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# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import os.path
from contextlib import contextmanager
from os import PathLike
from pathlib import Path
from typing import Dict, List, Optional, Union
import pydash
from promptflow._sdk._constants import DAG_FILE_NAME, SERVICE_FLOW_TYPE_2_CLIENT_FLOW_TYPE, AzureFlowSource, FlowType
from promptflow.azure._ml import AdditionalIncludesMixin, Code
from ..._constants import FlowLanguage
from ..._sdk._utils import PromptflowIgnoreFile, load_yaml, remove_empty_element_from_dict
from ..._utils.flow_utils import dump_flow_dag, load_flow_dag
from ..._utils.logger_utils import LoggerFactory
from .._constants._flow import ADDITIONAL_INCLUDES, DEFAULT_STORAGE, ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from .._restclient.flow.models import FlowDto
# pylint: disable=redefined-builtin, unused-argument, f-string-without-interpolation
logger = LoggerFactory.get_logger(__name__)
class Flow(AdditionalIncludesMixin):
DEFAULT_REQUIREMENTS_FILE_NAME = "requirements.txt"
def __init__(
self,
path: Union[str, PathLike],
name: Optional[str] = None,
type: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs,
):
self._flow_source = kwargs.pop("flow_source", AzureFlowSource.LOCAL)
self.path = path
self.name = name
self.type = type or FlowType.STANDARD
self.display_name = kwargs.get("display_name", None) or name
self.description = description
self.tags = tags
self.owner = kwargs.get("owner", None)
self.is_archived = kwargs.get("is_archived", None)
self.created_date = kwargs.get("created_date", None)
self.flow_portal_url = kwargs.get("flow_portal_url", None)
if self._flow_source == AzureFlowSource.LOCAL:
absolute_path = self._validate_flow_from_source(path)
# flow snapshot folder
self.code = absolute_path.parent.as_posix()
self._code_uploaded = False
self.path = absolute_path.name
self._flow_dict = self._load_flow_yaml(absolute_path)
self.display_name = self.display_name or absolute_path.parent.name
self.description = description or self._flow_dict.get("description", None)
self.tags = tags or self._flow_dict.get("tags", None)
elif self._flow_source == AzureFlowSource.PF_SERVICE:
self.code = kwargs.get("flow_resource_id", None)
elif self._flow_source == AzureFlowSource.INDEX:
self.code = kwargs.get("entity_id", None)
def _validate_flow_from_source(self, source: Union[str, PathLike]) -> Path:
"""Validate flow from source.
:param source: The source of the flow.
:type source: Union[str, PathLike]
"""
absolute_path = Path(source).resolve().absolute()
if absolute_path.is_dir():
absolute_path = absolute_path / DAG_FILE_NAME
if not absolute_path.exists():
raise ValueError(f"Flow file {absolute_path.as_posix()} does not exist.")
return absolute_path
def _load_flow_yaml(self, path: Union[str, Path]) -> Dict:
"""Load flow yaml file.
:param path: The path of the flow yaml file.
:type path: str
"""
return load_yaml(path)
@classmethod
def _resolve_requirements(cls, flow_path: Union[str, Path], flow_dag: dict):
"""If requirements.txt exists, add it to the flow snapshot. Return True if flow_dag is updated."""
flow_dir = Path(flow_path)
if not (flow_dir / cls.DEFAULT_REQUIREMENTS_FILE_NAME).exists():
return False
if pydash.get(flow_dag, f"{ENVIRONMENT}.{PYTHON_REQUIREMENTS_TXT}"):
return False
logger.debug(
f"requirements.txt is found in the flow folder: {flow_path.resolve().as_posix()}, "
"adding it to flow.dag.yaml."
)
pydash.set_(flow_dag, f"{ENVIRONMENT}.{PYTHON_REQUIREMENTS_TXT}", cls.DEFAULT_REQUIREMENTS_FILE_NAME)
return True
@classmethod
def _remove_additional_includes(cls, flow_dag: dict):
"""Remove additional includes from flow dag. Return True if removed."""
if ADDITIONAL_INCLUDES not in flow_dag:
return False
logger.debug("Additional includes are found in the flow dag, removing them from flow.dag.yaml after resolved.")
flow_dag.pop(ADDITIONAL_INCLUDES, None)
return True
# region AdditionalIncludesMixin
@contextmanager
def _try_build_local_code(self) -> Optional[Code]:
"""Try to create a Code object pointing to local code and yield it.
If there is no local code to upload, yield None. Otherwise, yield a Code object pointing to the code.
"""
with super()._try_build_local_code() as code:
dag_updated = False
if isinstance(code, Code):
flow_dir = Path(code.path)
_, flow_dag = load_flow_dag(flow_path=flow_dir)
original_flow_dag = copy.deepcopy(flow_dag)
if self._get_all_additional_includes_configs():
# Remove additional include in the flow yaml.
dag_updated = self._remove_additional_includes(flow_dag)
# promptflow snapshot has specific ignore logic, like it should ignore `.run` by default
code._ignore_file = PromptflowIgnoreFile(flow_dir)
# promptflow snapshot will always be uploaded to default storage
code.datastore = DEFAULT_STORAGE
dag_updated = self._resolve_requirements(flow_dir, flow_dag) or dag_updated
if dag_updated:
dump_flow_dag(flow_dag, flow_dir)
try:
yield code
finally:
if dag_updated:
dump_flow_dag(original_flow_dag, flow_dir)
def _get_base_path_for_code(self) -> Path:
"""Get base path for additional includes."""
# note that self.code is an absolute path, so it is safe to use it as base path
return Path(self.code)
def _get_all_additional_includes_configs(self) -> List:
"""Get all additional include configs.
For flow, its additional include need to be read from dag with a helper function.
"""
from promptflow._sdk._utils import _get_additional_includes
return _get_additional_includes(os.path.join(self.code, self.path))
# endregion
@classmethod
def _from_pf_service(cls, rest_object: FlowDto):
return cls(
flow_source=AzureFlowSource.PF_SERVICE,
path=rest_object.flow_definition_file_path,
name=rest_object.flow_id,
type=SERVICE_FLOW_TYPE_2_CLIENT_FLOW_TYPE[str(rest_object.flow_type).lower()],
description=rest_object.description,
tags=rest_object.tags,
display_name=rest_object.flow_name,
flow_resource_id=rest_object.flow_resource_id,
owner=rest_object.owner.as_dict(),
is_archived=rest_object.is_archived,
created_date=rest_object.created_date,
flow_portal_url=rest_object.studio_portal_endpoint,
)
@classmethod
def _from_index_service(cls, rest_object: Dict):
properties = rest_object["properties"]
annotations = rest_object["annotations"]
flow_type = properties.get("flowType", None).lower()
# rag type flow is shown as standard flow in UX, not sure why this type exists in service code
if flow_type == "rag":
flow_type = FlowType.STANDARD
elif flow_type:
flow_type = SERVICE_FLOW_TYPE_2_CLIENT_FLOW_TYPE[flow_type]
return cls(
flow_source=AzureFlowSource.INDEX,
path=properties.get("flowDefinitionFilePath", None),
name=properties.get("flowId", None),
display_name=annotations.get("flowName", None),
type=flow_type,
description=annotations.get("description", None),
tags=annotations.get("tags", None),
entity_id=rest_object["entityId"],
owner=annotations.get("owner", None),
is_archived=annotations.get("isArchived", None),
created_date=annotations.get("createdDate", None),
)
def _to_dict(self):
result = {
"name": self.name,
"type": self.type,
"description": self.description,
"tags": self.tags,
"path": self.path,
"code": str(self.code),
"display_name": self.display_name,
"owner": self.owner,
"is_archived": self.is_archived,
"created_date": str(self.created_date),
"flow_portal_url": self.flow_portal_url,
}
return remove_empty_element_from_dict(result)
@property
def language(self):
return self._flow_dict.get("language", FlowLanguage.Python)
| promptflow/src/promptflow/promptflow/azure/_entities/_flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_entities/_flow.py",
"repo_id": "promptflow",
"token_count": 3966
} | 16 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import datetime
from typing import Any, Dict, IO, List, Optional, Union
from azure.core.exceptions import HttpResponseError
import msrest.serialization
from ._azure_machine_learning_designer_service_client_enums import *
class ACIAdvanceSettings(msrest.serialization.Model):
"""ACIAdvanceSettings.
:ivar container_resource_requirements:
:vartype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar ssl_enabled:
:vartype ssl_enabled: bool
:ivar ssl_certificate:
:vartype ssl_certificate: str
:ivar ssl_key:
:vartype ssl_key: str
:ivar c_name:
:vartype c_name: str
:ivar dns_name_label:
:vartype dns_name_label: str
"""
_attribute_map = {
'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'ssl_enabled': {'key': 'sslEnabled', 'type': 'bool'},
'ssl_certificate': {'key': 'sslCertificate', 'type': 'str'},
'ssl_key': {'key': 'sslKey', 'type': 'str'},
'c_name': {'key': 'cName', 'type': 'str'},
'dns_name_label': {'key': 'dnsNameLabel', 'type': 'str'},
}
def __init__(
self,
*,
container_resource_requirements: Optional["ContainerResourceRequirements"] = None,
app_insights_enabled: Optional[bool] = None,
ssl_enabled: Optional[bool] = None,
ssl_certificate: Optional[str] = None,
ssl_key: Optional[str] = None,
c_name: Optional[str] = None,
dns_name_label: Optional[str] = None,
**kwargs
):
"""
:keyword container_resource_requirements:
:paramtype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword ssl_enabled:
:paramtype ssl_enabled: bool
:keyword ssl_certificate:
:paramtype ssl_certificate: str
:keyword ssl_key:
:paramtype ssl_key: str
:keyword c_name:
:paramtype c_name: str
:keyword dns_name_label:
:paramtype dns_name_label: str
"""
super(ACIAdvanceSettings, self).__init__(**kwargs)
self.container_resource_requirements = container_resource_requirements
self.app_insights_enabled = app_insights_enabled
self.ssl_enabled = ssl_enabled
self.ssl_certificate = ssl_certificate
self.ssl_key = ssl_key
self.c_name = c_name
self.dns_name_label = dns_name_label
class Activate(msrest.serialization.Model):
"""Activate.
:ivar when:
:vartype when: str
:ivar is_property: Anything.
:vartype is_property: any
"""
_attribute_map = {
'when': {'key': 'when', 'type': 'str'},
'is_property': {'key': 'is', 'type': 'object'},
}
def __init__(
self,
*,
when: Optional[str] = None,
is_property: Optional[Any] = None,
**kwargs
):
"""
:keyword when:
:paramtype when: str
:keyword is_property: Anything.
:paramtype is_property: any
"""
super(Activate, self).__init__(**kwargs)
self.when = when
self.is_property = is_property
class AdditionalErrorInfo(msrest.serialization.Model):
"""AdditionalErrorInfo.
:ivar type:
:vartype type: str
:ivar info: Anything.
:vartype info: any
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'info': {'key': 'info', 'type': 'object'},
}
def __init__(
self,
*,
type: Optional[str] = None,
info: Optional[Any] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword info: Anything.
:paramtype info: any
"""
super(AdditionalErrorInfo, self).__init__(**kwargs)
self.type = type
self.info = info
class AdhocTriggerScheduledCommandJobRequest(msrest.serialization.Model):
"""AdhocTriggerScheduledCommandJobRequest.
:ivar job_name:
:vartype job_name: str
:ivar job_display_name:
:vartype job_display_name: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
"""
_attribute_map = {
'job_name': {'key': 'jobName', 'type': 'str'},
'job_display_name': {'key': 'jobDisplayName', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
}
def __init__(
self,
*,
job_name: Optional[str] = None,
job_display_name: Optional[str] = None,
trigger_time_string: Optional[str] = None,
**kwargs
):
"""
:keyword job_name:
:paramtype job_name: str
:keyword job_display_name:
:paramtype job_display_name: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
"""
super(AdhocTriggerScheduledCommandJobRequest, self).__init__(**kwargs)
self.job_name = job_name
self.job_display_name = job_display_name
self.trigger_time_string = trigger_time_string
class AdhocTriggerScheduledSparkJobRequest(msrest.serialization.Model):
"""AdhocTriggerScheduledSparkJobRequest.
:ivar job_name:
:vartype job_name: str
:ivar job_display_name:
:vartype job_display_name: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
"""
_attribute_map = {
'job_name': {'key': 'jobName', 'type': 'str'},
'job_display_name': {'key': 'jobDisplayName', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
}
def __init__(
self,
*,
job_name: Optional[str] = None,
job_display_name: Optional[str] = None,
trigger_time_string: Optional[str] = None,
**kwargs
):
"""
:keyword job_name:
:paramtype job_name: str
:keyword job_display_name:
:paramtype job_display_name: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
"""
super(AdhocTriggerScheduledSparkJobRequest, self).__init__(**kwargs)
self.job_name = job_name
self.job_display_name = job_display_name
self.trigger_time_string = trigger_time_string
class AetherAmlDataset(msrest.serialization.Model):
"""AetherAmlDataset.
:ivar registered_data_set_reference:
:vartype registered_data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'registered_data_set_reference': {'key': 'registeredDataSetReference', 'type': 'AetherRegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'AetherSavedDataSetReference'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
registered_data_set_reference: Optional["AetherRegisteredDataSetReference"] = None,
saved_data_set_reference: Optional["AetherSavedDataSetReference"] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword registered_data_set_reference:
:paramtype registered_data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherAmlDataset, self).__init__(**kwargs)
self.registered_data_set_reference = registered_data_set_reference
self.saved_data_set_reference = saved_data_set_reference
self.additional_transformations = additional_transformations
class AetherAmlSparkCloudSetting(msrest.serialization.Model):
"""AetherAmlSparkCloudSetting.
:ivar entry:
:vartype entry: ~flow.models.AetherEntrySetting
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar inline_environment_definition_string:
:vartype inline_environment_definition_string: str
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar compute:
:vartype compute: str
:ivar resources:
:vartype resources: ~flow.models.AetherResourcesSetting
:ivar identity:
:vartype identity: ~flow.models.AetherIdentitySetting
"""
_attribute_map = {
'entry': {'key': 'entry', 'type': 'AetherEntrySetting'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'inline_environment_definition_string': {'key': 'inlineEnvironmentDefinitionString', 'type': 'str'},
'conf': {'key': 'conf', 'type': '{str}'},
'compute': {'key': 'compute', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'AetherResourcesSetting'},
'identity': {'key': 'identity', 'type': 'AetherIdentitySetting'},
}
def __init__(
self,
*,
entry: Optional["AetherEntrySetting"] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
py_files: Optional[List[str]] = None,
driver_memory: Optional[str] = None,
driver_cores: Optional[int] = None,
executor_memory: Optional[str] = None,
executor_cores: Optional[int] = None,
number_executors: Optional[int] = None,
environment_asset_id: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
inline_environment_definition_string: Optional[str] = None,
conf: Optional[Dict[str, str]] = None,
compute: Optional[str] = None,
resources: Optional["AetherResourcesSetting"] = None,
identity: Optional["AetherIdentitySetting"] = None,
**kwargs
):
"""
:keyword entry:
:paramtype entry: ~flow.models.AetherEntrySetting
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword inline_environment_definition_string:
:paramtype inline_environment_definition_string: str
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword compute:
:paramtype compute: str
:keyword resources:
:paramtype resources: ~flow.models.AetherResourcesSetting
:keyword identity:
:paramtype identity: ~flow.models.AetherIdentitySetting
"""
super(AetherAmlSparkCloudSetting, self).__init__(**kwargs)
self.entry = entry
self.files = files
self.archives = archives
self.jars = jars
self.py_files = py_files
self.driver_memory = driver_memory
self.driver_cores = driver_cores
self.executor_memory = executor_memory
self.executor_cores = executor_cores
self.number_executors = number_executors
self.environment_asset_id = environment_asset_id
self.environment_variables = environment_variables
self.inline_environment_definition_string = inline_environment_definition_string
self.conf = conf
self.compute = compute
self.resources = resources
self.identity = identity
class AetherAPCloudConfiguration(msrest.serialization.Model):
"""AetherAPCloudConfiguration.
:ivar referenced_ap_module_guid:
:vartype referenced_ap_module_guid: str
:ivar user_alias:
:vartype user_alias: str
:ivar aether_module_type:
:vartype aether_module_type: str
:ivar allow_overwrite:
:vartype allow_overwrite: bool
:ivar destination_expiration_days:
:vartype destination_expiration_days: int
:ivar should_respect_line_boundaries:
:vartype should_respect_line_boundaries: bool
"""
_attribute_map = {
'referenced_ap_module_guid': {'key': 'referencedAPModuleGuid', 'type': 'str'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'aether_module_type': {'key': 'aetherModuleType', 'type': 'str'},
'allow_overwrite': {'key': 'allowOverwrite', 'type': 'bool'},
'destination_expiration_days': {'key': 'destinationExpirationDays', 'type': 'int'},
'should_respect_line_boundaries': {'key': 'shouldRespectLineBoundaries', 'type': 'bool'},
}
def __init__(
self,
*,
referenced_ap_module_guid: Optional[str] = None,
user_alias: Optional[str] = None,
aether_module_type: Optional[str] = None,
allow_overwrite: Optional[bool] = None,
destination_expiration_days: Optional[int] = None,
should_respect_line_boundaries: Optional[bool] = None,
**kwargs
):
"""
:keyword referenced_ap_module_guid:
:paramtype referenced_ap_module_guid: str
:keyword user_alias:
:paramtype user_alias: str
:keyword aether_module_type:
:paramtype aether_module_type: str
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
:keyword destination_expiration_days:
:paramtype destination_expiration_days: int
:keyword should_respect_line_boundaries:
:paramtype should_respect_line_boundaries: bool
"""
super(AetherAPCloudConfiguration, self).__init__(**kwargs)
self.referenced_ap_module_guid = referenced_ap_module_guid
self.user_alias = user_alias
self.aether_module_type = aether_module_type
self.allow_overwrite = allow_overwrite
self.destination_expiration_days = destination_expiration_days
self.should_respect_line_boundaries = should_respect_line_boundaries
class AetherArgumentAssignment(msrest.serialization.Model):
"""AetherArgumentAssignment.
:ivar value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:vartype value_type: str or ~flow.models.AetherArgumentValueType
:ivar value:
:vartype value: str
:ivar nested_argument_list:
:vartype nested_argument_list: list[~flow.models.AetherArgumentAssignment]
:ivar string_interpolation_argument_list:
:vartype string_interpolation_argument_list: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'nested_argument_list': {'key': 'nestedArgumentList', 'type': '[AetherArgumentAssignment]'},
'string_interpolation_argument_list': {'key': 'stringInterpolationArgumentList', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
*,
value_type: Optional[Union[str, "AetherArgumentValueType"]] = None,
value: Optional[str] = None,
nested_argument_list: Optional[List["AetherArgumentAssignment"]] = None,
string_interpolation_argument_list: Optional[List["AetherArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:paramtype value_type: str or ~flow.models.AetherArgumentValueType
:keyword value:
:paramtype value: str
:keyword nested_argument_list:
:paramtype nested_argument_list: list[~flow.models.AetherArgumentAssignment]
:keyword string_interpolation_argument_list:
:paramtype string_interpolation_argument_list: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherArgumentAssignment, self).__init__(**kwargs)
self.value_type = value_type
self.value = value
self.nested_argument_list = nested_argument_list
self.string_interpolation_argument_list = string_interpolation_argument_list
class AetherAssetDefinition(msrest.serialization.Model):
"""AetherAssetDefinition.
:ivar path:
:vartype path: str
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AetherAssetType
:ivar asset_id:
:vartype asset_id: str
:ivar initial_asset_id:
:vartype initial_asset_id: str
:ivar serialized_asset_id:
:vartype serialized_asset_id: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'initial_asset_id': {'key': 'initialAssetId', 'type': 'str'},
'serialized_asset_id': {'key': 'serializedAssetId', 'type': 'str'},
}
def __init__(
self,
*,
path: Optional[str] = None,
type: Optional[Union[str, "AetherAssetType"]] = None,
asset_id: Optional[str] = None,
initial_asset_id: Optional[str] = None,
serialized_asset_id: Optional[str] = None,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AetherAssetType
:keyword asset_id:
:paramtype asset_id: str
:keyword initial_asset_id:
:paramtype initial_asset_id: str
:keyword serialized_asset_id:
:paramtype serialized_asset_id: str
"""
super(AetherAssetDefinition, self).__init__(**kwargs)
self.path = path
self.type = type
self.asset_id = asset_id
self.initial_asset_id = initial_asset_id
self.serialized_asset_id = serialized_asset_id
class AetherAssetOutputSettings(msrest.serialization.Model):
"""AetherAssetOutputSettings.
:ivar path:
:vartype path: str
:ivar path_parameter_assignment:
:vartype path_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AetherAssetType
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'path_parameter_assignment': {'key': 'PathParameterAssignment', 'type': 'AetherParameterAssignment'},
'type': {'key': 'type', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
path: Optional[str] = None,
path_parameter_assignment: Optional["AetherParameterAssignment"] = None,
type: Optional[Union[str, "AetherAssetType"]] = None,
options: Optional[Dict[str, str]] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword path_parameter_assignment:
:paramtype path_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AetherAssetType
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AetherAssetOutputSettings, self).__init__(**kwargs)
self.path = path
self.path_parameter_assignment = path_parameter_assignment
self.type = type
self.options = options
self.data_store_mode = data_store_mode
self.name = name
self.version = version
class AetherAutoFeaturizeConfiguration(msrest.serialization.Model):
"""AetherAutoFeaturizeConfiguration.
:ivar featurization_config:
:vartype featurization_config: ~flow.models.AetherFeaturizationSettings
"""
_attribute_map = {
'featurization_config': {'key': 'featurizationConfig', 'type': 'AetherFeaturizationSettings'},
}
def __init__(
self,
*,
featurization_config: Optional["AetherFeaturizationSettings"] = None,
**kwargs
):
"""
:keyword featurization_config:
:paramtype featurization_config: ~flow.models.AetherFeaturizationSettings
"""
super(AetherAutoFeaturizeConfiguration, self).__init__(**kwargs)
self.featurization_config = featurization_config
class AetherAutoMLComponentConfiguration(msrest.serialization.Model):
"""AetherAutoMLComponentConfiguration.
:ivar auto_train_config:
:vartype auto_train_config: ~flow.models.AetherAutoTrainConfiguration
:ivar auto_featurize_config:
:vartype auto_featurize_config: ~flow.models.AetherAutoFeaturizeConfiguration
"""
_attribute_map = {
'auto_train_config': {'key': 'autoTrainConfig', 'type': 'AetherAutoTrainConfiguration'},
'auto_featurize_config': {'key': 'autoFeaturizeConfig', 'type': 'AetherAutoFeaturizeConfiguration'},
}
def __init__(
self,
*,
auto_train_config: Optional["AetherAutoTrainConfiguration"] = None,
auto_featurize_config: Optional["AetherAutoFeaturizeConfiguration"] = None,
**kwargs
):
"""
:keyword auto_train_config:
:paramtype auto_train_config: ~flow.models.AetherAutoTrainConfiguration
:keyword auto_featurize_config:
:paramtype auto_featurize_config: ~flow.models.AetherAutoFeaturizeConfiguration
"""
super(AetherAutoMLComponentConfiguration, self).__init__(**kwargs)
self.auto_train_config = auto_train_config
self.auto_featurize_config = auto_featurize_config
class AetherAutoTrainConfiguration(msrest.serialization.Model):
"""AetherAutoTrainConfiguration.
:ivar general_settings:
:vartype general_settings: ~flow.models.AetherGeneralSettings
:ivar limit_settings:
:vartype limit_settings: ~flow.models.AetherLimitSettings
:ivar data_settings:
:vartype data_settings: ~flow.models.AetherDataSettings
:ivar forecasting_settings:
:vartype forecasting_settings: ~flow.models.AetherForecastingSettings
:ivar training_settings:
:vartype training_settings: ~flow.models.AetherTrainingSettings
:ivar sweep_settings:
:vartype sweep_settings: ~flow.models.AetherSweepSettings
:ivar image_model_settings: Dictionary of :code:`<any>`.
:vartype image_model_settings: dict[str, any]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar compute_configuration:
:vartype compute_configuration: ~flow.models.AetherComputeConfiguration
:ivar resource_configurtion:
:vartype resource_configurtion: ~flow.models.AetherResourceConfiguration
:ivar environment_id:
:vartype environment_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
"""
_attribute_map = {
'general_settings': {'key': 'generalSettings', 'type': 'AetherGeneralSettings'},
'limit_settings': {'key': 'limitSettings', 'type': 'AetherLimitSettings'},
'data_settings': {'key': 'dataSettings', 'type': 'AetherDataSettings'},
'forecasting_settings': {'key': 'forecastingSettings', 'type': 'AetherForecastingSettings'},
'training_settings': {'key': 'trainingSettings', 'type': 'AetherTrainingSettings'},
'sweep_settings': {'key': 'sweepSettings', 'type': 'AetherSweepSettings'},
'image_model_settings': {'key': 'imageModelSettings', 'type': '{object}'},
'properties': {'key': 'properties', 'type': '{str}'},
'compute_configuration': {'key': 'computeConfiguration', 'type': 'AetherComputeConfiguration'},
'resource_configurtion': {'key': 'resourceConfigurtion', 'type': 'AetherResourceConfiguration'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
}
def __init__(
self,
*,
general_settings: Optional["AetherGeneralSettings"] = None,
limit_settings: Optional["AetherLimitSettings"] = None,
data_settings: Optional["AetherDataSettings"] = None,
forecasting_settings: Optional["AetherForecastingSettings"] = None,
training_settings: Optional["AetherTrainingSettings"] = None,
sweep_settings: Optional["AetherSweepSettings"] = None,
image_model_settings: Optional[Dict[str, Any]] = None,
properties: Optional[Dict[str, str]] = None,
compute_configuration: Optional["AetherComputeConfiguration"] = None,
resource_configurtion: Optional["AetherResourceConfiguration"] = None,
environment_id: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword general_settings:
:paramtype general_settings: ~flow.models.AetherGeneralSettings
:keyword limit_settings:
:paramtype limit_settings: ~flow.models.AetherLimitSettings
:keyword data_settings:
:paramtype data_settings: ~flow.models.AetherDataSettings
:keyword forecasting_settings:
:paramtype forecasting_settings: ~flow.models.AetherForecastingSettings
:keyword training_settings:
:paramtype training_settings: ~flow.models.AetherTrainingSettings
:keyword sweep_settings:
:paramtype sweep_settings: ~flow.models.AetherSweepSettings
:keyword image_model_settings: Dictionary of :code:`<any>`.
:paramtype image_model_settings: dict[str, any]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword compute_configuration:
:paramtype compute_configuration: ~flow.models.AetherComputeConfiguration
:keyword resource_configurtion:
:paramtype resource_configurtion: ~flow.models.AetherResourceConfiguration
:keyword environment_id:
:paramtype environment_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
"""
super(AetherAutoTrainConfiguration, self).__init__(**kwargs)
self.general_settings = general_settings
self.limit_settings = limit_settings
self.data_settings = data_settings
self.forecasting_settings = forecasting_settings
self.training_settings = training_settings
self.sweep_settings = sweep_settings
self.image_model_settings = image_model_settings
self.properties = properties
self.compute_configuration = compute_configuration
self.resource_configurtion = resource_configurtion
self.environment_id = environment_id
self.environment_variables = environment_variables
class AetherAzureBlobReference(msrest.serialization.Model):
"""AetherAzureBlobReference.
:ivar container:
:vartype container: str
:ivar sas_token:
:vartype sas_token: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'container': {'key': 'container', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
container: Optional[str] = None,
sas_token: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
path_type: Optional[Union[str, "AetherFileBasedPathType"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword container:
:paramtype container: str
:keyword sas_token:
:paramtype sas_token: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureBlobReference, self).__init__(**kwargs)
self.container = container
self.sas_token = sas_token
self.uri = uri
self.account = account
self.relative_path = relative_path
self.path_type = path_type
self.aml_data_store_name = aml_data_store_name
class AetherAzureDatabaseReference(msrest.serialization.Model):
"""AetherAzureDatabaseReference.
:ivar server_uri:
:vartype server_uri: str
:ivar database_name:
:vartype database_name: str
:ivar table_name:
:vartype table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'server_uri': {'key': 'serverUri', 'type': 'str'},
'database_name': {'key': 'databaseName', 'type': 'str'},
'table_name': {'key': 'tableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[AetherStoredProcedureParameter]'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
server_uri: Optional[str] = None,
database_name: Optional[str] = None,
table_name: Optional[str] = None,
sql_query: Optional[str] = None,
stored_procedure_name: Optional[str] = None,
stored_procedure_parameters: Optional[List["AetherStoredProcedureParameter"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword server_uri:
:paramtype server_uri: str
:keyword database_name:
:paramtype database_name: str
:keyword table_name:
:paramtype table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDatabaseReference, self).__init__(**kwargs)
self.server_uri = server_uri
self.database_name = database_name
self.table_name = table_name
self.sql_query = sql_query
self.stored_procedure_name = stored_procedure_name
self.stored_procedure_parameters = stored_procedure_parameters
self.aml_data_store_name = aml_data_store_name
class AetherAzureDataLakeGen2Reference(msrest.serialization.Model):
"""AetherAzureDataLakeGen2Reference.
:ivar file_system_name:
:vartype file_system_name: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'file_system_name': {'key': 'fileSystemName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
file_system_name: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
path_type: Optional[Union[str, "AetherFileBasedPathType"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword file_system_name:
:paramtype file_system_name: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDataLakeGen2Reference, self).__init__(**kwargs)
self.file_system_name = file_system_name
self.uri = uri
self.account = account
self.relative_path = relative_path
self.path_type = path_type
self.aml_data_store_name = aml_data_store_name
class AetherAzureDataLakeReference(msrest.serialization.Model):
"""AetherAzureDataLakeReference.
:ivar tenant:
:vartype tenant: str
:ivar subscription:
:vartype subscription: str
:ivar resource_group:
:vartype resource_group: str
:ivar data_lake_uri:
:vartype data_lake_uri: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'tenant': {'key': 'tenant', 'type': 'str'},
'subscription': {'key': 'subscription', 'type': 'str'},
'resource_group': {'key': 'resourceGroup', 'type': 'str'},
'data_lake_uri': {'key': 'dataLakeUri', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
tenant: Optional[str] = None,
subscription: Optional[str] = None,
resource_group: Optional[str] = None,
data_lake_uri: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
path_type: Optional[Union[str, "AetherFileBasedPathType"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword tenant:
:paramtype tenant: str
:keyword subscription:
:paramtype subscription: str
:keyword resource_group:
:paramtype resource_group: str
:keyword data_lake_uri:
:paramtype data_lake_uri: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureDataLakeReference, self).__init__(**kwargs)
self.tenant = tenant
self.subscription = subscription
self.resource_group = resource_group
self.data_lake_uri = data_lake_uri
self.uri = uri
self.account = account
self.relative_path = relative_path
self.path_type = path_type
self.aml_data_store_name = aml_data_store_name
class AetherAzureFilesReference(msrest.serialization.Model):
"""AetherAzureFilesReference.
:ivar share:
:vartype share: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar path_type: Possible values include: "Unknown", "File", "Folder".
:vartype path_type: str or ~flow.models.AetherFileBasedPathType
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'share': {'key': 'share', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'path_type': {'key': 'pathType', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
share: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
path_type: Optional[Union[str, "AetherFileBasedPathType"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword share:
:paramtype share: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword path_type: Possible values include: "Unknown", "File", "Folder".
:paramtype path_type: str or ~flow.models.AetherFileBasedPathType
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherAzureFilesReference, self).__init__(**kwargs)
self.share = share
self.uri = uri
self.account = account
self.relative_path = relative_path
self.path_type = path_type
self.aml_data_store_name = aml_data_store_name
class AetherBatchAiComputeInfo(msrest.serialization.Model):
"""AetherBatchAiComputeInfo.
:ivar batch_ai_subscription_id:
:vartype batch_ai_subscription_id: str
:ivar batch_ai_resource_group:
:vartype batch_ai_resource_group: str
:ivar batch_ai_workspace_name:
:vartype batch_ai_workspace_name: str
:ivar cluster_name:
:vartype cluster_name: str
:ivar native_shared_directory:
:vartype native_shared_directory: str
"""
_attribute_map = {
'batch_ai_subscription_id': {'key': 'batchAiSubscriptionId', 'type': 'str'},
'batch_ai_resource_group': {'key': 'batchAiResourceGroup', 'type': 'str'},
'batch_ai_workspace_name': {'key': 'batchAiWorkspaceName', 'type': 'str'},
'cluster_name': {'key': 'clusterName', 'type': 'str'},
'native_shared_directory': {'key': 'nativeSharedDirectory', 'type': 'str'},
}
def __init__(
self,
*,
batch_ai_subscription_id: Optional[str] = None,
batch_ai_resource_group: Optional[str] = None,
batch_ai_workspace_name: Optional[str] = None,
cluster_name: Optional[str] = None,
native_shared_directory: Optional[str] = None,
**kwargs
):
"""
:keyword batch_ai_subscription_id:
:paramtype batch_ai_subscription_id: str
:keyword batch_ai_resource_group:
:paramtype batch_ai_resource_group: str
:keyword batch_ai_workspace_name:
:paramtype batch_ai_workspace_name: str
:keyword cluster_name:
:paramtype cluster_name: str
:keyword native_shared_directory:
:paramtype native_shared_directory: str
"""
super(AetherBatchAiComputeInfo, self).__init__(**kwargs)
self.batch_ai_subscription_id = batch_ai_subscription_id
self.batch_ai_resource_group = batch_ai_resource_group
self.batch_ai_workspace_name = batch_ai_workspace_name
self.cluster_name = cluster_name
self.native_shared_directory = native_shared_directory
class AetherBuildArtifactInfo(msrest.serialization.Model):
"""AetherBuildArtifactInfo.
:ivar type: Possible values include: "CloudBuild", "Vso", "VsoGit".
:vartype type: str or ~flow.models.AetherBuildSourceType
:ivar cloud_build_drop_path_info:
:vartype cloud_build_drop_path_info: ~flow.models.AetherCloudBuildDropPathInfo
:ivar vso_build_artifact_info:
:vartype vso_build_artifact_info: ~flow.models.AetherVsoBuildArtifactInfo
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'cloud_build_drop_path_info': {'key': 'cloudBuildDropPathInfo', 'type': 'AetherCloudBuildDropPathInfo'},
'vso_build_artifact_info': {'key': 'vsoBuildArtifactInfo', 'type': 'AetherVsoBuildArtifactInfo'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AetherBuildSourceType"]] = None,
cloud_build_drop_path_info: Optional["AetherCloudBuildDropPathInfo"] = None,
vso_build_artifact_info: Optional["AetherVsoBuildArtifactInfo"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "CloudBuild", "Vso", "VsoGit".
:paramtype type: str or ~flow.models.AetherBuildSourceType
:keyword cloud_build_drop_path_info:
:paramtype cloud_build_drop_path_info: ~flow.models.AetherCloudBuildDropPathInfo
:keyword vso_build_artifact_info:
:paramtype vso_build_artifact_info: ~flow.models.AetherVsoBuildArtifactInfo
"""
super(AetherBuildArtifactInfo, self).__init__(**kwargs)
self.type = type
self.cloud_build_drop_path_info = cloud_build_drop_path_info
self.vso_build_artifact_info = vso_build_artifact_info
class AetherCloudBuildDropPathInfo(msrest.serialization.Model):
"""AetherCloudBuildDropPathInfo.
:ivar build_info:
:vartype build_info: ~flow.models.AetherCloudBuildInfo
:ivar root:
:vartype root: str
"""
_attribute_map = {
'build_info': {'key': 'buildInfo', 'type': 'AetherCloudBuildInfo'},
'root': {'key': 'root', 'type': 'str'},
}
def __init__(
self,
*,
build_info: Optional["AetherCloudBuildInfo"] = None,
root: Optional[str] = None,
**kwargs
):
"""
:keyword build_info:
:paramtype build_info: ~flow.models.AetherCloudBuildInfo
:keyword root:
:paramtype root: str
"""
super(AetherCloudBuildDropPathInfo, self).__init__(**kwargs)
self.build_info = build_info
self.root = root
class AetherCloudBuildInfo(msrest.serialization.Model):
"""AetherCloudBuildInfo.
:ivar queue_info:
:vartype queue_info: ~flow.models.AetherCloudBuildQueueInfo
:ivar build_id:
:vartype build_id: str
:ivar drop_url:
:vartype drop_url: str
"""
_attribute_map = {
'queue_info': {'key': 'queueInfo', 'type': 'AetherCloudBuildQueueInfo'},
'build_id': {'key': 'buildId', 'type': 'str'},
'drop_url': {'key': 'dropUrl', 'type': 'str'},
}
def __init__(
self,
*,
queue_info: Optional["AetherCloudBuildQueueInfo"] = None,
build_id: Optional[str] = None,
drop_url: Optional[str] = None,
**kwargs
):
"""
:keyword queue_info:
:paramtype queue_info: ~flow.models.AetherCloudBuildQueueInfo
:keyword build_id:
:paramtype build_id: str
:keyword drop_url:
:paramtype drop_url: str
"""
super(AetherCloudBuildInfo, self).__init__(**kwargs)
self.queue_info = queue_info
self.build_id = build_id
self.drop_url = drop_url
class AetherCloudBuildQueueInfo(msrest.serialization.Model):
"""AetherCloudBuildQueueInfo.
:ivar build_queue:
:vartype build_queue: str
:ivar build_role:
:vartype build_role: str
"""
_attribute_map = {
'build_queue': {'key': 'buildQueue', 'type': 'str'},
'build_role': {'key': 'buildRole', 'type': 'str'},
}
def __init__(
self,
*,
build_queue: Optional[str] = None,
build_role: Optional[str] = None,
**kwargs
):
"""
:keyword build_queue:
:paramtype build_queue: str
:keyword build_role:
:paramtype build_role: str
"""
super(AetherCloudBuildQueueInfo, self).__init__(**kwargs)
self.build_queue = build_queue
self.build_role = build_role
class AetherCloudPrioritySetting(msrest.serialization.Model):
"""AetherCloudPrioritySetting.
:ivar scope_priority:
:vartype scope_priority: ~flow.models.AetherPriorityConfiguration
:ivar aml_compute_priority:
:vartype aml_compute_priority: ~flow.models.AetherPriorityConfiguration
:ivar itp_priority:
:vartype itp_priority: ~flow.models.AetherPriorityConfiguration
:ivar singularity_priority:
:vartype singularity_priority: ~flow.models.AetherPriorityConfiguration
"""
_attribute_map = {
'scope_priority': {'key': 'scopePriority', 'type': 'AetherPriorityConfiguration'},
'aml_compute_priority': {'key': 'AmlComputePriority', 'type': 'AetherPriorityConfiguration'},
'itp_priority': {'key': 'ItpPriority', 'type': 'AetherPriorityConfiguration'},
'singularity_priority': {'key': 'SingularityPriority', 'type': 'AetherPriorityConfiguration'},
}
def __init__(
self,
*,
scope_priority: Optional["AetherPriorityConfiguration"] = None,
aml_compute_priority: Optional["AetherPriorityConfiguration"] = None,
itp_priority: Optional["AetherPriorityConfiguration"] = None,
singularity_priority: Optional["AetherPriorityConfiguration"] = None,
**kwargs
):
"""
:keyword scope_priority:
:paramtype scope_priority: ~flow.models.AetherPriorityConfiguration
:keyword aml_compute_priority:
:paramtype aml_compute_priority: ~flow.models.AetherPriorityConfiguration
:keyword itp_priority:
:paramtype itp_priority: ~flow.models.AetherPriorityConfiguration
:keyword singularity_priority:
:paramtype singularity_priority: ~flow.models.AetherPriorityConfiguration
"""
super(AetherCloudPrioritySetting, self).__init__(**kwargs)
self.scope_priority = scope_priority
self.aml_compute_priority = aml_compute_priority
self.itp_priority = itp_priority
self.singularity_priority = singularity_priority
class AetherCloudSettings(msrest.serialization.Model):
"""AetherCloudSettings.
:ivar linked_settings:
:vartype linked_settings: list[~flow.models.AetherParameterAssignment]
:ivar priority_config:
:vartype priority_config: ~flow.models.AetherPriorityConfiguration
:ivar hdi_run_config:
:vartype hdi_run_config: ~flow.models.AetherHdiRunConfiguration
:ivar sub_graph_config:
:vartype sub_graph_config: ~flow.models.AetherSubGraphConfiguration
:ivar auto_ml_component_config:
:vartype auto_ml_component_config: ~flow.models.AetherAutoMLComponentConfiguration
:ivar ap_cloud_config:
:vartype ap_cloud_config: ~flow.models.AetherAPCloudConfiguration
:ivar scope_cloud_config:
:vartype scope_cloud_config: ~flow.models.AetherScopeCloudConfiguration
:ivar es_cloud_config:
:vartype es_cloud_config: ~flow.models.AetherEsCloudConfiguration
:ivar data_transfer_cloud_config:
:vartype data_transfer_cloud_config: ~flow.models.AetherDataTransferCloudConfiguration
:ivar aml_spark_cloud_setting:
:vartype aml_spark_cloud_setting: ~flow.models.AetherAmlSparkCloudSetting
:ivar data_transfer_v2_cloud_setting:
:vartype data_transfer_v2_cloud_setting: ~flow.models.AetherDataTransferV2CloudSetting
"""
_attribute_map = {
'linked_settings': {'key': 'linkedSettings', 'type': '[AetherParameterAssignment]'},
'priority_config': {'key': 'priorityConfig', 'type': 'AetherPriorityConfiguration'},
'hdi_run_config': {'key': 'hdiRunConfig', 'type': 'AetherHdiRunConfiguration'},
'sub_graph_config': {'key': 'subGraphConfig', 'type': 'AetherSubGraphConfiguration'},
'auto_ml_component_config': {'key': 'autoMLComponentConfig', 'type': 'AetherAutoMLComponentConfiguration'},
'ap_cloud_config': {'key': 'apCloudConfig', 'type': 'AetherAPCloudConfiguration'},
'scope_cloud_config': {'key': 'scopeCloudConfig', 'type': 'AetherScopeCloudConfiguration'},
'es_cloud_config': {'key': 'esCloudConfig', 'type': 'AetherEsCloudConfiguration'},
'data_transfer_cloud_config': {'key': 'dataTransferCloudConfig', 'type': 'AetherDataTransferCloudConfiguration'},
'aml_spark_cloud_setting': {'key': 'amlSparkCloudSetting', 'type': 'AetherAmlSparkCloudSetting'},
'data_transfer_v2_cloud_setting': {'key': 'dataTransferV2CloudSetting', 'type': 'AetherDataTransferV2CloudSetting'},
}
def __init__(
self,
*,
linked_settings: Optional[List["AetherParameterAssignment"]] = None,
priority_config: Optional["AetherPriorityConfiguration"] = None,
hdi_run_config: Optional["AetherHdiRunConfiguration"] = None,
sub_graph_config: Optional["AetherSubGraphConfiguration"] = None,
auto_ml_component_config: Optional["AetherAutoMLComponentConfiguration"] = None,
ap_cloud_config: Optional["AetherAPCloudConfiguration"] = None,
scope_cloud_config: Optional["AetherScopeCloudConfiguration"] = None,
es_cloud_config: Optional["AetherEsCloudConfiguration"] = None,
data_transfer_cloud_config: Optional["AetherDataTransferCloudConfiguration"] = None,
aml_spark_cloud_setting: Optional["AetherAmlSparkCloudSetting"] = None,
data_transfer_v2_cloud_setting: Optional["AetherDataTransferV2CloudSetting"] = None,
**kwargs
):
"""
:keyword linked_settings:
:paramtype linked_settings: list[~flow.models.AetherParameterAssignment]
:keyword priority_config:
:paramtype priority_config: ~flow.models.AetherPriorityConfiguration
:keyword hdi_run_config:
:paramtype hdi_run_config: ~flow.models.AetherHdiRunConfiguration
:keyword sub_graph_config:
:paramtype sub_graph_config: ~flow.models.AetherSubGraphConfiguration
:keyword auto_ml_component_config:
:paramtype auto_ml_component_config: ~flow.models.AetherAutoMLComponentConfiguration
:keyword ap_cloud_config:
:paramtype ap_cloud_config: ~flow.models.AetherAPCloudConfiguration
:keyword scope_cloud_config:
:paramtype scope_cloud_config: ~flow.models.AetherScopeCloudConfiguration
:keyword es_cloud_config:
:paramtype es_cloud_config: ~flow.models.AetherEsCloudConfiguration
:keyword data_transfer_cloud_config:
:paramtype data_transfer_cloud_config: ~flow.models.AetherDataTransferCloudConfiguration
:keyword aml_spark_cloud_setting:
:paramtype aml_spark_cloud_setting: ~flow.models.AetherAmlSparkCloudSetting
:keyword data_transfer_v2_cloud_setting:
:paramtype data_transfer_v2_cloud_setting: ~flow.models.AetherDataTransferV2CloudSetting
"""
super(AetherCloudSettings, self).__init__(**kwargs)
self.linked_settings = linked_settings
self.priority_config = priority_config
self.hdi_run_config = hdi_run_config
self.sub_graph_config = sub_graph_config
self.auto_ml_component_config = auto_ml_component_config
self.ap_cloud_config = ap_cloud_config
self.scope_cloud_config = scope_cloud_config
self.es_cloud_config = es_cloud_config
self.data_transfer_cloud_config = data_transfer_cloud_config
self.aml_spark_cloud_setting = aml_spark_cloud_setting
self.data_transfer_v2_cloud_setting = data_transfer_v2_cloud_setting
class AetherColumnTransformer(msrest.serialization.Model):
"""AetherColumnTransformer.
:ivar fields:
:vartype fields: list[str]
:ivar parameters: Anything.
:vartype parameters: any
"""
_attribute_map = {
'fields': {'key': 'fields', 'type': '[str]'},
'parameters': {'key': 'parameters', 'type': 'object'},
}
def __init__(
self,
*,
fields: Optional[List[str]] = None,
parameters: Optional[Any] = None,
**kwargs
):
"""
:keyword fields:
:paramtype fields: list[str]
:keyword parameters: Anything.
:paramtype parameters: any
"""
super(AetherColumnTransformer, self).__init__(**kwargs)
self.fields = fields
self.parameters = parameters
class AetherComputeConfiguration(msrest.serialization.Model):
"""AetherComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar is_preemptable:
:vartype is_preemptable: bool
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'is_preemptable': {'key': 'isPreemptable', 'type': 'bool'},
}
def __init__(
self,
*,
target: Optional[str] = None,
instance_count: Optional[int] = None,
is_local: Optional[bool] = None,
location: Optional[str] = None,
is_clusterless: Optional[bool] = None,
instance_type: Optional[str] = None,
properties: Optional[Dict[str, Any]] = None,
is_preemptable: Optional[bool] = None,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword is_preemptable:
:paramtype is_preemptable: bool
"""
super(AetherComputeConfiguration, self).__init__(**kwargs)
self.target = target
self.instance_count = instance_count
self.is_local = is_local
self.location = location
self.is_clusterless = is_clusterless
self.instance_type = instance_type
self.properties = properties
self.is_preemptable = is_preemptable
class AetherComputeSetting(msrest.serialization.Model):
"""AetherComputeSetting.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:vartype compute_type: str or ~flow.models.AetherComputeType
:ivar batch_ai_compute_info:
:vartype batch_ai_compute_info: ~flow.models.AetherBatchAiComputeInfo
:ivar remote_docker_compute_info:
:vartype remote_docker_compute_info: ~flow.models.AetherRemoteDockerComputeInfo
:ivar hdi_cluster_compute_info:
:vartype hdi_cluster_compute_info: ~flow.models.AetherHdiClusterComputeInfo
:ivar mlc_compute_info:
:vartype mlc_compute_info: ~flow.models.AetherMlcComputeInfo
:ivar databricks_compute_info:
:vartype databricks_compute_info: ~flow.models.AetherDatabricksComputeInfo
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'batch_ai_compute_info': {'key': 'batchAiComputeInfo', 'type': 'AetherBatchAiComputeInfo'},
'remote_docker_compute_info': {'key': 'remoteDockerComputeInfo', 'type': 'AetherRemoteDockerComputeInfo'},
'hdi_cluster_compute_info': {'key': 'hdiClusterComputeInfo', 'type': 'AetherHdiClusterComputeInfo'},
'mlc_compute_info': {'key': 'mlcComputeInfo', 'type': 'AetherMlcComputeInfo'},
'databricks_compute_info': {'key': 'databricksComputeInfo', 'type': 'AetherDatabricksComputeInfo'},
}
def __init__(
self,
*,
name: Optional[str] = None,
compute_type: Optional[Union[str, "AetherComputeType"]] = None,
batch_ai_compute_info: Optional["AetherBatchAiComputeInfo"] = None,
remote_docker_compute_info: Optional["AetherRemoteDockerComputeInfo"] = None,
hdi_cluster_compute_info: Optional["AetherHdiClusterComputeInfo"] = None,
mlc_compute_info: Optional["AetherMlcComputeInfo"] = None,
databricks_compute_info: Optional["AetherDatabricksComputeInfo"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:paramtype compute_type: str or ~flow.models.AetherComputeType
:keyword batch_ai_compute_info:
:paramtype batch_ai_compute_info: ~flow.models.AetherBatchAiComputeInfo
:keyword remote_docker_compute_info:
:paramtype remote_docker_compute_info: ~flow.models.AetherRemoteDockerComputeInfo
:keyword hdi_cluster_compute_info:
:paramtype hdi_cluster_compute_info: ~flow.models.AetherHdiClusterComputeInfo
:keyword mlc_compute_info:
:paramtype mlc_compute_info: ~flow.models.AetherMlcComputeInfo
:keyword databricks_compute_info:
:paramtype databricks_compute_info: ~flow.models.AetherDatabricksComputeInfo
"""
super(AetherComputeSetting, self).__init__(**kwargs)
self.name = name
self.compute_type = compute_type
self.batch_ai_compute_info = batch_ai_compute_info
self.remote_docker_compute_info = remote_docker_compute_info
self.hdi_cluster_compute_info = hdi_cluster_compute_info
self.mlc_compute_info = mlc_compute_info
self.databricks_compute_info = databricks_compute_info
class AetherControlInput(msrest.serialization.Model):
"""AetherControlInput.
:ivar name:
:vartype name: str
:ivar default_value: Possible values include: "None", "False", "True", "Skipped".
:vartype default_value: str or ~flow.models.AetherControlInputValue
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
default_value: Optional[Union[str, "AetherControlInputValue"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword default_value: Possible values include: "None", "False", "True", "Skipped".
:paramtype default_value: str or ~flow.models.AetherControlInputValue
"""
super(AetherControlInput, self).__init__(**kwargs)
self.name = name
self.default_value = default_value
class AetherControlOutput(msrest.serialization.Model):
"""AetherControlOutput.
:ivar name:
:vartype name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
"""
super(AetherControlOutput, self).__init__(**kwargs)
self.name = name
class AetherCopyDataTask(msrest.serialization.Model):
"""AetherCopyDataTask.
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
_attribute_map = {
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
*,
data_copy_mode: Optional[Union[str, "AetherDataCopyMode"]] = None,
**kwargs
):
"""
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
super(AetherCopyDataTask, self).__init__(**kwargs)
self.data_copy_mode = data_copy_mode
class AetherCosmosReference(msrest.serialization.Model):
"""AetherCosmosReference.
:ivar cluster:
:vartype cluster: str
:ivar vc:
:vartype vc: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'cluster': {'key': 'cluster', 'type': 'str'},
'vc': {'key': 'vc', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
cluster: Optional[str] = None,
vc: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword cluster:
:paramtype cluster: str
:keyword vc:
:paramtype vc: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherCosmosReference, self).__init__(**kwargs)
self.cluster = cluster
self.vc = vc
self.relative_path = relative_path
class AetherCreatedBy(msrest.serialization.Model):
"""AetherCreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
:ivar puid:
:vartype puid: str
:ivar iss:
:vartype iss: str
:ivar idp:
:vartype idp: str
:ivar altsec_id:
:vartype altsec_id: str
:ivar source_ip:
:vartype source_ip: str
:ivar skip_registry_private_link_check:
:vartype skip_registry_private_link_check: bool
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'puid': {'key': 'puid', 'type': 'str'},
'iss': {'key': 'iss', 'type': 'str'},
'idp': {'key': 'idp', 'type': 'str'},
'altsec_id': {'key': 'altsecId', 'type': 'str'},
'source_ip': {'key': 'sourceIp', 'type': 'str'},
'skip_registry_private_link_check': {'key': 'skipRegistryPrivateLinkCheck', 'type': 'bool'},
}
def __init__(
self,
*,
user_object_id: Optional[str] = None,
user_tenant_id: Optional[str] = None,
user_name: Optional[str] = None,
puid: Optional[str] = None,
iss: Optional[str] = None,
idp: Optional[str] = None,
altsec_id: Optional[str] = None,
source_ip: Optional[str] = None,
skip_registry_private_link_check: Optional[bool] = None,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
:keyword puid:
:paramtype puid: str
:keyword iss:
:paramtype iss: str
:keyword idp:
:paramtype idp: str
:keyword altsec_id:
:paramtype altsec_id: str
:keyword source_ip:
:paramtype source_ip: str
:keyword skip_registry_private_link_check:
:paramtype skip_registry_private_link_check: bool
"""
super(AetherCreatedBy, self).__init__(**kwargs)
self.user_object_id = user_object_id
self.user_tenant_id = user_tenant_id
self.user_name = user_name
self.puid = puid
self.iss = iss
self.idp = idp
self.altsec_id = altsec_id
self.source_ip = source_ip
self.skip_registry_private_link_check = skip_registry_private_link_check
class AetherCustomReference(msrest.serialization.Model):
"""AetherCustomReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherCustomReference, self).__init__(**kwargs)
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
class AetherDatabaseSink(msrest.serialization.Model):
"""AetherDatabaseSink.
:ivar connection:
:vartype connection: str
:ivar table:
:vartype table: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'table': {'key': 'table', 'type': 'str'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
table: Optional[str] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword table:
:paramtype table: str
"""
super(AetherDatabaseSink, self).__init__(**kwargs)
self.connection = connection
self.table = table
class AetherDatabaseSource(msrest.serialization.Model):
"""AetherDatabaseSource.
:ivar connection:
:vartype connection: str
:ivar query:
:vartype query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'query': {'key': 'query', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[AetherStoredProcedureParameter]'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
query: Optional[str] = None,
stored_procedure_name: Optional[str] = None,
stored_procedure_parameters: Optional[List["AetherStoredProcedureParameter"]] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword query:
:paramtype query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.AetherStoredProcedureParameter]
"""
super(AetherDatabaseSource, self).__init__(**kwargs)
self.connection = connection
self.query = query
self.stored_procedure_name = stored_procedure_name
self.stored_procedure_parameters = stored_procedure_parameters
class AetherDatabricksComputeInfo(msrest.serialization.Model):
"""AetherDatabricksComputeInfo.
:ivar existing_cluster_id:
:vartype existing_cluster_id: str
"""
_attribute_map = {
'existing_cluster_id': {'key': 'existingClusterId', 'type': 'str'},
}
def __init__(
self,
*,
existing_cluster_id: Optional[str] = None,
**kwargs
):
"""
:keyword existing_cluster_id:
:paramtype existing_cluster_id: str
"""
super(AetherDatabricksComputeInfo, self).__init__(**kwargs)
self.existing_cluster_id = existing_cluster_id
class AetherDataLocation(msrest.serialization.Model):
"""AetherDataLocation.
:ivar storage_type: Possible values include: "Cosmos", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:vartype storage_type: str or ~flow.models.AetherDataLocationStorageType
:ivar storage_id:
:vartype storage_id: str
:ivar uri:
:vartype uri: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference:
:vartype data_reference: ~flow.models.AetherDataReference
:ivar aml_dataset:
:vartype aml_dataset: ~flow.models.AetherAmlDataset
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AetherAssetDefinition
:ivar is_compliant:
:vartype is_compliant: bool
:ivar reuse_calculation_fields:
:vartype reuse_calculation_fields: ~flow.models.AetherDataLocationReuseCalculationFields
"""
_attribute_map = {
'storage_type': {'key': 'storageType', 'type': 'str'},
'storage_id': {'key': 'storageId', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference': {'key': 'dataReference', 'type': 'AetherDataReference'},
'aml_dataset': {'key': 'amlDataset', 'type': 'AetherAmlDataset'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AetherAssetDefinition'},
'is_compliant': {'key': 'isCompliant', 'type': 'bool'},
'reuse_calculation_fields': {'key': 'reuseCalculationFields', 'type': 'AetherDataLocationReuseCalculationFields'},
}
def __init__(
self,
*,
storage_type: Optional[Union[str, "AetherDataLocationStorageType"]] = None,
storage_id: Optional[str] = None,
uri: Optional[str] = None,
data_store_name: Optional[str] = None,
data_reference: Optional["AetherDataReference"] = None,
aml_dataset: Optional["AetherAmlDataset"] = None,
asset_definition: Optional["AetherAssetDefinition"] = None,
is_compliant: Optional[bool] = None,
reuse_calculation_fields: Optional["AetherDataLocationReuseCalculationFields"] = None,
**kwargs
):
"""
:keyword storage_type: Possible values include: "Cosmos", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:paramtype storage_type: str or ~flow.models.AetherDataLocationStorageType
:keyword storage_id:
:paramtype storage_id: str
:keyword uri:
:paramtype uri: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference:
:paramtype data_reference: ~flow.models.AetherDataReference
:keyword aml_dataset:
:paramtype aml_dataset: ~flow.models.AetherAmlDataset
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AetherAssetDefinition
:keyword is_compliant:
:paramtype is_compliant: bool
:keyword reuse_calculation_fields:
:paramtype reuse_calculation_fields: ~flow.models.AetherDataLocationReuseCalculationFields
"""
super(AetherDataLocation, self).__init__(**kwargs)
self.storage_type = storage_type
self.storage_id = storage_id
self.uri = uri
self.data_store_name = data_store_name
self.data_reference = data_reference
self.aml_dataset = aml_dataset
self.asset_definition = asset_definition
self.is_compliant = is_compliant
self.reuse_calculation_fields = reuse_calculation_fields
class AetherDataLocationReuseCalculationFields(msrest.serialization.Model):
"""AetherDataLocationReuseCalculationFields.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar data_experiment_id:
:vartype data_experiment_id: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'data_experiment_id': {'key': 'dataExperimentId', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
data_experiment_id: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword data_experiment_id:
:paramtype data_experiment_id: str
"""
super(AetherDataLocationReuseCalculationFields, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.relative_path = relative_path
self.data_experiment_id = data_experiment_id
class AetherDataPath(msrest.serialization.Model):
"""AetherDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar sql_data_path:
:vartype sql_data_path: ~flow.models.AetherSqlDataPath
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'sql_data_path': {'key': 'sqlDataPath', 'type': 'AetherSqlDataPath'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
sql_data_path: Optional["AetherSqlDataPath"] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword sql_data_path:
:paramtype sql_data_path: ~flow.models.AetherSqlDataPath
"""
super(AetherDataPath, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.relative_path = relative_path
self.sql_data_path = sql_data_path
class AetherDataReference(msrest.serialization.Model):
"""AetherDataReference.
:ivar type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"Cosmos", "PhillyHdfs", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2",
"DBFS", "AzureMySqlDatabase", "Custom", "Hdfs".
:vartype type: str or ~flow.models.AetherDataReferenceType
:ivar azure_blob_reference:
:vartype azure_blob_reference: ~flow.models.AetherAzureBlobReference
:ivar azure_data_lake_reference:
:vartype azure_data_lake_reference: ~flow.models.AetherAzureDataLakeReference
:ivar azure_files_reference:
:vartype azure_files_reference: ~flow.models.AetherAzureFilesReference
:ivar cosmos_reference:
:vartype cosmos_reference: ~flow.models.AetherCosmosReference
:ivar philly_hdfs_reference:
:vartype philly_hdfs_reference: ~flow.models.AetherPhillyHdfsReference
:ivar azure_sql_database_reference:
:vartype azure_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar azure_postgres_database_reference:
:vartype azure_postgres_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar azure_data_lake_gen2_reference:
:vartype azure_data_lake_gen2_reference: ~flow.models.AetherAzureDataLakeGen2Reference
:ivar dbfs_reference:
:vartype dbfs_reference: ~flow.models.AetherDBFSReference
:ivar azure_my_sql_database_reference:
:vartype azure_my_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:ivar custom_reference:
:vartype custom_reference: ~flow.models.AetherCustomReference
:ivar hdfs_reference:
:vartype hdfs_reference: ~flow.models.AetherHdfsReference
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'azure_blob_reference': {'key': 'azureBlobReference', 'type': 'AetherAzureBlobReference'},
'azure_data_lake_reference': {'key': 'azureDataLakeReference', 'type': 'AetherAzureDataLakeReference'},
'azure_files_reference': {'key': 'azureFilesReference', 'type': 'AetherAzureFilesReference'},
'cosmos_reference': {'key': 'cosmosReference', 'type': 'AetherCosmosReference'},
'philly_hdfs_reference': {'key': 'phillyHdfsReference', 'type': 'AetherPhillyHdfsReference'},
'azure_sql_database_reference': {'key': 'azureSqlDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'azure_postgres_database_reference': {'key': 'azurePostgresDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'azure_data_lake_gen2_reference': {'key': 'azureDataLakeGen2Reference', 'type': 'AetherAzureDataLakeGen2Reference'},
'dbfs_reference': {'key': 'dbfsReference', 'type': 'AetherDBFSReference'},
'azure_my_sql_database_reference': {'key': 'azureMySqlDatabaseReference', 'type': 'AetherAzureDatabaseReference'},
'custom_reference': {'key': 'customReference', 'type': 'AetherCustomReference'},
'hdfs_reference': {'key': 'hdfsReference', 'type': 'AetherHdfsReference'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AetherDataReferenceType"]] = None,
azure_blob_reference: Optional["AetherAzureBlobReference"] = None,
azure_data_lake_reference: Optional["AetherAzureDataLakeReference"] = None,
azure_files_reference: Optional["AetherAzureFilesReference"] = None,
cosmos_reference: Optional["AetherCosmosReference"] = None,
philly_hdfs_reference: Optional["AetherPhillyHdfsReference"] = None,
azure_sql_database_reference: Optional["AetherAzureDatabaseReference"] = None,
azure_postgres_database_reference: Optional["AetherAzureDatabaseReference"] = None,
azure_data_lake_gen2_reference: Optional["AetherAzureDataLakeGen2Reference"] = None,
dbfs_reference: Optional["AetherDBFSReference"] = None,
azure_my_sql_database_reference: Optional["AetherAzureDatabaseReference"] = None,
custom_reference: Optional["AetherCustomReference"] = None,
hdfs_reference: Optional["AetherHdfsReference"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"Cosmos", "PhillyHdfs", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2",
"DBFS", "AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype type: str or ~flow.models.AetherDataReferenceType
:keyword azure_blob_reference:
:paramtype azure_blob_reference: ~flow.models.AetherAzureBlobReference
:keyword azure_data_lake_reference:
:paramtype azure_data_lake_reference: ~flow.models.AetherAzureDataLakeReference
:keyword azure_files_reference:
:paramtype azure_files_reference: ~flow.models.AetherAzureFilesReference
:keyword cosmos_reference:
:paramtype cosmos_reference: ~flow.models.AetherCosmosReference
:keyword philly_hdfs_reference:
:paramtype philly_hdfs_reference: ~flow.models.AetherPhillyHdfsReference
:keyword azure_sql_database_reference:
:paramtype azure_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword azure_postgres_database_reference:
:paramtype azure_postgres_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword azure_data_lake_gen2_reference:
:paramtype azure_data_lake_gen2_reference: ~flow.models.AetherAzureDataLakeGen2Reference
:keyword dbfs_reference:
:paramtype dbfs_reference: ~flow.models.AetherDBFSReference
:keyword azure_my_sql_database_reference:
:paramtype azure_my_sql_database_reference: ~flow.models.AetherAzureDatabaseReference
:keyword custom_reference:
:paramtype custom_reference: ~flow.models.AetherCustomReference
:keyword hdfs_reference:
:paramtype hdfs_reference: ~flow.models.AetherHdfsReference
"""
super(AetherDataReference, self).__init__(**kwargs)
self.type = type
self.azure_blob_reference = azure_blob_reference
self.azure_data_lake_reference = azure_data_lake_reference
self.azure_files_reference = azure_files_reference
self.cosmos_reference = cosmos_reference
self.philly_hdfs_reference = philly_hdfs_reference
self.azure_sql_database_reference = azure_sql_database_reference
self.azure_postgres_database_reference = azure_postgres_database_reference
self.azure_data_lake_gen2_reference = azure_data_lake_gen2_reference
self.dbfs_reference = dbfs_reference
self.azure_my_sql_database_reference = azure_my_sql_database_reference
self.custom_reference = custom_reference
self.hdfs_reference = hdfs_reference
class AetherDataSetDefinition(msrest.serialization.Model):
"""AetherDataSetDefinition.
:ivar data_type_short_name:
:vartype data_type_short_name: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar value:
:vartype value: ~flow.models.AetherDataSetDefinitionValue
"""
_attribute_map = {
'data_type_short_name': {'key': 'dataTypeShortName', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'value': {'key': 'value', 'type': 'AetherDataSetDefinitionValue'},
}
def __init__(
self,
*,
data_type_short_name: Optional[str] = None,
parameter_name: Optional[str] = None,
value: Optional["AetherDataSetDefinitionValue"] = None,
**kwargs
):
"""
:keyword data_type_short_name:
:paramtype data_type_short_name: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword value:
:paramtype value: ~flow.models.AetherDataSetDefinitionValue
"""
super(AetherDataSetDefinition, self).__init__(**kwargs)
self.data_type_short_name = data_type_short_name
self.parameter_name = parameter_name
self.value = value
class AetherDataSetDefinitionValue(msrest.serialization.Model):
"""AetherDataSetDefinitionValue.
:ivar literal_value:
:vartype literal_value: ~flow.models.AetherDataPath
:ivar data_set_reference:
:vartype data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AetherAssetDefinition
"""
_attribute_map = {
'literal_value': {'key': 'literalValue', 'type': 'AetherDataPath'},
'data_set_reference': {'key': 'dataSetReference', 'type': 'AetherRegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'AetherSavedDataSetReference'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AetherAssetDefinition'},
}
def __init__(
self,
*,
literal_value: Optional["AetherDataPath"] = None,
data_set_reference: Optional["AetherRegisteredDataSetReference"] = None,
saved_data_set_reference: Optional["AetherSavedDataSetReference"] = None,
asset_definition: Optional["AetherAssetDefinition"] = None,
**kwargs
):
"""
:keyword literal_value:
:paramtype literal_value: ~flow.models.AetherDataPath
:keyword data_set_reference:
:paramtype data_set_reference: ~flow.models.AetherRegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.AetherSavedDataSetReference
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AetherAssetDefinition
"""
super(AetherDataSetDefinitionValue, self).__init__(**kwargs)
self.literal_value = literal_value
self.data_set_reference = data_set_reference
self.saved_data_set_reference = saved_data_set_reference
self.asset_definition = asset_definition
class AetherDatasetOutput(msrest.serialization.Model):
"""AetherDatasetOutput.
:ivar dataset_type: Possible values include: "File", "Tabular".
:vartype dataset_type: str or ~flow.models.AetherDatasetType
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.AetherDatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
"""
_attribute_map = {
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'AetherDatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'AetherDatasetOutputOptions'},
}
def __init__(
self,
*,
dataset_type: Optional[Union[str, "AetherDatasetType"]] = None,
dataset_registration: Optional["AetherDatasetRegistration"] = None,
dataset_output_options: Optional["AetherDatasetOutputOptions"] = None,
**kwargs
):
"""
:keyword dataset_type: Possible values include: "File", "Tabular".
:paramtype dataset_type: str or ~flow.models.AetherDatasetType
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.AetherDatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
"""
super(AetherDatasetOutput, self).__init__(**kwargs)
self.dataset_type = dataset_type
self.dataset_registration = dataset_registration
self.dataset_output_options = dataset_output_options
class AetherDatasetOutputOptions(msrest.serialization.Model):
"""AetherDatasetOutputOptions.
:ivar source_globs:
:vartype source_globs: ~flow.models.AetherGlobsOptions
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_datastore_parameter_assignment:
:vartype path_on_datastore_parameter_assignment: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'source_globs': {'key': 'sourceGlobs', 'type': 'AetherGlobsOptions'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_datastore_parameter_assignment': {'key': 'PathOnDatastoreParameterAssignment', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
*,
source_globs: Optional["AetherGlobsOptions"] = None,
path_on_datastore: Optional[str] = None,
path_on_datastore_parameter_assignment: Optional["AetherParameterAssignment"] = None,
**kwargs
):
"""
:keyword source_globs:
:paramtype source_globs: ~flow.models.AetherGlobsOptions
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_datastore_parameter_assignment:
:paramtype path_on_datastore_parameter_assignment: ~flow.models.AetherParameterAssignment
"""
super(AetherDatasetOutputOptions, self).__init__(**kwargs)
self.source_globs = source_globs
self.path_on_datastore = path_on_datastore
self.path_on_datastore_parameter_assignment = path_on_datastore_parameter_assignment
class AetherDatasetRegistration(msrest.serialization.Model):
"""AetherDatasetRegistration.
:ivar name:
:vartype name: str
:ivar create_new_version:
:vartype create_new_version: bool
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'create_new_version': {'key': 'createNewVersion', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
create_new_version: Optional[bool] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword create_new_version:
:paramtype create_new_version: bool
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherDatasetRegistration, self).__init__(**kwargs)
self.name = name
self.create_new_version = create_new_version
self.description = description
self.tags = tags
self.additional_transformations = additional_transformations
class AetherDataSettings(msrest.serialization.Model):
"""AetherDataSettings.
:ivar target_column_name:
:vartype target_column_name: str
:ivar weight_column_name:
:vartype weight_column_name: str
:ivar positive_label:
:vartype positive_label: str
:ivar validation_data:
:vartype validation_data: ~flow.models.AetherValidationDataSettings
:ivar test_data:
:vartype test_data: ~flow.models.AetherTestDataSettings
"""
_attribute_map = {
'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
'positive_label': {'key': 'positiveLabel', 'type': 'str'},
'validation_data': {'key': 'validationData', 'type': 'AetherValidationDataSettings'},
'test_data': {'key': 'testData', 'type': 'AetherTestDataSettings'},
}
def __init__(
self,
*,
target_column_name: Optional[str] = None,
weight_column_name: Optional[str] = None,
positive_label: Optional[str] = None,
validation_data: Optional["AetherValidationDataSettings"] = None,
test_data: Optional["AetherTestDataSettings"] = None,
**kwargs
):
"""
:keyword target_column_name:
:paramtype target_column_name: str
:keyword weight_column_name:
:paramtype weight_column_name: str
:keyword positive_label:
:paramtype positive_label: str
:keyword validation_data:
:paramtype validation_data: ~flow.models.AetherValidationDataSettings
:keyword test_data:
:paramtype test_data: ~flow.models.AetherTestDataSettings
"""
super(AetherDataSettings, self).__init__(**kwargs)
self.target_column_name = target_column_name
self.weight_column_name = weight_column_name
self.positive_label = positive_label
self.validation_data = validation_data
self.test_data = test_data
class AetherDatastoreSetting(msrest.serialization.Model):
"""AetherDatastoreSetting.
:ivar data_store_name:
:vartype data_store_name: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
"""
super(AetherDatastoreSetting, self).__init__(**kwargs)
self.data_store_name = data_store_name
class AetherDataTransferCloudConfiguration(msrest.serialization.Model):
"""AetherDataTransferCloudConfiguration.
:ivar allow_overwrite:
:vartype allow_overwrite: bool
"""
_attribute_map = {
'allow_overwrite': {'key': 'AllowOverwrite', 'type': 'bool'},
}
def __init__(
self,
*,
allow_overwrite: Optional[bool] = None,
**kwargs
):
"""
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
"""
super(AetherDataTransferCloudConfiguration, self).__init__(**kwargs)
self.allow_overwrite = allow_overwrite
class AetherDataTransferSink(msrest.serialization.Model):
"""AetherDataTransferSink.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.AetherDataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.AetherFileSystem
:ivar database_sink:
:vartype database_sink: ~flow.models.AetherDatabaseSink
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'AetherFileSystem'},
'database_sink': {'key': 'databaseSink', 'type': 'AetherDatabaseSink'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AetherDataTransferStorageType"]] = None,
file_system: Optional["AetherFileSystem"] = None,
database_sink: Optional["AetherDatabaseSink"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.AetherDataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.AetherFileSystem
:keyword database_sink:
:paramtype database_sink: ~flow.models.AetherDatabaseSink
"""
super(AetherDataTransferSink, self).__init__(**kwargs)
self.type = type
self.file_system = file_system
self.database_sink = database_sink
class AetherDataTransferSource(msrest.serialization.Model):
"""AetherDataTransferSource.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.AetherDataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.AetherFileSystem
:ivar database_source:
:vartype database_source: ~flow.models.AetherDatabaseSource
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'AetherFileSystem'},
'database_source': {'key': 'databaseSource', 'type': 'AetherDatabaseSource'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AetherDataTransferStorageType"]] = None,
file_system: Optional["AetherFileSystem"] = None,
database_source: Optional["AetherDatabaseSource"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.AetherDataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.AetherFileSystem
:keyword database_source:
:paramtype database_source: ~flow.models.AetherDatabaseSource
"""
super(AetherDataTransferSource, self).__init__(**kwargs)
self.type = type
self.file_system = file_system
self.database_source = database_source
class AetherDataTransferV2CloudSetting(msrest.serialization.Model):
"""AetherDataTransferV2CloudSetting.
:ivar task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:vartype task_type: str or ~flow.models.AetherDataTransferTaskType
:ivar compute_name:
:vartype compute_name: str
:ivar copy_data_task:
:vartype copy_data_task: ~flow.models.AetherCopyDataTask
:ivar import_data_task:
:vartype import_data_task: ~flow.models.AetherImportDataTask
:ivar export_data_task:
:vartype export_data_task: ~flow.models.AetherExportDataTask
:ivar data_transfer_sources: This is a dictionary.
:vartype data_transfer_sources: dict[str, ~flow.models.AetherDataTransferSource]
:ivar data_transfer_sinks: This is a dictionary.
:vartype data_transfer_sinks: dict[str, ~flow.models.AetherDataTransferSink]
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
_attribute_map = {
'task_type': {'key': 'taskType', 'type': 'str'},
'compute_name': {'key': 'ComputeName', 'type': 'str'},
'copy_data_task': {'key': 'CopyDataTask', 'type': 'AetherCopyDataTask'},
'import_data_task': {'key': 'ImportDataTask', 'type': 'AetherImportDataTask'},
'export_data_task': {'key': 'ExportDataTask', 'type': 'AetherExportDataTask'},
'data_transfer_sources': {'key': 'DataTransferSources', 'type': '{AetherDataTransferSource}'},
'data_transfer_sinks': {'key': 'DataTransferSinks', 'type': '{AetherDataTransferSink}'},
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
*,
task_type: Optional[Union[str, "AetherDataTransferTaskType"]] = None,
compute_name: Optional[str] = None,
copy_data_task: Optional["AetherCopyDataTask"] = None,
import_data_task: Optional["AetherImportDataTask"] = None,
export_data_task: Optional["AetherExportDataTask"] = None,
data_transfer_sources: Optional[Dict[str, "AetherDataTransferSource"]] = None,
data_transfer_sinks: Optional[Dict[str, "AetherDataTransferSink"]] = None,
data_copy_mode: Optional[Union[str, "AetherDataCopyMode"]] = None,
**kwargs
):
"""
:keyword task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:paramtype task_type: str or ~flow.models.AetherDataTransferTaskType
:keyword compute_name:
:paramtype compute_name: str
:keyword copy_data_task:
:paramtype copy_data_task: ~flow.models.AetherCopyDataTask
:keyword import_data_task:
:paramtype import_data_task: ~flow.models.AetherImportDataTask
:keyword export_data_task:
:paramtype export_data_task: ~flow.models.AetherExportDataTask
:keyword data_transfer_sources: This is a dictionary.
:paramtype data_transfer_sources: dict[str, ~flow.models.AetherDataTransferSource]
:keyword data_transfer_sinks: This is a dictionary.
:paramtype data_transfer_sinks: dict[str, ~flow.models.AetherDataTransferSink]
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.AetherDataCopyMode
"""
super(AetherDataTransferV2CloudSetting, self).__init__(**kwargs)
self.task_type = task_type
self.compute_name = compute_name
self.copy_data_task = copy_data_task
self.import_data_task = import_data_task
self.export_data_task = export_data_task
self.data_transfer_sources = data_transfer_sources
self.data_transfer_sinks = data_transfer_sinks
self.data_copy_mode = data_copy_mode
class AetherDBFSReference(msrest.serialization.Model):
"""AetherDBFSReference.
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AetherDBFSReference, self).__init__(**kwargs)
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class AetherDockerSettingConfiguration(msrest.serialization.Model):
"""AetherDockerSettingConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar shm_size:
:vartype shm_size: str
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
*,
use_docker: Optional[bool] = None,
shared_volumes: Optional[bool] = None,
shm_size: Optional[str] = None,
arguments: Optional[List[str]] = None,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword shm_size:
:paramtype shm_size: str
:keyword arguments:
:paramtype arguments: list[str]
"""
super(AetherDockerSettingConfiguration, self).__init__(**kwargs)
self.use_docker = use_docker
self.shared_volumes = shared_volumes
self.shm_size = shm_size
self.arguments = arguments
class AetherDoWhileControlFlowInfo(msrest.serialization.Model):
"""AetherDoWhileControlFlowInfo.
:ivar output_port_name_to_input_port_names_mapping: Dictionary of
<components·1f2aigm·schemas·aetherdowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:vartype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:ivar condition_output_port_name:
:vartype condition_output_port_name: str
:ivar run_settings:
:vartype run_settings: ~flow.models.AetherDoWhileControlFlowRunSettings
"""
_attribute_map = {
'output_port_name_to_input_port_names_mapping': {'key': 'outputPortNameToInputPortNamesMapping', 'type': '{[str]}'},
'condition_output_port_name': {'key': 'conditionOutputPortName', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': 'AetherDoWhileControlFlowRunSettings'},
}
def __init__(
self,
*,
output_port_name_to_input_port_names_mapping: Optional[Dict[str, List[str]]] = None,
condition_output_port_name: Optional[str] = None,
run_settings: Optional["AetherDoWhileControlFlowRunSettings"] = None,
**kwargs
):
"""
:keyword output_port_name_to_input_port_names_mapping: Dictionary of
<components·1f2aigm·schemas·aetherdowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:paramtype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:keyword condition_output_port_name:
:paramtype condition_output_port_name: str
:keyword run_settings:
:paramtype run_settings: ~flow.models.AetherDoWhileControlFlowRunSettings
"""
super(AetherDoWhileControlFlowInfo, self).__init__(**kwargs)
self.output_port_name_to_input_port_names_mapping = output_port_name_to_input_port_names_mapping
self.condition_output_port_name = condition_output_port_name
self.run_settings = run_settings
class AetherDoWhileControlFlowRunSettings(msrest.serialization.Model):
"""AetherDoWhileControlFlowRunSettings.
:ivar max_loop_iteration_count:
:vartype max_loop_iteration_count: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'max_loop_iteration_count': {'key': 'maxLoopIterationCount', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
*,
max_loop_iteration_count: Optional["AetherParameterAssignment"] = None,
**kwargs
):
"""
:keyword max_loop_iteration_count:
:paramtype max_loop_iteration_count: ~flow.models.AetherParameterAssignment
"""
super(AetherDoWhileControlFlowRunSettings, self).__init__(**kwargs)
self.max_loop_iteration_count = max_loop_iteration_count
class AetherEntityInterfaceDocumentation(msrest.serialization.Model):
"""AetherEntityInterfaceDocumentation.
:ivar inputs_documentation: Dictionary of :code:`<string>`.
:vartype inputs_documentation: dict[str, str]
:ivar outputs_documentation: Dictionary of :code:`<string>`.
:vartype outputs_documentation: dict[str, str]
:ivar parameters_documentation: Dictionary of :code:`<string>`.
:vartype parameters_documentation: dict[str, str]
"""
_attribute_map = {
'inputs_documentation': {'key': 'inputsDocumentation', 'type': '{str}'},
'outputs_documentation': {'key': 'outputsDocumentation', 'type': '{str}'},
'parameters_documentation': {'key': 'parametersDocumentation', 'type': '{str}'},
}
def __init__(
self,
*,
inputs_documentation: Optional[Dict[str, str]] = None,
outputs_documentation: Optional[Dict[str, str]] = None,
parameters_documentation: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword inputs_documentation: Dictionary of :code:`<string>`.
:paramtype inputs_documentation: dict[str, str]
:keyword outputs_documentation: Dictionary of :code:`<string>`.
:paramtype outputs_documentation: dict[str, str]
:keyword parameters_documentation: Dictionary of :code:`<string>`.
:paramtype parameters_documentation: dict[str, str]
"""
super(AetherEntityInterfaceDocumentation, self).__init__(**kwargs)
self.inputs_documentation = inputs_documentation
self.outputs_documentation = outputs_documentation
self.parameters_documentation = parameters_documentation
class AetherEntrySetting(msrest.serialization.Model):
"""AetherEntrySetting.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
class_name: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(AetherEntrySetting, self).__init__(**kwargs)
self.file = file
self.class_name = class_name
class AetherEnvironmentConfiguration(msrest.serialization.Model):
"""AetherEnvironmentConfiguration.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar use_environment_definition:
:vartype use_environment_definition: bool
:ivar environment_definition_string:
:vartype environment_definition_string: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'use_environment_definition': {'key': 'useEnvironmentDefinition', 'type': 'bool'},
'environment_definition_string': {'key': 'environmentDefinitionString', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
version: Optional[str] = None,
use_environment_definition: Optional[bool] = None,
environment_definition_string: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword use_environment_definition:
:paramtype use_environment_definition: bool
:keyword environment_definition_string:
:paramtype environment_definition_string: str
"""
super(AetherEnvironmentConfiguration, self).__init__(**kwargs)
self.name = name
self.version = version
self.use_environment_definition = use_environment_definition
self.environment_definition_string = environment_definition_string
class AetherEsCloudConfiguration(msrest.serialization.Model):
"""AetherEsCloudConfiguration.
:ivar enable_output_to_file_based_on_data_type_id:
:vartype enable_output_to_file_based_on_data_type_id: bool
:ivar aml_compute_priority_internal:
:vartype aml_compute_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar itp_priority_internal:
:vartype itp_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar singularity_priority_internal:
:vartype singularity_priority_internal: ~flow.models.AetherPriorityConfiguration
:ivar environment:
:vartype environment: ~flow.models.AetherEnvironmentConfiguration
:ivar hyper_drive_configuration:
:vartype hyper_drive_configuration: ~flow.models.AetherHyperDriveConfiguration
:ivar k8_s_config:
:vartype k8_s_config: ~flow.models.AetherK8SConfiguration
:ivar resource_config:
:vartype resource_config: ~flow.models.AetherResourceConfiguration
:ivar torch_distributed_config:
:vartype torch_distributed_config: ~flow.models.AetherTorchDistributedConfiguration
:ivar target_selector_config:
:vartype target_selector_config: ~flow.models.AetherTargetSelectorConfiguration
:ivar docker_config:
:vartype docker_config: ~flow.models.AetherDockerSettingConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: int
:ivar identity:
:vartype identity: ~flow.models.AetherIdentitySetting
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar run_config:
:vartype run_config: str
"""
_attribute_map = {
'enable_output_to_file_based_on_data_type_id': {'key': 'enableOutputToFileBasedOnDataTypeId', 'type': 'bool'},
'aml_compute_priority_internal': {'key': 'amlComputePriorityInternal', 'type': 'AetherPriorityConfiguration'},
'itp_priority_internal': {'key': 'itpPriorityInternal', 'type': 'AetherPriorityConfiguration'},
'singularity_priority_internal': {'key': 'singularityPriorityInternal', 'type': 'AetherPriorityConfiguration'},
'environment': {'key': 'environment', 'type': 'AetherEnvironmentConfiguration'},
'hyper_drive_configuration': {'key': 'hyperDriveConfiguration', 'type': 'AetherHyperDriveConfiguration'},
'k8_s_config': {'key': 'k8sConfig', 'type': 'AetherK8SConfiguration'},
'resource_config': {'key': 'resourceConfig', 'type': 'AetherResourceConfiguration'},
'torch_distributed_config': {'key': 'torchDistributedConfig', 'type': 'AetherTorchDistributedConfiguration'},
'target_selector_config': {'key': 'targetSelectorConfig', 'type': 'AetherTargetSelectorConfiguration'},
'docker_config': {'key': 'dockerConfig', 'type': 'AetherDockerSettingConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'int'},
'identity': {'key': 'identity', 'type': 'AetherIdentitySetting'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'run_config': {'key': 'runConfig', 'type': 'str'},
}
def __init__(
self,
*,
enable_output_to_file_based_on_data_type_id: Optional[bool] = None,
aml_compute_priority_internal: Optional["AetherPriorityConfiguration"] = None,
itp_priority_internal: Optional["AetherPriorityConfiguration"] = None,
singularity_priority_internal: Optional["AetherPriorityConfiguration"] = None,
environment: Optional["AetherEnvironmentConfiguration"] = None,
hyper_drive_configuration: Optional["AetherHyperDriveConfiguration"] = None,
k8_s_config: Optional["AetherK8SConfiguration"] = None,
resource_config: Optional["AetherResourceConfiguration"] = None,
torch_distributed_config: Optional["AetherTorchDistributedConfiguration"] = None,
target_selector_config: Optional["AetherTargetSelectorConfiguration"] = None,
docker_config: Optional["AetherDockerSettingConfiguration"] = None,
environment_variables: Optional[Dict[str, str]] = None,
max_run_duration_seconds: Optional[int] = None,
identity: Optional["AetherIdentitySetting"] = None,
application_endpoints: Optional[Dict[str, "ApplicationEndpointConfiguration"]] = None,
run_config: Optional[str] = None,
**kwargs
):
"""
:keyword enable_output_to_file_based_on_data_type_id:
:paramtype enable_output_to_file_based_on_data_type_id: bool
:keyword aml_compute_priority_internal:
:paramtype aml_compute_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword itp_priority_internal:
:paramtype itp_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword singularity_priority_internal:
:paramtype singularity_priority_internal: ~flow.models.AetherPriorityConfiguration
:keyword environment:
:paramtype environment: ~flow.models.AetherEnvironmentConfiguration
:keyword hyper_drive_configuration:
:paramtype hyper_drive_configuration: ~flow.models.AetherHyperDriveConfiguration
:keyword k8_s_config:
:paramtype k8_s_config: ~flow.models.AetherK8SConfiguration
:keyword resource_config:
:paramtype resource_config: ~flow.models.AetherResourceConfiguration
:keyword torch_distributed_config:
:paramtype torch_distributed_config: ~flow.models.AetherTorchDistributedConfiguration
:keyword target_selector_config:
:paramtype target_selector_config: ~flow.models.AetherTargetSelectorConfiguration
:keyword docker_config:
:paramtype docker_config: ~flow.models.AetherDockerSettingConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: int
:keyword identity:
:paramtype identity: ~flow.models.AetherIdentitySetting
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword run_config:
:paramtype run_config: str
"""
super(AetherEsCloudConfiguration, self).__init__(**kwargs)
self.enable_output_to_file_based_on_data_type_id = enable_output_to_file_based_on_data_type_id
self.aml_compute_priority_internal = aml_compute_priority_internal
self.itp_priority_internal = itp_priority_internal
self.singularity_priority_internal = singularity_priority_internal
self.environment = environment
self.hyper_drive_configuration = hyper_drive_configuration
self.k8_s_config = k8_s_config
self.resource_config = resource_config
self.torch_distributed_config = torch_distributed_config
self.target_selector_config = target_selector_config
self.docker_config = docker_config
self.environment_variables = environment_variables
self.max_run_duration_seconds = max_run_duration_seconds
self.identity = identity
self.application_endpoints = application_endpoints
self.run_config = run_config
class AetherExportDataTask(msrest.serialization.Model):
"""AetherExportDataTask.
:ivar data_transfer_sink:
:vartype data_transfer_sink: ~flow.models.AetherDataTransferSink
"""
_attribute_map = {
'data_transfer_sink': {'key': 'DataTransferSink', 'type': 'AetherDataTransferSink'},
}
def __init__(
self,
*,
data_transfer_sink: Optional["AetherDataTransferSink"] = None,
**kwargs
):
"""
:keyword data_transfer_sink:
:paramtype data_transfer_sink: ~flow.models.AetherDataTransferSink
"""
super(AetherExportDataTask, self).__init__(**kwargs)
self.data_transfer_sink = data_transfer_sink
class AetherFeaturizationSettings(msrest.serialization.Model):
"""AetherFeaturizationSettings.
:ivar mode: Possible values include: "Auto", "Custom", "Off".
:vartype mode: str or ~flow.models.AetherFeaturizationMode
:ivar blocked_transformers:
:vartype blocked_transformers: list[str]
:ivar column_purposes: Dictionary of :code:`<string>`.
:vartype column_purposes: dict[str, str]
:ivar drop_columns:
:vartype drop_columns: list[str]
:ivar transformer_params: Dictionary of
<components·1y90i4m·schemas·aetherfeaturizationsettings·properties·transformerparams·additionalproperties>.
:vartype transformer_params: dict[str, list[~flow.models.AetherColumnTransformer]]
:ivar dataset_language:
:vartype dataset_language: str
:ivar enable_dnn_featurization:
:vartype enable_dnn_featurization: bool
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
'column_purposes': {'key': 'columnPurposes', 'type': '{str}'},
'drop_columns': {'key': 'dropColumns', 'type': '[str]'},
'transformer_params': {'key': 'transformerParams', 'type': '{[AetherColumnTransformer]}'},
'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherFeaturizationMode"]] = None,
blocked_transformers: Optional[List[str]] = None,
column_purposes: Optional[Dict[str, str]] = None,
drop_columns: Optional[List[str]] = None,
transformer_params: Optional[Dict[str, List["AetherColumnTransformer"]]] = None,
dataset_language: Optional[str] = None,
enable_dnn_featurization: Optional[bool] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom", "Off".
:paramtype mode: str or ~flow.models.AetherFeaturizationMode
:keyword blocked_transformers:
:paramtype blocked_transformers: list[str]
:keyword column_purposes: Dictionary of :code:`<string>`.
:paramtype column_purposes: dict[str, str]
:keyword drop_columns:
:paramtype drop_columns: list[str]
:keyword transformer_params: Dictionary of
<components·1y90i4m·schemas·aetherfeaturizationsettings·properties·transformerparams·additionalproperties>.
:paramtype transformer_params: dict[str, list[~flow.models.AetherColumnTransformer]]
:keyword dataset_language:
:paramtype dataset_language: str
:keyword enable_dnn_featurization:
:paramtype enable_dnn_featurization: bool
"""
super(AetherFeaturizationSettings, self).__init__(**kwargs)
self.mode = mode
self.blocked_transformers = blocked_transformers
self.column_purposes = column_purposes
self.drop_columns = drop_columns
self.transformer_params = transformer_params
self.dataset_language = dataset_language
self.enable_dnn_featurization = enable_dnn_featurization
class AetherFileSystem(msrest.serialization.Model):
"""AetherFileSystem.
:ivar connection:
:vartype connection: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
path: Optional[str] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword path:
:paramtype path: str
"""
super(AetherFileSystem, self).__init__(**kwargs)
self.connection = connection
self.path = path
class AetherForecastHorizon(msrest.serialization.Model):
"""AetherForecastHorizon.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherForecastHorizonMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherForecastHorizonMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherForecastHorizonMode
:keyword value:
:paramtype value: int
"""
super(AetherForecastHorizon, self).__init__(**kwargs)
self.mode = mode
self.value = value
class AetherForecastingSettings(msrest.serialization.Model):
"""AetherForecastingSettings.
:ivar country_or_region_for_holidays:
:vartype country_or_region_for_holidays: str
:ivar time_column_name:
:vartype time_column_name: str
:ivar target_lags:
:vartype target_lags: ~flow.models.AetherTargetLags
:ivar target_rolling_window_size:
:vartype target_rolling_window_size: ~flow.models.AetherTargetRollingWindowSize
:ivar forecast_horizon:
:vartype forecast_horizon: ~flow.models.AetherForecastHorizon
:ivar time_series_id_column_names:
:vartype time_series_id_column_names: list[str]
:ivar frequency:
:vartype frequency: str
:ivar feature_lags:
:vartype feature_lags: str
:ivar seasonality:
:vartype seasonality: ~flow.models.AetherSeasonality
:ivar short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:vartype short_series_handling_config: str or
~flow.models.AetherShortSeriesHandlingConfiguration
:ivar use_stl: Possible values include: "Season", "SeasonTrend".
:vartype use_stl: str or ~flow.models.AetherUseStl
:ivar target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:vartype target_aggregate_function: str or ~flow.models.AetherTargetAggregationFunction
:ivar cv_step_size:
:vartype cv_step_size: int
:ivar features_unknown_at_forecast_time:
:vartype features_unknown_at_forecast_time: list[str]
"""
_attribute_map = {
'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
'target_lags': {'key': 'targetLags', 'type': 'AetherTargetLags'},
'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'AetherTargetRollingWindowSize'},
'forecast_horizon': {'key': 'forecastHorizon', 'type': 'AetherForecastHorizon'},
'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
'frequency': {'key': 'frequency', 'type': 'str'},
'feature_lags': {'key': 'featureLags', 'type': 'str'},
'seasonality': {'key': 'seasonality', 'type': 'AetherSeasonality'},
'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
'use_stl': {'key': 'useStl', 'type': 'str'},
'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
'features_unknown_at_forecast_time': {'key': 'featuresUnknownAtForecastTime', 'type': '[str]'},
}
def __init__(
self,
*,
country_or_region_for_holidays: Optional[str] = None,
time_column_name: Optional[str] = None,
target_lags: Optional["AetherTargetLags"] = None,
target_rolling_window_size: Optional["AetherTargetRollingWindowSize"] = None,
forecast_horizon: Optional["AetherForecastHorizon"] = None,
time_series_id_column_names: Optional[List[str]] = None,
frequency: Optional[str] = None,
feature_lags: Optional[str] = None,
seasonality: Optional["AetherSeasonality"] = None,
short_series_handling_config: Optional[Union[str, "AetherShortSeriesHandlingConfiguration"]] = None,
use_stl: Optional[Union[str, "AetherUseStl"]] = None,
target_aggregate_function: Optional[Union[str, "AetherTargetAggregationFunction"]] = None,
cv_step_size: Optional[int] = None,
features_unknown_at_forecast_time: Optional[List[str]] = None,
**kwargs
):
"""
:keyword country_or_region_for_holidays:
:paramtype country_or_region_for_holidays: str
:keyword time_column_name:
:paramtype time_column_name: str
:keyword target_lags:
:paramtype target_lags: ~flow.models.AetherTargetLags
:keyword target_rolling_window_size:
:paramtype target_rolling_window_size: ~flow.models.AetherTargetRollingWindowSize
:keyword forecast_horizon:
:paramtype forecast_horizon: ~flow.models.AetherForecastHorizon
:keyword time_series_id_column_names:
:paramtype time_series_id_column_names: list[str]
:keyword frequency:
:paramtype frequency: str
:keyword feature_lags:
:paramtype feature_lags: str
:keyword seasonality:
:paramtype seasonality: ~flow.models.AetherSeasonality
:keyword short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:paramtype short_series_handling_config: str or
~flow.models.AetherShortSeriesHandlingConfiguration
:keyword use_stl: Possible values include: "Season", "SeasonTrend".
:paramtype use_stl: str or ~flow.models.AetherUseStl
:keyword target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:paramtype target_aggregate_function: str or ~flow.models.AetherTargetAggregationFunction
:keyword cv_step_size:
:paramtype cv_step_size: int
:keyword features_unknown_at_forecast_time:
:paramtype features_unknown_at_forecast_time: list[str]
"""
super(AetherForecastingSettings, self).__init__(**kwargs)
self.country_or_region_for_holidays = country_or_region_for_holidays
self.time_column_name = time_column_name
self.target_lags = target_lags
self.target_rolling_window_size = target_rolling_window_size
self.forecast_horizon = forecast_horizon
self.time_series_id_column_names = time_series_id_column_names
self.frequency = frequency
self.feature_lags = feature_lags
self.seasonality = seasonality
self.short_series_handling_config = short_series_handling_config
self.use_stl = use_stl
self.target_aggregate_function = target_aggregate_function
self.cv_step_size = cv_step_size
self.features_unknown_at_forecast_time = features_unknown_at_forecast_time
class AetherGeneralSettings(msrest.serialization.Model):
"""AetherGeneralSettings.
:ivar primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:vartype primary_metric: str or ~flow.models.AetherPrimaryMetrics
:ivar task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:vartype task_type: str or ~flow.models.AetherTaskType
:ivar log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:vartype log_verbosity: str or ~flow.models.AetherLogVerbosity
"""
_attribute_map = {
'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
'task_type': {'key': 'taskType', 'type': 'str'},
'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
}
def __init__(
self,
*,
primary_metric: Optional[Union[str, "AetherPrimaryMetrics"]] = None,
task_type: Optional[Union[str, "AetherTaskType"]] = None,
log_verbosity: Optional[Union[str, "AetherLogVerbosity"]] = None,
**kwargs
):
"""
:keyword primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:paramtype primary_metric: str or ~flow.models.AetherPrimaryMetrics
:keyword task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:paramtype task_type: str or ~flow.models.AetherTaskType
:keyword log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:paramtype log_verbosity: str or ~flow.models.AetherLogVerbosity
"""
super(AetherGeneralSettings, self).__init__(**kwargs)
self.primary_metric = primary_metric
self.task_type = task_type
self.log_verbosity = log_verbosity
class AetherGlobsOptions(msrest.serialization.Model):
"""AetherGlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
*,
glob_patterns: Optional[List[str]] = None,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(AetherGlobsOptions, self).__init__(**kwargs)
self.glob_patterns = glob_patterns
class AetherGraphControlNode(msrest.serialization.Model):
"""AetherGraphControlNode.
:ivar id:
:vartype id: str
:ivar control_type: The only acceptable values to pass in are None and "IfElse". The default
value is None.
:vartype control_type: str
:ivar control_parameter:
:vartype control_parameter: ~flow.models.AetherParameterAssignment
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'control_type': {'key': 'controlType', 'type': 'str'},
'control_parameter': {'key': 'controlParameter', 'type': 'AetherParameterAssignment'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
control_type: Optional[str] = None,
control_parameter: Optional["AetherParameterAssignment"] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword control_type: The only acceptable values to pass in are None and "IfElse". The
default value is None.
:paramtype control_type: str
:keyword control_parameter:
:paramtype control_parameter: ~flow.models.AetherParameterAssignment
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphControlNode, self).__init__(**kwargs)
self.id = id
self.control_type = control_type
self.control_parameter = control_parameter
self.run_attribution = run_attribution
class AetherGraphControlReferenceNode(msrest.serialization.Model):
"""AetherGraphControlReferenceNode.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar comment:
:vartype comment: str
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.AetherControlFlowType
:ivar reference_node_id:
:vartype reference_node_id: str
:ivar do_while_control_flow_info:
:vartype do_while_control_flow_info: ~flow.models.AetherDoWhileControlFlowInfo
:ivar parallel_for_control_flow_info:
:vartype parallel_for_control_flow_info: ~flow.models.AetherParallelForControlFlowInfo
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'reference_node_id': {'key': 'referenceNodeId', 'type': 'str'},
'do_while_control_flow_info': {'key': 'doWhileControlFlowInfo', 'type': 'AetherDoWhileControlFlowInfo'},
'parallel_for_control_flow_info': {'key': 'parallelForControlFlowInfo', 'type': 'AetherParallelForControlFlowInfo'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
comment: Optional[str] = None,
control_flow_type: Optional[Union[str, "AetherControlFlowType"]] = None,
reference_node_id: Optional[str] = None,
do_while_control_flow_info: Optional["AetherDoWhileControlFlowInfo"] = None,
parallel_for_control_flow_info: Optional["AetherParallelForControlFlowInfo"] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword comment:
:paramtype comment: str
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.AetherControlFlowType
:keyword reference_node_id:
:paramtype reference_node_id: str
:keyword do_while_control_flow_info:
:paramtype do_while_control_flow_info: ~flow.models.AetherDoWhileControlFlowInfo
:keyword parallel_for_control_flow_info:
:paramtype parallel_for_control_flow_info: ~flow.models.AetherParallelForControlFlowInfo
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphControlReferenceNode, self).__init__(**kwargs)
self.id = id
self.name = name
self.comment = comment
self.control_flow_type = control_flow_type
self.reference_node_id = reference_node_id
self.do_while_control_flow_info = do_while_control_flow_info
self.parallel_for_control_flow_info = parallel_for_control_flow_info
self.run_attribution = run_attribution
class AetherGraphDatasetNode(msrest.serialization.Model):
"""AetherGraphDatasetNode.
:ivar id:
:vartype id: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar data_path_parameter_name:
:vartype data_path_parameter_name: str
:ivar data_set_definition:
:vartype data_set_definition: ~flow.models.AetherDataSetDefinition
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'data_path_parameter_name': {'key': 'dataPathParameterName', 'type': 'str'},
'data_set_definition': {'key': 'dataSetDefinition', 'type': 'AetherDataSetDefinition'},
}
def __init__(
self,
*,
id: Optional[str] = None,
dataset_id: Optional[str] = None,
data_path_parameter_name: Optional[str] = None,
data_set_definition: Optional["AetherDataSetDefinition"] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword data_path_parameter_name:
:paramtype data_path_parameter_name: str
:keyword data_set_definition:
:paramtype data_set_definition: ~flow.models.AetherDataSetDefinition
"""
super(AetherGraphDatasetNode, self).__init__(**kwargs)
self.id = id
self.dataset_id = dataset_id
self.data_path_parameter_name = data_path_parameter_name
self.data_set_definition = data_set_definition
class AetherGraphEdge(msrest.serialization.Model):
"""AetherGraphEdge.
:ivar source_output_port:
:vartype source_output_port: ~flow.models.AetherPortInfo
:ivar destination_input_port:
:vartype destination_input_port: ~flow.models.AetherPortInfo
"""
_attribute_map = {
'source_output_port': {'key': 'sourceOutputPort', 'type': 'AetherPortInfo'},
'destination_input_port': {'key': 'destinationInputPort', 'type': 'AetherPortInfo'},
}
def __init__(
self,
*,
source_output_port: Optional["AetherPortInfo"] = None,
destination_input_port: Optional["AetherPortInfo"] = None,
**kwargs
):
"""
:keyword source_output_port:
:paramtype source_output_port: ~flow.models.AetherPortInfo
:keyword destination_input_port:
:paramtype destination_input_port: ~flow.models.AetherPortInfo
"""
super(AetherGraphEdge, self).__init__(**kwargs)
self.source_output_port = source_output_port
self.destination_input_port = destination_input_port
class AetherGraphEntity(msrest.serialization.Model):
"""AetherGraphEntity.
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.AetherGraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.AetherGraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.AetherGraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.AetherGraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.AetherGraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.AetherGraphEdge]
:ivar default_compute:
:vartype default_compute: ~flow.models.AetherComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.AetherDatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar etag:
:vartype etag: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.AetherEntityStatus
"""
_attribute_map = {
'module_nodes': {'key': 'moduleNodes', 'type': '[AetherGraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[AetherGraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[AetherGraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[AetherGraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[AetherGraphControlNode]'},
'edges': {'key': 'edges', 'type': '[AetherGraphEdge]'},
'default_compute': {'key': 'defaultCompute', 'type': 'AetherComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'AetherDatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'AetherCloudPrioritySetting'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
}
def __init__(
self,
*,
module_nodes: Optional[List["AetherGraphModuleNode"]] = None,
dataset_nodes: Optional[List["AetherGraphDatasetNode"]] = None,
sub_graph_nodes: Optional[List["AetherGraphReferenceNode"]] = None,
control_reference_nodes: Optional[List["AetherGraphControlReferenceNode"]] = None,
control_nodes: Optional[List["AetherGraphControlNode"]] = None,
edges: Optional[List["AetherGraphEdge"]] = None,
default_compute: Optional["AetherComputeSetting"] = None,
default_datastore: Optional["AetherDatastoreSetting"] = None,
default_cloud_priority: Optional["AetherCloudPrioritySetting"] = None,
parent_sub_graph_module_ids: Optional[List[str]] = None,
id: Optional[str] = None,
workspace_id: Optional[str] = None,
etag: Optional[str] = None,
tags: Optional[List[str]] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
entity_status: Optional[Union[str, "AetherEntityStatus"]] = None,
**kwargs
):
"""
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.AetherGraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.AetherGraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.AetherGraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.AetherGraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.AetherGraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.AetherGraphEdge]
:keyword default_compute:
:paramtype default_compute: ~flow.models.AetherComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.AetherDatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword etag:
:paramtype etag: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.AetherEntityStatus
"""
super(AetherGraphEntity, self).__init__(**kwargs)
self.module_nodes = module_nodes
self.dataset_nodes = dataset_nodes
self.sub_graph_nodes = sub_graph_nodes
self.control_reference_nodes = control_reference_nodes
self.control_nodes = control_nodes
self.edges = edges
self.default_compute = default_compute
self.default_datastore = default_datastore
self.default_cloud_priority = default_cloud_priority
self.parent_sub_graph_module_ids = parent_sub_graph_module_ids
self.id = id
self.workspace_id = workspace_id
self.etag = etag
self.tags = tags
self.created_date = created_date
self.last_modified_date = last_modified_date
self.entity_status = entity_status
class AetherGraphModuleNode(msrest.serialization.Model):
"""AetherGraphModuleNode.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar default_data_retention_hint:
:vartype default_data_retention_hint: int
:ivar compliance_cluster:
:vartype compliance_cluster: str
:ivar euclid_workspace_id:
:vartype euclid_workspace_id: str
:ivar attached_modules:
:vartype attached_modules: list[str]
:ivar acceptable_machine_clusters:
:vartype acceptable_machine_clusters: list[str]
:ivar custom_data_location_id:
:vartype custom_data_location_id: str
:ivar alert_timeout_duration:
:vartype alert_timeout_duration: str
:ivar runconfig:
:vartype runconfig: str
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.AetherOutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.AetherInputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.AetherControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.AetherExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'default_data_retention_hint': {'key': 'defaultDataRetentionHint', 'type': 'int'},
'compliance_cluster': {'key': 'complianceCluster', 'type': 'str'},
'euclid_workspace_id': {'key': 'euclidWorkspaceId', 'type': 'str'},
'attached_modules': {'key': 'attachedModules', 'type': '[str]'},
'acceptable_machine_clusters': {'key': 'acceptableMachineClusters', 'type': '[str]'},
'custom_data_location_id': {'key': 'customDataLocationId', 'type': 'str'},
'alert_timeout_duration': {'key': 'alertTimeoutDuration', 'type': 'str'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[AetherParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[AetherParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[AetherOutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[AetherInputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[AetherControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
cloud_priority: Optional[int] = None,
default_data_retention_hint: Optional[int] = None,
compliance_cluster: Optional[str] = None,
euclid_workspace_id: Optional[str] = None,
attached_modules: Optional[List[str]] = None,
acceptable_machine_clusters: Optional[List[str]] = None,
custom_data_location_id: Optional[str] = None,
alert_timeout_duration: Optional[str] = None,
runconfig: Optional[str] = None,
id: Optional[str] = None,
module_id: Optional[str] = None,
comment: Optional[str] = None,
name: Optional[str] = None,
module_parameters: Optional[List["AetherParameterAssignment"]] = None,
module_metadata_parameters: Optional[List["AetherParameterAssignment"]] = None,
module_output_settings: Optional[List["AetherOutputSetting"]] = None,
module_input_settings: Optional[List["AetherInputSetting"]] = None,
use_graph_default_compute: Optional[bool] = None,
use_graph_default_datastore: Optional[bool] = None,
regenerate_output: Optional[bool] = None,
control_inputs: Optional[List["AetherControlInput"]] = None,
cloud_settings: Optional["AetherCloudSettings"] = None,
execution_phase: Optional[Union[str, "AetherExecutionPhase"]] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword default_data_retention_hint:
:paramtype default_data_retention_hint: int
:keyword compliance_cluster:
:paramtype compliance_cluster: str
:keyword euclid_workspace_id:
:paramtype euclid_workspace_id: str
:keyword attached_modules:
:paramtype attached_modules: list[str]
:keyword acceptable_machine_clusters:
:paramtype acceptable_machine_clusters: list[str]
:keyword custom_data_location_id:
:paramtype custom_data_location_id: str
:keyword alert_timeout_duration:
:paramtype alert_timeout_duration: str
:keyword runconfig:
:paramtype runconfig: str
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.AetherOutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.AetherInputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.AetherControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.AetherExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphModuleNode, self).__init__(**kwargs)
self.cloud_priority = cloud_priority
self.default_data_retention_hint = default_data_retention_hint
self.compliance_cluster = compliance_cluster
self.euclid_workspace_id = euclid_workspace_id
self.attached_modules = attached_modules
self.acceptable_machine_clusters = acceptable_machine_clusters
self.custom_data_location_id = custom_data_location_id
self.alert_timeout_duration = alert_timeout_duration
self.runconfig = runconfig
self.id = id
self.module_id = module_id
self.comment = comment
self.name = name
self.module_parameters = module_parameters
self.module_metadata_parameters = module_metadata_parameters
self.module_output_settings = module_output_settings
self.module_input_settings = module_input_settings
self.use_graph_default_compute = use_graph_default_compute
self.use_graph_default_datastore = use_graph_default_datastore
self.regenerate_output = regenerate_output
self.control_inputs = control_inputs
self.cloud_settings = cloud_settings
self.execution_phase = execution_phase
self.run_attribution = run_attribution
class AetherGraphReferenceNode(msrest.serialization.Model):
"""AetherGraphReferenceNode.
:ivar graph_id:
:vartype graph_id: str
:ivar default_compute:
:vartype default_compute: ~flow.models.AetherComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.AetherDatastoreSetting
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.AetherOutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.AetherInputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.AetherControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.AetherExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'default_compute': {'key': 'defaultCompute', 'type': 'AetherComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'AetherDatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[AetherParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[AetherParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[AetherOutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[AetherInputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[AetherControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
graph_id: Optional[str] = None,
default_compute: Optional["AetherComputeSetting"] = None,
default_datastore: Optional["AetherDatastoreSetting"] = None,
id: Optional[str] = None,
module_id: Optional[str] = None,
comment: Optional[str] = None,
name: Optional[str] = None,
module_parameters: Optional[List["AetherParameterAssignment"]] = None,
module_metadata_parameters: Optional[List["AetherParameterAssignment"]] = None,
module_output_settings: Optional[List["AetherOutputSetting"]] = None,
module_input_settings: Optional[List["AetherInputSetting"]] = None,
use_graph_default_compute: Optional[bool] = None,
use_graph_default_datastore: Optional[bool] = None,
regenerate_output: Optional[bool] = None,
control_inputs: Optional[List["AetherControlInput"]] = None,
cloud_settings: Optional["AetherCloudSettings"] = None,
execution_phase: Optional[Union[str, "AetherExecutionPhase"]] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword default_compute:
:paramtype default_compute: ~flow.models.AetherComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.AetherDatastoreSetting
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.AetherParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.AetherOutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.AetherInputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.AetherControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.AetherExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(AetherGraphReferenceNode, self).__init__(**kwargs)
self.graph_id = graph_id
self.default_compute = default_compute
self.default_datastore = default_datastore
self.id = id
self.module_id = module_id
self.comment = comment
self.name = name
self.module_parameters = module_parameters
self.module_metadata_parameters = module_metadata_parameters
self.module_output_settings = module_output_settings
self.module_input_settings = module_input_settings
self.use_graph_default_compute = use_graph_default_compute
self.use_graph_default_datastore = use_graph_default_datastore
self.regenerate_output = regenerate_output
self.control_inputs = control_inputs
self.cloud_settings = cloud_settings
self.execution_phase = execution_phase
self.run_attribution = run_attribution
class AetherHdfsReference(msrest.serialization.Model):
"""AetherHdfsReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherHdfsReference, self).__init__(**kwargs)
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
class AetherHdiClusterComputeInfo(msrest.serialization.Model):
"""AetherHdiClusterComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
*,
address: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
private_key: Optional[str] = None,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(AetherHdiClusterComputeInfo, self).__init__(**kwargs)
self.address = address
self.username = username
self.password = password
self.private_key = private_key
class AetherHdiRunConfiguration(msrest.serialization.Model):
"""AetherHdiRunConfiguration.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar compute_name:
:vartype compute_name: str
:ivar queue:
:vartype queue: str
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar name:
:vartype name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'queue': {'key': 'queue', 'type': 'str'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'conf': {'key': 'conf', 'type': '{str}'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
class_name: Optional[str] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
py_files: Optional[List[str]] = None,
compute_name: Optional[str] = None,
queue: Optional[str] = None,
driver_memory: Optional[str] = None,
driver_cores: Optional[int] = None,
executor_memory: Optional[str] = None,
executor_cores: Optional[int] = None,
number_executors: Optional[int] = None,
conf: Optional[Dict[str, str]] = None,
name: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword compute_name:
:paramtype compute_name: str
:keyword queue:
:paramtype queue: str
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword name:
:paramtype name: str
"""
super(AetherHdiRunConfiguration, self).__init__(**kwargs)
self.file = file
self.class_name = class_name
self.files = files
self.archives = archives
self.jars = jars
self.py_files = py_files
self.compute_name = compute_name
self.queue = queue
self.driver_memory = driver_memory
self.driver_cores = driver_cores
self.executor_memory = executor_memory
self.executor_cores = executor_cores
self.number_executors = number_executors
self.conf = conf
self.name = name
class AetherHyperDriveConfiguration(msrest.serialization.Model):
"""AetherHyperDriveConfiguration.
:ivar hyper_drive_run_config:
:vartype hyper_drive_run_config: str
:ivar primary_metric_goal:
:vartype primary_metric_goal: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar arguments:
:vartype arguments: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'hyper_drive_run_config': {'key': 'hyperDriveRunConfig', 'type': 'str'},
'primary_metric_goal': {'key': 'primaryMetricGoal', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
*,
hyper_drive_run_config: Optional[str] = None,
primary_metric_goal: Optional[str] = None,
primary_metric_name: Optional[str] = None,
arguments: Optional[List["AetherArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword hyper_drive_run_config:
:paramtype hyper_drive_run_config: str
:keyword primary_metric_goal:
:paramtype primary_metric_goal: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword arguments:
:paramtype arguments: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherHyperDriveConfiguration, self).__init__(**kwargs)
self.hyper_drive_run_config = hyper_drive_run_config
self.primary_metric_goal = primary_metric_goal
self.primary_metric_name = primary_metric_name
self.arguments = arguments
class AetherIdentitySetting(msrest.serialization.Model):
"""AetherIdentitySetting.
:ivar type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:vartype type: str or ~flow.models.AetherIdentityType
:ivar client_id:
:vartype client_id: str
:ivar object_id:
:vartype object_id: str
:ivar msi_resource_id:
:vartype msi_resource_id: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'object_id': {'key': 'objectId', 'type': 'str'},
'msi_resource_id': {'key': 'msiResourceId', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AetherIdentityType"]] = None,
client_id: Optional[str] = None,
object_id: Optional[str] = None,
msi_resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:paramtype type: str or ~flow.models.AetherIdentityType
:keyword client_id:
:paramtype client_id: str
:keyword object_id:
:paramtype object_id: str
:keyword msi_resource_id:
:paramtype msi_resource_id: str
"""
super(AetherIdentitySetting, self).__init__(**kwargs)
self.type = type
self.client_id = client_id
self.object_id = object_id
self.msi_resource_id = msi_resource_id
class AetherImportDataTask(msrest.serialization.Model):
"""AetherImportDataTask.
:ivar data_transfer_source:
:vartype data_transfer_source: ~flow.models.AetherDataTransferSource
"""
_attribute_map = {
'data_transfer_source': {'key': 'DataTransferSource', 'type': 'AetherDataTransferSource'},
}
def __init__(
self,
*,
data_transfer_source: Optional["AetherDataTransferSource"] = None,
**kwargs
):
"""
:keyword data_transfer_source:
:paramtype data_transfer_source: ~flow.models.AetherDataTransferSource
"""
super(AetherImportDataTask, self).__init__(**kwargs)
self.data_transfer_source = data_transfer_source
class AetherInputSetting(msrest.serialization.Model):
"""AetherInputSetting.
:ivar name:
:vartype name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
options: Optional[Dict[str, str]] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherInputSetting, self).__init__(**kwargs)
self.name = name
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.options = options
self.additional_transformations = additional_transformations
class AetherInteractiveConfig(msrest.serialization.Model):
"""AetherInteractiveConfig.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
*,
is_ssh_enabled: Optional[bool] = None,
ssh_public_key: Optional[str] = None,
is_i_python_enabled: Optional[bool] = None,
is_tensor_board_enabled: Optional[bool] = None,
interactive_port: Optional[int] = None,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(AetherInteractiveConfig, self).__init__(**kwargs)
self.is_ssh_enabled = is_ssh_enabled
self.ssh_public_key = ssh_public_key
self.is_i_python_enabled = is_i_python_enabled
self.is_tensor_board_enabled = is_tensor_board_enabled
self.interactive_port = interactive_port
class AetherK8SConfiguration(msrest.serialization.Model):
"""AetherK8SConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.AetherResourceConfig
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.AetherPriorityConfig
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.AetherInteractiveConfig
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'AetherResourceConfig'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'AetherPriorityConfig'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'AetherInteractiveConfig'},
}
def __init__(
self,
*,
max_retry_count: Optional[int] = None,
resource_configuration: Optional["AetherResourceConfig"] = None,
priority_configuration: Optional["AetherPriorityConfig"] = None,
interactive_configuration: Optional["AetherInteractiveConfig"] = None,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.AetherResourceConfig
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.AetherPriorityConfig
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.AetherInteractiveConfig
"""
super(AetherK8SConfiguration, self).__init__(**kwargs)
self.max_retry_count = max_retry_count
self.resource_configuration = resource_configuration
self.priority_configuration = priority_configuration
self.interactive_configuration = interactive_configuration
class AetherLegacyDataPath(msrest.serialization.Model):
"""AetherLegacyDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherLegacyDataPath, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.data_store_mode = data_store_mode
self.relative_path = relative_path
class AetherLimitSettings(msrest.serialization.Model):
"""AetherLimitSettings.
:ivar max_trials:
:vartype max_trials: int
:ivar timeout:
:vartype timeout: str
:ivar trial_timeout:
:vartype trial_timeout: str
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
:ivar max_cores_per_trial:
:vartype max_cores_per_trial: int
:ivar exit_score:
:vartype exit_score: float
:ivar enable_early_termination:
:vartype enable_early_termination: bool
:ivar max_nodes:
:vartype max_nodes: int
"""
_attribute_map = {
'max_trials': {'key': 'maxTrials', 'type': 'int'},
'timeout': {'key': 'timeout', 'type': 'str'},
'trial_timeout': {'key': 'trialTimeout', 'type': 'str'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
'exit_score': {'key': 'exitScore', 'type': 'float'},
'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
'max_nodes': {'key': 'maxNodes', 'type': 'int'},
}
def __init__(
self,
*,
max_trials: Optional[int] = None,
timeout: Optional[str] = None,
trial_timeout: Optional[str] = None,
max_concurrent_trials: Optional[int] = None,
max_cores_per_trial: Optional[int] = None,
exit_score: Optional[float] = None,
enable_early_termination: Optional[bool] = None,
max_nodes: Optional[int] = None,
**kwargs
):
"""
:keyword max_trials:
:paramtype max_trials: int
:keyword timeout:
:paramtype timeout: str
:keyword trial_timeout:
:paramtype trial_timeout: str
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
:keyword max_cores_per_trial:
:paramtype max_cores_per_trial: int
:keyword exit_score:
:paramtype exit_score: float
:keyword enable_early_termination:
:paramtype enable_early_termination: bool
:keyword max_nodes:
:paramtype max_nodes: int
"""
super(AetherLimitSettings, self).__init__(**kwargs)
self.max_trials = max_trials
self.timeout = timeout
self.trial_timeout = trial_timeout
self.max_concurrent_trials = max_concurrent_trials
self.max_cores_per_trial = max_cores_per_trial
self.exit_score = exit_score
self.enable_early_termination = enable_early_termination
self.max_nodes = max_nodes
class AetherMlcComputeInfo(msrest.serialization.Model):
"""AetherMlcComputeInfo.
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
"""
_attribute_map = {
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
}
def __init__(
self,
*,
mlc_compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
"""
super(AetherMlcComputeInfo, self).__init__(**kwargs)
self.mlc_compute_type = mlc_compute_type
class AetherModuleEntity(msrest.serialization.Model):
"""AetherModuleEntity.
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.AetherCreatedBy
:ivar display_name:
:vartype display_name: str
:ivar module_execution_type:
:vartype module_execution_type: str
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.AetherModuleType
:ivar module_type_version:
:vartype module_type_version: str
:ivar resource_requirements:
:vartype resource_requirements: ~flow.models.AetherResourceModel
:ivar machine_cluster:
:vartype machine_cluster: list[str]
:ivar default_compliance_cluster:
:vartype default_compliance_cluster: str
:ivar repository_type: Possible values include: "None", "Other", "Git", "SourceDepot",
"Cosmos".
:vartype repository_type: str or ~flow.models.AetherRepositoryType
:ivar relative_path_to_source_code:
:vartype relative_path_to_source_code: str
:ivar commit_id:
:vartype commit_id: str
:ivar code_review_link:
:vartype code_review_link: str
:ivar unit_tests_available:
:vartype unit_tests_available: bool
:ivar is_compressed:
:vartype is_compressed: bool
:ivar execution_environment: Possible values include: "ExeWorkerMachine",
"DockerContainerWithoutNetwork", "DockerContainerWithNetwork", "HyperVWithoutNetwork",
"HyperVWithNetwork".
:vartype execution_environment: str or ~flow.models.AetherExecutionEnvironment
:ivar is_output_markup_enabled:
:vartype is_output_markup_enabled: bool
:ivar docker_image_id:
:vartype docker_image_id: str
:ivar docker_image_reference:
:vartype docker_image_reference: str
:ivar docker_image_security_groups:
:vartype docker_image_security_groups: str
:ivar extended_properties:
:vartype extended_properties: ~flow.models.AetherModuleExtendedProperties
:ivar deployment_source: Possible values include: "Client", "AutoDeployment", "Vsts".
:vartype deployment_source: str or ~flow.models.AetherModuleDeploymentSource
:ivar deployment_source_metadata:
:vartype deployment_source_metadata: str
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
:ivar kv_tags: This is a dictionary.
:vartype kv_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar created_by:
:vartype created_by: ~flow.models.AetherCreatedBy
:ivar runconfig:
:vartype runconfig: str
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.AetherCloudSettings
:ivar category:
:vartype category: str
:ivar step_type:
:vartype step_type: str
:ivar stage:
:vartype stage: str
:ivar upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:vartype upload_state: str or ~flow.models.AetherUploadState
:ivar source_code_location:
:vartype source_code_location: str
:ivar size_in_bytes:
:vartype size_in_bytes: long
:ivar download_location:
:vartype download_location: str
:ivar data_location:
:vartype data_location: ~flow.models.AetherDataLocation
:ivar scripting_runtime_id:
:vartype scripting_runtime_id: str
:ivar interface_documentation:
:vartype interface_documentation: ~flow.models.AetherEntityInterfaceDocumentation
:ivar is_eyes_on:
:vartype is_eyes_on: bool
:ivar compliance_cluster:
:vartype compliance_cluster: str
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar information_url:
:vartype information_url: str
:ivar is_experiment_id_in_parameters:
:vartype is_experiment_id_in_parameters: bool
:ivar interface_string:
:vartype interface_string: str
:ivar default_parameters: This is a dictionary.
:vartype default_parameters: dict[str, str]
:ivar structured_interface:
:vartype structured_interface: ~flow.models.AetherStructuredInterface
:ivar family_id:
:vartype family_id: str
:ivar name:
:vartype name: str
:ivar hash:
:vartype hash: str
:ivar description:
:vartype description: str
:ivar version:
:vartype version: str
:ivar sequence_number_in_family:
:vartype sequence_number_in_family: int
:ivar owner:
:vartype owner: str
:ivar azure_tenant_id:
:vartype azure_tenant_id: str
:ivar azure_user_id:
:vartype azure_user_id: str
:ivar collaborators:
:vartype collaborators: list[str]
:ivar id:
:vartype id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar etag:
:vartype etag: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.AetherEntityStatus
"""
_attribute_map = {
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'AetherCreatedBy'},
'display_name': {'key': 'displayName', 'type': 'str'},
'module_execution_type': {'key': 'moduleExecutionType', 'type': 'str'},
'module_type': {'key': 'moduleType', 'type': 'str'},
'module_type_version': {'key': 'moduleTypeVersion', 'type': 'str'},
'resource_requirements': {'key': 'resourceRequirements', 'type': 'AetherResourceModel'},
'machine_cluster': {'key': 'machineCluster', 'type': '[str]'},
'default_compliance_cluster': {'key': 'defaultComplianceCluster', 'type': 'str'},
'repository_type': {'key': 'repositoryType', 'type': 'str'},
'relative_path_to_source_code': {'key': 'relativePathToSourceCode', 'type': 'str'},
'commit_id': {'key': 'commitId', 'type': 'str'},
'code_review_link': {'key': 'codeReviewLink', 'type': 'str'},
'unit_tests_available': {'key': 'unitTestsAvailable', 'type': 'bool'},
'is_compressed': {'key': 'isCompressed', 'type': 'bool'},
'execution_environment': {'key': 'executionEnvironment', 'type': 'str'},
'is_output_markup_enabled': {'key': 'isOutputMarkupEnabled', 'type': 'bool'},
'docker_image_id': {'key': 'dockerImageId', 'type': 'str'},
'docker_image_reference': {'key': 'dockerImageReference', 'type': 'str'},
'docker_image_security_groups': {'key': 'dockerImageSecurityGroups', 'type': 'str'},
'extended_properties': {'key': 'extendedProperties', 'type': 'AetherModuleExtendedProperties'},
'deployment_source': {'key': 'deploymentSource', 'type': 'str'},
'deployment_source_metadata': {'key': 'deploymentSourceMetadata', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'identifierHashV2', 'type': 'str'},
'kv_tags': {'key': 'kvTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'created_by': {'key': 'createdBy', 'type': 'AetherCreatedBy'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'AetherCloudSettings'},
'category': {'key': 'category', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'stage': {'key': 'stage', 'type': 'str'},
'upload_state': {'key': 'uploadState', 'type': 'str'},
'source_code_location': {'key': 'sourceCodeLocation', 'type': 'str'},
'size_in_bytes': {'key': 'sizeInBytes', 'type': 'long'},
'download_location': {'key': 'downloadLocation', 'type': 'str'},
'data_location': {'key': 'dataLocation', 'type': 'AetherDataLocation'},
'scripting_runtime_id': {'key': 'scriptingRuntimeId', 'type': 'str'},
'interface_documentation': {'key': 'interfaceDocumentation', 'type': 'AetherEntityInterfaceDocumentation'},
'is_eyes_on': {'key': 'isEyesOn', 'type': 'bool'},
'compliance_cluster': {'key': 'complianceCluster', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'information_url': {'key': 'informationUrl', 'type': 'str'},
'is_experiment_id_in_parameters': {'key': 'isExperimentIdInParameters', 'type': 'bool'},
'interface_string': {'key': 'interfaceString', 'type': 'str'},
'default_parameters': {'key': 'defaultParameters', 'type': '{str}'},
'structured_interface': {'key': 'structuredInterface', 'type': 'AetherStructuredInterface'},
'family_id': {'key': 'familyId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'hash': {'key': 'hash', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'sequence_number_in_family': {'key': 'sequenceNumberInFamily', 'type': 'int'},
'owner': {'key': 'owner', 'type': 'str'},
'azure_tenant_id': {'key': 'azureTenantId', 'type': 'str'},
'azure_user_id': {'key': 'azureUserId', 'type': 'str'},
'collaborators': {'key': 'collaborators', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
}
def __init__(
self,
*,
last_updated_by: Optional["AetherCreatedBy"] = None,
display_name: Optional[str] = None,
module_execution_type: Optional[str] = None,
module_type: Optional[Union[str, "AetherModuleType"]] = None,
module_type_version: Optional[str] = None,
resource_requirements: Optional["AetherResourceModel"] = None,
machine_cluster: Optional[List[str]] = None,
default_compliance_cluster: Optional[str] = None,
repository_type: Optional[Union[str, "AetherRepositoryType"]] = None,
relative_path_to_source_code: Optional[str] = None,
commit_id: Optional[str] = None,
code_review_link: Optional[str] = None,
unit_tests_available: Optional[bool] = None,
is_compressed: Optional[bool] = None,
execution_environment: Optional[Union[str, "AetherExecutionEnvironment"]] = None,
is_output_markup_enabled: Optional[bool] = None,
docker_image_id: Optional[str] = None,
docker_image_reference: Optional[str] = None,
docker_image_security_groups: Optional[str] = None,
extended_properties: Optional["AetherModuleExtendedProperties"] = None,
deployment_source: Optional[Union[str, "AetherModuleDeploymentSource"]] = None,
deployment_source_metadata: Optional[str] = None,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
kv_tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
created_by: Optional["AetherCreatedBy"] = None,
runconfig: Optional[str] = None,
cloud_settings: Optional["AetherCloudSettings"] = None,
category: Optional[str] = None,
step_type: Optional[str] = None,
stage: Optional[str] = None,
upload_state: Optional[Union[str, "AetherUploadState"]] = None,
source_code_location: Optional[str] = None,
size_in_bytes: Optional[int] = None,
download_location: Optional[str] = None,
data_location: Optional["AetherDataLocation"] = None,
scripting_runtime_id: Optional[str] = None,
interface_documentation: Optional["AetherEntityInterfaceDocumentation"] = None,
is_eyes_on: Optional[bool] = None,
compliance_cluster: Optional[str] = None,
is_deterministic: Optional[bool] = None,
information_url: Optional[str] = None,
is_experiment_id_in_parameters: Optional[bool] = None,
interface_string: Optional[str] = None,
default_parameters: Optional[Dict[str, str]] = None,
structured_interface: Optional["AetherStructuredInterface"] = None,
family_id: Optional[str] = None,
name: Optional[str] = None,
hash: Optional[str] = None,
description: Optional[str] = None,
version: Optional[str] = None,
sequence_number_in_family: Optional[int] = None,
owner: Optional[str] = None,
azure_tenant_id: Optional[str] = None,
azure_user_id: Optional[str] = None,
collaborators: Optional[List[str]] = None,
id: Optional[str] = None,
workspace_id: Optional[str] = None,
etag: Optional[str] = None,
tags: Optional[List[str]] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
entity_status: Optional[Union[str, "AetherEntityStatus"]] = None,
**kwargs
):
"""
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.AetherCreatedBy
:keyword display_name:
:paramtype display_name: str
:keyword module_execution_type:
:paramtype module_execution_type: str
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.AetherModuleType
:keyword module_type_version:
:paramtype module_type_version: str
:keyword resource_requirements:
:paramtype resource_requirements: ~flow.models.AetherResourceModel
:keyword machine_cluster:
:paramtype machine_cluster: list[str]
:keyword default_compliance_cluster:
:paramtype default_compliance_cluster: str
:keyword repository_type: Possible values include: "None", "Other", "Git", "SourceDepot",
"Cosmos".
:paramtype repository_type: str or ~flow.models.AetherRepositoryType
:keyword relative_path_to_source_code:
:paramtype relative_path_to_source_code: str
:keyword commit_id:
:paramtype commit_id: str
:keyword code_review_link:
:paramtype code_review_link: str
:keyword unit_tests_available:
:paramtype unit_tests_available: bool
:keyword is_compressed:
:paramtype is_compressed: bool
:keyword execution_environment: Possible values include: "ExeWorkerMachine",
"DockerContainerWithoutNetwork", "DockerContainerWithNetwork", "HyperVWithoutNetwork",
"HyperVWithNetwork".
:paramtype execution_environment: str or ~flow.models.AetherExecutionEnvironment
:keyword is_output_markup_enabled:
:paramtype is_output_markup_enabled: bool
:keyword docker_image_id:
:paramtype docker_image_id: str
:keyword docker_image_reference:
:paramtype docker_image_reference: str
:keyword docker_image_security_groups:
:paramtype docker_image_security_groups: str
:keyword extended_properties:
:paramtype extended_properties: ~flow.models.AetherModuleExtendedProperties
:keyword deployment_source: Possible values include: "Client", "AutoDeployment", "Vsts".
:paramtype deployment_source: str or ~flow.models.AetherModuleDeploymentSource
:keyword deployment_source_metadata:
:paramtype deployment_source_metadata: str
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
:keyword kv_tags: This is a dictionary.
:paramtype kv_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword created_by:
:paramtype created_by: ~flow.models.AetherCreatedBy
:keyword runconfig:
:paramtype runconfig: str
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.AetherCloudSettings
:keyword category:
:paramtype category: str
:keyword step_type:
:paramtype step_type: str
:keyword stage:
:paramtype stage: str
:keyword upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:paramtype upload_state: str or ~flow.models.AetherUploadState
:keyword source_code_location:
:paramtype source_code_location: str
:keyword size_in_bytes:
:paramtype size_in_bytes: long
:keyword download_location:
:paramtype download_location: str
:keyword data_location:
:paramtype data_location: ~flow.models.AetherDataLocation
:keyword scripting_runtime_id:
:paramtype scripting_runtime_id: str
:keyword interface_documentation:
:paramtype interface_documentation: ~flow.models.AetherEntityInterfaceDocumentation
:keyword is_eyes_on:
:paramtype is_eyes_on: bool
:keyword compliance_cluster:
:paramtype compliance_cluster: str
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword information_url:
:paramtype information_url: str
:keyword is_experiment_id_in_parameters:
:paramtype is_experiment_id_in_parameters: bool
:keyword interface_string:
:paramtype interface_string: str
:keyword default_parameters: This is a dictionary.
:paramtype default_parameters: dict[str, str]
:keyword structured_interface:
:paramtype structured_interface: ~flow.models.AetherStructuredInterface
:keyword family_id:
:paramtype family_id: str
:keyword name:
:paramtype name: str
:keyword hash:
:paramtype hash: str
:keyword description:
:paramtype description: str
:keyword version:
:paramtype version: str
:keyword sequence_number_in_family:
:paramtype sequence_number_in_family: int
:keyword owner:
:paramtype owner: str
:keyword azure_tenant_id:
:paramtype azure_tenant_id: str
:keyword azure_user_id:
:paramtype azure_user_id: str
:keyword collaborators:
:paramtype collaborators: list[str]
:keyword id:
:paramtype id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword etag:
:paramtype etag: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.AetherEntityStatus
"""
super(AetherModuleEntity, self).__init__(**kwargs)
self.last_updated_by = last_updated_by
self.display_name = display_name
self.module_execution_type = module_execution_type
self.module_type = module_type
self.module_type_version = module_type_version
self.resource_requirements = resource_requirements
self.machine_cluster = machine_cluster
self.default_compliance_cluster = default_compliance_cluster
self.repository_type = repository_type
self.relative_path_to_source_code = relative_path_to_source_code
self.commit_id = commit_id
self.code_review_link = code_review_link
self.unit_tests_available = unit_tests_available
self.is_compressed = is_compressed
self.execution_environment = execution_environment
self.is_output_markup_enabled = is_output_markup_enabled
self.docker_image_id = docker_image_id
self.docker_image_reference = docker_image_reference
self.docker_image_security_groups = docker_image_security_groups
self.extended_properties = extended_properties
self.deployment_source = deployment_source
self.deployment_source_metadata = deployment_source_metadata
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
self.kv_tags = kv_tags
self.properties = properties
self.created_by = created_by
self.runconfig = runconfig
self.cloud_settings = cloud_settings
self.category = category
self.step_type = step_type
self.stage = stage
self.upload_state = upload_state
self.source_code_location = source_code_location
self.size_in_bytes = size_in_bytes
self.download_location = download_location
self.data_location = data_location
self.scripting_runtime_id = scripting_runtime_id
self.interface_documentation = interface_documentation
self.is_eyes_on = is_eyes_on
self.compliance_cluster = compliance_cluster
self.is_deterministic = is_deterministic
self.information_url = information_url
self.is_experiment_id_in_parameters = is_experiment_id_in_parameters
self.interface_string = interface_string
self.default_parameters = default_parameters
self.structured_interface = structured_interface
self.family_id = family_id
self.name = name
self.hash = hash
self.description = description
self.version = version
self.sequence_number_in_family = sequence_number_in_family
self.owner = owner
self.azure_tenant_id = azure_tenant_id
self.azure_user_id = azure_user_id
self.collaborators = collaborators
self.id = id
self.workspace_id = workspace_id
self.etag = etag
self.tags = tags
self.created_date = created_date
self.last_modified_date = last_modified_date
self.entity_status = entity_status
class AetherModuleExtendedProperties(msrest.serialization.Model):
"""AetherModuleExtendedProperties.
:ivar auto_deployed_artifact:
:vartype auto_deployed_artifact: ~flow.models.AetherBuildArtifactInfo
:ivar script_needs_approval:
:vartype script_needs_approval: bool
"""
_attribute_map = {
'auto_deployed_artifact': {'key': 'autoDeployedArtifact', 'type': 'AetherBuildArtifactInfo'},
'script_needs_approval': {'key': 'scriptNeedsApproval', 'type': 'bool'},
}
def __init__(
self,
*,
auto_deployed_artifact: Optional["AetherBuildArtifactInfo"] = None,
script_needs_approval: Optional[bool] = None,
**kwargs
):
"""
:keyword auto_deployed_artifact:
:paramtype auto_deployed_artifact: ~flow.models.AetherBuildArtifactInfo
:keyword script_needs_approval:
:paramtype script_needs_approval: bool
"""
super(AetherModuleExtendedProperties, self).__init__(**kwargs)
self.auto_deployed_artifact = auto_deployed_artifact
self.script_needs_approval = script_needs_approval
class AetherNCrossValidations(msrest.serialization.Model):
"""AetherNCrossValidations.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherNCrossValidationMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherNCrossValidationMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherNCrossValidationMode
:keyword value:
:paramtype value: int
"""
super(AetherNCrossValidations, self).__init__(**kwargs)
self.mode = mode
self.value = value
class AetherOutputSetting(msrest.serialization.Model):
"""AetherOutputSetting.
:ivar name:
:vartype name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_name_parameter_assignment:
:vartype data_store_name_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar data_store_mode_parameter_assignment:
:vartype data_store_mode_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar path_on_compute_parameter_assignment:
:vartype path_on_compute_parameter_assignment: ~flow.models.AetherParameterAssignment
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar web_service_port:
:vartype web_service_port: str
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.AetherDatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:ivar parameter_name:
:vartype parameter_name: str
:ivar asset_output_settings_parameter_name:
:vartype asset_output_settings_parameter_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_name_parameter_assignment': {'key': 'DataStoreNameParameterAssignment', 'type': 'AetherParameterAssignment'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'data_store_mode_parameter_assignment': {'key': 'DataStoreModeParameterAssignment', 'type': 'AetherParameterAssignment'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'path_on_compute_parameter_assignment': {'key': 'PathOnComputeParameterAssignment', 'type': 'AetherParameterAssignment'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'AetherDatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'AetherDatasetOutputOptions'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AetherAssetOutputSettings'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'asset_output_settings_parameter_name': {'key': 'AssetOutputSettingsParameterName', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
data_store_name: Optional[str] = None,
data_store_name_parameter_assignment: Optional["AetherParameterAssignment"] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
data_store_mode_parameter_assignment: Optional["AetherParameterAssignment"] = None,
path_on_compute: Optional[str] = None,
path_on_compute_parameter_assignment: Optional["AetherParameterAssignment"] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
web_service_port: Optional[str] = None,
dataset_registration: Optional["AetherDatasetRegistration"] = None,
dataset_output_options: Optional["AetherDatasetOutputOptions"] = None,
asset_output_settings: Optional["AetherAssetOutputSettings"] = None,
parameter_name: Optional[str] = None,
asset_output_settings_parameter_name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_name_parameter_assignment:
:paramtype data_store_name_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword data_store_mode_parameter_assignment:
:paramtype data_store_mode_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword path_on_compute_parameter_assignment:
:paramtype path_on_compute_parameter_assignment: ~flow.models.AetherParameterAssignment
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword web_service_port:
:paramtype web_service_port: str
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.AetherDatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.AetherDatasetOutputOptions
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:keyword parameter_name:
:paramtype parameter_name: str
:keyword asset_output_settings_parameter_name:
:paramtype asset_output_settings_parameter_name: str
"""
super(AetherOutputSetting, self).__init__(**kwargs)
self.name = name
self.data_store_name = data_store_name
self.data_store_name_parameter_assignment = data_store_name_parameter_assignment
self.data_store_mode = data_store_mode
self.data_store_mode_parameter_assignment = data_store_mode_parameter_assignment
self.path_on_compute = path_on_compute
self.path_on_compute_parameter_assignment = path_on_compute_parameter_assignment
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.web_service_port = web_service_port
self.dataset_registration = dataset_registration
self.dataset_output_options = dataset_output_options
self.asset_output_settings = asset_output_settings
self.parameter_name = parameter_name
self.asset_output_settings_parameter_name = asset_output_settings_parameter_name
class AetherParallelForControlFlowInfo(msrest.serialization.Model):
"""AetherParallelForControlFlowInfo.
:ivar parallel_for_items_input:
:vartype parallel_for_items_input: ~flow.models.AetherParameterAssignment
"""
_attribute_map = {
'parallel_for_items_input': {'key': 'parallelForItemsInput', 'type': 'AetherParameterAssignment'},
}
def __init__(
self,
*,
parallel_for_items_input: Optional["AetherParameterAssignment"] = None,
**kwargs
):
"""
:keyword parallel_for_items_input:
:paramtype parallel_for_items_input: ~flow.models.AetherParameterAssignment
"""
super(AetherParallelForControlFlowInfo, self).__init__(**kwargs)
self.parallel_for_items_input = parallel_for_items_input
class AetherParameterAssignment(msrest.serialization.Model):
"""AetherParameterAssignment.
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.AetherParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.AetherParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.AetherLegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.AetherDataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[AetherParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'AetherLegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'AetherDataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
value_type: Optional[Union[str, "AetherParameterValueType"]] = None,
assignments_to_concatenate: Optional[List["AetherParameterAssignment"]] = None,
data_path_assignment: Optional["AetherLegacyDataPath"] = None,
data_set_definition_value_assignment: Optional["AetherDataSetDefinitionValue"] = None,
name: Optional[str] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.AetherParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.AetherParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.AetherLegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.AetherDataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(AetherParameterAssignment, self).__init__(**kwargs)
self.value_type = value_type
self.assignments_to_concatenate = assignments_to_concatenate
self.data_path_assignment = data_path_assignment
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.name = name
self.value = value
class AetherPhillyHdfsReference(msrest.serialization.Model):
"""AetherPhillyHdfsReference.
:ivar cluster:
:vartype cluster: str
:ivar vc:
:vartype vc: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'cluster': {'key': 'cluster', 'type': 'str'},
'vc': {'key': 'vc', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
cluster: Optional[str] = None,
vc: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword cluster:
:paramtype cluster: str
:keyword vc:
:paramtype vc: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AetherPhillyHdfsReference, self).__init__(**kwargs)
self.cluster = cluster
self.vc = vc
self.relative_path = relative_path
class AetherPortInfo(msrest.serialization.Model):
"""AetherPortInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar graph_port_name:
:vartype graph_port_name: str
:ivar is_parameter:
:vartype is_parameter: bool
:ivar web_service_port:
:vartype web_service_port: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'graph_port_name': {'key': 'graphPortName', 'type': 'str'},
'is_parameter': {'key': 'isParameter', 'type': 'bool'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
port_name: Optional[str] = None,
graph_port_name: Optional[str] = None,
is_parameter: Optional[bool] = None,
web_service_port: Optional[str] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword graph_port_name:
:paramtype graph_port_name: str
:keyword is_parameter:
:paramtype is_parameter: bool
:keyword web_service_port:
:paramtype web_service_port: str
"""
super(AetherPortInfo, self).__init__(**kwargs)
self.node_id = node_id
self.port_name = port_name
self.graph_port_name = graph_port_name
self.is_parameter = is_parameter
self.web_service_port = web_service_port
class AetherPriorityConfig(msrest.serialization.Model):
"""AetherPriorityConfig.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
*,
job_priority: Optional[int] = None,
is_preemptible: Optional[bool] = None,
node_count_set: Optional[List[int]] = None,
scale_interval: Optional[int] = None,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(AetherPriorityConfig, self).__init__(**kwargs)
self.job_priority = job_priority
self.is_preemptible = is_preemptible
self.node_count_set = node_count_set
self.scale_interval = scale_interval
class AetherPriorityConfiguration(msrest.serialization.Model):
"""AetherPriorityConfiguration.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar string_type_priority:
:vartype string_type_priority: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'string_type_priority': {'key': 'stringTypePriority', 'type': 'str'},
}
def __init__(
self,
*,
cloud_priority: Optional[int] = None,
string_type_priority: Optional[str] = None,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword string_type_priority:
:paramtype string_type_priority: str
"""
super(AetherPriorityConfiguration, self).__init__(**kwargs)
self.cloud_priority = cloud_priority
self.string_type_priority = string_type_priority
class AetherRegisteredDataSetReference(msrest.serialization.Model):
"""AetherRegisteredDataSetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AetherRegisteredDataSetReference, self).__init__(**kwargs)
self.id = id
self.name = name
self.version = version
class AetherRemoteDockerComputeInfo(msrest.serialization.Model):
"""AetherRemoteDockerComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
*,
address: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
private_key: Optional[str] = None,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(AetherRemoteDockerComputeInfo, self).__init__(**kwargs)
self.address = address
self.username = username
self.password = password
self.private_key = private_key
class AetherResourceAssignment(msrest.serialization.Model):
"""AetherResourceAssignment.
:ivar attributes: Dictionary of :code:`<AetherResourceAttributeAssignment>`.
:vartype attributes: dict[str, ~flow.models.AetherResourceAttributeAssignment]
"""
_attribute_map = {
'attributes': {'key': 'attributes', 'type': '{AetherResourceAttributeAssignment}'},
}
def __init__(
self,
*,
attributes: Optional[Dict[str, "AetherResourceAttributeAssignment"]] = None,
**kwargs
):
"""
:keyword attributes: Dictionary of :code:`<AetherResourceAttributeAssignment>`.
:paramtype attributes: dict[str, ~flow.models.AetherResourceAttributeAssignment]
"""
super(AetherResourceAssignment, self).__init__(**kwargs)
self.attributes = attributes
class AetherResourceAttributeAssignment(msrest.serialization.Model):
"""AetherResourceAttributeAssignment.
:ivar attribute:
:vartype attribute: ~flow.models.AetherResourceAttributeDefinition
:ivar operator: Possible values include: "Equal", "Contain", "GreaterOrEqual".
:vartype operator: str or ~flow.models.AetherResourceOperator
:ivar value:
:vartype value: str
"""
_attribute_map = {
'attribute': {'key': 'attribute', 'type': 'AetherResourceAttributeDefinition'},
'operator': {'key': 'operator', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
attribute: Optional["AetherResourceAttributeDefinition"] = None,
operator: Optional[Union[str, "AetherResourceOperator"]] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword attribute:
:paramtype attribute: ~flow.models.AetherResourceAttributeDefinition
:keyword operator: Possible values include: "Equal", "Contain", "GreaterOrEqual".
:paramtype operator: str or ~flow.models.AetherResourceOperator
:keyword value:
:paramtype value: str
"""
super(AetherResourceAttributeAssignment, self).__init__(**kwargs)
self.attribute = attribute
self.operator = operator
self.value = value
class AetherResourceAttributeDefinition(msrest.serialization.Model):
"""AetherResourceAttributeDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "String", "Double".
:vartype type: str or ~flow.models.AetherResourceValueType
:ivar units:
:vartype units: str
:ivar allowed_operators:
:vartype allowed_operators: list[str or ~flow.models.AetherResourceOperator]
"""
_validation = {
'allowed_operators': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'units': {'key': 'units', 'type': 'str'},
'allowed_operators': {'key': 'allowedOperators', 'type': '[str]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "AetherResourceValueType"]] = None,
units: Optional[str] = None,
allowed_operators: Optional[List[Union[str, "AetherResourceOperator"]]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "String", "Double".
:paramtype type: str or ~flow.models.AetherResourceValueType
:keyword units:
:paramtype units: str
:keyword allowed_operators:
:paramtype allowed_operators: list[str or ~flow.models.AetherResourceOperator]
"""
super(AetherResourceAttributeDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.units = units
self.allowed_operators = allowed_operators
class AetherResourceConfig(msrest.serialization.Model):
"""AetherResourceConfig.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
*,
gpu_count: Optional[int] = None,
cpu_count: Optional[int] = None,
memory_request_in_gb: Optional[int] = None,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(AetherResourceConfig, self).__init__(**kwargs)
self.gpu_count = gpu_count
self.cpu_count = cpu_count
self.memory_request_in_gb = memory_request_in_gb
class AetherResourceConfiguration(msrest.serialization.Model):
"""AetherResourceConfiguration.
:ivar instance_count:
:vartype instance_count: int
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar locations:
:vartype locations: list[str]
:ivar instance_priority:
:vartype instance_priority: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
"""
_attribute_map = {
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'locations': {'key': 'locations', 'type': '[str]'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
}
def __init__(
self,
*,
instance_count: Optional[int] = None,
instance_type: Optional[str] = None,
properties: Optional[Dict[str, Any]] = None,
locations: Optional[List[str]] = None,
instance_priority: Optional[str] = None,
quota_enforcement_resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword instance_count:
:paramtype instance_count: int
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword locations:
:paramtype locations: list[str]
:keyword instance_priority:
:paramtype instance_priority: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
"""
super(AetherResourceConfiguration, self).__init__(**kwargs)
self.instance_count = instance_count
self.instance_type = instance_type
self.properties = properties
self.locations = locations
self.instance_priority = instance_priority
self.quota_enforcement_resource_id = quota_enforcement_resource_id
class AetherResourceModel(msrest.serialization.Model):
"""AetherResourceModel.
:ivar resources:
:vartype resources: list[~flow.models.AetherResourceAssignment]
"""
_attribute_map = {
'resources': {'key': 'resources', 'type': '[AetherResourceAssignment]'},
}
def __init__(
self,
*,
resources: Optional[List["AetherResourceAssignment"]] = None,
**kwargs
):
"""
:keyword resources:
:paramtype resources: list[~flow.models.AetherResourceAssignment]
"""
super(AetherResourceModel, self).__init__(**kwargs)
self.resources = resources
class AetherResourcesSetting(msrest.serialization.Model):
"""AetherResourcesSetting.
:ivar instance_size:
:vartype instance_size: str
:ivar spark_version:
:vartype spark_version: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
}
def __init__(
self,
*,
instance_size: Optional[str] = None,
spark_version: Optional[str] = None,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword spark_version:
:paramtype spark_version: str
"""
super(AetherResourcesSetting, self).__init__(**kwargs)
self.instance_size = instance_size
self.spark_version = spark_version
class AetherSavedDataSetReference(msrest.serialization.Model):
"""AetherSavedDataSetReference.
:ivar id:
:vartype id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
"""
super(AetherSavedDataSetReference, self).__init__(**kwargs)
self.id = id
class AetherScopeCloudConfiguration(msrest.serialization.Model):
"""AetherScopeCloudConfiguration.
:ivar input_path_suffixes: This is a dictionary.
:vartype input_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:ivar output_path_suffixes: This is a dictionary.
:vartype output_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:ivar user_alias:
:vartype user_alias: str
:ivar tokens:
:vartype tokens: int
:ivar auto_token:
:vartype auto_token: int
:ivar vcp:
:vartype vcp: float
"""
_attribute_map = {
'input_path_suffixes': {'key': 'inputPathSuffixes', 'type': '{AetherArgumentAssignment}'},
'output_path_suffixes': {'key': 'outputPathSuffixes', 'type': '{AetherArgumentAssignment}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'tokens': {'key': 'tokens', 'type': 'int'},
'auto_token': {'key': 'autoToken', 'type': 'int'},
'vcp': {'key': 'vcp', 'type': 'float'},
}
def __init__(
self,
*,
input_path_suffixes: Optional[Dict[str, "AetherArgumentAssignment"]] = None,
output_path_suffixes: Optional[Dict[str, "AetherArgumentAssignment"]] = None,
user_alias: Optional[str] = None,
tokens: Optional[int] = None,
auto_token: Optional[int] = None,
vcp: Optional[float] = None,
**kwargs
):
"""
:keyword input_path_suffixes: This is a dictionary.
:paramtype input_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:keyword output_path_suffixes: This is a dictionary.
:paramtype output_path_suffixes: dict[str, ~flow.models.AetherArgumentAssignment]
:keyword user_alias:
:paramtype user_alias: str
:keyword tokens:
:paramtype tokens: int
:keyword auto_token:
:paramtype auto_token: int
:keyword vcp:
:paramtype vcp: float
"""
super(AetherScopeCloudConfiguration, self).__init__(**kwargs)
self.input_path_suffixes = input_path_suffixes
self.output_path_suffixes = output_path_suffixes
self.user_alias = user_alias
self.tokens = tokens
self.auto_token = auto_token
self.vcp = vcp
class AetherSeasonality(msrest.serialization.Model):
"""AetherSeasonality.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherSeasonalityMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherSeasonalityMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherSeasonalityMode
:keyword value:
:paramtype value: int
"""
super(AetherSeasonality, self).__init__(**kwargs)
self.mode = mode
self.value = value
class AetherSqlDataPath(msrest.serialization.Model):
"""AetherSqlDataPath.
:ivar sql_table_name:
:vartype sql_table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar sql_stored_procedure_name:
:vartype sql_stored_procedure_name: str
:ivar sql_stored_procedure_params:
:vartype sql_stored_procedure_params: list[~flow.models.AetherStoredProcedureParameter]
"""
_attribute_map = {
'sql_table_name': {'key': 'sqlTableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'},
'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[AetherStoredProcedureParameter]'},
}
def __init__(
self,
*,
sql_table_name: Optional[str] = None,
sql_query: Optional[str] = None,
sql_stored_procedure_name: Optional[str] = None,
sql_stored_procedure_params: Optional[List["AetherStoredProcedureParameter"]] = None,
**kwargs
):
"""
:keyword sql_table_name:
:paramtype sql_table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword sql_stored_procedure_name:
:paramtype sql_stored_procedure_name: str
:keyword sql_stored_procedure_params:
:paramtype sql_stored_procedure_params: list[~flow.models.AetherStoredProcedureParameter]
"""
super(AetherSqlDataPath, self).__init__(**kwargs)
self.sql_table_name = sql_table_name
self.sql_query = sql_query
self.sql_stored_procedure_name = sql_stored_procedure_name
self.sql_stored_procedure_params = sql_stored_procedure_params
class AetherStackEnsembleSettings(msrest.serialization.Model):
"""AetherStackEnsembleSettings.
:ivar stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:vartype stack_meta_learner_type: str or ~flow.models.AetherStackMetaLearnerType
:ivar stack_meta_learner_train_percentage:
:vartype stack_meta_learner_train_percentage: float
:ivar stack_meta_learner_k_wargs: Anything.
:vartype stack_meta_learner_k_wargs: any
"""
_attribute_map = {
'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
}
def __init__(
self,
*,
stack_meta_learner_type: Optional[Union[str, "AetherStackMetaLearnerType"]] = None,
stack_meta_learner_train_percentage: Optional[float] = None,
stack_meta_learner_k_wargs: Optional[Any] = None,
**kwargs
):
"""
:keyword stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:paramtype stack_meta_learner_type: str or ~flow.models.AetherStackMetaLearnerType
:keyword stack_meta_learner_train_percentage:
:paramtype stack_meta_learner_train_percentage: float
:keyword stack_meta_learner_k_wargs: Anything.
:paramtype stack_meta_learner_k_wargs: any
"""
super(AetherStackEnsembleSettings, self).__init__(**kwargs)
self.stack_meta_learner_type = stack_meta_learner_type
self.stack_meta_learner_train_percentage = stack_meta_learner_train_percentage
self.stack_meta_learner_k_wargs = stack_meta_learner_k_wargs
class AetherStoredProcedureParameter(msrest.serialization.Model):
"""AetherStoredProcedureParameter.
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
:ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:vartype type: str or ~flow.models.AetherStoredProcedureParameterType
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
value: Optional[str] = None,
type: Optional[Union[str, "AetherStoredProcedureParameterType"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
:keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:paramtype type: str or ~flow.models.AetherStoredProcedureParameterType
"""
super(AetherStoredProcedureParameter, self).__init__(**kwargs)
self.name = name
self.value = value
self.type = type
class AetherStructuredInterface(msrest.serialization.Model):
"""AetherStructuredInterface.
:ivar command_line_pattern:
:vartype command_line_pattern: str
:ivar inputs:
:vartype inputs: list[~flow.models.AetherStructuredInterfaceInput]
:ivar outputs:
:vartype outputs: list[~flow.models.AetherStructuredInterfaceOutput]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.AetherControlOutput]
:ivar parameters:
:vartype parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:ivar arguments:
:vartype arguments: list[~flow.models.AetherArgumentAssignment]
"""
_attribute_map = {
'command_line_pattern': {'key': 'commandLinePattern', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[AetherStructuredInterfaceInput]'},
'outputs': {'key': 'outputs', 'type': '[AetherStructuredInterfaceOutput]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[AetherControlOutput]'},
'parameters': {'key': 'parameters', 'type': '[AetherStructuredInterfaceParameter]'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[AetherStructuredInterfaceParameter]'},
'arguments': {'key': 'arguments', 'type': '[AetherArgumentAssignment]'},
}
def __init__(
self,
*,
command_line_pattern: Optional[str] = None,
inputs: Optional[List["AetherStructuredInterfaceInput"]] = None,
outputs: Optional[List["AetherStructuredInterfaceOutput"]] = None,
control_outputs: Optional[List["AetherControlOutput"]] = None,
parameters: Optional[List["AetherStructuredInterfaceParameter"]] = None,
metadata_parameters: Optional[List["AetherStructuredInterfaceParameter"]] = None,
arguments: Optional[List["AetherArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword command_line_pattern:
:paramtype command_line_pattern: str
:keyword inputs:
:paramtype inputs: list[~flow.models.AetherStructuredInterfaceInput]
:keyword outputs:
:paramtype outputs: list[~flow.models.AetherStructuredInterfaceOutput]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.AetherControlOutput]
:keyword parameters:
:paramtype parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.AetherStructuredInterfaceParameter]
:keyword arguments:
:paramtype arguments: list[~flow.models.AetherArgumentAssignment]
"""
super(AetherStructuredInterface, self).__init__(**kwargs)
self.command_line_pattern = command_line_pattern
self.inputs = inputs
self.outputs = outputs
self.control_outputs = control_outputs
self.parameters = parameters
self.metadata_parameters = metadata_parameters
self.arguments = arguments
class AetherStructuredInterfaceInput(msrest.serialization.Model):
"""AetherStructuredInterfaceInput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_ids_list:
:vartype data_type_ids_list: list[str]
:ivar is_optional:
:vartype is_optional: bool
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_resource:
:vartype is_resource: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar dataset_types:
:vartype dataset_types: list[str or ~flow.models.AetherDatasetType]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_validation = {
'dataset_types': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_ids_list': {'key': 'dataTypeIdsList', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_resource': {'key': 'isResource', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'dataset_types': {'key': 'datasetTypes', 'type': '[str]'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
data_type_ids_list: Optional[List[str]] = None,
is_optional: Optional[bool] = None,
description: Optional[str] = None,
skip_processing: Optional[bool] = None,
is_resource: Optional[bool] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
dataset_types: Optional[List[Union[str, "AetherDatasetType"]]] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_ids_list:
:paramtype data_type_ids_list: list[str]
:keyword is_optional:
:paramtype is_optional: bool
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_resource:
:paramtype is_resource: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword dataset_types:
:paramtype dataset_types: list[str or ~flow.models.AetherDatasetType]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AetherStructuredInterfaceInput, self).__init__(**kwargs)
self.name = name
self.label = label
self.data_type_ids_list = data_type_ids_list
self.is_optional = is_optional
self.description = description
self.skip_processing = skip_processing
self.is_resource = is_resource
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.dataset_types = dataset_types
self.additional_transformations = additional_transformations
class AetherStructuredInterfaceOutput(msrest.serialization.Model):
"""AetherStructuredInterfaceOutput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_data_type_input_name:
:vartype pass_through_data_type_input_name: str
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_artifact:
:vartype is_artifact: bool
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AetherDataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar training_output:
:vartype training_output: ~flow.models.AetherTrainingOutput
:ivar dataset_output:
:vartype dataset_output: ~flow.models.AetherDatasetOutput
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:ivar early_available:
:vartype early_available: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_data_type_input_name': {'key': 'passThroughDataTypeInputName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_artifact': {'key': 'isArtifact', 'type': 'bool'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'training_output': {'key': 'trainingOutput', 'type': 'AetherTrainingOutput'},
'dataset_output': {'key': 'datasetOutput', 'type': 'AetherDatasetOutput'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AetherAssetOutputSettings'},
'early_available': {'key': 'earlyAvailable', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
data_type_id: Optional[str] = None,
pass_through_data_type_input_name: Optional[str] = None,
description: Optional[str] = None,
skip_processing: Optional[bool] = None,
is_artifact: Optional[bool] = None,
data_store_name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AetherDataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
training_output: Optional["AetherTrainingOutput"] = None,
dataset_output: Optional["AetherDatasetOutput"] = None,
asset_output_settings: Optional["AetherAssetOutputSettings"] = None,
early_available: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_data_type_input_name:
:paramtype pass_through_data_type_input_name: str
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_artifact:
:paramtype is_artifact: bool
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AetherDataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword training_output:
:paramtype training_output: ~flow.models.AetherTrainingOutput
:keyword dataset_output:
:paramtype dataset_output: ~flow.models.AetherDatasetOutput
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AetherAssetOutputSettings
:keyword early_available:
:paramtype early_available: bool
"""
super(AetherStructuredInterfaceOutput, self).__init__(**kwargs)
self.name = name
self.label = label
self.data_type_id = data_type_id
self.pass_through_data_type_input_name = pass_through_data_type_input_name
self.description = description
self.skip_processing = skip_processing
self.is_artifact = is_artifact
self.data_store_name = data_store_name
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.training_output = training_output
self.dataset_output = dataset_output
self.asset_output_settings = asset_output_settings
self.early_available = early_available
class AetherStructuredInterfaceParameter(msrest.serialization.Model):
"""AetherStructuredInterfaceParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype parameter_type: str or ~flow.models.AetherParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar description:
:vartype description: str
:ivar set_environment_variable:
:vartype set_environment_variable: bool
:ivar environment_variable_override:
:vartype environment_variable_override: str
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar ui_hint:
:vartype ui_hint: ~flow.models.AetherUIParameterHint
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'description': {'key': 'description', 'type': 'str'},
'set_environment_variable': {'key': 'setEnvironmentVariable', 'type': 'bool'},
'environment_variable_override': {'key': 'environmentVariableOverride', 'type': 'str'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'ui_hint': {'key': 'uiHint', 'type': 'AetherUIParameterHint'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
parameter_type: Optional[Union[str, "AetherParameterType"]] = None,
is_optional: Optional[bool] = None,
default_value: Optional[str] = None,
lower_bound: Optional[str] = None,
upper_bound: Optional[str] = None,
enum_values: Optional[List[str]] = None,
enum_values_to_argument_strings: Optional[Dict[str, str]] = None,
description: Optional[str] = None,
set_environment_variable: Optional[bool] = None,
environment_variable_override: Optional[str] = None,
enabled_by_parameter_name: Optional[str] = None,
enabled_by_parameter_values: Optional[List[str]] = None,
ui_hint: Optional["AetherUIParameterHint"] = None,
group_names: Optional[List[str]] = None,
argument_name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Int", "Double", "Bool", "String",
"Undefined".
:paramtype parameter_type: str or ~flow.models.AetherParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword description:
:paramtype description: str
:keyword set_environment_variable:
:paramtype set_environment_variable: bool
:keyword environment_variable_override:
:paramtype environment_variable_override: str
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.AetherUIParameterHint
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
"""
super(AetherStructuredInterfaceParameter, self).__init__(**kwargs)
self.name = name
self.label = label
self.parameter_type = parameter_type
self.is_optional = is_optional
self.default_value = default_value
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.enum_values = enum_values
self.enum_values_to_argument_strings = enum_values_to_argument_strings
self.description = description
self.set_environment_variable = set_environment_variable
self.environment_variable_override = environment_variable_override
self.enabled_by_parameter_name = enabled_by_parameter_name
self.enabled_by_parameter_values = enabled_by_parameter_values
self.ui_hint = ui_hint
self.group_names = group_names
self.argument_name = argument_name
class AetherSubGraphConfiguration(msrest.serialization.Model):
"""AetherSubGraphConfiguration.
:ivar graph_id:
:vartype graph_id: str
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar default_compute_internal:
:vartype default_compute_internal: ~flow.models.AetherComputeSetting
:ivar default_datastore_internal:
:vartype default_datastore_internal: ~flow.models.AetherDatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:ivar user_alias:
:vartype user_alias: str
:ivar is_dynamic:
:vartype is_dynamic: bool
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'default_compute_internal': {'key': 'defaultComputeInternal', 'type': 'AetherComputeSetting'},
'default_datastore_internal': {'key': 'defaultDatastoreInternal', 'type': 'AetherDatastoreSetting'},
'default_cloud_priority': {'key': 'DefaultCloudPriority', 'type': 'AetherCloudPrioritySetting'},
'user_alias': {'key': 'UserAlias', 'type': 'str'},
'is_dynamic': {'key': 'IsDynamic', 'type': 'bool'},
}
def __init__(
self,
*,
graph_id: Optional[str] = None,
graph_draft_id: Optional[str] = None,
default_compute_internal: Optional["AetherComputeSetting"] = None,
default_datastore_internal: Optional["AetherDatastoreSetting"] = None,
default_cloud_priority: Optional["AetherCloudPrioritySetting"] = None,
user_alias: Optional[str] = None,
is_dynamic: Optional[bool] = False,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword default_compute_internal:
:paramtype default_compute_internal: ~flow.models.AetherComputeSetting
:keyword default_datastore_internal:
:paramtype default_datastore_internal: ~flow.models.AetherDatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.AetherCloudPrioritySetting
:keyword user_alias:
:paramtype user_alias: str
:keyword is_dynamic:
:paramtype is_dynamic: bool
"""
super(AetherSubGraphConfiguration, self).__init__(**kwargs)
self.graph_id = graph_id
self.graph_draft_id = graph_draft_id
self.default_compute_internal = default_compute_internal
self.default_datastore_internal = default_datastore_internal
self.default_cloud_priority = default_cloud_priority
self.user_alias = user_alias
self.is_dynamic = is_dynamic
class AetherSweepEarlyTerminationPolicy(msrest.serialization.Model):
"""AetherSweepEarlyTerminationPolicy.
:ivar policy_type: Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
:vartype policy_type: str or ~flow.models.AetherEarlyTerminationPolicyType
:ivar evaluation_interval:
:vartype evaluation_interval: int
:ivar delay_evaluation:
:vartype delay_evaluation: int
:ivar slack_factor:
:vartype slack_factor: float
:ivar slack_amount:
:vartype slack_amount: float
:ivar truncation_percentage:
:vartype truncation_percentage: int
"""
_attribute_map = {
'policy_type': {'key': 'policyType', 'type': 'str'},
'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
'slack_factor': {'key': 'slackFactor', 'type': 'float'},
'slack_amount': {'key': 'slackAmount', 'type': 'float'},
'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
}
def __init__(
self,
*,
policy_type: Optional[Union[str, "AetherEarlyTerminationPolicyType"]] = None,
evaluation_interval: Optional[int] = None,
delay_evaluation: Optional[int] = None,
slack_factor: Optional[float] = None,
slack_amount: Optional[float] = None,
truncation_percentage: Optional[int] = None,
**kwargs
):
"""
:keyword policy_type: Possible values include: "Bandit", "MedianStopping",
"TruncationSelection".
:paramtype policy_type: str or ~flow.models.AetherEarlyTerminationPolicyType
:keyword evaluation_interval:
:paramtype evaluation_interval: int
:keyword delay_evaluation:
:paramtype delay_evaluation: int
:keyword slack_factor:
:paramtype slack_factor: float
:keyword slack_amount:
:paramtype slack_amount: float
:keyword truncation_percentage:
:paramtype truncation_percentage: int
"""
super(AetherSweepEarlyTerminationPolicy, self).__init__(**kwargs)
self.policy_type = policy_type
self.evaluation_interval = evaluation_interval
self.delay_evaluation = delay_evaluation
self.slack_factor = slack_factor
self.slack_amount = slack_amount
self.truncation_percentage = truncation_percentage
class AetherSweepSettings(msrest.serialization.Model):
"""AetherSweepSettings.
:ivar limits:
:vartype limits: ~flow.models.AetherSweepSettingsLimits
:ivar search_space:
:vartype search_space: list[dict[str, str]]
:ivar sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:vartype sampling_algorithm: str or ~flow.models.AetherSamplingAlgorithmType
:ivar early_termination:
:vartype early_termination: ~flow.models.AetherSweepEarlyTerminationPolicy
"""
_attribute_map = {
'limits': {'key': 'limits', 'type': 'AetherSweepSettingsLimits'},
'search_space': {'key': 'searchSpace', 'type': '[{str}]'},
'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
'early_termination': {'key': 'earlyTermination', 'type': 'AetherSweepEarlyTerminationPolicy'},
}
def __init__(
self,
*,
limits: Optional["AetherSweepSettingsLimits"] = None,
search_space: Optional[List[Dict[str, str]]] = None,
sampling_algorithm: Optional[Union[str, "AetherSamplingAlgorithmType"]] = None,
early_termination: Optional["AetherSweepEarlyTerminationPolicy"] = None,
**kwargs
):
"""
:keyword limits:
:paramtype limits: ~flow.models.AetherSweepSettingsLimits
:keyword search_space:
:paramtype search_space: list[dict[str, str]]
:keyword sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:paramtype sampling_algorithm: str or ~flow.models.AetherSamplingAlgorithmType
:keyword early_termination:
:paramtype early_termination: ~flow.models.AetherSweepEarlyTerminationPolicy
"""
super(AetherSweepSettings, self).__init__(**kwargs)
self.limits = limits
self.search_space = search_space
self.sampling_algorithm = sampling_algorithm
self.early_termination = early_termination
class AetherSweepSettingsLimits(msrest.serialization.Model):
"""AetherSweepSettingsLimits.
:ivar max_total_trials:
:vartype max_total_trials: int
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
"""
_attribute_map = {
'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
}
def __init__(
self,
*,
max_total_trials: Optional[int] = None,
max_concurrent_trials: Optional[int] = None,
**kwargs
):
"""
:keyword max_total_trials:
:paramtype max_total_trials: int
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
"""
super(AetherSweepSettingsLimits, self).__init__(**kwargs)
self.max_total_trials = max_total_trials
self.max_concurrent_trials = max_concurrent_trials
class AetherTargetLags(msrest.serialization.Model):
"""AetherTargetLags.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherTargetLagsMode
:ivar values:
:vartype values: list[int]
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherTargetLagsMode"]] = None,
values: Optional[List[int]] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherTargetLagsMode
:keyword values:
:paramtype values: list[int]
"""
super(AetherTargetLags, self).__init__(**kwargs)
self.mode = mode
self.values = values
class AetherTargetRollingWindowSize(msrest.serialization.Model):
"""AetherTargetRollingWindowSize.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.AetherTargetRollingWindowSizeMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "AetherTargetRollingWindowSizeMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.AetherTargetRollingWindowSizeMode
:keyword value:
:paramtype value: int
"""
super(AetherTargetRollingWindowSize, self).__init__(**kwargs)
self.mode = mode
self.value = value
class AetherTargetSelectorConfiguration(msrest.serialization.Model):
"""AetherTargetSelectorConfiguration.
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
:ivar compute_type:
:vartype compute_type: str
:ivar instance_type:
:vartype instance_type: list[str]
:ivar instance_types:
:vartype instance_types: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar region:
:vartype region: list[str]
:ivar regions:
:vartype regions: list[str]
:ivar vc_block_list:
:vartype vc_block_list: list[str]
"""
_attribute_map = {
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': '[str]'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'regions': {'key': 'regions', 'type': '[str]'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
}
def __init__(
self,
*,
low_priority_vm_tolerant: Optional[bool] = None,
cluster_block_list: Optional[List[str]] = None,
compute_type: Optional[str] = None,
instance_type: Optional[List[str]] = None,
instance_types: Optional[List[str]] = None,
my_resource_only: Optional[bool] = None,
plan_id: Optional[str] = None,
plan_region_id: Optional[str] = None,
region: Optional[List[str]] = None,
regions: Optional[List[str]] = None,
vc_block_list: Optional[List[str]] = None,
**kwargs
):
"""
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
:keyword compute_type:
:paramtype compute_type: str
:keyword instance_type:
:paramtype instance_type: list[str]
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword region:
:paramtype region: list[str]
:keyword regions:
:paramtype regions: list[str]
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
"""
super(AetherTargetSelectorConfiguration, self).__init__(**kwargs)
self.low_priority_vm_tolerant = low_priority_vm_tolerant
self.cluster_block_list = cluster_block_list
self.compute_type = compute_type
self.instance_type = instance_type
self.instance_types = instance_types
self.my_resource_only = my_resource_only
self.plan_id = plan_id
self.plan_region_id = plan_region_id
self.region = region
self.regions = regions
self.vc_block_list = vc_block_list
class AetherTestDataSettings(msrest.serialization.Model):
"""AetherTestDataSettings.
:ivar test_data_size:
:vartype test_data_size: float
"""
_attribute_map = {
'test_data_size': {'key': 'testDataSize', 'type': 'float'},
}
def __init__(
self,
*,
test_data_size: Optional[float] = None,
**kwargs
):
"""
:keyword test_data_size:
:paramtype test_data_size: float
"""
super(AetherTestDataSettings, self).__init__(**kwargs)
self.test_data_size = test_data_size
class AetherTorchDistributedConfiguration(msrest.serialization.Model):
"""AetherTorchDistributedConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
*,
process_count_per_node: Optional[int] = None,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(AetherTorchDistributedConfiguration, self).__init__(**kwargs)
self.process_count_per_node = process_count_per_node
class AetherTrainingOutput(msrest.serialization.Model):
"""AetherTrainingOutput.
:ivar training_output_type: Possible values include: "Metrics", "Model".
:vartype training_output_type: str or ~flow.models.AetherTrainingOutputType
:ivar iteration:
:vartype iteration: int
:ivar metric:
:vartype metric: str
:ivar model_file:
:vartype model_file: str
"""
_attribute_map = {
'training_output_type': {'key': 'trainingOutputType', 'type': 'str'},
'iteration': {'key': 'iteration', 'type': 'int'},
'metric': {'key': 'metric', 'type': 'str'},
'model_file': {'key': 'modelFile', 'type': 'str'},
}
def __init__(
self,
*,
training_output_type: Optional[Union[str, "AetherTrainingOutputType"]] = None,
iteration: Optional[int] = None,
metric: Optional[str] = None,
model_file: Optional[str] = None,
**kwargs
):
"""
:keyword training_output_type: Possible values include: "Metrics", "Model".
:paramtype training_output_type: str or ~flow.models.AetherTrainingOutputType
:keyword iteration:
:paramtype iteration: int
:keyword metric:
:paramtype metric: str
:keyword model_file:
:paramtype model_file: str
"""
super(AetherTrainingOutput, self).__init__(**kwargs)
self.training_output_type = training_output_type
self.iteration = iteration
self.metric = metric
self.model_file = model_file
class AetherTrainingSettings(msrest.serialization.Model):
"""AetherTrainingSettings.
:ivar block_list_models:
:vartype block_list_models: list[str]
:ivar allow_list_models:
:vartype allow_list_models: list[str]
:ivar enable_dnn_training:
:vartype enable_dnn_training: bool
:ivar enable_onnx_compatible_models:
:vartype enable_onnx_compatible_models: bool
:ivar stack_ensemble_settings:
:vartype stack_ensemble_settings: ~flow.models.AetherStackEnsembleSettings
:ivar enable_stack_ensemble:
:vartype enable_stack_ensemble: bool
:ivar enable_vote_ensemble:
:vartype enable_vote_ensemble: bool
:ivar ensemble_model_download_timeout:
:vartype ensemble_model_download_timeout: str
:ivar enable_model_explainability:
:vartype enable_model_explainability: bool
:ivar training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:vartype training_mode: str or ~flow.models.AetherTabularTrainingMode
"""
_attribute_map = {
'block_list_models': {'key': 'blockListModels', 'type': '[str]'},
'allow_list_models': {'key': 'allowListModels', 'type': '[str]'},
'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'AetherStackEnsembleSettings'},
'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'str'},
'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
'training_mode': {'key': 'trainingMode', 'type': 'str'},
}
def __init__(
self,
*,
block_list_models: Optional[List[str]] = None,
allow_list_models: Optional[List[str]] = None,
enable_dnn_training: Optional[bool] = None,
enable_onnx_compatible_models: Optional[bool] = None,
stack_ensemble_settings: Optional["AetherStackEnsembleSettings"] = None,
enable_stack_ensemble: Optional[bool] = None,
enable_vote_ensemble: Optional[bool] = None,
ensemble_model_download_timeout: Optional[str] = None,
enable_model_explainability: Optional[bool] = None,
training_mode: Optional[Union[str, "AetherTabularTrainingMode"]] = None,
**kwargs
):
"""
:keyword block_list_models:
:paramtype block_list_models: list[str]
:keyword allow_list_models:
:paramtype allow_list_models: list[str]
:keyword enable_dnn_training:
:paramtype enable_dnn_training: bool
:keyword enable_onnx_compatible_models:
:paramtype enable_onnx_compatible_models: bool
:keyword stack_ensemble_settings:
:paramtype stack_ensemble_settings: ~flow.models.AetherStackEnsembleSettings
:keyword enable_stack_ensemble:
:paramtype enable_stack_ensemble: bool
:keyword enable_vote_ensemble:
:paramtype enable_vote_ensemble: bool
:keyword ensemble_model_download_timeout:
:paramtype ensemble_model_download_timeout: str
:keyword enable_model_explainability:
:paramtype enable_model_explainability: bool
:keyword training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:paramtype training_mode: str or ~flow.models.AetherTabularTrainingMode
"""
super(AetherTrainingSettings, self).__init__(**kwargs)
self.block_list_models = block_list_models
self.allow_list_models = allow_list_models
self.enable_dnn_training = enable_dnn_training
self.enable_onnx_compatible_models = enable_onnx_compatible_models
self.stack_ensemble_settings = stack_ensemble_settings
self.enable_stack_ensemble = enable_stack_ensemble
self.enable_vote_ensemble = enable_vote_ensemble
self.ensemble_model_download_timeout = ensemble_model_download_timeout
self.enable_model_explainability = enable_model_explainability
self.training_mode = training_mode
class AetherUIAzureOpenAIDeploymentNameSelector(msrest.serialization.Model):
"""AetherUIAzureOpenAIDeploymentNameSelector.
:ivar capabilities:
:vartype capabilities: ~flow.models.AetherUIAzureOpenAIModelCapabilities
"""
_attribute_map = {
'capabilities': {'key': 'Capabilities', 'type': 'AetherUIAzureOpenAIModelCapabilities'},
}
def __init__(
self,
*,
capabilities: Optional["AetherUIAzureOpenAIModelCapabilities"] = None,
**kwargs
):
"""
:keyword capabilities:
:paramtype capabilities: ~flow.models.AetherUIAzureOpenAIModelCapabilities
"""
super(AetherUIAzureOpenAIDeploymentNameSelector, self).__init__(**kwargs)
self.capabilities = capabilities
class AetherUIAzureOpenAIModelCapabilities(msrest.serialization.Model):
"""AetherUIAzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'Completion', 'type': 'bool'},
'chat_completion': {'key': 'ChatCompletion', 'type': 'bool'},
'embeddings': {'key': 'Embeddings', 'type': 'bool'},
}
def __init__(
self,
*,
completion: Optional[bool] = None,
chat_completion: Optional[bool] = None,
embeddings: Optional[bool] = None,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(AetherUIAzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = completion
self.chat_completion = chat_completion
self.embeddings = embeddings
class AetherUIColumnPicker(msrest.serialization.Model):
"""AetherUIColumnPicker.
:ivar column_picker_for:
:vartype column_picker_for: str
:ivar column_selection_categories:
:vartype column_selection_categories: list[str]
:ivar single_column_selection:
:vartype single_column_selection: bool
"""
_attribute_map = {
'column_picker_for': {'key': 'columnPickerFor', 'type': 'str'},
'column_selection_categories': {'key': 'columnSelectionCategories', 'type': '[str]'},
'single_column_selection': {'key': 'singleColumnSelection', 'type': 'bool'},
}
def __init__(
self,
*,
column_picker_for: Optional[str] = None,
column_selection_categories: Optional[List[str]] = None,
single_column_selection: Optional[bool] = None,
**kwargs
):
"""
:keyword column_picker_for:
:paramtype column_picker_for: str
:keyword column_selection_categories:
:paramtype column_selection_categories: list[str]
:keyword single_column_selection:
:paramtype single_column_selection: bool
"""
super(AetherUIColumnPicker, self).__init__(**kwargs)
self.column_picker_for = column_picker_for
self.column_selection_categories = column_selection_categories
self.single_column_selection = single_column_selection
class AetherUIJsonEditor(msrest.serialization.Model):
"""AetherUIJsonEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
*,
json_schema: Optional[str] = None,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(AetherUIJsonEditor, self).__init__(**kwargs)
self.json_schema = json_schema
class AetherUIParameterHint(msrest.serialization.Model):
"""AetherUIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.AetherUIWidgetTypeEnum
:ivar column_picker:
:vartype column_picker: ~flow.models.AetherUIColumnPicker
:ivar ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:vartype ui_script_language: str or ~flow.models.AetherUIScriptLanguageEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.AetherUIJsonEditor
:ivar prompt_flow_connection_selector:
:vartype prompt_flow_connection_selector: ~flow.models.AetherUIPromptFlowConnectionSelector
:ivar azure_open_ai_deployment_name_selector:
:vartype azure_open_ai_deployment_name_selector:
~flow.models.AetherUIAzureOpenAIDeploymentNameSelector
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'column_picker': {'key': 'columnPicker', 'type': 'AetherUIColumnPicker'},
'ui_script_language': {'key': 'uiScriptLanguage', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'AetherUIJsonEditor'},
'prompt_flow_connection_selector': {'key': 'PromptFlowConnectionSelector', 'type': 'AetherUIPromptFlowConnectionSelector'},
'azure_open_ai_deployment_name_selector': {'key': 'AzureOpenAIDeploymentNameSelector', 'type': 'AetherUIAzureOpenAIDeploymentNameSelector'},
'ux_ignore': {'key': 'UxIgnore', 'type': 'bool'},
'anonymous': {'key': 'Anonymous', 'type': 'bool'},
}
def __init__(
self,
*,
ui_widget_type: Optional[Union[str, "AetherUIWidgetTypeEnum"]] = None,
column_picker: Optional["AetherUIColumnPicker"] = None,
ui_script_language: Optional[Union[str, "AetherUIScriptLanguageEnum"]] = None,
json_editor: Optional["AetherUIJsonEditor"] = None,
prompt_flow_connection_selector: Optional["AetherUIPromptFlowConnectionSelector"] = None,
azure_open_ai_deployment_name_selector: Optional["AetherUIAzureOpenAIDeploymentNameSelector"] = None,
ux_ignore: Optional[bool] = None,
anonymous: Optional[bool] = None,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.AetherUIWidgetTypeEnum
:keyword column_picker:
:paramtype column_picker: ~flow.models.AetherUIColumnPicker
:keyword ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:paramtype ui_script_language: str or ~flow.models.AetherUIScriptLanguageEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.AetherUIJsonEditor
:keyword prompt_flow_connection_selector:
:paramtype prompt_flow_connection_selector: ~flow.models.AetherUIPromptFlowConnectionSelector
:keyword azure_open_ai_deployment_name_selector:
:paramtype azure_open_ai_deployment_name_selector:
~flow.models.AetherUIAzureOpenAIDeploymentNameSelector
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
"""
super(AetherUIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = ui_widget_type
self.column_picker = column_picker
self.ui_script_language = ui_script_language
self.json_editor = json_editor
self.prompt_flow_connection_selector = prompt_flow_connection_selector
self.azure_open_ai_deployment_name_selector = azure_open_ai_deployment_name_selector
self.ux_ignore = ux_ignore
self.anonymous = anonymous
class AetherUIPromptFlowConnectionSelector(msrest.serialization.Model):
"""AetherUIPromptFlowConnectionSelector.
:ivar prompt_flow_connection_type:
:vartype prompt_flow_connection_type: str
"""
_attribute_map = {
'prompt_flow_connection_type': {'key': 'PromptFlowConnectionType', 'type': 'str'},
}
def __init__(
self,
*,
prompt_flow_connection_type: Optional[str] = None,
**kwargs
):
"""
:keyword prompt_flow_connection_type:
:paramtype prompt_flow_connection_type: str
"""
super(AetherUIPromptFlowConnectionSelector, self).__init__(**kwargs)
self.prompt_flow_connection_type = prompt_flow_connection_type
class AetherValidationDataSettings(msrest.serialization.Model):
"""AetherValidationDataSettings.
:ivar n_cross_validations:
:vartype n_cross_validations: ~flow.models.AetherNCrossValidations
:ivar validation_data_size:
:vartype validation_data_size: float
:ivar cv_split_column_names:
:vartype cv_split_column_names: list[str]
:ivar validation_type:
:vartype validation_type: str
"""
_attribute_map = {
'n_cross_validations': {'key': 'nCrossValidations', 'type': 'AetherNCrossValidations'},
'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
'validation_type': {'key': 'validationType', 'type': 'str'},
}
def __init__(
self,
*,
n_cross_validations: Optional["AetherNCrossValidations"] = None,
validation_data_size: Optional[float] = None,
cv_split_column_names: Optional[List[str]] = None,
validation_type: Optional[str] = None,
**kwargs
):
"""
:keyword n_cross_validations:
:paramtype n_cross_validations: ~flow.models.AetherNCrossValidations
:keyword validation_data_size:
:paramtype validation_data_size: float
:keyword cv_split_column_names:
:paramtype cv_split_column_names: list[str]
:keyword validation_type:
:paramtype validation_type: str
"""
super(AetherValidationDataSettings, self).__init__(**kwargs)
self.n_cross_validations = n_cross_validations
self.validation_data_size = validation_data_size
self.cv_split_column_names = cv_split_column_names
self.validation_type = validation_type
class AetherVsoBuildArtifactInfo(msrest.serialization.Model):
"""AetherVsoBuildArtifactInfo.
:ivar build_info:
:vartype build_info: ~flow.models.AetherVsoBuildInfo
:ivar download_url:
:vartype download_url: str
"""
_attribute_map = {
'build_info': {'key': 'buildInfo', 'type': 'AetherVsoBuildInfo'},
'download_url': {'key': 'downloadUrl', 'type': 'str'},
}
def __init__(
self,
*,
build_info: Optional["AetherVsoBuildInfo"] = None,
download_url: Optional[str] = None,
**kwargs
):
"""
:keyword build_info:
:paramtype build_info: ~flow.models.AetherVsoBuildInfo
:keyword download_url:
:paramtype download_url: str
"""
super(AetherVsoBuildArtifactInfo, self).__init__(**kwargs)
self.build_info = build_info
self.download_url = download_url
class AetherVsoBuildDefinitionInfo(msrest.serialization.Model):
"""AetherVsoBuildDefinitionInfo.
:ivar account_name:
:vartype account_name: str
:ivar project_id:
:vartype project_id: str
:ivar build_definition_id:
:vartype build_definition_id: int
"""
_attribute_map = {
'account_name': {'key': 'accountName', 'type': 'str'},
'project_id': {'key': 'projectId', 'type': 'str'},
'build_definition_id': {'key': 'buildDefinitionId', 'type': 'int'},
}
def __init__(
self,
*,
account_name: Optional[str] = None,
project_id: Optional[str] = None,
build_definition_id: Optional[int] = None,
**kwargs
):
"""
:keyword account_name:
:paramtype account_name: str
:keyword project_id:
:paramtype project_id: str
:keyword build_definition_id:
:paramtype build_definition_id: int
"""
super(AetherVsoBuildDefinitionInfo, self).__init__(**kwargs)
self.account_name = account_name
self.project_id = project_id
self.build_definition_id = build_definition_id
class AetherVsoBuildInfo(msrest.serialization.Model):
"""AetherVsoBuildInfo.
:ivar definition_info:
:vartype definition_info: ~flow.models.AetherVsoBuildDefinitionInfo
:ivar build_id:
:vartype build_id: int
"""
_attribute_map = {
'definition_info': {'key': 'definitionInfo', 'type': 'AetherVsoBuildDefinitionInfo'},
'build_id': {'key': 'buildId', 'type': 'int'},
}
def __init__(
self,
*,
definition_info: Optional["AetherVsoBuildDefinitionInfo"] = None,
build_id: Optional[int] = None,
**kwargs
):
"""
:keyword definition_info:
:paramtype definition_info: ~flow.models.AetherVsoBuildDefinitionInfo
:keyword build_id:
:paramtype build_id: int
"""
super(AetherVsoBuildInfo, self).__init__(**kwargs)
self.definition_info = definition_info
self.build_id = build_id
class AEVAComputeConfiguration(msrest.serialization.Model):
"""AEVAComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar is_preemptable:
:vartype is_preemptable: bool
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'is_preemptable': {'key': 'isPreemptable', 'type': 'bool'},
}
def __init__(
self,
*,
target: Optional[str] = None,
instance_count: Optional[int] = None,
is_local: Optional[bool] = None,
location: Optional[str] = None,
is_clusterless: Optional[bool] = None,
instance_type: Optional[str] = None,
properties: Optional[Dict[str, Any]] = None,
is_preemptable: Optional[bool] = None,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword is_preemptable:
:paramtype is_preemptable: bool
"""
super(AEVAComputeConfiguration, self).__init__(**kwargs)
self.target = target
self.instance_count = instance_count
self.is_local = is_local
self.location = location
self.is_clusterless = is_clusterless
self.instance_type = instance_type
self.properties = properties
self.is_preemptable = is_preemptable
class AEVAResourceConfiguration(msrest.serialization.Model):
"""AEVAResourceConfiguration.
:ivar instance_count:
:vartype instance_count: int
:ivar instance_type:
:vartype instance_type: str
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
:ivar locations:
:vartype locations: list[str]
:ivar instance_priority:
:vartype instance_priority: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
"""
_attribute_map = {
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{object}'},
'locations': {'key': 'locations', 'type': '[str]'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
}
def __init__(
self,
*,
instance_count: Optional[int] = None,
instance_type: Optional[str] = None,
properties: Optional[Dict[str, Any]] = None,
locations: Optional[List[str]] = None,
instance_priority: Optional[str] = None,
quota_enforcement_resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword instance_count:
:paramtype instance_count: int
:keyword instance_type:
:paramtype instance_type: str
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
:keyword locations:
:paramtype locations: list[str]
:keyword instance_priority:
:paramtype instance_priority: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
"""
super(AEVAResourceConfiguration, self).__init__(**kwargs)
self.instance_count = instance_count
self.instance_type = instance_type
self.properties = properties
self.locations = locations
self.instance_priority = instance_priority
self.quota_enforcement_resource_id = quota_enforcement_resource_id
class AISuperComputerConfiguration(msrest.serialization.Model):
"""AISuperComputerConfiguration.
:ivar instance_type:
:vartype instance_type: str
:ivar instance_types:
:vartype instance_types: list[str]
:ivar image_version:
:vartype image_version: str
:ivar location:
:vartype location: str
:ivar locations:
:vartype locations: list[str]
:ivar ai_super_computer_storage_data: Dictionary of
:code:`<AISuperComputerStorageReferenceConfiguration>`.
:vartype ai_super_computer_storage_data: dict[str,
~flow.models.AISuperComputerStorageReferenceConfiguration]
:ivar interactive:
:vartype interactive: bool
:ivar scale_policy:
:vartype scale_policy: ~flow.models.AISuperComputerScalePolicy
:ivar virtual_cluster_arm_id:
:vartype virtual_cluster_arm_id: str
:ivar tensorboard_log_directory:
:vartype tensorboard_log_directory: str
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar ssh_public_keys:
:vartype ssh_public_keys: list[str]
:ivar enable_azml_int:
:vartype enable_azml_int: bool
:ivar priority:
:vartype priority: str
:ivar sla_tier:
:vartype sla_tier: str
:ivar suspend_on_idle_time_hours:
:vartype suspend_on_idle_time_hours: long
:ivar user_alias:
:vartype user_alias: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
:ivar model_compute_specification_id:
:vartype model_compute_specification_id: str
:ivar group_policy_name:
:vartype group_policy_name: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'image_version': {'key': 'imageVersion', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'locations': {'key': 'locations', 'type': '[str]'},
'ai_super_computer_storage_data': {'key': 'aiSuperComputerStorageData', 'type': '{AISuperComputerStorageReferenceConfiguration}'},
'interactive': {'key': 'interactive', 'type': 'bool'},
'scale_policy': {'key': 'scalePolicy', 'type': 'AISuperComputerScalePolicy'},
'virtual_cluster_arm_id': {'key': 'virtualClusterArmId', 'type': 'str'},
'tensorboard_log_directory': {'key': 'tensorboardLogDirectory', 'type': 'str'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'ssh_public_keys': {'key': 'sshPublicKeys', 'type': '[str]'},
'enable_azml_int': {'key': 'enableAzmlInt', 'type': 'bool'},
'priority': {'key': 'priority', 'type': 'str'},
'sla_tier': {'key': 'slaTier', 'type': 'str'},
'suspend_on_idle_time_hours': {'key': 'suspendOnIdleTimeHours', 'type': 'long'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
'model_compute_specification_id': {'key': 'modelComputeSpecificationId', 'type': 'str'},
'group_policy_name': {'key': 'groupPolicyName', 'type': 'str'},
}
def __init__(
self,
*,
instance_type: Optional[str] = None,
instance_types: Optional[List[str]] = None,
image_version: Optional[str] = None,
location: Optional[str] = None,
locations: Optional[List[str]] = None,
ai_super_computer_storage_data: Optional[Dict[str, "AISuperComputerStorageReferenceConfiguration"]] = None,
interactive: Optional[bool] = None,
scale_policy: Optional["AISuperComputerScalePolicy"] = None,
virtual_cluster_arm_id: Optional[str] = None,
tensorboard_log_directory: Optional[str] = None,
ssh_public_key: Optional[str] = None,
ssh_public_keys: Optional[List[str]] = None,
enable_azml_int: Optional[bool] = None,
priority: Optional[str] = None,
sla_tier: Optional[str] = None,
suspend_on_idle_time_hours: Optional[int] = None,
user_alias: Optional[str] = None,
quota_enforcement_resource_id: Optional[str] = None,
model_compute_specification_id: Optional[str] = None,
group_policy_name: Optional[str] = None,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword image_version:
:paramtype image_version: str
:keyword location:
:paramtype location: str
:keyword locations:
:paramtype locations: list[str]
:keyword ai_super_computer_storage_data: Dictionary of
:code:`<AISuperComputerStorageReferenceConfiguration>`.
:paramtype ai_super_computer_storage_data: dict[str,
~flow.models.AISuperComputerStorageReferenceConfiguration]
:keyword interactive:
:paramtype interactive: bool
:keyword scale_policy:
:paramtype scale_policy: ~flow.models.AISuperComputerScalePolicy
:keyword virtual_cluster_arm_id:
:paramtype virtual_cluster_arm_id: str
:keyword tensorboard_log_directory:
:paramtype tensorboard_log_directory: str
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword ssh_public_keys:
:paramtype ssh_public_keys: list[str]
:keyword enable_azml_int:
:paramtype enable_azml_int: bool
:keyword priority:
:paramtype priority: str
:keyword sla_tier:
:paramtype sla_tier: str
:keyword suspend_on_idle_time_hours:
:paramtype suspend_on_idle_time_hours: long
:keyword user_alias:
:paramtype user_alias: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
:keyword model_compute_specification_id:
:paramtype model_compute_specification_id: str
:keyword group_policy_name:
:paramtype group_policy_name: str
"""
super(AISuperComputerConfiguration, self).__init__(**kwargs)
self.instance_type = instance_type
self.instance_types = instance_types
self.image_version = image_version
self.location = location
self.locations = locations
self.ai_super_computer_storage_data = ai_super_computer_storage_data
self.interactive = interactive
self.scale_policy = scale_policy
self.virtual_cluster_arm_id = virtual_cluster_arm_id
self.tensorboard_log_directory = tensorboard_log_directory
self.ssh_public_key = ssh_public_key
self.ssh_public_keys = ssh_public_keys
self.enable_azml_int = enable_azml_int
self.priority = priority
self.sla_tier = sla_tier
self.suspend_on_idle_time_hours = suspend_on_idle_time_hours
self.user_alias = user_alias
self.quota_enforcement_resource_id = quota_enforcement_resource_id
self.model_compute_specification_id = model_compute_specification_id
self.group_policy_name = group_policy_name
class AISuperComputerScalePolicy(msrest.serialization.Model):
"""AISuperComputerScalePolicy.
:ivar auto_scale_instance_type_count_set:
:vartype auto_scale_instance_type_count_set: list[int]
:ivar auto_scale_interval_in_sec:
:vartype auto_scale_interval_in_sec: int
:ivar max_instance_type_count:
:vartype max_instance_type_count: int
:ivar min_instance_type_count:
:vartype min_instance_type_count: int
"""
_attribute_map = {
'auto_scale_instance_type_count_set': {'key': 'autoScaleInstanceTypeCountSet', 'type': '[int]'},
'auto_scale_interval_in_sec': {'key': 'autoScaleIntervalInSec', 'type': 'int'},
'max_instance_type_count': {'key': 'maxInstanceTypeCount', 'type': 'int'},
'min_instance_type_count': {'key': 'minInstanceTypeCount', 'type': 'int'},
}
def __init__(
self,
*,
auto_scale_instance_type_count_set: Optional[List[int]] = None,
auto_scale_interval_in_sec: Optional[int] = None,
max_instance_type_count: Optional[int] = None,
min_instance_type_count: Optional[int] = None,
**kwargs
):
"""
:keyword auto_scale_instance_type_count_set:
:paramtype auto_scale_instance_type_count_set: list[int]
:keyword auto_scale_interval_in_sec:
:paramtype auto_scale_interval_in_sec: int
:keyword max_instance_type_count:
:paramtype max_instance_type_count: int
:keyword min_instance_type_count:
:paramtype min_instance_type_count: int
"""
super(AISuperComputerScalePolicy, self).__init__(**kwargs)
self.auto_scale_instance_type_count_set = auto_scale_instance_type_count_set
self.auto_scale_interval_in_sec = auto_scale_interval_in_sec
self.max_instance_type_count = max_instance_type_count
self.min_instance_type_count = min_instance_type_count
class AISuperComputerStorageReferenceConfiguration(msrest.serialization.Model):
"""AISuperComputerStorageReferenceConfiguration.
:ivar container_name:
:vartype container_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'container_name': {'key': 'containerName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
container_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword container_name:
:paramtype container_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(AISuperComputerStorageReferenceConfiguration, self).__init__(**kwargs)
self.container_name = container_name
self.relative_path = relative_path
class AKSAdvanceSettings(msrest.serialization.Model):
"""AKSAdvanceSettings.
:ivar auto_scaler:
:vartype auto_scaler: ~flow.models.AutoScaler
:ivar container_resource_requirements:
:vartype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar scoring_timeout_ms:
:vartype scoring_timeout_ms: int
:ivar num_replicas:
:vartype num_replicas: int
"""
_attribute_map = {
'auto_scaler': {'key': 'autoScaler', 'type': 'AutoScaler'},
'container_resource_requirements': {'key': 'containerResourceRequirements', 'type': 'ContainerResourceRequirements'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'scoring_timeout_ms': {'key': 'scoringTimeoutMs', 'type': 'int'},
'num_replicas': {'key': 'numReplicas', 'type': 'int'},
}
def __init__(
self,
*,
auto_scaler: Optional["AutoScaler"] = None,
container_resource_requirements: Optional["ContainerResourceRequirements"] = None,
app_insights_enabled: Optional[bool] = None,
scoring_timeout_ms: Optional[int] = None,
num_replicas: Optional[int] = None,
**kwargs
):
"""
:keyword auto_scaler:
:paramtype auto_scaler: ~flow.models.AutoScaler
:keyword container_resource_requirements:
:paramtype container_resource_requirements: ~flow.models.ContainerResourceRequirements
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword scoring_timeout_ms:
:paramtype scoring_timeout_ms: int
:keyword num_replicas:
:paramtype num_replicas: int
"""
super(AKSAdvanceSettings, self).__init__(**kwargs)
self.auto_scaler = auto_scaler
self.container_resource_requirements = container_resource_requirements
self.app_insights_enabled = app_insights_enabled
self.scoring_timeout_ms = scoring_timeout_ms
self.num_replicas = num_replicas
class AKSReplicaStatus(msrest.serialization.Model):
"""AKSReplicaStatus.
:ivar desired_replicas:
:vartype desired_replicas: int
:ivar updated_replicas:
:vartype updated_replicas: int
:ivar available_replicas:
:vartype available_replicas: int
:ivar error:
:vartype error: ~flow.models.ModelManagementErrorResponse
"""
_attribute_map = {
'desired_replicas': {'key': 'desiredReplicas', 'type': 'int'},
'updated_replicas': {'key': 'updatedReplicas', 'type': 'int'},
'available_replicas': {'key': 'availableReplicas', 'type': 'int'},
'error': {'key': 'error', 'type': 'ModelManagementErrorResponse'},
}
def __init__(
self,
*,
desired_replicas: Optional[int] = None,
updated_replicas: Optional[int] = None,
available_replicas: Optional[int] = None,
error: Optional["ModelManagementErrorResponse"] = None,
**kwargs
):
"""
:keyword desired_replicas:
:paramtype desired_replicas: int
:keyword updated_replicas:
:paramtype updated_replicas: int
:keyword available_replicas:
:paramtype available_replicas: int
:keyword error:
:paramtype error: ~flow.models.ModelManagementErrorResponse
"""
super(AKSReplicaStatus, self).__init__(**kwargs)
self.desired_replicas = desired_replicas
self.updated_replicas = updated_replicas
self.available_replicas = available_replicas
self.error = error
class AMLComputeConfiguration(msrest.serialization.Model):
"""AMLComputeConfiguration.
:ivar name:
:vartype name: str
:ivar vm_size:
:vartype vm_size: str
:ivar vm_priority: Possible values include: "Dedicated", "Lowpriority".
:vartype vm_priority: str or ~flow.models.VmPriority
:ivar retain_cluster:
:vartype retain_cluster: bool
:ivar cluster_max_node_count:
:vartype cluster_max_node_count: int
:ivar os_type:
:vartype os_type: str
:ivar virtual_machine_image:
:vartype virtual_machine_image: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'vm_priority': {'key': 'vmPriority', 'type': 'str'},
'retain_cluster': {'key': 'retainCluster', 'type': 'bool'},
'cluster_max_node_count': {'key': 'clusterMaxNodeCount', 'type': 'int'},
'os_type': {'key': 'osType', 'type': 'str'},
'virtual_machine_image': {'key': 'virtualMachineImage', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
vm_size: Optional[str] = None,
vm_priority: Optional[Union[str, "VmPriority"]] = None,
retain_cluster: Optional[bool] = None,
cluster_max_node_count: Optional[int] = None,
os_type: Optional[str] = None,
virtual_machine_image: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword vm_size:
:paramtype vm_size: str
:keyword vm_priority: Possible values include: "Dedicated", "Lowpriority".
:paramtype vm_priority: str or ~flow.models.VmPriority
:keyword retain_cluster:
:paramtype retain_cluster: bool
:keyword cluster_max_node_count:
:paramtype cluster_max_node_count: int
:keyword os_type:
:paramtype os_type: str
:keyword virtual_machine_image:
:paramtype virtual_machine_image: str
"""
super(AMLComputeConfiguration, self).__init__(**kwargs)
self.name = name
self.vm_size = vm_size
self.vm_priority = vm_priority
self.retain_cluster = retain_cluster
self.cluster_max_node_count = cluster_max_node_count
self.os_type = os_type
self.virtual_machine_image = virtual_machine_image
class AmlDataset(msrest.serialization.Model):
"""AmlDataset.
:ivar registered_data_set_reference:
:vartype registered_data_set_reference: ~flow.models.RegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.SavedDataSetReference
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'registered_data_set_reference': {'key': 'registeredDataSetReference', 'type': 'RegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'SavedDataSetReference'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
registered_data_set_reference: Optional["RegisteredDataSetReference"] = None,
saved_data_set_reference: Optional["SavedDataSetReference"] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword registered_data_set_reference:
:paramtype registered_data_set_reference: ~flow.models.RegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.SavedDataSetReference
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(AmlDataset, self).__init__(**kwargs)
self.registered_data_set_reference = registered_data_set_reference
self.saved_data_set_reference = saved_data_set_reference
self.additional_transformations = additional_transformations
class AmlK8SConfiguration(msrest.serialization.Model):
"""AmlK8SConfiguration.
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.ResourceConfiguration
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.AmlK8SPriorityConfiguration
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.InteractiveConfiguration
"""
_attribute_map = {
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'ResourceConfiguration'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'AmlK8SPriorityConfiguration'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'InteractiveConfiguration'},
}
def __init__(
self,
*,
resource_configuration: Optional["ResourceConfiguration"] = None,
priority_configuration: Optional["AmlK8SPriorityConfiguration"] = None,
interactive_configuration: Optional["InteractiveConfiguration"] = None,
**kwargs
):
"""
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.ResourceConfiguration
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.AmlK8SPriorityConfiguration
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.InteractiveConfiguration
"""
super(AmlK8SConfiguration, self).__init__(**kwargs)
self.resource_configuration = resource_configuration
self.priority_configuration = priority_configuration
self.interactive_configuration = interactive_configuration
class AmlK8SPriorityConfiguration(msrest.serialization.Model):
"""AmlK8SPriorityConfiguration.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
*,
job_priority: Optional[int] = None,
is_preemptible: Optional[bool] = None,
node_count_set: Optional[List[int]] = None,
scale_interval: Optional[int] = None,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(AmlK8SPriorityConfiguration, self).__init__(**kwargs)
self.job_priority = job_priority
self.is_preemptible = is_preemptible
self.node_count_set = node_count_set
self.scale_interval = scale_interval
class AmlSparkCloudSetting(msrest.serialization.Model):
"""AmlSparkCloudSetting.
:ivar entry:
:vartype entry: ~flow.models.EntrySetting
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar inline_environment_definition_string:
:vartype inline_environment_definition_string: str
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar compute:
:vartype compute: str
:ivar resources:
:vartype resources: ~flow.models.ResourcesSetting
:ivar identity:
:vartype identity: ~flow.models.IdentitySetting
"""
_attribute_map = {
'entry': {'key': 'entry', 'type': 'EntrySetting'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'inline_environment_definition_string': {'key': 'inlineEnvironmentDefinitionString', 'type': 'str'},
'conf': {'key': 'conf', 'type': '{str}'},
'compute': {'key': 'compute', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'ResourcesSetting'},
'identity': {'key': 'identity', 'type': 'IdentitySetting'},
}
def __init__(
self,
*,
entry: Optional["EntrySetting"] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
py_files: Optional[List[str]] = None,
driver_memory: Optional[str] = None,
driver_cores: Optional[int] = None,
executor_memory: Optional[str] = None,
executor_cores: Optional[int] = None,
number_executors: Optional[int] = None,
environment_asset_id: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
inline_environment_definition_string: Optional[str] = None,
conf: Optional[Dict[str, str]] = None,
compute: Optional[str] = None,
resources: Optional["ResourcesSetting"] = None,
identity: Optional["IdentitySetting"] = None,
**kwargs
):
"""
:keyword entry:
:paramtype entry: ~flow.models.EntrySetting
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword inline_environment_definition_string:
:paramtype inline_environment_definition_string: str
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword compute:
:paramtype compute: str
:keyword resources:
:paramtype resources: ~flow.models.ResourcesSetting
:keyword identity:
:paramtype identity: ~flow.models.IdentitySetting
"""
super(AmlSparkCloudSetting, self).__init__(**kwargs)
self.entry = entry
self.files = files
self.archives = archives
self.jars = jars
self.py_files = py_files
self.driver_memory = driver_memory
self.driver_cores = driver_cores
self.executor_memory = executor_memory
self.executor_cores = executor_cores
self.number_executors = number_executors
self.environment_asset_id = environment_asset_id
self.environment_variables = environment_variables
self.inline_environment_definition_string = inline_environment_definition_string
self.conf = conf
self.compute = compute
self.resources = resources
self.identity = identity
class APCloudConfiguration(msrest.serialization.Model):
"""APCloudConfiguration.
:ivar referenced_ap_module_guid:
:vartype referenced_ap_module_guid: str
:ivar user_alias:
:vartype user_alias: str
:ivar aether_module_type:
:vartype aether_module_type: str
:ivar allow_overwrite:
:vartype allow_overwrite: bool
:ivar destination_expiration_days:
:vartype destination_expiration_days: int
:ivar should_respect_line_boundaries:
:vartype should_respect_line_boundaries: bool
"""
_attribute_map = {
'referenced_ap_module_guid': {'key': 'referencedAPModuleGuid', 'type': 'str'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'aether_module_type': {'key': 'aetherModuleType', 'type': 'str'},
'allow_overwrite': {'key': 'allowOverwrite', 'type': 'bool'},
'destination_expiration_days': {'key': 'destinationExpirationDays', 'type': 'int'},
'should_respect_line_boundaries': {'key': 'shouldRespectLineBoundaries', 'type': 'bool'},
}
def __init__(
self,
*,
referenced_ap_module_guid: Optional[str] = None,
user_alias: Optional[str] = None,
aether_module_type: Optional[str] = None,
allow_overwrite: Optional[bool] = None,
destination_expiration_days: Optional[int] = None,
should_respect_line_boundaries: Optional[bool] = None,
**kwargs
):
"""
:keyword referenced_ap_module_guid:
:paramtype referenced_ap_module_guid: str
:keyword user_alias:
:paramtype user_alias: str
:keyword aether_module_type:
:paramtype aether_module_type: str
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
:keyword destination_expiration_days:
:paramtype destination_expiration_days: int
:keyword should_respect_line_boundaries:
:paramtype should_respect_line_boundaries: bool
"""
super(APCloudConfiguration, self).__init__(**kwargs)
self.referenced_ap_module_guid = referenced_ap_module_guid
self.user_alias = user_alias
self.aether_module_type = aether_module_type
self.allow_overwrite = allow_overwrite
self.destination_expiration_days = destination_expiration_days
self.should_respect_line_boundaries = should_respect_line_boundaries
class ApiAndParameters(msrest.serialization.Model):
"""ApiAndParameters.
:ivar api:
:vartype api: str
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, ~flow.models.FlowToolSettingParameter]
:ivar default_prompt:
:vartype default_prompt: str
"""
_attribute_map = {
'api': {'key': 'api', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{FlowToolSettingParameter}'},
'default_prompt': {'key': 'default_prompt', 'type': 'str'},
}
def __init__(
self,
*,
api: Optional[str] = None,
parameters: Optional[Dict[str, "FlowToolSettingParameter"]] = None,
default_prompt: Optional[str] = None,
**kwargs
):
"""
:keyword api:
:paramtype api: str
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, ~flow.models.FlowToolSettingParameter]
:keyword default_prompt:
:paramtype default_prompt: str
"""
super(ApiAndParameters, self).__init__(**kwargs)
self.api = api
self.parameters = parameters
self.default_prompt = default_prompt
class ApplicationEndpointConfiguration(msrest.serialization.Model):
"""ApplicationEndpointConfiguration.
:ivar type: Possible values include: "Jupyter", "JupyterLab", "SSH", "TensorBoard", "VSCode",
"Theia", "Grafana", "Custom", "RayDashboard".
:vartype type: str or ~flow.models.ApplicationEndpointType
:ivar port:
:vartype port: int
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: ~flow.models.Nodes
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'Nodes'},
}
def __init__(
self,
*,
type: Optional[Union[str, "ApplicationEndpointType"]] = None,
port: Optional[int] = None,
properties: Optional[Dict[str, str]] = None,
nodes: Optional["Nodes"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "Jupyter", "JupyterLab", "SSH", "TensorBoard",
"VSCode", "Theia", "Grafana", "Custom", "RayDashboard".
:paramtype type: str or ~flow.models.ApplicationEndpointType
:keyword port:
:paramtype port: int
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: ~flow.models.Nodes
"""
super(ApplicationEndpointConfiguration, self).__init__(**kwargs)
self.type = type
self.port = port
self.properties = properties
self.nodes = nodes
class ArgumentAssignment(msrest.serialization.Model):
"""ArgumentAssignment.
:ivar value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:vartype value_type: str or ~flow.models.ArgumentValueType
:ivar value:
:vartype value: str
:ivar nested_argument_list:
:vartype nested_argument_list: list[~flow.models.ArgumentAssignment]
:ivar string_interpolation_argument_list:
:vartype string_interpolation_argument_list: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'nested_argument_list': {'key': 'nestedArgumentList', 'type': '[ArgumentAssignment]'},
'string_interpolation_argument_list': {'key': 'stringInterpolationArgumentList', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
*,
value_type: Optional[Union[str, "ArgumentValueType"]] = None,
value: Optional[str] = None,
nested_argument_list: Optional[List["ArgumentAssignment"]] = None,
string_interpolation_argument_list: Optional[List["ArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "Parameter", "Input", "Output",
"NestedList", "StringInterpolationList".
:paramtype value_type: str or ~flow.models.ArgumentValueType
:keyword value:
:paramtype value: str
:keyword nested_argument_list:
:paramtype nested_argument_list: list[~flow.models.ArgumentAssignment]
:keyword string_interpolation_argument_list:
:paramtype string_interpolation_argument_list: list[~flow.models.ArgumentAssignment]
"""
super(ArgumentAssignment, self).__init__(**kwargs)
self.value_type = value_type
self.value = value
self.nested_argument_list = nested_argument_list
self.string_interpolation_argument_list = string_interpolation_argument_list
class Asset(msrest.serialization.Model):
"""Asset.
:ivar asset_id:
:vartype asset_id: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'asset_id': {'key': 'assetId', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
asset_id: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword asset_id:
:paramtype asset_id: str
:keyword type:
:paramtype type: str
"""
super(Asset, self).__init__(**kwargs)
self.asset_id = asset_id
self.type = type
class AssetDefinition(msrest.serialization.Model):
"""AssetDefinition.
:ivar path:
:vartype path: str
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AEVAAssetType
:ivar asset_id:
:vartype asset_id: str
:ivar serialized_asset_id:
:vartype serialized_asset_id: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'serialized_asset_id': {'key': 'serializedAssetId', 'type': 'str'},
}
def __init__(
self,
*,
path: Optional[str] = None,
type: Optional[Union[str, "AEVAAssetType"]] = None,
asset_id: Optional[str] = None,
serialized_asset_id: Optional[str] = None,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AEVAAssetType
:keyword asset_id:
:paramtype asset_id: str
:keyword serialized_asset_id:
:paramtype serialized_asset_id: str
"""
super(AssetDefinition, self).__init__(**kwargs)
self.path = path
self.type = type
self.asset_id = asset_id
self.serialized_asset_id = serialized_asset_id
class AssetNameAndVersionIdentifier(msrest.serialization.Model):
"""AssetNameAndVersionIdentifier.
:ivar asset_name:
:vartype asset_name: str
:ivar version:
:vartype version: str
:ivar feed_name:
:vartype feed_name: str
"""
_attribute_map = {
'asset_name': {'key': 'assetName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
}
def __init__(
self,
*,
asset_name: Optional[str] = None,
version: Optional[str] = None,
feed_name: Optional[str] = None,
**kwargs
):
"""
:keyword asset_name:
:paramtype asset_name: str
:keyword version:
:paramtype version: str
:keyword feed_name:
:paramtype feed_name: str
"""
super(AssetNameAndVersionIdentifier, self).__init__(**kwargs)
self.asset_name = asset_name
self.version = version
self.feed_name = feed_name
class AssetOutputSettings(msrest.serialization.Model):
"""AssetOutputSettings.
:ivar path:
:vartype path: str
:ivar path_parameter_assignment:
:vartype path_parameter_assignment: ~flow.models.ParameterAssignment
:ivar type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:vartype type: str or ~flow.models.AEVAAssetType
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'path_parameter_assignment': {'key': 'PathParameterAssignment', 'type': 'ParameterAssignment'},
'type': {'key': 'type', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
path: Optional[str] = None,
path_parameter_assignment: Optional["ParameterAssignment"] = None,
type: Optional[Union[str, "AEVAAssetType"]] = None,
options: Optional[Dict[str, str]] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword path_parameter_assignment:
:paramtype path_parameter_assignment: ~flow.models.ParameterAssignment
:keyword type: Possible values include: "UriFile", "UriFolder", "MLTable", "CustomModel",
"MLFlowModel", "TritonModel", "OpenAIModel".
:paramtype type: str or ~flow.models.AEVAAssetType
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(AssetOutputSettings, self).__init__(**kwargs)
self.path = path
self.path_parameter_assignment = path_parameter_assignment
self.type = type
self.options = options
self.data_store_mode = data_store_mode
self.name = name
self.version = version
class AssetOutputSettingsParameter(msrest.serialization.Model):
"""AssetOutputSettingsParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.AssetOutputSettings
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'AssetOutputSettings'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
default_value: Optional["AssetOutputSettings"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.AssetOutputSettings
"""
super(AssetOutputSettingsParameter, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.default_value = default_value
class AssetPublishResult(msrest.serialization.Model):
"""AssetPublishResult.
:ivar feed_name:
:vartype feed_name: str
:ivar asset_name:
:vartype asset_name: str
:ivar asset_version:
:vartype asset_version: str
:ivar step_name:
:vartype step_name: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar last_updated_time:
:vartype last_updated_time: ~datetime.datetime
:ivar regional_publish_results: Dictionary of :code:`<AssetPublishSingleRegionResult>`.
:vartype regional_publish_results: dict[str, ~flow.models.AssetPublishSingleRegionResult]
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'asset_name': {'key': 'assetName', 'type': 'str'},
'asset_version': {'key': 'assetVersion', 'type': 'str'},
'step_name': {'key': 'stepName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'last_updated_time': {'key': 'lastUpdatedTime', 'type': 'iso-8601'},
'regional_publish_results': {'key': 'regionalPublishResults', 'type': '{AssetPublishSingleRegionResult}'},
}
def __init__(
self,
*,
feed_name: Optional[str] = None,
asset_name: Optional[str] = None,
asset_version: Optional[str] = None,
step_name: Optional[str] = None,
status: Optional[str] = None,
error_message: Optional[str] = None,
created_time: Optional[datetime.datetime] = None,
last_updated_time: Optional[datetime.datetime] = None,
regional_publish_results: Optional[Dict[str, "AssetPublishSingleRegionResult"]] = None,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword asset_name:
:paramtype asset_name: str
:keyword asset_version:
:paramtype asset_version: str
:keyword step_name:
:paramtype step_name: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword last_updated_time:
:paramtype last_updated_time: ~datetime.datetime
:keyword regional_publish_results: Dictionary of :code:`<AssetPublishSingleRegionResult>`.
:paramtype regional_publish_results: dict[str, ~flow.models.AssetPublishSingleRegionResult]
"""
super(AssetPublishResult, self).__init__(**kwargs)
self.feed_name = feed_name
self.asset_name = asset_name
self.asset_version = asset_version
self.step_name = step_name
self.status = status
self.error_message = error_message
self.created_time = created_time
self.last_updated_time = last_updated_time
self.regional_publish_results = regional_publish_results
class AssetPublishSingleRegionResult(msrest.serialization.Model):
"""AssetPublishSingleRegionResult.
:ivar step_name:
:vartype step_name: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar last_updated_time:
:vartype last_updated_time: ~datetime.datetime
:ivar total_steps:
:vartype total_steps: int
:ivar finished_steps:
:vartype finished_steps: int
:ivar remaining_steps:
:vartype remaining_steps: int
"""
_attribute_map = {
'step_name': {'key': 'stepName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'last_updated_time': {'key': 'lastUpdatedTime', 'type': 'iso-8601'},
'total_steps': {'key': 'totalSteps', 'type': 'int'},
'finished_steps': {'key': 'finishedSteps', 'type': 'int'},
'remaining_steps': {'key': 'remainingSteps', 'type': 'int'},
}
def __init__(
self,
*,
step_name: Optional[str] = None,
status: Optional[str] = None,
error_message: Optional[str] = None,
last_updated_time: Optional[datetime.datetime] = None,
total_steps: Optional[int] = None,
finished_steps: Optional[int] = None,
remaining_steps: Optional[int] = None,
**kwargs
):
"""
:keyword step_name:
:paramtype step_name: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword last_updated_time:
:paramtype last_updated_time: ~datetime.datetime
:keyword total_steps:
:paramtype total_steps: int
:keyword finished_steps:
:paramtype finished_steps: int
:keyword remaining_steps:
:paramtype remaining_steps: int
"""
super(AssetPublishSingleRegionResult, self).__init__(**kwargs)
self.step_name = step_name
self.status = status
self.error_message = error_message
self.last_updated_time = last_updated_time
self.total_steps = total_steps
self.finished_steps = finished_steps
self.remaining_steps = remaining_steps
class AssetTypeMetaInfo(msrest.serialization.Model):
"""AssetTypeMetaInfo.
:ivar consumption_mode: Possible values include: "Reference", "Copy", "CopyAndAutoUpgrade".
:vartype consumption_mode: str or ~flow.models.ConsumeMode
"""
_attribute_map = {
'consumption_mode': {'key': 'consumptionMode', 'type': 'str'},
}
def __init__(
self,
*,
consumption_mode: Optional[Union[str, "ConsumeMode"]] = None,
**kwargs
):
"""
:keyword consumption_mode: Possible values include: "Reference", "Copy", "CopyAndAutoUpgrade".
:paramtype consumption_mode: str or ~flow.models.ConsumeMode
"""
super(AssetTypeMetaInfo, self).__init__(**kwargs)
self.consumption_mode = consumption_mode
class AssetVersionPublishRequest(msrest.serialization.Model):
"""AssetVersionPublishRequest.
:ivar asset_type: Possible values include: "Component", "Model", "Environment", "Dataset",
"DataStore", "SampleGraph", "FlowTool", "FlowToolSetting", "FlowConnection", "FlowSample",
"FlowRuntimeSpec".
:vartype asset_type: str or ~flow.models.AssetType
:ivar asset_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip".
:vartype asset_source_type: str or ~flow.models.AssetSourceType
:ivar yaml_file:
:vartype yaml_file: str
:ivar source_zip_url:
:vartype source_zip_url: str
:ivar source_zip_file:
:vartype source_zip_file: IO
:ivar feed_name:
:vartype feed_name: str
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar referenced_assets:
:vartype referenced_assets: list[~flow.models.AssetNameAndVersionIdentifier]
:ivar flow_file:
:vartype flow_file: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'asset_type': {'key': 'assetType', 'type': 'str'},
'asset_source_type': {'key': 'assetSourceType', 'type': 'str'},
'yaml_file': {'key': 'yamlFile', 'type': 'str'},
'source_zip_url': {'key': 'sourceZipUrl', 'type': 'str'},
'source_zip_file': {'key': 'sourceZipFile', 'type': 'IO'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'referenced_assets': {'key': 'referencedAssets', 'type': '[AssetNameAndVersionIdentifier]'},
'flow_file': {'key': 'flowFile', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
asset_type: Optional[Union[str, "AssetType"]] = None,
asset_source_type: Optional[Union[str, "AssetSourceType"]] = None,
yaml_file: Optional[str] = None,
source_zip_url: Optional[str] = None,
source_zip_file: Optional[IO] = None,
feed_name: Optional[str] = None,
set_as_default_version: Optional[bool] = None,
referenced_assets: Optional[List["AssetNameAndVersionIdentifier"]] = None,
flow_file: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword asset_type: Possible values include: "Component", "Model", "Environment", "Dataset",
"DataStore", "SampleGraph", "FlowTool", "FlowToolSetting", "FlowConnection", "FlowSample",
"FlowRuntimeSpec".
:paramtype asset_type: str or ~flow.models.AssetType
:keyword asset_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip".
:paramtype asset_source_type: str or ~flow.models.AssetSourceType
:keyword yaml_file:
:paramtype yaml_file: str
:keyword source_zip_url:
:paramtype source_zip_url: str
:keyword source_zip_file:
:paramtype source_zip_file: IO
:keyword feed_name:
:paramtype feed_name: str
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword referenced_assets:
:paramtype referenced_assets: list[~flow.models.AssetNameAndVersionIdentifier]
:keyword flow_file:
:paramtype flow_file: str
:keyword version:
:paramtype version: str
"""
super(AssetVersionPublishRequest, self).__init__(**kwargs)
self.asset_type = asset_type
self.asset_source_type = asset_source_type
self.yaml_file = yaml_file
self.source_zip_url = source_zip_url
self.source_zip_file = source_zip_file
self.feed_name = feed_name
self.set_as_default_version = set_as_default_version
self.referenced_assets = referenced_assets
self.flow_file = flow_file
self.version = version
class AssignedUser(msrest.serialization.Model):
"""AssignedUser.
:ivar object_id:
:vartype object_id: str
:ivar tenant_id:
:vartype tenant_id: str
"""
_attribute_map = {
'object_id': {'key': 'objectId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
}
def __init__(
self,
*,
object_id: Optional[str] = None,
tenant_id: Optional[str] = None,
**kwargs
):
"""
:keyword object_id:
:paramtype object_id: str
:keyword tenant_id:
:paramtype tenant_id: str
"""
super(AssignedUser, self).__init__(**kwargs)
self.object_id = object_id
self.tenant_id = tenant_id
class AttachCosmosRequest(msrest.serialization.Model):
"""AttachCosmosRequest.
:ivar account_endpoint:
:vartype account_endpoint: str
:ivar resource_arm_id:
:vartype resource_arm_id: str
:ivar database_name:
:vartype database_name: str
"""
_attribute_map = {
'account_endpoint': {'key': 'accountEndpoint', 'type': 'str'},
'resource_arm_id': {'key': 'resourceArmId', 'type': 'str'},
'database_name': {'key': 'databaseName', 'type': 'str'},
}
def __init__(
self,
*,
account_endpoint: Optional[str] = None,
resource_arm_id: Optional[str] = None,
database_name: Optional[str] = None,
**kwargs
):
"""
:keyword account_endpoint:
:paramtype account_endpoint: str
:keyword resource_arm_id:
:paramtype resource_arm_id: str
:keyword database_name:
:paramtype database_name: str
"""
super(AttachCosmosRequest, self).__init__(**kwargs)
self.account_endpoint = account_endpoint
self.resource_arm_id = resource_arm_id
self.database_name = database_name
class AuthKeys(msrest.serialization.Model):
"""AuthKeys.
:ivar primary_key:
:vartype primary_key: str
:ivar secondary_key:
:vartype secondary_key: str
"""
_attribute_map = {
'primary_key': {'key': 'primaryKey', 'type': 'str'},
'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
}
def __init__(
self,
*,
primary_key: Optional[str] = None,
secondary_key: Optional[str] = None,
**kwargs
):
"""
:keyword primary_key:
:paramtype primary_key: str
:keyword secondary_key:
:paramtype secondary_key: str
"""
super(AuthKeys, self).__init__(**kwargs)
self.primary_key = primary_key
self.secondary_key = secondary_key
class AutoClusterComputeSpecification(msrest.serialization.Model):
"""AutoClusterComputeSpecification.
:ivar instance_size:
:vartype instance_size: str
:ivar instance_priority:
:vartype instance_priority: str
:ivar os_type:
:vartype os_type: str
:ivar location:
:vartype location: str
:ivar runtime_version:
:vartype runtime_version: str
:ivar quota_enforcement_resource_id:
:vartype quota_enforcement_resource_id: str
:ivar model_compute_specification_id:
:vartype model_compute_specification_id: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'runtime_version': {'key': 'runtimeVersion', 'type': 'str'},
'quota_enforcement_resource_id': {'key': 'quotaEnforcementResourceId', 'type': 'str'},
'model_compute_specification_id': {'key': 'modelComputeSpecificationId', 'type': 'str'},
}
def __init__(
self,
*,
instance_size: Optional[str] = None,
instance_priority: Optional[str] = None,
os_type: Optional[str] = None,
location: Optional[str] = None,
runtime_version: Optional[str] = None,
quota_enforcement_resource_id: Optional[str] = None,
model_compute_specification_id: Optional[str] = None,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword instance_priority:
:paramtype instance_priority: str
:keyword os_type:
:paramtype os_type: str
:keyword location:
:paramtype location: str
:keyword runtime_version:
:paramtype runtime_version: str
:keyword quota_enforcement_resource_id:
:paramtype quota_enforcement_resource_id: str
:keyword model_compute_specification_id:
:paramtype model_compute_specification_id: str
"""
super(AutoClusterComputeSpecification, self).__init__(**kwargs)
self.instance_size = instance_size
self.instance_priority = instance_priority
self.os_type = os_type
self.location = location
self.runtime_version = runtime_version
self.quota_enforcement_resource_id = quota_enforcement_resource_id
self.model_compute_specification_id = model_compute_specification_id
class AutoDeleteSetting(msrest.serialization.Model):
"""AutoDeleteSetting.
:ivar condition: Possible values include: "CreatedGreaterThan", "LastAccessedGreaterThan".
:vartype condition: str or ~flow.models.AutoDeleteCondition
:ivar value:
:vartype value: str
"""
_attribute_map = {
'condition': {'key': 'condition', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
condition: Optional[Union[str, "AutoDeleteCondition"]] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword condition: Possible values include: "CreatedGreaterThan", "LastAccessedGreaterThan".
:paramtype condition: str or ~flow.models.AutoDeleteCondition
:keyword value:
:paramtype value: str
"""
super(AutoDeleteSetting, self).__init__(**kwargs)
self.condition = condition
self.value = value
class AutoFeaturizeConfiguration(msrest.serialization.Model):
"""AutoFeaturizeConfiguration.
:ivar featurization_config:
:vartype featurization_config: ~flow.models.FeaturizationSettings
"""
_attribute_map = {
'featurization_config': {'key': 'featurizationConfig', 'type': 'FeaturizationSettings'},
}
def __init__(
self,
*,
featurization_config: Optional["FeaturizationSettings"] = None,
**kwargs
):
"""
:keyword featurization_config:
:paramtype featurization_config: ~flow.models.FeaturizationSettings
"""
super(AutoFeaturizeConfiguration, self).__init__(**kwargs)
self.featurization_config = featurization_config
class AutologgerSettings(msrest.serialization.Model):
"""AutologgerSettings.
:ivar ml_flow_autologger: Possible values include: "Enabled", "Disabled".
:vartype ml_flow_autologger: str or ~flow.models.MLFlowAutologgerState
"""
_attribute_map = {
'ml_flow_autologger': {'key': 'mlFlowAutologger', 'type': 'str'},
}
def __init__(
self,
*,
ml_flow_autologger: Optional[Union[str, "MLFlowAutologgerState"]] = None,
**kwargs
):
"""
:keyword ml_flow_autologger: Possible values include: "Enabled", "Disabled".
:paramtype ml_flow_autologger: str or ~flow.models.MLFlowAutologgerState
"""
super(AutologgerSettings, self).__init__(**kwargs)
self.ml_flow_autologger = ml_flow_autologger
class AutoMLComponentConfiguration(msrest.serialization.Model):
"""AutoMLComponentConfiguration.
:ivar auto_train_config:
:vartype auto_train_config: ~flow.models.AutoTrainConfiguration
:ivar auto_featurize_config:
:vartype auto_featurize_config: ~flow.models.AutoFeaturizeConfiguration
"""
_attribute_map = {
'auto_train_config': {'key': 'autoTrainConfig', 'type': 'AutoTrainConfiguration'},
'auto_featurize_config': {'key': 'autoFeaturizeConfig', 'type': 'AutoFeaturizeConfiguration'},
}
def __init__(
self,
*,
auto_train_config: Optional["AutoTrainConfiguration"] = None,
auto_featurize_config: Optional["AutoFeaturizeConfiguration"] = None,
**kwargs
):
"""
:keyword auto_train_config:
:paramtype auto_train_config: ~flow.models.AutoTrainConfiguration
:keyword auto_featurize_config:
:paramtype auto_featurize_config: ~flow.models.AutoFeaturizeConfiguration
"""
super(AutoMLComponentConfiguration, self).__init__(**kwargs)
self.auto_train_config = auto_train_config
self.auto_featurize_config = auto_featurize_config
class AutoScaler(msrest.serialization.Model):
"""AutoScaler.
:ivar autoscale_enabled:
:vartype autoscale_enabled: bool
:ivar min_replicas:
:vartype min_replicas: int
:ivar max_replicas:
:vartype max_replicas: int
:ivar target_utilization:
:vartype target_utilization: int
:ivar refresh_period_in_seconds:
:vartype refresh_period_in_seconds: int
"""
_attribute_map = {
'autoscale_enabled': {'key': 'autoscaleEnabled', 'type': 'bool'},
'min_replicas': {'key': 'minReplicas', 'type': 'int'},
'max_replicas': {'key': 'maxReplicas', 'type': 'int'},
'target_utilization': {'key': 'targetUtilization', 'type': 'int'},
'refresh_period_in_seconds': {'key': 'refreshPeriodInSeconds', 'type': 'int'},
}
def __init__(
self,
*,
autoscale_enabled: Optional[bool] = None,
min_replicas: Optional[int] = None,
max_replicas: Optional[int] = None,
target_utilization: Optional[int] = None,
refresh_period_in_seconds: Optional[int] = None,
**kwargs
):
"""
:keyword autoscale_enabled:
:paramtype autoscale_enabled: bool
:keyword min_replicas:
:paramtype min_replicas: int
:keyword max_replicas:
:paramtype max_replicas: int
:keyword target_utilization:
:paramtype target_utilization: int
:keyword refresh_period_in_seconds:
:paramtype refresh_period_in_seconds: int
"""
super(AutoScaler, self).__init__(**kwargs)
self.autoscale_enabled = autoscale_enabled
self.min_replicas = min_replicas
self.max_replicas = max_replicas
self.target_utilization = target_utilization
self.refresh_period_in_seconds = refresh_period_in_seconds
class AutoTrainConfiguration(msrest.serialization.Model):
"""AutoTrainConfiguration.
:ivar general_settings:
:vartype general_settings: ~flow.models.GeneralSettings
:ivar limit_settings:
:vartype limit_settings: ~flow.models.LimitSettings
:ivar data_settings:
:vartype data_settings: ~flow.models.DataSettings
:ivar forecasting_settings:
:vartype forecasting_settings: ~flow.models.ForecastingSettings
:ivar training_settings:
:vartype training_settings: ~flow.models.TrainingSettings
:ivar sweep_settings:
:vartype sweep_settings: ~flow.models.SweepSettings
:ivar image_model_settings: Dictionary of :code:`<any>`.
:vartype image_model_settings: dict[str, any]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar compute_configuration:
:vartype compute_configuration: ~flow.models.AEVAComputeConfiguration
:ivar resource_configurtion:
:vartype resource_configurtion: ~flow.models.AEVAResourceConfiguration
:ivar environment_id:
:vartype environment_id: str
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
"""
_attribute_map = {
'general_settings': {'key': 'generalSettings', 'type': 'GeneralSettings'},
'limit_settings': {'key': 'limitSettings', 'type': 'LimitSettings'},
'data_settings': {'key': 'dataSettings', 'type': 'DataSettings'},
'forecasting_settings': {'key': 'forecastingSettings', 'type': 'ForecastingSettings'},
'training_settings': {'key': 'trainingSettings', 'type': 'TrainingSettings'},
'sweep_settings': {'key': 'sweepSettings', 'type': 'SweepSettings'},
'image_model_settings': {'key': 'imageModelSettings', 'type': '{object}'},
'properties': {'key': 'properties', 'type': '{str}'},
'compute_configuration': {'key': 'computeConfiguration', 'type': 'AEVAComputeConfiguration'},
'resource_configurtion': {'key': 'resourceConfigurtion', 'type': 'AEVAResourceConfiguration'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
}
def __init__(
self,
*,
general_settings: Optional["GeneralSettings"] = None,
limit_settings: Optional["LimitSettings"] = None,
data_settings: Optional["DataSettings"] = None,
forecasting_settings: Optional["ForecastingSettings"] = None,
training_settings: Optional["TrainingSettings"] = None,
sweep_settings: Optional["SweepSettings"] = None,
image_model_settings: Optional[Dict[str, Any]] = None,
properties: Optional[Dict[str, str]] = None,
compute_configuration: Optional["AEVAComputeConfiguration"] = None,
resource_configurtion: Optional["AEVAResourceConfiguration"] = None,
environment_id: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword general_settings:
:paramtype general_settings: ~flow.models.GeneralSettings
:keyword limit_settings:
:paramtype limit_settings: ~flow.models.LimitSettings
:keyword data_settings:
:paramtype data_settings: ~flow.models.DataSettings
:keyword forecasting_settings:
:paramtype forecasting_settings: ~flow.models.ForecastingSettings
:keyword training_settings:
:paramtype training_settings: ~flow.models.TrainingSettings
:keyword sweep_settings:
:paramtype sweep_settings: ~flow.models.SweepSettings
:keyword image_model_settings: Dictionary of :code:`<any>`.
:paramtype image_model_settings: dict[str, any]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword compute_configuration:
:paramtype compute_configuration: ~flow.models.AEVAComputeConfiguration
:keyword resource_configurtion:
:paramtype resource_configurtion: ~flow.models.AEVAResourceConfiguration
:keyword environment_id:
:paramtype environment_id: str
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
"""
super(AutoTrainConfiguration, self).__init__(**kwargs)
self.general_settings = general_settings
self.limit_settings = limit_settings
self.data_settings = data_settings
self.forecasting_settings = forecasting_settings
self.training_settings = training_settings
self.sweep_settings = sweep_settings
self.image_model_settings = image_model_settings
self.properties = properties
self.compute_configuration = compute_configuration
self.resource_configurtion = resource_configurtion
self.environment_id = environment_id
self.environment_variables = environment_variables
class AvailabilityResponse(msrest.serialization.Model):
"""AvailabilityResponse.
:ivar is_available:
:vartype is_available: bool
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
"""
_attribute_map = {
'is_available': {'key': 'isAvailable', 'type': 'bool'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
}
def __init__(
self,
*,
is_available: Optional[bool] = None,
error: Optional["ErrorResponse"] = None,
**kwargs
):
"""
:keyword is_available:
:paramtype is_available: bool
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
"""
super(AvailabilityResponse, self).__init__(**kwargs)
self.is_available = is_available
self.error = error
class AzureBlobReference(msrest.serialization.Model):
"""AzureBlobReference.
:ivar container:
:vartype container: str
:ivar sas_token:
:vartype sas_token: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'container': {'key': 'container', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
container: Optional[str] = None,
sas_token: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword container:
:paramtype container: str
:keyword sas_token:
:paramtype sas_token: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureBlobReference, self).__init__(**kwargs)
self.container = container
self.sas_token = sas_token
self.uri = uri
self.account = account
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class AzureDatabaseReference(msrest.serialization.Model):
"""AzureDatabaseReference.
:ivar table_name:
:vartype table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'table_name': {'key': 'tableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[StoredProcedureParameter]'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
table_name: Optional[str] = None,
sql_query: Optional[str] = None,
stored_procedure_name: Optional[str] = None,
stored_procedure_parameters: Optional[List["StoredProcedureParameter"]] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword table_name:
:paramtype table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDatabaseReference, self).__init__(**kwargs)
self.table_name = table_name
self.sql_query = sql_query
self.stored_procedure_name = stored_procedure_name
self.stored_procedure_parameters = stored_procedure_parameters
self.aml_data_store_name = aml_data_store_name
class AzureDataLakeGen2Reference(msrest.serialization.Model):
"""AzureDataLakeGen2Reference.
:ivar file_system_name:
:vartype file_system_name: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'file_system_name': {'key': 'fileSystemName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
file_system_name: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword file_system_name:
:paramtype file_system_name: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDataLakeGen2Reference, self).__init__(**kwargs)
self.file_system_name = file_system_name
self.uri = uri
self.account = account
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class AzureDataLakeReference(msrest.serialization.Model):
"""AzureDataLakeReference.
:ivar tenant:
:vartype tenant: str
:ivar subscription:
:vartype subscription: str
:ivar resource_group:
:vartype resource_group: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'tenant': {'key': 'tenant', 'type': 'str'},
'subscription': {'key': 'subscription', 'type': 'str'},
'resource_group': {'key': 'resourceGroup', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
tenant: Optional[str] = None,
subscription: Optional[str] = None,
resource_group: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword tenant:
:paramtype tenant: str
:keyword subscription:
:paramtype subscription: str
:keyword resource_group:
:paramtype resource_group: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureDataLakeReference, self).__init__(**kwargs)
self.tenant = tenant
self.subscription = subscription
self.resource_group = resource_group
self.uri = uri
self.account = account
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class AzureFilesReference(msrest.serialization.Model):
"""AzureFilesReference.
:ivar share:
:vartype share: str
:ivar uri:
:vartype uri: str
:ivar account:
:vartype account: str
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'share': {'key': 'share', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'account': {'key': 'account', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
share: Optional[str] = None,
uri: Optional[str] = None,
account: Optional[str] = None,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword share:
:paramtype share: str
:keyword uri:
:paramtype uri: str
:keyword account:
:paramtype account: str
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(AzureFilesReference, self).__init__(**kwargs)
self.share = share
self.uri = uri
self.account = account
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class AzureMLModuleVersionDescriptor(msrest.serialization.Model):
"""AzureMLModuleVersionDescriptor.
:ivar module_version_id:
:vartype module_version_id: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
module_version_id: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword module_version_id:
:paramtype module_version_id: str
:keyword version:
:paramtype version: str
"""
super(AzureMLModuleVersionDescriptor, self).__init__(**kwargs)
self.module_version_id = module_version_id
self.version = version
class AzureOpenAIDeploymentDto(msrest.serialization.Model):
"""AzureOpenAIDeploymentDto.
:ivar name:
:vartype name: str
:ivar model_name:
:vartype model_name: str
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'model_name': {'key': 'modelName', 'type': 'str'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
}
def __init__(
self,
*,
name: Optional[str] = None,
model_name: Optional[str] = None,
capabilities: Optional["AzureOpenAIModelCapabilities"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword model_name:
:paramtype model_name: str
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
"""
super(AzureOpenAIDeploymentDto, self).__init__(**kwargs)
self.name = name
self.model_name = model_name
self.capabilities = capabilities
class AzureOpenAIModelCapabilities(msrest.serialization.Model):
"""AzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'completion', 'type': 'bool'},
'chat_completion': {'key': 'chat_completion', 'type': 'bool'},
'embeddings': {'key': 'embeddings', 'type': 'bool'},
}
def __init__(
self,
*,
completion: Optional[bool] = None,
chat_completion: Optional[bool] = None,
embeddings: Optional[bool] = None,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(AzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = completion
self.chat_completion = chat_completion
self.embeddings = embeddings
class BatchAiComputeInfo(msrest.serialization.Model):
"""BatchAiComputeInfo.
:ivar batch_ai_subscription_id:
:vartype batch_ai_subscription_id: str
:ivar batch_ai_resource_group:
:vartype batch_ai_resource_group: str
:ivar batch_ai_workspace_name:
:vartype batch_ai_workspace_name: str
:ivar cluster_name:
:vartype cluster_name: str
:ivar native_shared_directory:
:vartype native_shared_directory: str
"""
_attribute_map = {
'batch_ai_subscription_id': {'key': 'batchAiSubscriptionId', 'type': 'str'},
'batch_ai_resource_group': {'key': 'batchAiResourceGroup', 'type': 'str'},
'batch_ai_workspace_name': {'key': 'batchAiWorkspaceName', 'type': 'str'},
'cluster_name': {'key': 'clusterName', 'type': 'str'},
'native_shared_directory': {'key': 'nativeSharedDirectory', 'type': 'str'},
}
def __init__(
self,
*,
batch_ai_subscription_id: Optional[str] = None,
batch_ai_resource_group: Optional[str] = None,
batch_ai_workspace_name: Optional[str] = None,
cluster_name: Optional[str] = None,
native_shared_directory: Optional[str] = None,
**kwargs
):
"""
:keyword batch_ai_subscription_id:
:paramtype batch_ai_subscription_id: str
:keyword batch_ai_resource_group:
:paramtype batch_ai_resource_group: str
:keyword batch_ai_workspace_name:
:paramtype batch_ai_workspace_name: str
:keyword cluster_name:
:paramtype cluster_name: str
:keyword native_shared_directory:
:paramtype native_shared_directory: str
"""
super(BatchAiComputeInfo, self).__init__(**kwargs)
self.batch_ai_subscription_id = batch_ai_subscription_id
self.batch_ai_resource_group = batch_ai_resource_group
self.batch_ai_workspace_name = batch_ai_workspace_name
self.cluster_name = cluster_name
self.native_shared_directory = native_shared_directory
class BatchDataInput(msrest.serialization.Model):
"""BatchDataInput.
:ivar data_uri:
:vartype data_uri: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'data_uri': {'key': 'dataUri', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
data_uri: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword data_uri:
:paramtype data_uri: str
:keyword type:
:paramtype type: str
"""
super(BatchDataInput, self).__init__(**kwargs)
self.data_uri = data_uri
self.type = type
class BatchExportComponentSpecResponse(msrest.serialization.Model):
"""BatchExportComponentSpecResponse.
:ivar component_spec_meta_infos:
:vartype component_spec_meta_infos: list[~flow.models.ComponentSpecMetaInfo]
:ivar errors:
:vartype errors: list[~flow.models.ErrorResponse]
"""
_attribute_map = {
'component_spec_meta_infos': {'key': 'componentSpecMetaInfos', 'type': '[ComponentSpecMetaInfo]'},
'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
}
def __init__(
self,
*,
component_spec_meta_infos: Optional[List["ComponentSpecMetaInfo"]] = None,
errors: Optional[List["ErrorResponse"]] = None,
**kwargs
):
"""
:keyword component_spec_meta_infos:
:paramtype component_spec_meta_infos: list[~flow.models.ComponentSpecMetaInfo]
:keyword errors:
:paramtype errors: list[~flow.models.ErrorResponse]
"""
super(BatchExportComponentSpecResponse, self).__init__(**kwargs)
self.component_spec_meta_infos = component_spec_meta_infos
self.errors = errors
class BatchExportRawComponentResponse(msrest.serialization.Model):
"""BatchExportRawComponentResponse.
:ivar raw_component_dtos:
:vartype raw_component_dtos: list[~flow.models.RawComponentDto]
:ivar errors:
:vartype errors: list[~flow.models.ErrorResponse]
"""
_attribute_map = {
'raw_component_dtos': {'key': 'rawComponentDtos', 'type': '[RawComponentDto]'},
'errors': {'key': 'errors', 'type': '[ErrorResponse]'},
}
def __init__(
self,
*,
raw_component_dtos: Optional[List["RawComponentDto"]] = None,
errors: Optional[List["ErrorResponse"]] = None,
**kwargs
):
"""
:keyword raw_component_dtos:
:paramtype raw_component_dtos: list[~flow.models.RawComponentDto]
:keyword errors:
:paramtype errors: list[~flow.models.ErrorResponse]
"""
super(BatchExportRawComponentResponse, self).__init__(**kwargs)
self.raw_component_dtos = raw_component_dtos
self.errors = errors
class BatchGetComponentHashesRequest(msrest.serialization.Model):
"""BatchGetComponentHashesRequest.
:ivar module_hash_version: Possible values include: "IdentifierHash", "IdentifierHashV2".
:vartype module_hash_version: str or ~flow.models.AetherModuleHashVersion
:ivar module_entities: Dictionary of :code:`<AetherModuleEntity>`.
:vartype module_entities: dict[str, ~flow.models.AetherModuleEntity]
"""
_attribute_map = {
'module_hash_version': {'key': 'moduleHashVersion', 'type': 'str'},
'module_entities': {'key': 'moduleEntities', 'type': '{AetherModuleEntity}'},
}
def __init__(
self,
*,
module_hash_version: Optional[Union[str, "AetherModuleHashVersion"]] = None,
module_entities: Optional[Dict[str, "AetherModuleEntity"]] = None,
**kwargs
):
"""
:keyword module_hash_version: Possible values include: "IdentifierHash", "IdentifierHashV2".
:paramtype module_hash_version: str or ~flow.models.AetherModuleHashVersion
:keyword module_entities: Dictionary of :code:`<AetherModuleEntity>`.
:paramtype module_entities: dict[str, ~flow.models.AetherModuleEntity]
"""
super(BatchGetComponentHashesRequest, self).__init__(**kwargs)
self.module_hash_version = module_hash_version
self.module_entities = module_entities
class BatchGetComponentRequest(msrest.serialization.Model):
"""BatchGetComponentRequest.
:ivar version_ids:
:vartype version_ids: list[str]
:ivar name_and_versions:
:vartype name_and_versions: list[~flow.models.ComponentNameMetaInfo]
"""
_attribute_map = {
'version_ids': {'key': 'versionIds', 'type': '[str]'},
'name_and_versions': {'key': 'nameAndVersions', 'type': '[ComponentNameMetaInfo]'},
}
def __init__(
self,
*,
version_ids: Optional[List[str]] = None,
name_and_versions: Optional[List["ComponentNameMetaInfo"]] = None,
**kwargs
):
"""
:keyword version_ids:
:paramtype version_ids: list[str]
:keyword name_and_versions:
:paramtype name_and_versions: list[~flow.models.ComponentNameMetaInfo]
"""
super(BatchGetComponentRequest, self).__init__(**kwargs)
self.version_ids = version_ids
self.name_and_versions = name_and_versions
class Binding(msrest.serialization.Model):
"""Binding.
:ivar binding_type: The only acceptable values to pass in are None and "Basic". The default
value is None.
:vartype binding_type: str
"""
_attribute_map = {
'binding_type': {'key': 'bindingType', 'type': 'str'},
}
def __init__(
self,
*,
binding_type: Optional[str] = None,
**kwargs
):
"""
:keyword binding_type: The only acceptable values to pass in are None and "Basic". The default
value is None.
:paramtype binding_type: str
"""
super(Binding, self).__init__(**kwargs)
self.binding_type = binding_type
class BulkTestDto(msrest.serialization.Model):
"""BulkTestDto.
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar runtime:
:vartype runtime: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar evaluation_count:
:vartype evaluation_count: int
:ivar variant_count:
:vartype variant_count: int
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
"""
_attribute_map = {
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'runtime': {'key': 'runtime', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'evaluation_count': {'key': 'evaluationCount', 'type': 'int'},
'variant_count': {'key': 'variantCount', 'type': 'int'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
}
def __init__(
self,
*,
bulk_test_id: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
runtime: Optional[str] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
created_on: Optional[datetime.datetime] = None,
evaluation_count: Optional[int] = None,
variant_count: Optional[int] = None,
flow_submit_run_settings: Optional["FlowSubmitRunSettings"] = None,
inputs: Optional[Dict[str, "FlowInputDefinition"]] = None,
outputs: Optional[Dict[str, "FlowOutputDefinition"]] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
batch_data_input: Optional["BatchDataInput"] = None,
**kwargs
):
"""
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword runtime:
:paramtype runtime: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword evaluation_count:
:paramtype evaluation_count: int
:keyword variant_count:
:paramtype variant_count: int
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
"""
super(BulkTestDto, self).__init__(**kwargs)
self.bulk_test_id = bulk_test_id
self.display_name = display_name
self.description = description
self.tags = tags
self.runtime = runtime
self.created_by = created_by
self.created_on = created_on
self.evaluation_count = evaluation_count
self.variant_count = variant_count
self.flow_submit_run_settings = flow_submit_run_settings
self.inputs = inputs
self.outputs = outputs
self.batch_inputs = batch_inputs
self.batch_data_input = batch_data_input
class CloudError(msrest.serialization.Model):
"""CloudError.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
:ivar details:
:vartype details: list[~flow.models.CloudError]
:ivar additional_info:
:vartype additional_info: list[~flow.models.AdditionalErrorInfo]
"""
_validation = {
'details': {'readonly': True},
'additional_info': {'readonly': True},
}
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[CloudError]'},
'additional_info': {'key': 'additionalInfo', 'type': '[AdditionalErrorInfo]'},
}
def __init__(
self,
*,
code: Optional[str] = None,
message: Optional[str] = None,
target: Optional[str] = None,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
"""
super(CloudError, self).__init__(**kwargs)
self.code = code
self.message = message
self.target = target
self.details = None
self.additional_info = None
class CloudPrioritySetting(msrest.serialization.Model):
"""CloudPrioritySetting.
:ivar scope_priority:
:vartype scope_priority: ~flow.models.PriorityConfiguration
:ivar aml_compute_priority:
:vartype aml_compute_priority: ~flow.models.PriorityConfiguration
:ivar itp_priority:
:vartype itp_priority: ~flow.models.PriorityConfiguration
:ivar singularity_priority:
:vartype singularity_priority: ~flow.models.PriorityConfiguration
"""
_attribute_map = {
'scope_priority': {'key': 'scopePriority', 'type': 'PriorityConfiguration'},
'aml_compute_priority': {'key': 'AmlComputePriority', 'type': 'PriorityConfiguration'},
'itp_priority': {'key': 'ItpPriority', 'type': 'PriorityConfiguration'},
'singularity_priority': {'key': 'SingularityPriority', 'type': 'PriorityConfiguration'},
}
def __init__(
self,
*,
scope_priority: Optional["PriorityConfiguration"] = None,
aml_compute_priority: Optional["PriorityConfiguration"] = None,
itp_priority: Optional["PriorityConfiguration"] = None,
singularity_priority: Optional["PriorityConfiguration"] = None,
**kwargs
):
"""
:keyword scope_priority:
:paramtype scope_priority: ~flow.models.PriorityConfiguration
:keyword aml_compute_priority:
:paramtype aml_compute_priority: ~flow.models.PriorityConfiguration
:keyword itp_priority:
:paramtype itp_priority: ~flow.models.PriorityConfiguration
:keyword singularity_priority:
:paramtype singularity_priority: ~flow.models.PriorityConfiguration
"""
super(CloudPrioritySetting, self).__init__(**kwargs)
self.scope_priority = scope_priority
self.aml_compute_priority = aml_compute_priority
self.itp_priority = itp_priority
self.singularity_priority = singularity_priority
class CloudSettings(msrest.serialization.Model):
"""CloudSettings.
:ivar linked_settings:
:vartype linked_settings: list[~flow.models.ParameterAssignment]
:ivar priority_config:
:vartype priority_config: ~flow.models.PriorityConfiguration
:ivar hdi_run_config:
:vartype hdi_run_config: ~flow.models.HdiRunConfiguration
:ivar sub_graph_config:
:vartype sub_graph_config: ~flow.models.SubGraphConfiguration
:ivar auto_ml_component_config:
:vartype auto_ml_component_config: ~flow.models.AutoMLComponentConfiguration
:ivar ap_cloud_config:
:vartype ap_cloud_config: ~flow.models.APCloudConfiguration
:ivar scope_cloud_config:
:vartype scope_cloud_config: ~flow.models.ScopeCloudConfiguration
:ivar es_cloud_config:
:vartype es_cloud_config: ~flow.models.EsCloudConfiguration
:ivar data_transfer_cloud_config:
:vartype data_transfer_cloud_config: ~flow.models.DataTransferCloudConfiguration
:ivar aml_spark_cloud_setting:
:vartype aml_spark_cloud_setting: ~flow.models.AmlSparkCloudSetting
:ivar data_transfer_v2_cloud_setting:
:vartype data_transfer_v2_cloud_setting: ~flow.models.DataTransferV2CloudSetting
"""
_attribute_map = {
'linked_settings': {'key': 'linkedSettings', 'type': '[ParameterAssignment]'},
'priority_config': {'key': 'priorityConfig', 'type': 'PriorityConfiguration'},
'hdi_run_config': {'key': 'hdiRunConfig', 'type': 'HdiRunConfiguration'},
'sub_graph_config': {'key': 'subGraphConfig', 'type': 'SubGraphConfiguration'},
'auto_ml_component_config': {'key': 'autoMLComponentConfig', 'type': 'AutoMLComponentConfiguration'},
'ap_cloud_config': {'key': 'apCloudConfig', 'type': 'APCloudConfiguration'},
'scope_cloud_config': {'key': 'scopeCloudConfig', 'type': 'ScopeCloudConfiguration'},
'es_cloud_config': {'key': 'esCloudConfig', 'type': 'EsCloudConfiguration'},
'data_transfer_cloud_config': {'key': 'dataTransferCloudConfig', 'type': 'DataTransferCloudConfiguration'},
'aml_spark_cloud_setting': {'key': 'amlSparkCloudSetting', 'type': 'AmlSparkCloudSetting'},
'data_transfer_v2_cloud_setting': {'key': 'dataTransferV2CloudSetting', 'type': 'DataTransferV2CloudSetting'},
}
def __init__(
self,
*,
linked_settings: Optional[List["ParameterAssignment"]] = None,
priority_config: Optional["PriorityConfiguration"] = None,
hdi_run_config: Optional["HdiRunConfiguration"] = None,
sub_graph_config: Optional["SubGraphConfiguration"] = None,
auto_ml_component_config: Optional["AutoMLComponentConfiguration"] = None,
ap_cloud_config: Optional["APCloudConfiguration"] = None,
scope_cloud_config: Optional["ScopeCloudConfiguration"] = None,
es_cloud_config: Optional["EsCloudConfiguration"] = None,
data_transfer_cloud_config: Optional["DataTransferCloudConfiguration"] = None,
aml_spark_cloud_setting: Optional["AmlSparkCloudSetting"] = None,
data_transfer_v2_cloud_setting: Optional["DataTransferV2CloudSetting"] = None,
**kwargs
):
"""
:keyword linked_settings:
:paramtype linked_settings: list[~flow.models.ParameterAssignment]
:keyword priority_config:
:paramtype priority_config: ~flow.models.PriorityConfiguration
:keyword hdi_run_config:
:paramtype hdi_run_config: ~flow.models.HdiRunConfiguration
:keyword sub_graph_config:
:paramtype sub_graph_config: ~flow.models.SubGraphConfiguration
:keyword auto_ml_component_config:
:paramtype auto_ml_component_config: ~flow.models.AutoMLComponentConfiguration
:keyword ap_cloud_config:
:paramtype ap_cloud_config: ~flow.models.APCloudConfiguration
:keyword scope_cloud_config:
:paramtype scope_cloud_config: ~flow.models.ScopeCloudConfiguration
:keyword es_cloud_config:
:paramtype es_cloud_config: ~flow.models.EsCloudConfiguration
:keyword data_transfer_cloud_config:
:paramtype data_transfer_cloud_config: ~flow.models.DataTransferCloudConfiguration
:keyword aml_spark_cloud_setting:
:paramtype aml_spark_cloud_setting: ~flow.models.AmlSparkCloudSetting
:keyword data_transfer_v2_cloud_setting:
:paramtype data_transfer_v2_cloud_setting: ~flow.models.DataTransferV2CloudSetting
"""
super(CloudSettings, self).__init__(**kwargs)
self.linked_settings = linked_settings
self.priority_config = priority_config
self.hdi_run_config = hdi_run_config
self.sub_graph_config = sub_graph_config
self.auto_ml_component_config = auto_ml_component_config
self.ap_cloud_config = ap_cloud_config
self.scope_cloud_config = scope_cloud_config
self.es_cloud_config = es_cloud_config
self.data_transfer_cloud_config = data_transfer_cloud_config
self.aml_spark_cloud_setting = aml_spark_cloud_setting
self.data_transfer_v2_cloud_setting = data_transfer_v2_cloud_setting
class ColumnTransformer(msrest.serialization.Model):
"""ColumnTransformer.
:ivar fields:
:vartype fields: list[str]
:ivar parameters: Anything.
:vartype parameters: any
"""
_attribute_map = {
'fields': {'key': 'fields', 'type': '[str]'},
'parameters': {'key': 'parameters', 'type': 'object'},
}
def __init__(
self,
*,
fields: Optional[List[str]] = None,
parameters: Optional[Any] = None,
**kwargs
):
"""
:keyword fields:
:paramtype fields: list[str]
:keyword parameters: Anything.
:paramtype parameters: any
"""
super(ColumnTransformer, self).__init__(**kwargs)
self.fields = fields
self.parameters = parameters
class CommandJob(msrest.serialization.Model):
"""CommandJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar code_id:
:vartype code_id: str
:ivar command:
:vartype command: str
:ivar environment_id:
:vartype environment_id: str
:ivar input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:vartype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:vartype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar distribution:
:vartype distribution: ~flow.models.DistributionConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar autologger_settings:
:vartype autologger_settings: ~flow.models.MfeInternalAutologgerSettings
:ivar limits:
:vartype limits: ~flow.models.CommandJobLimits
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_validation = {
'command': {'min_length': 1},
}
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'code_id': {'key': 'codeId', 'type': 'str'},
'command': {'key': 'command', 'type': 'str'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'input_data_bindings': {'key': 'inputDataBindings', 'type': '{InputDataBinding}'},
'output_data_bindings': {'key': 'outputDataBindings', 'type': '{OutputDataBinding}'},
'distribution': {'key': 'distribution', 'type': 'DistributionConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'autologger_settings': {'key': 'autologgerSettings', 'type': 'MfeInternalAutologgerSettings'},
'limits': {'key': 'limits', 'type': 'CommandJobLimits'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
job_type: Optional[Union[str, "JobType"]] = None,
code_id: Optional[str] = None,
command: Optional[str] = None,
environment_id: Optional[str] = None,
input_data_bindings: Optional[Dict[str, "InputDataBinding"]] = None,
output_data_bindings: Optional[Dict[str, "OutputDataBinding"]] = None,
distribution: Optional["DistributionConfiguration"] = None,
environment_variables: Optional[Dict[str, str]] = None,
parameters: Optional[Dict[str, Any]] = None,
autologger_settings: Optional["MfeInternalAutologgerSettings"] = None,
limits: Optional["CommandJobLimits"] = None,
provisioning_state: Optional[Union[str, "JobProvisioningState"]] = None,
parent_job_name: Optional[str] = None,
display_name: Optional[str] = None,
experiment_name: Optional[str] = None,
status: Optional[Union[str, "JobStatus"]] = None,
interaction_endpoints: Optional[Dict[str, "JobEndpoint"]] = None,
identity: Optional["MfeInternalIdentityConfiguration"] = None,
compute: Optional["ComputeConfiguration"] = None,
priority: Optional[int] = None,
output: Optional["JobOutputArtifacts"] = None,
is_archived: Optional[bool] = None,
schedule: Optional["ScheduleBase"] = None,
component_id: Optional[str] = None,
notification_setting: Optional["NotificationSetting"] = None,
secrets_configuration: Optional[Dict[str, "MfeInternalSecretConfiguration"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword code_id:
:paramtype code_id: str
:keyword command:
:paramtype command: str
:keyword environment_id:
:paramtype environment_id: str
:keyword input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:paramtype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:paramtype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword distribution:
:paramtype distribution: ~flow.models.DistributionConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword autologger_settings:
:paramtype autologger_settings: ~flow.models.MfeInternalAutologgerSettings
:keyword limits:
:paramtype limits: ~flow.models.CommandJobLimits
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(CommandJob, self).__init__(**kwargs)
self.job_type = job_type
self.code_id = code_id
self.command = command
self.environment_id = environment_id
self.input_data_bindings = input_data_bindings
self.output_data_bindings = output_data_bindings
self.distribution = distribution
self.environment_variables = environment_variables
self.parameters = parameters
self.autologger_settings = autologger_settings
self.limits = limits
self.provisioning_state = provisioning_state
self.parent_job_name = parent_job_name
self.display_name = display_name
self.experiment_name = experiment_name
self.status = status
self.interaction_endpoints = interaction_endpoints
self.identity = identity
self.compute = compute
self.priority = priority
self.output = output
self.is_archived = is_archived
self.schedule = schedule
self.component_id = component_id
self.notification_setting = notification_setting
self.secrets_configuration = secrets_configuration
self.description = description
self.tags = tags
self.properties = properties
class CommandJobLimits(msrest.serialization.Model):
"""CommandJobLimits.
:ivar job_limits_type: Possible values include: "Command", "Sweep".
:vartype job_limits_type: str or ~flow.models.JobLimitsType
:ivar timeout:
:vartype timeout: str
"""
_attribute_map = {
'job_limits_type': {'key': 'jobLimitsType', 'type': 'str'},
'timeout': {'key': 'timeout', 'type': 'str'},
}
def __init__(
self,
*,
job_limits_type: Optional[Union[str, "JobLimitsType"]] = None,
timeout: Optional[str] = None,
**kwargs
):
"""
:keyword job_limits_type: Possible values include: "Command", "Sweep".
:paramtype job_limits_type: str or ~flow.models.JobLimitsType
:keyword timeout:
:paramtype timeout: str
"""
super(CommandJobLimits, self).__init__(**kwargs)
self.job_limits_type = job_limits_type
self.timeout = timeout
class CommandReturnCodeConfig(msrest.serialization.Model):
"""CommandReturnCodeConfig.
:ivar return_code: Possible values include: "Zero", "ZeroOrGreater".
:vartype return_code: str or ~flow.models.SuccessfulCommandReturnCode
:ivar successful_return_codes:
:vartype successful_return_codes: list[int]
"""
_attribute_map = {
'return_code': {'key': 'returnCode', 'type': 'str'},
'successful_return_codes': {'key': 'successfulReturnCodes', 'type': '[int]'},
}
def __init__(
self,
*,
return_code: Optional[Union[str, "SuccessfulCommandReturnCode"]] = None,
successful_return_codes: Optional[List[int]] = None,
**kwargs
):
"""
:keyword return_code: Possible values include: "Zero", "ZeroOrGreater".
:paramtype return_code: str or ~flow.models.SuccessfulCommandReturnCode
:keyword successful_return_codes:
:paramtype successful_return_codes: list[int]
"""
super(CommandReturnCodeConfig, self).__init__(**kwargs)
self.return_code = return_code
self.successful_return_codes = successful_return_codes
class ComponentConfiguration(msrest.serialization.Model):
"""ComponentConfiguration.
:ivar component_identifier:
:vartype component_identifier: str
"""
_attribute_map = {
'component_identifier': {'key': 'componentIdentifier', 'type': 'str'},
}
def __init__(
self,
*,
component_identifier: Optional[str] = None,
**kwargs
):
"""
:keyword component_identifier:
:paramtype component_identifier: str
"""
super(ComponentConfiguration, self).__init__(**kwargs)
self.component_identifier = component_identifier
class ComponentInput(msrest.serialization.Model):
"""ComponentInput.
:ivar name:
:vartype name: str
:ivar optional:
:vartype optional: bool
:ivar description:
:vartype description: str
:ivar type:
:vartype type: str
:ivar default:
:vartype default: str
:ivar enum:
:vartype enum: list[str]
:ivar min:
:vartype min: str
:ivar max:
:vartype max: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'optional': {'key': 'optional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'default': {'key': 'default', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[str]'},
'min': {'key': 'min', 'type': 'str'},
'max': {'key': 'max', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
optional: Optional[bool] = None,
description: Optional[str] = None,
type: Optional[str] = None,
default: Optional[str] = None,
enum: Optional[List[str]] = None,
min: Optional[str] = None,
max: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword optional:
:paramtype optional: bool
:keyword description:
:paramtype description: str
:keyword type:
:paramtype type: str
:keyword default:
:paramtype default: str
:keyword enum:
:paramtype enum: list[str]
:keyword min:
:paramtype min: str
:keyword max:
:paramtype max: str
"""
super(ComponentInput, self).__init__(**kwargs)
self.name = name
self.optional = optional
self.description = description
self.type = type
self.default = default
self.enum = enum
self.min = min
self.max = max
class ComponentJob(msrest.serialization.Model):
"""ComponentJob.
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar component_id:
:vartype component_id: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.ComponentJobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.ComponentJobOutput]
"""
_attribute_map = {
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'component_id': {'key': 'componentId', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{ComponentJobInput}'},
'outputs': {'key': 'outputs', 'type': '{ComponentJobOutput}'},
}
def __init__(
self,
*,
compute: Optional["ComputeConfiguration"] = None,
component_id: Optional[str] = None,
inputs: Optional[Dict[str, "ComponentJobInput"]] = None,
outputs: Optional[Dict[str, "ComponentJobOutput"]] = None,
**kwargs
):
"""
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword component_id:
:paramtype component_id: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.ComponentJobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.ComponentJobOutput]
"""
super(ComponentJob, self).__init__(**kwargs)
self.compute = compute
self.component_id = component_id
self.inputs = inputs
self.outputs = outputs
class ComponentJobInput(msrest.serialization.Model):
"""ComponentJobInput.
:ivar data:
:vartype data: ~flow.models.InputData
:ivar input_binding:
:vartype input_binding: str
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'InputData'},
'input_binding': {'key': 'inputBinding', 'type': 'str'},
}
def __init__(
self,
*,
data: Optional["InputData"] = None,
input_binding: Optional[str] = None,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.InputData
:keyword input_binding:
:paramtype input_binding: str
"""
super(ComponentJobInput, self).__init__(**kwargs)
self.data = data
self.input_binding = input_binding
class ComponentJobOutput(msrest.serialization.Model):
"""ComponentJobOutput.
:ivar data:
:vartype data: ~flow.models.MfeInternalOutputData
:ivar output_binding:
:vartype output_binding: str
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'MfeInternalOutputData'},
'output_binding': {'key': 'outputBinding', 'type': 'str'},
}
def __init__(
self,
*,
data: Optional["MfeInternalOutputData"] = None,
output_binding: Optional[str] = None,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.MfeInternalOutputData
:keyword output_binding:
:paramtype output_binding: str
"""
super(ComponentJobOutput, self).__init__(**kwargs)
self.data = data
self.output_binding = output_binding
class ComponentNameAndDefaultVersion(msrest.serialization.Model):
"""ComponentNameAndDefaultVersion.
:ivar component_name:
:vartype component_name: str
:ivar version:
:vartype version: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
"""
_attribute_map = {
'component_name': {'key': 'componentName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
}
def __init__(
self,
*,
component_name: Optional[str] = None,
version: Optional[str] = None,
feed_name: Optional[str] = None,
registry_name: Optional[str] = None,
**kwargs
):
"""
:keyword component_name:
:paramtype component_name: str
:keyword version:
:paramtype version: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
"""
super(ComponentNameAndDefaultVersion, self).__init__(**kwargs)
self.component_name = component_name
self.version = version
self.feed_name = feed_name
self.registry_name = registry_name
class ComponentNameMetaInfo(msrest.serialization.Model):
"""ComponentNameMetaInfo.
:ivar feed_name:
:vartype feed_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar registry_name:
:vartype registry_name: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
}
def __init__(
self,
*,
feed_name: Optional[str] = None,
component_name: Optional[str] = None,
component_version: Optional[str] = None,
registry_name: Optional[str] = None,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword registry_name:
:paramtype registry_name: str
"""
super(ComponentNameMetaInfo, self).__init__(**kwargs)
self.feed_name = feed_name
self.component_name = component_name
self.component_version = component_version
self.registry_name = registry_name
class ComponentOutput(msrest.serialization.Model):
"""ComponentOutput.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword type:
:paramtype type: str
"""
super(ComponentOutput, self).__init__(**kwargs)
self.name = name
self.description = description
self.type = type
class ComponentPreflightResult(msrest.serialization.Model):
"""ComponentPreflightResult.
:ivar error_details:
:vartype error_details: list[~flow.models.RootError]
"""
_attribute_map = {
'error_details': {'key': 'errorDetails', 'type': '[RootError]'},
}
def __init__(
self,
*,
error_details: Optional[List["RootError"]] = None,
**kwargs
):
"""
:keyword error_details:
:paramtype error_details: list[~flow.models.RootError]
"""
super(ComponentPreflightResult, self).__init__(**kwargs)
self.error_details = error_details
class ComponentSpecMetaInfo(msrest.serialization.Model):
"""ComponentSpecMetaInfo.
:ivar component_spec: Anything.
:vartype component_spec: any
:ivar component_version:
:vartype component_version: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar component_name:
:vartype component_name: str
:ivar description:
:vartype description: str
:ivar is_archived:
:vartype is_archived: bool
"""
_attribute_map = {
'component_spec': {'key': 'componentSpec', 'type': 'object'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'tags': {'key': 'tags', 'type': '{str}'},
'component_name': {'key': 'componentName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
}
def __init__(
self,
*,
component_spec: Optional[Any] = None,
component_version: Optional[str] = None,
is_anonymous: Optional[bool] = None,
properties: Optional[Dict[str, str]] = None,
tags: Optional[Dict[str, str]] = None,
component_name: Optional[str] = None,
description: Optional[str] = None,
is_archived: Optional[bool] = None,
**kwargs
):
"""
:keyword component_spec: Anything.
:paramtype component_spec: any
:keyword component_version:
:paramtype component_version: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword component_name:
:paramtype component_name: str
:keyword description:
:paramtype description: str
:keyword is_archived:
:paramtype is_archived: bool
"""
super(ComponentSpecMetaInfo, self).__init__(**kwargs)
self.component_spec = component_spec
self.component_version = component_version
self.is_anonymous = is_anonymous
self.properties = properties
self.tags = tags
self.component_name = component_name
self.description = description
self.is_archived = is_archived
class ComponentUpdateRequest(msrest.serialization.Model):
"""ComponentUpdateRequest.
:ivar original_module_entity:
:vartype original_module_entity: ~flow.models.ModuleEntity
:ivar update_module_entity:
:vartype update_module_entity: ~flow.models.ModuleEntity
:ivar module_name:
:vartype module_name: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar overwrite_with_original_name_and_version:
:vartype overwrite_with_original_name_and_version: bool
:ivar snapshot_id:
:vartype snapshot_id: str
"""
_attribute_map = {
'original_module_entity': {'key': 'originalModuleEntity', 'type': 'ModuleEntity'},
'update_module_entity': {'key': 'updateModuleEntity', 'type': 'ModuleEntity'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'overwrite_with_original_name_and_version': {'key': 'overwriteWithOriginalNameAndVersion', 'type': 'bool'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
}
def __init__(
self,
*,
original_module_entity: Optional["ModuleEntity"] = None,
update_module_entity: Optional["ModuleEntity"] = None,
module_name: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
overwrite_with_original_name_and_version: Optional[bool] = None,
snapshot_id: Optional[str] = None,
**kwargs
):
"""
:keyword original_module_entity:
:paramtype original_module_entity: ~flow.models.ModuleEntity
:keyword update_module_entity:
:paramtype update_module_entity: ~flow.models.ModuleEntity
:keyword module_name:
:paramtype module_name: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword overwrite_with_original_name_and_version:
:paramtype overwrite_with_original_name_and_version: bool
:keyword snapshot_id:
:paramtype snapshot_id: str
"""
super(ComponentUpdateRequest, self).__init__(**kwargs)
self.original_module_entity = original_module_entity
self.update_module_entity = update_module_entity
self.module_name = module_name
self.properties = properties
self.overwrite_with_original_name_and_version = overwrite_with_original_name_and_version
self.snapshot_id = snapshot_id
class ComponentValidationRequest(msrest.serialization.Model):
"""ComponentValidationRequest.
:ivar component_identifier:
:vartype component_identifier: str
:ivar compute_identity:
:vartype compute_identity: ~flow.models.ComputeIdentityDto
:ivar execution_context_dto:
:vartype execution_context_dto: ~flow.models.ExecutionContextDto
:ivar environment_definition:
:vartype environment_definition: ~flow.models.EnvironmentDefinitionDto
:ivar data_port_dtos:
:vartype data_port_dtos: list[~flow.models.DataPortDto]
"""
_attribute_map = {
'component_identifier': {'key': 'componentIdentifier', 'type': 'str'},
'compute_identity': {'key': 'computeIdentity', 'type': 'ComputeIdentityDto'},
'execution_context_dto': {'key': 'executionContextDto', 'type': 'ExecutionContextDto'},
'environment_definition': {'key': 'environmentDefinition', 'type': 'EnvironmentDefinitionDto'},
'data_port_dtos': {'key': 'dataPortDtos', 'type': '[DataPortDto]'},
}
def __init__(
self,
*,
component_identifier: Optional[str] = None,
compute_identity: Optional["ComputeIdentityDto"] = None,
execution_context_dto: Optional["ExecutionContextDto"] = None,
environment_definition: Optional["EnvironmentDefinitionDto"] = None,
data_port_dtos: Optional[List["DataPortDto"]] = None,
**kwargs
):
"""
:keyword component_identifier:
:paramtype component_identifier: str
:keyword compute_identity:
:paramtype compute_identity: ~flow.models.ComputeIdentityDto
:keyword execution_context_dto:
:paramtype execution_context_dto: ~flow.models.ExecutionContextDto
:keyword environment_definition:
:paramtype environment_definition: ~flow.models.EnvironmentDefinitionDto
:keyword data_port_dtos:
:paramtype data_port_dtos: list[~flow.models.DataPortDto]
"""
super(ComponentValidationRequest, self).__init__(**kwargs)
self.component_identifier = component_identifier
self.compute_identity = compute_identity
self.execution_context_dto = execution_context_dto
self.environment_definition = environment_definition
self.data_port_dtos = data_port_dtos
class ComponentValidationResponse(msrest.serialization.Model):
"""ComponentValidationResponse.
:ivar status: Possible values include: "Succeeded", "Failed".
:vartype status: str or ~flow.models.ValidationStatus
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
}
def __init__(
self,
*,
status: Optional[Union[str, "ValidationStatus"]] = None,
error: Optional["ErrorResponse"] = None,
**kwargs
):
"""
:keyword status: Possible values include: "Succeeded", "Failed".
:paramtype status: str or ~flow.models.ValidationStatus
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
"""
super(ComponentValidationResponse, self).__init__(**kwargs)
self.status = status
self.error = error
class Compute(msrest.serialization.Model):
"""Compute.
:ivar target:
:vartype target: str
:ivar target_type:
:vartype target_type: str
:ivar vm_size:
:vartype vm_size: str
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar gpu_count:
:vartype gpu_count: int
:ivar priority:
:vartype priority: str
:ivar region:
:vartype region: str
:ivar arm_id:
:vartype arm_id: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'target_type': {'key': 'targetType', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'priority': {'key': 'priority', 'type': 'str'},
'region': {'key': 'region', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
target: Optional[str] = None,
target_type: Optional[str] = None,
vm_size: Optional[str] = None,
instance_type: Optional[str] = None,
instance_count: Optional[int] = None,
gpu_count: Optional[int] = None,
priority: Optional[str] = None,
region: Optional[str] = None,
arm_id: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword target_type:
:paramtype target_type: str
:keyword vm_size:
:paramtype vm_size: str
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword gpu_count:
:paramtype gpu_count: int
:keyword priority:
:paramtype priority: str
:keyword region:
:paramtype region: str
:keyword arm_id:
:paramtype arm_id: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(Compute, self).__init__(**kwargs)
self.target = target
self.target_type = target_type
self.vm_size = vm_size
self.instance_type = instance_type
self.instance_count = instance_count
self.gpu_count = gpu_count
self.priority = priority
self.region = region
self.arm_id = arm_id
self.properties = properties
class ComputeConfiguration(msrest.serialization.Model):
"""ComputeConfiguration.
:ivar target:
:vartype target: str
:ivar instance_count:
:vartype instance_count: int
:ivar max_instance_count:
:vartype max_instance_count: int
:ivar is_local:
:vartype is_local: bool
:ivar location:
:vartype location: str
:ivar is_clusterless:
:vartype is_clusterless: bool
:ivar instance_type:
:vartype instance_type: str
:ivar instance_priority:
:vartype instance_priority: str
:ivar job_priority:
:vartype job_priority: int
:ivar shm_size:
:vartype shm_size: str
:ivar docker_args:
:vartype docker_args: str
:ivar locations:
:vartype locations: list[str]
:ivar properties: Dictionary of :code:`<any>`.
:vartype properties: dict[str, any]
"""
_attribute_map = {
'target': {'key': 'target', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'max_instance_count': {'key': 'maxInstanceCount', 'type': 'int'},
'is_local': {'key': 'isLocal', 'type': 'bool'},
'location': {'key': 'location', 'type': 'str'},
'is_clusterless': {'key': 'isClusterless', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_priority': {'key': 'instancePriority', 'type': 'str'},
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'docker_args': {'key': 'dockerArgs', 'type': 'str'},
'locations': {'key': 'locations', 'type': '[str]'},
'properties': {'key': 'properties', 'type': '{object}'},
}
def __init__(
self,
*,
target: Optional[str] = None,
instance_count: Optional[int] = None,
max_instance_count: Optional[int] = None,
is_local: Optional[bool] = None,
location: Optional[str] = None,
is_clusterless: Optional[bool] = None,
instance_type: Optional[str] = None,
instance_priority: Optional[str] = None,
job_priority: Optional[int] = None,
shm_size: Optional[str] = None,
docker_args: Optional[str] = None,
locations: Optional[List[str]] = None,
properties: Optional[Dict[str, Any]] = None,
**kwargs
):
"""
:keyword target:
:paramtype target: str
:keyword instance_count:
:paramtype instance_count: int
:keyword max_instance_count:
:paramtype max_instance_count: int
:keyword is_local:
:paramtype is_local: bool
:keyword location:
:paramtype location: str
:keyword is_clusterless:
:paramtype is_clusterless: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_priority:
:paramtype instance_priority: str
:keyword job_priority:
:paramtype job_priority: int
:keyword shm_size:
:paramtype shm_size: str
:keyword docker_args:
:paramtype docker_args: str
:keyword locations:
:paramtype locations: list[str]
:keyword properties: Dictionary of :code:`<any>`.
:paramtype properties: dict[str, any]
"""
super(ComputeConfiguration, self).__init__(**kwargs)
self.target = target
self.instance_count = instance_count
self.max_instance_count = max_instance_count
self.is_local = is_local
self.location = location
self.is_clusterless = is_clusterless
self.instance_type = instance_type
self.instance_priority = instance_priority
self.job_priority = job_priority
self.shm_size = shm_size
self.docker_args = docker_args
self.locations = locations
self.properties = properties
class ComputeContract(msrest.serialization.Model):
"""ComputeContract.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
:ivar location:
:vartype location: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar identity:
:vartype identity: ~flow.models.ComputeIdentityContract
:ivar properties:
:vartype properties: ~flow.models.ComputeProperties
"""
_validation = {
'type': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'identity': {'key': 'identity', 'type': 'ComputeIdentityContract'},
'properties': {'key': 'properties', 'type': 'ComputeProperties'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
location: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
identity: Optional["ComputeIdentityContract"] = None,
properties: Optional["ComputeProperties"] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword location:
:paramtype location: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword identity:
:paramtype identity: ~flow.models.ComputeIdentityContract
:keyword properties:
:paramtype properties: ~flow.models.ComputeProperties
"""
super(ComputeContract, self).__init__(**kwargs)
self.id = id
self.name = name
self.type = None
self.location = location
self.tags = tags
self.identity = identity
self.properties = properties
class ComputeIdentityContract(msrest.serialization.Model):
"""ComputeIdentityContract.
:ivar type:
:vartype type: str
:ivar system_identity_url:
:vartype system_identity_url: str
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar client_id:
:vartype client_id: str
:ivar client_secret_url:
:vartype client_secret_url: str
:ivar user_assigned_identities: This is a dictionary.
:vartype user_assigned_identities: dict[str, ~flow.models.ComputeRPUserAssignedIdentity]
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'system_identity_url': {'key': 'systemIdentityUrl', 'type': 'str'},
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'client_secret_url': {'key': 'clientSecretUrl', 'type': 'str'},
'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{ComputeRPUserAssignedIdentity}'},
}
def __init__(
self,
*,
type: Optional[str] = None,
system_identity_url: Optional[str] = None,
principal_id: Optional[str] = None,
tenant_id: Optional[str] = None,
client_id: Optional[str] = None,
client_secret_url: Optional[str] = None,
user_assigned_identities: Optional[Dict[str, "ComputeRPUserAssignedIdentity"]] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword system_identity_url:
:paramtype system_identity_url: str
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword client_id:
:paramtype client_id: str
:keyword client_secret_url:
:paramtype client_secret_url: str
:keyword user_assigned_identities: This is a dictionary.
:paramtype user_assigned_identities: dict[str, ~flow.models.ComputeRPUserAssignedIdentity]
"""
super(ComputeIdentityContract, self).__init__(**kwargs)
self.type = type
self.system_identity_url = system_identity_url
self.principal_id = principal_id
self.tenant_id = tenant_id
self.client_id = client_id
self.client_secret_url = client_secret_url
self.user_assigned_identities = user_assigned_identities
class ComputeIdentityDto(msrest.serialization.Model):
"""ComputeIdentityDto.
:ivar compute_name:
:vartype compute_name: str
:ivar compute_target_type: Possible values include: "Local", "Remote", "HdiCluster",
"ContainerInstance", "AmlCompute", "ComputeInstance", "Cmk8s", "SynapseSpark", "Kubernetes",
"Aisc", "GlobalJobDispatcher", "Databricks", "MockedCompute".
:vartype compute_target_type: str or ~flow.models.ComputeTargetType
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'compute_name': {'key': 'computeName', 'type': 'str'},
'compute_target_type': {'key': 'computeTargetType', 'type': 'str'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
*,
compute_name: Optional[str] = None,
compute_target_type: Optional[Union[str, "ComputeTargetType"]] = None,
intellectual_property_publisher: Optional[str] = None,
**kwargs
):
"""
:keyword compute_name:
:paramtype compute_name: str
:keyword compute_target_type: Possible values include: "Local", "Remote", "HdiCluster",
"ContainerInstance", "AmlCompute", "ComputeInstance", "Cmk8s", "SynapseSpark", "Kubernetes",
"Aisc", "GlobalJobDispatcher", "Databricks", "MockedCompute".
:paramtype compute_target_type: str or ~flow.models.ComputeTargetType
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(ComputeIdentityDto, self).__init__(**kwargs)
self.compute_name = compute_name
self.compute_target_type = compute_target_type
self.intellectual_property_publisher = intellectual_property_publisher
class ComputeInfo(msrest.serialization.Model):
"""ComputeInfo.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar is_ssl_enabled:
:vartype is_ssl_enabled: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar cluster_purpose:
:vartype cluster_purpose: str
:ivar public_ip_address:
:vartype public_ip_address: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'is_ssl_enabled': {'key': 'isSslEnabled', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
compute_type: Optional[Union[str, "ComputeEnvironmentType"]] = None,
is_ssl_enabled: Optional[bool] = None,
is_gpu_type: Optional[bool] = None,
cluster_purpose: Optional[str] = None,
public_ip_address: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword is_ssl_enabled:
:paramtype is_ssl_enabled: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword cluster_purpose:
:paramtype cluster_purpose: str
:keyword public_ip_address:
:paramtype public_ip_address: str
"""
super(ComputeInfo, self).__init__(**kwargs)
self.name = name
self.compute_type = compute_type
self.is_ssl_enabled = is_ssl_enabled
self.is_gpu_type = is_gpu_type
self.cluster_purpose = cluster_purpose
self.public_ip_address = public_ip_address
class ComputeProperties(msrest.serialization.Model):
"""ComputeProperties.
All required parameters must be populated in order to send to Azure.
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar disable_local_auth:
:vartype disable_local_auth: bool
:ivar description:
:vartype description: str
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type: Required.
:vartype compute_type: str
:ivar compute_location:
:vartype compute_location: str
:ivar provisioning_state: Possible values include: "Unknown", "Updating", "Creating",
"Deleting", "Accepted", "Succeeded", "Failed", "Canceled".
:vartype provisioning_state: str or ~flow.models.ProvisioningState
:ivar provisioning_errors:
:vartype provisioning_errors: list[~flow.models.ODataErrorResponse]
:ivar provisioning_warnings: This is a dictionary.
:vartype provisioning_warnings: dict[str, str]
:ivar is_attached_compute:
:vartype is_attached_compute: bool
:ivar properties: Any object.
:vartype properties: any
:ivar status:
:vartype status: ~flow.models.ComputeStatus
:ivar warnings:
:vartype warnings: list[~flow.models.ComputeWarning]
"""
_validation = {
'compute_type': {'required': True, 'min_length': 1},
}
_attribute_map = {
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'disable_local_auth': {'key': 'disableLocalAuth', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'compute_location': {'key': 'computeLocation', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'provisioning_errors': {'key': 'provisioningErrors', 'type': '[ODataErrorResponse]'},
'provisioning_warnings': {'key': 'provisioningWarnings', 'type': '{str}'},
'is_attached_compute': {'key': 'isAttachedCompute', 'type': 'bool'},
'properties': {'key': 'properties', 'type': 'object'},
'status': {'key': 'status', 'type': 'ComputeStatus'},
'warnings': {'key': 'warnings', 'type': '[ComputeWarning]'},
}
def __init__(
self,
*,
compute_type: str,
created_on: Optional[datetime.datetime] = None,
modified_on: Optional[datetime.datetime] = None,
disable_local_auth: Optional[bool] = None,
description: Optional[str] = None,
resource_id: Optional[str] = None,
compute_location: Optional[str] = None,
provisioning_state: Optional[Union[str, "ProvisioningState"]] = None,
provisioning_errors: Optional[List["ODataErrorResponse"]] = None,
provisioning_warnings: Optional[Dict[str, str]] = None,
is_attached_compute: Optional[bool] = None,
properties: Optional[Any] = None,
status: Optional["ComputeStatus"] = None,
warnings: Optional[List["ComputeWarning"]] = None,
**kwargs
):
"""
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword disable_local_auth:
:paramtype disable_local_auth: bool
:keyword description:
:paramtype description: str
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type: Required.
:paramtype compute_type: str
:keyword compute_location:
:paramtype compute_location: str
:keyword provisioning_state: Possible values include: "Unknown", "Updating", "Creating",
"Deleting", "Accepted", "Succeeded", "Failed", "Canceled".
:paramtype provisioning_state: str or ~flow.models.ProvisioningState
:keyword provisioning_errors:
:paramtype provisioning_errors: list[~flow.models.ODataErrorResponse]
:keyword provisioning_warnings: This is a dictionary.
:paramtype provisioning_warnings: dict[str, str]
:keyword is_attached_compute:
:paramtype is_attached_compute: bool
:keyword properties: Any object.
:paramtype properties: any
:keyword status:
:paramtype status: ~flow.models.ComputeStatus
:keyword warnings:
:paramtype warnings: list[~flow.models.ComputeWarning]
"""
super(ComputeProperties, self).__init__(**kwargs)
self.created_on = created_on
self.modified_on = modified_on
self.disable_local_auth = disable_local_auth
self.description = description
self.resource_id = resource_id
self.compute_type = compute_type
self.compute_location = compute_location
self.provisioning_state = provisioning_state
self.provisioning_errors = provisioning_errors
self.provisioning_warnings = provisioning_warnings
self.is_attached_compute = is_attached_compute
self.properties = properties
self.status = status
self.warnings = warnings
class ComputeRequest(msrest.serialization.Model):
"""ComputeRequest.
:ivar node_count:
:vartype node_count: int
:ivar gpu_count:
:vartype gpu_count: int
"""
_attribute_map = {
'node_count': {'key': 'nodeCount', 'type': 'int'},
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
}
def __init__(
self,
*,
node_count: Optional[int] = None,
gpu_count: Optional[int] = None,
**kwargs
):
"""
:keyword node_count:
:paramtype node_count: int
:keyword gpu_count:
:paramtype gpu_count: int
"""
super(ComputeRequest, self).__init__(**kwargs)
self.node_count = node_count
self.gpu_count = gpu_count
class ComputeRPUserAssignedIdentity(msrest.serialization.Model):
"""ComputeRPUserAssignedIdentity.
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar client_id:
:vartype client_id: str
:ivar client_secret_url:
:vartype client_secret_url: str
:ivar resource_id:
:vartype resource_id: str
"""
_attribute_map = {
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'client_secret_url': {'key': 'clientSecretUrl', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
}
def __init__(
self,
*,
principal_id: Optional[str] = None,
tenant_id: Optional[str] = None,
client_id: Optional[str] = None,
client_secret_url: Optional[str] = None,
resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword client_id:
:paramtype client_id: str
:keyword client_secret_url:
:paramtype client_secret_url: str
:keyword resource_id:
:paramtype resource_id: str
"""
super(ComputeRPUserAssignedIdentity, self).__init__(**kwargs)
self.principal_id = principal_id
self.tenant_id = tenant_id
self.client_id = client_id
self.client_secret_url = client_secret_url
self.resource_id = resource_id
class ComputeSetting(msrest.serialization.Model):
"""ComputeSetting.
:ivar name:
:vartype name: str
:ivar compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:vartype compute_type: str or ~flow.models.ComputeType
:ivar batch_ai_compute_info:
:vartype batch_ai_compute_info: ~flow.models.BatchAiComputeInfo
:ivar remote_docker_compute_info:
:vartype remote_docker_compute_info: ~flow.models.RemoteDockerComputeInfo
:ivar hdi_cluster_compute_info:
:vartype hdi_cluster_compute_info: ~flow.models.HdiClusterComputeInfo
:ivar mlc_compute_info:
:vartype mlc_compute_info: ~flow.models.MlcComputeInfo
:ivar databricks_compute_info:
:vartype databricks_compute_info: ~flow.models.DatabricksComputeInfo
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'batch_ai_compute_info': {'key': 'batchAiComputeInfo', 'type': 'BatchAiComputeInfo'},
'remote_docker_compute_info': {'key': 'remoteDockerComputeInfo', 'type': 'RemoteDockerComputeInfo'},
'hdi_cluster_compute_info': {'key': 'hdiClusterComputeInfo', 'type': 'HdiClusterComputeInfo'},
'mlc_compute_info': {'key': 'mlcComputeInfo', 'type': 'MlcComputeInfo'},
'databricks_compute_info': {'key': 'databricksComputeInfo', 'type': 'DatabricksComputeInfo'},
}
def __init__(
self,
*,
name: Optional[str] = None,
compute_type: Optional[Union[str, "ComputeType"]] = None,
batch_ai_compute_info: Optional["BatchAiComputeInfo"] = None,
remote_docker_compute_info: Optional["RemoteDockerComputeInfo"] = None,
hdi_cluster_compute_info: Optional["HdiClusterComputeInfo"] = None,
mlc_compute_info: Optional["MlcComputeInfo"] = None,
databricks_compute_info: Optional["DatabricksComputeInfo"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_type: Possible values include: "BatchAi", "MLC", "HdiCluster", "RemoteDocker",
"Databricks", "Aisc".
:paramtype compute_type: str or ~flow.models.ComputeType
:keyword batch_ai_compute_info:
:paramtype batch_ai_compute_info: ~flow.models.BatchAiComputeInfo
:keyword remote_docker_compute_info:
:paramtype remote_docker_compute_info: ~flow.models.RemoteDockerComputeInfo
:keyword hdi_cluster_compute_info:
:paramtype hdi_cluster_compute_info: ~flow.models.HdiClusterComputeInfo
:keyword mlc_compute_info:
:paramtype mlc_compute_info: ~flow.models.MlcComputeInfo
:keyword databricks_compute_info:
:paramtype databricks_compute_info: ~flow.models.DatabricksComputeInfo
"""
super(ComputeSetting, self).__init__(**kwargs)
self.name = name
self.compute_type = compute_type
self.batch_ai_compute_info = batch_ai_compute_info
self.remote_docker_compute_info = remote_docker_compute_info
self.hdi_cluster_compute_info = hdi_cluster_compute_info
self.mlc_compute_info = mlc_compute_info
self.databricks_compute_info = databricks_compute_info
class ComputeStatus(msrest.serialization.Model):
"""ComputeStatus.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar is_status_available:
:vartype is_status_available: bool
:ivar detailed_status: Anything.
:vartype detailed_status: any
:ivar error: Represents OData v4 error object.
:vartype error: ~flow.models.ODataError
"""
_validation = {
'is_status_available': {'readonly': True},
}
_attribute_map = {
'is_status_available': {'key': 'isStatusAvailable', 'type': 'bool'},
'detailed_status': {'key': 'detailedStatus', 'type': 'object'},
'error': {'key': 'error', 'type': 'ODataError'},
}
def __init__(
self,
*,
detailed_status: Optional[Any] = None,
error: Optional["ODataError"] = None,
**kwargs
):
"""
:keyword detailed_status: Anything.
:paramtype detailed_status: any
:keyword error: Represents OData v4 error object.
:paramtype error: ~flow.models.ODataError
"""
super(ComputeStatus, self).__init__(**kwargs)
self.is_status_available = None
self.detailed_status = detailed_status
self.error = error
class ComputeStatusDetail(msrest.serialization.Model):
"""ComputeStatusDetail.
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar provisioning_error_message:
:vartype provisioning_error_message: str
"""
_attribute_map = {
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'provisioning_error_message': {'key': 'provisioningErrorMessage', 'type': 'str'},
}
def __init__(
self,
*,
provisioning_state: Optional[str] = None,
provisioning_error_message: Optional[str] = None,
**kwargs
):
"""
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword provisioning_error_message:
:paramtype provisioning_error_message: str
"""
super(ComputeStatusDetail, self).__init__(**kwargs)
self.provisioning_state = provisioning_state
self.provisioning_error_message = provisioning_error_message
class ComputeWarning(msrest.serialization.Model):
"""ComputeWarning.
:ivar title:
:vartype title: str
:ivar message:
:vartype message: str
:ivar code:
:vartype code: str
:ivar severity: Possible values include: "Critical", "Error", "Warning", "Info".
:vartype severity: str or ~flow.models.SeverityLevel
"""
_attribute_map = {
'title': {'key': 'title', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'code': {'key': 'code', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'str'},
}
def __init__(
self,
*,
title: Optional[str] = None,
message: Optional[str] = None,
code: Optional[str] = None,
severity: Optional[Union[str, "SeverityLevel"]] = None,
**kwargs
):
"""
:keyword title:
:paramtype title: str
:keyword message:
:paramtype message: str
:keyword code:
:paramtype code: str
:keyword severity: Possible values include: "Critical", "Error", "Warning", "Info".
:paramtype severity: str or ~flow.models.SeverityLevel
"""
super(ComputeWarning, self).__init__(**kwargs)
self.title = title
self.message = message
self.code = code
self.severity = severity
class ConnectionConfigSpec(msrest.serialization.Model):
"""ConnectionConfigSpec.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar config_value_type: Possible values include: "String", "Secret".
:vartype config_value_type: str or ~flow.models.ConfigValueType
:ivar description:
:vartype description: str
:ivar default_value:
:vartype default_value: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'config_value_type': {'key': 'configValueType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
display_name: Optional[str] = None,
config_value_type: Optional[Union[str, "ConfigValueType"]] = None,
description: Optional[str] = None,
default_value: Optional[str] = None,
enum_values: Optional[List[str]] = None,
is_optional: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword config_value_type: Possible values include: "String", "Secret".
:paramtype config_value_type: str or ~flow.models.ConfigValueType
:keyword description:
:paramtype description: str
:keyword default_value:
:paramtype default_value: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword is_optional:
:paramtype is_optional: bool
"""
super(ConnectionConfigSpec, self).__init__(**kwargs)
self.name = name
self.display_name = display_name
self.config_value_type = config_value_type
self.description = description
self.default_value = default_value
self.enum_values = enum_values
self.is_optional = is_optional
class ConnectionDto(msrest.serialization.Model):
"""ConnectionDto.
:ivar connection_name:
:vartype connection_name: str
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'connection_name': {'key': 'connectionName', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
connection_name: Optional[str] = None,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
configs: Optional[Dict[str, str]] = None,
custom_configs: Optional[Dict[str, "CustomConnectionConfig"]] = None,
expiry_time: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword connection_name:
:paramtype connection_name: str
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ConnectionDto, self).__init__(**kwargs)
self.connection_name = connection_name
self.connection_type = connection_type
self.configs = configs
self.custom_configs = custom_configs
self.expiry_time = expiry_time
self.owner = owner
self.created_date = created_date
self.last_modified_date = last_modified_date
class ConnectionEntity(msrest.serialization.Model):
"""ConnectionEntity.
:ivar connection_id:
:vartype connection_id: str
:ivar connection_name:
:vartype connection_name: str
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_scope: Possible values include: "User", "WorkspaceShared".
:vartype connection_scope: str or ~flow.models.ConnectionScope
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
:ivar secret_name:
:vartype secret_name: str
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'connection_id': {'key': 'connectionId', 'type': 'str'},
'connection_name': {'key': 'connectionName', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_scope': {'key': 'connectionScope', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
'secret_name': {'key': 'secretName', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
connection_id: Optional[str] = None,
connection_name: Optional[str] = None,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
connection_scope: Optional[Union[str, "ConnectionScope"]] = None,
configs: Optional[Dict[str, str]] = None,
custom_configs: Optional[Dict[str, "CustomConnectionConfig"]] = None,
expiry_time: Optional[datetime.datetime] = None,
secret_name: Optional[str] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword connection_id:
:paramtype connection_id: str
:keyword connection_name:
:paramtype connection_name: str
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_scope: Possible values include: "User", "WorkspaceShared".
:paramtype connection_scope: str or ~flow.models.ConnectionScope
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
:keyword secret_name:
:paramtype secret_name: str
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ConnectionEntity, self).__init__(**kwargs)
self.connection_id = connection_id
self.connection_name = connection_name
self.connection_type = connection_type
self.connection_scope = connection_scope
self.configs = configs
self.custom_configs = custom_configs
self.expiry_time = expiry_time
self.secret_name = secret_name
self.owner = owner
self.created_date = created_date
self.last_modified_date = last_modified_date
class ConnectionOverrideSetting(msrest.serialization.Model):
"""ConnectionOverrideSetting.
:ivar connection_source_type: Possible values include: "Node", "NodeInput".
:vartype connection_source_type: str or ~flow.models.ConnectionSourceType
:ivar node_name:
:vartype node_name: str
:ivar node_input_name:
:vartype node_input_name: str
:ivar node_deployment_name_input:
:vartype node_deployment_name_input: str
:ivar node_model_input:
:vartype node_model_input: str
:ivar connection_name:
:vartype connection_name: str
:ivar deployment_name:
:vartype deployment_name: str
:ivar model:
:vartype model: str
:ivar connection_types:
:vartype connection_types: list[str or ~flow.models.ConnectionType]
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar model_enum:
:vartype model_enum: list[str]
"""
_attribute_map = {
'connection_source_type': {'key': 'connectionSourceType', 'type': 'str'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'node_input_name': {'key': 'nodeInputName', 'type': 'str'},
'node_deployment_name_input': {'key': 'nodeDeploymentNameInput', 'type': 'str'},
'node_model_input': {'key': 'nodeModelInput', 'type': 'str'},
'connection_name': {'key': 'connectionName', 'type': 'str'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'model': {'key': 'model', 'type': 'str'},
'connection_types': {'key': 'connectionTypes', 'type': '[str]'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'model_enum': {'key': 'modelEnum', 'type': '[str]'},
}
def __init__(
self,
*,
connection_source_type: Optional[Union[str, "ConnectionSourceType"]] = None,
node_name: Optional[str] = None,
node_input_name: Optional[str] = None,
node_deployment_name_input: Optional[str] = None,
node_model_input: Optional[str] = None,
connection_name: Optional[str] = None,
deployment_name: Optional[str] = None,
model: Optional[str] = None,
connection_types: Optional[List[Union[str, "ConnectionType"]]] = None,
capabilities: Optional["AzureOpenAIModelCapabilities"] = None,
model_enum: Optional[List[str]] = None,
**kwargs
):
"""
:keyword connection_source_type: Possible values include: "Node", "NodeInput".
:paramtype connection_source_type: str or ~flow.models.ConnectionSourceType
:keyword node_name:
:paramtype node_name: str
:keyword node_input_name:
:paramtype node_input_name: str
:keyword node_deployment_name_input:
:paramtype node_deployment_name_input: str
:keyword node_model_input:
:paramtype node_model_input: str
:keyword connection_name:
:paramtype connection_name: str
:keyword deployment_name:
:paramtype deployment_name: str
:keyword model:
:paramtype model: str
:keyword connection_types:
:paramtype connection_types: list[str or ~flow.models.ConnectionType]
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword model_enum:
:paramtype model_enum: list[str]
"""
super(ConnectionOverrideSetting, self).__init__(**kwargs)
self.connection_source_type = connection_source_type
self.node_name = node_name
self.node_input_name = node_input_name
self.node_deployment_name_input = node_deployment_name_input
self.node_model_input = node_model_input
self.connection_name = connection_name
self.deployment_name = deployment_name
self.model = model
self.connection_types = connection_types
self.capabilities = capabilities
self.model_enum = model_enum
class ConnectionSpec(msrest.serialization.Model):
"""ConnectionSpec.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar config_specs:
:vartype config_specs: list[~flow.models.ConnectionConfigSpec]
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'config_specs': {'key': 'configSpecs', 'type': '[ConnectionConfigSpec]'},
}
def __init__(
self,
*,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
config_specs: Optional[List["ConnectionConfigSpec"]] = None,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword config_specs:
:paramtype config_specs: list[~flow.models.ConnectionConfigSpec]
"""
super(ConnectionSpec, self).__init__(**kwargs)
self.connection_type = connection_type
self.config_specs = config_specs
class ContainerInstanceConfiguration(msrest.serialization.Model):
"""ContainerInstanceConfiguration.
:ivar region:
:vartype region: str
:ivar cpu_cores:
:vartype cpu_cores: float
:ivar memory_gb:
:vartype memory_gb: float
"""
_attribute_map = {
'region': {'key': 'region', 'type': 'str'},
'cpu_cores': {'key': 'cpuCores', 'type': 'float'},
'memory_gb': {'key': 'memoryGb', 'type': 'float'},
}
def __init__(
self,
*,
region: Optional[str] = None,
cpu_cores: Optional[float] = None,
memory_gb: Optional[float] = None,
**kwargs
):
"""
:keyword region:
:paramtype region: str
:keyword cpu_cores:
:paramtype cpu_cores: float
:keyword memory_gb:
:paramtype memory_gb: float
"""
super(ContainerInstanceConfiguration, self).__init__(**kwargs)
self.region = region
self.cpu_cores = cpu_cores
self.memory_gb = memory_gb
class ContainerRegistry(msrest.serialization.Model):
"""ContainerRegistry.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar credential_type:
:vartype credential_type: str
:ivar registry_identity:
:vartype registry_identity: ~flow.models.RegistryIdentity
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'credential_type': {'key': 'credentialType', 'type': 'str'},
'registry_identity': {'key': 'registryIdentity', 'type': 'RegistryIdentity'},
}
def __init__(
self,
*,
address: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
credential_type: Optional[str] = None,
registry_identity: Optional["RegistryIdentity"] = None,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword credential_type:
:paramtype credential_type: str
:keyword registry_identity:
:paramtype registry_identity: ~flow.models.RegistryIdentity
"""
super(ContainerRegistry, self).__init__(**kwargs)
self.address = address
self.username = username
self.password = password
self.credential_type = credential_type
self.registry_identity = registry_identity
class ContainerResourceRequirements(msrest.serialization.Model):
"""ContainerResourceRequirements.
:ivar cpu:
:vartype cpu: float
:ivar cpu_limit:
:vartype cpu_limit: float
:ivar memory_in_gb:
:vartype memory_in_gb: float
:ivar memory_in_gb_limit:
:vartype memory_in_gb_limit: float
:ivar gpu_enabled:
:vartype gpu_enabled: bool
:ivar gpu:
:vartype gpu: int
:ivar fpga:
:vartype fpga: int
"""
_attribute_map = {
'cpu': {'key': 'cpu', 'type': 'float'},
'cpu_limit': {'key': 'cpuLimit', 'type': 'float'},
'memory_in_gb': {'key': 'memoryInGB', 'type': 'float'},
'memory_in_gb_limit': {'key': 'memoryInGBLimit', 'type': 'float'},
'gpu_enabled': {'key': 'gpuEnabled', 'type': 'bool'},
'gpu': {'key': 'gpu', 'type': 'int'},
'fpga': {'key': 'fpga', 'type': 'int'},
}
def __init__(
self,
*,
cpu: Optional[float] = None,
cpu_limit: Optional[float] = None,
memory_in_gb: Optional[float] = None,
memory_in_gb_limit: Optional[float] = None,
gpu_enabled: Optional[bool] = None,
gpu: Optional[int] = None,
fpga: Optional[int] = None,
**kwargs
):
"""
:keyword cpu:
:paramtype cpu: float
:keyword cpu_limit:
:paramtype cpu_limit: float
:keyword memory_in_gb:
:paramtype memory_in_gb: float
:keyword memory_in_gb_limit:
:paramtype memory_in_gb_limit: float
:keyword gpu_enabled:
:paramtype gpu_enabled: bool
:keyword gpu:
:paramtype gpu: int
:keyword fpga:
:paramtype fpga: int
"""
super(ContainerResourceRequirements, self).__init__(**kwargs)
self.cpu = cpu
self.cpu_limit = cpu_limit
self.memory_in_gb = memory_in_gb
self.memory_in_gb_limit = memory_in_gb_limit
self.gpu_enabled = gpu_enabled
self.gpu = gpu
self.fpga = fpga
class ControlInput(msrest.serialization.Model):
"""ControlInput.
:ivar name:
:vartype name: str
:ivar default_value: Possible values include: "None", "False", "True", "Skipped".
:vartype default_value: str or ~flow.models.ControlInputValue
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
default_value: Optional[Union[str, "ControlInputValue"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword default_value: Possible values include: "None", "False", "True", "Skipped".
:paramtype default_value: str or ~flow.models.ControlInputValue
"""
super(ControlInput, self).__init__(**kwargs)
self.name = name
self.default_value = default_value
class ControlOutput(msrest.serialization.Model):
"""ControlOutput.
:ivar name:
:vartype name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
"""
super(ControlOutput, self).__init__(**kwargs)
self.name = name
class CopyDataTask(msrest.serialization.Model):
"""CopyDataTask.
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.DataCopyMode
"""
_attribute_map = {
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
*,
data_copy_mode: Optional[Union[str, "DataCopyMode"]] = None,
**kwargs
):
"""
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.DataCopyMode
"""
super(CopyDataTask, self).__init__(**kwargs)
self.data_copy_mode = data_copy_mode
class CreatedBy(msrest.serialization.Model):
"""CreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
}
def __init__(
self,
*,
user_object_id: Optional[str] = None,
user_tenant_id: Optional[str] = None,
user_name: Optional[str] = None,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
"""
super(CreatedBy, self).__init__(**kwargs)
self.user_object_id = user_object_id
self.user_tenant_id = user_tenant_id
self.user_name = user_name
class CreatedFromDto(msrest.serialization.Model):
"""CreatedFromDto.
:ivar type: The only acceptable values to pass in are None and "Notebook". The default value
is None.
:vartype type: str
:ivar location_type: The only acceptable values to pass in are None and "ArtifactId". The
default value is None.
:vartype location_type: str
:ivar location:
:vartype location: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'location_type': {'key': 'locationType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[str] = None,
location_type: Optional[str] = None,
location: Optional[str] = None,
**kwargs
):
"""
:keyword type: The only acceptable values to pass in are None and "Notebook". The default
value is None.
:paramtype type: str
:keyword location_type: The only acceptable values to pass in are None and "ArtifactId". The
default value is None.
:paramtype location_type: str
:keyword location:
:paramtype location: str
"""
super(CreatedFromDto, self).__init__(**kwargs)
self.type = type
self.location_type = location_type
self.location = location
class CreateFlowRequest(msrest.serialization.Model):
"""CreateFlowRequest.
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
*,
flow_name: Optional[str] = None,
description: Optional[str] = None,
details: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
flow: Optional["Flow"] = None,
flow_definition_file_path: Optional[str] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
flow_run_settings: Optional["FlowRunSettings"] = None,
is_archived: Optional[bool] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
**kwargs
):
"""
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(CreateFlowRequest, self).__init__(**kwargs)
self.flow_name = flow_name
self.description = description
self.details = details
self.tags = tags
self.flow = flow
self.flow_definition_file_path = flow_definition_file_path
self.flow_type = flow_type
self.flow_run_settings = flow_run_settings
self.is_archived = is_archived
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
class CreateFlowRuntimeRequest(msrest.serialization.Model):
"""CreateFlowRuntimeRequest.
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar deployment_name:
:vartype deployment_name: str
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar environment:
:vartype environment: str
:ivar instance_count:
:vartype instance_count: int
"""
_attribute_map = {
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
}
def __init__(
self,
*,
runtime_type: Optional[Union[str, "RuntimeType"]] = None,
identity: Optional["ManagedServiceIdentity"] = None,
instance_type: Optional[str] = None,
from_existing_endpoint: Optional[bool] = None,
from_existing_deployment: Optional[bool] = None,
endpoint_name: Optional[str] = None,
deployment_name: Optional[str] = None,
compute_instance_name: Optional[str] = None,
from_existing_custom_app: Optional[bool] = None,
custom_app_name: Optional[str] = None,
runtime_description: Optional[str] = None,
environment: Optional[str] = None,
instance_count: Optional[int] = None,
**kwargs
):
"""
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword deployment_name:
:paramtype deployment_name: str
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword environment:
:paramtype environment: str
:keyword instance_count:
:paramtype instance_count: int
"""
super(CreateFlowRuntimeRequest, self).__init__(**kwargs)
self.runtime_type = runtime_type
self.identity = identity
self.instance_type = instance_type
self.from_existing_endpoint = from_existing_endpoint
self.from_existing_deployment = from_existing_deployment
self.endpoint_name = endpoint_name
self.deployment_name = deployment_name
self.compute_instance_name = compute_instance_name
self.from_existing_custom_app = from_existing_custom_app
self.custom_app_name = custom_app_name
self.runtime_description = runtime_description
self.environment = environment
self.instance_count = instance_count
class CreateFlowSessionRequest(msrest.serialization.Model):
"""CreateFlowSessionRequest.
:ivar python_pip_requirements:
:vartype python_pip_requirements: list[str]
:ivar base_image:
:vartype base_image: str
:ivar action: Possible values include: "Install", "Reset", "Update", "Delete".
:vartype action: str or ~flow.models.SetupFlowSessionAction
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
:ivar compute_name:
:vartype compute_name: str
"""
_attribute_map = {
'python_pip_requirements': {'key': 'pythonPipRequirements', 'type': '[str]'},
'base_image': {'key': 'baseImage', 'type': 'str'},
'action': {'key': 'action', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
'compute_name': {'key': 'computeName', 'type': 'str'},
}
def __init__(
self,
*,
python_pip_requirements: Optional[List[str]] = None,
base_image: Optional[str] = None,
action: Optional[Union[str, "SetupFlowSessionAction"]] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
compute_name: Optional[str] = None,
**kwargs
):
"""
:keyword python_pip_requirements:
:paramtype python_pip_requirements: list[str]
:keyword base_image:
:paramtype base_image: str
:keyword action: Possible values include: "Install", "Reset", "Update", "Delete".
:paramtype action: str or ~flow.models.SetupFlowSessionAction
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
:keyword compute_name:
:paramtype compute_name: str
"""
super(CreateFlowSessionRequest, self).__init__(**kwargs)
self.python_pip_requirements = python_pip_requirements
self.base_image = base_image
self.action = action
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
self.compute_name = compute_name
class CreateInferencePipelineRequest(msrest.serialization.Model):
"""CreateInferencePipelineRequest.
:ivar module_node_id:
:vartype module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar training_pipeline_draft_name:
:vartype training_pipeline_draft_name: str
:ivar training_pipeline_run_display_name:
:vartype training_pipeline_run_display_name: str
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'training_pipeline_draft_name': {'key': 'trainingPipelineDraftName', 'type': 'str'},
'training_pipeline_run_display_name': {'key': 'trainingPipelineRunDisplayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
module_node_id: Optional[str] = None,
port_name: Optional[str] = None,
training_pipeline_draft_name: Optional[str] = None,
training_pipeline_run_display_name: Optional[str] = None,
name: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
graph_components_mode: Optional[Union[str, "GraphComponentsMode"]] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
flattened_sub_graphs: Optional[Dict[str, "PipelineSubDraft"]] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword module_node_id:
:paramtype module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword training_pipeline_draft_name:
:paramtype training_pipeline_draft_name: str
:keyword training_pipeline_run_display_name:
:paramtype training_pipeline_run_display_name: str
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreateInferencePipelineRequest, self).__init__(**kwargs)
self.module_node_id = module_node_id
self.port_name = port_name
self.training_pipeline_draft_name = training_pipeline_draft_name
self.training_pipeline_run_display_name = training_pipeline_run_display_name
self.name = name
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.graph_components_mode = graph_components_mode
self.sub_pipelines_info = sub_pipelines_info
self.flattened_sub_graphs = flattened_sub_graphs
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class CreateOrUpdateConnectionRequest(msrest.serialization.Model):
"""CreateOrUpdateConnectionRequest.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_scope: Possible values include: "User", "WorkspaceShared".
:vartype connection_scope: str or ~flow.models.ConnectionScope
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_scope': {'key': 'connectionScope', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
connection_scope: Optional[Union[str, "ConnectionScope"]] = None,
configs: Optional[Dict[str, str]] = None,
custom_configs: Optional[Dict[str, "CustomConnectionConfig"]] = None,
expiry_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_scope: Possible values include: "User", "WorkspaceShared".
:paramtype connection_scope: str or ~flow.models.ConnectionScope
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
"""
super(CreateOrUpdateConnectionRequest, self).__init__(**kwargs)
self.connection_type = connection_type
self.connection_scope = connection_scope
self.configs = configs
self.custom_configs = custom_configs
self.expiry_time = expiry_time
class CreateOrUpdateConnectionRequestDto(msrest.serialization.Model):
"""CreateOrUpdateConnectionRequestDto.
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar configs: This is a dictionary.
:vartype configs: dict[str, str]
:ivar custom_configs: This is a dictionary.
:vartype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:ivar expiry_time:
:vartype expiry_time: ~datetime.datetime
"""
_attribute_map = {
'connection_type': {'key': 'connectionType', 'type': 'str'},
'configs': {'key': 'configs', 'type': '{str}'},
'custom_configs': {'key': 'customConfigs', 'type': '{CustomConnectionConfig}'},
'expiry_time': {'key': 'expiryTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
configs: Optional[Dict[str, str]] = None,
custom_configs: Optional[Dict[str, "CustomConnectionConfig"]] = None,
expiry_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword configs: This is a dictionary.
:paramtype configs: dict[str, str]
:keyword custom_configs: This is a dictionary.
:paramtype custom_configs: dict[str, ~flow.models.CustomConnectionConfig]
:keyword expiry_time:
:paramtype expiry_time: ~datetime.datetime
"""
super(CreateOrUpdateConnectionRequestDto, self).__init__(**kwargs)
self.connection_type = connection_type
self.configs = configs
self.custom_configs = custom_configs
self.expiry_time = expiry_time
class CreatePipelineDraftRequest(msrest.serialization.Model):
"""CreatePipelineDraftRequest.
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
graph_components_mode: Optional[Union[str, "GraphComponentsMode"]] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
flattened_sub_graphs: Optional[Dict[str, "PipelineSubDraft"]] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreatePipelineDraftRequest, self).__init__(**kwargs)
self.name = name
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.graph_components_mode = graph_components_mode
self.sub_pipelines_info = sub_pipelines_info
self.flattened_sub_graphs = flattened_sub_graphs
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class CreatePipelineJobScheduleDto(msrest.serialization.Model):
"""CreatePipelineJobScheduleDto.
:ivar name:
:vartype name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
name: Optional[str] = None,
pipeline_job_name: Optional[str] = None,
pipeline_job_runtime_settings: Optional["PipelineJobRuntimeBasicSettings"] = None,
display_name: Optional[str] = None,
trigger_type: Optional[Union[str, "TriggerType"]] = None,
recurrence: Optional["Recurrence"] = None,
cron: Optional["Cron"] = None,
status: Optional[Union[str, "ScheduleStatus"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(CreatePipelineJobScheduleDto, self).__init__(**kwargs)
self.name = name
self.pipeline_job_name = pipeline_job_name
self.pipeline_job_runtime_settings = pipeline_job_runtime_settings
self.display_name = display_name
self.trigger_type = trigger_type
self.recurrence = recurrence
self.cron = cron
self.status = status
self.description = description
self.tags = tags
self.properties = properties
class CreatePublishedPipelineRequest(msrest.serialization.Model):
"""CreatePublishedPipelineRequest.
:ivar use_pipeline_endpoint:
:vartype use_pipeline_endpoint: bool
:ivar pipeline_name:
:vartype pipeline_name: str
:ivar pipeline_description:
:vartype pipeline_description: str
:ivar use_existing_pipeline_endpoint:
:vartype use_existing_pipeline_endpoint: bool
:ivar pipeline_endpoint_name:
:vartype pipeline_endpoint_name: str
:ivar pipeline_endpoint_description:
:vartype pipeline_endpoint_description: str
:ivar set_as_default_pipeline_for_endpoint:
:vartype set_as_default_pipeline_for_endpoint: bool
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar enable_notification:
:vartype enable_notification: bool
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar display_name:
:vartype display_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'use_pipeline_endpoint': {'key': 'usePipelineEndpoint', 'type': 'bool'},
'pipeline_name': {'key': 'pipelineName', 'type': 'str'},
'pipeline_description': {'key': 'pipelineDescription', 'type': 'str'},
'use_existing_pipeline_endpoint': {'key': 'useExistingPipelineEndpoint', 'type': 'bool'},
'pipeline_endpoint_name': {'key': 'pipelineEndpointName', 'type': 'str'},
'pipeline_endpoint_description': {'key': 'pipelineEndpointDescription', 'type': 'str'},
'set_as_default_pipeline_for_endpoint': {'key': 'setAsDefaultPipelineForEndpoint', 'type': 'bool'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'enable_notification': {'key': 'enableNotification', 'type': 'bool'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
use_pipeline_endpoint: Optional[bool] = None,
pipeline_name: Optional[str] = None,
pipeline_description: Optional[str] = None,
use_existing_pipeline_endpoint: Optional[bool] = None,
pipeline_endpoint_name: Optional[str] = None,
pipeline_endpoint_description: Optional[str] = None,
set_as_default_pipeline_for_endpoint: Optional[bool] = None,
step_tags: Optional[Dict[str, str]] = None,
experiment_name: Optional[str] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
enable_notification: Optional[bool] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
display_name: Optional[str] = None,
run_id: Optional[str] = None,
parent_run_id: Optional[str] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword use_pipeline_endpoint:
:paramtype use_pipeline_endpoint: bool
:keyword pipeline_name:
:paramtype pipeline_name: str
:keyword pipeline_description:
:paramtype pipeline_description: str
:keyword use_existing_pipeline_endpoint:
:paramtype use_existing_pipeline_endpoint: bool
:keyword pipeline_endpoint_name:
:paramtype pipeline_endpoint_name: str
:keyword pipeline_endpoint_description:
:paramtype pipeline_endpoint_description: str
:keyword set_as_default_pipeline_for_endpoint:
:paramtype set_as_default_pipeline_for_endpoint: bool
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword enable_notification:
:paramtype enable_notification: bool
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword display_name:
:paramtype display_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(CreatePublishedPipelineRequest, self).__init__(**kwargs)
self.use_pipeline_endpoint = use_pipeline_endpoint
self.pipeline_name = pipeline_name
self.pipeline_description = pipeline_description
self.use_existing_pipeline_endpoint = use_existing_pipeline_endpoint
self.pipeline_endpoint_name = pipeline_endpoint_name
self.pipeline_endpoint_description = pipeline_endpoint_description
self.set_as_default_pipeline_for_endpoint = set_as_default_pipeline_for_endpoint
self.step_tags = step_tags
self.experiment_name = experiment_name
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.enable_notification = enable_notification
self.sub_pipelines_info = sub_pipelines_info
self.display_name = display_name
self.run_id = run_id
self.parent_run_id = parent_run_id
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class CreateRealTimeEndpointRequest(msrest.serialization.Model):
"""CreateRealTimeEndpointRequest.
:ivar name:
:vartype name: str
:ivar compute_info:
:vartype compute_info: ~flow.models.ComputeInfo
:ivar description:
:vartype description: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar aks_advance_settings:
:vartype aks_advance_settings: ~flow.models.AKSAdvanceSettings
:ivar aci_advance_settings:
:vartype aci_advance_settings: ~flow.models.ACIAdvanceSettings
:ivar linked_training_pipeline_run_id:
:vartype linked_training_pipeline_run_id: str
:ivar linked_experiment_name:
:vartype linked_experiment_name: str
:ivar graph_nodes_run_id_mapping: This is a dictionary.
:vartype graph_nodes_run_id_mapping: dict[str, str]
:ivar workflow:
:vartype workflow: ~flow.models.PipelineGraph
:ivar inputs:
:vartype inputs: list[~flow.models.InputOutputPortMetadata]
:ivar outputs:
:vartype outputs: list[~flow.models.InputOutputPortMetadata]
:ivar example_request:
:vartype example_request: ~flow.models.ExampleRequest
:ivar user_storage_connection_string:
:vartype user_storage_connection_string: str
:ivar user_storage_endpoint_uri:
:vartype user_storage_endpoint_uri: str
:ivar user_storage_workspace_sai_token:
:vartype user_storage_workspace_sai_token: str
:ivar user_storage_container_name:
:vartype user_storage_container_name: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar root_pipeline_run_id:
:vartype root_pipeline_run_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'compute_info': {'key': 'computeInfo', 'type': 'ComputeInfo'},
'description': {'key': 'description', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'aks_advance_settings': {'key': 'aksAdvanceSettings', 'type': 'AKSAdvanceSettings'},
'aci_advance_settings': {'key': 'aciAdvanceSettings', 'type': 'ACIAdvanceSettings'},
'linked_training_pipeline_run_id': {'key': 'linkedTrainingPipelineRunId', 'type': 'str'},
'linked_experiment_name': {'key': 'linkedExperimentName', 'type': 'str'},
'graph_nodes_run_id_mapping': {'key': 'graphNodesRunIdMapping', 'type': '{str}'},
'workflow': {'key': 'workflow', 'type': 'PipelineGraph'},
'inputs': {'key': 'inputs', 'type': '[InputOutputPortMetadata]'},
'outputs': {'key': 'outputs', 'type': '[InputOutputPortMetadata]'},
'example_request': {'key': 'exampleRequest', 'type': 'ExampleRequest'},
'user_storage_connection_string': {'key': 'userStorageConnectionString', 'type': 'str'},
'user_storage_endpoint_uri': {'key': 'userStorageEndpointUri', 'type': 'str'},
'user_storage_workspace_sai_token': {'key': 'userStorageWorkspaceSaiToken', 'type': 'str'},
'user_storage_container_name': {'key': 'userStorageContainerName', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'root_pipeline_run_id': {'key': 'rootPipelineRunId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
compute_info: Optional["ComputeInfo"] = None,
description: Optional[str] = None,
linked_pipeline_draft_id: Optional[str] = None,
linked_pipeline_run_id: Optional[str] = None,
aks_advance_settings: Optional["AKSAdvanceSettings"] = None,
aci_advance_settings: Optional["ACIAdvanceSettings"] = None,
linked_training_pipeline_run_id: Optional[str] = None,
linked_experiment_name: Optional[str] = None,
graph_nodes_run_id_mapping: Optional[Dict[str, str]] = None,
workflow: Optional["PipelineGraph"] = None,
inputs: Optional[List["InputOutputPortMetadata"]] = None,
outputs: Optional[List["InputOutputPortMetadata"]] = None,
example_request: Optional["ExampleRequest"] = None,
user_storage_connection_string: Optional[str] = None,
user_storage_endpoint_uri: Optional[str] = None,
user_storage_workspace_sai_token: Optional[str] = None,
user_storage_container_name: Optional[str] = None,
pipeline_run_id: Optional[str] = None,
root_pipeline_run_id: Optional[str] = None,
experiment_name: Optional[str] = None,
experiment_id: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword compute_info:
:paramtype compute_info: ~flow.models.ComputeInfo
:keyword description:
:paramtype description: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword aks_advance_settings:
:paramtype aks_advance_settings: ~flow.models.AKSAdvanceSettings
:keyword aci_advance_settings:
:paramtype aci_advance_settings: ~flow.models.ACIAdvanceSettings
:keyword linked_training_pipeline_run_id:
:paramtype linked_training_pipeline_run_id: str
:keyword linked_experiment_name:
:paramtype linked_experiment_name: str
:keyword graph_nodes_run_id_mapping: This is a dictionary.
:paramtype graph_nodes_run_id_mapping: dict[str, str]
:keyword workflow:
:paramtype workflow: ~flow.models.PipelineGraph
:keyword inputs:
:paramtype inputs: list[~flow.models.InputOutputPortMetadata]
:keyword outputs:
:paramtype outputs: list[~flow.models.InputOutputPortMetadata]
:keyword example_request:
:paramtype example_request: ~flow.models.ExampleRequest
:keyword user_storage_connection_string:
:paramtype user_storage_connection_string: str
:keyword user_storage_endpoint_uri:
:paramtype user_storage_endpoint_uri: str
:keyword user_storage_workspace_sai_token:
:paramtype user_storage_workspace_sai_token: str
:keyword user_storage_container_name:
:paramtype user_storage_container_name: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword root_pipeline_run_id:
:paramtype root_pipeline_run_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(CreateRealTimeEndpointRequest, self).__init__(**kwargs)
self.name = name
self.compute_info = compute_info
self.description = description
self.linked_pipeline_draft_id = linked_pipeline_draft_id
self.linked_pipeline_run_id = linked_pipeline_run_id
self.aks_advance_settings = aks_advance_settings
self.aci_advance_settings = aci_advance_settings
self.linked_training_pipeline_run_id = linked_training_pipeline_run_id
self.linked_experiment_name = linked_experiment_name
self.graph_nodes_run_id_mapping = graph_nodes_run_id_mapping
self.workflow = workflow
self.inputs = inputs
self.outputs = outputs
self.example_request = example_request
self.user_storage_connection_string = user_storage_connection_string
self.user_storage_endpoint_uri = user_storage_endpoint_uri
self.user_storage_workspace_sai_token = user_storage_workspace_sai_token
self.user_storage_container_name = user_storage_container_name
self.pipeline_run_id = pipeline_run_id
self.root_pipeline_run_id = root_pipeline_run_id
self.experiment_name = experiment_name
self.experiment_id = experiment_id
class CreationContext(msrest.serialization.Model):
"""CreationContext.
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar creation_source:
:vartype creation_source: str
"""
_attribute_map = {
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'creation_source': {'key': 'creationSource', 'type': 'str'},
}
def __init__(
self,
*,
created_time: Optional[datetime.datetime] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
creation_source: Optional[str] = None,
**kwargs
):
"""
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword creation_source:
:paramtype creation_source: str
"""
super(CreationContext, self).__init__(**kwargs)
self.created_time = created_time
self.created_by = created_by
self.creation_source = creation_source
class Cron(msrest.serialization.Model):
"""Cron.
:ivar expression:
:vartype expression: str
:ivar end_time:
:vartype end_time: str
:ivar start_time:
:vartype start_time: str
:ivar time_zone:
:vartype time_zone: str
"""
_attribute_map = {
'expression': {'key': 'expression', 'type': 'str'},
'end_time': {'key': 'endTime', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'str'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
}
def __init__(
self,
*,
expression: Optional[str] = None,
end_time: Optional[str] = None,
start_time: Optional[str] = None,
time_zone: Optional[str] = None,
**kwargs
):
"""
:keyword expression:
:paramtype expression: str
:keyword end_time:
:paramtype end_time: str
:keyword start_time:
:paramtype start_time: str
:keyword time_zone:
:paramtype time_zone: str
"""
super(Cron, self).__init__(**kwargs)
self.expression = expression
self.end_time = end_time
self.start_time = start_time
self.time_zone = time_zone
class CustomConnectionConfig(msrest.serialization.Model):
"""CustomConnectionConfig.
:ivar config_value_type: Possible values include: "String", "Secret".
:vartype config_value_type: str or ~flow.models.ConfigValueType
:ivar value:
:vartype value: str
"""
_attribute_map = {
'config_value_type': {'key': 'configValueType', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
config_value_type: Optional[Union[str, "ConfigValueType"]] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword config_value_type: Possible values include: "String", "Secret".
:paramtype config_value_type: str or ~flow.models.ConfigValueType
:keyword value:
:paramtype value: str
"""
super(CustomConnectionConfig, self).__init__(**kwargs)
self.config_value_type = config_value_type
self.value = value
class CustomReference(msrest.serialization.Model):
"""CustomReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(CustomReference, self).__init__(**kwargs)
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
class Data(msrest.serialization.Model):
"""Data.
:ivar data_location:
:vartype data_location: ~flow.models.ExecutionDataLocation
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DeliveryMechanism
:ivar environment_variable_name:
:vartype environment_variable_name: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar options: Dictionary of :code:`<string>`.
:vartype options: dict[str, str]
"""
_attribute_map = {
'data_location': {'key': 'dataLocation', 'type': 'ExecutionDataLocation'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'options': {'key': 'options', 'type': '{str}'},
}
def __init__(
self,
*,
data_location: Optional["ExecutionDataLocation"] = None,
mechanism: Optional[Union[str, "DeliveryMechanism"]] = None,
environment_variable_name: Optional[str] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
options: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword data_location:
:paramtype data_location: ~flow.models.ExecutionDataLocation
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DeliveryMechanism
:keyword environment_variable_name:
:paramtype environment_variable_name: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword options: Dictionary of :code:`<string>`.
:paramtype options: dict[str, str]
"""
super(Data, self).__init__(**kwargs)
self.data_location = data_location
self.mechanism = mechanism
self.environment_variable_name = environment_variable_name
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.options = options
class DatabaseSink(msrest.serialization.Model):
"""DatabaseSink.
:ivar connection:
:vartype connection: str
:ivar table:
:vartype table: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'table': {'key': 'table', 'type': 'str'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
table: Optional[str] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword table:
:paramtype table: str
"""
super(DatabaseSink, self).__init__(**kwargs)
self.connection = connection
self.table = table
class DatabaseSource(msrest.serialization.Model):
"""DatabaseSource.
:ivar connection:
:vartype connection: str
:ivar query:
:vartype query: str
:ivar stored_procedure_name:
:vartype stored_procedure_name: str
:ivar stored_procedure_parameters:
:vartype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'query': {'key': 'query', 'type': 'str'},
'stored_procedure_name': {'key': 'storedProcedureName', 'type': 'str'},
'stored_procedure_parameters': {'key': 'storedProcedureParameters', 'type': '[StoredProcedureParameter]'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
query: Optional[str] = None,
stored_procedure_name: Optional[str] = None,
stored_procedure_parameters: Optional[List["StoredProcedureParameter"]] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword query:
:paramtype query: str
:keyword stored_procedure_name:
:paramtype stored_procedure_name: str
:keyword stored_procedure_parameters:
:paramtype stored_procedure_parameters: list[~flow.models.StoredProcedureParameter]
"""
super(DatabaseSource, self).__init__(**kwargs)
self.connection = connection
self.query = query
self.stored_procedure_name = stored_procedure_name
self.stored_procedure_parameters = stored_procedure_parameters
class DatabricksComputeInfo(msrest.serialization.Model):
"""DatabricksComputeInfo.
:ivar existing_cluster_id:
:vartype existing_cluster_id: str
"""
_attribute_map = {
'existing_cluster_id': {'key': 'existingClusterId', 'type': 'str'},
}
def __init__(
self,
*,
existing_cluster_id: Optional[str] = None,
**kwargs
):
"""
:keyword existing_cluster_id:
:paramtype existing_cluster_id: str
"""
super(DatabricksComputeInfo, self).__init__(**kwargs)
self.existing_cluster_id = existing_cluster_id
class DatabricksConfiguration(msrest.serialization.Model):
"""DatabricksConfiguration.
:ivar workers:
:vartype workers: int
:ivar minimum_worker_count:
:vartype minimum_worker_count: int
:ivar max_mum_worker_count:
:vartype max_mum_worker_count: int
:ivar spark_version:
:vartype spark_version: str
:ivar node_type_id:
:vartype node_type_id: str
:ivar spark_conf: Dictionary of :code:`<string>`.
:vartype spark_conf: dict[str, str]
:ivar spark_env_vars: Dictionary of :code:`<string>`.
:vartype spark_env_vars: dict[str, str]
:ivar cluster_log_conf_dbfs_path:
:vartype cluster_log_conf_dbfs_path: str
:ivar dbfs_init_scripts:
:vartype dbfs_init_scripts: list[~flow.models.InitScriptInfoDto]
:ivar instance_pool_id:
:vartype instance_pool_id: str
:ivar timeout_seconds:
:vartype timeout_seconds: int
:ivar notebook_task:
:vartype notebook_task: ~flow.models.NoteBookTaskDto
:ivar spark_python_task:
:vartype spark_python_task: ~flow.models.SparkPythonTaskDto
:ivar spark_jar_task:
:vartype spark_jar_task: ~flow.models.SparkJarTaskDto
:ivar spark_submit_task:
:vartype spark_submit_task: ~flow.models.SparkSubmitTaskDto
:ivar jar_libraries:
:vartype jar_libraries: list[str]
:ivar egg_libraries:
:vartype egg_libraries: list[str]
:ivar whl_libraries:
:vartype whl_libraries: list[str]
:ivar pypi_libraries:
:vartype pypi_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:ivar r_cran_libraries:
:vartype r_cran_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:ivar maven_libraries:
:vartype maven_libraries: list[~flow.models.MavenLibraryDto]
:ivar libraries:
:vartype libraries: list[any]
:ivar linked_adb_workspace_metadata:
:vartype linked_adb_workspace_metadata: ~flow.models.LinkedADBWorkspaceMetadata
:ivar databrick_resource_id:
:vartype databrick_resource_id: str
:ivar auto_scale:
:vartype auto_scale: bool
"""
_attribute_map = {
'workers': {'key': 'workers', 'type': 'int'},
'minimum_worker_count': {'key': 'minimumWorkerCount', 'type': 'int'},
'max_mum_worker_count': {'key': 'maxMumWorkerCount', 'type': 'int'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
'node_type_id': {'key': 'nodeTypeId', 'type': 'str'},
'spark_conf': {'key': 'sparkConf', 'type': '{str}'},
'spark_env_vars': {'key': 'sparkEnvVars', 'type': '{str}'},
'cluster_log_conf_dbfs_path': {'key': 'clusterLogConfDbfsPath', 'type': 'str'},
'dbfs_init_scripts': {'key': 'dbfsInitScripts', 'type': '[InitScriptInfoDto]'},
'instance_pool_id': {'key': 'instancePoolId', 'type': 'str'},
'timeout_seconds': {'key': 'timeoutSeconds', 'type': 'int'},
'notebook_task': {'key': 'notebookTask', 'type': 'NoteBookTaskDto'},
'spark_python_task': {'key': 'sparkPythonTask', 'type': 'SparkPythonTaskDto'},
'spark_jar_task': {'key': 'sparkJarTask', 'type': 'SparkJarTaskDto'},
'spark_submit_task': {'key': 'sparkSubmitTask', 'type': 'SparkSubmitTaskDto'},
'jar_libraries': {'key': 'jarLibraries', 'type': '[str]'},
'egg_libraries': {'key': 'eggLibraries', 'type': '[str]'},
'whl_libraries': {'key': 'whlLibraries', 'type': '[str]'},
'pypi_libraries': {'key': 'pypiLibraries', 'type': '[PythonPyPiOrRCranLibraryDto]'},
'r_cran_libraries': {'key': 'rCranLibraries', 'type': '[PythonPyPiOrRCranLibraryDto]'},
'maven_libraries': {'key': 'mavenLibraries', 'type': '[MavenLibraryDto]'},
'libraries': {'key': 'libraries', 'type': '[object]'},
'linked_adb_workspace_metadata': {'key': 'linkedADBWorkspaceMetadata', 'type': 'LinkedADBWorkspaceMetadata'},
'databrick_resource_id': {'key': 'databrickResourceId', 'type': 'str'},
'auto_scale': {'key': 'autoScale', 'type': 'bool'},
}
def __init__(
self,
*,
workers: Optional[int] = None,
minimum_worker_count: Optional[int] = None,
max_mum_worker_count: Optional[int] = None,
spark_version: Optional[str] = None,
node_type_id: Optional[str] = None,
spark_conf: Optional[Dict[str, str]] = None,
spark_env_vars: Optional[Dict[str, str]] = None,
cluster_log_conf_dbfs_path: Optional[str] = None,
dbfs_init_scripts: Optional[List["InitScriptInfoDto"]] = None,
instance_pool_id: Optional[str] = None,
timeout_seconds: Optional[int] = None,
notebook_task: Optional["NoteBookTaskDto"] = None,
spark_python_task: Optional["SparkPythonTaskDto"] = None,
spark_jar_task: Optional["SparkJarTaskDto"] = None,
spark_submit_task: Optional["SparkSubmitTaskDto"] = None,
jar_libraries: Optional[List[str]] = None,
egg_libraries: Optional[List[str]] = None,
whl_libraries: Optional[List[str]] = None,
pypi_libraries: Optional[List["PythonPyPiOrRCranLibraryDto"]] = None,
r_cran_libraries: Optional[List["PythonPyPiOrRCranLibraryDto"]] = None,
maven_libraries: Optional[List["MavenLibraryDto"]] = None,
libraries: Optional[List[Any]] = None,
linked_adb_workspace_metadata: Optional["LinkedADBWorkspaceMetadata"] = None,
databrick_resource_id: Optional[str] = None,
auto_scale: Optional[bool] = None,
**kwargs
):
"""
:keyword workers:
:paramtype workers: int
:keyword minimum_worker_count:
:paramtype minimum_worker_count: int
:keyword max_mum_worker_count:
:paramtype max_mum_worker_count: int
:keyword spark_version:
:paramtype spark_version: str
:keyword node_type_id:
:paramtype node_type_id: str
:keyword spark_conf: Dictionary of :code:`<string>`.
:paramtype spark_conf: dict[str, str]
:keyword spark_env_vars: Dictionary of :code:`<string>`.
:paramtype spark_env_vars: dict[str, str]
:keyword cluster_log_conf_dbfs_path:
:paramtype cluster_log_conf_dbfs_path: str
:keyword dbfs_init_scripts:
:paramtype dbfs_init_scripts: list[~flow.models.InitScriptInfoDto]
:keyword instance_pool_id:
:paramtype instance_pool_id: str
:keyword timeout_seconds:
:paramtype timeout_seconds: int
:keyword notebook_task:
:paramtype notebook_task: ~flow.models.NoteBookTaskDto
:keyword spark_python_task:
:paramtype spark_python_task: ~flow.models.SparkPythonTaskDto
:keyword spark_jar_task:
:paramtype spark_jar_task: ~flow.models.SparkJarTaskDto
:keyword spark_submit_task:
:paramtype spark_submit_task: ~flow.models.SparkSubmitTaskDto
:keyword jar_libraries:
:paramtype jar_libraries: list[str]
:keyword egg_libraries:
:paramtype egg_libraries: list[str]
:keyword whl_libraries:
:paramtype whl_libraries: list[str]
:keyword pypi_libraries:
:paramtype pypi_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:keyword r_cran_libraries:
:paramtype r_cran_libraries: list[~flow.models.PythonPyPiOrRCranLibraryDto]
:keyword maven_libraries:
:paramtype maven_libraries: list[~flow.models.MavenLibraryDto]
:keyword libraries:
:paramtype libraries: list[any]
:keyword linked_adb_workspace_metadata:
:paramtype linked_adb_workspace_metadata: ~flow.models.LinkedADBWorkspaceMetadata
:keyword databrick_resource_id:
:paramtype databrick_resource_id: str
:keyword auto_scale:
:paramtype auto_scale: bool
"""
super(DatabricksConfiguration, self).__init__(**kwargs)
self.workers = workers
self.minimum_worker_count = minimum_worker_count
self.max_mum_worker_count = max_mum_worker_count
self.spark_version = spark_version
self.node_type_id = node_type_id
self.spark_conf = spark_conf
self.spark_env_vars = spark_env_vars
self.cluster_log_conf_dbfs_path = cluster_log_conf_dbfs_path
self.dbfs_init_scripts = dbfs_init_scripts
self.instance_pool_id = instance_pool_id
self.timeout_seconds = timeout_seconds
self.notebook_task = notebook_task
self.spark_python_task = spark_python_task
self.spark_jar_task = spark_jar_task
self.spark_submit_task = spark_submit_task
self.jar_libraries = jar_libraries
self.egg_libraries = egg_libraries
self.whl_libraries = whl_libraries
self.pypi_libraries = pypi_libraries
self.r_cran_libraries = r_cran_libraries
self.maven_libraries = maven_libraries
self.libraries = libraries
self.linked_adb_workspace_metadata = linked_adb_workspace_metadata
self.databrick_resource_id = databrick_resource_id
self.auto_scale = auto_scale
class DatacacheConfiguration(msrest.serialization.Model):
"""DatacacheConfiguration.
:ivar datacache_id:
:vartype datacache_id: str
:ivar datacache_store:
:vartype datacache_store: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar mode: The only acceptable values to pass in are None and "Mount". The default value is
None.
:vartype mode: str
:ivar replica:
:vartype replica: int
:ivar failure_fallback:
:vartype failure_fallback: bool
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'datacache_id': {'key': 'datacacheId', 'type': 'str'},
'datacache_store': {'key': 'datacacheStore', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'replica': {'key': 'replica', 'type': 'int'},
'failure_fallback': {'key': 'failureFallback', 'type': 'bool'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
*,
datacache_id: Optional[str] = None,
datacache_store: Optional[str] = None,
dataset_id: Optional[str] = None,
mode: Optional[str] = None,
replica: Optional[int] = None,
failure_fallback: Optional[bool] = None,
path_on_compute: Optional[str] = None,
**kwargs
):
"""
:keyword datacache_id:
:paramtype datacache_id: str
:keyword datacache_store:
:paramtype datacache_store: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword mode: The only acceptable values to pass in are None and "Mount". The default value
is None.
:paramtype mode: str
:keyword replica:
:paramtype replica: int
:keyword failure_fallback:
:paramtype failure_fallback: bool
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(DatacacheConfiguration, self).__init__(**kwargs)
self.datacache_id = datacache_id
self.datacache_store = datacache_store
self.dataset_id = dataset_id
self.mode = mode
self.replica = replica
self.failure_fallback = failure_fallback
self.path_on_compute = path_on_compute
class DataInfo(msrest.serialization.Model):
"""DataInfo.
:ivar feed_name:
:vartype feed_name: str
:ivar id:
:vartype id: str
:ivar data_source_type: Possible values include: "None", "PipelineDataSource", "AmlDataset",
"GlobalDataset", "FeedModel", "FeedDataset", "AmlDataVersion", "AMLModelVersion".
:vartype data_source_type: str or ~flow.models.DataSourceType
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar modified_date:
:vartype modified_date: ~datetime.datetime
:ivar registered_by:
:vartype registered_by: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:vartype data_reference_type: str or ~flow.models.DataReferenceType
:ivar dataset_type:
:vartype dataset_type: str
:ivar saved_dataset_id:
:vartype saved_dataset_id: str
:ivar dataset_version_id:
:vartype dataset_version_id: str
:ivar is_visible:
:vartype is_visible: bool
:ivar is_registered:
:vartype is_registered: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, any]
:ivar connection_string:
:vartype connection_string: str
:ivar container_name:
:vartype container_name: str
:ivar data_storage_endpoint_uri:
:vartype data_storage_endpoint_uri: str
:ivar workspace_sai_token:
:vartype workspace_sai_token: str
:ivar aml_dataset_data_flow:
:vartype aml_dataset_data_flow: str
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar arm_id:
:vartype arm_id: str
:ivar asset_id:
:vartype asset_id: str
:ivar asset_uri:
:vartype asset_uri: str
:ivar asset_type:
:vartype asset_type: str
:ivar is_data_v2:
:vartype is_data_v2: bool
:ivar asset_scope_type: Possible values include: "Workspace", "Global", "Feed", "All".
:vartype asset_scope_type: str or ~flow.models.AssetScopeTypes
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar module_node_id:
:vartype module_node_id: str
:ivar output_port_name:
:vartype output_port_name: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'data_source_type': {'key': 'dataSourceType', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'modified_date': {'key': 'modifiedDate', 'type': 'iso-8601'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'data_reference_type': {'key': 'dataReferenceType', 'type': 'str'},
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'saved_dataset_id': {'key': 'savedDatasetId', 'type': 'str'},
'dataset_version_id': {'key': 'datasetVersionId', 'type': 'str'},
'is_visible': {'key': 'isVisible', 'type': 'bool'},
'is_registered': {'key': 'isRegistered', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{object}'},
'connection_string': {'key': 'connectionString', 'type': 'str'},
'container_name': {'key': 'containerName', 'type': 'str'},
'data_storage_endpoint_uri': {'key': 'dataStorageEndpointUri', 'type': 'str'},
'workspace_sai_token': {'key': 'workspaceSaiToken', 'type': 'str'},
'aml_dataset_data_flow': {'key': 'amlDatasetDataFlow', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'arm_id': {'key': 'armId', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'asset_type': {'key': 'assetType', 'type': 'str'},
'is_data_v2': {'key': 'isDataV2', 'type': 'bool'},
'asset_scope_type': {'key': 'assetScopeType', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'output_port_name': {'key': 'outputPortName', 'type': 'str'},
}
def __init__(
self,
*,
feed_name: Optional[str] = None,
id: Optional[str] = None,
data_source_type: Optional[Union[str, "DataSourceType"]] = None,
name: Optional[str] = None,
description: Optional[str] = None,
data_type_id: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
modified_date: Optional[datetime.datetime] = None,
registered_by: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
created_by_studio: Optional[bool] = None,
data_reference_type: Optional[Union[str, "DataReferenceType"]] = None,
dataset_type: Optional[str] = None,
saved_dataset_id: Optional[str] = None,
dataset_version_id: Optional[str] = None,
is_visible: Optional[bool] = None,
is_registered: Optional[bool] = None,
properties: Optional[Dict[str, Any]] = None,
connection_string: Optional[str] = None,
container_name: Optional[str] = None,
data_storage_endpoint_uri: Optional[str] = None,
workspace_sai_token: Optional[str] = None,
aml_dataset_data_flow: Optional[str] = None,
system_data: Optional["SystemData"] = None,
arm_id: Optional[str] = None,
asset_id: Optional[str] = None,
asset_uri: Optional[str] = None,
asset_type: Optional[str] = None,
is_data_v2: Optional[bool] = None,
asset_scope_type: Optional[Union[str, "AssetScopeTypes"]] = None,
pipeline_run_id: Optional[str] = None,
module_node_id: Optional[str] = None,
output_port_name: Optional[str] = None,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword id:
:paramtype id: str
:keyword data_source_type: Possible values include: "None", "PipelineDataSource", "AmlDataset",
"GlobalDataset", "FeedModel", "FeedDataset", "AmlDataVersion", "AMLModelVersion".
:paramtype data_source_type: str or ~flow.models.DataSourceType
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword modified_date:
:paramtype modified_date: ~datetime.datetime
:keyword registered_by:
:paramtype registered_by: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype data_reference_type: str or ~flow.models.DataReferenceType
:keyword dataset_type:
:paramtype dataset_type: str
:keyword saved_dataset_id:
:paramtype saved_dataset_id: str
:keyword dataset_version_id:
:paramtype dataset_version_id: str
:keyword is_visible:
:paramtype is_visible: bool
:keyword is_registered:
:paramtype is_registered: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, any]
:keyword connection_string:
:paramtype connection_string: str
:keyword container_name:
:paramtype container_name: str
:keyword data_storage_endpoint_uri:
:paramtype data_storage_endpoint_uri: str
:keyword workspace_sai_token:
:paramtype workspace_sai_token: str
:keyword aml_dataset_data_flow:
:paramtype aml_dataset_data_flow: str
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword arm_id:
:paramtype arm_id: str
:keyword asset_id:
:paramtype asset_id: str
:keyword asset_uri:
:paramtype asset_uri: str
:keyword asset_type:
:paramtype asset_type: str
:keyword is_data_v2:
:paramtype is_data_v2: bool
:keyword asset_scope_type: Possible values include: "Workspace", "Global", "Feed", "All".
:paramtype asset_scope_type: str or ~flow.models.AssetScopeTypes
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword module_node_id:
:paramtype module_node_id: str
:keyword output_port_name:
:paramtype output_port_name: str
"""
super(DataInfo, self).__init__(**kwargs)
self.feed_name = feed_name
self.id = id
self.data_source_type = data_source_type
self.name = name
self.description = description
self.data_type_id = data_type_id
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
self.created_date = created_date
self.modified_date = modified_date
self.registered_by = registered_by
self.tags = tags
self.created_by_studio = created_by_studio
self.data_reference_type = data_reference_type
self.dataset_type = dataset_type
self.saved_dataset_id = saved_dataset_id
self.dataset_version_id = dataset_version_id
self.is_visible = is_visible
self.is_registered = is_registered
self.properties = properties
self.connection_string = connection_string
self.container_name = container_name
self.data_storage_endpoint_uri = data_storage_endpoint_uri
self.workspace_sai_token = workspace_sai_token
self.aml_dataset_data_flow = aml_dataset_data_flow
self.system_data = system_data
self.arm_id = arm_id
self.asset_id = asset_id
self.asset_uri = asset_uri
self.asset_type = asset_type
self.is_data_v2 = is_data_v2
self.asset_scope_type = asset_scope_type
self.pipeline_run_id = pipeline_run_id
self.module_node_id = module_node_id
self.output_port_name = output_port_name
class DataLocation(msrest.serialization.Model):
"""DataLocation.
:ivar storage_type: Possible values include: "None", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:vartype storage_type: str or ~flow.models.DataLocationStorageType
:ivar storage_id:
:vartype storage_id: str
:ivar uri:
:vartype uri: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference:
:vartype data_reference: ~flow.models.DataReference
:ivar aml_dataset:
:vartype aml_dataset: ~flow.models.AmlDataset
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AssetDefinition
"""
_attribute_map = {
'storage_type': {'key': 'storageType', 'type': 'str'},
'storage_id': {'key': 'storageId', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference': {'key': 'dataReference', 'type': 'DataReference'},
'aml_dataset': {'key': 'amlDataset', 'type': 'AmlDataset'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AssetDefinition'},
}
def __init__(
self,
*,
storage_type: Optional[Union[str, "DataLocationStorageType"]] = None,
storage_id: Optional[str] = None,
uri: Optional[str] = None,
data_store_name: Optional[str] = None,
data_reference: Optional["DataReference"] = None,
aml_dataset: Optional["AmlDataset"] = None,
asset_definition: Optional["AssetDefinition"] = None,
**kwargs
):
"""
:keyword storage_type: Possible values include: "None", "AzureBlob", "Artifact", "Snapshot",
"SavedAmlDataset", "Asset".
:paramtype storage_type: str or ~flow.models.DataLocationStorageType
:keyword storage_id:
:paramtype storage_id: str
:keyword uri:
:paramtype uri: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference:
:paramtype data_reference: ~flow.models.DataReference
:keyword aml_dataset:
:paramtype aml_dataset: ~flow.models.AmlDataset
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AssetDefinition
"""
super(DataLocation, self).__init__(**kwargs)
self.storage_type = storage_type
self.storage_id = storage_id
self.uri = uri
self.data_store_name = data_store_name
self.data_reference = data_reference
self.aml_dataset = aml_dataset
self.asset_definition = asset_definition
class DataPath(msrest.serialization.Model):
"""DataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar sql_data_path:
:vartype sql_data_path: ~flow.models.SqlDataPath
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'sql_data_path': {'key': 'sqlDataPath', 'type': 'SqlDataPath'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
sql_data_path: Optional["SqlDataPath"] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword sql_data_path:
:paramtype sql_data_path: ~flow.models.SqlDataPath
"""
super(DataPath, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.relative_path = relative_path
self.sql_data_path = sql_data_path
class DataPathParameter(msrest.serialization.Model):
"""DataPathParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.LegacyDataPath
:ivar is_optional:
:vartype is_optional: bool
:ivar data_type_id:
:vartype data_type_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'LegacyDataPath'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
default_value: Optional["LegacyDataPath"] = None,
is_optional: Optional[bool] = None,
data_type_id: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.LegacyDataPath
:keyword is_optional:
:paramtype is_optional: bool
:keyword data_type_id:
:paramtype data_type_id: str
"""
super(DataPathParameter, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.default_value = default_value
self.is_optional = is_optional
self.data_type_id = data_type_id
class DataPortDto(msrest.serialization.Model):
"""DataPortDto.
:ivar data_port_type: Possible values include: "Input", "Output".
:vartype data_port_type: str or ~flow.models.DataPortType
:ivar data_port_name:
:vartype data_port_name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_intellectual_property_access_mode: Possible values include: "ReadOnly",
"ReadWrite".
:vartype data_store_intellectual_property_access_mode: str or
~flow.models.IntellectualPropertyAccessMode
:ivar data_store_intellectual_property_publisher:
:vartype data_store_intellectual_property_publisher: str
"""
_attribute_map = {
'data_port_type': {'key': 'dataPortType', 'type': 'str'},
'data_port_name': {'key': 'dataPortName', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_intellectual_property_access_mode': {'key': 'dataStoreIntellectualPropertyAccessMode', 'type': 'str'},
'data_store_intellectual_property_publisher': {'key': 'dataStoreIntellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
*,
data_port_type: Optional[Union[str, "DataPortType"]] = None,
data_port_name: Optional[str] = None,
data_store_name: Optional[str] = None,
data_store_intellectual_property_access_mode: Optional[Union[str, "IntellectualPropertyAccessMode"]] = None,
data_store_intellectual_property_publisher: Optional[str] = None,
**kwargs
):
"""
:keyword data_port_type: Possible values include: "Input", "Output".
:paramtype data_port_type: str or ~flow.models.DataPortType
:keyword data_port_name:
:paramtype data_port_name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_intellectual_property_access_mode: Possible values include: "ReadOnly",
"ReadWrite".
:paramtype data_store_intellectual_property_access_mode: str or
~flow.models.IntellectualPropertyAccessMode
:keyword data_store_intellectual_property_publisher:
:paramtype data_store_intellectual_property_publisher: str
"""
super(DataPortDto, self).__init__(**kwargs)
self.data_port_type = data_port_type
self.data_port_name = data_port_name
self.data_store_name = data_store_name
self.data_store_intellectual_property_access_mode = data_store_intellectual_property_access_mode
self.data_store_intellectual_property_publisher = data_store_intellectual_property_publisher
class DataReference(msrest.serialization.Model):
"""DataReference.
:ivar type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS", "AzureMySqlDatabase",
"Custom", "Hdfs".
:vartype type: str or ~flow.models.DataReferenceType
:ivar azure_blob_reference:
:vartype azure_blob_reference: ~flow.models.AzureBlobReference
:ivar azure_data_lake_reference:
:vartype azure_data_lake_reference: ~flow.models.AzureDataLakeReference
:ivar azure_files_reference:
:vartype azure_files_reference: ~flow.models.AzureFilesReference
:ivar azure_sql_database_reference:
:vartype azure_sql_database_reference: ~flow.models.AzureDatabaseReference
:ivar azure_postgres_database_reference:
:vartype azure_postgres_database_reference: ~flow.models.AzureDatabaseReference
:ivar azure_data_lake_gen2_reference:
:vartype azure_data_lake_gen2_reference: ~flow.models.AzureDataLakeGen2Reference
:ivar dbfs_reference:
:vartype dbfs_reference: ~flow.models.DBFSReference
:ivar azure_my_sql_database_reference:
:vartype azure_my_sql_database_reference: ~flow.models.AzureDatabaseReference
:ivar custom_reference:
:vartype custom_reference: ~flow.models.CustomReference
:ivar hdfs_reference:
:vartype hdfs_reference: ~flow.models.HdfsReference
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'azure_blob_reference': {'key': 'azureBlobReference', 'type': 'AzureBlobReference'},
'azure_data_lake_reference': {'key': 'azureDataLakeReference', 'type': 'AzureDataLakeReference'},
'azure_files_reference': {'key': 'azureFilesReference', 'type': 'AzureFilesReference'},
'azure_sql_database_reference': {'key': 'azureSqlDatabaseReference', 'type': 'AzureDatabaseReference'},
'azure_postgres_database_reference': {'key': 'azurePostgresDatabaseReference', 'type': 'AzureDatabaseReference'},
'azure_data_lake_gen2_reference': {'key': 'azureDataLakeGen2Reference', 'type': 'AzureDataLakeGen2Reference'},
'dbfs_reference': {'key': 'dbfsReference', 'type': 'DBFSReference'},
'azure_my_sql_database_reference': {'key': 'azureMySqlDatabaseReference', 'type': 'AzureDatabaseReference'},
'custom_reference': {'key': 'customReference', 'type': 'CustomReference'},
'hdfs_reference': {'key': 'hdfsReference', 'type': 'HdfsReference'},
}
def __init__(
self,
*,
type: Optional[Union[str, "DataReferenceType"]] = None,
azure_blob_reference: Optional["AzureBlobReference"] = None,
azure_data_lake_reference: Optional["AzureDataLakeReference"] = None,
azure_files_reference: Optional["AzureFilesReference"] = None,
azure_sql_database_reference: Optional["AzureDatabaseReference"] = None,
azure_postgres_database_reference: Optional["AzureDatabaseReference"] = None,
azure_data_lake_gen2_reference: Optional["AzureDataLakeGen2Reference"] = None,
dbfs_reference: Optional["DBFSReference"] = None,
azure_my_sql_database_reference: Optional["AzureDatabaseReference"] = None,
custom_reference: Optional["CustomReference"] = None,
hdfs_reference: Optional["HdfsReference"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "None", "AzureBlob", "AzureDataLake", "AzureFiles",
"AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS", "AzureMySqlDatabase",
"Custom", "Hdfs".
:paramtype type: str or ~flow.models.DataReferenceType
:keyword azure_blob_reference:
:paramtype azure_blob_reference: ~flow.models.AzureBlobReference
:keyword azure_data_lake_reference:
:paramtype azure_data_lake_reference: ~flow.models.AzureDataLakeReference
:keyword azure_files_reference:
:paramtype azure_files_reference: ~flow.models.AzureFilesReference
:keyword azure_sql_database_reference:
:paramtype azure_sql_database_reference: ~flow.models.AzureDatabaseReference
:keyword azure_postgres_database_reference:
:paramtype azure_postgres_database_reference: ~flow.models.AzureDatabaseReference
:keyword azure_data_lake_gen2_reference:
:paramtype azure_data_lake_gen2_reference: ~flow.models.AzureDataLakeGen2Reference
:keyword dbfs_reference:
:paramtype dbfs_reference: ~flow.models.DBFSReference
:keyword azure_my_sql_database_reference:
:paramtype azure_my_sql_database_reference: ~flow.models.AzureDatabaseReference
:keyword custom_reference:
:paramtype custom_reference: ~flow.models.CustomReference
:keyword hdfs_reference:
:paramtype hdfs_reference: ~flow.models.HdfsReference
"""
super(DataReference, self).__init__(**kwargs)
self.type = type
self.azure_blob_reference = azure_blob_reference
self.azure_data_lake_reference = azure_data_lake_reference
self.azure_files_reference = azure_files_reference
self.azure_sql_database_reference = azure_sql_database_reference
self.azure_postgres_database_reference = azure_postgres_database_reference
self.azure_data_lake_gen2_reference = azure_data_lake_gen2_reference
self.dbfs_reference = dbfs_reference
self.azure_my_sql_database_reference = azure_my_sql_database_reference
self.custom_reference = custom_reference
self.hdfs_reference = hdfs_reference
class DataReferenceConfiguration(msrest.serialization.Model):
"""DataReferenceConfiguration.
:ivar data_store_name:
:vartype data_store_name: str
:ivar mode: Possible values include: "Mount", "Download", "Upload".
:vartype mode: str or ~flow.models.DataStoreMode
:ivar path_on_data_store:
:vartype path_on_data_store: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'path_on_data_store': {'key': 'pathOnDataStore', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
mode: Optional[Union[str, "DataStoreMode"]] = None,
path_on_data_store: Optional[str] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword mode: Possible values include: "Mount", "Download", "Upload".
:paramtype mode: str or ~flow.models.DataStoreMode
:keyword path_on_data_store:
:paramtype path_on_data_store: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
"""
super(DataReferenceConfiguration, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.mode = mode
self.path_on_data_store = path_on_data_store
self.path_on_compute = path_on_compute
self.overwrite = overwrite
class DataSetDefinition(msrest.serialization.Model):
"""DataSetDefinition.
:ivar data_type_short_name:
:vartype data_type_short_name: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar value:
:vartype value: ~flow.models.DataSetDefinitionValue
"""
_attribute_map = {
'data_type_short_name': {'key': 'dataTypeShortName', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'value': {'key': 'value', 'type': 'DataSetDefinitionValue'},
}
def __init__(
self,
*,
data_type_short_name: Optional[str] = None,
parameter_name: Optional[str] = None,
value: Optional["DataSetDefinitionValue"] = None,
**kwargs
):
"""
:keyword data_type_short_name:
:paramtype data_type_short_name: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword value:
:paramtype value: ~flow.models.DataSetDefinitionValue
"""
super(DataSetDefinition, self).__init__(**kwargs)
self.data_type_short_name = data_type_short_name
self.parameter_name = parameter_name
self.value = value
class DataSetDefinitionValue(msrest.serialization.Model):
"""DataSetDefinitionValue.
:ivar literal_value:
:vartype literal_value: ~flow.models.DataPath
:ivar data_set_reference:
:vartype data_set_reference: ~flow.models.RegisteredDataSetReference
:ivar saved_data_set_reference:
:vartype saved_data_set_reference: ~flow.models.SavedDataSetReference
:ivar asset_definition:
:vartype asset_definition: ~flow.models.AssetDefinition
"""
_attribute_map = {
'literal_value': {'key': 'literalValue', 'type': 'DataPath'},
'data_set_reference': {'key': 'dataSetReference', 'type': 'RegisteredDataSetReference'},
'saved_data_set_reference': {'key': 'savedDataSetReference', 'type': 'SavedDataSetReference'},
'asset_definition': {'key': 'assetDefinition', 'type': 'AssetDefinition'},
}
def __init__(
self,
*,
literal_value: Optional["DataPath"] = None,
data_set_reference: Optional["RegisteredDataSetReference"] = None,
saved_data_set_reference: Optional["SavedDataSetReference"] = None,
asset_definition: Optional["AssetDefinition"] = None,
**kwargs
):
"""
:keyword literal_value:
:paramtype literal_value: ~flow.models.DataPath
:keyword data_set_reference:
:paramtype data_set_reference: ~flow.models.RegisteredDataSetReference
:keyword saved_data_set_reference:
:paramtype saved_data_set_reference: ~flow.models.SavedDataSetReference
:keyword asset_definition:
:paramtype asset_definition: ~flow.models.AssetDefinition
"""
super(DataSetDefinitionValue, self).__init__(**kwargs)
self.literal_value = literal_value
self.data_set_reference = data_set_reference
self.saved_data_set_reference = saved_data_set_reference
self.asset_definition = asset_definition
class DatasetIdentifier(msrest.serialization.Model):
"""DatasetIdentifier.
:ivar saved_id:
:vartype saved_id: str
:ivar registered_id:
:vartype registered_id: str
:ivar registered_version:
:vartype registered_version: str
"""
_attribute_map = {
'saved_id': {'key': 'savedId', 'type': 'str'},
'registered_id': {'key': 'registeredId', 'type': 'str'},
'registered_version': {'key': 'registeredVersion', 'type': 'str'},
}
def __init__(
self,
*,
saved_id: Optional[str] = None,
registered_id: Optional[str] = None,
registered_version: Optional[str] = None,
**kwargs
):
"""
:keyword saved_id:
:paramtype saved_id: str
:keyword registered_id:
:paramtype registered_id: str
:keyword registered_version:
:paramtype registered_version: str
"""
super(DatasetIdentifier, self).__init__(**kwargs)
self.saved_id = saved_id
self.registered_id = registered_id
self.registered_version = registered_version
class DatasetInputDetails(msrest.serialization.Model):
"""DatasetInputDetails.
:ivar input_name:
:vartype input_name: str
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DatasetDeliveryMechanism
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'input_name': {'key': 'inputName', 'type': 'str'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
*,
input_name: Optional[str] = None,
mechanism: Optional[Union[str, "DatasetDeliveryMechanism"]] = None,
path_on_compute: Optional[str] = None,
**kwargs
):
"""
:keyword input_name:
:paramtype input_name: str
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DatasetDeliveryMechanism
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(DatasetInputDetails, self).__init__(**kwargs)
self.input_name = input_name
self.mechanism = mechanism
self.path_on_compute = path_on_compute
class DatasetLineage(msrest.serialization.Model):
"""DatasetLineage.
:ivar identifier:
:vartype identifier: ~flow.models.DatasetIdentifier
:ivar consumption_type: Possible values include: "RunInput", "Reference".
:vartype consumption_type: str or ~flow.models.DatasetConsumptionType
:ivar input_details:
:vartype input_details: ~flow.models.DatasetInputDetails
"""
_attribute_map = {
'identifier': {'key': 'identifier', 'type': 'DatasetIdentifier'},
'consumption_type': {'key': 'consumptionType', 'type': 'str'},
'input_details': {'key': 'inputDetails', 'type': 'DatasetInputDetails'},
}
def __init__(
self,
*,
identifier: Optional["DatasetIdentifier"] = None,
consumption_type: Optional[Union[str, "DatasetConsumptionType"]] = None,
input_details: Optional["DatasetInputDetails"] = None,
**kwargs
):
"""
:keyword identifier:
:paramtype identifier: ~flow.models.DatasetIdentifier
:keyword consumption_type: Possible values include: "RunInput", "Reference".
:paramtype consumption_type: str or ~flow.models.DatasetConsumptionType
:keyword input_details:
:paramtype input_details: ~flow.models.DatasetInputDetails
"""
super(DatasetLineage, self).__init__(**kwargs)
self.identifier = identifier
self.consumption_type = consumption_type
self.input_details = input_details
class DatasetOutput(msrest.serialization.Model):
"""DatasetOutput.
:ivar dataset_type: Possible values include: "File", "Tabular".
:vartype dataset_type: str or ~flow.models.DatasetType
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.DatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.DatasetOutputOptions
"""
_attribute_map = {
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'DatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'DatasetOutputOptions'},
}
def __init__(
self,
*,
dataset_type: Optional[Union[str, "DatasetType"]] = None,
dataset_registration: Optional["DatasetRegistration"] = None,
dataset_output_options: Optional["DatasetOutputOptions"] = None,
**kwargs
):
"""
:keyword dataset_type: Possible values include: "File", "Tabular".
:paramtype dataset_type: str or ~flow.models.DatasetType
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.DatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.DatasetOutputOptions
"""
super(DatasetOutput, self).__init__(**kwargs)
self.dataset_type = dataset_type
self.dataset_registration = dataset_registration
self.dataset_output_options = dataset_output_options
class DatasetOutputDetails(msrest.serialization.Model):
"""DatasetOutputDetails.
:ivar output_name:
:vartype output_name: str
"""
_attribute_map = {
'output_name': {'key': 'outputName', 'type': 'str'},
}
def __init__(
self,
*,
output_name: Optional[str] = None,
**kwargs
):
"""
:keyword output_name:
:paramtype output_name: str
"""
super(DatasetOutputDetails, self).__init__(**kwargs)
self.output_name = output_name
class DatasetOutputOptions(msrest.serialization.Model):
"""DatasetOutputOptions.
:ivar source_globs:
:vartype source_globs: ~flow.models.GlobsOptions
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_datastore_parameter_assignment:
:vartype path_on_datastore_parameter_assignment: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'source_globs': {'key': 'sourceGlobs', 'type': 'GlobsOptions'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_datastore_parameter_assignment': {'key': 'PathOnDatastoreParameterAssignment', 'type': 'ParameterAssignment'},
}
def __init__(
self,
*,
source_globs: Optional["GlobsOptions"] = None,
path_on_datastore: Optional[str] = None,
path_on_datastore_parameter_assignment: Optional["ParameterAssignment"] = None,
**kwargs
):
"""
:keyword source_globs:
:paramtype source_globs: ~flow.models.GlobsOptions
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_datastore_parameter_assignment:
:paramtype path_on_datastore_parameter_assignment: ~flow.models.ParameterAssignment
"""
super(DatasetOutputOptions, self).__init__(**kwargs)
self.source_globs = source_globs
self.path_on_datastore = path_on_datastore
self.path_on_datastore_parameter_assignment = path_on_datastore_parameter_assignment
class DataSetPathParameter(msrest.serialization.Model):
"""DataSetPathParameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: ~flow.models.DataSetDefinitionValue
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'DataSetDefinitionValue'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
default_value: Optional["DataSetDefinitionValue"] = None,
is_optional: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: ~flow.models.DataSetDefinitionValue
:keyword is_optional:
:paramtype is_optional: bool
"""
super(DataSetPathParameter, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.default_value = default_value
self.is_optional = is_optional
class DatasetRegistration(msrest.serialization.Model):
"""DatasetRegistration.
:ivar name:
:vartype name: str
:ivar create_new_version:
:vartype create_new_version: bool
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'create_new_version': {'key': 'createNewVersion', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
create_new_version: Optional[bool] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword create_new_version:
:paramtype create_new_version: bool
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(DatasetRegistration, self).__init__(**kwargs)
self.name = name
self.create_new_version = create_new_version
self.description = description
self.tags = tags
self.additional_transformations = additional_transformations
class DatasetRegistrationOptions(msrest.serialization.Model):
"""DatasetRegistrationOptions.
:ivar additional_transformation:
:vartype additional_transformation: str
"""
_attribute_map = {
'additional_transformation': {'key': 'additionalTransformation', 'type': 'str'},
}
def __init__(
self,
*,
additional_transformation: Optional[str] = None,
**kwargs
):
"""
:keyword additional_transformation:
:paramtype additional_transformation: str
"""
super(DatasetRegistrationOptions, self).__init__(**kwargs)
self.additional_transformation = additional_transformation
class DataSettings(msrest.serialization.Model):
"""DataSettings.
:ivar target_column_name:
:vartype target_column_name: str
:ivar weight_column_name:
:vartype weight_column_name: str
:ivar positive_label:
:vartype positive_label: str
:ivar validation_data:
:vartype validation_data: ~flow.models.ValidationDataSettings
:ivar test_data:
:vartype test_data: ~flow.models.TestDataSettings
"""
_attribute_map = {
'target_column_name': {'key': 'targetColumnName', 'type': 'str'},
'weight_column_name': {'key': 'weightColumnName', 'type': 'str'},
'positive_label': {'key': 'positiveLabel', 'type': 'str'},
'validation_data': {'key': 'validationData', 'type': 'ValidationDataSettings'},
'test_data': {'key': 'testData', 'type': 'TestDataSettings'},
}
def __init__(
self,
*,
target_column_name: Optional[str] = None,
weight_column_name: Optional[str] = None,
positive_label: Optional[str] = None,
validation_data: Optional["ValidationDataSettings"] = None,
test_data: Optional["TestDataSettings"] = None,
**kwargs
):
"""
:keyword target_column_name:
:paramtype target_column_name: str
:keyword weight_column_name:
:paramtype weight_column_name: str
:keyword positive_label:
:paramtype positive_label: str
:keyword validation_data:
:paramtype validation_data: ~flow.models.ValidationDataSettings
:keyword test_data:
:paramtype test_data: ~flow.models.TestDataSettings
"""
super(DataSettings, self).__init__(**kwargs)
self.target_column_name = target_column_name
self.weight_column_name = weight_column_name
self.positive_label = positive_label
self.validation_data = validation_data
self.test_data = test_data
class DatastoreSetting(msrest.serialization.Model):
"""DatastoreSetting.
:ivar data_store_name:
:vartype data_store_name: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
"""
super(DatastoreSetting, self).__init__(**kwargs)
self.data_store_name = data_store_name
class DataTransferCloudConfiguration(msrest.serialization.Model):
"""DataTransferCloudConfiguration.
:ivar allow_overwrite:
:vartype allow_overwrite: bool
"""
_attribute_map = {
'allow_overwrite': {'key': 'AllowOverwrite', 'type': 'bool'},
}
def __init__(
self,
*,
allow_overwrite: Optional[bool] = None,
**kwargs
):
"""
:keyword allow_overwrite:
:paramtype allow_overwrite: bool
"""
super(DataTransferCloudConfiguration, self).__init__(**kwargs)
self.allow_overwrite = allow_overwrite
class DataTransferSink(msrest.serialization.Model):
"""DataTransferSink.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.DataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.FileSystem
:ivar database_sink:
:vartype database_sink: ~flow.models.DatabaseSink
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'FileSystem'},
'database_sink': {'key': 'databaseSink', 'type': 'DatabaseSink'},
}
def __init__(
self,
*,
type: Optional[Union[str, "DataTransferStorageType"]] = None,
file_system: Optional["FileSystem"] = None,
database_sink: Optional["DatabaseSink"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.DataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.FileSystem
:keyword database_sink:
:paramtype database_sink: ~flow.models.DatabaseSink
"""
super(DataTransferSink, self).__init__(**kwargs)
self.type = type
self.file_system = file_system
self.database_sink = database_sink
class DataTransferSource(msrest.serialization.Model):
"""DataTransferSource.
:ivar type: Possible values include: "DataBase", "FileSystem".
:vartype type: str or ~flow.models.DataTransferStorageType
:ivar file_system:
:vartype file_system: ~flow.models.FileSystem
:ivar database_source:
:vartype database_source: ~flow.models.DatabaseSource
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'file_system': {'key': 'fileSystem', 'type': 'FileSystem'},
'database_source': {'key': 'databaseSource', 'type': 'DatabaseSource'},
}
def __init__(
self,
*,
type: Optional[Union[str, "DataTransferStorageType"]] = None,
file_system: Optional["FileSystem"] = None,
database_source: Optional["DatabaseSource"] = None,
**kwargs
):
"""
:keyword type: Possible values include: "DataBase", "FileSystem".
:paramtype type: str or ~flow.models.DataTransferStorageType
:keyword file_system:
:paramtype file_system: ~flow.models.FileSystem
:keyword database_source:
:paramtype database_source: ~flow.models.DatabaseSource
"""
super(DataTransferSource, self).__init__(**kwargs)
self.type = type
self.file_system = file_system
self.database_source = database_source
class DataTransferV2CloudSetting(msrest.serialization.Model):
"""DataTransferV2CloudSetting.
:ivar task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:vartype task_type: str or ~flow.models.DataTransferTaskType
:ivar compute_name:
:vartype compute_name: str
:ivar copy_data_task:
:vartype copy_data_task: ~flow.models.CopyDataTask
:ivar import_data_task:
:vartype import_data_task: ~flow.models.ImportDataTask
:ivar export_data_task:
:vartype export_data_task: ~flow.models.ExportDataTask
:ivar data_transfer_sources: This is a dictionary.
:vartype data_transfer_sources: dict[str, ~flow.models.DataTransferSource]
:ivar data_transfer_sinks: This is a dictionary.
:vartype data_transfer_sinks: dict[str, ~flow.models.DataTransferSink]
:ivar data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:vartype data_copy_mode: str or ~flow.models.DataCopyMode
"""
_attribute_map = {
'task_type': {'key': 'taskType', 'type': 'str'},
'compute_name': {'key': 'ComputeName', 'type': 'str'},
'copy_data_task': {'key': 'CopyDataTask', 'type': 'CopyDataTask'},
'import_data_task': {'key': 'ImportDataTask', 'type': 'ImportDataTask'},
'export_data_task': {'key': 'ExportDataTask', 'type': 'ExportDataTask'},
'data_transfer_sources': {'key': 'DataTransferSources', 'type': '{DataTransferSource}'},
'data_transfer_sinks': {'key': 'DataTransferSinks', 'type': '{DataTransferSink}'},
'data_copy_mode': {'key': 'DataCopyMode', 'type': 'str'},
}
def __init__(
self,
*,
task_type: Optional[Union[str, "DataTransferTaskType"]] = None,
compute_name: Optional[str] = None,
copy_data_task: Optional["CopyDataTask"] = None,
import_data_task: Optional["ImportDataTask"] = None,
export_data_task: Optional["ExportDataTask"] = None,
data_transfer_sources: Optional[Dict[str, "DataTransferSource"]] = None,
data_transfer_sinks: Optional[Dict[str, "DataTransferSink"]] = None,
data_copy_mode: Optional[Union[str, "DataCopyMode"]] = None,
**kwargs
):
"""
:keyword task_type: Possible values include: "ImportData", "ExportData", "CopyData".
:paramtype task_type: str or ~flow.models.DataTransferTaskType
:keyword compute_name:
:paramtype compute_name: str
:keyword copy_data_task:
:paramtype copy_data_task: ~flow.models.CopyDataTask
:keyword import_data_task:
:paramtype import_data_task: ~flow.models.ImportDataTask
:keyword export_data_task:
:paramtype export_data_task: ~flow.models.ExportDataTask
:keyword data_transfer_sources: This is a dictionary.
:paramtype data_transfer_sources: dict[str, ~flow.models.DataTransferSource]
:keyword data_transfer_sinks: This is a dictionary.
:paramtype data_transfer_sinks: dict[str, ~flow.models.DataTransferSink]
:keyword data_copy_mode: Possible values include: "MergeWithOverwrite", "FailIfConflict".
:paramtype data_copy_mode: str or ~flow.models.DataCopyMode
"""
super(DataTransferV2CloudSetting, self).__init__(**kwargs)
self.task_type = task_type
self.compute_name = compute_name
self.copy_data_task = copy_data_task
self.import_data_task = import_data_task
self.export_data_task = export_data_task
self.data_transfer_sources = data_transfer_sources
self.data_transfer_sinks = data_transfer_sinks
self.data_copy_mode = data_copy_mode
class DataTypeCreationInfo(msrest.serialization.Model):
"""DataTypeCreationInfo.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar is_directory:
:vartype is_directory: bool
:ivar file_extension:
:vartype file_extension: str
:ivar parent_data_type_ids:
:vartype parent_data_type_ids: list[str]
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'is_directory': {'key': 'isDirectory', 'type': 'bool'},
'file_extension': {'key': 'fileExtension', 'type': 'str'},
'parent_data_type_ids': {'key': 'parentDataTypeIds', 'type': '[str]'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
is_directory: Optional[bool] = None,
file_extension: Optional[str] = None,
parent_data_type_ids: Optional[List[str]] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword is_directory:
:paramtype is_directory: bool
:keyword file_extension:
:paramtype file_extension: str
:keyword parent_data_type_ids:
:paramtype parent_data_type_ids: list[str]
"""
super(DataTypeCreationInfo, self).__init__(**kwargs)
self.id = id
self.name = name
self.description = description
self.is_directory = is_directory
self.file_extension = file_extension
self.parent_data_type_ids = parent_data_type_ids
class DBFSReference(msrest.serialization.Model):
"""DBFSReference.
:ivar relative_path:
:vartype relative_path: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
"""
_attribute_map = {
'relative_path': {'key': 'relativePath', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
}
def __init__(
self,
*,
relative_path: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
**kwargs
):
"""
:keyword relative_path:
:paramtype relative_path: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
"""
super(DBFSReference, self).__init__(**kwargs)
self.relative_path = relative_path
self.aml_data_store_name = aml_data_store_name
class DbfsStorageInfoDto(msrest.serialization.Model):
"""DbfsStorageInfoDto.
:ivar destination:
:vartype destination: str
"""
_attribute_map = {
'destination': {'key': 'destination', 'type': 'str'},
}
def __init__(
self,
*,
destination: Optional[str] = None,
**kwargs
):
"""
:keyword destination:
:paramtype destination: str
"""
super(DbfsStorageInfoDto, self).__init__(**kwargs)
self.destination = destination
class DebugInfoResponse(msrest.serialization.Model):
"""Internal debugging information not intended for external clients.
:ivar type: The type.
:vartype type: str
:ivar message: The message.
:vartype message: str
:ivar stack_trace: The stack trace.
:vartype stack_trace: str
:ivar inner_exception: Internal debugging information not intended for external clients.
:vartype inner_exception: ~flow.models.DebugInfoResponse
:ivar data: This is a dictionary.
:vartype data: dict[str, any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'stack_trace': {'key': 'stackTrace', 'type': 'str'},
'inner_exception': {'key': 'innerException', 'type': 'DebugInfoResponse'},
'data': {'key': 'data', 'type': '{object}'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
}
def __init__(
self,
*,
type: Optional[str] = None,
message: Optional[str] = None,
stack_trace: Optional[str] = None,
inner_exception: Optional["DebugInfoResponse"] = None,
data: Optional[Dict[str, Any]] = None,
error_response: Optional["ErrorResponse"] = None,
**kwargs
):
"""
:keyword type: The type.
:paramtype type: str
:keyword message: The message.
:paramtype message: str
:keyword stack_trace: The stack trace.
:paramtype stack_trace: str
:keyword inner_exception: Internal debugging information not intended for external clients.
:paramtype inner_exception: ~flow.models.DebugInfoResponse
:keyword data: This is a dictionary.
:paramtype data: dict[str, any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
"""
super(DebugInfoResponse, self).__init__(**kwargs)
self.type = type
self.message = message
self.stack_trace = stack_trace
self.inner_exception = inner_exception
self.data = data
self.error_response = error_response
class DeployFlowRequest(msrest.serialization.Model):
"""DeployFlowRequest.
:ivar source_resource_id:
:vartype source_resource_id: str
:ivar source_flow_run_id:
:vartype source_flow_run_id: str
:ivar source_flow_id:
:vartype source_flow_id: str
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar output_names_included_in_endpoint_response:
:vartype output_names_included_in_endpoint_response: list[str]
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar endpoint_description:
:vartype endpoint_description: str
:ivar auth_mode: Possible values include: "AMLToken", "Key", "AADToken".
:vartype auth_mode: str or ~flow.models.EndpointAuthMode
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar endpoint_tags: This is a dictionary.
:vartype endpoint_tags: dict[str, str]
:ivar enable_public_network_access:
:vartype enable_public_network_access: bool
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar use_workspace_connection:
:vartype use_workspace_connection: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar environment:
:vartype environment: str
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar deployment_tags: This is a dictionary.
:vartype deployment_tags: dict[str, str]
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar enable_model_data_collector:
:vartype enable_model_data_collector: bool
:ivar skip_update_traffic_to_full:
:vartype skip_update_traffic_to_full: bool
:ivar enable_streaming_response:
:vartype enable_streaming_response: bool
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar auto_grant_connection_permission:
:vartype auto_grant_connection_permission: bool
"""
_attribute_map = {
'source_resource_id': {'key': 'sourceResourceId', 'type': 'str'},
'source_flow_run_id': {'key': 'sourceFlowRunId', 'type': 'str'},
'source_flow_id': {'key': 'sourceFlowId', 'type': 'str'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'output_names_included_in_endpoint_response': {'key': 'outputNamesIncludedInEndpointResponse', 'type': '[str]'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'endpoint_description': {'key': 'endpointDescription', 'type': 'str'},
'auth_mode': {'key': 'authMode', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'endpoint_tags': {'key': 'endpointTags', 'type': '{str}'},
'enable_public_network_access': {'key': 'enablePublicNetworkAccess', 'type': 'bool'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'use_workspace_connection': {'key': 'useWorkspaceConnection', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'deployment_tags': {'key': 'deploymentTags', 'type': '{str}'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'enable_model_data_collector': {'key': 'enableModelDataCollector', 'type': 'bool'},
'skip_update_traffic_to_full': {'key': 'skipUpdateTrafficToFull', 'type': 'bool'},
'enable_streaming_response': {'key': 'enableStreamingResponse', 'type': 'bool'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'auto_grant_connection_permission': {'key': 'autoGrantConnectionPermission', 'type': 'bool'},
}
def __init__(
self,
*,
source_resource_id: Optional[str] = None,
source_flow_run_id: Optional[str] = None,
source_flow_id: Optional[str] = None,
flow: Optional["Flow"] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
flow_submit_run_settings: Optional["FlowSubmitRunSettings"] = None,
output_names_included_in_endpoint_response: Optional[List[str]] = None,
endpoint_name: Optional[str] = None,
endpoint_description: Optional[str] = None,
auth_mode: Optional[Union[str, "EndpointAuthMode"]] = None,
identity: Optional["ManagedServiceIdentity"] = None,
endpoint_tags: Optional[Dict[str, str]] = None,
enable_public_network_access: Optional[bool] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
use_workspace_connection: Optional[bool] = None,
deployment_name: Optional[str] = None,
environment: Optional[str] = None,
environment_variables: Optional[Dict[str, str]] = None,
deployment_tags: Optional[Dict[str, str]] = None,
app_insights_enabled: Optional[bool] = None,
enable_model_data_collector: Optional[bool] = None,
skip_update_traffic_to_full: Optional[bool] = None,
enable_streaming_response: Optional[bool] = None,
instance_type: Optional[str] = None,
instance_count: Optional[int] = None,
auto_grant_connection_permission: Optional[bool] = None,
**kwargs
):
"""
:keyword source_resource_id:
:paramtype source_resource_id: str
:keyword source_flow_run_id:
:paramtype source_flow_run_id: str
:keyword source_flow_id:
:paramtype source_flow_id: str
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword output_names_included_in_endpoint_response:
:paramtype output_names_included_in_endpoint_response: list[str]
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword endpoint_description:
:paramtype endpoint_description: str
:keyword auth_mode: Possible values include: "AMLToken", "Key", "AADToken".
:paramtype auth_mode: str or ~flow.models.EndpointAuthMode
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword endpoint_tags: This is a dictionary.
:paramtype endpoint_tags: dict[str, str]
:keyword enable_public_network_access:
:paramtype enable_public_network_access: bool
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword use_workspace_connection:
:paramtype use_workspace_connection: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword environment:
:paramtype environment: str
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword deployment_tags: This is a dictionary.
:paramtype deployment_tags: dict[str, str]
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword enable_model_data_collector:
:paramtype enable_model_data_collector: bool
:keyword skip_update_traffic_to_full:
:paramtype skip_update_traffic_to_full: bool
:keyword enable_streaming_response:
:paramtype enable_streaming_response: bool
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword auto_grant_connection_permission:
:paramtype auto_grant_connection_permission: bool
"""
super(DeployFlowRequest, self).__init__(**kwargs)
self.source_resource_id = source_resource_id
self.source_flow_run_id = source_flow_run_id
self.source_flow_id = source_flow_id
self.flow = flow
self.flow_type = flow_type
self.flow_submit_run_settings = flow_submit_run_settings
self.output_names_included_in_endpoint_response = output_names_included_in_endpoint_response
self.endpoint_name = endpoint_name
self.endpoint_description = endpoint_description
self.auth_mode = auth_mode
self.identity = identity
self.endpoint_tags = endpoint_tags
self.enable_public_network_access = enable_public_network_access
self.connection_overrides = connection_overrides
self.use_workspace_connection = use_workspace_connection
self.deployment_name = deployment_name
self.environment = environment
self.environment_variables = environment_variables
self.deployment_tags = deployment_tags
self.app_insights_enabled = app_insights_enabled
self.enable_model_data_collector = enable_model_data_collector
self.skip_update_traffic_to_full = skip_update_traffic_to_full
self.enable_streaming_response = enable_streaming_response
self.instance_type = instance_type
self.instance_count = instance_count
self.auto_grant_connection_permission = auto_grant_connection_permission
class DeploymentInfo(msrest.serialization.Model):
"""DeploymentInfo.
:ivar operation_id:
:vartype operation_id: str
:ivar service_id:
:vartype service_id: str
:ivar service_name:
:vartype service_name: str
:ivar status_detail:
:vartype status_detail: str
"""
_attribute_map = {
'operation_id': {'key': 'operationId', 'type': 'str'},
'service_id': {'key': 'serviceId', 'type': 'str'},
'service_name': {'key': 'serviceName', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
}
def __init__(
self,
*,
operation_id: Optional[str] = None,
service_id: Optional[str] = None,
service_name: Optional[str] = None,
status_detail: Optional[str] = None,
**kwargs
):
"""
:keyword operation_id:
:paramtype operation_id: str
:keyword service_id:
:paramtype service_id: str
:keyword service_name:
:paramtype service_name: str
:keyword status_detail:
:paramtype status_detail: str
"""
super(DeploymentInfo, self).__init__(**kwargs)
self.operation_id = operation_id
self.service_id = service_id
self.service_name = service_name
self.status_detail = status_detail
class DistributionConfiguration(msrest.serialization.Model):
"""DistributionConfiguration.
:ivar distribution_type: Possible values include: "PyTorch", "TensorFlow", "Mpi", "Ray".
:vartype distribution_type: str or ~flow.models.DistributionType
"""
_attribute_map = {
'distribution_type': {'key': 'distributionType', 'type': 'str'},
}
def __init__(
self,
*,
distribution_type: Optional[Union[str, "DistributionType"]] = None,
**kwargs
):
"""
:keyword distribution_type: Possible values include: "PyTorch", "TensorFlow", "Mpi", "Ray".
:paramtype distribution_type: str or ~flow.models.DistributionType
"""
super(DistributionConfiguration, self).__init__(**kwargs)
self.distribution_type = distribution_type
class DistributionParameter(msrest.serialization.Model):
"""DistributionParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar description:
:vartype description: str
:ivar input_type: Possible values include: "Text", "Number".
:vartype input_type: str or ~flow.models.DistributionParameterEnum
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'input_type': {'key': 'inputType', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
description: Optional[str] = None,
input_type: Optional[Union[str, "DistributionParameterEnum"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword description:
:paramtype description: str
:keyword input_type: Possible values include: "Text", "Number".
:paramtype input_type: str or ~flow.models.DistributionParameterEnum
"""
super(DistributionParameter, self).__init__(**kwargs)
self.name = name
self.label = label
self.description = description
self.input_type = input_type
class DockerBuildContext(msrest.serialization.Model):
"""DockerBuildContext.
:ivar location_type: Possible values include: "Git", "StorageAccount".
:vartype location_type: str or ~flow.models.BuildContextLocationType
:ivar location:
:vartype location: str
:ivar dockerfile_path:
:vartype dockerfile_path: str
"""
_attribute_map = {
'location_type': {'key': 'locationType', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'dockerfile_path': {'key': 'dockerfilePath', 'type': 'str'},
}
def __init__(
self,
*,
location_type: Optional[Union[str, "BuildContextLocationType"]] = None,
location: Optional[str] = None,
dockerfile_path: Optional[str] = "Dockerfile",
**kwargs
):
"""
:keyword location_type: Possible values include: "Git", "StorageAccount".
:paramtype location_type: str or ~flow.models.BuildContextLocationType
:keyword location:
:paramtype location: str
:keyword dockerfile_path:
:paramtype dockerfile_path: str
"""
super(DockerBuildContext, self).__init__(**kwargs)
self.location_type = location_type
self.location = location
self.dockerfile_path = dockerfile_path
class DockerConfiguration(msrest.serialization.Model):
"""DockerConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
*,
use_docker: Optional[bool] = None,
shared_volumes: Optional[bool] = None,
arguments: Optional[List[str]] = None,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword arguments:
:paramtype arguments: list[str]
"""
super(DockerConfiguration, self).__init__(**kwargs)
self.use_docker = use_docker
self.shared_volumes = shared_volumes
self.arguments = arguments
class DockerImagePlatform(msrest.serialization.Model):
"""DockerImagePlatform.
:ivar os:
:vartype os: str
:ivar architecture:
:vartype architecture: str
"""
_attribute_map = {
'os': {'key': 'os', 'type': 'str'},
'architecture': {'key': 'architecture', 'type': 'str'},
}
def __init__(
self,
*,
os: Optional[str] = None,
architecture: Optional[str] = None,
**kwargs
):
"""
:keyword os:
:paramtype os: str
:keyword architecture:
:paramtype architecture: str
"""
super(DockerImagePlatform, self).__init__(**kwargs)
self.os = os
self.architecture = architecture
class DockerSection(msrest.serialization.Model):
"""DockerSection.
:ivar base_image:
:vartype base_image: str
:ivar platform:
:vartype platform: ~flow.models.DockerImagePlatform
:ivar base_dockerfile:
:vartype base_dockerfile: str
:ivar build_context:
:vartype build_context: ~flow.models.DockerBuildContext
:ivar base_image_registry:
:vartype base_image_registry: ~flow.models.ContainerRegistry
"""
_attribute_map = {
'base_image': {'key': 'baseImage', 'type': 'str'},
'platform': {'key': 'platform', 'type': 'DockerImagePlatform'},
'base_dockerfile': {'key': 'baseDockerfile', 'type': 'str'},
'build_context': {'key': 'buildContext', 'type': 'DockerBuildContext'},
'base_image_registry': {'key': 'baseImageRegistry', 'type': 'ContainerRegistry'},
}
def __init__(
self,
*,
base_image: Optional[str] = None,
platform: Optional["DockerImagePlatform"] = None,
base_dockerfile: Optional[str] = None,
build_context: Optional["DockerBuildContext"] = None,
base_image_registry: Optional["ContainerRegistry"] = None,
**kwargs
):
"""
:keyword base_image:
:paramtype base_image: str
:keyword platform:
:paramtype platform: ~flow.models.DockerImagePlatform
:keyword base_dockerfile:
:paramtype base_dockerfile: str
:keyword build_context:
:paramtype build_context: ~flow.models.DockerBuildContext
:keyword base_image_registry:
:paramtype base_image_registry: ~flow.models.ContainerRegistry
"""
super(DockerSection, self).__init__(**kwargs)
self.base_image = base_image
self.platform = platform
self.base_dockerfile = base_dockerfile
self.build_context = build_context
self.base_image_registry = base_image_registry
class DockerSettingConfiguration(msrest.serialization.Model):
"""DockerSettingConfiguration.
:ivar use_docker:
:vartype use_docker: bool
:ivar shared_volumes:
:vartype shared_volumes: bool
:ivar shm_size:
:vartype shm_size: str
:ivar arguments:
:vartype arguments: list[str]
"""
_attribute_map = {
'use_docker': {'key': 'useDocker', 'type': 'bool'},
'shared_volumes': {'key': 'sharedVolumes', 'type': 'bool'},
'shm_size': {'key': 'shmSize', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
}
def __init__(
self,
*,
use_docker: Optional[bool] = None,
shared_volumes: Optional[bool] = None,
shm_size: Optional[str] = None,
arguments: Optional[List[str]] = None,
**kwargs
):
"""
:keyword use_docker:
:paramtype use_docker: bool
:keyword shared_volumes:
:paramtype shared_volumes: bool
:keyword shm_size:
:paramtype shm_size: str
:keyword arguments:
:paramtype arguments: list[str]
"""
super(DockerSettingConfiguration, self).__init__(**kwargs)
self.use_docker = use_docker
self.shared_volumes = shared_volumes
self.shm_size = shm_size
self.arguments = arguments
class DoWhileControlFlowInfo(msrest.serialization.Model):
"""DoWhileControlFlowInfo.
:ivar output_port_name_to_input_port_names_mapping: Dictionary of
<components·1sqg750·schemas·dowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:vartype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:ivar condition_output_port_name:
:vartype condition_output_port_name: str
:ivar run_settings:
:vartype run_settings: ~flow.models.DoWhileControlFlowRunSettings
"""
_attribute_map = {
'output_port_name_to_input_port_names_mapping': {'key': 'outputPortNameToInputPortNamesMapping', 'type': '{[str]}'},
'condition_output_port_name': {'key': 'conditionOutputPortName', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': 'DoWhileControlFlowRunSettings'},
}
def __init__(
self,
*,
output_port_name_to_input_port_names_mapping: Optional[Dict[str, List[str]]] = None,
condition_output_port_name: Optional[str] = None,
run_settings: Optional["DoWhileControlFlowRunSettings"] = None,
**kwargs
):
"""
:keyword output_port_name_to_input_port_names_mapping: Dictionary of
<components·1sqg750·schemas·dowhilecontrolflowinfo·properties·outputportnametoinputportnamesmapping·additionalproperties>.
:paramtype output_port_name_to_input_port_names_mapping: dict[str, list[str]]
:keyword condition_output_port_name:
:paramtype condition_output_port_name: str
:keyword run_settings:
:paramtype run_settings: ~flow.models.DoWhileControlFlowRunSettings
"""
super(DoWhileControlFlowInfo, self).__init__(**kwargs)
self.output_port_name_to_input_port_names_mapping = output_port_name_to_input_port_names_mapping
self.condition_output_port_name = condition_output_port_name
self.run_settings = run_settings
class DoWhileControlFlowRunSettings(msrest.serialization.Model):
"""DoWhileControlFlowRunSettings.
:ivar max_loop_iteration_count:
:vartype max_loop_iteration_count: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'max_loop_iteration_count': {'key': 'maxLoopIterationCount', 'type': 'ParameterAssignment'},
}
def __init__(
self,
*,
max_loop_iteration_count: Optional["ParameterAssignment"] = None,
**kwargs
):
"""
:keyword max_loop_iteration_count:
:paramtype max_loop_iteration_count: ~flow.models.ParameterAssignment
"""
super(DoWhileControlFlowRunSettings, self).__init__(**kwargs)
self.max_loop_iteration_count = max_loop_iteration_count
class DownloadResourceInfo(msrest.serialization.Model):
"""DownloadResourceInfo.
:ivar download_url:
:vartype download_url: str
:ivar size:
:vartype size: long
"""
_attribute_map = {
'download_url': {'key': 'downloadUrl', 'type': 'str'},
'size': {'key': 'size', 'type': 'long'},
}
def __init__(
self,
*,
download_url: Optional[str] = None,
size: Optional[int] = None,
**kwargs
):
"""
:keyword download_url:
:paramtype download_url: str
:keyword size:
:paramtype size: long
"""
super(DownloadResourceInfo, self).__init__(**kwargs)
self.download_url = download_url
self.size = size
class EndpointSetting(msrest.serialization.Model):
"""EndpointSetting.
:ivar type:
:vartype type: str
:ivar port:
:vartype port: int
:ivar ssl_thumbprint:
:vartype ssl_thumbprint: str
:ivar endpoint:
:vartype endpoint: str
:ivar proxy_endpoint:
:vartype proxy_endpoint: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar enabled:
:vartype enabled: bool
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'ssl_thumbprint': {'key': 'sslThumbprint', 'type': 'str'},
'endpoint': {'key': 'endpoint', 'type': 'str'},
'proxy_endpoint': {'key': 'proxyEndpoint', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'enabled': {'key': 'enabled', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[str] = None,
port: Optional[int] = None,
ssl_thumbprint: Optional[str] = None,
endpoint: Optional[str] = None,
proxy_endpoint: Optional[str] = None,
status: Optional[str] = None,
error_message: Optional[str] = None,
enabled: Optional[bool] = None,
properties: Optional[Dict[str, str]] = None,
nodes: Optional[str] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword port:
:paramtype port: int
:keyword ssl_thumbprint:
:paramtype ssl_thumbprint: str
:keyword endpoint:
:paramtype endpoint: str
:keyword proxy_endpoint:
:paramtype proxy_endpoint: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword enabled:
:paramtype enabled: bool
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: str
"""
super(EndpointSetting, self).__init__(**kwargs)
self.type = type
self.port = port
self.ssl_thumbprint = ssl_thumbprint
self.endpoint = endpoint
self.proxy_endpoint = proxy_endpoint
self.status = status
self.error_message = error_message
self.enabled = enabled
self.properties = properties
self.nodes = nodes
class EntityInterface(msrest.serialization.Model):
"""EntityInterface.
:ivar parameters:
:vartype parameters: list[~flow.models.Parameter]
:ivar ports:
:vartype ports: ~flow.models.NodePortInterface
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.Parameter]
:ivar data_path_parameters:
:vartype data_path_parameters: list[~flow.models.DataPathParameter]
:ivar data_path_parameter_list:
:vartype data_path_parameter_list: list[~flow.models.DataSetPathParameter]
:ivar asset_output_settings_parameter_list:
:vartype asset_output_settings_parameter_list: list[~flow.models.AssetOutputSettingsParameter]
"""
_attribute_map = {
'parameters': {'key': 'parameters', 'type': '[Parameter]'},
'ports': {'key': 'ports', 'type': 'NodePortInterface'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[Parameter]'},
'data_path_parameters': {'key': 'dataPathParameters', 'type': '[DataPathParameter]'},
'data_path_parameter_list': {'key': 'dataPathParameterList', 'type': '[DataSetPathParameter]'},
'asset_output_settings_parameter_list': {'key': 'AssetOutputSettingsParameterList', 'type': '[AssetOutputSettingsParameter]'},
}
def __init__(
self,
*,
parameters: Optional[List["Parameter"]] = None,
ports: Optional["NodePortInterface"] = None,
metadata_parameters: Optional[List["Parameter"]] = None,
data_path_parameters: Optional[List["DataPathParameter"]] = None,
data_path_parameter_list: Optional[List["DataSetPathParameter"]] = None,
asset_output_settings_parameter_list: Optional[List["AssetOutputSettingsParameter"]] = None,
**kwargs
):
"""
:keyword parameters:
:paramtype parameters: list[~flow.models.Parameter]
:keyword ports:
:paramtype ports: ~flow.models.NodePortInterface
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.Parameter]
:keyword data_path_parameters:
:paramtype data_path_parameters: list[~flow.models.DataPathParameter]
:keyword data_path_parameter_list:
:paramtype data_path_parameter_list: list[~flow.models.DataSetPathParameter]
:keyword asset_output_settings_parameter_list:
:paramtype asset_output_settings_parameter_list:
list[~flow.models.AssetOutputSettingsParameter]
"""
super(EntityInterface, self).__init__(**kwargs)
self.parameters = parameters
self.ports = ports
self.metadata_parameters = metadata_parameters
self.data_path_parameters = data_path_parameters
self.data_path_parameter_list = data_path_parameter_list
self.asset_output_settings_parameter_list = asset_output_settings_parameter_list
class EntrySetting(msrest.serialization.Model):
"""EntrySetting.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
class_name: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(EntrySetting, self).__init__(**kwargs)
self.file = file
self.class_name = class_name
class EnumParameterRule(msrest.serialization.Model):
"""EnumParameterRule.
:ivar valid_values:
:vartype valid_values: list[str]
"""
_attribute_map = {
'valid_values': {'key': 'validValues', 'type': '[str]'},
}
def __init__(
self,
*,
valid_values: Optional[List[str]] = None,
**kwargs
):
"""
:keyword valid_values:
:paramtype valid_values: list[str]
"""
super(EnumParameterRule, self).__init__(**kwargs)
self.valid_values = valid_values
class EnvironmentConfiguration(msrest.serialization.Model):
"""EnvironmentConfiguration.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar use_environment_definition:
:vartype use_environment_definition: bool
:ivar environment_definition_string:
:vartype environment_definition_string: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'use_environment_definition': {'key': 'useEnvironmentDefinition', 'type': 'bool'},
'environment_definition_string': {'key': 'environmentDefinitionString', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
version: Optional[str] = None,
use_environment_definition: Optional[bool] = None,
environment_definition_string: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword use_environment_definition:
:paramtype use_environment_definition: bool
:keyword environment_definition_string:
:paramtype environment_definition_string: str
"""
super(EnvironmentConfiguration, self).__init__(**kwargs)
self.name = name
self.version = version
self.use_environment_definition = use_environment_definition
self.environment_definition_string = environment_definition_string
class EnvironmentDefinition(msrest.serialization.Model):
"""EnvironmentDefinition.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar asset_id:
:vartype asset_id: str
:ivar auto_rebuild:
:vartype auto_rebuild: bool
:ivar python:
:vartype python: ~flow.models.PythonSection
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar docker:
:vartype docker: ~flow.models.DockerSection
:ivar spark:
:vartype spark: ~flow.models.SparkSection
:ivar r:
:vartype r: ~flow.models.RSection
:ivar inferencing_stack_version:
:vartype inferencing_stack_version: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'auto_rebuild': {'key': 'autoRebuild', 'type': 'bool'},
'python': {'key': 'python', 'type': 'PythonSection'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'docker': {'key': 'docker', 'type': 'DockerSection'},
'spark': {'key': 'spark', 'type': 'SparkSection'},
'r': {'key': 'r', 'type': 'RSection'},
'inferencing_stack_version': {'key': 'inferencingStackVersion', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
version: Optional[str] = None,
asset_id: Optional[str] = None,
auto_rebuild: Optional[bool] = None,
python: Optional["PythonSection"] = None,
environment_variables: Optional[Dict[str, str]] = None,
docker: Optional["DockerSection"] = None,
spark: Optional["SparkSection"] = None,
r: Optional["RSection"] = None,
inferencing_stack_version: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword asset_id:
:paramtype asset_id: str
:keyword auto_rebuild:
:paramtype auto_rebuild: bool
:keyword python:
:paramtype python: ~flow.models.PythonSection
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword docker:
:paramtype docker: ~flow.models.DockerSection
:keyword spark:
:paramtype spark: ~flow.models.SparkSection
:keyword r:
:paramtype r: ~flow.models.RSection
:keyword inferencing_stack_version:
:paramtype inferencing_stack_version: str
"""
super(EnvironmentDefinition, self).__init__(**kwargs)
self.name = name
self.version = version
self.asset_id = asset_id
self.auto_rebuild = auto_rebuild
self.python = python
self.environment_variables = environment_variables
self.docker = docker
self.spark = spark
self.r = r
self.inferencing_stack_version = inferencing_stack_version
class EnvironmentDefinitionDto(msrest.serialization.Model):
"""EnvironmentDefinitionDto.
:ivar environment_name:
:vartype environment_name: str
:ivar environment_version:
:vartype environment_version: str
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'environment_name': {'key': 'environmentName', 'type': 'str'},
'environment_version': {'key': 'environmentVersion', 'type': 'str'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
*,
environment_name: Optional[str] = None,
environment_version: Optional[str] = None,
intellectual_property_publisher: Optional[str] = None,
**kwargs
):
"""
:keyword environment_name:
:paramtype environment_name: str
:keyword environment_version:
:paramtype environment_version: str
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(EnvironmentDefinitionDto, self).__init__(**kwargs)
self.environment_name = environment_name
self.environment_version = environment_version
self.intellectual_property_publisher = intellectual_property_publisher
class EPRPipelineRunErrorClassificationRequest(msrest.serialization.Model):
"""EPRPipelineRunErrorClassificationRequest.
:ivar root_run_id:
:vartype root_run_id: str
:ivar run_id:
:vartype run_id: str
:ivar task_result:
:vartype task_result: str
:ivar failure_type:
:vartype failure_type: str
:ivar failure_name:
:vartype failure_name: str
:ivar responsible_team:
:vartype responsible_team: str
"""
_attribute_map = {
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'task_result': {'key': 'taskResult', 'type': 'str'},
'failure_type': {'key': 'failureType', 'type': 'str'},
'failure_name': {'key': 'failureName', 'type': 'str'},
'responsible_team': {'key': 'responsibleTeam', 'type': 'str'},
}
def __init__(
self,
*,
root_run_id: Optional[str] = None,
run_id: Optional[str] = None,
task_result: Optional[str] = None,
failure_type: Optional[str] = None,
failure_name: Optional[str] = None,
responsible_team: Optional[str] = None,
**kwargs
):
"""
:keyword root_run_id:
:paramtype root_run_id: str
:keyword run_id:
:paramtype run_id: str
:keyword task_result:
:paramtype task_result: str
:keyword failure_type:
:paramtype failure_type: str
:keyword failure_name:
:paramtype failure_name: str
:keyword responsible_team:
:paramtype responsible_team: str
"""
super(EPRPipelineRunErrorClassificationRequest, self).__init__(**kwargs)
self.root_run_id = root_run_id
self.run_id = run_id
self.task_result = task_result
self.failure_type = failure_type
self.failure_name = failure_name
self.responsible_team = responsible_team
class ErrorAdditionalInfo(msrest.serialization.Model):
"""The resource management error additional info.
:ivar type: The additional info type.
:vartype type: str
:ivar info: The additional info.
:vartype info: any
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'info': {'key': 'info', 'type': 'object'},
}
def __init__(
self,
*,
type: Optional[str] = None,
info: Optional[Any] = None,
**kwargs
):
"""
:keyword type: The additional info type.
:paramtype type: str
:keyword info: The additional info.
:paramtype info: any
"""
super(ErrorAdditionalInfo, self).__init__(**kwargs)
self.type = type
self.info = info
class ErrorResponse(msrest.serialization.Model):
"""The error response.
:ivar error: The root error.
:vartype error: ~flow.models.RootError
:ivar correlation: Dictionary containing correlation details for the error.
:vartype correlation: dict[str, str]
:ivar environment: The hosting environment.
:vartype environment: str
:ivar location: The Azure region.
:vartype location: str
:ivar time: The time in UTC.
:vartype time: ~datetime.datetime
:ivar component_name: Component name where error originated/encountered.
:vartype component_name: str
"""
_attribute_map = {
'error': {'key': 'error', 'type': 'RootError'},
'correlation': {'key': 'correlation', 'type': '{str}'},
'environment': {'key': 'environment', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'time': {'key': 'time', 'type': 'iso-8601'},
'component_name': {'key': 'componentName', 'type': 'str'},
}
def __init__(
self,
*,
error: Optional["RootError"] = None,
correlation: Optional[Dict[str, str]] = None,
environment: Optional[str] = None,
location: Optional[str] = None,
time: Optional[datetime.datetime] = None,
component_name: Optional[str] = None,
**kwargs
):
"""
:keyword error: The root error.
:paramtype error: ~flow.models.RootError
:keyword correlation: Dictionary containing correlation details for the error.
:paramtype correlation: dict[str, str]
:keyword environment: The hosting environment.
:paramtype environment: str
:keyword location: The Azure region.
:paramtype location: str
:keyword time: The time in UTC.
:paramtype time: ~datetime.datetime
:keyword component_name: Component name where error originated/encountered.
:paramtype component_name: str
"""
super(ErrorResponse, self).__init__(**kwargs)
self.error = error
self.correlation = correlation
self.environment = environment
self.location = location
self.time = time
self.component_name = component_name
class EsCloudConfiguration(msrest.serialization.Model):
"""EsCloudConfiguration.
:ivar enable_output_to_file_based_on_data_type_id:
:vartype enable_output_to_file_based_on_data_type_id: bool
:ivar environment:
:vartype environment: ~flow.models.EnvironmentConfiguration
:ivar hyper_drive_configuration:
:vartype hyper_drive_configuration: ~flow.models.HyperDriveConfiguration
:ivar k8_s_config:
:vartype k8_s_config: ~flow.models.K8SConfiguration
:ivar resource_config:
:vartype resource_config: ~flow.models.AEVAResourceConfiguration
:ivar torch_distributed_config:
:vartype torch_distributed_config: ~flow.models.TorchDistributedConfiguration
:ivar target_selector_config:
:vartype target_selector_config: ~flow.models.TargetSelectorConfiguration
:ivar docker_config:
:vartype docker_config: ~flow.models.DockerSettingConfiguration
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: int
:ivar identity:
:vartype identity: ~flow.models.IdentitySetting
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar run_config:
:vartype run_config: str
"""
_attribute_map = {
'enable_output_to_file_based_on_data_type_id': {'key': 'enableOutputToFileBasedOnDataTypeId', 'type': 'bool'},
'environment': {'key': 'environment', 'type': 'EnvironmentConfiguration'},
'hyper_drive_configuration': {'key': 'hyperDriveConfiguration', 'type': 'HyperDriveConfiguration'},
'k8_s_config': {'key': 'k8sConfig', 'type': 'K8SConfiguration'},
'resource_config': {'key': 'resourceConfig', 'type': 'AEVAResourceConfiguration'},
'torch_distributed_config': {'key': 'torchDistributedConfig', 'type': 'TorchDistributedConfiguration'},
'target_selector_config': {'key': 'targetSelectorConfig', 'type': 'TargetSelectorConfiguration'},
'docker_config': {'key': 'dockerConfig', 'type': 'DockerSettingConfiguration'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'int'},
'identity': {'key': 'identity', 'type': 'IdentitySetting'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'run_config': {'key': 'runConfig', 'type': 'str'},
}
def __init__(
self,
*,
enable_output_to_file_based_on_data_type_id: Optional[bool] = None,
environment: Optional["EnvironmentConfiguration"] = None,
hyper_drive_configuration: Optional["HyperDriveConfiguration"] = None,
k8_s_config: Optional["K8SConfiguration"] = None,
resource_config: Optional["AEVAResourceConfiguration"] = None,
torch_distributed_config: Optional["TorchDistributedConfiguration"] = None,
target_selector_config: Optional["TargetSelectorConfiguration"] = None,
docker_config: Optional["DockerSettingConfiguration"] = None,
environment_variables: Optional[Dict[str, str]] = None,
max_run_duration_seconds: Optional[int] = None,
identity: Optional["IdentitySetting"] = None,
application_endpoints: Optional[Dict[str, "ApplicationEndpointConfiguration"]] = None,
run_config: Optional[str] = None,
**kwargs
):
"""
:keyword enable_output_to_file_based_on_data_type_id:
:paramtype enable_output_to_file_based_on_data_type_id: bool
:keyword environment:
:paramtype environment: ~flow.models.EnvironmentConfiguration
:keyword hyper_drive_configuration:
:paramtype hyper_drive_configuration: ~flow.models.HyperDriveConfiguration
:keyword k8_s_config:
:paramtype k8_s_config: ~flow.models.K8SConfiguration
:keyword resource_config:
:paramtype resource_config: ~flow.models.AEVAResourceConfiguration
:keyword torch_distributed_config:
:paramtype torch_distributed_config: ~flow.models.TorchDistributedConfiguration
:keyword target_selector_config:
:paramtype target_selector_config: ~flow.models.TargetSelectorConfiguration
:keyword docker_config:
:paramtype docker_config: ~flow.models.DockerSettingConfiguration
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: int
:keyword identity:
:paramtype identity: ~flow.models.IdentitySetting
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword run_config:
:paramtype run_config: str
"""
super(EsCloudConfiguration, self).__init__(**kwargs)
self.enable_output_to_file_based_on_data_type_id = enable_output_to_file_based_on_data_type_id
self.environment = environment
self.hyper_drive_configuration = hyper_drive_configuration
self.k8_s_config = k8_s_config
self.resource_config = resource_config
self.torch_distributed_config = torch_distributed_config
self.target_selector_config = target_selector_config
self.docker_config = docker_config
self.environment_variables = environment_variables
self.max_run_duration_seconds = max_run_duration_seconds
self.identity = identity
self.application_endpoints = application_endpoints
self.run_config = run_config
class EvaluationFlowRunSettings(msrest.serialization.Model):
"""EvaluationFlowRunSettings.
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar variant_run_variants:
:vartype variant_run_variants: list[~flow.models.VariantIdentifier]
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar input_universal_link:
:vartype input_universal_link: str
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'variant_run_variants': {'key': 'variantRunVariants', 'type': '[VariantIdentifier]'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'input_universal_link': {'key': 'inputUniversalLink', 'type': 'str'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
flow_run_id: Optional[str] = None,
variant_run_variants: Optional[List["VariantIdentifier"]] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
input_universal_link: Optional[str] = None,
data_inputs: Optional[Dict[str, str]] = None,
flow_run_output_directory: Optional[str] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword variant_run_variants:
:paramtype variant_run_variants: list[~flow.models.VariantIdentifier]
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword input_universal_link:
:paramtype input_universal_link: str
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(EvaluationFlowRunSettings, self).__init__(**kwargs)
self.flow_run_id = flow_run_id
self.variant_run_variants = variant_run_variants
self.batch_inputs = batch_inputs
self.input_universal_link = input_universal_link
self.data_inputs = data_inputs
self.flow_run_output_directory = flow_run_output_directory
self.connection_overrides = connection_overrides
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class ExampleRequest(msrest.serialization.Model):
"""ExampleRequest.
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, list[list[any]]]
:ivar global_parameters: This is a dictionary.
:vartype global_parameters: dict[str, any]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '{[[object]]}'},
'global_parameters': {'key': 'globalParameters', 'type': '{object}'},
}
def __init__(
self,
*,
inputs: Optional[Dict[str, List[List[Any]]]] = None,
global_parameters: Optional[Dict[str, Any]] = None,
**kwargs
):
"""
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, list[list[any]]]
:keyword global_parameters: This is a dictionary.
:paramtype global_parameters: dict[str, any]
"""
super(ExampleRequest, self).__init__(**kwargs)
self.inputs = inputs
self.global_parameters = global_parameters
class ExecutionContextDto(msrest.serialization.Model):
"""ExecutionContextDto.
:ivar executable:
:vartype executable: str
:ivar user_code:
:vartype user_code: str
:ivar arguments:
:vartype arguments: str
"""
_attribute_map = {
'executable': {'key': 'executable', 'type': 'str'},
'user_code': {'key': 'userCode', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': 'str'},
}
def __init__(
self,
*,
executable: Optional[str] = None,
user_code: Optional[str] = None,
arguments: Optional[str] = None,
**kwargs
):
"""
:keyword executable:
:paramtype executable: str
:keyword user_code:
:paramtype user_code: str
:keyword arguments:
:paramtype arguments: str
"""
super(ExecutionContextDto, self).__init__(**kwargs)
self.executable = executable
self.user_code = user_code
self.arguments = arguments
class ExecutionDataLocation(msrest.serialization.Model):
"""ExecutionDataLocation.
:ivar dataset:
:vartype dataset: ~flow.models.RunDatasetReference
:ivar data_path:
:vartype data_path: ~flow.models.ExecutionDataPath
:ivar uri:
:vartype uri: ~flow.models.UriReference
:ivar type:
:vartype type: str
"""
_attribute_map = {
'dataset': {'key': 'dataset', 'type': 'RunDatasetReference'},
'data_path': {'key': 'dataPath', 'type': 'ExecutionDataPath'},
'uri': {'key': 'uri', 'type': 'UriReference'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
dataset: Optional["RunDatasetReference"] = None,
data_path: Optional["ExecutionDataPath"] = None,
uri: Optional["UriReference"] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword dataset:
:paramtype dataset: ~flow.models.RunDatasetReference
:keyword data_path:
:paramtype data_path: ~flow.models.ExecutionDataPath
:keyword uri:
:paramtype uri: ~flow.models.UriReference
:keyword type:
:paramtype type: str
"""
super(ExecutionDataLocation, self).__init__(**kwargs)
self.dataset = dataset
self.data_path = data_path
self.uri = uri
self.type = type
class ExecutionDataPath(msrest.serialization.Model):
"""ExecutionDataPath.
:ivar datastore_name:
:vartype datastore_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'datastore_name': {'key': 'datastoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
datastore_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword datastore_name:
:paramtype datastore_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(ExecutionDataPath, self).__init__(**kwargs)
self.datastore_name = datastore_name
self.relative_path = relative_path
class ExecutionGlobsOptions(msrest.serialization.Model):
"""ExecutionGlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
*,
glob_patterns: Optional[List[str]] = None,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(ExecutionGlobsOptions, self).__init__(**kwargs)
self.glob_patterns = glob_patterns
class ExperimentComputeMetaInfo(msrest.serialization.Model):
"""ExperimentComputeMetaInfo.
:ivar current_node_count:
:vartype current_node_count: int
:ivar target_node_count:
:vartype target_node_count: int
:ivar max_node_count:
:vartype max_node_count: int
:ivar min_node_count:
:vartype min_node_count: int
:ivar idle_node_count:
:vartype idle_node_count: int
:ivar running_node_count:
:vartype running_node_count: int
:ivar preparing_node_count:
:vartype preparing_node_count: int
:ivar unusable_node_count:
:vartype unusable_node_count: int
:ivar leaving_node_count:
:vartype leaving_node_count: int
:ivar preempted_node_count:
:vartype preempted_node_count: int
:ivar vm_size:
:vartype vm_size: str
:ivar location:
:vartype location: str
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar state:
:vartype state: str
:ivar os_type:
:vartype os_type: str
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'current_node_count': {'key': 'currentNodeCount', 'type': 'int'},
'target_node_count': {'key': 'targetNodeCount', 'type': 'int'},
'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
'min_node_count': {'key': 'minNodeCount', 'type': 'int'},
'idle_node_count': {'key': 'idleNodeCount', 'type': 'int'},
'running_node_count': {'key': 'runningNodeCount', 'type': 'int'},
'preparing_node_count': {'key': 'preparingNodeCount', 'type': 'int'},
'unusable_node_count': {'key': 'unusableNodeCount', 'type': 'int'},
'leaving_node_count': {'key': 'leavingNodeCount', 'type': 'int'},
'preempted_node_count': {'key': 'preemptedNodeCount', 'type': 'int'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'state': {'key': 'state', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
*,
current_node_count: Optional[int] = None,
target_node_count: Optional[int] = None,
max_node_count: Optional[int] = None,
min_node_count: Optional[int] = None,
idle_node_count: Optional[int] = None,
running_node_count: Optional[int] = None,
preparing_node_count: Optional[int] = None,
unusable_node_count: Optional[int] = None,
leaving_node_count: Optional[int] = None,
preempted_node_count: Optional[int] = None,
vm_size: Optional[str] = None,
location: Optional[str] = None,
provisioning_state: Optional[str] = None,
state: Optional[str] = None,
os_type: Optional[str] = None,
id: Optional[str] = None,
name: Optional[str] = None,
created_by_studio: Optional[bool] = None,
is_gpu_type: Optional[bool] = None,
resource_id: Optional[str] = None,
compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword current_node_count:
:paramtype current_node_count: int
:keyword target_node_count:
:paramtype target_node_count: int
:keyword max_node_count:
:paramtype max_node_count: int
:keyword min_node_count:
:paramtype min_node_count: int
:keyword idle_node_count:
:paramtype idle_node_count: int
:keyword running_node_count:
:paramtype running_node_count: int
:keyword preparing_node_count:
:paramtype preparing_node_count: int
:keyword unusable_node_count:
:paramtype unusable_node_count: int
:keyword leaving_node_count:
:paramtype leaving_node_count: int
:keyword preempted_node_count:
:paramtype preempted_node_count: int
:keyword vm_size:
:paramtype vm_size: str
:keyword location:
:paramtype location: str
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword state:
:paramtype state: str
:keyword os_type:
:paramtype os_type: str
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(ExperimentComputeMetaInfo, self).__init__(**kwargs)
self.current_node_count = current_node_count
self.target_node_count = target_node_count
self.max_node_count = max_node_count
self.min_node_count = min_node_count
self.idle_node_count = idle_node_count
self.running_node_count = running_node_count
self.preparing_node_count = preparing_node_count
self.unusable_node_count = unusable_node_count
self.leaving_node_count = leaving_node_count
self.preempted_node_count = preempted_node_count
self.vm_size = vm_size
self.location = location
self.provisioning_state = provisioning_state
self.state = state
self.os_type = os_type
self.id = id
self.name = name
self.created_by_studio = created_by_studio
self.is_gpu_type = is_gpu_type
self.resource_id = resource_id
self.compute_type = compute_type
class ExperimentInfo(msrest.serialization.Model):
"""ExperimentInfo.
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
*,
experiment_name: Optional[str] = None,
experiment_id: Optional[str] = None,
**kwargs
):
"""
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(ExperimentInfo, self).__init__(**kwargs)
self.experiment_name = experiment_name
self.experiment_id = experiment_id
class ExportComponentMetaInfo(msrest.serialization.Model):
"""ExportComponentMetaInfo.
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar module_version:
:vartype module_version: str
:ivar is_anonymous:
:vartype is_anonymous: bool
"""
_attribute_map = {
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
}
def __init__(
self,
*,
module_entity: Optional["ModuleEntity"] = None,
module_version: Optional[str] = None,
is_anonymous: Optional[bool] = None,
**kwargs
):
"""
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword module_version:
:paramtype module_version: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
"""
super(ExportComponentMetaInfo, self).__init__(**kwargs)
self.module_entity = module_entity
self.module_version = module_version
self.is_anonymous = is_anonymous
class ExportDataTask(msrest.serialization.Model):
"""ExportDataTask.
:ivar data_transfer_sink:
:vartype data_transfer_sink: ~flow.models.DataTransferSink
"""
_attribute_map = {
'data_transfer_sink': {'key': 'DataTransferSink', 'type': 'DataTransferSink'},
}
def __init__(
self,
*,
data_transfer_sink: Optional["DataTransferSink"] = None,
**kwargs
):
"""
:keyword data_transfer_sink:
:paramtype data_transfer_sink: ~flow.models.DataTransferSink
"""
super(ExportDataTask, self).__init__(**kwargs)
self.data_transfer_sink = data_transfer_sink
class FeaturizationSettings(msrest.serialization.Model):
"""FeaturizationSettings.
:ivar mode: Possible values include: "Auto", "Custom", "Off".
:vartype mode: str or ~flow.models.FeaturizationMode
:ivar blocked_transformers:
:vartype blocked_transformers: list[str]
:ivar column_purposes: Dictionary of :code:`<string>`.
:vartype column_purposes: dict[str, str]
:ivar drop_columns:
:vartype drop_columns: list[str]
:ivar transformer_params: Dictionary of
<components·1gi3krm·schemas·featurizationsettings·properties·transformerparams·additionalproperties>.
:vartype transformer_params: dict[str, list[~flow.models.ColumnTransformer]]
:ivar dataset_language:
:vartype dataset_language: str
:ivar enable_dnn_featurization:
:vartype enable_dnn_featurization: bool
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'blocked_transformers': {'key': 'blockedTransformers', 'type': '[str]'},
'column_purposes': {'key': 'columnPurposes', 'type': '{str}'},
'drop_columns': {'key': 'dropColumns', 'type': '[str]'},
'transformer_params': {'key': 'transformerParams', 'type': '{[ColumnTransformer]}'},
'dataset_language': {'key': 'datasetLanguage', 'type': 'str'},
'enable_dnn_featurization': {'key': 'enableDnnFeaturization', 'type': 'bool'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "FeaturizationMode"]] = None,
blocked_transformers: Optional[List[str]] = None,
column_purposes: Optional[Dict[str, str]] = None,
drop_columns: Optional[List[str]] = None,
transformer_params: Optional[Dict[str, List["ColumnTransformer"]]] = None,
dataset_language: Optional[str] = None,
enable_dnn_featurization: Optional[bool] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom", "Off".
:paramtype mode: str or ~flow.models.FeaturizationMode
:keyword blocked_transformers:
:paramtype blocked_transformers: list[str]
:keyword column_purposes: Dictionary of :code:`<string>`.
:paramtype column_purposes: dict[str, str]
:keyword drop_columns:
:paramtype drop_columns: list[str]
:keyword transformer_params: Dictionary of
<components·1gi3krm·schemas·featurizationsettings·properties·transformerparams·additionalproperties>.
:paramtype transformer_params: dict[str, list[~flow.models.ColumnTransformer]]
:keyword dataset_language:
:paramtype dataset_language: str
:keyword enable_dnn_featurization:
:paramtype enable_dnn_featurization: bool
"""
super(FeaturizationSettings, self).__init__(**kwargs)
self.mode = mode
self.blocked_transformers = blocked_transformers
self.column_purposes = column_purposes
self.drop_columns = drop_columns
self.transformer_params = transformer_params
self.dataset_language = dataset_language
self.enable_dnn_featurization = enable_dnn_featurization
class FeedDto(msrest.serialization.Model):
"""FeedDto.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar sharing_scopes:
:vartype sharing_scopes: list[~flow.models.SharingScope]
:ivar supported_asset_types:
:vartype supported_asset_types: ~flow.models.FeedDtoSupportedAssetTypes
:ivar regional_workspace_storage: This is a dictionary.
:vartype regional_workspace_storage: dict[str, list[str]]
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'sharing_scopes': {'key': 'sharingScopes', 'type': '[SharingScope]'},
'supported_asset_types': {'key': 'supportedAssetTypes', 'type': 'FeedDtoSupportedAssetTypes'},
'regional_workspace_storage': {'key': 'regionalWorkspaceStorage', 'type': '{[str]}'},
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
sharing_scopes: Optional[List["SharingScope"]] = None,
supported_asset_types: Optional["FeedDtoSupportedAssetTypes"] = None,
regional_workspace_storage: Optional[Dict[str, List[str]]] = None,
intellectual_property_publisher: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword sharing_scopes:
:paramtype sharing_scopes: list[~flow.models.SharingScope]
:keyword supported_asset_types:
:paramtype supported_asset_types: ~flow.models.FeedDtoSupportedAssetTypes
:keyword regional_workspace_storage: This is a dictionary.
:paramtype regional_workspace_storage: dict[str, list[str]]
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(FeedDto, self).__init__(**kwargs)
self.name = name
self.display_name = display_name
self.description = description
self.sharing_scopes = sharing_scopes
self.supported_asset_types = supported_asset_types
self.regional_workspace_storage = regional_workspace_storage
self.intellectual_property_publisher = intellectual_property_publisher
class FeedDtoSupportedAssetTypes(msrest.serialization.Model):
"""FeedDtoSupportedAssetTypes.
:ivar component:
:vartype component: ~flow.models.AssetTypeMetaInfo
:ivar model:
:vartype model: ~flow.models.AssetTypeMetaInfo
:ivar environment:
:vartype environment: ~flow.models.AssetTypeMetaInfo
:ivar dataset:
:vartype dataset: ~flow.models.AssetTypeMetaInfo
:ivar data_store:
:vartype data_store: ~flow.models.AssetTypeMetaInfo
:ivar sample_graph:
:vartype sample_graph: ~flow.models.AssetTypeMetaInfo
:ivar flow_tool:
:vartype flow_tool: ~flow.models.AssetTypeMetaInfo
:ivar flow_tool_setting:
:vartype flow_tool_setting: ~flow.models.AssetTypeMetaInfo
:ivar flow_connection:
:vartype flow_connection: ~flow.models.AssetTypeMetaInfo
:ivar flow_sample:
:vartype flow_sample: ~flow.models.AssetTypeMetaInfo
:ivar flow_runtime_spec:
:vartype flow_runtime_spec: ~flow.models.AssetTypeMetaInfo
"""
_attribute_map = {
'component': {'key': 'Component', 'type': 'AssetTypeMetaInfo'},
'model': {'key': 'Model', 'type': 'AssetTypeMetaInfo'},
'environment': {'key': 'Environment', 'type': 'AssetTypeMetaInfo'},
'dataset': {'key': 'Dataset', 'type': 'AssetTypeMetaInfo'},
'data_store': {'key': 'DataStore', 'type': 'AssetTypeMetaInfo'},
'sample_graph': {'key': 'SampleGraph', 'type': 'AssetTypeMetaInfo'},
'flow_tool': {'key': 'FlowTool', 'type': 'AssetTypeMetaInfo'},
'flow_tool_setting': {'key': 'FlowToolSetting', 'type': 'AssetTypeMetaInfo'},
'flow_connection': {'key': 'FlowConnection', 'type': 'AssetTypeMetaInfo'},
'flow_sample': {'key': 'FlowSample', 'type': 'AssetTypeMetaInfo'},
'flow_runtime_spec': {'key': 'FlowRuntimeSpec', 'type': 'AssetTypeMetaInfo'},
}
def __init__(
self,
*,
component: Optional["AssetTypeMetaInfo"] = None,
model: Optional["AssetTypeMetaInfo"] = None,
environment: Optional["AssetTypeMetaInfo"] = None,
dataset: Optional["AssetTypeMetaInfo"] = None,
data_store: Optional["AssetTypeMetaInfo"] = None,
sample_graph: Optional["AssetTypeMetaInfo"] = None,
flow_tool: Optional["AssetTypeMetaInfo"] = None,
flow_tool_setting: Optional["AssetTypeMetaInfo"] = None,
flow_connection: Optional["AssetTypeMetaInfo"] = None,
flow_sample: Optional["AssetTypeMetaInfo"] = None,
flow_runtime_spec: Optional["AssetTypeMetaInfo"] = None,
**kwargs
):
"""
:keyword component:
:paramtype component: ~flow.models.AssetTypeMetaInfo
:keyword model:
:paramtype model: ~flow.models.AssetTypeMetaInfo
:keyword environment:
:paramtype environment: ~flow.models.AssetTypeMetaInfo
:keyword dataset:
:paramtype dataset: ~flow.models.AssetTypeMetaInfo
:keyword data_store:
:paramtype data_store: ~flow.models.AssetTypeMetaInfo
:keyword sample_graph:
:paramtype sample_graph: ~flow.models.AssetTypeMetaInfo
:keyword flow_tool:
:paramtype flow_tool: ~flow.models.AssetTypeMetaInfo
:keyword flow_tool_setting:
:paramtype flow_tool_setting: ~flow.models.AssetTypeMetaInfo
:keyword flow_connection:
:paramtype flow_connection: ~flow.models.AssetTypeMetaInfo
:keyword flow_sample:
:paramtype flow_sample: ~flow.models.AssetTypeMetaInfo
:keyword flow_runtime_spec:
:paramtype flow_runtime_spec: ~flow.models.AssetTypeMetaInfo
"""
super(FeedDtoSupportedAssetTypes, self).__init__(**kwargs)
self.component = component
self.model = model
self.environment = environment
self.dataset = dataset
self.data_store = data_store
self.sample_graph = sample_graph
self.flow_tool = flow_tool
self.flow_tool_setting = flow_tool_setting
self.flow_connection = flow_connection
self.flow_sample = flow_sample
self.flow_runtime_spec = flow_runtime_spec
class FileSystem(msrest.serialization.Model):
"""FileSystem.
:ivar connection:
:vartype connection: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'connection': {'key': 'connection', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
*,
connection: Optional[str] = None,
path: Optional[str] = None,
**kwargs
):
"""
:keyword connection:
:paramtype connection: str
:keyword path:
:paramtype path: str
"""
super(FileSystem, self).__init__(**kwargs)
self.connection = connection
self.path = path
class Flow(msrest.serialization.Model):
"""Flow.
:ivar source_resource_id:
:vartype source_resource_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar node_variants: This is a dictionary.
:vartype node_variants: dict[str, ~flow.models.NodeVariant]
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar bulk_test_data: This is a dictionary.
:vartype bulk_test_data: dict[str, str]
:ivar evaluation_flows: This is a dictionary.
:vartype evaluation_flows: dict[str, ~flow.models.FlowGraphReference]
"""
_attribute_map = {
'source_resource_id': {'key': 'sourceResourceId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'node_variants': {'key': 'nodeVariants', 'type': '{NodeVariant}'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'bulk_test_data': {'key': 'bulkTestData', 'type': '{str}'},
'evaluation_flows': {'key': 'evaluationFlows', 'type': '{FlowGraphReference}'},
}
def __init__(
self,
*,
source_resource_id: Optional[str] = None,
flow_graph: Optional["FlowGraph"] = None,
node_variants: Optional[Dict[str, "NodeVariant"]] = None,
flow_graph_layout: Optional["FlowGraphLayout"] = None,
bulk_test_data: Optional[Dict[str, str]] = None,
evaluation_flows: Optional[Dict[str, "FlowGraphReference"]] = None,
**kwargs
):
"""
:keyword source_resource_id:
:paramtype source_resource_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword node_variants: This is a dictionary.
:paramtype node_variants: dict[str, ~flow.models.NodeVariant]
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword bulk_test_data: This is a dictionary.
:paramtype bulk_test_data: dict[str, str]
:keyword evaluation_flows: This is a dictionary.
:paramtype evaluation_flows: dict[str, ~flow.models.FlowGraphReference]
"""
super(Flow, self).__init__(**kwargs)
self.source_resource_id = source_resource_id
self.flow_graph = flow_graph
self.node_variants = node_variants
self.flow_graph_layout = flow_graph_layout
self.bulk_test_data = bulk_test_data
self.evaluation_flows = evaluation_flows
class FlowAnnotations(msrest.serialization.Model):
"""FlowAnnotations.
:ivar flow_name:
:vartype flow_name: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar archived:
:vartype archived: bool
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'flow_name': {'key': 'flowName', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'archived': {'key': 'archived', 'type': 'bool'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
*,
flow_name: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
is_archived: Optional[bool] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
name: Optional[str] = None,
description: Optional[str] = None,
archived: Optional[bool] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword flow_name:
:paramtype flow_name: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword archived:
:paramtype archived: bool
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(FlowAnnotations, self).__init__(**kwargs)
self.flow_name = flow_name
self.created_date = created_date
self.last_modified_date = last_modified_date
self.owner = owner
self.is_archived = is_archived
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.name = name
self.description = description
self.archived = archived
self.tags = tags
class FlowBaseDto(msrest.serialization.Model):
"""FlowBaseDto.
:ivar flow_id:
:vartype flow_id: str
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar experiment_id:
:vartype experiment_id: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar flow_resource_id:
:vartype flow_resource_id: str
:ivar is_archived:
:vartype is_archived: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'flow_resource_id': {'key': 'flowResourceId', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
*,
flow_id: Optional[str] = None,
flow_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
experiment_id: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
flow_resource_id: Optional[str] = None,
is_archived: Optional[bool] = None,
flow_definition_file_path: Optional[str] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
**kwargs
):
"""
:keyword flow_id:
:paramtype flow_id: str
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword experiment_id:
:paramtype experiment_id: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword flow_resource_id:
:paramtype flow_resource_id: str
:keyword is_archived:
:paramtype is_archived: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowBaseDto, self).__init__(**kwargs)
self.flow_id = flow_id
self.flow_name = flow_name
self.description = description
self.tags = tags
self.flow_type = flow_type
self.experiment_id = experiment_id
self.created_date = created_date
self.last_modified_date = last_modified_date
self.owner = owner
self.flow_resource_id = flow_resource_id
self.is_archived = is_archived
self.flow_definition_file_path = flow_definition_file_path
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
class FlowDto(msrest.serialization.Model):
"""FlowDto.
:ivar timestamp:
:vartype timestamp: ~datetime.datetime
:ivar e_tag: Any object.
:vartype e_tag: any
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar flow_run_result:
:vartype flow_run_result: ~flow.models.FlowRunResult
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar studio_portal_endpoint:
:vartype studio_portal_endpoint: str
:ivar flow_id:
:vartype flow_id: str
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar experiment_id:
:vartype experiment_id: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
:ivar flow_resource_id:
:vartype flow_resource_id: str
:ivar is_archived:
:vartype is_archived: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},
'e_tag': {'key': 'eTag', 'type': 'object'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'flow_run_result': {'key': 'flowRunResult', 'type': 'FlowRunResult'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'studio_portal_endpoint': {'key': 'studioPortalEndpoint', 'type': 'str'},
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
'flow_resource_id': {'key': 'flowResourceId', 'type': 'str'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
*,
timestamp: Optional[datetime.datetime] = None,
e_tag: Optional[Any] = None,
flow: Optional["Flow"] = None,
flow_run_settings: Optional["FlowRunSettings"] = None,
flow_run_result: Optional["FlowRunResult"] = None,
flow_test_mode: Optional[Union[str, "FlowTestMode"]] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
studio_portal_endpoint: Optional[str] = None,
flow_id: Optional[str] = None,
flow_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
experiment_id: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
flow_resource_id: Optional[str] = None,
is_archived: Optional[bool] = None,
flow_definition_file_path: Optional[str] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
**kwargs
):
"""
:keyword timestamp:
:paramtype timestamp: ~datetime.datetime
:keyword e_tag: Any object.
:paramtype e_tag: any
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword flow_run_result:
:paramtype flow_run_result: ~flow.models.FlowRunResult
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword studio_portal_endpoint:
:paramtype studio_portal_endpoint: str
:keyword flow_id:
:paramtype flow_id: str
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword experiment_id:
:paramtype experiment_id: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
:keyword flow_resource_id:
:paramtype flow_resource_id: str
:keyword is_archived:
:paramtype is_archived: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowDto, self).__init__(**kwargs)
self.timestamp = timestamp
self.e_tag = e_tag
self.flow = flow
self.flow_run_settings = flow_run_settings
self.flow_run_result = flow_run_result
self.flow_test_mode = flow_test_mode
self.flow_test_infos = flow_test_infos
self.studio_portal_endpoint = studio_portal_endpoint
self.flow_id = flow_id
self.flow_name = flow_name
self.description = description
self.tags = tags
self.flow_type = flow_type
self.experiment_id = experiment_id
self.created_date = created_date
self.last_modified_date = last_modified_date
self.owner = owner
self.flow_resource_id = flow_resource_id
self.is_archived = is_archived
self.flow_definition_file_path = flow_definition_file_path
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
class FlowEnvironment(msrest.serialization.Model):
"""FlowEnvironment.
:ivar image:
:vartype image: str
:ivar python_requirements_txt:
:vartype python_requirements_txt: str
"""
_attribute_map = {
'image': {'key': 'image', 'type': 'str'},
'python_requirements_txt': {'key': 'python_requirements_txt', 'type': 'str'},
}
def __init__(
self,
*,
image: Optional[str] = None,
python_requirements_txt: Optional[str] = None,
**kwargs
):
"""
:keyword image:
:paramtype image: str
:keyword python_requirements_txt:
:paramtype python_requirements_txt: str
"""
super(FlowEnvironment, self).__init__(**kwargs)
self.image = image
self.python_requirements_txt = python_requirements_txt
class FlowFeature(msrest.serialization.Model):
"""FlowFeature.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar state:
:vartype state: ~flow.models.FlowFeatureState
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'state': {'key': 'state', 'type': 'FlowFeatureState'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
state: Optional["FlowFeatureState"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword state:
:paramtype state: ~flow.models.FlowFeatureState
"""
super(FlowFeature, self).__init__(**kwargs)
self.name = name
self.description = description
self.state = state
class FlowFeatureState(msrest.serialization.Model):
"""FlowFeatureState.
:ivar runtime: Possible values include: "Ready", "E2ETest".
:vartype runtime: str or ~flow.models.FlowFeatureStateEnum
:ivar executor: Possible values include: "Ready", "E2ETest".
:vartype executor: str or ~flow.models.FlowFeatureStateEnum
:ivar pfs: Possible values include: "Ready", "E2ETest".
:vartype pfs: str or ~flow.models.FlowFeatureStateEnum
"""
_attribute_map = {
'runtime': {'key': 'Runtime', 'type': 'str'},
'executor': {'key': 'Executor', 'type': 'str'},
'pfs': {'key': 'PFS', 'type': 'str'},
}
def __init__(
self,
*,
runtime: Optional[Union[str, "FlowFeatureStateEnum"]] = None,
executor: Optional[Union[str, "FlowFeatureStateEnum"]] = None,
pfs: Optional[Union[str, "FlowFeatureStateEnum"]] = None,
**kwargs
):
"""
:keyword runtime: Possible values include: "Ready", "E2ETest".
:paramtype runtime: str or ~flow.models.FlowFeatureStateEnum
:keyword executor: Possible values include: "Ready", "E2ETest".
:paramtype executor: str or ~flow.models.FlowFeatureStateEnum
:keyword pfs: Possible values include: "Ready", "E2ETest".
:paramtype pfs: str or ~flow.models.FlowFeatureStateEnum
"""
super(FlowFeatureState, self).__init__(**kwargs)
self.runtime = runtime
self.executor = executor
self.pfs = pfs
class FlowGraph(msrest.serialization.Model):
"""FlowGraph.
:ivar nodes:
:vartype nodes: list[~flow.models.Node]
:ivar tools:
:vartype tools: list[~flow.models.Tool]
:ivar codes: This is a dictionary.
:vartype codes: dict[str, str]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
"""
_attribute_map = {
'nodes': {'key': 'nodes', 'type': '[Node]'},
'tools': {'key': 'tools', 'type': '[Tool]'},
'codes': {'key': 'codes', 'type': '{str}'},
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
}
def __init__(
self,
*,
nodes: Optional[List["Node"]] = None,
tools: Optional[List["Tool"]] = None,
codes: Optional[Dict[str, str]] = None,
inputs: Optional[Dict[str, "FlowInputDefinition"]] = None,
outputs: Optional[Dict[str, "FlowOutputDefinition"]] = None,
**kwargs
):
"""
:keyword nodes:
:paramtype nodes: list[~flow.models.Node]
:keyword tools:
:paramtype tools: list[~flow.models.Tool]
:keyword codes: This is a dictionary.
:paramtype codes: dict[str, str]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
"""
super(FlowGraph, self).__init__(**kwargs)
self.nodes = nodes
self.tools = tools
self.codes = codes
self.inputs = inputs
self.outputs = outputs
class FlowGraphAnnotationNode(msrest.serialization.Model):
"""FlowGraphAnnotationNode.
:ivar id:
:vartype id: str
:ivar content:
:vartype content: str
:ivar mentioned_node_names:
:vartype mentioned_node_names: list[str]
:ivar structured_content:
:vartype structured_content: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'content': {'key': 'content', 'type': 'str'},
'mentioned_node_names': {'key': 'mentionedNodeNames', 'type': '[str]'},
'structured_content': {'key': 'structuredContent', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
content: Optional[str] = None,
mentioned_node_names: Optional[List[str]] = None,
structured_content: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword content:
:paramtype content: str
:keyword mentioned_node_names:
:paramtype mentioned_node_names: list[str]
:keyword structured_content:
:paramtype structured_content: str
"""
super(FlowGraphAnnotationNode, self).__init__(**kwargs)
self.id = id
self.content = content
self.mentioned_node_names = mentioned_node_names
self.structured_content = structured_content
class FlowGraphLayout(msrest.serialization.Model):
"""FlowGraphLayout.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.FlowNodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.FlowGraphAnnotationNode]
:ivar orientation: Possible values include: "Horizontal", "Vertical".
:vartype orientation: str or ~flow.models.Orientation
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{FlowNodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[FlowGraphAnnotationNode]'},
'orientation': {'key': 'orientation', 'type': 'str'},
}
def __init__(
self,
*,
node_layouts: Optional[Dict[str, "FlowNodeLayout"]] = None,
extended_data: Optional[str] = None,
annotation_nodes: Optional[List["FlowGraphAnnotationNode"]] = None,
orientation: Optional[Union[str, "Orientation"]] = None,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.FlowNodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.FlowGraphAnnotationNode]
:keyword orientation: Possible values include: "Horizontal", "Vertical".
:paramtype orientation: str or ~flow.models.Orientation
"""
super(FlowGraphLayout, self).__init__(**kwargs)
self.node_layouts = node_layouts
self.extended_data = extended_data
self.annotation_nodes = annotation_nodes
self.orientation = orientation
class FlowGraphReference(msrest.serialization.Model):
"""FlowGraphReference.
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar reference_resource_id:
:vartype reference_resource_id: str
:ivar variant:
:vartype variant: ~flow.models.VariantIdentifier
"""
_attribute_map = {
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'reference_resource_id': {'key': 'referenceResourceId', 'type': 'str'},
'variant': {'key': 'variant', 'type': 'VariantIdentifier'},
}
def __init__(
self,
*,
flow_graph: Optional["FlowGraph"] = None,
reference_resource_id: Optional[str] = None,
variant: Optional["VariantIdentifier"] = None,
**kwargs
):
"""
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword reference_resource_id:
:paramtype reference_resource_id: str
:keyword variant:
:paramtype variant: ~flow.models.VariantIdentifier
"""
super(FlowGraphReference, self).__init__(**kwargs)
self.flow_graph = flow_graph
self.reference_resource_id = reference_resource_id
self.variant = variant
class FlowIndexEntity(msrest.serialization.Model):
"""FlowIndexEntity.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar schema_id:
:vartype schema_id: str
:ivar entity_id:
:vartype entity_id: str
:ivar kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:vartype kind: str or ~flow.models.EntityKind
:ivar annotations:
:vartype annotations: ~flow.models.FlowAnnotations
:ivar properties:
:vartype properties: ~flow.models.FlowProperties
:ivar internal: Any object.
:vartype internal: any
:ivar update_sequence:
:vartype update_sequence: long
:ivar type:
:vartype type: str
:ivar version:
:vartype version: str
:ivar entity_container_id:
:vartype entity_container_id: str
:ivar entity_object_id:
:vartype entity_object_id: str
:ivar resource_type:
:vartype resource_type: str
:ivar relationships:
:vartype relationships: list[~flow.models.Relationship]
:ivar asset_id:
:vartype asset_id: str
"""
_validation = {
'version': {'readonly': True},
'entity_container_id': {'readonly': True},
'entity_object_id': {'readonly': True},
'resource_type': {'readonly': True},
}
_attribute_map = {
'schema_id': {'key': 'schemaId', 'type': 'str'},
'entity_id': {'key': 'entityId', 'type': 'str'},
'kind': {'key': 'kind', 'type': 'str'},
'annotations': {'key': 'annotations', 'type': 'FlowAnnotations'},
'properties': {'key': 'properties', 'type': 'FlowProperties'},
'internal': {'key': 'internal', 'type': 'object'},
'update_sequence': {'key': 'updateSequence', 'type': 'long'},
'type': {'key': 'type', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
'entity_object_id': {'key': 'entityObjectId', 'type': 'str'},
'resource_type': {'key': 'resourceType', 'type': 'str'},
'relationships': {'key': 'relationships', 'type': '[Relationship]'},
'asset_id': {'key': 'assetId', 'type': 'str'},
}
def __init__(
self,
*,
schema_id: Optional[str] = None,
entity_id: Optional[str] = None,
kind: Optional[Union[str, "EntityKind"]] = None,
annotations: Optional["FlowAnnotations"] = None,
properties: Optional["FlowProperties"] = None,
internal: Optional[Any] = None,
update_sequence: Optional[int] = None,
type: Optional[str] = None,
relationships: Optional[List["Relationship"]] = None,
asset_id: Optional[str] = None,
**kwargs
):
"""
:keyword schema_id:
:paramtype schema_id: str
:keyword entity_id:
:paramtype entity_id: str
:keyword kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:paramtype kind: str or ~flow.models.EntityKind
:keyword annotations:
:paramtype annotations: ~flow.models.FlowAnnotations
:keyword properties:
:paramtype properties: ~flow.models.FlowProperties
:keyword internal: Any object.
:paramtype internal: any
:keyword update_sequence:
:paramtype update_sequence: long
:keyword type:
:paramtype type: str
:keyword relationships:
:paramtype relationships: list[~flow.models.Relationship]
:keyword asset_id:
:paramtype asset_id: str
"""
super(FlowIndexEntity, self).__init__(**kwargs)
self.schema_id = schema_id
self.entity_id = entity_id
self.kind = kind
self.annotations = annotations
self.properties = properties
self.internal = internal
self.update_sequence = update_sequence
self.type = type
self.version = None
self.entity_container_id = None
self.entity_object_id = None
self.resource_type = None
self.relationships = relationships
self.asset_id = asset_id
class FlowInputDefinition(msrest.serialization.Model):
"""FlowInputDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:vartype type: str or ~flow.models.ValueType
:ivar default: Anything.
:vartype default: any
:ivar description:
:vartype description: str
:ivar is_chat_input:
:vartype is_chat_input: bool
:ivar is_chat_history:
:vartype is_chat_history: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'default': {'key': 'default', 'type': 'object'},
'description': {'key': 'description', 'type': 'str'},
'is_chat_input': {'key': 'is_chat_input', 'type': 'bool'},
'is_chat_history': {'key': 'is_chat_history', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "ValueType"]] = None,
default: Optional[Any] = None,
description: Optional[str] = None,
is_chat_input: Optional[bool] = None,
is_chat_history: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:paramtype type: str or ~flow.models.ValueType
:keyword default: Anything.
:paramtype default: any
:keyword description:
:paramtype description: str
:keyword is_chat_input:
:paramtype is_chat_input: bool
:keyword is_chat_history:
:paramtype is_chat_history: bool
"""
super(FlowInputDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.default = default
self.description = description
self.is_chat_input = is_chat_input
self.is_chat_history = is_chat_history
class FlowNode(msrest.serialization.Model):
"""FlowNode.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:vartype type: str or ~flow.models.ToolType
:ivar source:
:vartype source: ~flow.models.NodeSource
:ivar inputs: Dictionary of :code:`<any>`.
:vartype inputs: dict[str, any]
:ivar activate:
:vartype activate: ~flow.models.Activate
:ivar use_variants:
:vartype use_variants: bool
:ivar comment:
:vartype comment: str
:ivar api:
:vartype api: str
:ivar provider:
:vartype provider: str
:ivar connection:
:vartype connection: str
:ivar module:
:vartype module: str
:ivar aggregation:
:vartype aggregation: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'source': {'key': 'source', 'type': 'NodeSource'},
'inputs': {'key': 'inputs', 'type': '{object}'},
'activate': {'key': 'activate', 'type': 'Activate'},
'use_variants': {'key': 'use_variants', 'type': 'bool'},
'comment': {'key': 'comment', 'type': 'str'},
'api': {'key': 'api', 'type': 'str'},
'provider': {'key': 'provider', 'type': 'str'},
'connection': {'key': 'connection', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
'aggregation': {'key': 'aggregation', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "ToolType"]] = None,
source: Optional["NodeSource"] = None,
inputs: Optional[Dict[str, Any]] = None,
activate: Optional["Activate"] = None,
use_variants: Optional[bool] = None,
comment: Optional[str] = None,
api: Optional[str] = None,
provider: Optional[str] = None,
connection: Optional[str] = None,
module: Optional[str] = None,
aggregation: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:paramtype type: str or ~flow.models.ToolType
:keyword source:
:paramtype source: ~flow.models.NodeSource
:keyword inputs: Dictionary of :code:`<any>`.
:paramtype inputs: dict[str, any]
:keyword activate:
:paramtype activate: ~flow.models.Activate
:keyword use_variants:
:paramtype use_variants: bool
:keyword comment:
:paramtype comment: str
:keyword api:
:paramtype api: str
:keyword provider:
:paramtype provider: str
:keyword connection:
:paramtype connection: str
:keyword module:
:paramtype module: str
:keyword aggregation:
:paramtype aggregation: bool
"""
super(FlowNode, self).__init__(**kwargs)
self.name = name
self.type = type
self.source = source
self.inputs = inputs
self.activate = activate
self.use_variants = use_variants
self.comment = comment
self.api = api
self.provider = provider
self.connection = connection
self.module = module
self.aggregation = aggregation
class FlowNodeLayout(msrest.serialization.Model):
"""FlowNodeLayout.
:ivar x:
:vartype x: float
:ivar y:
:vartype y: float
:ivar width:
:vartype width: float
:ivar height:
:vartype height: float
:ivar index:
:vartype index: int
:ivar extended_data:
:vartype extended_data: str
"""
_attribute_map = {
'x': {'key': 'x', 'type': 'float'},
'y': {'key': 'y', 'type': 'float'},
'width': {'key': 'width', 'type': 'float'},
'height': {'key': 'height', 'type': 'float'},
'index': {'key': 'index', 'type': 'int'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
}
def __init__(
self,
*,
x: Optional[float] = None,
y: Optional[float] = None,
width: Optional[float] = None,
height: Optional[float] = None,
index: Optional[int] = None,
extended_data: Optional[str] = None,
**kwargs
):
"""
:keyword x:
:paramtype x: float
:keyword y:
:paramtype y: float
:keyword width:
:paramtype width: float
:keyword height:
:paramtype height: float
:keyword index:
:paramtype index: int
:keyword extended_data:
:paramtype extended_data: str
"""
super(FlowNodeLayout, self).__init__(**kwargs)
self.x = x
self.y = y
self.width = width
self.height = height
self.index = index
self.extended_data = extended_data
class FlowNodeVariant(msrest.serialization.Model):
"""FlowNodeVariant.
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, ~flow.models.FlowVariantNode]
"""
_attribute_map = {
'default_variant_id': {'key': 'default_variant_id', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{FlowVariantNode}'},
}
def __init__(
self,
*,
default_variant_id: Optional[str] = None,
variants: Optional[Dict[str, "FlowVariantNode"]] = None,
**kwargs
):
"""
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, ~flow.models.FlowVariantNode]
"""
super(FlowNodeVariant, self).__init__(**kwargs)
self.default_variant_id = default_variant_id
self.variants = variants
class FlowOutputDefinition(msrest.serialization.Model):
"""FlowOutputDefinition.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:vartype type: str or ~flow.models.ValueType
:ivar description:
:vartype description: str
:ivar reference:
:vartype reference: str
:ivar evaluation_only:
:vartype evaluation_only: bool
:ivar is_chat_output:
:vartype is_chat_output: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'reference': {'key': 'reference', 'type': 'str'},
'evaluation_only': {'key': 'evaluation_only', 'type': 'bool'},
'is_chat_output': {'key': 'is_chat_output', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "ValueType"]] = None,
description: Optional[str] = None,
reference: Optional[str] = None,
evaluation_only: Optional[bool] = None,
is_chat_output: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:paramtype type: str or ~flow.models.ValueType
:keyword description:
:paramtype description: str
:keyword reference:
:paramtype reference: str
:keyword evaluation_only:
:paramtype evaluation_only: bool
:keyword is_chat_output:
:paramtype is_chat_output: bool
"""
super(FlowOutputDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.description = description
self.reference = reference
self.evaluation_only = evaluation_only
self.is_chat_output = is_chat_output
class FlowProperties(msrest.serialization.Model):
"""FlowProperties.
:ivar flow_id:
:vartype flow_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar creation_context:
:vartype creation_context: ~flow.models.CreationContext
"""
_attribute_map = {
'flow_id': {'key': 'flowId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'creation_context': {'key': 'creationContext', 'type': 'CreationContext'},
}
def __init__(
self,
*,
flow_id: Optional[str] = None,
experiment_id: Optional[str] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
flow_definition_file_path: Optional[str] = None,
creation_context: Optional["CreationContext"] = None,
**kwargs
):
"""
:keyword flow_id:
:paramtype flow_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword creation_context:
:paramtype creation_context: ~flow.models.CreationContext
"""
super(FlowProperties, self).__init__(**kwargs)
self.flow_id = flow_id
self.experiment_id = experiment_id
self.flow_type = flow_type
self.flow_definition_file_path = flow_definition_file_path
self.creation_context = creation_context
class FlowRunBasePath(msrest.serialization.Model):
"""FlowRunBasePath.
:ivar output_datastore_name:
:vartype output_datastore_name: str
:ivar base_path:
:vartype base_path: str
"""
_attribute_map = {
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
'base_path': {'key': 'basePath', 'type': 'str'},
}
def __init__(
self,
*,
output_datastore_name: Optional[str] = None,
base_path: Optional[str] = None,
**kwargs
):
"""
:keyword output_datastore_name:
:paramtype output_datastore_name: str
:keyword base_path:
:paramtype base_path: str
"""
super(FlowRunBasePath, self).__init__(**kwargs)
self.output_datastore_name = output_datastore_name
self.base_path = base_path
class FlowRunInfo(msrest.serialization.Model):
"""FlowRunInfo.
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar output_datastore_name:
:vartype output_datastore_name: str
:ivar child_run_base_path:
:vartype child_run_base_path: str
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar studio_portal_endpoint:
:vartype studio_portal_endpoint: str
"""
_attribute_map = {
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
'child_run_base_path': {'key': 'childRunBasePath', 'type': 'str'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'studio_portal_endpoint': {'key': 'studioPortalEndpoint', 'type': 'str'},
}
def __init__(
self,
*,
flow_graph: Optional["FlowGraph"] = None,
flow_graph_layout: Optional["FlowGraphLayout"] = None,
flow_name: Optional[str] = None,
flow_run_resource_id: Optional[str] = None,
flow_run_id: Optional[str] = None,
flow_run_display_name: Optional[str] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
batch_data_input: Optional["BatchDataInput"] = None,
flow_run_type: Optional[Union[str, "FlowRunTypeEnum"]] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
runtime_name: Optional[str] = None,
bulk_test_id: Optional[str] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
created_on: Optional[datetime.datetime] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
output_datastore_name: Optional[str] = None,
child_run_base_path: Optional[str] = None,
working_directory: Optional[str] = None,
flow_dag_file_relative_path: Optional[str] = None,
flow_snapshot_id: Optional[str] = None,
studio_portal_endpoint: Optional[str] = None,
**kwargs
):
"""
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword output_datastore_name:
:paramtype output_datastore_name: str
:keyword child_run_base_path:
:paramtype child_run_base_path: str
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword studio_portal_endpoint:
:paramtype studio_portal_endpoint: str
"""
super(FlowRunInfo, self).__init__(**kwargs)
self.flow_graph = flow_graph
self.flow_graph_layout = flow_graph_layout
self.flow_name = flow_name
self.flow_run_resource_id = flow_run_resource_id
self.flow_run_id = flow_run_id
self.flow_run_display_name = flow_run_display_name
self.batch_inputs = batch_inputs
self.batch_data_input = batch_data_input
self.flow_run_type = flow_run_type
self.flow_type = flow_type
self.runtime_name = runtime_name
self.bulk_test_id = bulk_test_id
self.created_by = created_by
self.created_on = created_on
self.inputs_mapping = inputs_mapping
self.output_datastore_name = output_datastore_name
self.child_run_base_path = child_run_base_path
self.working_directory = working_directory
self.flow_dag_file_relative_path = flow_dag_file_relative_path
self.flow_snapshot_id = flow_snapshot_id
self.studio_portal_endpoint = studio_portal_endpoint
class FlowRunResult(msrest.serialization.Model):
"""FlowRunResult.
:ivar flow_runs:
:vartype flow_runs: list[any]
:ivar node_runs:
:vartype node_runs: list[any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar flow_run_logs: Dictionary of :code:`<string>`.
:vartype flow_run_logs: dict[str, str]
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1k1eaeg·schemas·flowrunresult·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:vartype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
_attribute_map = {
'flow_runs': {'key': 'flow_runs', 'type': '[object]'},
'node_runs': {'key': 'node_runs', 'type': '[object]'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'flow_run_logs': {'key': 'flowRunLogs', 'type': '{str}'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'variant_run_to_evaluation_runs_id_mapping': {'key': 'variantRunToEvaluationRunsIdMapping', 'type': '{[str]}'},
}
def __init__(
self,
*,
flow_runs: Optional[List[Any]] = None,
node_runs: Optional[List[Any]] = None,
error_response: Optional["ErrorResponse"] = None,
flow_name: Optional[str] = None,
flow_run_display_name: Optional[str] = None,
flow_run_id: Optional[str] = None,
flow_graph: Optional["FlowGraph"] = None,
flow_graph_layout: Optional["FlowGraphLayout"] = None,
flow_run_resource_id: Optional[str] = None,
bulk_test_id: Optional[str] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
batch_data_input: Optional["BatchDataInput"] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
created_on: Optional[datetime.datetime] = None,
flow_run_type: Optional[Union[str, "FlowRunTypeEnum"]] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
runtime_name: Optional[str] = None,
aml_compute_name: Optional[str] = None,
flow_run_logs: Optional[Dict[str, str]] = None,
flow_test_mode: Optional[Union[str, "FlowTestMode"]] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
working_directory: Optional[str] = None,
flow_dag_file_relative_path: Optional[str] = None,
flow_snapshot_id: Optional[str] = None,
variant_run_to_evaluation_runs_id_mapping: Optional[Dict[str, List[str]]] = None,
**kwargs
):
"""
:keyword flow_runs:
:paramtype flow_runs: list[any]
:keyword node_runs:
:paramtype node_runs: list[any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword flow_run_logs: Dictionary of :code:`<string>`.
:paramtype flow_run_logs: dict[str, str]
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1k1eaeg·schemas·flowrunresult·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:paramtype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
super(FlowRunResult, self).__init__(**kwargs)
self.flow_runs = flow_runs
self.node_runs = node_runs
self.error_response = error_response
self.flow_name = flow_name
self.flow_run_display_name = flow_run_display_name
self.flow_run_id = flow_run_id
self.flow_graph = flow_graph
self.flow_graph_layout = flow_graph_layout
self.flow_run_resource_id = flow_run_resource_id
self.bulk_test_id = bulk_test_id
self.batch_inputs = batch_inputs
self.batch_data_input = batch_data_input
self.created_by = created_by
self.created_on = created_on
self.flow_run_type = flow_run_type
self.flow_type = flow_type
self.runtime_name = runtime_name
self.aml_compute_name = aml_compute_name
self.flow_run_logs = flow_run_logs
self.flow_test_mode = flow_test_mode
self.flow_test_infos = flow_test_infos
self.working_directory = working_directory
self.flow_dag_file_relative_path = flow_dag_file_relative_path
self.flow_snapshot_id = flow_snapshot_id
self.variant_run_to_evaluation_runs_id_mapping = variant_run_to_evaluation_runs_id_mapping
class FlowRunSettings(msrest.serialization.Model):
"""FlowRunSettings.
:ivar run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest", "Eval",
"PairwiseEval", "ExperimentTest", "ExperimentEval".
:vartype run_mode: str or ~flow.models.FlowRunMode
:ivar tuning_node_names:
:vartype tuning_node_names: list[str]
:ivar tuning_node_settings: This is a dictionary.
:vartype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:ivar baseline_variant_id:
:vartype baseline_variant_id: str
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, list[~flow.models.Node]]
:ivar node_name:
:vartype node_name: str
:ivar is_default_variant:
:vartype is_default_variant: bool
:ivar node_variant_id:
:vartype node_variant_id: str
:ivar node_output_paths: Dictionary of :code:`<string>`.
:vartype node_output_paths: dict[str, str]
:ivar base_flow_run_id:
:vartype base_flow_run_id: str
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar evaluation_flow_run_settings: This is a dictionary.
:vartype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:ivar bulk_test_flow_id:
:vartype bulk_test_flow_id: str
:ivar bulk_test_flow_run_ids:
:vartype bulk_test_flow_run_ids: list[str]
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar input_universal_link:
:vartype input_universal_link: str
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'run_mode': {'key': 'runMode', 'type': 'str'},
'tuning_node_names': {'key': 'tuningNodeNames', 'type': '[str]'},
'tuning_node_settings': {'key': 'tuningNodeSettings', 'type': '{TuningNodeSetting}'},
'baseline_variant_id': {'key': 'baselineVariantId', 'type': 'str'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{[Node]}'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'is_default_variant': {'key': 'isDefaultVariant', 'type': 'bool'},
'node_variant_id': {'key': 'nodeVariantId', 'type': 'str'},
'node_output_paths': {'key': 'nodeOutputPaths', 'type': '{str}'},
'base_flow_run_id': {'key': 'baseFlowRunId', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'evaluation_flow_run_settings': {'key': 'evaluationFlowRunSettings', 'type': '{EvaluationFlowRunSettings}'},
'bulk_test_flow_id': {'key': 'bulkTestFlowId', 'type': 'str'},
'bulk_test_flow_run_ids': {'key': 'bulkTestFlowRunIds', 'type': '[str]'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'input_universal_link': {'key': 'inputUniversalLink', 'type': 'str'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
run_mode: Optional[Union[str, "FlowRunMode"]] = None,
tuning_node_names: Optional[List[str]] = None,
tuning_node_settings: Optional[Dict[str, "TuningNodeSetting"]] = None,
baseline_variant_id: Optional[str] = None,
default_variant_id: Optional[str] = None,
variants: Optional[Dict[str, List["Node"]]] = None,
node_name: Optional[str] = None,
is_default_variant: Optional[bool] = None,
node_variant_id: Optional[str] = None,
node_output_paths: Optional[Dict[str, str]] = None,
base_flow_run_id: Optional[str] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
bulk_test_id: Optional[str] = None,
evaluation_flow_run_settings: Optional[Dict[str, "EvaluationFlowRunSettings"]] = None,
bulk_test_flow_id: Optional[str] = None,
bulk_test_flow_run_ids: Optional[List[str]] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
input_universal_link: Optional[str] = None,
data_inputs: Optional[Dict[str, str]] = None,
flow_run_output_directory: Optional[str] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest",
"Eval", "PairwiseEval", "ExperimentTest", "ExperimentEval".
:paramtype run_mode: str or ~flow.models.FlowRunMode
:keyword tuning_node_names:
:paramtype tuning_node_names: list[str]
:keyword tuning_node_settings: This is a dictionary.
:paramtype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:keyword baseline_variant_id:
:paramtype baseline_variant_id: str
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, list[~flow.models.Node]]
:keyword node_name:
:paramtype node_name: str
:keyword is_default_variant:
:paramtype is_default_variant: bool
:keyword node_variant_id:
:paramtype node_variant_id: str
:keyword node_output_paths: Dictionary of :code:`<string>`.
:paramtype node_output_paths: dict[str, str]
:keyword base_flow_run_id:
:paramtype base_flow_run_id: str
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword evaluation_flow_run_settings: This is a dictionary.
:paramtype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:keyword bulk_test_flow_id:
:paramtype bulk_test_flow_id: str
:keyword bulk_test_flow_run_ids:
:paramtype bulk_test_flow_run_ids: list[str]
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword input_universal_link:
:paramtype input_universal_link: str
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(FlowRunSettings, self).__init__(**kwargs)
self.run_mode = run_mode
self.tuning_node_names = tuning_node_names
self.tuning_node_settings = tuning_node_settings
self.baseline_variant_id = baseline_variant_id
self.default_variant_id = default_variant_id
self.variants = variants
self.node_name = node_name
self.is_default_variant = is_default_variant
self.node_variant_id = node_variant_id
self.node_output_paths = node_output_paths
self.base_flow_run_id = base_flow_run_id
self.flow_test_infos = flow_test_infos
self.bulk_test_id = bulk_test_id
self.evaluation_flow_run_settings = evaluation_flow_run_settings
self.bulk_test_flow_id = bulk_test_flow_id
self.bulk_test_flow_run_ids = bulk_test_flow_run_ids
self.batch_inputs = batch_inputs
self.input_universal_link = input_universal_link
self.data_inputs = data_inputs
self.flow_run_output_directory = flow_run_output_directory
self.connection_overrides = connection_overrides
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class FlowRunSettingsBase(msrest.serialization.Model):
"""FlowRunSettingsBase.
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar input_universal_link:
:vartype input_universal_link: str
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'input_universal_link': {'key': 'inputUniversalLink', 'type': 'str'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
input_universal_link: Optional[str] = None,
data_inputs: Optional[Dict[str, str]] = None,
flow_run_output_directory: Optional[str] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword input_universal_link:
:paramtype input_universal_link: str
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(FlowRunSettingsBase, self).__init__(**kwargs)
self.batch_inputs = batch_inputs
self.input_universal_link = input_universal_link
self.data_inputs = data_inputs
self.flow_run_output_directory = flow_run_output_directory
self.connection_overrides = connection_overrides
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class FlowRunStatusResponse(msrest.serialization.Model):
"""FlowRunStatusResponse.
:ivar flow_run_status: Possible values include: "Started", "Completed", "Failed", "Cancelled",
"NotStarted", "Running", "Queued", "Paused", "Unapproved", "Starting", "Preparing",
"CancelRequested", "Pausing", "Finalizing", "Canceled", "Bypassed".
:vartype flow_run_status: str or ~flow.models.FlowRunStatusEnum
:ivar last_checked_time:
:vartype last_checked_time: ~datetime.datetime
:ivar flow_run_created_time:
:vartype flow_run_created_time: ~datetime.datetime
"""
_attribute_map = {
'flow_run_status': {'key': 'flowRunStatus', 'type': 'str'},
'last_checked_time': {'key': 'lastCheckedTime', 'type': 'iso-8601'},
'flow_run_created_time': {'key': 'flowRunCreatedTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
flow_run_status: Optional[Union[str, "FlowRunStatusEnum"]] = None,
last_checked_time: Optional[datetime.datetime] = None,
flow_run_created_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword flow_run_status: Possible values include: "Started", "Completed", "Failed",
"Cancelled", "NotStarted", "Running", "Queued", "Paused", "Unapproved", "Starting",
"Preparing", "CancelRequested", "Pausing", "Finalizing", "Canceled", "Bypassed".
:paramtype flow_run_status: str or ~flow.models.FlowRunStatusEnum
:keyword last_checked_time:
:paramtype last_checked_time: ~datetime.datetime
:keyword flow_run_created_time:
:paramtype flow_run_created_time: ~datetime.datetime
"""
super(FlowRunStatusResponse, self).__init__(**kwargs)
self.flow_run_status = flow_run_status
self.last_checked_time = last_checked_time
self.flow_run_created_time = flow_run_created_time
class FlowRuntimeCapability(msrest.serialization.Model):
"""FlowRuntimeCapability.
:ivar flow_features:
:vartype flow_features: list[~flow.models.FlowFeature]
"""
_attribute_map = {
'flow_features': {'key': 'flowFeatures', 'type': '[FlowFeature]'},
}
def __init__(
self,
*,
flow_features: Optional[List["FlowFeature"]] = None,
**kwargs
):
"""
:keyword flow_features:
:paramtype flow_features: list[~flow.models.FlowFeature]
"""
super(FlowRuntimeCapability, self).__init__(**kwargs)
self.flow_features = flow_features
class FlowRuntimeDto(msrest.serialization.Model):
"""FlowRuntimeDto.
:ivar runtime_name:
:vartype runtime_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar environment:
:vartype environment: str
:ivar status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:vartype status: str or ~flow.models.RuntimeStatusEnum
:ivar status_message:
:vartype status_message: str
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar docker_image:
:vartype docker_image: str
:ivar published_port:
:vartype published_port: int
:ivar target_port:
:vartype target_port: int
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar assigned_to:
:vartype assigned_to: ~flow.models.AssignedUser
:ivar endpoint_url:
:vartype endpoint_url: str
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
"""
_attribute_map = {
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_message': {'key': 'statusMessage', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'docker_image': {'key': 'dockerImage', 'type': 'str'},
'published_port': {'key': 'publishedPort', 'type': 'int'},
'target_port': {'key': 'targetPort', 'type': 'int'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'assigned_to': {'key': 'assignedTo', 'type': 'AssignedUser'},
'endpoint_url': {'key': 'endpointUrl', 'type': 'str'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
}
def __init__(
self,
*,
runtime_name: Optional[str] = None,
runtime_description: Optional[str] = None,
runtime_type: Optional[Union[str, "RuntimeType"]] = None,
environment: Optional[str] = None,
status: Optional[Union[str, "RuntimeStatusEnum"]] = None,
status_message: Optional[str] = None,
error: Optional["ErrorResponse"] = None,
from_existing_endpoint: Optional[bool] = None,
endpoint_name: Optional[str] = None,
from_existing_deployment: Optional[bool] = None,
deployment_name: Optional[str] = None,
identity: Optional["ManagedServiceIdentity"] = None,
instance_type: Optional[str] = None,
instance_count: Optional[int] = None,
compute_instance_name: Optional[str] = None,
docker_image: Optional[str] = None,
published_port: Optional[int] = None,
target_port: Optional[int] = None,
from_existing_custom_app: Optional[bool] = None,
custom_app_name: Optional[str] = None,
assigned_to: Optional["AssignedUser"] = None,
endpoint_url: Optional[str] = None,
created_on: Optional[datetime.datetime] = None,
modified_on: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
**kwargs
):
"""
:keyword runtime_name:
:paramtype runtime_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword environment:
:paramtype environment: str
:keyword status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:paramtype status: str or ~flow.models.RuntimeStatusEnum
:keyword status_message:
:paramtype status_message: str
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword docker_image:
:paramtype docker_image: str
:keyword published_port:
:paramtype published_port: int
:keyword target_port:
:paramtype target_port: int
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword assigned_to:
:paramtype assigned_to: ~flow.models.AssignedUser
:keyword endpoint_url:
:paramtype endpoint_url: str
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
"""
super(FlowRuntimeDto, self).__init__(**kwargs)
self.runtime_name = runtime_name
self.runtime_description = runtime_description
self.runtime_type = runtime_type
self.environment = environment
self.status = status
self.status_message = status_message
self.error = error
self.from_existing_endpoint = from_existing_endpoint
self.endpoint_name = endpoint_name
self.from_existing_deployment = from_existing_deployment
self.deployment_name = deployment_name
self.identity = identity
self.instance_type = instance_type
self.instance_count = instance_count
self.compute_instance_name = compute_instance_name
self.docker_image = docker_image
self.published_port = published_port
self.target_port = target_port
self.from_existing_custom_app = from_existing_custom_app
self.custom_app_name = custom_app_name
self.assigned_to = assigned_to
self.endpoint_url = endpoint_url
self.created_on = created_on
self.modified_on = modified_on
self.owner = owner
class FlowSampleDto(msrest.serialization.Model):
"""FlowSampleDto.
:ivar sample_resource_id:
:vartype sample_resource_id: str
:ivar section: Possible values include: "Gallery", "Template".
:vartype section: str or ~flow.models.Section
:ivar index_number:
:vartype index_number: int
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'sample_resource_id': {'key': 'sampleResourceId', 'type': 'str'},
'section': {'key': 'section', 'type': 'str'},
'index_number': {'key': 'indexNumber', 'type': 'int'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
*,
sample_resource_id: Optional[str] = None,
section: Optional[Union[str, "Section"]] = None,
index_number: Optional[int] = None,
flow_name: Optional[str] = None,
description: Optional[str] = None,
details: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
flow: Optional["Flow"] = None,
flow_definition_file_path: Optional[str] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
flow_run_settings: Optional["FlowRunSettings"] = None,
is_archived: Optional[bool] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
**kwargs
):
"""
:keyword sample_resource_id:
:paramtype sample_resource_id: str
:keyword section: Possible values include: "Gallery", "Template".
:paramtype section: str or ~flow.models.Section
:keyword index_number:
:paramtype index_number: int
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(FlowSampleDto, self).__init__(**kwargs)
self.sample_resource_id = sample_resource_id
self.section = section
self.index_number = index_number
self.flow_name = flow_name
self.description = description
self.details = details
self.tags = tags
self.flow = flow
self.flow_definition_file_path = flow_definition_file_path
self.flow_type = flow_type
self.flow_run_settings = flow_run_settings
self.is_archived = is_archived
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
class FlowSessionDto(msrest.serialization.Model):
"""FlowSessionDto.
:ivar session_id:
:vartype session_id: str
:ivar base_image:
:vartype base_image: str
:ivar packages:
:vartype packages: list[str]
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar compute_name:
:vartype compute_name: str
:ivar flow_features:
:vartype flow_features: list[~flow.models.FlowFeature]
:ivar runtime_name:
:vartype runtime_name: str
:ivar runtime_description:
:vartype runtime_description: str
:ivar runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:vartype runtime_type: str or ~flow.models.RuntimeType
:ivar environment:
:vartype environment: str
:ivar status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:vartype status: str or ~flow.models.RuntimeStatusEnum
:ivar status_message:
:vartype status_message: str
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar from_existing_endpoint:
:vartype from_existing_endpoint: bool
:ivar endpoint_name:
:vartype endpoint_name: str
:ivar from_existing_deployment:
:vartype from_existing_deployment: bool
:ivar deployment_name:
:vartype deployment_name: str
:ivar identity:
:vartype identity: ~flow.models.ManagedServiceIdentity
:ivar instance_type:
:vartype instance_type: str
:ivar instance_count:
:vartype instance_count: int
:ivar compute_instance_name:
:vartype compute_instance_name: str
:ivar docker_image:
:vartype docker_image: str
:ivar published_port:
:vartype published_port: int
:ivar target_port:
:vartype target_port: int
:ivar from_existing_custom_app:
:vartype from_existing_custom_app: bool
:ivar custom_app_name:
:vartype custom_app_name: str
:ivar assigned_to:
:vartype assigned_to: ~flow.models.AssignedUser
:ivar endpoint_url:
:vartype endpoint_url: str
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar modified_on:
:vartype modified_on: ~datetime.datetime
:ivar owner:
:vartype owner: ~flow.models.SchemaContractsCreatedBy
"""
_attribute_map = {
'session_id': {'key': 'sessionId', 'type': 'str'},
'base_image': {'key': 'baseImage', 'type': 'str'},
'packages': {'key': 'packages', 'type': '[str]'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'flow_features': {'key': 'flowFeatures', 'type': '[FlowFeature]'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'runtime_type': {'key': 'runtimeType', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_message': {'key': 'statusMessage', 'type': 'str'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'from_existing_endpoint': {'key': 'fromExistingEndpoint', 'type': 'bool'},
'endpoint_name': {'key': 'endpointName', 'type': 'str'},
'from_existing_deployment': {'key': 'fromExistingDeployment', 'type': 'bool'},
'deployment_name': {'key': 'deploymentName', 'type': 'str'},
'identity': {'key': 'identity', 'type': 'ManagedServiceIdentity'},
'instance_type': {'key': 'instanceType', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
'compute_instance_name': {'key': 'computeInstanceName', 'type': 'str'},
'docker_image': {'key': 'dockerImage', 'type': 'str'},
'published_port': {'key': 'publishedPort', 'type': 'int'},
'target_port': {'key': 'targetPort', 'type': 'int'},
'from_existing_custom_app': {'key': 'fromExistingCustomApp', 'type': 'bool'},
'custom_app_name': {'key': 'customAppName', 'type': 'str'},
'assigned_to': {'key': 'assignedTo', 'type': 'AssignedUser'},
'endpoint_url': {'key': 'endpointUrl', 'type': 'str'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'modified_on': {'key': 'modifiedOn', 'type': 'iso-8601'},
'owner': {'key': 'owner', 'type': 'SchemaContractsCreatedBy'},
}
def __init__(
self,
*,
session_id: Optional[str] = None,
base_image: Optional[str] = None,
packages: Optional[List[str]] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
compute_name: Optional[str] = None,
flow_features: Optional[List["FlowFeature"]] = None,
runtime_name: Optional[str] = None,
runtime_description: Optional[str] = None,
runtime_type: Optional[Union[str, "RuntimeType"]] = None,
environment: Optional[str] = None,
status: Optional[Union[str, "RuntimeStatusEnum"]] = None,
status_message: Optional[str] = None,
error: Optional["ErrorResponse"] = None,
from_existing_endpoint: Optional[bool] = None,
endpoint_name: Optional[str] = None,
from_existing_deployment: Optional[bool] = None,
deployment_name: Optional[str] = None,
identity: Optional["ManagedServiceIdentity"] = None,
instance_type: Optional[str] = None,
instance_count: Optional[int] = None,
compute_instance_name: Optional[str] = None,
docker_image: Optional[str] = None,
published_port: Optional[int] = None,
target_port: Optional[int] = None,
from_existing_custom_app: Optional[bool] = None,
custom_app_name: Optional[str] = None,
assigned_to: Optional["AssignedUser"] = None,
endpoint_url: Optional[str] = None,
created_on: Optional[datetime.datetime] = None,
modified_on: Optional[datetime.datetime] = None,
owner: Optional["SchemaContractsCreatedBy"] = None,
**kwargs
):
"""
:keyword session_id:
:paramtype session_id: str
:keyword base_image:
:paramtype base_image: str
:keyword packages:
:paramtype packages: list[str]
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword compute_name:
:paramtype compute_name: str
:keyword flow_features:
:paramtype flow_features: list[~flow.models.FlowFeature]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword runtime_description:
:paramtype runtime_description: str
:keyword runtime_type: Possible values include: "ManagedOnlineEndpoint", "ComputeInstance",
"TrainingSession".
:paramtype runtime_type: str or ~flow.models.RuntimeType
:keyword environment:
:paramtype environment: str
:keyword status: Possible values include: "Unavailable", "Failed", "NotExist", "Starting",
"Stopping".
:paramtype status: str or ~flow.models.RuntimeStatusEnum
:keyword status_message:
:paramtype status_message: str
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword from_existing_endpoint:
:paramtype from_existing_endpoint: bool
:keyword endpoint_name:
:paramtype endpoint_name: str
:keyword from_existing_deployment:
:paramtype from_existing_deployment: bool
:keyword deployment_name:
:paramtype deployment_name: str
:keyword identity:
:paramtype identity: ~flow.models.ManagedServiceIdentity
:keyword instance_type:
:paramtype instance_type: str
:keyword instance_count:
:paramtype instance_count: int
:keyword compute_instance_name:
:paramtype compute_instance_name: str
:keyword docker_image:
:paramtype docker_image: str
:keyword published_port:
:paramtype published_port: int
:keyword target_port:
:paramtype target_port: int
:keyword from_existing_custom_app:
:paramtype from_existing_custom_app: bool
:keyword custom_app_name:
:paramtype custom_app_name: str
:keyword assigned_to:
:paramtype assigned_to: ~flow.models.AssignedUser
:keyword endpoint_url:
:paramtype endpoint_url: str
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword modified_on:
:paramtype modified_on: ~datetime.datetime
:keyword owner:
:paramtype owner: ~flow.models.SchemaContractsCreatedBy
"""
super(FlowSessionDto, self).__init__(**kwargs)
self.session_id = session_id
self.base_image = base_image
self.packages = packages
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.compute_name = compute_name
self.flow_features = flow_features
self.runtime_name = runtime_name
self.runtime_description = runtime_description
self.runtime_type = runtime_type
self.environment = environment
self.status = status
self.status_message = status_message
self.error = error
self.from_existing_endpoint = from_existing_endpoint
self.endpoint_name = endpoint_name
self.from_existing_deployment = from_existing_deployment
self.deployment_name = deployment_name
self.identity = identity
self.instance_type = instance_type
self.instance_count = instance_count
self.compute_instance_name = compute_instance_name
self.docker_image = docker_image
self.published_port = published_port
self.target_port = target_port
self.from_existing_custom_app = from_existing_custom_app
self.custom_app_name = custom_app_name
self.assigned_to = assigned_to
self.endpoint_url = endpoint_url
self.created_on = created_on
self.modified_on = modified_on
self.owner = owner
class FlowSnapshot(msrest.serialization.Model):
"""FlowSnapshot.
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.FlowInputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:ivar nodes:
:vartype nodes: list[~flow.models.FlowNode]
:ivar node_variants: This is a dictionary.
:vartype node_variants: dict[str, ~flow.models.FlowNodeVariant]
:ivar environment:
:vartype environment: ~flow.models.FlowEnvironment
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, any]
:ivar language: Possible values include: "Python", "CSharp", "TypeScript", "JavaScript".
:vartype language: str or ~flow.models.FlowLanguage
:ivar path:
:vartype path: str
:ivar entry:
:vartype entry: str
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '{FlowInputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{FlowOutputDefinition}'},
'nodes': {'key': 'nodes', 'type': '[FlowNode]'},
'node_variants': {'key': 'node_variants', 'type': '{FlowNodeVariant}'},
'environment': {'key': 'environment', 'type': 'FlowEnvironment'},
'environment_variables': {'key': 'environment_variables', 'type': '{object}'},
'language': {'key': 'language', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'str'},
}
def __init__(
self,
*,
inputs: Optional[Dict[str, "FlowInputDefinition"]] = None,
outputs: Optional[Dict[str, "FlowOutputDefinition"]] = None,
nodes: Optional[List["FlowNode"]] = None,
node_variants: Optional[Dict[str, "FlowNodeVariant"]] = None,
environment: Optional["FlowEnvironment"] = None,
environment_variables: Optional[Dict[str, Any]] = None,
language: Optional[Union[str, "FlowLanguage"]] = None,
path: Optional[str] = None,
entry: Optional[str] = None,
**kwargs
):
"""
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.FlowInputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.FlowOutputDefinition]
:keyword nodes:
:paramtype nodes: list[~flow.models.FlowNode]
:keyword node_variants: This is a dictionary.
:paramtype node_variants: dict[str, ~flow.models.FlowNodeVariant]
:keyword environment:
:paramtype environment: ~flow.models.FlowEnvironment
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, any]
:keyword language: Possible values include: "Python", "CSharp", "TypeScript", "JavaScript".
:paramtype language: str or ~flow.models.FlowLanguage
:keyword path:
:paramtype path: str
:keyword entry:
:paramtype entry: str
"""
super(FlowSnapshot, self).__init__(**kwargs)
self.inputs = inputs
self.outputs = outputs
self.nodes = nodes
self.node_variants = node_variants
self.environment = environment
self.environment_variables = environment_variables
self.language = language
self.path = path
self.entry = entry
class FlowSubmitRunSettings(msrest.serialization.Model):
"""FlowSubmitRunSettings.
:ivar node_inputs: This is a dictionary.
:vartype node_inputs: dict[str, any]
:ivar run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest", "Eval",
"PairwiseEval", "ExperimentTest", "ExperimentEval".
:vartype run_mode: str or ~flow.models.FlowRunMode
:ivar tuning_node_names:
:vartype tuning_node_names: list[str]
:ivar tuning_node_settings: This is a dictionary.
:vartype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:ivar baseline_variant_id:
:vartype baseline_variant_id: str
:ivar default_variant_id:
:vartype default_variant_id: str
:ivar variants: This is a dictionary.
:vartype variants: dict[str, list[~flow.models.Node]]
:ivar node_name:
:vartype node_name: str
:ivar is_default_variant:
:vartype is_default_variant: bool
:ivar node_variant_id:
:vartype node_variant_id: str
:ivar node_output_paths: Dictionary of :code:`<string>`.
:vartype node_output_paths: dict[str, str]
:ivar base_flow_run_id:
:vartype base_flow_run_id: str
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar evaluation_flow_run_settings: This is a dictionary.
:vartype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:ivar bulk_test_flow_id:
:vartype bulk_test_flow_id: str
:ivar bulk_test_flow_run_ids:
:vartype bulk_test_flow_run_ids: list[str]
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar input_universal_link:
:vartype input_universal_link: str
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'node_inputs': {'key': 'nodeInputs', 'type': '{object}'},
'run_mode': {'key': 'runMode', 'type': 'str'},
'tuning_node_names': {'key': 'tuningNodeNames', 'type': '[str]'},
'tuning_node_settings': {'key': 'tuningNodeSettings', 'type': '{TuningNodeSetting}'},
'baseline_variant_id': {'key': 'baselineVariantId', 'type': 'str'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
'variants': {'key': 'variants', 'type': '{[Node]}'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'is_default_variant': {'key': 'isDefaultVariant', 'type': 'bool'},
'node_variant_id': {'key': 'nodeVariantId', 'type': 'str'},
'node_output_paths': {'key': 'nodeOutputPaths', 'type': '{str}'},
'base_flow_run_id': {'key': 'baseFlowRunId', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'evaluation_flow_run_settings': {'key': 'evaluationFlowRunSettings', 'type': '{EvaluationFlowRunSettings}'},
'bulk_test_flow_id': {'key': 'bulkTestFlowId', 'type': 'str'},
'bulk_test_flow_run_ids': {'key': 'bulkTestFlowRunIds', 'type': '[str]'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'input_universal_link': {'key': 'inputUniversalLink', 'type': 'str'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
node_inputs: Optional[Dict[str, Any]] = None,
run_mode: Optional[Union[str, "FlowRunMode"]] = None,
tuning_node_names: Optional[List[str]] = None,
tuning_node_settings: Optional[Dict[str, "TuningNodeSetting"]] = None,
baseline_variant_id: Optional[str] = None,
default_variant_id: Optional[str] = None,
variants: Optional[Dict[str, List["Node"]]] = None,
node_name: Optional[str] = None,
is_default_variant: Optional[bool] = None,
node_variant_id: Optional[str] = None,
node_output_paths: Optional[Dict[str, str]] = None,
base_flow_run_id: Optional[str] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
bulk_test_id: Optional[str] = None,
evaluation_flow_run_settings: Optional[Dict[str, "EvaluationFlowRunSettings"]] = None,
bulk_test_flow_id: Optional[str] = None,
bulk_test_flow_run_ids: Optional[List[str]] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
input_universal_link: Optional[str] = None,
data_inputs: Optional[Dict[str, str]] = None,
flow_run_output_directory: Optional[str] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword node_inputs: This is a dictionary.
:paramtype node_inputs: dict[str, any]
:keyword run_mode: Possible values include: "Flow", "SingleNode", "FromNode", "BulkTest",
"Eval", "PairwiseEval", "ExperimentTest", "ExperimentEval".
:paramtype run_mode: str or ~flow.models.FlowRunMode
:keyword tuning_node_names:
:paramtype tuning_node_names: list[str]
:keyword tuning_node_settings: This is a dictionary.
:paramtype tuning_node_settings: dict[str, ~flow.models.TuningNodeSetting]
:keyword baseline_variant_id:
:paramtype baseline_variant_id: str
:keyword default_variant_id:
:paramtype default_variant_id: str
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, list[~flow.models.Node]]
:keyword node_name:
:paramtype node_name: str
:keyword is_default_variant:
:paramtype is_default_variant: bool
:keyword node_variant_id:
:paramtype node_variant_id: str
:keyword node_output_paths: Dictionary of :code:`<string>`.
:paramtype node_output_paths: dict[str, str]
:keyword base_flow_run_id:
:paramtype base_flow_run_id: str
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword evaluation_flow_run_settings: This is a dictionary.
:paramtype evaluation_flow_run_settings: dict[str, ~flow.models.EvaluationFlowRunSettings]
:keyword bulk_test_flow_id:
:paramtype bulk_test_flow_id: str
:keyword bulk_test_flow_run_ids:
:paramtype bulk_test_flow_run_ids: list[str]
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword input_universal_link:
:paramtype input_universal_link: str
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(FlowSubmitRunSettings, self).__init__(**kwargs)
self.node_inputs = node_inputs
self.run_mode = run_mode
self.tuning_node_names = tuning_node_names
self.tuning_node_settings = tuning_node_settings
self.baseline_variant_id = baseline_variant_id
self.default_variant_id = default_variant_id
self.variants = variants
self.node_name = node_name
self.is_default_variant = is_default_variant
self.node_variant_id = node_variant_id
self.node_output_paths = node_output_paths
self.base_flow_run_id = base_flow_run_id
self.flow_test_infos = flow_test_infos
self.bulk_test_id = bulk_test_id
self.evaluation_flow_run_settings = evaluation_flow_run_settings
self.bulk_test_flow_id = bulk_test_flow_id
self.bulk_test_flow_run_ids = bulk_test_flow_run_ids
self.batch_inputs = batch_inputs
self.input_universal_link = input_universal_link
self.data_inputs = data_inputs
self.flow_run_output_directory = flow_run_output_directory
self.connection_overrides = connection_overrides
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class FlowTestInfo(msrest.serialization.Model):
"""FlowTestInfo.
:ivar variant_id:
:vartype variant_id: str
:ivar tuning_node_name:
:vartype tuning_node_name: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_test_storage_setting:
:vartype flow_test_storage_setting: ~flow.models.FlowTestStorageSetting
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar variant_run_id:
:vartype variant_run_id: str
:ivar evaluation_name:
:vartype evaluation_name: str
:ivar output_universal_link:
:vartype output_universal_link: str
"""
_attribute_map = {
'variant_id': {'key': 'variantId', 'type': 'str'},
'tuning_node_name': {'key': 'tuningNodeName', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_test_storage_setting': {'key': 'flowTestStorageSetting', 'type': 'FlowTestStorageSetting'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'variant_run_id': {'key': 'variantRunId', 'type': 'str'},
'evaluation_name': {'key': 'evaluationName', 'type': 'str'},
'output_universal_link': {'key': 'outputUniversalLink', 'type': 'str'},
}
def __init__(
self,
*,
variant_id: Optional[str] = None,
tuning_node_name: Optional[str] = None,
flow_run_id: Optional[str] = None,
flow_test_storage_setting: Optional["FlowTestStorageSetting"] = None,
flow_run_type: Optional[Union[str, "FlowRunTypeEnum"]] = None,
variant_run_id: Optional[str] = None,
evaluation_name: Optional[str] = None,
output_universal_link: Optional[str] = None,
**kwargs
):
"""
:keyword variant_id:
:paramtype variant_id: str
:keyword tuning_node_name:
:paramtype tuning_node_name: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_test_storage_setting:
:paramtype flow_test_storage_setting: ~flow.models.FlowTestStorageSetting
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword variant_run_id:
:paramtype variant_run_id: str
:keyword evaluation_name:
:paramtype evaluation_name: str
:keyword output_universal_link:
:paramtype output_universal_link: str
"""
super(FlowTestInfo, self).__init__(**kwargs)
self.variant_id = variant_id
self.tuning_node_name = tuning_node_name
self.flow_run_id = flow_run_id
self.flow_test_storage_setting = flow_test_storage_setting
self.flow_run_type = flow_run_type
self.variant_run_id = variant_run_id
self.evaluation_name = evaluation_name
self.output_universal_link = output_universal_link
class FlowTestStorageSetting(msrest.serialization.Model):
"""FlowTestStorageSetting.
:ivar storage_account_name:
:vartype storage_account_name: str
:ivar blob_container_name:
:vartype blob_container_name: str
:ivar flow_artifacts_root_path:
:vartype flow_artifacts_root_path: str
:ivar output_datastore_name:
:vartype output_datastore_name: str
"""
_attribute_map = {
'storage_account_name': {'key': 'storageAccountName', 'type': 'str'},
'blob_container_name': {'key': 'blobContainerName', 'type': 'str'},
'flow_artifacts_root_path': {'key': 'flowArtifactsRootPath', 'type': 'str'},
'output_datastore_name': {'key': 'outputDatastoreName', 'type': 'str'},
}
def __init__(
self,
*,
storage_account_name: Optional[str] = None,
blob_container_name: Optional[str] = None,
flow_artifacts_root_path: Optional[str] = None,
output_datastore_name: Optional[str] = None,
**kwargs
):
"""
:keyword storage_account_name:
:paramtype storage_account_name: str
:keyword blob_container_name:
:paramtype blob_container_name: str
:keyword flow_artifacts_root_path:
:paramtype flow_artifacts_root_path: str
:keyword output_datastore_name:
:paramtype output_datastore_name: str
"""
super(FlowTestStorageSetting, self).__init__(**kwargs)
self.storage_account_name = storage_account_name
self.blob_container_name = blob_container_name
self.flow_artifacts_root_path = flow_artifacts_root_path
self.output_datastore_name = output_datastore_name
class FlowToolsDto(msrest.serialization.Model):
"""FlowToolsDto.
:ivar package: This is a dictionary.
:vartype package: dict[str, ~flow.models.Tool]
:ivar code: This is a dictionary.
:vartype code: dict[str, ~flow.models.Tool]
:ivar errors: This is a dictionary.
:vartype errors: dict[str, ~flow.models.ErrorResponse]
"""
_attribute_map = {
'package': {'key': 'package', 'type': '{Tool}'},
'code': {'key': 'code', 'type': '{Tool}'},
'errors': {'key': 'errors', 'type': '{ErrorResponse}'},
}
def __init__(
self,
*,
package: Optional[Dict[str, "Tool"]] = None,
code: Optional[Dict[str, "Tool"]] = None,
errors: Optional[Dict[str, "ErrorResponse"]] = None,
**kwargs
):
"""
:keyword package: This is a dictionary.
:paramtype package: dict[str, ~flow.models.Tool]
:keyword code: This is a dictionary.
:paramtype code: dict[str, ~flow.models.Tool]
:keyword errors: This is a dictionary.
:paramtype errors: dict[str, ~flow.models.ErrorResponse]
"""
super(FlowToolsDto, self).__init__(**kwargs)
self.package = package
self.code = code
self.errors = errors
class FlowToolSettingParameter(msrest.serialization.Model):
"""FlowToolSettingParameter.
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar default:
:vartype default: str
:ivar advanced:
:vartype advanced: bool
:ivar enum:
:vartype enum: list[any]
:ivar model_list:
:vartype model_list: list[str]
:ivar text_box_size:
:vartype text_box_size: int
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar allow_manual_entry:
:vartype allow_manual_entry: bool
:ivar ui_hints: This is a dictionary.
:vartype ui_hints: dict[str, any]
"""
_attribute_map = {
'type': {'key': 'type', 'type': '[str]'},
'default': {'key': 'default', 'type': 'str'},
'advanced': {'key': 'advanced', 'type': 'bool'},
'enum': {'key': 'enum', 'type': '[object]'},
'model_list': {'key': 'model_list', 'type': '[str]'},
'text_box_size': {'key': 'text_box_size', 'type': 'int'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'allow_manual_entry': {'key': 'allow_manual_entry', 'type': 'bool'},
'ui_hints': {'key': 'ui_hints', 'type': '{object}'},
}
def __init__(
self,
*,
type: Optional[List[Union[str, "ValueType"]]] = None,
default: Optional[str] = None,
advanced: Optional[bool] = None,
enum: Optional[List[Any]] = None,
model_list: Optional[List[str]] = None,
text_box_size: Optional[int] = None,
capabilities: Optional["AzureOpenAIModelCapabilities"] = None,
allow_manual_entry: Optional[bool] = None,
ui_hints: Optional[Dict[str, Any]] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword default:
:paramtype default: str
:keyword advanced:
:paramtype advanced: bool
:keyword enum:
:paramtype enum: list[any]
:keyword model_list:
:paramtype model_list: list[str]
:keyword text_box_size:
:paramtype text_box_size: int
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword allow_manual_entry:
:paramtype allow_manual_entry: bool
:keyword ui_hints: This is a dictionary.
:paramtype ui_hints: dict[str, any]
"""
super(FlowToolSettingParameter, self).__init__(**kwargs)
self.type = type
self.default = default
self.advanced = advanced
self.enum = enum
self.model_list = model_list
self.text_box_size = text_box_size
self.capabilities = capabilities
self.allow_manual_entry = allow_manual_entry
self.ui_hints = ui_hints
class FlowVariantNode(msrest.serialization.Model):
"""FlowVariantNode.
:ivar node:
:vartype node: ~flow.models.FlowNode
:ivar description:
:vartype description: str
"""
_attribute_map = {
'node': {'key': 'node', 'type': 'FlowNode'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
*,
node: Optional["FlowNode"] = None,
description: Optional[str] = None,
**kwargs
):
"""
:keyword node:
:paramtype node: ~flow.models.FlowNode
:keyword description:
:paramtype description: str
"""
super(FlowVariantNode, self).__init__(**kwargs)
self.node = node
self.description = description
class ForecastHorizon(msrest.serialization.Model):
"""ForecastHorizon.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.ForecastHorizonMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "ForecastHorizonMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.ForecastHorizonMode
:keyword value:
:paramtype value: int
"""
super(ForecastHorizon, self).__init__(**kwargs)
self.mode = mode
self.value = value
class ForecastingSettings(msrest.serialization.Model):
"""ForecastingSettings.
:ivar country_or_region_for_holidays:
:vartype country_or_region_for_holidays: str
:ivar time_column_name:
:vartype time_column_name: str
:ivar target_lags:
:vartype target_lags: ~flow.models.TargetLags
:ivar target_rolling_window_size:
:vartype target_rolling_window_size: ~flow.models.TargetRollingWindowSize
:ivar forecast_horizon:
:vartype forecast_horizon: ~flow.models.ForecastHorizon
:ivar time_series_id_column_names:
:vartype time_series_id_column_names: list[str]
:ivar frequency:
:vartype frequency: str
:ivar feature_lags:
:vartype feature_lags: str
:ivar seasonality:
:vartype seasonality: ~flow.models.Seasonality
:ivar short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:vartype short_series_handling_config: str or ~flow.models.ShortSeriesHandlingConfiguration
:ivar use_stl: Possible values include: "Season", "SeasonTrend".
:vartype use_stl: str or ~flow.models.UseStl
:ivar target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:vartype target_aggregate_function: str or ~flow.models.TargetAggregationFunction
:ivar cv_step_size:
:vartype cv_step_size: int
:ivar features_unknown_at_forecast_time:
:vartype features_unknown_at_forecast_time: list[str]
"""
_attribute_map = {
'country_or_region_for_holidays': {'key': 'countryOrRegionForHolidays', 'type': 'str'},
'time_column_name': {'key': 'timeColumnName', 'type': 'str'},
'target_lags': {'key': 'targetLags', 'type': 'TargetLags'},
'target_rolling_window_size': {'key': 'targetRollingWindowSize', 'type': 'TargetRollingWindowSize'},
'forecast_horizon': {'key': 'forecastHorizon', 'type': 'ForecastHorizon'},
'time_series_id_column_names': {'key': 'timeSeriesIdColumnNames', 'type': '[str]'},
'frequency': {'key': 'frequency', 'type': 'str'},
'feature_lags': {'key': 'featureLags', 'type': 'str'},
'seasonality': {'key': 'seasonality', 'type': 'Seasonality'},
'short_series_handling_config': {'key': 'shortSeriesHandlingConfig', 'type': 'str'},
'use_stl': {'key': 'useStl', 'type': 'str'},
'target_aggregate_function': {'key': 'targetAggregateFunction', 'type': 'str'},
'cv_step_size': {'key': 'cvStepSize', 'type': 'int'},
'features_unknown_at_forecast_time': {'key': 'featuresUnknownAtForecastTime', 'type': '[str]'},
}
def __init__(
self,
*,
country_or_region_for_holidays: Optional[str] = None,
time_column_name: Optional[str] = None,
target_lags: Optional["TargetLags"] = None,
target_rolling_window_size: Optional["TargetRollingWindowSize"] = None,
forecast_horizon: Optional["ForecastHorizon"] = None,
time_series_id_column_names: Optional[List[str]] = None,
frequency: Optional[str] = None,
feature_lags: Optional[str] = None,
seasonality: Optional["Seasonality"] = None,
short_series_handling_config: Optional[Union[str, "ShortSeriesHandlingConfiguration"]] = None,
use_stl: Optional[Union[str, "UseStl"]] = None,
target_aggregate_function: Optional[Union[str, "TargetAggregationFunction"]] = None,
cv_step_size: Optional[int] = None,
features_unknown_at_forecast_time: Optional[List[str]] = None,
**kwargs
):
"""
:keyword country_or_region_for_holidays:
:paramtype country_or_region_for_holidays: str
:keyword time_column_name:
:paramtype time_column_name: str
:keyword target_lags:
:paramtype target_lags: ~flow.models.TargetLags
:keyword target_rolling_window_size:
:paramtype target_rolling_window_size: ~flow.models.TargetRollingWindowSize
:keyword forecast_horizon:
:paramtype forecast_horizon: ~flow.models.ForecastHorizon
:keyword time_series_id_column_names:
:paramtype time_series_id_column_names: list[str]
:keyword frequency:
:paramtype frequency: str
:keyword feature_lags:
:paramtype feature_lags: str
:keyword seasonality:
:paramtype seasonality: ~flow.models.Seasonality
:keyword short_series_handling_config: Possible values include: "Auto", "Pad", "Drop".
:paramtype short_series_handling_config: str or ~flow.models.ShortSeriesHandlingConfiguration
:keyword use_stl: Possible values include: "Season", "SeasonTrend".
:paramtype use_stl: str or ~flow.models.UseStl
:keyword target_aggregate_function: Possible values include: "Sum", "Max", "Min", "Mean".
:paramtype target_aggregate_function: str or ~flow.models.TargetAggregationFunction
:keyword cv_step_size:
:paramtype cv_step_size: int
:keyword features_unknown_at_forecast_time:
:paramtype features_unknown_at_forecast_time: list[str]
"""
super(ForecastingSettings, self).__init__(**kwargs)
self.country_or_region_for_holidays = country_or_region_for_holidays
self.time_column_name = time_column_name
self.target_lags = target_lags
self.target_rolling_window_size = target_rolling_window_size
self.forecast_horizon = forecast_horizon
self.time_series_id_column_names = time_series_id_column_names
self.frequency = frequency
self.feature_lags = feature_lags
self.seasonality = seasonality
self.short_series_handling_config = short_series_handling_config
self.use_stl = use_stl
self.target_aggregate_function = target_aggregate_function
self.cv_step_size = cv_step_size
self.features_unknown_at_forecast_time = features_unknown_at_forecast_time
class GeneralSettings(msrest.serialization.Model):
"""GeneralSettings.
:ivar primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:vartype primary_metric: str or ~flow.models.PrimaryMetrics
:ivar task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:vartype task_type: str or ~flow.models.TaskType
:ivar log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:vartype log_verbosity: str or ~flow.models.LogVerbosity
"""
_attribute_map = {
'primary_metric': {'key': 'primaryMetric', 'type': 'str'},
'task_type': {'key': 'taskType', 'type': 'str'},
'log_verbosity': {'key': 'logVerbosity', 'type': 'str'},
}
def __init__(
self,
*,
primary_metric: Optional[Union[str, "PrimaryMetrics"]] = None,
task_type: Optional[Union[str, "TaskType"]] = None,
log_verbosity: Optional[Union[str, "LogVerbosity"]] = None,
**kwargs
):
"""
:keyword primary_metric: Possible values include: "AUCWeighted", "Accuracy", "NormMacroRecall",
"AveragePrecisionScoreWeighted", "PrecisionScoreWeighted", "SpearmanCorrelation",
"NormalizedRootMeanSquaredError", "R2Score", "NormalizedMeanAbsoluteError",
"NormalizedRootMeanSquaredLogError", "MeanAveragePrecision", "Iou".
:paramtype primary_metric: str or ~flow.models.PrimaryMetrics
:keyword task_type: Possible values include: "Classification", "Regression", "Forecasting",
"ImageClassification", "ImageClassificationMultilabel", "ImageObjectDetection",
"ImageInstanceSegmentation", "TextClassification", "TextMultiLabeling", "TextNER",
"TextClassificationMultilabel".
:paramtype task_type: str or ~flow.models.TaskType
:keyword log_verbosity: Possible values include: "NotSet", "Debug", "Info", "Warning", "Error",
"Critical".
:paramtype log_verbosity: str or ~flow.models.LogVerbosity
"""
super(GeneralSettings, self).__init__(**kwargs)
self.primary_metric = primary_metric
self.task_type = task_type
self.log_verbosity = log_verbosity
class GeneratePipelineComponentRequest(msrest.serialization.Model):
"""GeneratePipelineComponentRequest.
:ivar name:
:vartype name: str
:ivar display_name:
:vartype display_name: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar category:
:vartype category: str
:ivar version:
:vartype version: str
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar registry_name:
:vartype registry_name: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'category': {'key': 'category', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
display_name: Optional[str] = None,
module_scope: Optional[Union[str, "ModuleScope"]] = None,
is_deterministic: Optional[bool] = None,
category: Optional[str] = None,
version: Optional[str] = None,
set_as_default_version: Optional[bool] = None,
registry_name: Optional[str] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword display_name:
:paramtype display_name: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword category:
:paramtype category: str
:keyword version:
:paramtype version: str
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword registry_name:
:paramtype registry_name: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(GeneratePipelineComponentRequest, self).__init__(**kwargs)
self.name = name
self.display_name = display_name
self.module_scope = module_scope
self.is_deterministic = is_deterministic
self.category = category
self.version = version
self.set_as_default_version = set_as_default_version
self.registry_name = registry_name
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class GenerateToolMetaRequest(msrest.serialization.Model):
"""GenerateToolMetaRequest.
:ivar tools: This is a dictionary.
:vartype tools: dict[str, ~flow.models.ToolSourceMeta]
:ivar working_dir:
:vartype working_dir: str
"""
_attribute_map = {
'tools': {'key': 'tools', 'type': '{ToolSourceMeta}'},
'working_dir': {'key': 'working_dir', 'type': 'str'},
}
def __init__(
self,
*,
tools: Optional[Dict[str, "ToolSourceMeta"]] = None,
working_dir: Optional[str] = None,
**kwargs
):
"""
:keyword tools: This is a dictionary.
:paramtype tools: dict[str, ~flow.models.ToolSourceMeta]
:keyword working_dir:
:paramtype working_dir: str
"""
super(GenerateToolMetaRequest, self).__init__(**kwargs)
self.tools = tools
self.working_dir = working_dir
class GetDynamicListRequest(msrest.serialization.Model):
"""GetDynamicListRequest.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs: This is a dictionary.
:vartype func_kwargs: dict[str, any]
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '{object}'},
}
def __init__(
self,
*,
func_path: Optional[str] = None,
func_kwargs: Optional[Dict[str, Any]] = None,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs: This is a dictionary.
:paramtype func_kwargs: dict[str, any]
"""
super(GetDynamicListRequest, self).__init__(**kwargs)
self.func_path = func_path
self.func_kwargs = func_kwargs
class GetRunDataResultDto(msrest.serialization.Model):
"""GetRunDataResultDto.
:ivar run_metadata:
:vartype run_metadata: ~flow.models.RunDto
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar job_specification: Anything.
:vartype job_specification: any
:ivar system_settings: Dictionary of :code:`<string>`.
:vartype system_settings: dict[str, str]
"""
_attribute_map = {
'run_metadata': {'key': 'runMetadata', 'type': 'RunDto'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'job_specification': {'key': 'jobSpecification', 'type': 'object'},
'system_settings': {'key': 'systemSettings', 'type': '{str}'},
}
def __init__(
self,
*,
run_metadata: Optional["RunDto"] = None,
run_definition: Optional[Any] = None,
job_specification: Optional[Any] = None,
system_settings: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword run_metadata:
:paramtype run_metadata: ~flow.models.RunDto
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword job_specification: Anything.
:paramtype job_specification: any
:keyword system_settings: Dictionary of :code:`<string>`.
:paramtype system_settings: dict[str, str]
"""
super(GetRunDataResultDto, self).__init__(**kwargs)
self.run_metadata = run_metadata
self.run_definition = run_definition
self.job_specification = job_specification
self.system_settings = system_settings
class GetTrainingSessionDto(msrest.serialization.Model):
"""GetTrainingSessionDto.
:ivar properties:
:vartype properties: ~flow.models.SessionProperties
:ivar compute:
:vartype compute: ~flow.models.ComputeContract
"""
_attribute_map = {
'properties': {'key': 'properties', 'type': 'SessionProperties'},
'compute': {'key': 'compute', 'type': 'ComputeContract'},
}
def __init__(
self,
*,
properties: Optional["SessionProperties"] = None,
compute: Optional["ComputeContract"] = None,
**kwargs
):
"""
:keyword properties:
:paramtype properties: ~flow.models.SessionProperties
:keyword compute:
:paramtype compute: ~flow.models.ComputeContract
"""
super(GetTrainingSessionDto, self).__init__(**kwargs)
self.properties = properties
self.compute = compute
class GlobalJobDispatcherConfiguration(msrest.serialization.Model):
"""GlobalJobDispatcherConfiguration.
:ivar vm_size:
:vartype vm_size: list[str]
:ivar compute_type: Possible values include: "AmlCompute", "AmlK8s".
:vartype compute_type: str or ~flow.models.GlobalJobDispatcherSupportedComputeType
:ivar region:
:vartype region: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar redispatch_allowed:
:vartype redispatch_allowed: bool
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar vc_list:
:vartype vc_list: list[str]
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar vc_block_list:
:vartype vc_block_list: list[str]
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
"""
_attribute_map = {
'vm_size': {'key': 'vmSize', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'redispatch_allowed': {'key': 'redispatchAllowed', 'type': 'bool'},
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'vc_list': {'key': 'vcList', 'type': '[str]'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
}
def __init__(
self,
*,
vm_size: Optional[List[str]] = None,
compute_type: Optional[Union[str, "GlobalJobDispatcherSupportedComputeType"]] = None,
region: Optional[List[str]] = None,
my_resource_only: Optional[bool] = None,
redispatch_allowed: Optional[bool] = None,
low_priority_vm_tolerant: Optional[bool] = None,
vc_list: Optional[List[str]] = None,
plan_id: Optional[str] = None,
plan_region_id: Optional[str] = None,
vc_block_list: Optional[List[str]] = None,
cluster_block_list: Optional[List[str]] = None,
**kwargs
):
"""
:keyword vm_size:
:paramtype vm_size: list[str]
:keyword compute_type: Possible values include: "AmlCompute", "AmlK8s".
:paramtype compute_type: str or ~flow.models.GlobalJobDispatcherSupportedComputeType
:keyword region:
:paramtype region: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword redispatch_allowed:
:paramtype redispatch_allowed: bool
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword vc_list:
:paramtype vc_list: list[str]
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
"""
super(GlobalJobDispatcherConfiguration, self).__init__(**kwargs)
self.vm_size = vm_size
self.compute_type = compute_type
self.region = region
self.my_resource_only = my_resource_only
self.redispatch_allowed = redispatch_allowed
self.low_priority_vm_tolerant = low_priority_vm_tolerant
self.vc_list = vc_list
self.plan_id = plan_id
self.plan_region_id = plan_region_id
self.vc_block_list = vc_block_list
self.cluster_block_list = cluster_block_list
class GlobsOptions(msrest.serialization.Model):
"""GlobsOptions.
:ivar glob_patterns:
:vartype glob_patterns: list[str]
"""
_attribute_map = {
'glob_patterns': {'key': 'globPatterns', 'type': '[str]'},
}
def __init__(
self,
*,
glob_patterns: Optional[List[str]] = None,
**kwargs
):
"""
:keyword glob_patterns:
:paramtype glob_patterns: list[str]
"""
super(GlobsOptions, self).__init__(**kwargs)
self.glob_patterns = glob_patterns
class GraphAnnotationNode(msrest.serialization.Model):
"""GraphAnnotationNode.
:ivar id:
:vartype id: str
:ivar content:
:vartype content: str
:ivar mentioned_node_names:
:vartype mentioned_node_names: list[str]
:ivar structured_content:
:vartype structured_content: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'content': {'key': 'content', 'type': 'str'},
'mentioned_node_names': {'key': 'mentionedNodeNames', 'type': '[str]'},
'structured_content': {'key': 'structuredContent', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
content: Optional[str] = None,
mentioned_node_names: Optional[List[str]] = None,
structured_content: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword content:
:paramtype content: str
:keyword mentioned_node_names:
:paramtype mentioned_node_names: list[str]
:keyword structured_content:
:paramtype structured_content: str
"""
super(GraphAnnotationNode, self).__init__(**kwargs)
self.id = id
self.content = content
self.mentioned_node_names = mentioned_node_names
self.structured_content = structured_content
class GraphControlNode(msrest.serialization.Model):
"""GraphControlNode.
:ivar id:
:vartype id: str
:ivar control_type: The only acceptable values to pass in are None and "IfElse". The default
value is None.
:vartype control_type: str
:ivar control_parameter:
:vartype control_parameter: ~flow.models.ParameterAssignment
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'control_type': {'key': 'controlType', 'type': 'str'},
'control_parameter': {'key': 'controlParameter', 'type': 'ParameterAssignment'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
control_type: Optional[str] = None,
control_parameter: Optional["ParameterAssignment"] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword control_type: The only acceptable values to pass in are None and "IfElse". The
default value is None.
:paramtype control_type: str
:keyword control_parameter:
:paramtype control_parameter: ~flow.models.ParameterAssignment
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphControlNode, self).__init__(**kwargs)
self.id = id
self.control_type = control_type
self.control_parameter = control_parameter
self.run_attribution = run_attribution
class GraphControlReferenceNode(msrest.serialization.Model):
"""GraphControlReferenceNode.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar comment:
:vartype comment: str
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.ControlFlowType
:ivar reference_node_id:
:vartype reference_node_id: str
:ivar do_while_control_flow_info:
:vartype do_while_control_flow_info: ~flow.models.DoWhileControlFlowInfo
:ivar parallel_for_control_flow_info:
:vartype parallel_for_control_flow_info: ~flow.models.ParallelForControlFlowInfo
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'reference_node_id': {'key': 'referenceNodeId', 'type': 'str'},
'do_while_control_flow_info': {'key': 'doWhileControlFlowInfo', 'type': 'DoWhileControlFlowInfo'},
'parallel_for_control_flow_info': {'key': 'parallelForControlFlowInfo', 'type': 'ParallelForControlFlowInfo'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
comment: Optional[str] = None,
control_flow_type: Optional[Union[str, "ControlFlowType"]] = None,
reference_node_id: Optional[str] = None,
do_while_control_flow_info: Optional["DoWhileControlFlowInfo"] = None,
parallel_for_control_flow_info: Optional["ParallelForControlFlowInfo"] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword comment:
:paramtype comment: str
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.ControlFlowType
:keyword reference_node_id:
:paramtype reference_node_id: str
:keyword do_while_control_flow_info:
:paramtype do_while_control_flow_info: ~flow.models.DoWhileControlFlowInfo
:keyword parallel_for_control_flow_info:
:paramtype parallel_for_control_flow_info: ~flow.models.ParallelForControlFlowInfo
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphControlReferenceNode, self).__init__(**kwargs)
self.id = id
self.name = name
self.comment = comment
self.control_flow_type = control_flow_type
self.reference_node_id = reference_node_id
self.do_while_control_flow_info = do_while_control_flow_info
self.parallel_for_control_flow_info = parallel_for_control_flow_info
self.run_attribution = run_attribution
class GraphDatasetNode(msrest.serialization.Model):
"""GraphDatasetNode.
:ivar id:
:vartype id: str
:ivar dataset_id:
:vartype dataset_id: str
:ivar data_path_parameter_name:
:vartype data_path_parameter_name: str
:ivar data_set_definition:
:vartype data_set_definition: ~flow.models.DataSetDefinition
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'data_path_parameter_name': {'key': 'dataPathParameterName', 'type': 'str'},
'data_set_definition': {'key': 'dataSetDefinition', 'type': 'DataSetDefinition'},
}
def __init__(
self,
*,
id: Optional[str] = None,
dataset_id: Optional[str] = None,
data_path_parameter_name: Optional[str] = None,
data_set_definition: Optional["DataSetDefinition"] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword dataset_id:
:paramtype dataset_id: str
:keyword data_path_parameter_name:
:paramtype data_path_parameter_name: str
:keyword data_set_definition:
:paramtype data_set_definition: ~flow.models.DataSetDefinition
"""
super(GraphDatasetNode, self).__init__(**kwargs)
self.id = id
self.dataset_id = dataset_id
self.data_path_parameter_name = data_path_parameter_name
self.data_set_definition = data_set_definition
class GraphDraftEntity(msrest.serialization.Model):
"""GraphDraftEntity.
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.GraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.GraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.GraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.GraphEdge]
:ivar entity_interface:
:vartype entity_interface: ~flow.models.EntityInterface
:ivar graph_layout:
:vartype graph_layout: ~flow.models.GraphLayout
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar extended_properties: This is a dictionary.
:vartype extended_properties: dict[str, str]
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'module_nodes': {'key': 'moduleNodes', 'type': '[GraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[GraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[GraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[GraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[GraphControlNode]'},
'edges': {'key': 'edges', 'type': '[GraphEdge]'},
'entity_interface': {'key': 'entityInterface', 'type': 'EntityInterface'},
'graph_layout': {'key': 'graphLayout', 'type': 'GraphLayout'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'extended_properties': {'key': 'extendedProperties', 'type': '{str}'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
module_nodes: Optional[List["GraphModuleNode"]] = None,
dataset_nodes: Optional[List["GraphDatasetNode"]] = None,
sub_graph_nodes: Optional[List["GraphReferenceNode"]] = None,
control_reference_nodes: Optional[List["GraphControlReferenceNode"]] = None,
control_nodes: Optional[List["GraphControlNode"]] = None,
edges: Optional[List["GraphEdge"]] = None,
entity_interface: Optional["EntityInterface"] = None,
graph_layout: Optional["GraphLayout"] = None,
created_by: Optional["CreatedBy"] = None,
last_updated_by: Optional["CreatedBy"] = None,
default_compute: Optional["ComputeSetting"] = None,
default_datastore: Optional["DatastoreSetting"] = None,
default_cloud_priority: Optional["CloudPrioritySetting"] = None,
extended_properties: Optional[Dict[str, str]] = None,
parent_sub_graph_module_ids: Optional[List[str]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.GraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.GraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.GraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.GraphEdge]
:keyword entity_interface:
:paramtype entity_interface: ~flow.models.EntityInterface
:keyword graph_layout:
:paramtype graph_layout: ~flow.models.GraphLayout
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword extended_properties: This is a dictionary.
:paramtype extended_properties: dict[str, str]
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(GraphDraftEntity, self).__init__(**kwargs)
self.module_nodes = module_nodes
self.dataset_nodes = dataset_nodes
self.sub_graph_nodes = sub_graph_nodes
self.control_reference_nodes = control_reference_nodes
self.control_nodes = control_nodes
self.edges = edges
self.entity_interface = entity_interface
self.graph_layout = graph_layout
self.created_by = created_by
self.last_updated_by = last_updated_by
self.default_compute = default_compute
self.default_datastore = default_datastore
self.default_cloud_priority = default_cloud_priority
self.extended_properties = extended_properties
self.parent_sub_graph_module_ids = parent_sub_graph_module_ids
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class GraphEdge(msrest.serialization.Model):
"""GraphEdge.
:ivar source_output_port:
:vartype source_output_port: ~flow.models.PortInfo
:ivar destination_input_port:
:vartype destination_input_port: ~flow.models.PortInfo
"""
_attribute_map = {
'source_output_port': {'key': 'sourceOutputPort', 'type': 'PortInfo'},
'destination_input_port': {'key': 'destinationInputPort', 'type': 'PortInfo'},
}
def __init__(
self,
*,
source_output_port: Optional["PortInfo"] = None,
destination_input_port: Optional["PortInfo"] = None,
**kwargs
):
"""
:keyword source_output_port:
:paramtype source_output_port: ~flow.models.PortInfo
:keyword destination_input_port:
:paramtype destination_input_port: ~flow.models.PortInfo
"""
super(GraphEdge, self).__init__(**kwargs)
self.source_output_port = source_output_port
self.destination_input_port = destination_input_port
class GraphLayout(msrest.serialization.Model):
"""GraphLayout.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.NodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.GraphAnnotationNode]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{NodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[GraphAnnotationNode]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
node_layouts: Optional[Dict[str, "NodeLayout"]] = None,
extended_data: Optional[str] = None,
annotation_nodes: Optional[List["GraphAnnotationNode"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.NodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.GraphAnnotationNode]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(GraphLayout, self).__init__(**kwargs)
self.node_layouts = node_layouts
self.extended_data = extended_data
self.annotation_nodes = annotation_nodes
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class GraphLayoutCreationInfo(msrest.serialization.Model):
"""GraphLayoutCreationInfo.
:ivar node_layouts: This is a dictionary.
:vartype node_layouts: dict[str, ~flow.models.NodeLayout]
:ivar extended_data:
:vartype extended_data: str
:ivar annotation_nodes:
:vartype annotation_nodes: list[~flow.models.GraphAnnotationNode]
"""
_attribute_map = {
'node_layouts': {'key': 'nodeLayouts', 'type': '{NodeLayout}'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
'annotation_nodes': {'key': 'annotationNodes', 'type': '[GraphAnnotationNode]'},
}
def __init__(
self,
*,
node_layouts: Optional[Dict[str, "NodeLayout"]] = None,
extended_data: Optional[str] = None,
annotation_nodes: Optional[List["GraphAnnotationNode"]] = None,
**kwargs
):
"""
:keyword node_layouts: This is a dictionary.
:paramtype node_layouts: dict[str, ~flow.models.NodeLayout]
:keyword extended_data:
:paramtype extended_data: str
:keyword annotation_nodes:
:paramtype annotation_nodes: list[~flow.models.GraphAnnotationNode]
"""
super(GraphLayoutCreationInfo, self).__init__(**kwargs)
self.node_layouts = node_layouts
self.extended_data = extended_data
self.annotation_nodes = annotation_nodes
class GraphModuleNode(msrest.serialization.Model):
"""GraphModuleNode.
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.ModuleType
:ivar runconfig:
:vartype runconfig: str
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.ParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.OutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.InputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.ControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.ExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'module_type': {'key': 'moduleType', 'type': 'str'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[ParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[ParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[OutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[InputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[ControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
module_type: Optional[Union[str, "ModuleType"]] = None,
runconfig: Optional[str] = None,
id: Optional[str] = None,
module_id: Optional[str] = None,
comment: Optional[str] = None,
name: Optional[str] = None,
module_parameters: Optional[List["ParameterAssignment"]] = None,
module_metadata_parameters: Optional[List["ParameterAssignment"]] = None,
module_output_settings: Optional[List["OutputSetting"]] = None,
module_input_settings: Optional[List["InputSetting"]] = None,
use_graph_default_compute: Optional[bool] = None,
use_graph_default_datastore: Optional[bool] = None,
regenerate_output: Optional[bool] = None,
control_inputs: Optional[List["ControlInput"]] = None,
cloud_settings: Optional["CloudSettings"] = None,
execution_phase: Optional[Union[str, "ExecutionPhase"]] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.ModuleType
:keyword runconfig:
:paramtype runconfig: str
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.ParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.OutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.InputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.ControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.ExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphModuleNode, self).__init__(**kwargs)
self.module_type = module_type
self.runconfig = runconfig
self.id = id
self.module_id = module_id
self.comment = comment
self.name = name
self.module_parameters = module_parameters
self.module_metadata_parameters = module_metadata_parameters
self.module_output_settings = module_output_settings
self.module_input_settings = module_input_settings
self.use_graph_default_compute = use_graph_default_compute
self.use_graph_default_datastore = use_graph_default_datastore
self.regenerate_output = regenerate_output
self.control_inputs = control_inputs
self.cloud_settings = cloud_settings
self.execution_phase = execution_phase
self.run_attribution = run_attribution
class GraphModuleNodeRunSetting(msrest.serialization.Model):
"""GraphModuleNodeRunSetting.
:ivar node_id:
:vartype node_id: str
:ivar module_id:
:vartype module_id: str
:ivar step_type:
:vartype step_type: str
:ivar run_settings:
:vartype run_settings: list[~flow.models.RunSettingParameterAssignment]
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'run_settings': {'key': 'runSettings', 'type': '[RunSettingParameterAssignment]'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
module_id: Optional[str] = None,
step_type: Optional[str] = None,
run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword module_id:
:paramtype module_id: str
:keyword step_type:
:paramtype step_type: str
:keyword run_settings:
:paramtype run_settings: list[~flow.models.RunSettingParameterAssignment]
"""
super(GraphModuleNodeRunSetting, self).__init__(**kwargs)
self.node_id = node_id
self.module_id = module_id
self.step_type = step_type
self.run_settings = run_settings
class GraphModuleNodeUIInputSetting(msrest.serialization.Model):
"""GraphModuleNodeUIInputSetting.
:ivar node_id:
:vartype node_id: str
:ivar module_id:
:vartype module_id: str
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.UIInputSetting]
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[UIInputSetting]'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
module_id: Optional[str] = None,
module_input_settings: Optional[List["UIInputSetting"]] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword module_id:
:paramtype module_id: str
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.UIInputSetting]
"""
super(GraphModuleNodeUIInputSetting, self).__init__(**kwargs)
self.node_id = node_id
self.module_id = module_id
self.module_input_settings = module_input_settings
class GraphNodeStatusInfo(msrest.serialization.Model):
"""GraphNodeStatusInfo.
:ivar status: Possible values include: "NotStarted", "Queued", "Running", "Failed", "Finished",
"Canceled", "PartiallyExecuted", "Bypassed".
:vartype status: str or ~flow.models.TaskStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar is_bypassed:
:vartype is_bypassed: bool
:ivar has_failed_child_run:
:vartype has_failed_child_run: bool
:ivar partially_executed:
:vartype partially_executed: bool
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar aether_creation_time:
:vartype aether_creation_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar run_history_creation_time:
:vartype run_history_creation_time: ~datetime.datetime
:ivar reuse_info:
:vartype reuse_info: ~flow.models.TaskReuseInfo
:ivar control_flow_info:
:vartype control_flow_info: ~flow.models.TaskControlFlowInfo
:ivar status_code: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:vartype status_code: str or ~flow.models.TaskStatusCode
:ivar status_detail:
:vartype status_detail: str
:ivar creation_time:
:vartype creation_time: ~datetime.datetime
:ivar schedule_time:
:vartype schedule_time: ~datetime.datetime
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar request_id:
:vartype request_id: str
:ivar run_id:
:vartype run_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar real_time_log_path:
:vartype real_time_log_path: str
:ivar has_warnings:
:vartype has_warnings: bool
:ivar composite_node_id:
:vartype composite_node_id: str
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'is_bypassed': {'key': 'isBypassed', 'type': 'bool'},
'has_failed_child_run': {'key': 'hasFailedChildRun', 'type': 'bool'},
'partially_executed': {'key': 'partiallyExecuted', 'type': 'bool'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'aether_creation_time': {'key': 'aetherCreationTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'run_history_creation_time': {'key': 'runHistoryCreationTime', 'type': 'iso-8601'},
'reuse_info': {'key': 'reuseInfo', 'type': 'TaskReuseInfo'},
'control_flow_info': {'key': 'controlFlowInfo', 'type': 'TaskControlFlowInfo'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'creation_time': {'key': 'creationTime', 'type': 'iso-8601'},
'schedule_time': {'key': 'scheduleTime', 'type': 'iso-8601'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'request_id': {'key': 'requestId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'real_time_log_path': {'key': 'realTimeLogPath', 'type': 'str'},
'has_warnings': {'key': 'hasWarnings', 'type': 'bool'},
'composite_node_id': {'key': 'compositeNodeId', 'type': 'str'},
}
def __init__(
self,
*,
status: Optional[Union[str, "TaskStatusCode"]] = None,
run_status: Optional[Union[str, "RunStatus"]] = None,
is_bypassed: Optional[bool] = None,
has_failed_child_run: Optional[bool] = None,
partially_executed: Optional[bool] = None,
properties: Optional[Dict[str, str]] = None,
aether_start_time: Optional[datetime.datetime] = None,
aether_end_time: Optional[datetime.datetime] = None,
aether_creation_time: Optional[datetime.datetime] = None,
run_history_start_time: Optional[datetime.datetime] = None,
run_history_end_time: Optional[datetime.datetime] = None,
run_history_creation_time: Optional[datetime.datetime] = None,
reuse_info: Optional["TaskReuseInfo"] = None,
control_flow_info: Optional["TaskControlFlowInfo"] = None,
status_code: Optional[Union[str, "TaskStatusCode"]] = None,
status_detail: Optional[str] = None,
creation_time: Optional[datetime.datetime] = None,
schedule_time: Optional[datetime.datetime] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
request_id: Optional[str] = None,
run_id: Optional[str] = None,
data_container_id: Optional[str] = None,
real_time_log_path: Optional[str] = None,
has_warnings: Optional[bool] = None,
composite_node_id: Optional[str] = None,
**kwargs
):
"""
:keyword status: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:paramtype status: str or ~flow.models.TaskStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword is_bypassed:
:paramtype is_bypassed: bool
:keyword has_failed_child_run:
:paramtype has_failed_child_run: bool
:keyword partially_executed:
:paramtype partially_executed: bool
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword aether_creation_time:
:paramtype aether_creation_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword run_history_creation_time:
:paramtype run_history_creation_time: ~datetime.datetime
:keyword reuse_info:
:paramtype reuse_info: ~flow.models.TaskReuseInfo
:keyword control_flow_info:
:paramtype control_flow_info: ~flow.models.TaskControlFlowInfo
:keyword status_code: Possible values include: "NotStarted", "Queued", "Running", "Failed",
"Finished", "Canceled", "PartiallyExecuted", "Bypassed".
:paramtype status_code: str or ~flow.models.TaskStatusCode
:keyword status_detail:
:paramtype status_detail: str
:keyword creation_time:
:paramtype creation_time: ~datetime.datetime
:keyword schedule_time:
:paramtype schedule_time: ~datetime.datetime
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword request_id:
:paramtype request_id: str
:keyword run_id:
:paramtype run_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword real_time_log_path:
:paramtype real_time_log_path: str
:keyword has_warnings:
:paramtype has_warnings: bool
:keyword composite_node_id:
:paramtype composite_node_id: str
"""
super(GraphNodeStatusInfo, self).__init__(**kwargs)
self.status = status
self.run_status = run_status
self.is_bypassed = is_bypassed
self.has_failed_child_run = has_failed_child_run
self.partially_executed = partially_executed
self.properties = properties
self.aether_start_time = aether_start_time
self.aether_end_time = aether_end_time
self.aether_creation_time = aether_creation_time
self.run_history_start_time = run_history_start_time
self.run_history_end_time = run_history_end_time
self.run_history_creation_time = run_history_creation_time
self.reuse_info = reuse_info
self.control_flow_info = control_flow_info
self.status_code = status_code
self.status_detail = status_detail
self.creation_time = creation_time
self.schedule_time = schedule_time
self.start_time = start_time
self.end_time = end_time
self.request_id = request_id
self.run_id = run_id
self.data_container_id = data_container_id
self.real_time_log_path = real_time_log_path
self.has_warnings = has_warnings
self.composite_node_id = composite_node_id
class GraphReferenceNode(msrest.serialization.Model):
"""GraphReferenceNode.
:ivar graph_id:
:vartype graph_id: str
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar id:
:vartype id: str
:ivar module_id:
:vartype module_id: str
:ivar comment:
:vartype comment: str
:ivar name:
:vartype name: str
:ivar module_parameters:
:vartype module_parameters: list[~flow.models.ParameterAssignment]
:ivar module_metadata_parameters:
:vartype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:ivar module_output_settings:
:vartype module_output_settings: list[~flow.models.OutputSetting]
:ivar module_input_settings:
:vartype module_input_settings: list[~flow.models.InputSetting]
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar use_graph_default_datastore:
:vartype use_graph_default_datastore: bool
:ivar regenerate_output:
:vartype regenerate_output: bool
:ivar control_inputs:
:vartype control_inputs: list[~flow.models.ControlInput]
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar execution_phase: Possible values include: "Execution", "Initialization", "Finalization".
:vartype execution_phase: str or ~flow.models.ExecutionPhase
:ivar run_attribution:
:vartype run_attribution: str
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'module_id': {'key': 'moduleId', 'type': 'str'},
'comment': {'key': 'comment', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'module_parameters': {'key': 'moduleParameters', 'type': '[ParameterAssignment]'},
'module_metadata_parameters': {'key': 'moduleMetadataParameters', 'type': '[ParameterAssignment]'},
'module_output_settings': {'key': 'moduleOutputSettings', 'type': '[OutputSetting]'},
'module_input_settings': {'key': 'moduleInputSettings', 'type': '[InputSetting]'},
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'use_graph_default_datastore': {'key': 'useGraphDefaultDatastore', 'type': 'bool'},
'regenerate_output': {'key': 'regenerateOutput', 'type': 'bool'},
'control_inputs': {'key': 'controlInputs', 'type': '[ControlInput]'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'execution_phase': {'key': 'executionPhase', 'type': 'str'},
'run_attribution': {'key': 'runAttribution', 'type': 'str'},
}
def __init__(
self,
*,
graph_id: Optional[str] = None,
default_compute: Optional["ComputeSetting"] = None,
default_datastore: Optional["DatastoreSetting"] = None,
id: Optional[str] = None,
module_id: Optional[str] = None,
comment: Optional[str] = None,
name: Optional[str] = None,
module_parameters: Optional[List["ParameterAssignment"]] = None,
module_metadata_parameters: Optional[List["ParameterAssignment"]] = None,
module_output_settings: Optional[List["OutputSetting"]] = None,
module_input_settings: Optional[List["InputSetting"]] = None,
use_graph_default_compute: Optional[bool] = None,
use_graph_default_datastore: Optional[bool] = None,
regenerate_output: Optional[bool] = None,
control_inputs: Optional[List["ControlInput"]] = None,
cloud_settings: Optional["CloudSettings"] = None,
execution_phase: Optional[Union[str, "ExecutionPhase"]] = None,
run_attribution: Optional[str] = None,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword id:
:paramtype id: str
:keyword module_id:
:paramtype module_id: str
:keyword comment:
:paramtype comment: str
:keyword name:
:paramtype name: str
:keyword module_parameters:
:paramtype module_parameters: list[~flow.models.ParameterAssignment]
:keyword module_metadata_parameters:
:paramtype module_metadata_parameters: list[~flow.models.ParameterAssignment]
:keyword module_output_settings:
:paramtype module_output_settings: list[~flow.models.OutputSetting]
:keyword module_input_settings:
:paramtype module_input_settings: list[~flow.models.InputSetting]
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword use_graph_default_datastore:
:paramtype use_graph_default_datastore: bool
:keyword regenerate_output:
:paramtype regenerate_output: bool
:keyword control_inputs:
:paramtype control_inputs: list[~flow.models.ControlInput]
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword execution_phase: Possible values include: "Execution", "Initialization",
"Finalization".
:paramtype execution_phase: str or ~flow.models.ExecutionPhase
:keyword run_attribution:
:paramtype run_attribution: str
"""
super(GraphReferenceNode, self).__init__(**kwargs)
self.graph_id = graph_id
self.default_compute = default_compute
self.default_datastore = default_datastore
self.id = id
self.module_id = module_id
self.comment = comment
self.name = name
self.module_parameters = module_parameters
self.module_metadata_parameters = module_metadata_parameters
self.module_output_settings = module_output_settings
self.module_input_settings = module_input_settings
self.use_graph_default_compute = use_graph_default_compute
self.use_graph_default_datastore = use_graph_default_datastore
self.regenerate_output = regenerate_output
self.control_inputs = control_inputs
self.cloud_settings = cloud_settings
self.execution_phase = execution_phase
self.run_attribution = run_attribution
class HdfsReference(msrest.serialization.Model):
"""HdfsReference.
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
"""
super(HdfsReference, self).__init__(**kwargs)
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
class HdiClusterComputeInfo(msrest.serialization.Model):
"""HdiClusterComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
*,
address: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
private_key: Optional[str] = None,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(HdiClusterComputeInfo, self).__init__(**kwargs)
self.address = address
self.username = username
self.password = password
self.private_key = private_key
class HdiConfiguration(msrest.serialization.Model):
"""HdiConfiguration.
:ivar yarn_deploy_mode: Possible values include: "None", "Client", "Cluster".
:vartype yarn_deploy_mode: str or ~flow.models.YarnDeployMode
"""
_attribute_map = {
'yarn_deploy_mode': {'key': 'yarnDeployMode', 'type': 'str'},
}
def __init__(
self,
*,
yarn_deploy_mode: Optional[Union[str, "YarnDeployMode"]] = None,
**kwargs
):
"""
:keyword yarn_deploy_mode: Possible values include: "None", "Client", "Cluster".
:paramtype yarn_deploy_mode: str or ~flow.models.YarnDeployMode
"""
super(HdiConfiguration, self).__init__(**kwargs)
self.yarn_deploy_mode = yarn_deploy_mode
class HdiRunConfiguration(msrest.serialization.Model):
"""HdiRunConfiguration.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar compute_name:
:vartype compute_name: str
:ivar queue:
:vartype queue: str
:ivar driver_memory:
:vartype driver_memory: str
:ivar driver_cores:
:vartype driver_cores: int
:ivar executor_memory:
:vartype executor_memory: str
:ivar executor_cores:
:vartype executor_cores: int
:ivar number_executors:
:vartype number_executors: int
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar name:
:vartype name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'queue': {'key': 'queue', 'type': 'str'},
'driver_memory': {'key': 'driverMemory', 'type': 'str'},
'driver_cores': {'key': 'driverCores', 'type': 'int'},
'executor_memory': {'key': 'executorMemory', 'type': 'str'},
'executor_cores': {'key': 'executorCores', 'type': 'int'},
'number_executors': {'key': 'numberExecutors', 'type': 'int'},
'conf': {'key': 'conf', 'type': '{str}'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
class_name: Optional[str] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
py_files: Optional[List[str]] = None,
compute_name: Optional[str] = None,
queue: Optional[str] = None,
driver_memory: Optional[str] = None,
driver_cores: Optional[int] = None,
executor_memory: Optional[str] = None,
executor_cores: Optional[int] = None,
number_executors: Optional[int] = None,
conf: Optional[Dict[str, str]] = None,
name: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword compute_name:
:paramtype compute_name: str
:keyword queue:
:paramtype queue: str
:keyword driver_memory:
:paramtype driver_memory: str
:keyword driver_cores:
:paramtype driver_cores: int
:keyword executor_memory:
:paramtype executor_memory: str
:keyword executor_cores:
:paramtype executor_cores: int
:keyword number_executors:
:paramtype number_executors: int
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword name:
:paramtype name: str
"""
super(HdiRunConfiguration, self).__init__(**kwargs)
self.file = file
self.class_name = class_name
self.files = files
self.archives = archives
self.jars = jars
self.py_files = py_files
self.compute_name = compute_name
self.queue = queue
self.driver_memory = driver_memory
self.driver_cores = driver_cores
self.executor_memory = executor_memory
self.executor_cores = executor_cores
self.number_executors = number_executors
self.conf = conf
self.name = name
class HistoryConfiguration(msrest.serialization.Model):
"""HistoryConfiguration.
:ivar output_collection:
:vartype output_collection: bool
:ivar directories_to_watch:
:vartype directories_to_watch: list[str]
:ivar enable_m_lflow_tracking:
:vartype enable_m_lflow_tracking: bool
"""
_attribute_map = {
'output_collection': {'key': 'outputCollection', 'type': 'bool'},
'directories_to_watch': {'key': 'directoriesToWatch', 'type': '[str]'},
'enable_m_lflow_tracking': {'key': 'enableMLflowTracking', 'type': 'bool'},
}
def __init__(
self,
*,
output_collection: Optional[bool] = True,
directories_to_watch: Optional[List[str]] = ['logs'],
enable_m_lflow_tracking: Optional[bool] = True,
**kwargs
):
"""
:keyword output_collection:
:paramtype output_collection: bool
:keyword directories_to_watch:
:paramtype directories_to_watch: list[str]
:keyword enable_m_lflow_tracking:
:paramtype enable_m_lflow_tracking: bool
"""
super(HistoryConfiguration, self).__init__(**kwargs)
self.output_collection = output_collection
self.directories_to_watch = directories_to_watch
self.enable_m_lflow_tracking = enable_m_lflow_tracking
class HyperDriveConfiguration(msrest.serialization.Model):
"""HyperDriveConfiguration.
:ivar hyper_drive_run_config:
:vartype hyper_drive_run_config: str
:ivar primary_metric_goal:
:vartype primary_metric_goal: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar arguments:
:vartype arguments: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'hyper_drive_run_config': {'key': 'hyperDriveRunConfig', 'type': 'str'},
'primary_metric_goal': {'key': 'primaryMetricGoal', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
*,
hyper_drive_run_config: Optional[str] = None,
primary_metric_goal: Optional[str] = None,
primary_metric_name: Optional[str] = None,
arguments: Optional[List["ArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword hyper_drive_run_config:
:paramtype hyper_drive_run_config: str
:keyword primary_metric_goal:
:paramtype primary_metric_goal: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword arguments:
:paramtype arguments: list[~flow.models.ArgumentAssignment]
"""
super(HyperDriveConfiguration, self).__init__(**kwargs)
self.hyper_drive_run_config = hyper_drive_run_config
self.primary_metric_goal = primary_metric_goal
self.primary_metric_name = primary_metric_name
self.arguments = arguments
class ICheckableLongRunningOperationResponse(msrest.serialization.Model):
"""ICheckableLongRunningOperationResponse.
:ivar completion_result: Any object.
:vartype completion_result: any
:ivar location:
:vartype location: str
:ivar operation_result:
:vartype operation_result: str
"""
_attribute_map = {
'completion_result': {'key': 'completionResult', 'type': 'object'},
'location': {'key': 'location', 'type': 'str'},
'operation_result': {'key': 'operationResult', 'type': 'str'},
}
def __init__(
self,
*,
completion_result: Optional[Any] = None,
location: Optional[str] = None,
operation_result: Optional[str] = None,
**kwargs
):
"""
:keyword completion_result: Any object.
:paramtype completion_result: any
:keyword location:
:paramtype location: str
:keyword operation_result:
:paramtype operation_result: str
"""
super(ICheckableLongRunningOperationResponse, self).__init__(**kwargs)
self.completion_result = completion_result
self.location = location
self.operation_result = operation_result
class IdentityConfiguration(msrest.serialization.Model):
"""IdentityConfiguration.
:ivar type: Possible values include: "Managed", "ServicePrincipal", "AMLToken".
:vartype type: str or ~flow.models.IdentityType
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar secret:
:vartype secret: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'secret': {'key': 'secret', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[Union[str, "IdentityType"]] = None,
properties: Optional[Dict[str, str]] = None,
secret: Optional[str] = None,
**kwargs
):
"""
:keyword type: Possible values include: "Managed", "ServicePrincipal", "AMLToken".
:paramtype type: str or ~flow.models.IdentityType
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword secret:
:paramtype secret: str
"""
super(IdentityConfiguration, self).__init__(**kwargs)
self.type = type
self.properties = properties
self.secret = secret
class IdentitySetting(msrest.serialization.Model):
"""IdentitySetting.
:ivar type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:vartype type: str or ~flow.models.AEVAIdentityType
:ivar client_id:
:vartype client_id: str
:ivar object_id:
:vartype object_id: str
:ivar msi_resource_id:
:vartype msi_resource_id: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
'object_id': {'key': 'objectId', 'type': 'str'},
'msi_resource_id': {'key': 'msiResourceId', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[Union[str, "AEVAIdentityType"]] = None,
client_id: Optional[str] = None,
object_id: Optional[str] = None,
msi_resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword type: Possible values include: "UserIdentity", "Managed", "AMLToken".
:paramtype type: str or ~flow.models.AEVAIdentityType
:keyword client_id:
:paramtype client_id: str
:keyword object_id:
:paramtype object_id: str
:keyword msi_resource_id:
:paramtype msi_resource_id: str
"""
super(IdentitySetting, self).__init__(**kwargs)
self.type = type
self.client_id = client_id
self.object_id = object_id
self.msi_resource_id = msi_resource_id
class ImportDataTask(msrest.serialization.Model):
"""ImportDataTask.
:ivar data_transfer_source:
:vartype data_transfer_source: ~flow.models.DataTransferSource
"""
_attribute_map = {
'data_transfer_source': {'key': 'DataTransferSource', 'type': 'DataTransferSource'},
}
def __init__(
self,
*,
data_transfer_source: Optional["DataTransferSource"] = None,
**kwargs
):
"""
:keyword data_transfer_source:
:paramtype data_transfer_source: ~flow.models.DataTransferSource
"""
super(ImportDataTask, self).__init__(**kwargs)
self.data_transfer_source = data_transfer_source
class IndexedErrorResponse(msrest.serialization.Model):
"""IndexedErrorResponse.
:ivar code:
:vartype code: str
:ivar error_code_hierarchy:
:vartype error_code_hierarchy: str
:ivar message:
:vartype message: str
:ivar time:
:vartype time: ~datetime.datetime
:ivar component_name:
:vartype component_name: str
:ivar severity:
:vartype severity: int
:ivar details_uri:
:vartype details_uri: str
:ivar reference_code:
:vartype reference_code: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'error_code_hierarchy': {'key': 'errorCodeHierarchy', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'time': {'key': 'time', 'type': 'iso-8601'},
'component_name': {'key': 'componentName', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'int'},
'details_uri': {'key': 'detailsUri', 'type': 'str'},
'reference_code': {'key': 'referenceCode', 'type': 'str'},
}
def __init__(
self,
*,
code: Optional[str] = None,
error_code_hierarchy: Optional[str] = None,
message: Optional[str] = None,
time: Optional[datetime.datetime] = None,
component_name: Optional[str] = None,
severity: Optional[int] = None,
details_uri: Optional[str] = None,
reference_code: Optional[str] = None,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword error_code_hierarchy:
:paramtype error_code_hierarchy: str
:keyword message:
:paramtype message: str
:keyword time:
:paramtype time: ~datetime.datetime
:keyword component_name:
:paramtype component_name: str
:keyword severity:
:paramtype severity: int
:keyword details_uri:
:paramtype details_uri: str
:keyword reference_code:
:paramtype reference_code: str
"""
super(IndexedErrorResponse, self).__init__(**kwargs)
self.code = code
self.error_code_hierarchy = error_code_hierarchy
self.message = message
self.time = time
self.component_name = component_name
self.severity = severity
self.details_uri = details_uri
self.reference_code = reference_code
class InitScriptInfoDto(msrest.serialization.Model):
"""InitScriptInfoDto.
:ivar dbfs:
:vartype dbfs: ~flow.models.DbfsStorageInfoDto
"""
_attribute_map = {
'dbfs': {'key': 'dbfs', 'type': 'DbfsStorageInfoDto'},
}
def __init__(
self,
*,
dbfs: Optional["DbfsStorageInfoDto"] = None,
**kwargs
):
"""
:keyword dbfs:
:paramtype dbfs: ~flow.models.DbfsStorageInfoDto
"""
super(InitScriptInfoDto, self).__init__(**kwargs)
self.dbfs = dbfs
class InnerErrorDetails(msrest.serialization.Model):
"""InnerErrorDetails.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
}
def __init__(
self,
*,
code: Optional[str] = None,
message: Optional[str] = None,
target: Optional[str] = None,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
"""
super(InnerErrorDetails, self).__init__(**kwargs)
self.code = code
self.message = message
self.target = target
class InnerErrorResponse(msrest.serialization.Model):
"""A nested structure of errors.
:ivar code: The error code.
:vartype code: str
:ivar inner_error: A nested structure of errors.
:vartype inner_error: ~flow.models.InnerErrorResponse
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'},
}
def __init__(
self,
*,
code: Optional[str] = None,
inner_error: Optional["InnerErrorResponse"] = None,
**kwargs
):
"""
:keyword code: The error code.
:paramtype code: str
:keyword inner_error: A nested structure of errors.
:paramtype inner_error: ~flow.models.InnerErrorResponse
"""
super(InnerErrorResponse, self).__init__(**kwargs)
self.code = code
self.inner_error = inner_error
class InputAsset(msrest.serialization.Model):
"""InputAsset.
:ivar asset:
:vartype asset: ~flow.models.Asset
:ivar mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:vartype mechanism: str or ~flow.models.DeliveryMechanism
:ivar environment_variable_name:
:vartype environment_variable_name: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar options: Dictionary of :code:`<string>`.
:vartype options: dict[str, str]
"""
_attribute_map = {
'asset': {'key': 'asset', 'type': 'Asset'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'options': {'key': 'options', 'type': '{str}'},
}
def __init__(
self,
*,
asset: Optional["Asset"] = None,
mechanism: Optional[Union[str, "DeliveryMechanism"]] = None,
environment_variable_name: Optional[str] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
options: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword asset:
:paramtype asset: ~flow.models.Asset
:keyword mechanism: Possible values include: "Direct", "Mount", "Download", "Hdfs".
:paramtype mechanism: str or ~flow.models.DeliveryMechanism
:keyword environment_variable_name:
:paramtype environment_variable_name: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword options: Dictionary of :code:`<string>`.
:paramtype options: dict[str, str]
"""
super(InputAsset, self).__init__(**kwargs)
self.asset = asset
self.mechanism = mechanism
self.environment_variable_name = environment_variable_name
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.options = options
class InputData(msrest.serialization.Model):
"""InputData.
:ivar dataset_id:
:vartype dataset_id: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar value:
:vartype value: str
"""
_attribute_map = {
'dataset_id': {'key': 'datasetId', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
dataset_id: Optional[str] = None,
mode: Optional[Union[str, "DataBindingMode"]] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword dataset_id:
:paramtype dataset_id: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword value:
:paramtype value: str
"""
super(InputData, self).__init__(**kwargs)
self.dataset_id = dataset_id
self.mode = mode
self.value = value
class InputDataBinding(msrest.serialization.Model):
"""InputDataBinding.
:ivar data_id:
:vartype data_id: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar description:
:vartype description: str
:ivar uri:
:vartype uri: ~flow.models.MfeInternalUriReference
:ivar value:
:vartype value: str
:ivar asset_uri:
:vartype asset_uri: str
:ivar job_input_type: Possible values include: "Dataset", "Uri", "Literal", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_input_type: str or ~flow.models.JobInputType
"""
_attribute_map = {
'data_id': {'key': 'dataId', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'MfeInternalUriReference'},
'value': {'key': 'value', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'job_input_type': {'key': 'jobInputType', 'type': 'str'},
}
def __init__(
self,
*,
data_id: Optional[str] = None,
path_on_compute: Optional[str] = None,
mode: Optional[Union[str, "DataBindingMode"]] = None,
description: Optional[str] = None,
uri: Optional["MfeInternalUriReference"] = None,
value: Optional[str] = None,
asset_uri: Optional[str] = None,
job_input_type: Optional[Union[str, "JobInputType"]] = None,
**kwargs
):
"""
:keyword data_id:
:paramtype data_id: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword description:
:paramtype description: str
:keyword uri:
:paramtype uri: ~flow.models.MfeInternalUriReference
:keyword value:
:paramtype value: str
:keyword asset_uri:
:paramtype asset_uri: str
:keyword job_input_type: Possible values include: "Dataset", "Uri", "Literal", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_input_type: str or ~flow.models.JobInputType
"""
super(InputDataBinding, self).__init__(**kwargs)
self.data_id = data_id
self.path_on_compute = path_on_compute
self.mode = mode
self.description = description
self.uri = uri
self.value = value
self.asset_uri = asset_uri
self.job_input_type = job_input_type
class InputDefinition(msrest.serialization.Model):
"""InputDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar default: Anything.
:vartype default: any
:ivar description:
:vartype description: str
:ivar enum:
:vartype enum: list[str]
:ivar enabled_by:
:vartype enabled_by: str
:ivar enabled_by_type:
:vartype enabled_by_type: list[str or ~flow.models.ValueType]
:ivar enabled_by_value:
:vartype enabled_by_value: list[any]
:ivar model_list:
:vartype model_list: list[str]
:ivar capabilities:
:vartype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:ivar dynamic_list:
:vartype dynamic_list: ~flow.models.ToolInputDynamicList
:ivar allow_manual_entry:
:vartype allow_manual_entry: bool
:ivar is_multi_select:
:vartype is_multi_select: bool
:ivar generated_by:
:vartype generated_by: ~flow.models.ToolInputGeneratedBy
:ivar input_type: Possible values include: "default", "uionly_hidden".
:vartype input_type: str or ~flow.models.InputType
:ivar advanced:
:vartype advanced: bool
:ivar ui_hints: This is a dictionary.
:vartype ui_hints: dict[str, any]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': '[str]'},
'default': {'key': 'default', 'type': 'object'},
'description': {'key': 'description', 'type': 'str'},
'enum': {'key': 'enum', 'type': '[str]'},
'enabled_by': {'key': 'enabled_by', 'type': 'str'},
'enabled_by_type': {'key': 'enabled_by_type', 'type': '[str]'},
'enabled_by_value': {'key': 'enabled_by_value', 'type': '[object]'},
'model_list': {'key': 'model_list', 'type': '[str]'},
'capabilities': {'key': 'capabilities', 'type': 'AzureOpenAIModelCapabilities'},
'dynamic_list': {'key': 'dynamic_list', 'type': 'ToolInputDynamicList'},
'allow_manual_entry': {'key': 'allow_manual_entry', 'type': 'bool'},
'is_multi_select': {'key': 'is_multi_select', 'type': 'bool'},
'generated_by': {'key': 'generated_by', 'type': 'ToolInputGeneratedBy'},
'input_type': {'key': 'input_type', 'type': 'str'},
'advanced': {'key': 'advanced', 'type': 'bool'},
'ui_hints': {'key': 'ui_hints', 'type': '{object}'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[List[Union[str, "ValueType"]]] = None,
default: Optional[Any] = None,
description: Optional[str] = None,
enum: Optional[List[str]] = None,
enabled_by: Optional[str] = None,
enabled_by_type: Optional[List[Union[str, "ValueType"]]] = None,
enabled_by_value: Optional[List[Any]] = None,
model_list: Optional[List[str]] = None,
capabilities: Optional["AzureOpenAIModelCapabilities"] = None,
dynamic_list: Optional["ToolInputDynamicList"] = None,
allow_manual_entry: Optional[bool] = None,
is_multi_select: Optional[bool] = None,
generated_by: Optional["ToolInputGeneratedBy"] = None,
input_type: Optional[Union[str, "InputType"]] = None,
advanced: Optional[bool] = None,
ui_hints: Optional[Dict[str, Any]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword default: Anything.
:paramtype default: any
:keyword description:
:paramtype description: str
:keyword enum:
:paramtype enum: list[str]
:keyword enabled_by:
:paramtype enabled_by: str
:keyword enabled_by_type:
:paramtype enabled_by_type: list[str or ~flow.models.ValueType]
:keyword enabled_by_value:
:paramtype enabled_by_value: list[any]
:keyword model_list:
:paramtype model_list: list[str]
:keyword capabilities:
:paramtype capabilities: ~flow.models.AzureOpenAIModelCapabilities
:keyword dynamic_list:
:paramtype dynamic_list: ~flow.models.ToolInputDynamicList
:keyword allow_manual_entry:
:paramtype allow_manual_entry: bool
:keyword is_multi_select:
:paramtype is_multi_select: bool
:keyword generated_by:
:paramtype generated_by: ~flow.models.ToolInputGeneratedBy
:keyword input_type: Possible values include: "default", "uionly_hidden".
:paramtype input_type: str or ~flow.models.InputType
:keyword advanced:
:paramtype advanced: bool
:keyword ui_hints: This is a dictionary.
:paramtype ui_hints: dict[str, any]
"""
super(InputDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.default = default
self.description = description
self.enum = enum
self.enabled_by = enabled_by
self.enabled_by_type = enabled_by_type
self.enabled_by_value = enabled_by_value
self.model_list = model_list
self.capabilities = capabilities
self.dynamic_list = dynamic_list
self.allow_manual_entry = allow_manual_entry
self.is_multi_select = is_multi_select
self.generated_by = generated_by
self.input_type = input_type
self.advanced = advanced
self.ui_hints = ui_hints
class InputOutputPortMetadata(msrest.serialization.Model):
"""InputOutputPortMetadata.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar graph_module_node_id:
:vartype graph_module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar schema:
:vartype schema: str
:ivar name:
:vartype name: str
:ivar id:
:vartype id: str
"""
_validation = {
'id': {'readonly': True},
}
_attribute_map = {
'graph_module_node_id': {'key': 'graphModuleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'schema': {'key': 'schema', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
*,
graph_module_node_id: Optional[str] = None,
port_name: Optional[str] = None,
schema: Optional[str] = None,
name: Optional[str] = None,
**kwargs
):
"""
:keyword graph_module_node_id:
:paramtype graph_module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword schema:
:paramtype schema: str
:keyword name:
:paramtype name: str
"""
super(InputOutputPortMetadata, self).__init__(**kwargs)
self.graph_module_node_id = graph_module_node_id
self.port_name = port_name
self.schema = schema
self.name = name
self.id = None
class InputSetting(msrest.serialization.Model):
"""InputSetting.
:ivar name:
:vartype name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar options: This is a dictionary.
:vartype options: dict[str, str]
:ivar additional_transformations:
:vartype additional_transformations: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'additional_transformations': {'key': 'additionalTransformations', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
options: Optional[Dict[str, str]] = None,
additional_transformations: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword options: This is a dictionary.
:paramtype options: dict[str, str]
:keyword additional_transformations:
:paramtype additional_transformations: str
"""
super(InputSetting, self).__init__(**kwargs)
self.name = name
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.options = options
self.additional_transformations = additional_transformations
class IntellectualPropertyPublisherInformation(msrest.serialization.Model):
"""IntellectualPropertyPublisherInformation.
:ivar intellectual_property_publisher:
:vartype intellectual_property_publisher: str
"""
_attribute_map = {
'intellectual_property_publisher': {'key': 'intellectualPropertyPublisher', 'type': 'str'},
}
def __init__(
self,
*,
intellectual_property_publisher: Optional[str] = None,
**kwargs
):
"""
:keyword intellectual_property_publisher:
:paramtype intellectual_property_publisher: str
"""
super(IntellectualPropertyPublisherInformation, self).__init__(**kwargs)
self.intellectual_property_publisher = intellectual_property_publisher
class InteractiveConfig(msrest.serialization.Model):
"""InteractiveConfig.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
*,
is_ssh_enabled: Optional[bool] = None,
ssh_public_key: Optional[str] = None,
is_i_python_enabled: Optional[bool] = None,
is_tensor_board_enabled: Optional[bool] = None,
interactive_port: Optional[int] = None,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(InteractiveConfig, self).__init__(**kwargs)
self.is_ssh_enabled = is_ssh_enabled
self.ssh_public_key = ssh_public_key
self.is_i_python_enabled = is_i_python_enabled
self.is_tensor_board_enabled = is_tensor_board_enabled
self.interactive_port = interactive_port
class InteractiveConfiguration(msrest.serialization.Model):
"""InteractiveConfiguration.
:ivar is_ssh_enabled:
:vartype is_ssh_enabled: bool
:ivar ssh_public_key:
:vartype ssh_public_key: str
:ivar is_i_python_enabled:
:vartype is_i_python_enabled: bool
:ivar is_tensor_board_enabled:
:vartype is_tensor_board_enabled: bool
:ivar interactive_port:
:vartype interactive_port: int
"""
_attribute_map = {
'is_ssh_enabled': {'key': 'isSSHEnabled', 'type': 'bool'},
'ssh_public_key': {'key': 'sshPublicKey', 'type': 'str'},
'is_i_python_enabled': {'key': 'isIPythonEnabled', 'type': 'bool'},
'is_tensor_board_enabled': {'key': 'isTensorBoardEnabled', 'type': 'bool'},
'interactive_port': {'key': 'interactivePort', 'type': 'int'},
}
def __init__(
self,
*,
is_ssh_enabled: Optional[bool] = None,
ssh_public_key: Optional[str] = None,
is_i_python_enabled: Optional[bool] = None,
is_tensor_board_enabled: Optional[bool] = None,
interactive_port: Optional[int] = None,
**kwargs
):
"""
:keyword is_ssh_enabled:
:paramtype is_ssh_enabled: bool
:keyword ssh_public_key:
:paramtype ssh_public_key: str
:keyword is_i_python_enabled:
:paramtype is_i_python_enabled: bool
:keyword is_tensor_board_enabled:
:paramtype is_tensor_board_enabled: bool
:keyword interactive_port:
:paramtype interactive_port: int
"""
super(InteractiveConfiguration, self).__init__(**kwargs)
self.is_ssh_enabled = is_ssh_enabled
self.ssh_public_key = ssh_public_key
self.is_i_python_enabled = is_i_python_enabled
self.is_tensor_board_enabled = is_tensor_board_enabled
self.interactive_port = interactive_port
class JobCost(msrest.serialization.Model):
"""JobCost.
:ivar charged_cpu_core_seconds:
:vartype charged_cpu_core_seconds: float
:ivar charged_cpu_memory_megabyte_seconds:
:vartype charged_cpu_memory_megabyte_seconds: float
:ivar charged_gpu_seconds:
:vartype charged_gpu_seconds: float
:ivar charged_node_utilization_seconds:
:vartype charged_node_utilization_seconds: float
"""
_attribute_map = {
'charged_cpu_core_seconds': {'key': 'chargedCpuCoreSeconds', 'type': 'float'},
'charged_cpu_memory_megabyte_seconds': {'key': 'chargedCpuMemoryMegabyteSeconds', 'type': 'float'},
'charged_gpu_seconds': {'key': 'chargedGpuSeconds', 'type': 'float'},
'charged_node_utilization_seconds': {'key': 'chargedNodeUtilizationSeconds', 'type': 'float'},
}
def __init__(
self,
*,
charged_cpu_core_seconds: Optional[float] = None,
charged_cpu_memory_megabyte_seconds: Optional[float] = None,
charged_gpu_seconds: Optional[float] = None,
charged_node_utilization_seconds: Optional[float] = None,
**kwargs
):
"""
:keyword charged_cpu_core_seconds:
:paramtype charged_cpu_core_seconds: float
:keyword charged_cpu_memory_megabyte_seconds:
:paramtype charged_cpu_memory_megabyte_seconds: float
:keyword charged_gpu_seconds:
:paramtype charged_gpu_seconds: float
:keyword charged_node_utilization_seconds:
:paramtype charged_node_utilization_seconds: float
"""
super(JobCost, self).__init__(**kwargs)
self.charged_cpu_core_seconds = charged_cpu_core_seconds
self.charged_cpu_memory_megabyte_seconds = charged_cpu_memory_megabyte_seconds
self.charged_gpu_seconds = charged_gpu_seconds
self.charged_node_utilization_seconds = charged_node_utilization_seconds
class JobEndpoint(msrest.serialization.Model):
"""JobEndpoint.
:ivar type:
:vartype type: str
:ivar port:
:vartype port: int
:ivar endpoint:
:vartype endpoint: str
:ivar status:
:vartype status: str
:ivar error_message:
:vartype error_message: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar nodes:
:vartype nodes: ~flow.models.MfeInternalNodes
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'port': {'key': 'port', 'type': 'int'},
'endpoint': {'key': 'endpoint', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'nodes': {'key': 'nodes', 'type': 'MfeInternalNodes'},
}
def __init__(
self,
*,
type: Optional[str] = None,
port: Optional[int] = None,
endpoint: Optional[str] = None,
status: Optional[str] = None,
error_message: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
nodes: Optional["MfeInternalNodes"] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword port:
:paramtype port: int
:keyword endpoint:
:paramtype endpoint: str
:keyword status:
:paramtype status: str
:keyword error_message:
:paramtype error_message: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword nodes:
:paramtype nodes: ~flow.models.MfeInternalNodes
"""
super(JobEndpoint, self).__init__(**kwargs)
self.type = type
self.port = port
self.endpoint = endpoint
self.status = status
self.error_message = error_message
self.properties = properties
self.nodes = nodes
class JobInput(msrest.serialization.Model):
"""JobInput.
All required parameters must be populated in order to send to Azure.
:ivar job_input_type: Required. Possible values include: "Dataset", "Uri", "Literal",
"UriFile", "UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_input_type: str or ~flow.models.JobInputType
:ivar description:
:vartype description: str
"""
_validation = {
'job_input_type': {'required': True},
}
_attribute_map = {
'job_input_type': {'key': 'jobInputType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
*,
job_input_type: Union[str, "JobInputType"],
description: Optional[str] = None,
**kwargs
):
"""
:keyword job_input_type: Required. Possible values include: "Dataset", "Uri", "Literal",
"UriFile", "UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_input_type: str or ~flow.models.JobInputType
:keyword description:
:paramtype description: str
"""
super(JobInput, self).__init__(**kwargs)
self.job_input_type = job_input_type
self.description = description
class JobOutput(msrest.serialization.Model):
"""JobOutput.
All required parameters must be populated in order to send to Azure.
:ivar job_output_type: Required. Possible values include: "Uri", "Dataset", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_output_type: str or ~flow.models.JobOutputType
:ivar description:
:vartype description: str
:ivar auto_delete_setting:
:vartype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
_validation = {
'job_output_type': {'required': True},
}
_attribute_map = {
'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'auto_delete_setting': {'key': 'autoDeleteSetting', 'type': 'AutoDeleteSetting'},
}
def __init__(
self,
*,
job_output_type: Union[str, "JobOutputType"],
description: Optional[str] = None,
auto_delete_setting: Optional["AutoDeleteSetting"] = None,
**kwargs
):
"""
:keyword job_output_type: Required. Possible values include: "Uri", "Dataset", "UriFile",
"UriFolder", "MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_output_type: str or ~flow.models.JobOutputType
:keyword description:
:paramtype description: str
:keyword auto_delete_setting:
:paramtype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
super(JobOutput, self).__init__(**kwargs)
self.job_output_type = job_output_type
self.description = description
self.auto_delete_setting = auto_delete_setting
class JobOutputArtifacts(msrest.serialization.Model):
"""JobOutputArtifacts.
:ivar datastore_id:
:vartype datastore_id: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'datastore_id': {'key': 'datastoreId', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
*,
datastore_id: Optional[str] = None,
path: Optional[str] = None,
**kwargs
):
"""
:keyword datastore_id:
:paramtype datastore_id: str
:keyword path:
:paramtype path: str
"""
super(JobOutputArtifacts, self).__init__(**kwargs)
self.datastore_id = datastore_id
self.path = path
class JobScheduleDto(msrest.serialization.Model):
"""JobScheduleDto.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar name:
:vartype name: str
:ivar job_definition_id:
:vartype job_definition_id: str
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'name': {'key': 'name', 'type': 'str'},
'job_definition_id': {'key': 'jobDefinitionId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
job_type: Optional[Union[str, "JobType"]] = None,
system_data: Optional["SystemData"] = None,
name: Optional[str] = None,
job_definition_id: Optional[str] = None,
display_name: Optional[str] = None,
trigger_type: Optional[Union[str, "TriggerType"]] = None,
recurrence: Optional["Recurrence"] = None,
cron: Optional["Cron"] = None,
status: Optional[Union[str, "ScheduleStatus"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword name:
:paramtype name: str
:keyword job_definition_id:
:paramtype job_definition_id: str
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(JobScheduleDto, self).__init__(**kwargs)
self.job_type = job_type
self.system_data = system_data
self.name = name
self.job_definition_id = job_definition_id
self.display_name = display_name
self.trigger_type = trigger_type
self.recurrence = recurrence
self.cron = cron
self.status = status
self.description = description
self.tags = tags
self.properties = properties
class K8SConfiguration(msrest.serialization.Model):
"""K8SConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
:ivar resource_configuration:
:vartype resource_configuration: ~flow.models.ResourceConfig
:ivar priority_configuration:
:vartype priority_configuration: ~flow.models.PriorityConfig
:ivar interactive_configuration:
:vartype interactive_configuration: ~flow.models.InteractiveConfig
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
'resource_configuration': {'key': 'resourceConfiguration', 'type': 'ResourceConfig'},
'priority_configuration': {'key': 'priorityConfiguration', 'type': 'PriorityConfig'},
'interactive_configuration': {'key': 'interactiveConfiguration', 'type': 'InteractiveConfig'},
}
def __init__(
self,
*,
max_retry_count: Optional[int] = None,
resource_configuration: Optional["ResourceConfig"] = None,
priority_configuration: Optional["PriorityConfig"] = None,
interactive_configuration: Optional["InteractiveConfig"] = None,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
:keyword resource_configuration:
:paramtype resource_configuration: ~flow.models.ResourceConfig
:keyword priority_configuration:
:paramtype priority_configuration: ~flow.models.PriorityConfig
:keyword interactive_configuration:
:paramtype interactive_configuration: ~flow.models.InteractiveConfig
"""
super(K8SConfiguration, self).__init__(**kwargs)
self.max_retry_count = max_retry_count
self.resource_configuration = resource_configuration
self.priority_configuration = priority_configuration
self.interactive_configuration = interactive_configuration
class KeyValuePairComponentNameMetaInfoErrorResponse(msrest.serialization.Model):
"""KeyValuePairComponentNameMetaInfoErrorResponse.
:ivar key:
:vartype key: ~flow.models.ComponentNameMetaInfo
:ivar value: The error response.
:vartype value: ~flow.models.ErrorResponse
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'ComponentNameMetaInfo'},
'value': {'key': 'value', 'type': 'ErrorResponse'},
}
def __init__(
self,
*,
key: Optional["ComponentNameMetaInfo"] = None,
value: Optional["ErrorResponse"] = None,
**kwargs
):
"""
:keyword key:
:paramtype key: ~flow.models.ComponentNameMetaInfo
:keyword value: The error response.
:paramtype value: ~flow.models.ErrorResponse
"""
super(KeyValuePairComponentNameMetaInfoErrorResponse, self).__init__(**kwargs)
self.key = key
self.value = value
class KeyValuePairComponentNameMetaInfoModuleDto(msrest.serialization.Model):
"""KeyValuePairComponentNameMetaInfoModuleDto.
:ivar key:
:vartype key: ~flow.models.ComponentNameMetaInfo
:ivar value:
:vartype value: ~flow.models.ModuleDto
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'ComponentNameMetaInfo'},
'value': {'key': 'value', 'type': 'ModuleDto'},
}
def __init__(
self,
*,
key: Optional["ComponentNameMetaInfo"] = None,
value: Optional["ModuleDto"] = None,
**kwargs
):
"""
:keyword key:
:paramtype key: ~flow.models.ComponentNameMetaInfo
:keyword value:
:paramtype value: ~flow.models.ModuleDto
"""
super(KeyValuePairComponentNameMetaInfoModuleDto, self).__init__(**kwargs)
self.key = key
self.value = value
class KeyValuePairStringObject(msrest.serialization.Model):
"""KeyValuePairStringObject.
:ivar key:
:vartype key: str
:ivar value: Anything.
:vartype value: any
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'str'},
'value': {'key': 'value', 'type': 'object'},
}
def __init__(
self,
*,
key: Optional[str] = None,
value: Optional[Any] = None,
**kwargs
):
"""
:keyword key:
:paramtype key: str
:keyword value: Anything.
:paramtype value: any
"""
super(KeyValuePairStringObject, self).__init__(**kwargs)
self.key = key
self.value = value
class KubernetesConfiguration(msrest.serialization.Model):
"""KubernetesConfiguration.
:ivar instance_type:
:vartype instance_type: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
}
def __init__(
self,
*,
instance_type: Optional[str] = None,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
"""
super(KubernetesConfiguration, self).__init__(**kwargs)
self.instance_type = instance_type
class Kwarg(msrest.serialization.Model):
"""Kwarg.
:ivar key:
:vartype key: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'key': {'key': 'key', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
key: Optional[str] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword key:
:paramtype key: str
:keyword value:
:paramtype value: str
"""
super(Kwarg, self).__init__(**kwargs)
self.key = key
self.value = value
class LegacyDataPath(msrest.serialization.Model):
"""LegacyDataPath.
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar relative_path:
:vartype relative_path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
relative_path: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword relative_path:
:paramtype relative_path: str
"""
super(LegacyDataPath, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.data_store_mode = data_store_mode
self.relative_path = relative_path
class LimitSettings(msrest.serialization.Model):
"""LimitSettings.
:ivar max_trials:
:vartype max_trials: int
:ivar timeout:
:vartype timeout: str
:ivar trial_timeout:
:vartype trial_timeout: str
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
:ivar max_cores_per_trial:
:vartype max_cores_per_trial: int
:ivar exit_score:
:vartype exit_score: float
:ivar enable_early_termination:
:vartype enable_early_termination: bool
:ivar max_nodes:
:vartype max_nodes: int
"""
_attribute_map = {
'max_trials': {'key': 'maxTrials', 'type': 'int'},
'timeout': {'key': 'timeout', 'type': 'str'},
'trial_timeout': {'key': 'trialTimeout', 'type': 'str'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
'max_cores_per_trial': {'key': 'maxCoresPerTrial', 'type': 'int'},
'exit_score': {'key': 'exitScore', 'type': 'float'},
'enable_early_termination': {'key': 'enableEarlyTermination', 'type': 'bool'},
'max_nodes': {'key': 'maxNodes', 'type': 'int'},
}
def __init__(
self,
*,
max_trials: Optional[int] = None,
timeout: Optional[str] = None,
trial_timeout: Optional[str] = None,
max_concurrent_trials: Optional[int] = None,
max_cores_per_trial: Optional[int] = None,
exit_score: Optional[float] = None,
enable_early_termination: Optional[bool] = None,
max_nodes: Optional[int] = None,
**kwargs
):
"""
:keyword max_trials:
:paramtype max_trials: int
:keyword timeout:
:paramtype timeout: str
:keyword trial_timeout:
:paramtype trial_timeout: str
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
:keyword max_cores_per_trial:
:paramtype max_cores_per_trial: int
:keyword exit_score:
:paramtype exit_score: float
:keyword enable_early_termination:
:paramtype enable_early_termination: bool
:keyword max_nodes:
:paramtype max_nodes: int
"""
super(LimitSettings, self).__init__(**kwargs)
self.max_trials = max_trials
self.timeout = timeout
self.trial_timeout = trial_timeout
self.max_concurrent_trials = max_concurrent_trials
self.max_cores_per_trial = max_cores_per_trial
self.exit_score = exit_score
self.enable_early_termination = enable_early_termination
self.max_nodes = max_nodes
class LinkedADBWorkspaceMetadata(msrest.serialization.Model):
"""LinkedADBWorkspaceMetadata.
:ivar workspace_id:
:vartype workspace_id: str
:ivar region:
:vartype region: str
"""
_attribute_map = {
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'region': {'key': 'region', 'type': 'str'},
}
def __init__(
self,
*,
workspace_id: Optional[str] = None,
region: Optional[str] = None,
**kwargs
):
"""
:keyword workspace_id:
:paramtype workspace_id: str
:keyword region:
:paramtype region: str
"""
super(LinkedADBWorkspaceMetadata, self).__init__(**kwargs)
self.workspace_id = workspace_id
self.region = region
class LinkedPipelineInfo(msrest.serialization.Model):
"""LinkedPipelineInfo.
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar module_node_id:
:vartype module_node_id: str
:ivar port_name:
:vartype port_name: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar is_direct_link:
:vartype is_direct_link: bool
"""
_attribute_map = {
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'is_direct_link': {'key': 'isDirectLink', 'type': 'bool'},
}
def __init__(
self,
*,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
module_node_id: Optional[str] = None,
port_name: Optional[str] = None,
linked_pipeline_draft_id: Optional[str] = None,
linked_pipeline_run_id: Optional[str] = None,
is_direct_link: Optional[bool] = None,
**kwargs
):
"""
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword module_node_id:
:paramtype module_node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword is_direct_link:
:paramtype is_direct_link: bool
"""
super(LinkedPipelineInfo, self).__init__(**kwargs)
self.pipeline_type = pipeline_type
self.module_node_id = module_node_id
self.port_name = port_name
self.linked_pipeline_draft_id = linked_pipeline_draft_id
self.linked_pipeline_run_id = linked_pipeline_run_id
self.is_direct_link = is_direct_link
class LoadFlowAsComponentRequest(msrest.serialization.Model):
"""LoadFlowAsComponentRequest.
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_definition_resource_id:
:vartype flow_definition_resource_id: str
:ivar flow_definition_data_store_name:
:vartype flow_definition_data_store_name: str
:ivar flow_definition_blob_path:
:vartype flow_definition_blob_path: str
:ivar flow_definition_data_uri:
:vartype flow_definition_data_uri: str
:ivar node_variant:
:vartype node_variant: str
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar session_id:
:vartype session_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
"""
_attribute_map = {
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_definition_resource_id': {'key': 'flowDefinitionResourceId', 'type': 'str'},
'flow_definition_data_store_name': {'key': 'flowDefinitionDataStoreName', 'type': 'str'},
'flow_definition_blob_path': {'key': 'flowDefinitionBlobPath', 'type': 'str'},
'flow_definition_data_uri': {'key': 'flowDefinitionDataUri', 'type': 'str'},
'node_variant': {'key': 'nodeVariant', 'type': 'str'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'session_id': {'key': 'sessionId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
}
def __init__(
self,
*,
component_name: Optional[str] = None,
component_version: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
is_deterministic: Optional[bool] = None,
flow_definition_file_path: Optional[str] = None,
flow_definition_resource_id: Optional[str] = None,
flow_definition_data_store_name: Optional[str] = None,
flow_definition_blob_path: Optional[str] = None,
flow_definition_data_uri: Optional[str] = None,
node_variant: Optional[str] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
session_id: Optional[str] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
**kwargs
):
"""
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_definition_resource_id:
:paramtype flow_definition_resource_id: str
:keyword flow_definition_data_store_name:
:paramtype flow_definition_data_store_name: str
:keyword flow_definition_blob_path:
:paramtype flow_definition_blob_path: str
:keyword flow_definition_data_uri:
:paramtype flow_definition_data_uri: str
:keyword node_variant:
:paramtype node_variant: str
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword session_id:
:paramtype session_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
"""
super(LoadFlowAsComponentRequest, self).__init__(**kwargs)
self.component_name = component_name
self.component_version = component_version
self.display_name = display_name
self.description = description
self.tags = tags
self.properties = properties
self.is_deterministic = is_deterministic
self.flow_definition_file_path = flow_definition_file_path
self.flow_definition_resource_id = flow_definition_resource_id
self.flow_definition_data_store_name = flow_definition_data_store_name
self.flow_definition_blob_path = flow_definition_blob_path
self.flow_definition_data_uri = flow_definition_data_uri
self.node_variant = node_variant
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.runtime_name = runtime_name
self.session_id = session_id
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
class LogRunTerminatedEventDto(msrest.serialization.Model):
"""LogRunTerminatedEventDto.
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:vartype action_type: str or ~flow.models.ActionType
:ivar last_checked_time:
:vartype last_checked_time: ~datetime.datetime
"""
_attribute_map = {
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
'last_checked_time': {'key': 'lastCheckedTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
next_action_interval_in_seconds: Optional[int] = None,
action_type: Optional[Union[str, "ActionType"]] = None,
last_checked_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:paramtype action_type: str or ~flow.models.ActionType
:keyword last_checked_time:
:paramtype last_checked_time: ~datetime.datetime
"""
super(LogRunTerminatedEventDto, self).__init__(**kwargs)
self.next_action_interval_in_seconds = next_action_interval_in_seconds
self.action_type = action_type
self.last_checked_time = last_checked_time
class LongRunningOperationUriResponse(msrest.serialization.Model):
"""LongRunningOperationUriResponse.
:ivar location:
:vartype location: str
:ivar operation_result:
:vartype operation_result: str
"""
_attribute_map = {
'location': {'key': 'location', 'type': 'str'},
'operation_result': {'key': 'operationResult', 'type': 'str'},
}
def __init__(
self,
*,
location: Optional[str] = None,
operation_result: Optional[str] = None,
**kwargs
):
"""
:keyword location:
:paramtype location: str
:keyword operation_result:
:paramtype operation_result: str
"""
super(LongRunningOperationUriResponse, self).__init__(**kwargs)
self.location = location
self.operation_result = operation_result
class LongRunningUpdateRegistryComponentRequest(msrest.serialization.Model):
"""LongRunningUpdateRegistryComponentRequest.
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar registry_name:
:vartype registry_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar update_type: Possible values include: "EnableModule", "DisableModule",
"UpdateDisplayName", "UpdateDescription", "UpdateTags".
:vartype update_type: str or ~flow.models.LongRunningUpdateType
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'update_type': {'key': 'updateType', 'type': 'str'},
}
def __init__(
self,
*,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
registry_name: Optional[str] = None,
component_name: Optional[str] = None,
component_version: Optional[str] = None,
update_type: Optional[Union[str, "LongRunningUpdateType"]] = None,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword registry_name:
:paramtype registry_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword update_type: Possible values include: "EnableModule", "DisableModule",
"UpdateDisplayName", "UpdateDescription", "UpdateTags".
:paramtype update_type: str or ~flow.models.LongRunningUpdateType
"""
super(LongRunningUpdateRegistryComponentRequest, self).__init__(**kwargs)
self.display_name = display_name
self.description = description
self.tags = tags
self.registry_name = registry_name
self.component_name = component_name
self.component_version = component_version
self.update_type = update_type
class ManagedServiceIdentity(msrest.serialization.Model):
"""ManagedServiceIdentity.
All required parameters must be populated in order to send to Azure.
:ivar type: Required. Possible values include: "SystemAssigned", "UserAssigned",
"SystemAssignedUserAssigned", "None".
:vartype type: str or ~flow.models.ManagedServiceIdentityType
:ivar principal_id:
:vartype principal_id: str
:ivar tenant_id:
:vartype tenant_id: str
:ivar user_assigned_identities: Dictionary of :code:`<UserAssignedIdentity>`.
:vartype user_assigned_identities: dict[str, ~flow.models.UserAssignedIdentity]
"""
_validation = {
'type': {'required': True},
}
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'principal_id': {'key': 'principalId', 'type': 'str'},
'tenant_id': {'key': 'tenantId', 'type': 'str'},
'user_assigned_identities': {'key': 'userAssignedIdentities', 'type': '{UserAssignedIdentity}'},
}
def __init__(
self,
*,
type: Union[str, "ManagedServiceIdentityType"],
principal_id: Optional[str] = None,
tenant_id: Optional[str] = None,
user_assigned_identities: Optional[Dict[str, "UserAssignedIdentity"]] = None,
**kwargs
):
"""
:keyword type: Required. Possible values include: "SystemAssigned", "UserAssigned",
"SystemAssignedUserAssigned", "None".
:paramtype type: str or ~flow.models.ManagedServiceIdentityType
:keyword principal_id:
:paramtype principal_id: str
:keyword tenant_id:
:paramtype tenant_id: str
:keyword user_assigned_identities: Dictionary of :code:`<UserAssignedIdentity>`.
:paramtype user_assigned_identities: dict[str, ~flow.models.UserAssignedIdentity]
"""
super(ManagedServiceIdentity, self).__init__(**kwargs)
self.type = type
self.principal_id = principal_id
self.tenant_id = tenant_id
self.user_assigned_identities = user_assigned_identities
class MavenLibraryDto(msrest.serialization.Model):
"""MavenLibraryDto.
:ivar coordinates:
:vartype coordinates: str
:ivar repo:
:vartype repo: str
:ivar exclusions:
:vartype exclusions: list[str]
"""
_attribute_map = {
'coordinates': {'key': 'coordinates', 'type': 'str'},
'repo': {'key': 'repo', 'type': 'str'},
'exclusions': {'key': 'exclusions', 'type': '[str]'},
}
def __init__(
self,
*,
coordinates: Optional[str] = None,
repo: Optional[str] = None,
exclusions: Optional[List[str]] = None,
**kwargs
):
"""
:keyword coordinates:
:paramtype coordinates: str
:keyword repo:
:paramtype repo: str
:keyword exclusions:
:paramtype exclusions: list[str]
"""
super(MavenLibraryDto, self).__init__(**kwargs)
self.coordinates = coordinates
self.repo = repo
self.exclusions = exclusions
class MetricProperties(msrest.serialization.Model):
"""MetricProperties.
:ivar ux_metric_type:
:vartype ux_metric_type: str
"""
_attribute_map = {
'ux_metric_type': {'key': 'uxMetricType', 'type': 'str'},
}
def __init__(
self,
*,
ux_metric_type: Optional[str] = None,
**kwargs
):
"""
:keyword ux_metric_type:
:paramtype ux_metric_type: str
"""
super(MetricProperties, self).__init__(**kwargs)
self.ux_metric_type = ux_metric_type
class MetricSchemaDto(msrest.serialization.Model):
"""MetricSchemaDto.
:ivar num_properties:
:vartype num_properties: int
:ivar properties:
:vartype properties: list[~flow.models.MetricSchemaPropertyDto]
"""
_attribute_map = {
'num_properties': {'key': 'numProperties', 'type': 'int'},
'properties': {'key': 'properties', 'type': '[MetricSchemaPropertyDto]'},
}
def __init__(
self,
*,
num_properties: Optional[int] = None,
properties: Optional[List["MetricSchemaPropertyDto"]] = None,
**kwargs
):
"""
:keyword num_properties:
:paramtype num_properties: int
:keyword properties:
:paramtype properties: list[~flow.models.MetricSchemaPropertyDto]
"""
super(MetricSchemaDto, self).__init__(**kwargs)
self.num_properties = num_properties
self.properties = properties
class MetricSchemaPropertyDto(msrest.serialization.Model):
"""MetricSchemaPropertyDto.
:ivar property_id:
:vartype property_id: str
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'property_id': {'key': 'propertyId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
property_id: Optional[str] = None,
name: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword property_id:
:paramtype property_id: str
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
"""
super(MetricSchemaPropertyDto, self).__init__(**kwargs)
self.property_id = property_id
self.name = name
self.type = type
class MetricV2Dto(msrest.serialization.Model):
"""MetricV2Dto.
:ivar data_container_id:
:vartype data_container_id: str
:ivar name:
:vartype name: str
:ivar columns: This is a dictionary.
:vartype columns: dict[str, str or ~flow.models.MetricValueType]
:ivar properties:
:vartype properties: ~flow.models.MetricProperties
:ivar namespace:
:vartype namespace: str
:ivar standard_schema_id:
:vartype standard_schema_id: str
:ivar value:
:vartype value: list[~flow.models.MetricV2Value]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'columns': {'key': 'columns', 'type': '{str}'},
'properties': {'key': 'properties', 'type': 'MetricProperties'},
'namespace': {'key': 'namespace', 'type': 'str'},
'standard_schema_id': {'key': 'standardSchemaId', 'type': 'str'},
'value': {'key': 'value', 'type': '[MetricV2Value]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
data_container_id: Optional[str] = None,
name: Optional[str] = None,
columns: Optional[Dict[str, Union[str, "MetricValueType"]]] = None,
properties: Optional["MetricProperties"] = None,
namespace: Optional[str] = None,
standard_schema_id: Optional[str] = None,
value: Optional[List["MetricV2Value"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword data_container_id:
:paramtype data_container_id: str
:keyword name:
:paramtype name: str
:keyword columns: This is a dictionary.
:paramtype columns: dict[str, str or ~flow.models.MetricValueType]
:keyword properties:
:paramtype properties: ~flow.models.MetricProperties
:keyword namespace:
:paramtype namespace: str
:keyword standard_schema_id:
:paramtype standard_schema_id: str
:keyword value:
:paramtype value: list[~flow.models.MetricV2Value]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(MetricV2Dto, self).__init__(**kwargs)
self.data_container_id = data_container_id
self.name = name
self.columns = columns
self.properties = properties
self.namespace = namespace
self.standard_schema_id = standard_schema_id
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class MetricV2Value(msrest.serialization.Model):
"""MetricV2Value.
:ivar metric_id:
:vartype metric_id: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar step:
:vartype step: long
:ivar data: Dictionary of :code:`<any>`.
:vartype data: dict[str, any]
:ivar sas_uri:
:vartype sas_uri: str
"""
_attribute_map = {
'metric_id': {'key': 'metricId', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'step': {'key': 'step', 'type': 'long'},
'data': {'key': 'data', 'type': '{object}'},
'sas_uri': {'key': 'sasUri', 'type': 'str'},
}
def __init__(
self,
*,
metric_id: Optional[str] = None,
created_utc: Optional[datetime.datetime] = None,
step: Optional[int] = None,
data: Optional[Dict[str, Any]] = None,
sas_uri: Optional[str] = None,
**kwargs
):
"""
:keyword metric_id:
:paramtype metric_id: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword step:
:paramtype step: long
:keyword data: Dictionary of :code:`<any>`.
:paramtype data: dict[str, any]
:keyword sas_uri:
:paramtype sas_uri: str
"""
super(MetricV2Value, self).__init__(**kwargs)
self.metric_id = metric_id
self.created_utc = created_utc
self.step = step
self.data = data
self.sas_uri = sas_uri
class MfeInternalAutologgerSettings(msrest.serialization.Model):
"""MfeInternalAutologgerSettings.
:ivar mlflow_autologger: Possible values include: "Enabled", "Disabled".
:vartype mlflow_autologger: str or ~flow.models.MfeInternalMLFlowAutologgerState
"""
_attribute_map = {
'mlflow_autologger': {'key': 'mlflowAutologger', 'type': 'str'},
}
def __init__(
self,
*,
mlflow_autologger: Optional[Union[str, "MfeInternalMLFlowAutologgerState"]] = None,
**kwargs
):
"""
:keyword mlflow_autologger: Possible values include: "Enabled", "Disabled".
:paramtype mlflow_autologger: str or ~flow.models.MfeInternalMLFlowAutologgerState
"""
super(MfeInternalAutologgerSettings, self).__init__(**kwargs)
self.mlflow_autologger = mlflow_autologger
class MfeInternalIdentityConfiguration(msrest.serialization.Model):
"""MfeInternalIdentityConfiguration.
:ivar identity_type: Possible values include: "Managed", "AMLToken", "UserIdentity".
:vartype identity_type: str or ~flow.models.MfeInternalIdentityType
"""
_attribute_map = {
'identity_type': {'key': 'identityType', 'type': 'str'},
}
def __init__(
self,
*,
identity_type: Optional[Union[str, "MfeInternalIdentityType"]] = None,
**kwargs
):
"""
:keyword identity_type: Possible values include: "Managed", "AMLToken", "UserIdentity".
:paramtype identity_type: str or ~flow.models.MfeInternalIdentityType
"""
super(MfeInternalIdentityConfiguration, self).__init__(**kwargs)
self.identity_type = identity_type
class MfeInternalNodes(msrest.serialization.Model):
"""MfeInternalNodes.
:ivar nodes_value_type: The only acceptable values to pass in are None and "All". The default
value is None.
:vartype nodes_value_type: str
"""
_attribute_map = {
'nodes_value_type': {'key': 'nodesValueType', 'type': 'str'},
}
def __init__(
self,
*,
nodes_value_type: Optional[str] = None,
**kwargs
):
"""
:keyword nodes_value_type: The only acceptable values to pass in are None and "All". The
default value is None.
:paramtype nodes_value_type: str
"""
super(MfeInternalNodes, self).__init__(**kwargs)
self.nodes_value_type = nodes_value_type
class MfeInternalOutputData(msrest.serialization.Model):
"""MfeInternalOutputData.
:ivar dataset_name:
:vartype dataset_name: str
:ivar datastore:
:vartype datastore: str
:ivar datapath:
:vartype datapath: str
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
"""
_attribute_map = {
'dataset_name': {'key': 'datasetName', 'type': 'str'},
'datastore': {'key': 'datastore', 'type': 'str'},
'datapath': {'key': 'datapath', 'type': 'str'},
'mode': {'key': 'mode', 'type': 'str'},
}
def __init__(
self,
*,
dataset_name: Optional[str] = None,
datastore: Optional[str] = None,
datapath: Optional[str] = None,
mode: Optional[Union[str, "DataBindingMode"]] = None,
**kwargs
):
"""
:keyword dataset_name:
:paramtype dataset_name: str
:keyword datastore:
:paramtype datastore: str
:keyword datapath:
:paramtype datapath: str
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
"""
super(MfeInternalOutputData, self).__init__(**kwargs)
self.dataset_name = dataset_name
self.datastore = datastore
self.datapath = datapath
self.mode = mode
class MfeInternalSecretConfiguration(msrest.serialization.Model):
"""MfeInternalSecretConfiguration.
:ivar workspace_secret_name:
:vartype workspace_secret_name: str
:ivar uri:
:vartype uri: str
"""
_attribute_map = {
'workspace_secret_name': {'key': 'workspaceSecretName', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
}
def __init__(
self,
*,
workspace_secret_name: Optional[str] = None,
uri: Optional[str] = None,
**kwargs
):
"""
:keyword workspace_secret_name:
:paramtype workspace_secret_name: str
:keyword uri:
:paramtype uri: str
"""
super(MfeInternalSecretConfiguration, self).__init__(**kwargs)
self.workspace_secret_name = workspace_secret_name
self.uri = uri
class MfeInternalUriReference(msrest.serialization.Model):
"""MfeInternalUriReference.
:ivar file:
:vartype file: str
:ivar folder:
:vartype folder: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'folder': {'key': 'folder', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
folder: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword folder:
:paramtype folder: str
"""
super(MfeInternalUriReference, self).__init__(**kwargs)
self.file = file
self.folder = folder
class MfeInternalV20211001ComponentJob(msrest.serialization.Model):
"""MfeInternalV20211001ComponentJob.
:ivar compute_id:
:vartype compute_id: str
:ivar component_id:
:vartype component_id: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.JobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.JobOutput]
:ivar overrides: Anything.
:vartype overrides: any
"""
_attribute_map = {
'compute_id': {'key': 'computeId', 'type': 'str'},
'component_id': {'key': 'componentId', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{JobInput}'},
'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
'overrides': {'key': 'overrides', 'type': 'object'},
}
def __init__(
self,
*,
compute_id: Optional[str] = None,
component_id: Optional[str] = None,
inputs: Optional[Dict[str, "JobInput"]] = None,
outputs: Optional[Dict[str, "JobOutput"]] = None,
overrides: Optional[Any] = None,
**kwargs
):
"""
:keyword compute_id:
:paramtype compute_id: str
:keyword component_id:
:paramtype component_id: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.JobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.JobOutput]
:keyword overrides: Anything.
:paramtype overrides: any
"""
super(MfeInternalV20211001ComponentJob, self).__init__(**kwargs)
self.compute_id = compute_id
self.component_id = component_id
self.inputs = inputs
self.outputs = outputs
self.overrides = overrides
class MinMaxParameterRule(msrest.serialization.Model):
"""MinMaxParameterRule.
:ivar min:
:vartype min: float
:ivar max:
:vartype max: float
"""
_attribute_map = {
'min': {'key': 'min', 'type': 'float'},
'max': {'key': 'max', 'type': 'float'},
}
def __init__(
self,
*,
min: Optional[float] = None,
max: Optional[float] = None,
**kwargs
):
"""
:keyword min:
:paramtype min: float
:keyword max:
:paramtype max: float
"""
super(MinMaxParameterRule, self).__init__(**kwargs)
self.min = min
self.max = max
class MlcComputeInfo(msrest.serialization.Model):
"""MlcComputeInfo.
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
"""
_attribute_map = {
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
}
def __init__(
self,
*,
mlc_compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
"""
super(MlcComputeInfo, self).__init__(**kwargs)
self.mlc_compute_type = mlc_compute_type
class ModelDto(msrest.serialization.Model):
"""ModelDto.
:ivar feed_name:
:vartype feed_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar aml_data_store_name:
:vartype aml_data_store_name: str
:ivar relative_path:
:vartype relative_path: str
:ivar id:
:vartype id: str
:ivar version:
:vartype version: str
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar arm_id:
:vartype arm_id: str
:ivar online_endpoint_yaml_str:
:vartype online_endpoint_yaml_str: str
"""
_attribute_map = {
'feed_name': {'key': 'feedName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'aml_data_store_name': {'key': 'amlDataStoreName', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'arm_id': {'key': 'armId', 'type': 'str'},
'online_endpoint_yaml_str': {'key': 'onlineEndpointYamlStr', 'type': 'str'},
}
def __init__(
self,
*,
feed_name: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
aml_data_store_name: Optional[str] = None,
relative_path: Optional[str] = None,
id: Optional[str] = None,
version: Optional[str] = None,
system_data: Optional["SystemData"] = None,
arm_id: Optional[str] = None,
online_endpoint_yaml_str: Optional[str] = None,
**kwargs
):
"""
:keyword feed_name:
:paramtype feed_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword aml_data_store_name:
:paramtype aml_data_store_name: str
:keyword relative_path:
:paramtype relative_path: str
:keyword id:
:paramtype id: str
:keyword version:
:paramtype version: str
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword arm_id:
:paramtype arm_id: str
:keyword online_endpoint_yaml_str:
:paramtype online_endpoint_yaml_str: str
"""
super(ModelDto, self).__init__(**kwargs)
self.feed_name = feed_name
self.name = name
self.description = description
self.aml_data_store_name = aml_data_store_name
self.relative_path = relative_path
self.id = id
self.version = version
self.system_data = system_data
self.arm_id = arm_id
self.online_endpoint_yaml_str = online_endpoint_yaml_str
class ModelManagementErrorResponse(msrest.serialization.Model):
"""ModelManagementErrorResponse.
:ivar code:
:vartype code: str
:ivar status_code:
:vartype status_code: int
:ivar message:
:vartype message: str
:ivar target:
:vartype target: str
:ivar details:
:vartype details: list[~flow.models.InnerErrorDetails]
:ivar correlation: Dictionary of :code:`<string>`.
:vartype correlation: dict[str, str]
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'status_code': {'key': 'statusCode', 'type': 'int'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[InnerErrorDetails]'},
'correlation': {'key': 'correlation', 'type': '{str}'},
}
def __init__(
self,
*,
code: Optional[str] = None,
status_code: Optional[int] = None,
message: Optional[str] = None,
target: Optional[str] = None,
details: Optional[List["InnerErrorDetails"]] = None,
correlation: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword status_code:
:paramtype status_code: int
:keyword message:
:paramtype message: str
:keyword target:
:paramtype target: str
:keyword details:
:paramtype details: list[~flow.models.InnerErrorDetails]
:keyword correlation: Dictionary of :code:`<string>`.
:paramtype correlation: dict[str, str]
"""
super(ModelManagementErrorResponse, self).__init__(**kwargs)
self.code = code
self.status_code = status_code
self.message = message
self.target = target
self.details = details
self.correlation = correlation
class ModifyPipelineJobScheduleDto(msrest.serialization.Model):
"""ModifyPipelineJobScheduleDto.
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
pipeline_job_name: Optional[str] = None,
pipeline_job_runtime_settings: Optional["PipelineJobRuntimeBasicSettings"] = None,
display_name: Optional[str] = None,
trigger_type: Optional[Union[str, "TriggerType"]] = None,
recurrence: Optional["Recurrence"] = None,
cron: Optional["Cron"] = None,
status: Optional[Union[str, "ScheduleStatus"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(ModifyPipelineJobScheduleDto, self).__init__(**kwargs)
self.pipeline_job_name = pipeline_job_name
self.pipeline_job_runtime_settings = pipeline_job_runtime_settings
self.display_name = display_name
self.trigger_type = trigger_type
self.recurrence = recurrence
self.cron = cron
self.status = status
self.description = description
self.tags = tags
self.properties = properties
class ModuleDto(msrest.serialization.Model):
"""ModuleDto.
:ivar namespace:
:vartype namespace: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar display_name:
:vartype display_name: str
:ivar dict_tags: Dictionary of :code:`<string>`.
:vartype dict_tags: dict[str, str]
:ivar module_version_id:
:vartype module_version_id: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
:ivar module_name:
:vartype module_name: str
:ivar module_version:
:vartype module_version: str
:ivar description:
:vartype description: str
:ivar owner:
:vartype owner: str
:ivar job_type:
:vartype job_type: str
:ivar default_version:
:vartype default_version: str
:ivar family_id:
:vartype family_id: str
:ivar help_document:
:vartype help_document: str
:ivar codegen_by:
:vartype codegen_by: str
:ivar arm_id:
:vartype arm_id: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar input_types:
:vartype input_types: list[str]
:ivar output_types:
:vartype output_types: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar yaml_link:
:vartype yaml_link: str
:ivar yaml_link_with_commit_sha:
:vartype yaml_link_with_commit_sha: str
:ivar module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:vartype module_source_type: str or ~flow.models.ModuleSourceType
:ivar registered_by:
:vartype registered_by: str
:ivar versions:
:vartype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:ivar is_default_module_version:
:vartype is_default_module_version: bool
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar system_meta:
:vartype system_meta: ~flow.models.SystemMeta
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar entry:
:vartype entry: str
:ivar os_type:
:vartype os_type: str
:ivar require_gpu:
:vartype require_gpu: bool
:ivar module_python_interface:
:vartype module_python_interface: ~flow.models.ModulePythonInterface
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar run_setting_parameters:
:vartype run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar supported_ui_input_data_delivery_modes: Dictionary of
<components·9qwi7e·schemas·moduledto·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:vartype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:ivar output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:vartype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:ivar yaml_str:
:vartype yaml_str: str
"""
_attribute_map = {
'namespace': {'key': 'namespace', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'display_name': {'key': 'displayName', 'type': 'str'},
'dict_tags': {'key': 'dictTags', 'type': '{str}'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'str'},
'job_type': {'key': 'jobType', 'type': 'str'},
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'family_id': {'key': 'familyId', 'type': 'str'},
'help_document': {'key': 'helpDocument', 'type': 'str'},
'codegen_by': {'key': 'codegenBy', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'input_types': {'key': 'inputTypes', 'type': '[str]'},
'output_types': {'key': 'outputTypes', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'yaml_link': {'key': 'yamlLink', 'type': 'str'},
'yaml_link_with_commit_sha': {'key': 'yamlLinkWithCommitSha', 'type': 'str'},
'module_source_type': {'key': 'moduleSourceType', 'type': 'str'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'versions': {'key': 'versions', 'type': '[AzureMLModuleVersionDescriptor]'},
'is_default_module_version': {'key': 'isDefaultModuleVersion', 'type': 'bool'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'system_meta': {'key': 'systemMeta', 'type': 'SystemMeta'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'module_python_interface': {'key': 'modulePythonInterface', 'type': 'ModulePythonInterface'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'run_setting_parameters': {'key': 'runSettingParameters', 'type': '[RunSettingParameter]'},
'supported_ui_input_data_delivery_modes': {'key': 'supportedUIInputDataDeliveryModes', 'type': '{[str]}'},
'output_setting_specs': {'key': 'outputSettingSpecs', 'type': '{OutputSettingSpec}'},
'yaml_str': {'key': 'yamlStr', 'type': 'str'},
}
def __init__(
self,
*,
namespace: Optional[str] = None,
tags: Optional[List[str]] = None,
display_name: Optional[str] = None,
dict_tags: Optional[Dict[str, str]] = None,
module_version_id: Optional[str] = None,
feed_name: Optional[str] = None,
registry_name: Optional[str] = None,
module_name: Optional[str] = None,
module_version: Optional[str] = None,
description: Optional[str] = None,
owner: Optional[str] = None,
job_type: Optional[str] = None,
default_version: Optional[str] = None,
family_id: Optional[str] = None,
help_document: Optional[str] = None,
codegen_by: Optional[str] = None,
arm_id: Optional[str] = None,
module_scope: Optional[Union[str, "ModuleScope"]] = None,
module_entity: Optional["ModuleEntity"] = None,
input_types: Optional[List[str]] = None,
output_types: Optional[List[str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
yaml_link: Optional[str] = None,
yaml_link_with_commit_sha: Optional[str] = None,
module_source_type: Optional[Union[str, "ModuleSourceType"]] = None,
registered_by: Optional[str] = None,
versions: Optional[List["AzureMLModuleVersionDescriptor"]] = None,
is_default_module_version: Optional[bool] = None,
system_data: Optional["SystemData"] = None,
system_meta: Optional["SystemMeta"] = None,
snapshot_id: Optional[str] = None,
entry: Optional[str] = None,
os_type: Optional[str] = None,
require_gpu: Optional[bool] = None,
module_python_interface: Optional["ModulePythonInterface"] = None,
environment_asset_id: Optional[str] = None,
run_setting_parameters: Optional[List["RunSettingParameter"]] = None,
supported_ui_input_data_delivery_modes: Optional[Dict[str, List[Union[str, "UIInputDataDeliveryMode"]]]] = None,
output_setting_specs: Optional[Dict[str, "OutputSettingSpec"]] = None,
yaml_str: Optional[str] = None,
**kwargs
):
"""
:keyword namespace:
:paramtype namespace: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword display_name:
:paramtype display_name: str
:keyword dict_tags: Dictionary of :code:`<string>`.
:paramtype dict_tags: dict[str, str]
:keyword module_version_id:
:paramtype module_version_id: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
:keyword module_name:
:paramtype module_name: str
:keyword module_version:
:paramtype module_version: str
:keyword description:
:paramtype description: str
:keyword owner:
:paramtype owner: str
:keyword job_type:
:paramtype job_type: str
:keyword default_version:
:paramtype default_version: str
:keyword family_id:
:paramtype family_id: str
:keyword help_document:
:paramtype help_document: str
:keyword codegen_by:
:paramtype codegen_by: str
:keyword arm_id:
:paramtype arm_id: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword input_types:
:paramtype input_types: list[str]
:keyword output_types:
:paramtype output_types: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword yaml_link:
:paramtype yaml_link: str
:keyword yaml_link_with_commit_sha:
:paramtype yaml_link_with_commit_sha: str
:keyword module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:paramtype module_source_type: str or ~flow.models.ModuleSourceType
:keyword registered_by:
:paramtype registered_by: str
:keyword versions:
:paramtype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:keyword is_default_module_version:
:paramtype is_default_module_version: bool
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword system_meta:
:paramtype system_meta: ~flow.models.SystemMeta
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword entry:
:paramtype entry: str
:keyword os_type:
:paramtype os_type: str
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword module_python_interface:
:paramtype module_python_interface: ~flow.models.ModulePythonInterface
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword run_setting_parameters:
:paramtype run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword supported_ui_input_data_delivery_modes: Dictionary of
<components·9qwi7e·schemas·moduledto·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:paramtype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:keyword output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:paramtype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:keyword yaml_str:
:paramtype yaml_str: str
"""
super(ModuleDto, self).__init__(**kwargs)
self.namespace = namespace
self.tags = tags
self.display_name = display_name
self.dict_tags = dict_tags
self.module_version_id = module_version_id
self.feed_name = feed_name
self.registry_name = registry_name
self.module_name = module_name
self.module_version = module_version
self.description = description
self.owner = owner
self.job_type = job_type
self.default_version = default_version
self.family_id = family_id
self.help_document = help_document
self.codegen_by = codegen_by
self.arm_id = arm_id
self.module_scope = module_scope
self.module_entity = module_entity
self.input_types = input_types
self.output_types = output_types
self.entity_status = entity_status
self.created_date = created_date
self.last_modified_date = last_modified_date
self.yaml_link = yaml_link
self.yaml_link_with_commit_sha = yaml_link_with_commit_sha
self.module_source_type = module_source_type
self.registered_by = registered_by
self.versions = versions
self.is_default_module_version = is_default_module_version
self.system_data = system_data
self.system_meta = system_meta
self.snapshot_id = snapshot_id
self.entry = entry
self.os_type = os_type
self.require_gpu = require_gpu
self.module_python_interface = module_python_interface
self.environment_asset_id = environment_asset_id
self.run_setting_parameters = run_setting_parameters
self.supported_ui_input_data_delivery_modes = supported_ui_input_data_delivery_modes
self.output_setting_specs = output_setting_specs
self.yaml_str = yaml_str
class ModuleDtoWithErrors(msrest.serialization.Model):
"""ModuleDtoWithErrors.
:ivar version_id_to_module_dto: This is a dictionary.
:vartype version_id_to_module_dto: dict[str, ~flow.models.ModuleDto]
:ivar name_and_version_to_module_dto:
:vartype name_and_version_to_module_dto:
list[~flow.models.KeyValuePairComponentNameMetaInfoModuleDto]
:ivar version_id_to_error: This is a dictionary.
:vartype version_id_to_error: dict[str, ~flow.models.ErrorResponse]
:ivar name_and_version_to_error:
:vartype name_and_version_to_error:
list[~flow.models.KeyValuePairComponentNameMetaInfoErrorResponse]
"""
_attribute_map = {
'version_id_to_module_dto': {'key': 'versionIdToModuleDto', 'type': '{ModuleDto}'},
'name_and_version_to_module_dto': {'key': 'nameAndVersionToModuleDto', 'type': '[KeyValuePairComponentNameMetaInfoModuleDto]'},
'version_id_to_error': {'key': 'versionIdToError', 'type': '{ErrorResponse}'},
'name_and_version_to_error': {'key': 'nameAndVersionToError', 'type': '[KeyValuePairComponentNameMetaInfoErrorResponse]'},
}
def __init__(
self,
*,
version_id_to_module_dto: Optional[Dict[str, "ModuleDto"]] = None,
name_and_version_to_module_dto: Optional[List["KeyValuePairComponentNameMetaInfoModuleDto"]] = None,
version_id_to_error: Optional[Dict[str, "ErrorResponse"]] = None,
name_and_version_to_error: Optional[List["KeyValuePairComponentNameMetaInfoErrorResponse"]] = None,
**kwargs
):
"""
:keyword version_id_to_module_dto: This is a dictionary.
:paramtype version_id_to_module_dto: dict[str, ~flow.models.ModuleDto]
:keyword name_and_version_to_module_dto:
:paramtype name_and_version_to_module_dto:
list[~flow.models.KeyValuePairComponentNameMetaInfoModuleDto]
:keyword version_id_to_error: This is a dictionary.
:paramtype version_id_to_error: dict[str, ~flow.models.ErrorResponse]
:keyword name_and_version_to_error:
:paramtype name_and_version_to_error:
list[~flow.models.KeyValuePairComponentNameMetaInfoErrorResponse]
"""
super(ModuleDtoWithErrors, self).__init__(**kwargs)
self.version_id_to_module_dto = version_id_to_module_dto
self.name_and_version_to_module_dto = name_and_version_to_module_dto
self.version_id_to_error = version_id_to_error
self.name_and_version_to_error = name_and_version_to_error
class ModuleDtoWithValidateStatus(msrest.serialization.Model):
"""ModuleDtoWithValidateStatus.
:ivar existing_module_entity:
:vartype existing_module_entity: ~flow.models.ModuleEntity
:ivar status: Possible values include: "NewModule", "NewVersion", "Conflict", "ParseError",
"ProcessRequestError".
:vartype status: str or ~flow.models.ModuleInfoFromYamlStatusEnum
:ivar status_details:
:vartype status_details: str
:ivar error_details:
:vartype error_details: list[str]
:ivar serialized_module_info:
:vartype serialized_module_info: str
:ivar namespace:
:vartype namespace: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar display_name:
:vartype display_name: str
:ivar dict_tags: Dictionary of :code:`<string>`.
:vartype dict_tags: dict[str, str]
:ivar module_version_id:
:vartype module_version_id: str
:ivar feed_name:
:vartype feed_name: str
:ivar registry_name:
:vartype registry_name: str
:ivar module_name:
:vartype module_name: str
:ivar module_version:
:vartype module_version: str
:ivar description:
:vartype description: str
:ivar owner:
:vartype owner: str
:ivar job_type:
:vartype job_type: str
:ivar default_version:
:vartype default_version: str
:ivar family_id:
:vartype family_id: str
:ivar help_document:
:vartype help_document: str
:ivar codegen_by:
:vartype codegen_by: str
:ivar arm_id:
:vartype arm_id: str
:ivar module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous", "Step",
"Draft", "Feed", "Registry", "SystemAutoCreated".
:vartype module_scope: str or ~flow.models.ModuleScope
:ivar module_entity:
:vartype module_entity: ~flow.models.ModuleEntity
:ivar input_types:
:vartype input_types: list[str]
:ivar output_types:
:vartype output_types: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar yaml_link:
:vartype yaml_link: str
:ivar yaml_link_with_commit_sha:
:vartype yaml_link_with_commit_sha: str
:ivar module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:vartype module_source_type: str or ~flow.models.ModuleSourceType
:ivar registered_by:
:vartype registered_by: str
:ivar versions:
:vartype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:ivar is_default_module_version:
:vartype is_default_module_version: bool
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar system_meta:
:vartype system_meta: ~flow.models.SystemMeta
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar entry:
:vartype entry: str
:ivar os_type:
:vartype os_type: str
:ivar require_gpu:
:vartype require_gpu: bool
:ivar module_python_interface:
:vartype module_python_interface: ~flow.models.ModulePythonInterface
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar run_setting_parameters:
:vartype run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar supported_ui_input_data_delivery_modes: Dictionary of
<components·8o5zaj·schemas·moduledtowithvalidatestatus·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:vartype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:ivar output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:vartype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:ivar yaml_str:
:vartype yaml_str: str
"""
_attribute_map = {
'existing_module_entity': {'key': 'existingModuleEntity', 'type': 'ModuleEntity'},
'status': {'key': 'status', 'type': 'str'},
'status_details': {'key': 'statusDetails', 'type': 'str'},
'error_details': {'key': 'errorDetails', 'type': '[str]'},
'serialized_module_info': {'key': 'serializedModuleInfo', 'type': 'str'},
'namespace': {'key': 'namespace', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'display_name': {'key': 'displayName', 'type': 'str'},
'dict_tags': {'key': 'dictTags', 'type': '{str}'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'registry_name': {'key': 'registryName', 'type': 'str'},
'module_name': {'key': 'moduleName', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'owner': {'key': 'owner', 'type': 'str'},
'job_type': {'key': 'jobType', 'type': 'str'},
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'family_id': {'key': 'familyId', 'type': 'str'},
'help_document': {'key': 'helpDocument', 'type': 'str'},
'codegen_by': {'key': 'codegenBy', 'type': 'str'},
'arm_id': {'key': 'armId', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_entity': {'key': 'moduleEntity', 'type': 'ModuleEntity'},
'input_types': {'key': 'inputTypes', 'type': '[str]'},
'output_types': {'key': 'outputTypes', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'yaml_link': {'key': 'yamlLink', 'type': 'str'},
'yaml_link_with_commit_sha': {'key': 'yamlLinkWithCommitSha', 'type': 'str'},
'module_source_type': {'key': 'moduleSourceType', 'type': 'str'},
'registered_by': {'key': 'registeredBy', 'type': 'str'},
'versions': {'key': 'versions', 'type': '[AzureMLModuleVersionDescriptor]'},
'is_default_module_version': {'key': 'isDefaultModuleVersion', 'type': 'bool'},
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'system_meta': {'key': 'systemMeta', 'type': 'SystemMeta'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'module_python_interface': {'key': 'modulePythonInterface', 'type': 'ModulePythonInterface'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'run_setting_parameters': {'key': 'runSettingParameters', 'type': '[RunSettingParameter]'},
'supported_ui_input_data_delivery_modes': {'key': 'supportedUIInputDataDeliveryModes', 'type': '{[str]}'},
'output_setting_specs': {'key': 'outputSettingSpecs', 'type': '{OutputSettingSpec}'},
'yaml_str': {'key': 'yamlStr', 'type': 'str'},
}
def __init__(
self,
*,
existing_module_entity: Optional["ModuleEntity"] = None,
status: Optional[Union[str, "ModuleInfoFromYamlStatusEnum"]] = None,
status_details: Optional[str] = None,
error_details: Optional[List[str]] = None,
serialized_module_info: Optional[str] = None,
namespace: Optional[str] = None,
tags: Optional[List[str]] = None,
display_name: Optional[str] = None,
dict_tags: Optional[Dict[str, str]] = None,
module_version_id: Optional[str] = None,
feed_name: Optional[str] = None,
registry_name: Optional[str] = None,
module_name: Optional[str] = None,
module_version: Optional[str] = None,
description: Optional[str] = None,
owner: Optional[str] = None,
job_type: Optional[str] = None,
default_version: Optional[str] = None,
family_id: Optional[str] = None,
help_document: Optional[str] = None,
codegen_by: Optional[str] = None,
arm_id: Optional[str] = None,
module_scope: Optional[Union[str, "ModuleScope"]] = None,
module_entity: Optional["ModuleEntity"] = None,
input_types: Optional[List[str]] = None,
output_types: Optional[List[str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
yaml_link: Optional[str] = None,
yaml_link_with_commit_sha: Optional[str] = None,
module_source_type: Optional[Union[str, "ModuleSourceType"]] = None,
registered_by: Optional[str] = None,
versions: Optional[List["AzureMLModuleVersionDescriptor"]] = None,
is_default_module_version: Optional[bool] = None,
system_data: Optional["SystemData"] = None,
system_meta: Optional["SystemMeta"] = None,
snapshot_id: Optional[str] = None,
entry: Optional[str] = None,
os_type: Optional[str] = None,
require_gpu: Optional[bool] = None,
module_python_interface: Optional["ModulePythonInterface"] = None,
environment_asset_id: Optional[str] = None,
run_setting_parameters: Optional[List["RunSettingParameter"]] = None,
supported_ui_input_data_delivery_modes: Optional[Dict[str, List[Union[str, "UIInputDataDeliveryMode"]]]] = None,
output_setting_specs: Optional[Dict[str, "OutputSettingSpec"]] = None,
yaml_str: Optional[str] = None,
**kwargs
):
"""
:keyword existing_module_entity:
:paramtype existing_module_entity: ~flow.models.ModuleEntity
:keyword status: Possible values include: "NewModule", "NewVersion", "Conflict", "ParseError",
"ProcessRequestError".
:paramtype status: str or ~flow.models.ModuleInfoFromYamlStatusEnum
:keyword status_details:
:paramtype status_details: str
:keyword error_details:
:paramtype error_details: list[str]
:keyword serialized_module_info:
:paramtype serialized_module_info: str
:keyword namespace:
:paramtype namespace: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword display_name:
:paramtype display_name: str
:keyword dict_tags: Dictionary of :code:`<string>`.
:paramtype dict_tags: dict[str, str]
:keyword module_version_id:
:paramtype module_version_id: str
:keyword feed_name:
:paramtype feed_name: str
:keyword registry_name:
:paramtype registry_name: str
:keyword module_name:
:paramtype module_name: str
:keyword module_version:
:paramtype module_version: str
:keyword description:
:paramtype description: str
:keyword owner:
:paramtype owner: str
:keyword job_type:
:paramtype job_type: str
:keyword default_version:
:paramtype default_version: str
:keyword family_id:
:paramtype family_id: str
:keyword help_document:
:paramtype help_document: str
:keyword codegen_by:
:paramtype codegen_by: str
:keyword arm_id:
:paramtype arm_id: str
:keyword module_scope: Possible values include: "All", "Global", "Workspace", "Anonymous",
"Step", "Draft", "Feed", "Registry", "SystemAutoCreated".
:paramtype module_scope: str or ~flow.models.ModuleScope
:keyword module_entity:
:paramtype module_entity: ~flow.models.ModuleEntity
:keyword input_types:
:paramtype input_types: list[str]
:keyword output_types:
:paramtype output_types: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword yaml_link:
:paramtype yaml_link: str
:keyword yaml_link_with_commit_sha:
:paramtype yaml_link_with_commit_sha: str
:keyword module_source_type: Possible values include: "Unknown", "Local", "GithubFile",
"GithubFolder", "DevopsArtifactsZip", "SerializedModuleInfo".
:paramtype module_source_type: str or ~flow.models.ModuleSourceType
:keyword registered_by:
:paramtype registered_by: str
:keyword versions:
:paramtype versions: list[~flow.models.AzureMLModuleVersionDescriptor]
:keyword is_default_module_version:
:paramtype is_default_module_version: bool
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword system_meta:
:paramtype system_meta: ~flow.models.SystemMeta
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword entry:
:paramtype entry: str
:keyword os_type:
:paramtype os_type: str
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword module_python_interface:
:paramtype module_python_interface: ~flow.models.ModulePythonInterface
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword run_setting_parameters:
:paramtype run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword supported_ui_input_data_delivery_modes: Dictionary of
<components·8o5zaj·schemas·moduledtowithvalidatestatus·properties·supporteduiinputdatadeliverymodes·additionalproperties>.
:paramtype supported_ui_input_data_delivery_modes: dict[str, list[str or
~flow.models.UIInputDataDeliveryMode]]
:keyword output_setting_specs: Dictionary of :code:`<OutputSettingSpec>`.
:paramtype output_setting_specs: dict[str, ~flow.models.OutputSettingSpec]
:keyword yaml_str:
:paramtype yaml_str: str
"""
super(ModuleDtoWithValidateStatus, self).__init__(**kwargs)
self.existing_module_entity = existing_module_entity
self.status = status
self.status_details = status_details
self.error_details = error_details
self.serialized_module_info = serialized_module_info
self.namespace = namespace
self.tags = tags
self.display_name = display_name
self.dict_tags = dict_tags
self.module_version_id = module_version_id
self.feed_name = feed_name
self.registry_name = registry_name
self.module_name = module_name
self.module_version = module_version
self.description = description
self.owner = owner
self.job_type = job_type
self.default_version = default_version
self.family_id = family_id
self.help_document = help_document
self.codegen_by = codegen_by
self.arm_id = arm_id
self.module_scope = module_scope
self.module_entity = module_entity
self.input_types = input_types
self.output_types = output_types
self.entity_status = entity_status
self.created_date = created_date
self.last_modified_date = last_modified_date
self.yaml_link = yaml_link
self.yaml_link_with_commit_sha = yaml_link_with_commit_sha
self.module_source_type = module_source_type
self.registered_by = registered_by
self.versions = versions
self.is_default_module_version = is_default_module_version
self.system_data = system_data
self.system_meta = system_meta
self.snapshot_id = snapshot_id
self.entry = entry
self.os_type = os_type
self.require_gpu = require_gpu
self.module_python_interface = module_python_interface
self.environment_asset_id = environment_asset_id
self.run_setting_parameters = run_setting_parameters
self.supported_ui_input_data_delivery_modes = supported_ui_input_data_delivery_modes
self.output_setting_specs = output_setting_specs
self.yaml_str = yaml_str
class ModuleEntity(msrest.serialization.Model):
"""ModuleEntity.
:ivar display_name:
:vartype display_name: str
:ivar module_execution_type:
:vartype module_execution_type: str
:ivar module_type: Possible values include: "None", "BatchInferencing".
:vartype module_type: str or ~flow.models.ModuleType
:ivar module_type_version:
:vartype module_type_version: str
:ivar upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:vartype upload_state: str or ~flow.models.UploadState
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar structured_interface:
:vartype structured_interface: ~flow.models.StructuredInterface
:ivar data_location:
:vartype data_location: ~flow.models.DataLocation
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar runconfig:
:vartype runconfig: str
:ivar cloud_settings:
:vartype cloud_settings: ~flow.models.CloudSettings
:ivar category:
:vartype category: str
:ivar step_type:
:vartype step_type: str
:ivar stage:
:vartype stage: str
:ivar name:
:vartype name: str
:ivar hash:
:vartype hash: str
:ivar description:
:vartype description: str
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'module_execution_type': {'key': 'moduleExecutionType', 'type': 'str'},
'module_type': {'key': 'moduleType', 'type': 'str'},
'module_type_version': {'key': 'moduleTypeVersion', 'type': 'str'},
'upload_state': {'key': 'uploadState', 'type': 'str'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'structured_interface': {'key': 'structuredInterface', 'type': 'StructuredInterface'},
'data_location': {'key': 'dataLocation', 'type': 'DataLocation'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'identifierHashV2', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'runconfig': {'key': 'runconfig', 'type': 'str'},
'cloud_settings': {'key': 'cloudSettings', 'type': 'CloudSettings'},
'category': {'key': 'category', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'stage': {'key': 'stage', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'hash': {'key': 'hash', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
display_name: Optional[str] = None,
module_execution_type: Optional[str] = None,
module_type: Optional[Union[str, "ModuleType"]] = None,
module_type_version: Optional[str] = None,
upload_state: Optional[Union[str, "UploadState"]] = None,
is_deterministic: Optional[bool] = None,
structured_interface: Optional["StructuredInterface"] = None,
data_location: Optional["DataLocation"] = None,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
created_by: Optional["CreatedBy"] = None,
last_updated_by: Optional["CreatedBy"] = None,
runconfig: Optional[str] = None,
cloud_settings: Optional["CloudSettings"] = None,
category: Optional[str] = None,
step_type: Optional[str] = None,
stage: Optional[str] = None,
name: Optional[str] = None,
hash: Optional[str] = None,
description: Optional[str] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword module_execution_type:
:paramtype module_execution_type: str
:keyword module_type: Possible values include: "None", "BatchInferencing".
:paramtype module_type: str or ~flow.models.ModuleType
:keyword module_type_version:
:paramtype module_type_version: str
:keyword upload_state: Possible values include: "Uploading", "Completed", "Canceled", "Failed".
:paramtype upload_state: str or ~flow.models.UploadState
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword structured_interface:
:paramtype structured_interface: ~flow.models.StructuredInterface
:keyword data_location:
:paramtype data_location: ~flow.models.DataLocation
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword runconfig:
:paramtype runconfig: str
:keyword cloud_settings:
:paramtype cloud_settings: ~flow.models.CloudSettings
:keyword category:
:paramtype category: str
:keyword step_type:
:paramtype step_type: str
:keyword stage:
:paramtype stage: str
:keyword name:
:paramtype name: str
:keyword hash:
:paramtype hash: str
:keyword description:
:paramtype description: str
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(ModuleEntity, self).__init__(**kwargs)
self.display_name = display_name
self.module_execution_type = module_execution_type
self.module_type = module_type
self.module_type_version = module_type_version
self.upload_state = upload_state
self.is_deterministic = is_deterministic
self.structured_interface = structured_interface
self.data_location = data_location
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
self.tags = tags
self.properties = properties
self.created_by = created_by
self.last_updated_by = last_updated_by
self.runconfig = runconfig
self.cloud_settings = cloud_settings
self.category = category
self.step_type = step_type
self.stage = stage
self.name = name
self.hash = hash
self.description = description
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class ModulePythonInterface(msrest.serialization.Model):
"""ModulePythonInterface.
:ivar inputs:
:vartype inputs: list[~flow.models.PythonInterfaceMapping]
:ivar outputs:
:vartype outputs: list[~flow.models.PythonInterfaceMapping]
:ivar parameters:
:vartype parameters: list[~flow.models.PythonInterfaceMapping]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '[PythonInterfaceMapping]'},
'outputs': {'key': 'outputs', 'type': '[PythonInterfaceMapping]'},
'parameters': {'key': 'parameters', 'type': '[PythonInterfaceMapping]'},
}
def __init__(
self,
*,
inputs: Optional[List["PythonInterfaceMapping"]] = None,
outputs: Optional[List["PythonInterfaceMapping"]] = None,
parameters: Optional[List["PythonInterfaceMapping"]] = None,
**kwargs
):
"""
:keyword inputs:
:paramtype inputs: list[~flow.models.PythonInterfaceMapping]
:keyword outputs:
:paramtype outputs: list[~flow.models.PythonInterfaceMapping]
:keyword parameters:
:paramtype parameters: list[~flow.models.PythonInterfaceMapping]
"""
super(ModulePythonInterface, self).__init__(**kwargs)
self.inputs = inputs
self.outputs = outputs
self.parameters = parameters
class MpiConfiguration(msrest.serialization.Model):
"""MpiConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
*,
process_count_per_node: Optional[int] = None,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(MpiConfiguration, self).__init__(**kwargs)
self.process_count_per_node = process_count_per_node
class NCrossValidations(msrest.serialization.Model):
"""NCrossValidations.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.NCrossValidationMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "NCrossValidationMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.NCrossValidationMode
:keyword value:
:paramtype value: int
"""
super(NCrossValidations, self).__init__(**kwargs)
self.mode = mode
self.value = value
class Node(msrest.serialization.Model):
"""Node.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:vartype type: str or ~flow.models.ToolType
:ivar source:
:vartype source: ~flow.models.NodeSource
:ivar inputs: Dictionary of :code:`<any>`.
:vartype inputs: dict[str, any]
:ivar tool:
:vartype tool: str
:ivar reduce:
:vartype reduce: bool
:ivar activate:
:vartype activate: ~flow.models.Activate
:ivar use_variants:
:vartype use_variants: bool
:ivar comment:
:vartype comment: str
:ivar api:
:vartype api: str
:ivar provider:
:vartype provider: str
:ivar connection:
:vartype connection: str
:ivar module:
:vartype module: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'source': {'key': 'source', 'type': 'NodeSource'},
'inputs': {'key': 'inputs', 'type': '{object}'},
'tool': {'key': 'tool', 'type': 'str'},
'reduce': {'key': 'reduce', 'type': 'bool'},
'activate': {'key': 'activate', 'type': 'Activate'},
'use_variants': {'key': 'use_variants', 'type': 'bool'},
'comment': {'key': 'comment', 'type': 'str'},
'api': {'key': 'api', 'type': 'str'},
'provider': {'key': 'provider', 'type': 'str'},
'connection': {'key': 'connection', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "ToolType"]] = None,
source: Optional["NodeSource"] = None,
inputs: Optional[Dict[str, Any]] = None,
tool: Optional[str] = None,
reduce: Optional[bool] = None,
activate: Optional["Activate"] = None,
use_variants: Optional[bool] = None,
comment: Optional[str] = None,
api: Optional[str] = None,
provider: Optional[str] = None,
connection: Optional[str] = None,
module: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:paramtype type: str or ~flow.models.ToolType
:keyword source:
:paramtype source: ~flow.models.NodeSource
:keyword inputs: Dictionary of :code:`<any>`.
:paramtype inputs: dict[str, any]
:keyword tool:
:paramtype tool: str
:keyword reduce:
:paramtype reduce: bool
:keyword activate:
:paramtype activate: ~flow.models.Activate
:keyword use_variants:
:paramtype use_variants: bool
:keyword comment:
:paramtype comment: str
:keyword api:
:paramtype api: str
:keyword provider:
:paramtype provider: str
:keyword connection:
:paramtype connection: str
:keyword module:
:paramtype module: str
"""
super(Node, self).__init__(**kwargs)
self.name = name
self.type = type
self.source = source
self.inputs = inputs
self.tool = tool
self.reduce = reduce
self.activate = activate
self.use_variants = use_variants
self.comment = comment
self.api = api
self.provider = provider
self.connection = connection
self.module = module
class NodeInputPort(msrest.serialization.Model):
"""NodeInputPort.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar data_types_ids:
:vartype data_types_ids: list[str]
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'data_types_ids': {'key': 'dataTypesIds', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
data_types_ids: Optional[List[str]] = None,
is_optional: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword data_types_ids:
:paramtype data_types_ids: list[str]
:keyword is_optional:
:paramtype is_optional: bool
"""
super(NodeInputPort, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.data_types_ids = data_types_ids
self.is_optional = is_optional
class NodeLayout(msrest.serialization.Model):
"""NodeLayout.
:ivar x:
:vartype x: float
:ivar y:
:vartype y: float
:ivar width:
:vartype width: float
:ivar height:
:vartype height: float
:ivar extended_data:
:vartype extended_data: str
"""
_attribute_map = {
'x': {'key': 'x', 'type': 'float'},
'y': {'key': 'y', 'type': 'float'},
'width': {'key': 'width', 'type': 'float'},
'height': {'key': 'height', 'type': 'float'},
'extended_data': {'key': 'extendedData', 'type': 'str'},
}
def __init__(
self,
*,
x: Optional[float] = None,
y: Optional[float] = None,
width: Optional[float] = None,
height: Optional[float] = None,
extended_data: Optional[str] = None,
**kwargs
):
"""
:keyword x:
:paramtype x: float
:keyword y:
:paramtype y: float
:keyword width:
:paramtype width: float
:keyword height:
:paramtype height: float
:keyword extended_data:
:paramtype extended_data: str
"""
super(NodeLayout, self).__init__(**kwargs)
self.x = x
self.y = y
self.width = width
self.height = height
self.extended_data = extended_data
class NodeOutputPort(msrest.serialization.Model):
"""NodeOutputPort.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_input_name:
:vartype pass_through_input_name: str
:ivar early_available:
:vartype early_available: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_input_name': {'key': 'passThroughInputName', 'type': 'str'},
'early_available': {'key': 'EarlyAvailable', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
data_type_id: Optional[str] = None,
pass_through_input_name: Optional[str] = None,
early_available: Optional[bool] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_input_name:
:paramtype pass_through_input_name: str
:keyword early_available:
:paramtype early_available: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
"""
super(NodeOutputPort, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.data_type_id = data_type_id
self.pass_through_input_name = pass_through_input_name
self.early_available = early_available
self.data_store_mode = data_store_mode
class NodePortInterface(msrest.serialization.Model):
"""NodePortInterface.
:ivar inputs:
:vartype inputs: list[~flow.models.NodeInputPort]
:ivar outputs:
:vartype outputs: list[~flow.models.NodeOutputPort]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.ControlOutput]
"""
_attribute_map = {
'inputs': {'key': 'inputs', 'type': '[NodeInputPort]'},
'outputs': {'key': 'outputs', 'type': '[NodeOutputPort]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[ControlOutput]'},
}
def __init__(
self,
*,
inputs: Optional[List["NodeInputPort"]] = None,
outputs: Optional[List["NodeOutputPort"]] = None,
control_outputs: Optional[List["ControlOutput"]] = None,
**kwargs
):
"""
:keyword inputs:
:paramtype inputs: list[~flow.models.NodeInputPort]
:keyword outputs:
:paramtype outputs: list[~flow.models.NodeOutputPort]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.ControlOutput]
"""
super(NodePortInterface, self).__init__(**kwargs)
self.inputs = inputs
self.outputs = outputs
self.control_outputs = control_outputs
class Nodes(msrest.serialization.Model):
"""Nodes.
All required parameters must be populated in order to send to Azure.
:ivar nodes_value_type: Required. Possible values include: "All", "Custom".
:vartype nodes_value_type: str or ~flow.models.NodesValueType
:ivar values:
:vartype values: list[int]
"""
_validation = {
'nodes_value_type': {'required': True},
}
_attribute_map = {
'nodes_value_type': {'key': 'nodes_value_type', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
*,
nodes_value_type: Union[str, "NodesValueType"],
values: Optional[List[int]] = None,
**kwargs
):
"""
:keyword nodes_value_type: Required. Possible values include: "All", "Custom".
:paramtype nodes_value_type: str or ~flow.models.NodesValueType
:keyword values:
:paramtype values: list[int]
"""
super(Nodes, self).__init__(**kwargs)
self.nodes_value_type = nodes_value_type
self.values = values
class NodeSource(msrest.serialization.Model):
"""NodeSource.
:ivar type:
:vartype type: str
:ivar tool:
:vartype tool: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'tool': {'key': 'tool', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[str] = None,
tool: Optional[str] = None,
path: Optional[str] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword tool:
:paramtype tool: str
:keyword path:
:paramtype path: str
"""
super(NodeSource, self).__init__(**kwargs)
self.type = type
self.tool = tool
self.path = path
class NodeTelemetryMetaInfo(msrest.serialization.Model):
"""NodeTelemetryMetaInfo.
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar node_id:
:vartype node_id: str
:ivar version_id:
:vartype version_id: str
:ivar node_type:
:vartype node_type: str
:ivar node_source:
:vartype node_source: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar is_pipeline_component:
:vartype is_pipeline_component: bool
"""
_attribute_map = {
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'version_id': {'key': 'versionId', 'type': 'str'},
'node_type': {'key': 'nodeType', 'type': 'str'},
'node_source': {'key': 'nodeSource', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'is_pipeline_component': {'key': 'isPipelineComponent', 'type': 'bool'},
}
def __init__(
self,
*,
pipeline_run_id: Optional[str] = None,
node_id: Optional[str] = None,
version_id: Optional[str] = None,
node_type: Optional[str] = None,
node_source: Optional[str] = None,
is_anonymous: Optional[bool] = None,
is_pipeline_component: Optional[bool] = None,
**kwargs
):
"""
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword version_id:
:paramtype version_id: str
:keyword node_type:
:paramtype node_type: str
:keyword node_source:
:paramtype node_source: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword is_pipeline_component:
:paramtype is_pipeline_component: bool
"""
super(NodeTelemetryMetaInfo, self).__init__(**kwargs)
self.pipeline_run_id = pipeline_run_id
self.node_id = node_id
self.version_id = version_id
self.node_type = node_type
self.node_source = node_source
self.is_anonymous = is_anonymous
self.is_pipeline_component = is_pipeline_component
class NodeVariant(msrest.serialization.Model):
"""NodeVariant.
:ivar variants: This is a dictionary.
:vartype variants: dict[str, ~flow.models.VariantNode]
:ivar default_variant_id:
:vartype default_variant_id: str
"""
_attribute_map = {
'variants': {'key': 'variants', 'type': '{VariantNode}'},
'default_variant_id': {'key': 'defaultVariantId', 'type': 'str'},
}
def __init__(
self,
*,
variants: Optional[Dict[str, "VariantNode"]] = None,
default_variant_id: Optional[str] = None,
**kwargs
):
"""
:keyword variants: This is a dictionary.
:paramtype variants: dict[str, ~flow.models.VariantNode]
:keyword default_variant_id:
:paramtype default_variant_id: str
"""
super(NodeVariant, self).__init__(**kwargs)
self.variants = variants
self.default_variant_id = default_variant_id
class NoteBookTaskDto(msrest.serialization.Model):
"""NoteBookTaskDto.
:ivar notebook_path:
:vartype notebook_path: str
:ivar base_parameters: Dictionary of :code:`<string>`.
:vartype base_parameters: dict[str, str]
"""
_attribute_map = {
'notebook_path': {'key': 'notebook_path', 'type': 'str'},
'base_parameters': {'key': 'base_parameters', 'type': '{str}'},
}
def __init__(
self,
*,
notebook_path: Optional[str] = None,
base_parameters: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword notebook_path:
:paramtype notebook_path: str
:keyword base_parameters: Dictionary of :code:`<string>`.
:paramtype base_parameters: dict[str, str]
"""
super(NoteBookTaskDto, self).__init__(**kwargs)
self.notebook_path = notebook_path
self.base_parameters = base_parameters
class NotificationSetting(msrest.serialization.Model):
"""NotificationSetting.
:ivar emails:
:vartype emails: list[str]
:ivar email_on:
:vartype email_on: list[str or ~flow.models.EmailNotificationEnableType]
:ivar webhooks: Dictionary of :code:`<Webhook>`.
:vartype webhooks: dict[str, ~flow.models.Webhook]
"""
_attribute_map = {
'emails': {'key': 'emails', 'type': '[str]'},
'email_on': {'key': 'emailOn', 'type': '[str]'},
'webhooks': {'key': 'webhooks', 'type': '{Webhook}'},
}
def __init__(
self,
*,
emails: Optional[List[str]] = None,
email_on: Optional[List[Union[str, "EmailNotificationEnableType"]]] = None,
webhooks: Optional[Dict[str, "Webhook"]] = None,
**kwargs
):
"""
:keyword emails:
:paramtype emails: list[str]
:keyword email_on:
:paramtype email_on: list[str or ~flow.models.EmailNotificationEnableType]
:keyword webhooks: Dictionary of :code:`<Webhook>`.
:paramtype webhooks: dict[str, ~flow.models.Webhook]
"""
super(NotificationSetting, self).__init__(**kwargs)
self.emails = emails
self.email_on = email_on
self.webhooks = webhooks
class ODataError(msrest.serialization.Model):
"""Represents OData v4 error object.
:ivar code: Gets or sets a language-independent, service-defined error code.
This code serves as a sub-status for the HTTP error code specified
in the response.
:vartype code: str
:ivar message: Gets or sets a human-readable, language-dependent representation of the error.
The ``Content-Language`` header MUST contain the language code from [RFC5646]
corresponding to the language in which the value for message is written.
:vartype message: str
:ivar target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:vartype target: str
:ivar details: Gets or sets additional details about the error.
:vartype details: list[~flow.models.ODataErrorDetail]
:ivar innererror: The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:vartype innererror: ~flow.models.ODataInnerError
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[ODataErrorDetail]'},
'innererror': {'key': 'innererror', 'type': 'ODataInnerError'},
}
def __init__(
self,
*,
code: Optional[str] = None,
message: Optional[str] = None,
target: Optional[str] = None,
details: Optional[List["ODataErrorDetail"]] = None,
innererror: Optional["ODataInnerError"] = None,
**kwargs
):
"""
:keyword code: Gets or sets a language-independent, service-defined error code.
This code serves as a sub-status for the HTTP error code specified
in the response.
:paramtype code: str
:keyword message: Gets or sets a human-readable, language-dependent representation of the
error.
The ``Content-Language`` header MUST contain the language code from [RFC5646]
corresponding to the language in which the value for message is written.
:paramtype message: str
:keyword target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:paramtype target: str
:keyword details: Gets or sets additional details about the error.
:paramtype details: list[~flow.models.ODataErrorDetail]
:keyword innererror: The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:paramtype innererror: ~flow.models.ODataInnerError
"""
super(ODataError, self).__init__(**kwargs)
self.code = code
self.message = message
self.target = target
self.details = details
self.innererror = innererror
class ODataErrorDetail(msrest.serialization.Model):
"""Represents additional error details.
:ivar code: Gets or sets a language-independent, service-defined error code.
:vartype code: str
:ivar message: Gets or sets a human-readable, language-dependent representation of the error.
:vartype message: str
:ivar target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:vartype target: str
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
}
def __init__(
self,
*,
code: Optional[str] = None,
message: Optional[str] = None,
target: Optional[str] = None,
**kwargs
):
"""
:keyword code: Gets or sets a language-independent, service-defined error code.
:paramtype code: str
:keyword message: Gets or sets a human-readable, language-dependent representation of the
error.
:paramtype message: str
:keyword target: Gets or sets the target of the particular error
(for example, the name of the property in error).
:paramtype target: str
"""
super(ODataErrorDetail, self).__init__(**kwargs)
self.code = code
self.message = message
self.target = target
class ODataErrorResponse(msrest.serialization.Model):
"""Represents OData v4 compliant error response message.
:ivar error: Represents OData v4 error object.
:vartype error: ~flow.models.ODataError
"""
_attribute_map = {
'error': {'key': 'error', 'type': 'ODataError'},
}
def __init__(
self,
*,
error: Optional["ODataError"] = None,
**kwargs
):
"""
:keyword error: Represents OData v4 error object.
:paramtype error: ~flow.models.ODataError
"""
super(ODataErrorResponse, self).__init__(**kwargs)
self.error = error
class ODataInnerError(msrest.serialization.Model):
"""The contents of this object are service-defined.
Usually this object contains information that will help debug the service
and SHOULD only be used in development environments in order to guard
against potential security concerns around information disclosure.
:ivar client_request_id: Gets or sets the client provided request ID.
:vartype client_request_id: str
:ivar service_request_id: Gets or sets the server generated request ID.
:vartype service_request_id: str
:ivar trace: Gets or sets the exception stack trace.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:vartype trace: str
:ivar context: Gets or sets additional context for the exception.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:vartype context: str
"""
_attribute_map = {
'client_request_id': {'key': 'clientRequestId', 'type': 'str'},
'service_request_id': {'key': 'serviceRequestId', 'type': 'str'},
'trace': {'key': 'trace', 'type': 'str'},
'context': {'key': 'context', 'type': 'str'},
}
def __init__(
self,
*,
client_request_id: Optional[str] = None,
service_request_id: Optional[str] = None,
trace: Optional[str] = None,
context: Optional[str] = None,
**kwargs
):
"""
:keyword client_request_id: Gets or sets the client provided request ID.
:paramtype client_request_id: str
:keyword service_request_id: Gets or sets the server generated request ID.
:paramtype service_request_id: str
:keyword trace: Gets or sets the exception stack trace.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:paramtype trace: str
:keyword context: Gets or sets additional context for the exception.
DO NOT INCLUDE IT IN PRODUCTION ENVIRONMENT.
:paramtype context: str
"""
super(ODataInnerError, self).__init__(**kwargs)
self.client_request_id = client_request_id
self.service_request_id = service_request_id
self.trace = trace
self.context = context
class OutputData(msrest.serialization.Model):
"""OutputData.
:ivar output_location:
:vartype output_location: ~flow.models.ExecutionDataLocation
:ivar mechanism: Possible values include: "Upload", "Mount", "Hdfs", "Link", "Direct".
:vartype mechanism: str or ~flow.models.OutputMechanism
:ivar additional_options:
:vartype additional_options: ~flow.models.OutputOptions
:ivar environment_variable_name:
:vartype environment_variable_name: str
"""
_attribute_map = {
'output_location': {'key': 'outputLocation', 'type': 'ExecutionDataLocation'},
'mechanism': {'key': 'mechanism', 'type': 'str'},
'additional_options': {'key': 'additionalOptions', 'type': 'OutputOptions'},
'environment_variable_name': {'key': 'environmentVariableName', 'type': 'str'},
}
def __init__(
self,
*,
output_location: Optional["ExecutionDataLocation"] = None,
mechanism: Optional[Union[str, "OutputMechanism"]] = None,
additional_options: Optional["OutputOptions"] = None,
environment_variable_name: Optional[str] = None,
**kwargs
):
"""
:keyword output_location:
:paramtype output_location: ~flow.models.ExecutionDataLocation
:keyword mechanism: Possible values include: "Upload", "Mount", "Hdfs", "Link", "Direct".
:paramtype mechanism: str or ~flow.models.OutputMechanism
:keyword additional_options:
:paramtype additional_options: ~flow.models.OutputOptions
:keyword environment_variable_name:
:paramtype environment_variable_name: str
"""
super(OutputData, self).__init__(**kwargs)
self.output_location = output_location
self.mechanism = mechanism
self.additional_options = additional_options
self.environment_variable_name = environment_variable_name
class OutputDataBinding(msrest.serialization.Model):
"""OutputDataBinding.
:ivar datastore_id:
:vartype datastore_id: str
:ivar path_on_datastore:
:vartype path_on_datastore: str
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar description:
:vartype description: str
:ivar uri:
:vartype uri: ~flow.models.MfeInternalUriReference
:ivar mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:vartype mode: str or ~flow.models.DataBindingMode
:ivar asset_uri:
:vartype asset_uri: str
:ivar is_asset_job_output:
:vartype is_asset_job_output: bool
:ivar job_output_type: Possible values include: "Uri", "Dataset", "UriFile", "UriFolder",
"MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:vartype job_output_type: str or ~flow.models.JobOutputType
:ivar asset_name:
:vartype asset_name: str
:ivar asset_version:
:vartype asset_version: str
:ivar auto_delete_setting:
:vartype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
_attribute_map = {
'datastore_id': {'key': 'datastoreId', 'type': 'str'},
'path_on_datastore': {'key': 'pathOnDatastore', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'MfeInternalUriReference'},
'mode': {'key': 'mode', 'type': 'str'},
'asset_uri': {'key': 'assetUri', 'type': 'str'},
'is_asset_job_output': {'key': 'isAssetJobOutput', 'type': 'bool'},
'job_output_type': {'key': 'jobOutputType', 'type': 'str'},
'asset_name': {'key': 'assetName', 'type': 'str'},
'asset_version': {'key': 'assetVersion', 'type': 'str'},
'auto_delete_setting': {'key': 'autoDeleteSetting', 'type': 'AutoDeleteSetting'},
}
def __init__(
self,
*,
datastore_id: Optional[str] = None,
path_on_datastore: Optional[str] = None,
path_on_compute: Optional[str] = None,
description: Optional[str] = None,
uri: Optional["MfeInternalUriReference"] = None,
mode: Optional[Union[str, "DataBindingMode"]] = None,
asset_uri: Optional[str] = None,
is_asset_job_output: Optional[bool] = None,
job_output_type: Optional[Union[str, "JobOutputType"]] = None,
asset_name: Optional[str] = None,
asset_version: Optional[str] = None,
auto_delete_setting: Optional["AutoDeleteSetting"] = None,
**kwargs
):
"""
:keyword datastore_id:
:paramtype datastore_id: str
:keyword path_on_datastore:
:paramtype path_on_datastore: str
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword description:
:paramtype description: str
:keyword uri:
:paramtype uri: ~flow.models.MfeInternalUriReference
:keyword mode: Possible values include: "Mount", "Download", "Upload", "ReadOnlyMount",
"ReadWriteMount", "Direct", "EvalMount", "EvalDownload".
:paramtype mode: str or ~flow.models.DataBindingMode
:keyword asset_uri:
:paramtype asset_uri: str
:keyword is_asset_job_output:
:paramtype is_asset_job_output: bool
:keyword job_output_type: Possible values include: "Uri", "Dataset", "UriFile", "UriFolder",
"MLTable", "CustomModel", "MLFlowModel", "TritonModel".
:paramtype job_output_type: str or ~flow.models.JobOutputType
:keyword asset_name:
:paramtype asset_name: str
:keyword asset_version:
:paramtype asset_version: str
:keyword auto_delete_setting:
:paramtype auto_delete_setting: ~flow.models.AutoDeleteSetting
"""
super(OutputDataBinding, self).__init__(**kwargs)
self.datastore_id = datastore_id
self.path_on_datastore = path_on_datastore
self.path_on_compute = path_on_compute
self.description = description
self.uri = uri
self.mode = mode
self.asset_uri = asset_uri
self.is_asset_job_output = is_asset_job_output
self.job_output_type = job_output_type
self.asset_name = asset_name
self.asset_version = asset_version
self.auto_delete_setting = auto_delete_setting
class OutputDatasetLineage(msrest.serialization.Model):
"""OutputDatasetLineage.
:ivar identifier:
:vartype identifier: ~flow.models.DatasetIdentifier
:ivar output_type: Possible values include: "RunOutput", "Reference".
:vartype output_type: str or ~flow.models.DatasetOutputType
:ivar output_details:
:vartype output_details: ~flow.models.DatasetOutputDetails
"""
_attribute_map = {
'identifier': {'key': 'identifier', 'type': 'DatasetIdentifier'},
'output_type': {'key': 'outputType', 'type': 'str'},
'output_details': {'key': 'outputDetails', 'type': 'DatasetOutputDetails'},
}
def __init__(
self,
*,
identifier: Optional["DatasetIdentifier"] = None,
output_type: Optional[Union[str, "DatasetOutputType"]] = None,
output_details: Optional["DatasetOutputDetails"] = None,
**kwargs
):
"""
:keyword identifier:
:paramtype identifier: ~flow.models.DatasetIdentifier
:keyword output_type: Possible values include: "RunOutput", "Reference".
:paramtype output_type: str or ~flow.models.DatasetOutputType
:keyword output_details:
:paramtype output_details: ~flow.models.DatasetOutputDetails
"""
super(OutputDatasetLineage, self).__init__(**kwargs)
self.identifier = identifier
self.output_type = output_type
self.output_details = output_details
class OutputDefinition(msrest.serialization.Model):
"""OutputDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: list[str or ~flow.models.ValueType]
:ivar description:
:vartype description: str
:ivar is_property:
:vartype is_property: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': '[str]'},
'description': {'key': 'description', 'type': 'str'},
'is_property': {'key': 'isProperty', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[List[Union[str, "ValueType"]]] = None,
description: Optional[str] = None,
is_property: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: list[str or ~flow.models.ValueType]
:keyword description:
:paramtype description: str
:keyword is_property:
:paramtype is_property: bool
"""
super(OutputDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.description = description
self.is_property = is_property
class OutputOptions(msrest.serialization.Model):
"""OutputOptions.
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar registration_options:
:vartype registration_options: ~flow.models.RegistrationOptions
:ivar upload_options:
:vartype upload_options: ~flow.models.UploadOptions
:ivar mount_options: Dictionary of :code:`<string>`.
:vartype mount_options: dict[str, str]
"""
_attribute_map = {
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'registration_options': {'key': 'registrationOptions', 'type': 'RegistrationOptions'},
'upload_options': {'key': 'uploadOptions', 'type': 'UploadOptions'},
'mount_options': {'key': 'mountOptions', 'type': '{str}'},
}
def __init__(
self,
*,
path_on_compute: Optional[str] = None,
registration_options: Optional["RegistrationOptions"] = None,
upload_options: Optional["UploadOptions"] = None,
mount_options: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword registration_options:
:paramtype registration_options: ~flow.models.RegistrationOptions
:keyword upload_options:
:paramtype upload_options: ~flow.models.UploadOptions
:keyword mount_options: Dictionary of :code:`<string>`.
:paramtype mount_options: dict[str, str]
"""
super(OutputOptions, self).__init__(**kwargs)
self.path_on_compute = path_on_compute
self.registration_options = registration_options
self.upload_options = upload_options
self.mount_options = mount_options
class OutputSetting(msrest.serialization.Model):
"""OutputSetting.
:ivar name:
:vartype name: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_name_parameter_assignment:
:vartype data_store_name_parameter_assignment: ~flow.models.ParameterAssignment
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar data_store_mode_parameter_assignment:
:vartype data_store_mode_parameter_assignment: ~flow.models.ParameterAssignment
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar path_on_compute_parameter_assignment:
:vartype path_on_compute_parameter_assignment: ~flow.models.ParameterAssignment
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar web_service_port:
:vartype web_service_port: str
:ivar dataset_registration:
:vartype dataset_registration: ~flow.models.DatasetRegistration
:ivar dataset_output_options:
:vartype dataset_output_options: ~flow.models.DatasetOutputOptions
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AssetOutputSettings
:ivar parameter_name:
:vartype parameter_name: str
:ivar asset_output_settings_parameter_name:
:vartype asset_output_settings_parameter_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_name_parameter_assignment': {'key': 'DataStoreNameParameterAssignment', 'type': 'ParameterAssignment'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'data_store_mode_parameter_assignment': {'key': 'DataStoreModeParameterAssignment', 'type': 'ParameterAssignment'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'path_on_compute_parameter_assignment': {'key': 'PathOnComputeParameterAssignment', 'type': 'ParameterAssignment'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
'dataset_registration': {'key': 'datasetRegistration', 'type': 'DatasetRegistration'},
'dataset_output_options': {'key': 'datasetOutputOptions', 'type': 'DatasetOutputOptions'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AssetOutputSettings'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'asset_output_settings_parameter_name': {'key': 'AssetOutputSettingsParameterName', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
data_store_name: Optional[str] = None,
data_store_name_parameter_assignment: Optional["ParameterAssignment"] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
data_store_mode_parameter_assignment: Optional["ParameterAssignment"] = None,
path_on_compute: Optional[str] = None,
path_on_compute_parameter_assignment: Optional["ParameterAssignment"] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
web_service_port: Optional[str] = None,
dataset_registration: Optional["DatasetRegistration"] = None,
dataset_output_options: Optional["DatasetOutputOptions"] = None,
asset_output_settings: Optional["AssetOutputSettings"] = None,
parameter_name: Optional[str] = None,
asset_output_settings_parameter_name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_name_parameter_assignment:
:paramtype data_store_name_parameter_assignment: ~flow.models.ParameterAssignment
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword data_store_mode_parameter_assignment:
:paramtype data_store_mode_parameter_assignment: ~flow.models.ParameterAssignment
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword path_on_compute_parameter_assignment:
:paramtype path_on_compute_parameter_assignment: ~flow.models.ParameterAssignment
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword web_service_port:
:paramtype web_service_port: str
:keyword dataset_registration:
:paramtype dataset_registration: ~flow.models.DatasetRegistration
:keyword dataset_output_options:
:paramtype dataset_output_options: ~flow.models.DatasetOutputOptions
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AssetOutputSettings
:keyword parameter_name:
:paramtype parameter_name: str
:keyword asset_output_settings_parameter_name:
:paramtype asset_output_settings_parameter_name: str
"""
super(OutputSetting, self).__init__(**kwargs)
self.name = name
self.data_store_name = data_store_name
self.data_store_name_parameter_assignment = data_store_name_parameter_assignment
self.data_store_mode = data_store_mode
self.data_store_mode_parameter_assignment = data_store_mode_parameter_assignment
self.path_on_compute = path_on_compute
self.path_on_compute_parameter_assignment = path_on_compute_parameter_assignment
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.web_service_port = web_service_port
self.dataset_registration = dataset_registration
self.dataset_output_options = dataset_output_options
self.asset_output_settings = asset_output_settings
self.parameter_name = parameter_name
self.asset_output_settings_parameter_name = asset_output_settings_parameter_name
class OutputSettingSpec(msrest.serialization.Model):
"""OutputSettingSpec.
:ivar supported_data_store_modes:
:vartype supported_data_store_modes: list[str or ~flow.models.AEVADataStoreMode]
:ivar default_asset_output_path:
:vartype default_asset_output_path: str
"""
_attribute_map = {
'supported_data_store_modes': {'key': 'supportedDataStoreModes', 'type': '[str]'},
'default_asset_output_path': {'key': 'defaultAssetOutputPath', 'type': 'str'},
}
def __init__(
self,
*,
supported_data_store_modes: Optional[List[Union[str, "AEVADataStoreMode"]]] = None,
default_asset_output_path: Optional[str] = None,
**kwargs
):
"""
:keyword supported_data_store_modes:
:paramtype supported_data_store_modes: list[str or ~flow.models.AEVADataStoreMode]
:keyword default_asset_output_path:
:paramtype default_asset_output_path: str
"""
super(OutputSettingSpec, self).__init__(**kwargs)
self.supported_data_store_modes = supported_data_store_modes
self.default_asset_output_path = default_asset_output_path
class PaginatedDataInfoList(msrest.serialization.Model):
"""A paginated list of DataInfos.
:ivar value: An array of objects of type DataInfo.
:vartype value: list[~flow.models.DataInfo]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[DataInfo]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["DataInfo"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type DataInfo.
:paramtype value: list[~flow.models.DataInfo]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedDataInfoList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedModelDtoList(msrest.serialization.Model):
"""A paginated list of ModelDtos.
:ivar value: An array of objects of type ModelDto.
:vartype value: list[~flow.models.ModelDto]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[ModelDto]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["ModelDto"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type ModelDto.
:paramtype value: list[~flow.models.ModelDto]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedModelDtoList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedModuleDtoList(msrest.serialization.Model):
"""A paginated list of ModuleDtos.
:ivar value: An array of objects of type ModuleDto.
:vartype value: list[~flow.models.ModuleDto]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[ModuleDto]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["ModuleDto"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type ModuleDto.
:paramtype value: list[~flow.models.ModuleDto]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedModuleDtoList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedPipelineDraftSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineDraftSummarys.
:ivar value: An array of objects of type PipelineDraftSummary.
:vartype value: list[~flow.models.PipelineDraftSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineDraftSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["PipelineDraftSummary"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineDraftSummary.
:paramtype value: list[~flow.models.PipelineDraftSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineDraftSummaryList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedPipelineEndpointSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineEndpointSummarys.
:ivar value: An array of objects of type PipelineEndpointSummary.
:vartype value: list[~flow.models.PipelineEndpointSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineEndpointSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["PipelineEndpointSummary"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineEndpointSummary.
:paramtype value: list[~flow.models.PipelineEndpointSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineEndpointSummaryList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedPipelineRunSummaryList(msrest.serialization.Model):
"""A paginated list of PipelineRunSummarys.
:ivar value: An array of objects of type PipelineRunSummary.
:vartype value: list[~flow.models.PipelineRunSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PipelineRunSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["PipelineRunSummary"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type PipelineRunSummary.
:paramtype value: list[~flow.models.PipelineRunSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPipelineRunSummaryList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class PaginatedPublishedPipelineSummaryList(msrest.serialization.Model):
"""A paginated list of PublishedPipelineSummarys.
:ivar value: An array of objects of type PublishedPipelineSummary.
:vartype value: list[~flow.models.PublishedPipelineSummary]
:ivar continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:vartype continuation_token: str
:ivar next_link: The link to the next page constructed using the continuationToken. If null,
there are no additional pages.
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[PublishedPipelineSummary]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["PublishedPipelineSummary"]] = None,
continuation_token: Optional[str] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value: An array of objects of type PublishedPipelineSummary.
:paramtype value: list[~flow.models.PublishedPipelineSummary]
:keyword continuation_token: The token used in retrieving the next page. If null, there are no
additional pages.
:paramtype continuation_token: str
:keyword next_link: The link to the next page constructed using the continuationToken. If
null, there are no additional pages.
:paramtype next_link: str
"""
super(PaginatedPublishedPipelineSummaryList, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.next_link = next_link
class ParallelForControlFlowInfo(msrest.serialization.Model):
"""ParallelForControlFlowInfo.
:ivar parallel_for_items_input:
:vartype parallel_for_items_input: ~flow.models.ParameterAssignment
"""
_attribute_map = {
'parallel_for_items_input': {'key': 'parallelForItemsInput', 'type': 'ParameterAssignment'},
}
def __init__(
self,
*,
parallel_for_items_input: Optional["ParameterAssignment"] = None,
**kwargs
):
"""
:keyword parallel_for_items_input:
:paramtype parallel_for_items_input: ~flow.models.ParameterAssignment
"""
super(ParallelForControlFlowInfo, self).__init__(**kwargs)
self.parallel_for_items_input = parallel_for_items_input
class ParallelTaskConfiguration(msrest.serialization.Model):
"""ParallelTaskConfiguration.
:ivar max_retries_per_worker:
:vartype max_retries_per_worker: int
:ivar worker_count_per_node:
:vartype worker_count_per_node: int
:ivar terminal_exit_codes:
:vartype terminal_exit_codes: list[int]
:ivar configuration: Dictionary of :code:`<string>`.
:vartype configuration: dict[str, str]
"""
_attribute_map = {
'max_retries_per_worker': {'key': 'maxRetriesPerWorker', 'type': 'int'},
'worker_count_per_node': {'key': 'workerCountPerNode', 'type': 'int'},
'terminal_exit_codes': {'key': 'terminalExitCodes', 'type': '[int]'},
'configuration': {'key': 'configuration', 'type': '{str}'},
}
def __init__(
self,
*,
max_retries_per_worker: Optional[int] = None,
worker_count_per_node: Optional[int] = None,
terminal_exit_codes: Optional[List[int]] = None,
configuration: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword max_retries_per_worker:
:paramtype max_retries_per_worker: int
:keyword worker_count_per_node:
:paramtype worker_count_per_node: int
:keyword terminal_exit_codes:
:paramtype terminal_exit_codes: list[int]
:keyword configuration: Dictionary of :code:`<string>`.
:paramtype configuration: dict[str, str]
"""
super(ParallelTaskConfiguration, self).__init__(**kwargs)
self.max_retries_per_worker = max_retries_per_worker
self.worker_count_per_node = worker_count_per_node
self.terminal_exit_codes = terminal_exit_codes
self.configuration = configuration
class Parameter(msrest.serialization.Model):
"""Parameter.
:ivar name:
:vartype name: str
:ivar documentation:
:vartype documentation: str
:ivar default_value:
:vartype default_value: str
:ivar is_optional:
:vartype is_optional: bool
:ivar min_max_rules:
:vartype min_max_rules: list[~flow.models.MinMaxParameterRule]
:ivar enum_rules:
:vartype enum_rules: list[~flow.models.EnumParameterRule]
:ivar type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype type: str or ~flow.models.ParameterType
:ivar label:
:vartype label: str
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
:ivar ui_hint:
:vartype ui_hint: ~flow.models.UIParameterHint
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'documentation': {'key': 'documentation', 'type': 'str'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'min_max_rules': {'key': 'minMaxRules', 'type': '[MinMaxParameterRule]'},
'enum_rules': {'key': 'enumRules', 'type': '[EnumParameterRule]'},
'type': {'key': 'type', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'ui_hint': {'key': 'uiHint', 'type': 'UIParameterHint'},
}
def __init__(
self,
*,
name: Optional[str] = None,
documentation: Optional[str] = None,
default_value: Optional[str] = None,
is_optional: Optional[bool] = None,
min_max_rules: Optional[List["MinMaxParameterRule"]] = None,
enum_rules: Optional[List["EnumParameterRule"]] = None,
type: Optional[Union[str, "ParameterType"]] = None,
label: Optional[str] = None,
group_names: Optional[List[str]] = None,
argument_name: Optional[str] = None,
ui_hint: Optional["UIParameterHint"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword documentation:
:paramtype documentation: str
:keyword default_value:
:paramtype default_value: str
:keyword is_optional:
:paramtype is_optional: bool
:keyword min_max_rules:
:paramtype min_max_rules: list[~flow.models.MinMaxParameterRule]
:keyword enum_rules:
:paramtype enum_rules: list[~flow.models.EnumParameterRule]
:keyword type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:paramtype type: str or ~flow.models.ParameterType
:keyword label:
:paramtype label: str
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.UIParameterHint
"""
super(Parameter, self).__init__(**kwargs)
self.name = name
self.documentation = documentation
self.default_value = default_value
self.is_optional = is_optional
self.min_max_rules = min_max_rules
self.enum_rules = enum_rules
self.type = type
self.label = label
self.group_names = group_names
self.argument_name = argument_name
self.ui_hint = ui_hint
class ParameterAssignment(msrest.serialization.Model):
"""ParameterAssignment.
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.ParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.LegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[ParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'LegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'DataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
value_type: Optional[Union[str, "ParameterValueType"]] = None,
assignments_to_concatenate: Optional[List["ParameterAssignment"]] = None,
data_path_assignment: Optional["LegacyDataPath"] = None,
data_set_definition_value_assignment: Optional["DataSetDefinitionValue"] = None,
name: Optional[str] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.ParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.LegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(ParameterAssignment, self).__init__(**kwargs)
self.value_type = value_type
self.assignments_to_concatenate = assignments_to_concatenate
self.data_path_assignment = data_path_assignment
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.name = name
self.value = value
class ParameterDefinition(msrest.serialization.Model):
"""ParameterDefinition.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
:ivar value:
:vartype value: str
:ivar is_optional:
:vartype is_optional: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[str] = None,
value: Optional[str] = None,
is_optional: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
:keyword value:
:paramtype value: str
:keyword is_optional:
:paramtype is_optional: bool
"""
super(ParameterDefinition, self).__init__(**kwargs)
self.name = name
self.type = type
self.value = value
self.is_optional = is_optional
class PatchFlowRequest(msrest.serialization.Model):
"""PatchFlowRequest.
:ivar flow_patch_operation_type: Possible values include: "ArchiveFlow", "RestoreFlow",
"ExportFlowToFile".
:vartype flow_patch_operation_type: str or ~flow.models.FlowPatchOperationType
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
"""
_attribute_map = {
'flow_patch_operation_type': {'key': 'flowPatchOperationType', 'type': 'str'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
}
def __init__(
self,
*,
flow_patch_operation_type: Optional[Union[str, "FlowPatchOperationType"]] = None,
flow_definition_file_path: Optional[str] = None,
**kwargs
):
"""
:keyword flow_patch_operation_type: Possible values include: "ArchiveFlow", "RestoreFlow",
"ExportFlowToFile".
:paramtype flow_patch_operation_type: str or ~flow.models.FlowPatchOperationType
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
"""
super(PatchFlowRequest, self).__init__(**kwargs)
self.flow_patch_operation_type = flow_patch_operation_type
self.flow_definition_file_path = flow_definition_file_path
class Pipeline(msrest.serialization.Model):
"""Pipeline.
:ivar run_id:
:vartype run_id: str
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar default_datastore_name:
:vartype default_datastore_name: str
:ivar component_jobs: This is a dictionary.
:vartype component_jobs: dict[str, ~flow.models.ComponentJob]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.PipelineInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.PipelineOutput]
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'default_datastore_name': {'key': 'defaultDatastoreName', 'type': 'str'},
'component_jobs': {'key': 'componentJobs', 'type': '{ComponentJob}'},
'inputs': {'key': 'inputs', 'type': '{PipelineInput}'},
'outputs': {'key': 'outputs', 'type': '{PipelineOutput}'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
continue_run_on_step_failure: Optional[bool] = None,
default_datastore_name: Optional[str] = None,
component_jobs: Optional[Dict[str, "ComponentJob"]] = None,
inputs: Optional[Dict[str, "PipelineInput"]] = None,
outputs: Optional[Dict[str, "PipelineOutput"]] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword default_datastore_name:
:paramtype default_datastore_name: str
:keyword component_jobs: This is a dictionary.
:paramtype component_jobs: dict[str, ~flow.models.ComponentJob]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.PipelineInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.PipelineOutput]
"""
super(Pipeline, self).__init__(**kwargs)
self.run_id = run_id
self.continue_run_on_step_failure = continue_run_on_step_failure
self.default_datastore_name = default_datastore_name
self.component_jobs = component_jobs
self.inputs = inputs
self.outputs = outputs
class PipelineDraft(msrest.serialization.Model):
"""PipelineDraft.
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar source_pipeline_run_id:
:vartype source_pipeline_run_id: str
:ivar latest_pipeline_run_id:
:vartype latest_pipeline_run_id: str
:ivar latest_run_experiment_name:
:vartype latest_run_experiment_name: str
:ivar latest_run_experiment_id:
:vartype latest_run_experiment_id: str
:ivar is_latest_run_experiment_archived:
:vartype is_latest_run_experiment_archived: bool
:ivar status:
:vartype status: ~flow.models.PipelineStatus
:ivar graph_detail:
:vartype graph_detail: ~flow.models.PipelineRunGraphDetail
:ivar real_time_endpoint_info:
:vartype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:ivar linked_pipelines_info:
:vartype linked_pipelines_info: list[~flow.models.LinkedPipelineInfo]
:ivar nodes_in_draft:
:vartype nodes_in_draft: list[str]
:ivar studio_migration_info:
:vartype studio_migration_info: ~flow.models.StudioMigrationInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_run_setting_parameters:
:vartype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar continue_run_on_failed_optional_input:
:vartype continue_run_on_failed_optional_input: bool
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar pipeline_timeout:
:vartype pipeline_timeout: int
:ivar identity_config:
:vartype identity_config: ~flow.models.IdentitySetting
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'source_pipeline_run_id': {'key': 'sourcePipelineRunId', 'type': 'str'},
'latest_pipeline_run_id': {'key': 'latestPipelineRunId', 'type': 'str'},
'latest_run_experiment_name': {'key': 'latestRunExperimentName', 'type': 'str'},
'latest_run_experiment_id': {'key': 'latestRunExperimentId', 'type': 'str'},
'is_latest_run_experiment_archived': {'key': 'isLatestRunExperimentArchived', 'type': 'bool'},
'status': {'key': 'status', 'type': 'PipelineStatus'},
'graph_detail': {'key': 'graphDetail', 'type': 'PipelineRunGraphDetail'},
'real_time_endpoint_info': {'key': 'realTimeEndpointInfo', 'type': 'RealTimeEndpointInfo'},
'linked_pipelines_info': {'key': 'linkedPipelinesInfo', 'type': '[LinkedPipelineInfo]'},
'nodes_in_draft': {'key': 'nodesInDraft', 'type': '[str]'},
'studio_migration_info': {'key': 'studioMigrationInfo', 'type': 'StudioMigrationInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_run_setting_parameters': {'key': 'pipelineRunSettingParameters', 'type': '[RunSettingParameter]'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'continue_run_on_failed_optional_input': {'key': 'continueRunOnFailedOptionalInput', 'type': 'bool'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'pipeline_timeout': {'key': 'pipelineTimeout', 'type': 'int'},
'identity_config': {'key': 'identityConfig', 'type': 'IdentitySetting'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
graph_draft_id: Optional[str] = None,
source_pipeline_run_id: Optional[str] = None,
latest_pipeline_run_id: Optional[str] = None,
latest_run_experiment_name: Optional[str] = None,
latest_run_experiment_id: Optional[str] = None,
is_latest_run_experiment_archived: Optional[bool] = None,
status: Optional["PipelineStatus"] = None,
graph_detail: Optional["PipelineRunGraphDetail"] = None,
real_time_endpoint_info: Optional["RealTimeEndpointInfo"] = None,
linked_pipelines_info: Optional[List["LinkedPipelineInfo"]] = None,
nodes_in_draft: Optional[List[str]] = None,
studio_migration_info: Optional["StudioMigrationInfo"] = None,
flattened_sub_graphs: Optional[Dict[str, "PipelineSubDraft"]] = None,
pipeline_run_setting_parameters: Optional[List["RunSettingParameter"]] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
continue_run_on_step_failure: Optional[bool] = None,
continue_run_on_failed_optional_input: Optional[bool] = None,
default_compute: Optional["ComputeSetting"] = None,
default_datastore: Optional["DatastoreSetting"] = None,
default_cloud_priority: Optional["CloudPrioritySetting"] = None,
enforce_rerun: Optional[bool] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
pipeline_timeout: Optional[int] = None,
identity_config: Optional["IdentitySetting"] = None,
graph_components_mode: Optional[Union[str, "GraphComponentsMode"]] = None,
name: Optional[str] = None,
last_edited_by: Optional[str] = None,
created_by: Optional[str] = None,
description: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword source_pipeline_run_id:
:paramtype source_pipeline_run_id: str
:keyword latest_pipeline_run_id:
:paramtype latest_pipeline_run_id: str
:keyword latest_run_experiment_name:
:paramtype latest_run_experiment_name: str
:keyword latest_run_experiment_id:
:paramtype latest_run_experiment_id: str
:keyword is_latest_run_experiment_archived:
:paramtype is_latest_run_experiment_archived: bool
:keyword status:
:paramtype status: ~flow.models.PipelineStatus
:keyword graph_detail:
:paramtype graph_detail: ~flow.models.PipelineRunGraphDetail
:keyword real_time_endpoint_info:
:paramtype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:keyword linked_pipelines_info:
:paramtype linked_pipelines_info: list[~flow.models.LinkedPipelineInfo]
:keyword nodes_in_draft:
:paramtype nodes_in_draft: list[str]
:keyword studio_migration_info:
:paramtype studio_migration_info: ~flow.models.StudioMigrationInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_run_setting_parameters:
:paramtype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword continue_run_on_failed_optional_input:
:paramtype continue_run_on_failed_optional_input: bool
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword pipeline_timeout:
:paramtype pipeline_timeout: int
:keyword identity_config:
:paramtype identity_config: ~flow.models.IdentitySetting
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineDraft, self).__init__(**kwargs)
self.graph_draft_id = graph_draft_id
self.source_pipeline_run_id = source_pipeline_run_id
self.latest_pipeline_run_id = latest_pipeline_run_id
self.latest_run_experiment_name = latest_run_experiment_name
self.latest_run_experiment_id = latest_run_experiment_id
self.is_latest_run_experiment_archived = is_latest_run_experiment_archived
self.status = status
self.graph_detail = graph_detail
self.real_time_endpoint_info = real_time_endpoint_info
self.linked_pipelines_info = linked_pipelines_info
self.nodes_in_draft = nodes_in_draft
self.studio_migration_info = studio_migration_info
self.flattened_sub_graphs = flattened_sub_graphs
self.pipeline_run_setting_parameters = pipeline_run_setting_parameters
self.pipeline_run_settings = pipeline_run_settings
self.continue_run_on_step_failure = continue_run_on_step_failure
self.continue_run_on_failed_optional_input = continue_run_on_failed_optional_input
self.default_compute = default_compute
self.default_datastore = default_datastore
self.default_cloud_priority = default_cloud_priority
self.enforce_rerun = enforce_rerun
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.pipeline_timeout = pipeline_timeout
self.identity_config = identity_config
self.graph_components_mode = graph_components_mode
self.name = name
self.last_edited_by = last_edited_by
self.created_by = created_by
self.description = description
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.tags = tags
self.properties = properties
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineDraftStepDetails(msrest.serialization.Model):
"""PipelineDraftStepDetails.
:ivar run_id:
:vartype run_id: str
:ivar target:
:vartype target: str
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_reused:
:vartype is_reused: bool
:ivar reused_run_id:
:vartype reused_run_id: str
:ivar reused_pipeline_run_id:
:vartype reused_pipeline_run_id: str
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar output_log:
:vartype output_log: str
:ivar run_configuration:
:vartype run_configuration: ~flow.models.RunConfiguration
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar port_outputs: This is a dictionary.
:vartype port_outputs: dict[str, ~flow.models.PortOutputInfo]
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'reused_run_id': {'key': 'reusedRunId', 'type': 'str'},
'reused_pipeline_run_id': {'key': 'reusedPipelineRunId', 'type': 'str'},
'logs': {'key': 'logs', 'type': '{str}'},
'output_log': {'key': 'outputLog', 'type': 'str'},
'run_configuration': {'key': 'runConfiguration', 'type': 'RunConfiguration'},
'outputs': {'key': 'outputs', 'type': '{str}'},
'port_outputs': {'key': 'portOutputs', 'type': '{PortOutputInfo}'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
target: Optional[str] = None,
status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
parent_run_id: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
is_reused: Optional[bool] = None,
reused_run_id: Optional[str] = None,
reused_pipeline_run_id: Optional[str] = None,
logs: Optional[Dict[str, str]] = None,
output_log: Optional[str] = None,
run_configuration: Optional["RunConfiguration"] = None,
outputs: Optional[Dict[str, str]] = None,
port_outputs: Optional[Dict[str, "PortOutputInfo"]] = None,
is_experiment_archived: Optional[bool] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword target:
:paramtype target: str
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword is_reused:
:paramtype is_reused: bool
:keyword reused_run_id:
:paramtype reused_run_id: str
:keyword reused_pipeline_run_id:
:paramtype reused_pipeline_run_id: str
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword output_log:
:paramtype output_log: str
:keyword run_configuration:
:paramtype run_configuration: ~flow.models.RunConfiguration
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword port_outputs: This is a dictionary.
:paramtype port_outputs: dict[str, ~flow.models.PortOutputInfo]
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
"""
super(PipelineDraftStepDetails, self).__init__(**kwargs)
self.run_id = run_id
self.target = target
self.status = status
self.status_detail = status_detail
self.parent_run_id = parent_run_id
self.start_time = start_time
self.end_time = end_time
self.is_reused = is_reused
self.reused_run_id = reused_run_id
self.reused_pipeline_run_id = reused_pipeline_run_id
self.logs = logs
self.output_log = output_log
self.run_configuration = run_configuration
self.outputs = outputs
self.port_outputs = port_outputs
self.is_experiment_archived = is_experiment_archived
class PipelineDraftSummary(msrest.serialization.Model):
"""PipelineDraftSummary.
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
name: Optional[str] = None,
last_edited_by: Optional[str] = None,
created_by: Optional[str] = None,
description: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineDraftSummary, self).__init__(**kwargs)
self.name = name
self.last_edited_by = last_edited_by
self.created_by = created_by
self.description = description
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.tags = tags
self.properties = properties
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineEndpoint(msrest.serialization.Model):
"""PipelineEndpoint.
:ivar default_version:
:vartype default_version: str
:ivar default_pipeline_id:
:vartype default_pipeline_id: str
:ivar default_graph_id:
:vartype default_graph_id: str
:ivar rest_endpoint:
:vartype rest_endpoint: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar published_by:
:vartype published_by: str
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar default_pipeline_name:
:vartype default_pipeline_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar updated_by:
:vartype updated_by: str
:ivar swagger_url:
:vartype swagger_url: str
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'default_version': {'key': 'defaultVersion', 'type': 'str'},
'default_pipeline_id': {'key': 'defaultPipelineId', 'type': 'str'},
'default_graph_id': {'key': 'defaultGraphId', 'type': 'str'},
'rest_endpoint': {'key': 'restEndpoint', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'default_pipeline_name': {'key': 'defaultPipelineName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
'swagger_url': {'key': 'swaggerUrl', 'type': 'str'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
default_version: Optional[str] = None,
default_pipeline_id: Optional[str] = None,
default_graph_id: Optional[str] = None,
rest_endpoint: Optional[str] = None,
published_date: Optional[datetime.datetime] = None,
published_by: Optional[str] = None,
parameters: Optional[Dict[str, str]] = None,
data_set_definition_value_assignment: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
default_pipeline_name: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
updated_by: Optional[str] = None,
swagger_url: Optional[str] = None,
last_run_time: Optional[datetime.datetime] = None,
last_run_status: Optional[Union[str, "PipelineRunStatusCode"]] = None,
tags: Optional[Dict[str, str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword default_version:
:paramtype default_version: str
:keyword default_pipeline_id:
:paramtype default_pipeline_id: str
:keyword default_graph_id:
:paramtype default_graph_id: str
:keyword rest_endpoint:
:paramtype rest_endpoint: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword published_by:
:paramtype published_by: str
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword default_pipeline_name:
:paramtype default_pipeline_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword updated_by:
:paramtype updated_by: str
:keyword swagger_url:
:paramtype swagger_url: str
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineEndpoint, self).__init__(**kwargs)
self.default_version = default_version
self.default_pipeline_id = default_pipeline_id
self.default_graph_id = default_graph_id
self.rest_endpoint = rest_endpoint
self.published_date = published_date
self.published_by = published_by
self.parameters = parameters
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.default_pipeline_name = default_pipeline_name
self.name = name
self.description = description
self.updated_by = updated_by
self.swagger_url = swagger_url
self.last_run_time = last_run_time
self.last_run_status = last_run_status
self.tags = tags
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineEndpointSummary(msrest.serialization.Model):
"""PipelineEndpointSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar updated_by:
:vartype updated_by: str
:ivar swagger_url:
:vartype swagger_url: str
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
'swagger_url': {'key': 'swaggerUrl', 'type': 'str'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
updated_by: Optional[str] = None,
swagger_url: Optional[str] = None,
last_run_time: Optional[datetime.datetime] = None,
last_run_status: Optional[Union[str, "PipelineRunStatusCode"]] = None,
tags: Optional[Dict[str, str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword updated_by:
:paramtype updated_by: str
:keyword swagger_url:
:paramtype swagger_url: str
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineEndpointSummary, self).__init__(**kwargs)
self.name = name
self.description = description
self.updated_by = updated_by
self.swagger_url = swagger_url
self.last_run_time = last_run_time
self.last_run_status = last_run_status
self.tags = tags
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineGraph(msrest.serialization.Model):
"""PipelineGraph.
:ivar graph_module_dtos:
:vartype graph_module_dtos: list[~flow.models.ModuleDto]
:ivar graph_data_sources:
:vartype graph_data_sources: list[~flow.models.DataInfo]
:ivar graphs: This is a dictionary.
:vartype graphs: dict[str, ~flow.models.PipelineGraph]
:ivar graph_drafts: This is a dictionary.
:vartype graph_drafts: dict[str, ~flow.models.PipelineGraph]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar referenced_node_id:
:vartype referenced_node_id: str
:ivar pipeline_run_setting_parameters:
:vartype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar real_time_endpoint_info:
:vartype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:ivar node_telemetry_meta_infos:
:vartype node_telemetry_meta_infos: list[~flow.models.NodeTelemetryMetaInfo]
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar module_nodes:
:vartype module_nodes: list[~flow.models.GraphModuleNode]
:ivar dataset_nodes:
:vartype dataset_nodes: list[~flow.models.GraphDatasetNode]
:ivar sub_graph_nodes:
:vartype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:ivar control_reference_nodes:
:vartype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:ivar control_nodes:
:vartype control_nodes: list[~flow.models.GraphControlNode]
:ivar edges:
:vartype edges: list[~flow.models.GraphEdge]
:ivar entity_interface:
:vartype entity_interface: ~flow.models.EntityInterface
:ivar graph_layout:
:vartype graph_layout: ~flow.models.GraphLayout
:ivar created_by:
:vartype created_by: ~flow.models.CreatedBy
:ivar last_updated_by:
:vartype last_updated_by: ~flow.models.CreatedBy
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar extended_properties: This is a dictionary.
:vartype extended_properties: dict[str, str]
:ivar parent_sub_graph_module_ids:
:vartype parent_sub_graph_module_ids: list[str]
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'graph_module_dtos': {'key': 'graphModuleDtos', 'type': '[ModuleDto]'},
'graph_data_sources': {'key': 'graphDataSources', 'type': '[DataInfo]'},
'graphs': {'key': 'graphs', 'type': '{PipelineGraph}'},
'graph_drafts': {'key': 'graphDrafts', 'type': '{PipelineGraph}'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'referenced_node_id': {'key': 'referencedNodeId', 'type': 'str'},
'pipeline_run_setting_parameters': {'key': 'pipelineRunSettingParameters', 'type': '[RunSettingParameter]'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'real_time_endpoint_info': {'key': 'realTimeEndpointInfo', 'type': 'RealTimeEndpointInfo'},
'node_telemetry_meta_infos': {'key': 'nodeTelemetryMetaInfos', 'type': '[NodeTelemetryMetaInfo]'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'module_nodes': {'key': 'moduleNodes', 'type': '[GraphModuleNode]'},
'dataset_nodes': {'key': 'datasetNodes', 'type': '[GraphDatasetNode]'},
'sub_graph_nodes': {'key': 'subGraphNodes', 'type': '[GraphReferenceNode]'},
'control_reference_nodes': {'key': 'controlReferenceNodes', 'type': '[GraphControlReferenceNode]'},
'control_nodes': {'key': 'controlNodes', 'type': '[GraphControlNode]'},
'edges': {'key': 'edges', 'type': '[GraphEdge]'},
'entity_interface': {'key': 'entityInterface', 'type': 'EntityInterface'},
'graph_layout': {'key': 'graphLayout', 'type': 'GraphLayout'},
'created_by': {'key': 'createdBy', 'type': 'CreatedBy'},
'last_updated_by': {'key': 'lastUpdatedBy', 'type': 'CreatedBy'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'extended_properties': {'key': 'extendedProperties', 'type': '{str}'},
'parent_sub_graph_module_ids': {'key': 'parentSubGraphModuleIds', 'type': '[str]'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
graph_module_dtos: Optional[List["ModuleDto"]] = None,
graph_data_sources: Optional[List["DataInfo"]] = None,
graphs: Optional[Dict[str, "PipelineGraph"]] = None,
graph_drafts: Optional[Dict[str, "PipelineGraph"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
referenced_node_id: Optional[str] = None,
pipeline_run_setting_parameters: Optional[List["RunSettingParameter"]] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
real_time_endpoint_info: Optional["RealTimeEndpointInfo"] = None,
node_telemetry_meta_infos: Optional[List["NodeTelemetryMetaInfo"]] = None,
graph_components_mode: Optional[Union[str, "GraphComponentsMode"]] = None,
module_nodes: Optional[List["GraphModuleNode"]] = None,
dataset_nodes: Optional[List["GraphDatasetNode"]] = None,
sub_graph_nodes: Optional[List["GraphReferenceNode"]] = None,
control_reference_nodes: Optional[List["GraphControlReferenceNode"]] = None,
control_nodes: Optional[List["GraphControlNode"]] = None,
edges: Optional[List["GraphEdge"]] = None,
entity_interface: Optional["EntityInterface"] = None,
graph_layout: Optional["GraphLayout"] = None,
created_by: Optional["CreatedBy"] = None,
last_updated_by: Optional["CreatedBy"] = None,
default_compute: Optional["ComputeSetting"] = None,
default_datastore: Optional["DatastoreSetting"] = None,
default_cloud_priority: Optional["CloudPrioritySetting"] = None,
extended_properties: Optional[Dict[str, str]] = None,
parent_sub_graph_module_ids: Optional[List[str]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword graph_module_dtos:
:paramtype graph_module_dtos: list[~flow.models.ModuleDto]
:keyword graph_data_sources:
:paramtype graph_data_sources: list[~flow.models.DataInfo]
:keyword graphs: This is a dictionary.
:paramtype graphs: dict[str, ~flow.models.PipelineGraph]
:keyword graph_drafts: This is a dictionary.
:paramtype graph_drafts: dict[str, ~flow.models.PipelineGraph]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword referenced_node_id:
:paramtype referenced_node_id: str
:keyword pipeline_run_setting_parameters:
:paramtype pipeline_run_setting_parameters: list[~flow.models.RunSettingParameter]
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword real_time_endpoint_info:
:paramtype real_time_endpoint_info: ~flow.models.RealTimeEndpointInfo
:keyword node_telemetry_meta_infos:
:paramtype node_telemetry_meta_infos: list[~flow.models.NodeTelemetryMetaInfo]
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword module_nodes:
:paramtype module_nodes: list[~flow.models.GraphModuleNode]
:keyword dataset_nodes:
:paramtype dataset_nodes: list[~flow.models.GraphDatasetNode]
:keyword sub_graph_nodes:
:paramtype sub_graph_nodes: list[~flow.models.GraphReferenceNode]
:keyword control_reference_nodes:
:paramtype control_reference_nodes: list[~flow.models.GraphControlReferenceNode]
:keyword control_nodes:
:paramtype control_nodes: list[~flow.models.GraphControlNode]
:keyword edges:
:paramtype edges: list[~flow.models.GraphEdge]
:keyword entity_interface:
:paramtype entity_interface: ~flow.models.EntityInterface
:keyword graph_layout:
:paramtype graph_layout: ~flow.models.GraphLayout
:keyword created_by:
:paramtype created_by: ~flow.models.CreatedBy
:keyword last_updated_by:
:paramtype last_updated_by: ~flow.models.CreatedBy
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword extended_properties: This is a dictionary.
:paramtype extended_properties: dict[str, str]
:keyword parent_sub_graph_module_ids:
:paramtype parent_sub_graph_module_ids: list[str]
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineGraph, self).__init__(**kwargs)
self.graph_module_dtos = graph_module_dtos
self.graph_data_sources = graph_data_sources
self.graphs = graphs
self.graph_drafts = graph_drafts
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.sub_pipelines_info = sub_pipelines_info
self.referenced_node_id = referenced_node_id
self.pipeline_run_setting_parameters = pipeline_run_setting_parameters
self.pipeline_run_settings = pipeline_run_settings
self.real_time_endpoint_info = real_time_endpoint_info
self.node_telemetry_meta_infos = node_telemetry_meta_infos
self.graph_components_mode = graph_components_mode
self.module_nodes = module_nodes
self.dataset_nodes = dataset_nodes
self.sub_graph_nodes = sub_graph_nodes
self.control_reference_nodes = control_reference_nodes
self.control_nodes = control_nodes
self.edges = edges
self.entity_interface = entity_interface
self.graph_layout = graph_layout
self.created_by = created_by
self.last_updated_by = last_updated_by
self.default_compute = default_compute
self.default_datastore = default_datastore
self.default_cloud_priority = default_cloud_priority
self.extended_properties = extended_properties
self.parent_sub_graph_module_ids = parent_sub_graph_module_ids
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineInput(msrest.serialization.Model):
"""PipelineInput.
:ivar data:
:vartype data: ~flow.models.InputData
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'InputData'},
}
def __init__(
self,
*,
data: Optional["InputData"] = None,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.InputData
"""
super(PipelineInput, self).__init__(**kwargs)
self.data = data
class PipelineJob(msrest.serialization.Model):
"""PipelineJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar pipeline_job_type: The only acceptable values to pass in are None and "AzureML". The
default value is None.
:vartype pipeline_job_type: str
:ivar pipeline:
:vartype pipeline: ~flow.models.Pipeline
:ivar compute_id:
:vartype compute_id: str
:ivar run_id:
:vartype run_id: str
:ivar settings: Anything.
:vartype settings: any
:ivar component_jobs: This is a dictionary.
:vartype component_jobs: dict[str, ~flow.models.MfeInternalV20211001ComponentJob]
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.JobInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.JobOutput]
:ivar bindings:
:vartype bindings: list[~flow.models.Binding]
:ivar jobs: This is a dictionary.
:vartype jobs: dict[str, any]
:ivar input_bindings: This is a dictionary.
:vartype input_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_bindings: This is a dictionary.
:vartype output_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar source_job_id:
:vartype source_job_id: str
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'pipeline_job_type': {'key': 'pipelineJobType', 'type': 'str'},
'pipeline': {'key': 'pipeline', 'type': 'Pipeline'},
'compute_id': {'key': 'computeId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'settings': {'key': 'settings', 'type': 'object'},
'component_jobs': {'key': 'componentJobs', 'type': '{MfeInternalV20211001ComponentJob}'},
'inputs': {'key': 'inputs', 'type': '{JobInput}'},
'outputs': {'key': 'outputs', 'type': '{JobOutput}'},
'bindings': {'key': 'bindings', 'type': '[Binding]'},
'jobs': {'key': 'jobs', 'type': '{object}'},
'input_bindings': {'key': 'inputBindings', 'type': '{InputDataBinding}'},
'output_bindings': {'key': 'outputBindings', 'type': '{OutputDataBinding}'},
'source_job_id': {'key': 'sourceJobId', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
job_type: Optional[Union[str, "JobType"]] = None,
pipeline_job_type: Optional[str] = None,
pipeline: Optional["Pipeline"] = None,
compute_id: Optional[str] = None,
run_id: Optional[str] = None,
settings: Optional[Any] = None,
component_jobs: Optional[Dict[str, "MfeInternalV20211001ComponentJob"]] = None,
inputs: Optional[Dict[str, "JobInput"]] = None,
outputs: Optional[Dict[str, "JobOutput"]] = None,
bindings: Optional[List["Binding"]] = None,
jobs: Optional[Dict[str, Any]] = None,
input_bindings: Optional[Dict[str, "InputDataBinding"]] = None,
output_bindings: Optional[Dict[str, "OutputDataBinding"]] = None,
source_job_id: Optional[str] = None,
provisioning_state: Optional[Union[str, "JobProvisioningState"]] = None,
parent_job_name: Optional[str] = None,
display_name: Optional[str] = None,
experiment_name: Optional[str] = None,
status: Optional[Union[str, "JobStatus"]] = None,
interaction_endpoints: Optional[Dict[str, "JobEndpoint"]] = None,
identity: Optional["MfeInternalIdentityConfiguration"] = None,
compute: Optional["ComputeConfiguration"] = None,
priority: Optional[int] = None,
output: Optional["JobOutputArtifacts"] = None,
is_archived: Optional[bool] = None,
schedule: Optional["ScheduleBase"] = None,
component_id: Optional[str] = None,
notification_setting: Optional["NotificationSetting"] = None,
secrets_configuration: Optional[Dict[str, "MfeInternalSecretConfiguration"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword pipeline_job_type: The only acceptable values to pass in are None and "AzureML". The
default value is None.
:paramtype pipeline_job_type: str
:keyword pipeline:
:paramtype pipeline: ~flow.models.Pipeline
:keyword compute_id:
:paramtype compute_id: str
:keyword run_id:
:paramtype run_id: str
:keyword settings: Anything.
:paramtype settings: any
:keyword component_jobs: This is a dictionary.
:paramtype component_jobs: dict[str, ~flow.models.MfeInternalV20211001ComponentJob]
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.JobInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.JobOutput]
:keyword bindings:
:paramtype bindings: list[~flow.models.Binding]
:keyword jobs: This is a dictionary.
:paramtype jobs: dict[str, any]
:keyword input_bindings: This is a dictionary.
:paramtype input_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_bindings: This is a dictionary.
:paramtype output_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword source_job_id:
:paramtype source_job_id: str
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(PipelineJob, self).__init__(**kwargs)
self.job_type = job_type
self.pipeline_job_type = pipeline_job_type
self.pipeline = pipeline
self.compute_id = compute_id
self.run_id = run_id
self.settings = settings
self.component_jobs = component_jobs
self.inputs = inputs
self.outputs = outputs
self.bindings = bindings
self.jobs = jobs
self.input_bindings = input_bindings
self.output_bindings = output_bindings
self.source_job_id = source_job_id
self.provisioning_state = provisioning_state
self.parent_job_name = parent_job_name
self.display_name = display_name
self.experiment_name = experiment_name
self.status = status
self.interaction_endpoints = interaction_endpoints
self.identity = identity
self.compute = compute
self.priority = priority
self.output = output
self.is_archived = is_archived
self.schedule = schedule
self.component_id = component_id
self.notification_setting = notification_setting
self.secrets_configuration = secrets_configuration
self.description = description
self.tags = tags
self.properties = properties
class PipelineJobRuntimeBasicSettings(msrest.serialization.Model):
"""PipelineJobRuntimeBasicSettings.
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar trigger_time_string:
:vartype trigger_time_string: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
"""
_attribute_map = {
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'trigger_time_string': {'key': 'triggerTimeString', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
}
def __init__(
self,
*,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
experiment_name: Optional[str] = None,
pipeline_job_name: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
trigger_time_string: Optional[str] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
**kwargs
):
"""
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword trigger_time_string:
:paramtype trigger_time_string: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
"""
super(PipelineJobRuntimeBasicSettings, self).__init__(**kwargs)
self.pipeline_run_settings = pipeline_run_settings
self.experiment_name = experiment_name
self.pipeline_job_name = pipeline_job_name
self.tags = tags
self.display_name = display_name
self.description = description
self.trigger_time_string = trigger_time_string
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
class PipelineJobScheduleDto(msrest.serialization.Model):
"""PipelineJobScheduleDto.
:ivar system_data:
:vartype system_data: ~flow.models.SystemData
:ivar name:
:vartype name: str
:ivar pipeline_job_name:
:vartype pipeline_job_name: str
:ivar pipeline_job_runtime_settings:
:vartype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:ivar display_name:
:vartype display_name: str
:ivar trigger_type: Possible values include: "Recurrence", "Cron".
:vartype trigger_type: str or ~flow.models.TriggerType
:ivar recurrence:
:vartype recurrence: ~flow.models.Recurrence
:ivar cron:
:vartype cron: ~flow.models.Cron
:ivar status: Possible values include: "Enabled", "Disabled".
:vartype status: str or ~flow.models.ScheduleStatus
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'system_data': {'key': 'systemData', 'type': 'SystemData'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_job_name': {'key': 'pipelineJobName', 'type': 'str'},
'pipeline_job_runtime_settings': {'key': 'pipelineJobRuntimeSettings', 'type': 'PipelineJobRuntimeBasicSettings'},
'display_name': {'key': 'displayName', 'type': 'str'},
'trigger_type': {'key': 'triggerType', 'type': 'str'},
'recurrence': {'key': 'recurrence', 'type': 'Recurrence'},
'cron': {'key': 'cron', 'type': 'Cron'},
'status': {'key': 'status', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
system_data: Optional["SystemData"] = None,
name: Optional[str] = None,
pipeline_job_name: Optional[str] = None,
pipeline_job_runtime_settings: Optional["PipelineJobRuntimeBasicSettings"] = None,
display_name: Optional[str] = None,
trigger_type: Optional[Union[str, "TriggerType"]] = None,
recurrence: Optional["Recurrence"] = None,
cron: Optional["Cron"] = None,
status: Optional[Union[str, "ScheduleStatus"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword system_data:
:paramtype system_data: ~flow.models.SystemData
:keyword name:
:paramtype name: str
:keyword pipeline_job_name:
:paramtype pipeline_job_name: str
:keyword pipeline_job_runtime_settings:
:paramtype pipeline_job_runtime_settings: ~flow.models.PipelineJobRuntimeBasicSettings
:keyword display_name:
:paramtype display_name: str
:keyword trigger_type: Possible values include: "Recurrence", "Cron".
:paramtype trigger_type: str or ~flow.models.TriggerType
:keyword recurrence:
:paramtype recurrence: ~flow.models.Recurrence
:keyword cron:
:paramtype cron: ~flow.models.Cron
:keyword status: Possible values include: "Enabled", "Disabled".
:paramtype status: str or ~flow.models.ScheduleStatus
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(PipelineJobScheduleDto, self).__init__(**kwargs)
self.system_data = system_data
self.name = name
self.pipeline_job_name = pipeline_job_name
self.pipeline_job_runtime_settings = pipeline_job_runtime_settings
self.display_name = display_name
self.trigger_type = trigger_type
self.recurrence = recurrence
self.cron = cron
self.status = status
self.description = description
self.tags = tags
self.properties = properties
class PipelineOutput(msrest.serialization.Model):
"""PipelineOutput.
:ivar data:
:vartype data: ~flow.models.MfeInternalOutputData
"""
_attribute_map = {
'data': {'key': 'data', 'type': 'MfeInternalOutputData'},
}
def __init__(
self,
*,
data: Optional["MfeInternalOutputData"] = None,
**kwargs
):
"""
:keyword data:
:paramtype data: ~flow.models.MfeInternalOutputData
"""
super(PipelineOutput, self).__init__(**kwargs)
self.data = data
class PipelineRun(msrest.serialization.Model):
"""PipelineRun.
:ivar pipeline_id:
:vartype pipeline_id: str
:ivar run_source:
:vartype run_source: str
:ivar run_type: Possible values include: "HTTP", "SDK", "Schedule", "Portal".
:vartype run_type: str or ~flow.models.RunType
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar total_steps:
:vartype total_steps: int
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar user_alias:
:vartype user_alias: str
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar continue_run_on_failed_optional_input:
:vartype continue_run_on_failed_optional_input: bool
:ivar default_compute:
:vartype default_compute: ~flow.models.ComputeSetting
:ivar default_datastore:
:vartype default_datastore: ~flow.models.DatastoreSetting
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar pipeline_timeout_seconds:
:vartype pipeline_timeout_seconds: int
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar identity_config:
:vartype identity_config: ~flow.models.IdentitySetting
:ivar description:
:vartype description: str
:ivar display_name:
:vartype display_name: str
:ivar run_number:
:vartype run_number: int
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar graph_id:
:vartype graph_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
:ivar submitted_by:
:vartype submitted_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
}
_attribute_map = {
'pipeline_id': {'key': 'pipelineId', 'type': 'str'},
'run_source': {'key': 'runSource', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'total_steps': {'key': 'totalSteps', 'type': 'int'},
'logs': {'key': 'logs', 'type': '{str}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'continue_run_on_failed_optional_input': {'key': 'continueRunOnFailedOptionalInput', 'type': 'bool'},
'default_compute': {'key': 'defaultCompute', 'type': 'ComputeSetting'},
'default_datastore': {'key': 'defaultDatastore', 'type': 'DatastoreSetting'},
'default_cloud_priority': {'key': 'defaultCloudPriority', 'type': 'CloudPrioritySetting'},
'pipeline_timeout_seconds': {'key': 'pipelineTimeoutSeconds', 'type': 'int'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'identity_config': {'key': 'identityConfig', 'type': 'IdentitySetting'},
'description': {'key': 'description', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
'submitted_by': {'key': 'submittedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
pipeline_id: Optional[str] = None,
run_source: Optional[str] = None,
run_type: Optional[Union[str, "RunType"]] = None,
parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignment: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
total_steps: Optional[int] = None,
logs: Optional[Dict[str, str]] = None,
user_alias: Optional[str] = None,
enforce_rerun: Optional[bool] = None,
continue_run_on_failed_optional_input: Optional[bool] = None,
default_compute: Optional["ComputeSetting"] = None,
default_datastore: Optional["DatastoreSetting"] = None,
default_cloud_priority: Optional["CloudPrioritySetting"] = None,
pipeline_timeout_seconds: Optional[int] = None,
continue_run_on_step_failure: Optional[bool] = None,
identity_config: Optional["IdentitySetting"] = None,
description: Optional[str] = None,
display_name: Optional[str] = None,
run_number: Optional[int] = None,
status_code: Optional[Union[str, "PipelineStatusCode"]] = None,
run_status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
graph_id: Optional[str] = None,
experiment_id: Optional[str] = None,
experiment_name: Optional[str] = None,
is_experiment_archived: Optional[bool] = None,
submitted_by: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
step_tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
aether_start_time: Optional[datetime.datetime] = None,
aether_end_time: Optional[datetime.datetime] = None,
run_history_start_time: Optional[datetime.datetime] = None,
run_history_end_time: Optional[datetime.datetime] = None,
unique_child_run_compute_targets: Optional[List[str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword pipeline_id:
:paramtype pipeline_id: str
:keyword run_source:
:paramtype run_source: str
:keyword run_type: Possible values include: "HTTP", "SDK", "Schedule", "Portal".
:paramtype run_type: str or ~flow.models.RunType
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword total_steps:
:paramtype total_steps: int
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword user_alias:
:paramtype user_alias: str
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword continue_run_on_failed_optional_input:
:paramtype continue_run_on_failed_optional_input: bool
:keyword default_compute:
:paramtype default_compute: ~flow.models.ComputeSetting
:keyword default_datastore:
:paramtype default_datastore: ~flow.models.DatastoreSetting
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword pipeline_timeout_seconds:
:paramtype pipeline_timeout_seconds: int
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword identity_config:
:paramtype identity_config: ~flow.models.IdentitySetting
:keyword description:
:paramtype description: str
:keyword display_name:
:paramtype display_name: str
:keyword run_number:
:paramtype run_number: int
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword graph_id:
:paramtype graph_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
:keyword submitted_by:
:paramtype submitted_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineRun, self).__init__(**kwargs)
self.pipeline_id = pipeline_id
self.run_source = run_source
self.run_type = run_type
self.parameters = parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.asset_output_settings_assignments = asset_output_settings_assignments
self.total_steps = total_steps
self.logs = logs
self.user_alias = user_alias
self.enforce_rerun = enforce_rerun
self.continue_run_on_failed_optional_input = continue_run_on_failed_optional_input
self.default_compute = default_compute
self.default_datastore = default_datastore
self.default_cloud_priority = default_cloud_priority
self.pipeline_timeout_seconds = pipeline_timeout_seconds
self.continue_run_on_step_failure = continue_run_on_step_failure
self.identity_config = identity_config
self.description = description
self.display_name = display_name
self.run_number = run_number
self.status_code = status_code
self.run_status = run_status
self.status_detail = status_detail
self.start_time = start_time
self.end_time = end_time
self.graph_id = graph_id
self.experiment_id = experiment_id
self.experiment_name = experiment_name
self.is_experiment_archived = is_experiment_archived
self.submitted_by = submitted_by
self.tags = tags
self.step_tags = step_tags
self.properties = properties
self.aether_start_time = aether_start_time
self.aether_end_time = aether_end_time
self.run_history_start_time = run_history_start_time
self.run_history_end_time = run_history_end_time
self.unique_child_run_compute_targets = unique_child_run_compute_targets
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineRunGraphDetail(msrest.serialization.Model):
"""PipelineRunGraphDetail.
:ivar graph:
:vartype graph: ~flow.models.PipelineGraph
:ivar graph_nodes_status: This is a dictionary.
:vartype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
"""
_attribute_map = {
'graph': {'key': 'graph', 'type': 'PipelineGraph'},
'graph_nodes_status': {'key': 'graphNodesStatus', 'type': '{GraphNodeStatusInfo}'},
}
def __init__(
self,
*,
graph: Optional["PipelineGraph"] = None,
graph_nodes_status: Optional[Dict[str, "GraphNodeStatusInfo"]] = None,
**kwargs
):
"""
:keyword graph:
:paramtype graph: ~flow.models.PipelineGraph
:keyword graph_nodes_status: This is a dictionary.
:paramtype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
"""
super(PipelineRunGraphDetail, self).__init__(**kwargs)
self.graph = graph
self.graph_nodes_status = graph_nodes_status
class PipelineRunGraphStatus(msrest.serialization.Model):
"""PipelineRunGraphStatus.
:ivar status:
:vartype status: ~flow.models.PipelineStatus
:ivar graph_nodes_status: This is a dictionary.
:vartype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
:ivar experiment_id:
:vartype experiment_id: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'PipelineStatus'},
'graph_nodes_status': {'key': 'graphNodesStatus', 'type': '{GraphNodeStatusInfo}'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
}
def __init__(
self,
*,
status: Optional["PipelineStatus"] = None,
graph_nodes_status: Optional[Dict[str, "GraphNodeStatusInfo"]] = None,
experiment_id: Optional[str] = None,
is_experiment_archived: Optional[bool] = None,
**kwargs
):
"""
:keyword status:
:paramtype status: ~flow.models.PipelineStatus
:keyword graph_nodes_status: This is a dictionary.
:paramtype graph_nodes_status: dict[str, ~flow.models.GraphNodeStatusInfo]
:keyword experiment_id:
:paramtype experiment_id: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
"""
super(PipelineRunGraphStatus, self).__init__(**kwargs)
self.status = status
self.graph_nodes_status = graph_nodes_status
self.experiment_id = experiment_id
self.is_experiment_archived = is_experiment_archived
class PipelineRunProfile(msrest.serialization.Model):
"""PipelineRunProfile.
:ivar run_id:
:vartype run_id: str
:ivar node_id:
:vartype node_id: str
:ivar run_url:
:vartype run_url: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar description:
:vartype description: str
:ivar status:
:vartype status: ~flow.models.PipelineRunStatus
:ivar create_time:
:vartype create_time: long
:ivar start_time:
:vartype start_time: long
:ivar end_time:
:vartype end_time: long
:ivar profiling_time:
:vartype profiling_time: long
:ivar step_runs_profile:
:vartype step_runs_profile: list[~flow.models.StepRunProfile]
:ivar sub_pipeline_run_profile:
:vartype sub_pipeline_run_profile: list[~flow.models.PipelineRunProfile]
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'run_url': {'key': 'runUrl', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'status': {'key': 'status', 'type': 'PipelineRunStatus'},
'create_time': {'key': 'createTime', 'type': 'long'},
'start_time': {'key': 'startTime', 'type': 'long'},
'end_time': {'key': 'endTime', 'type': 'long'},
'profiling_time': {'key': 'profilingTime', 'type': 'long'},
'step_runs_profile': {'key': 'stepRunsProfile', 'type': '[StepRunProfile]'},
'sub_pipeline_run_profile': {'key': 'subPipelineRunProfile', 'type': '[PipelineRunProfile]'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
node_id: Optional[str] = None,
run_url: Optional[str] = None,
experiment_name: Optional[str] = None,
experiment_id: Optional[str] = None,
description: Optional[str] = None,
status: Optional["PipelineRunStatus"] = None,
create_time: Optional[int] = None,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
profiling_time: Optional[int] = None,
step_runs_profile: Optional[List["StepRunProfile"]] = None,
sub_pipeline_run_profile: Optional[List["PipelineRunProfile"]] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword run_url:
:paramtype run_url: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword description:
:paramtype description: str
:keyword status:
:paramtype status: ~flow.models.PipelineRunStatus
:keyword create_time:
:paramtype create_time: long
:keyword start_time:
:paramtype start_time: long
:keyword end_time:
:paramtype end_time: long
:keyword profiling_time:
:paramtype profiling_time: long
:keyword step_runs_profile:
:paramtype step_runs_profile: list[~flow.models.StepRunProfile]
:keyword sub_pipeline_run_profile:
:paramtype sub_pipeline_run_profile: list[~flow.models.PipelineRunProfile]
"""
super(PipelineRunProfile, self).__init__(**kwargs)
self.run_id = run_id
self.node_id = node_id
self.run_url = run_url
self.experiment_name = experiment_name
self.experiment_id = experiment_id
self.description = description
self.status = status
self.create_time = create_time
self.start_time = start_time
self.end_time = end_time
self.profiling_time = profiling_time
self.step_runs_profile = step_runs_profile
self.sub_pipeline_run_profile = sub_pipeline_run_profile
class PipelineRunStatus(msrest.serialization.Model):
"""PipelineRunStatus.
:ivar status_code: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype status_code: str or ~flow.models.PipelineRunStatusCode
:ivar status_detail:
:vartype status_detail: str
:ivar creation_time:
:vartype creation_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
"""
_attribute_map = {
'status_code': {'key': 'statusCode', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'creation_time': {'key': 'creationTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
status_code: Optional[Union[str, "PipelineRunStatusCode"]] = None,
status_detail: Optional[str] = None,
creation_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword status_code: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:paramtype status_code: str or ~flow.models.PipelineRunStatusCode
:keyword status_detail:
:paramtype status_detail: str
:keyword creation_time:
:paramtype creation_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(PipelineRunStatus, self).__init__(**kwargs)
self.status_code = status_code
self.status_detail = status_detail
self.creation_time = creation_time
self.end_time = end_time
class PipelineRunStepDetails(msrest.serialization.Model):
"""PipelineRunStepDetails.
:ivar run_id:
:vartype run_id: str
:ivar target:
:vartype target: str
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_reused:
:vartype is_reused: bool
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar snapshot_info:
:vartype snapshot_info: ~flow.models.SnapshotInfo
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
"""
_validation = {
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'logs': {'key': 'logs', 'type': '{str}'},
'outputs': {'key': 'outputs', 'type': '{str}'},
'snapshot_info': {'key': 'snapshotInfo', 'type': 'SnapshotInfo'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
target: Optional[str] = None,
status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
parent_run_id: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
is_reused: Optional[bool] = None,
logs: Optional[Dict[str, str]] = None,
outputs: Optional[Dict[str, str]] = None,
snapshot_info: Optional["SnapshotInfo"] = None,
input_datasets: Optional[List["DatasetLineage"]] = None,
output_datasets: Optional[List["OutputDatasetLineage"]] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword target:
:paramtype target: str
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword is_reused:
:paramtype is_reused: bool
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword snapshot_info:
:paramtype snapshot_info: ~flow.models.SnapshotInfo
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
"""
super(PipelineRunStepDetails, self).__init__(**kwargs)
self.run_id = run_id
self.target = target
self.status = status
self.status_detail = status_detail
self.parent_run_id = parent_run_id
self.start_time = start_time
self.end_time = end_time
self.is_reused = is_reused
self.logs = logs
self.outputs = outputs
self.snapshot_info = snapshot_info
self.input_datasets = input_datasets
self.output_datasets = output_datasets
class PipelineRunSummary(msrest.serialization.Model):
"""PipelineRunSummary.
:ivar description:
:vartype description: str
:ivar display_name:
:vartype display_name: str
:ivar run_number:
:vartype run_number: int
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar graph_id:
:vartype graph_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar is_experiment_archived:
:vartype is_experiment_archived: bool
:ivar submitted_by:
:vartype submitted_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar aether_start_time:
:vartype aether_start_time: ~datetime.datetime
:ivar aether_end_time:
:vartype aether_end_time: ~datetime.datetime
:ivar run_history_start_time:
:vartype run_history_start_time: ~datetime.datetime
:ivar run_history_end_time:
:vartype run_history_end_time: ~datetime.datetime
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
}
_attribute_map = {
'description': {'key': 'description', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'is_experiment_archived': {'key': 'isExperimentArchived', 'type': 'bool'},
'submitted_by': {'key': 'submittedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'aether_start_time': {'key': 'aetherStartTime', 'type': 'iso-8601'},
'aether_end_time': {'key': 'aetherEndTime', 'type': 'iso-8601'},
'run_history_start_time': {'key': 'runHistoryStartTime', 'type': 'iso-8601'},
'run_history_end_time': {'key': 'runHistoryEndTime', 'type': 'iso-8601'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
description: Optional[str] = None,
display_name: Optional[str] = None,
run_number: Optional[int] = None,
status_code: Optional[Union[str, "PipelineStatusCode"]] = None,
run_status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
graph_id: Optional[str] = None,
experiment_id: Optional[str] = None,
experiment_name: Optional[str] = None,
is_experiment_archived: Optional[bool] = None,
submitted_by: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
step_tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
aether_start_time: Optional[datetime.datetime] = None,
aether_end_time: Optional[datetime.datetime] = None,
run_history_start_time: Optional[datetime.datetime] = None,
run_history_end_time: Optional[datetime.datetime] = None,
unique_child_run_compute_targets: Optional[List[str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword description:
:paramtype description: str
:keyword display_name:
:paramtype display_name: str
:keyword run_number:
:paramtype run_number: int
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword graph_id:
:paramtype graph_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword is_experiment_archived:
:paramtype is_experiment_archived: bool
:keyword submitted_by:
:paramtype submitted_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword aether_start_time:
:paramtype aether_start_time: ~datetime.datetime
:keyword aether_end_time:
:paramtype aether_end_time: ~datetime.datetime
:keyword run_history_start_time:
:paramtype run_history_start_time: ~datetime.datetime
:keyword run_history_end_time:
:paramtype run_history_end_time: ~datetime.datetime
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineRunSummary, self).__init__(**kwargs)
self.description = description
self.display_name = display_name
self.run_number = run_number
self.status_code = status_code
self.run_status = run_status
self.status_detail = status_detail
self.start_time = start_time
self.end_time = end_time
self.graph_id = graph_id
self.experiment_id = experiment_id
self.experiment_name = experiment_name
self.is_experiment_archived = is_experiment_archived
self.submitted_by = submitted_by
self.tags = tags
self.step_tags = step_tags
self.properties = properties
self.aether_start_time = aether_start_time
self.aether_end_time = aether_end_time
self.run_history_start_time = run_history_start_time
self.run_history_end_time = run_history_end_time
self.unique_child_run_compute_targets = unique_child_run_compute_targets
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PipelineStatus(msrest.serialization.Model):
"""PipelineStatus.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:vartype status_code: str or ~flow.models.PipelineStatusCode
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar is_terminal_state:
:vartype is_terminal_state: bool
"""
_validation = {
'is_terminal_state': {'readonly': True},
}
_attribute_map = {
'status_code': {'key': 'statusCode', 'type': 'str'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'is_terminal_state': {'key': 'isTerminalState', 'type': 'bool'},
}
def __init__(
self,
*,
status_code: Optional[Union[str, "PipelineStatusCode"]] = None,
run_status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword status_code: Possible values include: "NotStarted", "InDraft", "Preparing", "Running",
"Failed", "Finished", "Canceled", "Throttled", "Unknown".
:paramtype status_code: str or ~flow.models.PipelineStatusCode
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(PipelineStatus, self).__init__(**kwargs)
self.status_code = status_code
self.run_status = run_status
self.status_detail = status_detail
self.start_time = start_time
self.end_time = end_time
self.is_terminal_state = None
class PipelineStepRun(msrest.serialization.Model):
"""PipelineStepRun.
:ivar step_name:
:vartype step_name: str
:ivar run_number:
:vartype run_number: int
:ivar run_id:
:vartype run_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype run_status: str or ~flow.models.RunStatus
:ivar compute_target:
:vartype compute_target: str
:ivar compute_type:
:vartype compute_type: str
:ivar run_type:
:vartype run_type: str
:ivar step_type:
:vartype step_type: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar is_reused:
:vartype is_reused: bool
:ivar display_name:
:vartype display_name: str
"""
_attribute_map = {
'step_name': {'key': 'stepName', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'run_id': {'key': 'runId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'run_status': {'key': 'runStatus', 'type': 'str'},
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'step_type': {'key': 'stepType', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'display_name': {'key': 'displayName', 'type': 'str'},
}
def __init__(
self,
*,
step_name: Optional[str] = None,
run_number: Optional[int] = None,
run_id: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
run_status: Optional[Union[str, "RunStatus"]] = None,
compute_target: Optional[str] = None,
compute_type: Optional[str] = None,
run_type: Optional[str] = None,
step_type: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
is_reused: Optional[bool] = None,
display_name: Optional[str] = None,
**kwargs
):
"""
:keyword step_name:
:paramtype step_name: str
:keyword run_number:
:paramtype run_number: int
:keyword run_id:
:paramtype run_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword run_status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype run_status: str or ~flow.models.RunStatus
:keyword compute_target:
:paramtype compute_target: str
:keyword compute_type:
:paramtype compute_type: str
:keyword run_type:
:paramtype run_type: str
:keyword step_type:
:paramtype step_type: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword is_reused:
:paramtype is_reused: bool
:keyword display_name:
:paramtype display_name: str
"""
super(PipelineStepRun, self).__init__(**kwargs)
self.step_name = step_name
self.run_number = run_number
self.run_id = run_id
self.start_time = start_time
self.end_time = end_time
self.run_status = run_status
self.compute_target = compute_target
self.compute_type = compute_type
self.run_type = run_type
self.step_type = step_type
self.tags = tags
self.is_reused = is_reused
self.display_name = display_name
class PipelineStepRunOutputs(msrest.serialization.Model):
"""PipelineStepRunOutputs.
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, str]
:ivar port_outputs: This is a dictionary.
:vartype port_outputs: dict[str, ~flow.models.PortOutputInfo]
"""
_attribute_map = {
'outputs': {'key': 'outputs', 'type': '{str}'},
'port_outputs': {'key': 'portOutputs', 'type': '{PortOutputInfo}'},
}
def __init__(
self,
*,
outputs: Optional[Dict[str, str]] = None,
port_outputs: Optional[Dict[str, "PortOutputInfo"]] = None,
**kwargs
):
"""
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, str]
:keyword port_outputs: This is a dictionary.
:paramtype port_outputs: dict[str, ~flow.models.PortOutputInfo]
"""
super(PipelineStepRunOutputs, self).__init__(**kwargs)
self.outputs = outputs
self.port_outputs = port_outputs
class PipelineSubDraft(msrest.serialization.Model):
"""PipelineSubDraft.
:ivar parent_graph_draft_id:
:vartype parent_graph_draft_id: str
:ivar parent_node_id:
:vartype parent_node_id: str
:ivar graph_detail:
:vartype graph_detail: ~flow.models.PipelineRunGraphDetail
:ivar module_dto:
:vartype module_dto: ~flow.models.ModuleDto
:ivar name:
:vartype name: str
:ivar last_edited_by:
:vartype last_edited_by: str
:ivar created_by:
:vartype created_by: str
:ivar description:
:vartype description: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'parent_graph_draft_id': {'key': 'parentGraphDraftId', 'type': 'str'},
'parent_node_id': {'key': 'parentNodeId', 'type': 'str'},
'graph_detail': {'key': 'graphDetail', 'type': 'PipelineRunGraphDetail'},
'module_dto': {'key': 'moduleDto', 'type': 'ModuleDto'},
'name': {'key': 'name', 'type': 'str'},
'last_edited_by': {'key': 'lastEditedBy', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
parent_graph_draft_id: Optional[str] = None,
parent_node_id: Optional[str] = None,
graph_detail: Optional["PipelineRunGraphDetail"] = None,
module_dto: Optional["ModuleDto"] = None,
name: Optional[str] = None,
last_edited_by: Optional[str] = None,
created_by: Optional[str] = None,
description: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword parent_graph_draft_id:
:paramtype parent_graph_draft_id: str
:keyword parent_node_id:
:paramtype parent_node_id: str
:keyword graph_detail:
:paramtype graph_detail: ~flow.models.PipelineRunGraphDetail
:keyword module_dto:
:paramtype module_dto: ~flow.models.ModuleDto
:keyword name:
:paramtype name: str
:keyword last_edited_by:
:paramtype last_edited_by: str
:keyword created_by:
:paramtype created_by: str
:keyword description:
:paramtype description: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PipelineSubDraft, self).__init__(**kwargs)
self.parent_graph_draft_id = parent_graph_draft_id
self.parent_node_id = parent_node_id
self.graph_detail = graph_detail
self.module_dto = module_dto
self.name = name
self.last_edited_by = last_edited_by
self.created_by = created_by
self.description = description
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.tags = tags
self.properties = properties
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PolicyValidationResponse(msrest.serialization.Model):
"""PolicyValidationResponse.
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:vartype action_type: str or ~flow.models.ActionType
"""
_attribute_map = {
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
}
def __init__(
self,
*,
error_response: Optional["ErrorResponse"] = None,
next_action_interval_in_seconds: Optional[int] = None,
action_type: Optional[Union[str, "ActionType"]] = None,
**kwargs
):
"""
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:paramtype action_type: str or ~flow.models.ActionType
"""
super(PolicyValidationResponse, self).__init__(**kwargs)
self.error_response = error_response
self.next_action_interval_in_seconds = next_action_interval_in_seconds
self.action_type = action_type
class PortInfo(msrest.serialization.Model):
"""PortInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar graph_port_name:
:vartype graph_port_name: str
:ivar is_parameter:
:vartype is_parameter: bool
:ivar web_service_port:
:vartype web_service_port: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'graph_port_name': {'key': 'graphPortName', 'type': 'str'},
'is_parameter': {'key': 'isParameter', 'type': 'bool'},
'web_service_port': {'key': 'webServicePort', 'type': 'str'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
port_name: Optional[str] = None,
graph_port_name: Optional[str] = None,
is_parameter: Optional[bool] = None,
web_service_port: Optional[str] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword graph_port_name:
:paramtype graph_port_name: str
:keyword is_parameter:
:paramtype is_parameter: bool
:keyword web_service_port:
:paramtype web_service_port: str
"""
super(PortInfo, self).__init__(**kwargs)
self.node_id = node_id
self.port_name = port_name
self.graph_port_name = graph_port_name
self.is_parameter = is_parameter
self.web_service_port = web_service_port
class PortOutputInfo(msrest.serialization.Model):
"""PortOutputInfo.
:ivar container_uri:
:vartype container_uri: str
:ivar relative_path:
:vartype relative_path: str
:ivar preview_params:
:vartype preview_params: str
:ivar model_output_path:
:vartype model_output_path: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:vartype data_reference_type: str or ~flow.models.DataReferenceType
:ivar is_file:
:vartype is_file: bool
:ivar supported_actions:
:vartype supported_actions: list[str or ~flow.models.PortAction]
"""
_attribute_map = {
'container_uri': {'key': 'containerUri', 'type': 'str'},
'relative_path': {'key': 'relativePath', 'type': 'str'},
'preview_params': {'key': 'previewParams', 'type': 'str'},
'model_output_path': {'key': 'modelOutputPath', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_reference_type': {'key': 'dataReferenceType', 'type': 'str'},
'is_file': {'key': 'isFile', 'type': 'bool'},
'supported_actions': {'key': 'supportedActions', 'type': '[str]'},
}
def __init__(
self,
*,
container_uri: Optional[str] = None,
relative_path: Optional[str] = None,
preview_params: Optional[str] = None,
model_output_path: Optional[str] = None,
data_store_name: Optional[str] = None,
data_reference_type: Optional[Union[str, "DataReferenceType"]] = None,
is_file: Optional[bool] = None,
supported_actions: Optional[List[Union[str, "PortAction"]]] = None,
**kwargs
):
"""
:keyword container_uri:
:paramtype container_uri: str
:keyword relative_path:
:paramtype relative_path: str
:keyword preview_params:
:paramtype preview_params: str
:keyword model_output_path:
:paramtype model_output_path: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_reference_type: Possible values include: "None", "AzureBlob", "AzureDataLake",
"AzureFiles", "AzureSqlDatabase", "AzurePostgresDatabase", "AzureDataLakeGen2", "DBFS",
"AzureMySqlDatabase", "Custom", "Hdfs".
:paramtype data_reference_type: str or ~flow.models.DataReferenceType
:keyword is_file:
:paramtype is_file: bool
:keyword supported_actions:
:paramtype supported_actions: list[str or ~flow.models.PortAction]
"""
super(PortOutputInfo, self).__init__(**kwargs)
self.container_uri = container_uri
self.relative_path = relative_path
self.preview_params = preview_params
self.model_output_path = model_output_path
self.data_store_name = data_store_name
self.data_reference_type = data_reference_type
self.is_file = is_file
self.supported_actions = supported_actions
class PriorityConfig(msrest.serialization.Model):
"""PriorityConfig.
:ivar job_priority:
:vartype job_priority: int
:ivar is_preemptible:
:vartype is_preemptible: bool
:ivar node_count_set:
:vartype node_count_set: list[int]
:ivar scale_interval:
:vartype scale_interval: int
"""
_attribute_map = {
'job_priority': {'key': 'jobPriority', 'type': 'int'},
'is_preemptible': {'key': 'isPreemptible', 'type': 'bool'},
'node_count_set': {'key': 'nodeCountSet', 'type': '[int]'},
'scale_interval': {'key': 'scaleInterval', 'type': 'int'},
}
def __init__(
self,
*,
job_priority: Optional[int] = None,
is_preemptible: Optional[bool] = None,
node_count_set: Optional[List[int]] = None,
scale_interval: Optional[int] = None,
**kwargs
):
"""
:keyword job_priority:
:paramtype job_priority: int
:keyword is_preemptible:
:paramtype is_preemptible: bool
:keyword node_count_set:
:paramtype node_count_set: list[int]
:keyword scale_interval:
:paramtype scale_interval: int
"""
super(PriorityConfig, self).__init__(**kwargs)
self.job_priority = job_priority
self.is_preemptible = is_preemptible
self.node_count_set = node_count_set
self.scale_interval = scale_interval
class PriorityConfiguration(msrest.serialization.Model):
"""PriorityConfiguration.
:ivar cloud_priority:
:vartype cloud_priority: int
:ivar string_type_priority:
:vartype string_type_priority: str
"""
_attribute_map = {
'cloud_priority': {'key': 'cloudPriority', 'type': 'int'},
'string_type_priority': {'key': 'stringTypePriority', 'type': 'str'},
}
def __init__(
self,
*,
cloud_priority: Optional[int] = None,
string_type_priority: Optional[str] = None,
**kwargs
):
"""
:keyword cloud_priority:
:paramtype cloud_priority: int
:keyword string_type_priority:
:paramtype string_type_priority: str
"""
super(PriorityConfiguration, self).__init__(**kwargs)
self.cloud_priority = cloud_priority
self.string_type_priority = string_type_priority
class PromoteDataSetRequest(msrest.serialization.Model):
"""PromoteDataSetRequest.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar module_node_id:
:vartype module_node_id: str
:ivar step_run_id:
:vartype step_run_id: str
:ivar output_port_name:
:vartype output_port_name: str
:ivar model_output_path:
:vartype model_output_path: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar dataset_type:
:vartype dataset_type: str
:ivar data_store_name:
:vartype data_store_name: str
:ivar output_relative_path:
:vartype output_relative_path: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar root_pipeline_run_id:
:vartype root_pipeline_run_id: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar experiment_id:
:vartype experiment_id: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'step_run_id': {'key': 'stepRunId', 'type': 'str'},
'output_port_name': {'key': 'outputPortName', 'type': 'str'},
'model_output_path': {'key': 'modelOutputPath', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'dataset_type': {'key': 'datasetType', 'type': 'str'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'output_relative_path': {'key': 'outputRelativePath', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'root_pipeline_run_id': {'key': 'rootPipelineRunId', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
module_node_id: Optional[str] = None,
step_run_id: Optional[str] = None,
output_port_name: Optional[str] = None,
model_output_path: Optional[str] = None,
data_type_id: Optional[str] = None,
dataset_type: Optional[str] = None,
data_store_name: Optional[str] = None,
output_relative_path: Optional[str] = None,
pipeline_run_id: Optional[str] = None,
root_pipeline_run_id: Optional[str] = None,
experiment_name: Optional[str] = None,
experiment_id: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword module_node_id:
:paramtype module_node_id: str
:keyword step_run_id:
:paramtype step_run_id: str
:keyword output_port_name:
:paramtype output_port_name: str
:keyword model_output_path:
:paramtype model_output_path: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword dataset_type:
:paramtype dataset_type: str
:keyword data_store_name:
:paramtype data_store_name: str
:keyword output_relative_path:
:paramtype output_relative_path: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword root_pipeline_run_id:
:paramtype root_pipeline_run_id: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword experiment_id:
:paramtype experiment_id: str
"""
super(PromoteDataSetRequest, self).__init__(**kwargs)
self.name = name
self.description = description
self.module_node_id = module_node_id
self.step_run_id = step_run_id
self.output_port_name = output_port_name
self.model_output_path = model_output_path
self.data_type_id = data_type_id
self.dataset_type = dataset_type
self.data_store_name = data_store_name
self.output_relative_path = output_relative_path
self.pipeline_run_id = pipeline_run_id
self.root_pipeline_run_id = root_pipeline_run_id
self.experiment_name = experiment_name
self.experiment_id = experiment_id
class ProviderEntity(msrest.serialization.Model):
"""ProviderEntity.
:ivar provider:
:vartype provider: str
:ivar module:
:vartype module: str
:ivar connection_type:
:vartype connection_type: list[str or ~flow.models.ConnectionType]
:ivar apis:
:vartype apis: list[~flow.models.ApiAndParameters]
"""
_attribute_map = {
'provider': {'key': 'provider', 'type': 'str'},
'module': {'key': 'module', 'type': 'str'},
'connection_type': {'key': 'connection_type', 'type': '[str]'},
'apis': {'key': 'apis', 'type': '[ApiAndParameters]'},
}
def __init__(
self,
*,
provider: Optional[str] = None,
module: Optional[str] = None,
connection_type: Optional[List[Union[str, "ConnectionType"]]] = None,
apis: Optional[List["ApiAndParameters"]] = None,
**kwargs
):
"""
:keyword provider:
:paramtype provider: str
:keyword module:
:paramtype module: str
:keyword connection_type:
:paramtype connection_type: list[str or ~flow.models.ConnectionType]
:keyword apis:
:paramtype apis: list[~flow.models.ApiAndParameters]
"""
super(ProviderEntity, self).__init__(**kwargs)
self.provider = provider
self.module = module
self.connection_type = connection_type
self.apis = apis
class PublishedPipeline(msrest.serialization.Model):
"""PublishedPipeline.
:ivar total_run_steps:
:vartype total_run_steps: int
:ivar total_runs:
:vartype total_runs: int
:ivar parameters: This is a dictionary.
:vartype parameters: dict[str, str]
:ivar data_set_definition_value_assignment: This is a dictionary.
:vartype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar rest_endpoint:
:vartype rest_endpoint: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar graph_id:
:vartype graph_id: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar published_by:
:vartype published_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar version:
:vartype version: str
:ivar is_default:
:vartype is_default: bool
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'total_run_steps': {'key': 'totalRunSteps', 'type': 'int'},
'total_runs': {'key': 'totalRuns', 'type': 'int'},
'parameters': {'key': 'parameters', 'type': '{str}'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': '{DataSetDefinitionValue}'},
'rest_endpoint': {'key': 'restEndpoint', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'version': {'key': 'version', 'type': 'str'},
'is_default': {'key': 'isDefault', 'type': 'bool'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
total_run_steps: Optional[int] = None,
total_runs: Optional[int] = None,
parameters: Optional[Dict[str, str]] = None,
data_set_definition_value_assignment: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
rest_endpoint: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
graph_id: Optional[str] = None,
published_date: Optional[datetime.datetime] = None,
last_run_time: Optional[datetime.datetime] = None,
last_run_status: Optional[Union[str, "PipelineRunStatusCode"]] = None,
published_by: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
version: Optional[str] = None,
is_default: Optional[bool] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword total_run_steps:
:paramtype total_run_steps: int
:keyword total_runs:
:paramtype total_runs: int
:keyword parameters: This is a dictionary.
:paramtype parameters: dict[str, str]
:keyword data_set_definition_value_assignment: This is a dictionary.
:paramtype data_set_definition_value_assignment: dict[str, ~flow.models.DataSetDefinitionValue]
:keyword rest_endpoint:
:paramtype rest_endpoint: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword graph_id:
:paramtype graph_id: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword published_by:
:paramtype published_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword version:
:paramtype version: str
:keyword is_default:
:paramtype is_default: bool
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PublishedPipeline, self).__init__(**kwargs)
self.total_run_steps = total_run_steps
self.total_runs = total_runs
self.parameters = parameters
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.rest_endpoint = rest_endpoint
self.name = name
self.description = description
self.graph_id = graph_id
self.published_date = published_date
self.last_run_time = last_run_time
self.last_run_status = last_run_status
self.published_by = published_by
self.tags = tags
self.properties = properties
self.version = version
self.is_default = is_default
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PublishedPipelineSummary(msrest.serialization.Model):
"""PublishedPipelineSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar graph_id:
:vartype graph_id: str
:ivar published_date:
:vartype published_date: ~datetime.datetime
:ivar last_run_time:
:vartype last_run_time: ~datetime.datetime
:ivar last_run_status: Possible values include: "NotStarted", "Running", "Failed", "Finished",
"Canceled", "Queued", "CancelRequested".
:vartype last_run_status: str or ~flow.models.PipelineRunStatusCode
:ivar published_by:
:vartype published_by: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar version:
:vartype version: str
:ivar is_default:
:vartype is_default: bool
:ivar entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:vartype entity_status: str or ~flow.models.EntityStatus
:ivar id:
:vartype id: str
:ivar etag:
:vartype etag: str
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'graph_id': {'key': 'graphId', 'type': 'str'},
'published_date': {'key': 'publishedDate', 'type': 'iso-8601'},
'last_run_time': {'key': 'lastRunTime', 'type': 'iso-8601'},
'last_run_status': {'key': 'lastRunStatus', 'type': 'str'},
'published_by': {'key': 'publishedBy', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'version': {'key': 'version', 'type': 'str'},
'is_default': {'key': 'isDefault', 'type': 'bool'},
'entity_status': {'key': 'entityStatus', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
graph_id: Optional[str] = None,
published_date: Optional[datetime.datetime] = None,
last_run_time: Optional[datetime.datetime] = None,
last_run_status: Optional[Union[str, "PipelineRunStatusCode"]] = None,
published_by: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
version: Optional[str] = None,
is_default: Optional[bool] = None,
entity_status: Optional[Union[str, "EntityStatus"]] = None,
id: Optional[str] = None,
etag: Optional[str] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword graph_id:
:paramtype graph_id: str
:keyword published_date:
:paramtype published_date: ~datetime.datetime
:keyword last_run_time:
:paramtype last_run_time: ~datetime.datetime
:keyword last_run_status: Possible values include: "NotStarted", "Running", "Failed",
"Finished", "Canceled", "Queued", "CancelRequested".
:paramtype last_run_status: str or ~flow.models.PipelineRunStatusCode
:keyword published_by:
:paramtype published_by: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword version:
:paramtype version: str
:keyword is_default:
:paramtype is_default: bool
:keyword entity_status: Possible values include: "Active", "Deprecated", "Disabled".
:paramtype entity_status: str or ~flow.models.EntityStatus
:keyword id:
:paramtype id: str
:keyword etag:
:paramtype etag: str
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
"""
super(PublishedPipelineSummary, self).__init__(**kwargs)
self.name = name
self.description = description
self.graph_id = graph_id
self.published_date = published_date
self.last_run_time = last_run_time
self.last_run_status = last_run_status
self.published_by = published_by
self.tags = tags
self.properties = properties
self.version = version
self.is_default = is_default
self.entity_status = entity_status
self.id = id
self.etag = etag
self.created_date = created_date
self.last_modified_date = last_modified_date
class PythonInterfaceMapping(msrest.serialization.Model):
"""PythonInterfaceMapping.
:ivar name:
:vartype name: str
:ivar name_in_yaml:
:vartype name_in_yaml: str
:ivar argument_name:
:vartype argument_name: str
:ivar command_line_option:
:vartype command_line_option: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'name_in_yaml': {'key': 'nameInYaml', 'type': 'str'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'command_line_option': {'key': 'commandLineOption', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
name_in_yaml: Optional[str] = None,
argument_name: Optional[str] = None,
command_line_option: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword name_in_yaml:
:paramtype name_in_yaml: str
:keyword argument_name:
:paramtype argument_name: str
:keyword command_line_option:
:paramtype command_line_option: str
"""
super(PythonInterfaceMapping, self).__init__(**kwargs)
self.name = name
self.name_in_yaml = name_in_yaml
self.argument_name = argument_name
self.command_line_option = command_line_option
class PythonPyPiOrRCranLibraryDto(msrest.serialization.Model):
"""PythonPyPiOrRCranLibraryDto.
:ivar package:
:vartype package: str
:ivar repo:
:vartype repo: str
"""
_attribute_map = {
'package': {'key': 'package', 'type': 'str'},
'repo': {'key': 'repo', 'type': 'str'},
}
def __init__(
self,
*,
package: Optional[str] = None,
repo: Optional[str] = None,
**kwargs
):
"""
:keyword package:
:paramtype package: str
:keyword repo:
:paramtype repo: str
"""
super(PythonPyPiOrRCranLibraryDto, self).__init__(**kwargs)
self.package = package
self.repo = repo
class PythonSection(msrest.serialization.Model):
"""PythonSection.
:ivar interpreter_path:
:vartype interpreter_path: str
:ivar user_managed_dependencies:
:vartype user_managed_dependencies: bool
:ivar conda_dependencies: Anything.
:vartype conda_dependencies: any
:ivar base_conda_environment:
:vartype base_conda_environment: str
"""
_attribute_map = {
'interpreter_path': {'key': 'interpreterPath', 'type': 'str'},
'user_managed_dependencies': {'key': 'userManagedDependencies', 'type': 'bool'},
'conda_dependencies': {'key': 'condaDependencies', 'type': 'object'},
'base_conda_environment': {'key': 'baseCondaEnvironment', 'type': 'str'},
}
def __init__(
self,
*,
interpreter_path: Optional[str] = None,
user_managed_dependencies: Optional[bool] = None,
conda_dependencies: Optional[Any] = None,
base_conda_environment: Optional[str] = None,
**kwargs
):
"""
:keyword interpreter_path:
:paramtype interpreter_path: str
:keyword user_managed_dependencies:
:paramtype user_managed_dependencies: bool
:keyword conda_dependencies: Anything.
:paramtype conda_dependencies: any
:keyword base_conda_environment:
:paramtype base_conda_environment: str
"""
super(PythonSection, self).__init__(**kwargs)
self.interpreter_path = interpreter_path
self.user_managed_dependencies = user_managed_dependencies
self.conda_dependencies = conda_dependencies
self.base_conda_environment = base_conda_environment
class PyTorchConfiguration(msrest.serialization.Model):
"""PyTorchConfiguration.
:ivar communication_backend:
:vartype communication_backend: str
:ivar process_count:
:vartype process_count: int
"""
_attribute_map = {
'communication_backend': {'key': 'communicationBackend', 'type': 'str'},
'process_count': {'key': 'processCount', 'type': 'int'},
}
def __init__(
self,
*,
communication_backend: Optional[str] = None,
process_count: Optional[int] = None,
**kwargs
):
"""
:keyword communication_backend:
:paramtype communication_backend: str
:keyword process_count:
:paramtype process_count: int
"""
super(PyTorchConfiguration, self).__init__(**kwargs)
self.communication_backend = communication_backend
self.process_count = process_count
class QueueingInfo(msrest.serialization.Model):
"""QueueingInfo.
:ivar code:
:vartype code: str
:ivar message:
:vartype message: str
:ivar last_refresh_timestamp:
:vartype last_refresh_timestamp: ~datetime.datetime
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'last_refresh_timestamp': {'key': 'lastRefreshTimestamp', 'type': 'iso-8601'},
}
def __init__(
self,
*,
code: Optional[str] = None,
message: Optional[str] = None,
last_refresh_timestamp: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword code:
:paramtype code: str
:keyword message:
:paramtype message: str
:keyword last_refresh_timestamp:
:paramtype last_refresh_timestamp: ~datetime.datetime
"""
super(QueueingInfo, self).__init__(**kwargs)
self.code = code
self.message = message
self.last_refresh_timestamp = last_refresh_timestamp
class RawComponentDto(msrest.serialization.Model):
"""RawComponentDto.
:ivar component_schema:
:vartype component_schema: str
:ivar is_anonymous:
:vartype is_anonymous: bool
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar type: Possible values include: "Unknown", "CommandComponent", "Command".
:vartype type: str or ~flow.models.ComponentType
:ivar component_type_version:
:vartype component_type_version: str
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar is_deterministic:
:vartype is_deterministic: bool
:ivar successful_return_code:
:vartype successful_return_code: str
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.ComponentInput]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.ComponentOutput]
:ivar command:
:vartype command: str
:ivar environment_name:
:vartype environment_name: str
:ivar environment_version:
:vartype environment_version: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_date:
:vartype created_date: ~datetime.datetime
:ivar last_modified_date:
:vartype last_modified_date: ~datetime.datetime
:ivar component_internal_id:
:vartype component_internal_id: str
"""
_attribute_map = {
'component_schema': {'key': 'componentSchema', 'type': 'str'},
'is_anonymous': {'key': 'isAnonymous', 'type': 'bool'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'component_type_version': {'key': 'componentTypeVersion', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'is_deterministic': {'key': 'isDeterministic', 'type': 'bool'},
'successful_return_code': {'key': 'successfulReturnCode', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{ComponentInput}'},
'outputs': {'key': 'outputs', 'type': '{ComponentOutput}'},
'command': {'key': 'command', 'type': 'str'},
'environment_name': {'key': 'environmentName', 'type': 'str'},
'environment_version': {'key': 'environmentVersion', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'SchemaContractsCreatedBy'},
'created_date': {'key': 'createdDate', 'type': 'iso-8601'},
'last_modified_date': {'key': 'lastModifiedDate', 'type': 'iso-8601'},
'component_internal_id': {'key': 'componentInternalId', 'type': 'str'},
}
def __init__(
self,
*,
component_schema: Optional[str] = None,
is_anonymous: Optional[bool] = None,
name: Optional[str] = None,
version: Optional[str] = None,
type: Optional[Union[str, "ComponentType"]] = None,
component_type_version: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
is_deterministic: Optional[bool] = None,
successful_return_code: Optional[str] = None,
inputs: Optional[Dict[str, "ComponentInput"]] = None,
outputs: Optional[Dict[str, "ComponentOutput"]] = None,
command: Optional[str] = None,
environment_name: Optional[str] = None,
environment_version: Optional[str] = None,
snapshot_id: Optional[str] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
last_modified_by: Optional["SchemaContractsCreatedBy"] = None,
created_date: Optional[datetime.datetime] = None,
last_modified_date: Optional[datetime.datetime] = None,
component_internal_id: Optional[str] = None,
**kwargs
):
"""
:keyword component_schema:
:paramtype component_schema: str
:keyword is_anonymous:
:paramtype is_anonymous: bool
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword type: Possible values include: "Unknown", "CommandComponent", "Command".
:paramtype type: str or ~flow.models.ComponentType
:keyword component_type_version:
:paramtype component_type_version: str
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword is_deterministic:
:paramtype is_deterministic: bool
:keyword successful_return_code:
:paramtype successful_return_code: str
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.ComponentInput]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.ComponentOutput]
:keyword command:
:paramtype command: str
:keyword environment_name:
:paramtype environment_name: str
:keyword environment_version:
:paramtype environment_version: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_date:
:paramtype created_date: ~datetime.datetime
:keyword last_modified_date:
:paramtype last_modified_date: ~datetime.datetime
:keyword component_internal_id:
:paramtype component_internal_id: str
"""
super(RawComponentDto, self).__init__(**kwargs)
self.component_schema = component_schema
self.is_anonymous = is_anonymous
self.name = name
self.version = version
self.type = type
self.component_type_version = component_type_version
self.display_name = display_name
self.description = description
self.tags = tags
self.properties = properties
self.is_deterministic = is_deterministic
self.successful_return_code = successful_return_code
self.inputs = inputs
self.outputs = outputs
self.command = command
self.environment_name = environment_name
self.environment_version = environment_version
self.snapshot_id = snapshot_id
self.created_by = created_by
self.last_modified_by = last_modified_by
self.created_date = created_date
self.last_modified_date = last_modified_date
self.component_internal_id = component_internal_id
class RayConfiguration(msrest.serialization.Model):
"""RayConfiguration.
:ivar port:
:vartype port: int
:ivar address:
:vartype address: str
:ivar include_dashboard:
:vartype include_dashboard: bool
:ivar dashboard_port:
:vartype dashboard_port: int
:ivar head_node_additional_args:
:vartype head_node_additional_args: str
:ivar worker_node_additional_args:
:vartype worker_node_additional_args: str
"""
_attribute_map = {
'port': {'key': 'port', 'type': 'int'},
'address': {'key': 'address', 'type': 'str'},
'include_dashboard': {'key': 'includeDashboard', 'type': 'bool'},
'dashboard_port': {'key': 'dashboardPort', 'type': 'int'},
'head_node_additional_args': {'key': 'headNodeAdditionalArgs', 'type': 'str'},
'worker_node_additional_args': {'key': 'workerNodeAdditionalArgs', 'type': 'str'},
}
def __init__(
self,
*,
port: Optional[int] = None,
address: Optional[str] = None,
include_dashboard: Optional[bool] = None,
dashboard_port: Optional[int] = None,
head_node_additional_args: Optional[str] = None,
worker_node_additional_args: Optional[str] = None,
**kwargs
):
"""
:keyword port:
:paramtype port: int
:keyword address:
:paramtype address: str
:keyword include_dashboard:
:paramtype include_dashboard: bool
:keyword dashboard_port:
:paramtype dashboard_port: int
:keyword head_node_additional_args:
:paramtype head_node_additional_args: str
:keyword worker_node_additional_args:
:paramtype worker_node_additional_args: str
"""
super(RayConfiguration, self).__init__(**kwargs)
self.port = port
self.address = address
self.include_dashboard = include_dashboard
self.dashboard_port = dashboard_port
self.head_node_additional_args = head_node_additional_args
self.worker_node_additional_args = worker_node_additional_args
class RCranPackage(msrest.serialization.Model):
"""RCranPackage.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar repository:
:vartype repository: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'repository': {'key': 'repository', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
version: Optional[str] = None,
repository: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword repository:
:paramtype repository: str
"""
super(RCranPackage, self).__init__(**kwargs)
self.name = name
self.version = version
self.repository = repository
class RealTimeEndpoint(msrest.serialization.Model):
"""RealTimeEndpoint.
:ivar created_by:
:vartype created_by: str
:ivar kv_tags: Dictionary of :code:`<string>`.
:vartype kv_tags: dict[str, str]
:ivar state: Possible values include: "Transitioning", "Healthy", "Unhealthy", "Failed",
"Unschedulable".
:vartype state: str or ~flow.models.WebServiceState
:ivar error:
:vartype error: ~flow.models.ModelManagementErrorResponse
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar image_id:
:vartype image_id: str
:ivar cpu:
:vartype cpu: float
:ivar memory_in_gb:
:vartype memory_in_gb: float
:ivar max_concurrent_requests_per_container:
:vartype max_concurrent_requests_per_container: int
:ivar num_replicas:
:vartype num_replicas: int
:ivar event_hub_enabled:
:vartype event_hub_enabled: bool
:ivar storage_enabled:
:vartype storage_enabled: bool
:ivar app_insights_enabled:
:vartype app_insights_enabled: bool
:ivar auto_scale_enabled:
:vartype auto_scale_enabled: bool
:ivar min_replicas:
:vartype min_replicas: int
:ivar max_replicas:
:vartype max_replicas: int
:ivar target_utilization:
:vartype target_utilization: int
:ivar refresh_period_in_seconds:
:vartype refresh_period_in_seconds: int
:ivar scoring_uri:
:vartype scoring_uri: str
:ivar deployment_status:
:vartype deployment_status: ~flow.models.AKSReplicaStatus
:ivar scoring_timeout_ms:
:vartype scoring_timeout_ms: int
:ivar auth_enabled:
:vartype auth_enabled: bool
:ivar aad_auth_enabled:
:vartype aad_auth_enabled: bool
:ivar region:
:vartype region: str
:ivar primary_key:
:vartype primary_key: str
:ivar secondary_key:
:vartype secondary_key: str
:ivar swagger_uri:
:vartype swagger_uri: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
:ivar linked_pipeline_run_id:
:vartype linked_pipeline_run_id: str
:ivar warning:
:vartype warning: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar id:
:vartype id: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar updated_time:
:vartype updated_time: ~datetime.datetime
:ivar compute_name:
:vartype compute_name: str
:ivar updated_by:
:vartype updated_by: str
"""
_attribute_map = {
'created_by': {'key': 'createdBy', 'type': 'str'},
'kv_tags': {'key': 'kvTags', 'type': '{str}'},
'state': {'key': 'state', 'type': 'str'},
'error': {'key': 'error', 'type': 'ModelManagementErrorResponse'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'image_id': {'key': 'imageId', 'type': 'str'},
'cpu': {'key': 'cpu', 'type': 'float'},
'memory_in_gb': {'key': 'memoryInGB', 'type': 'float'},
'max_concurrent_requests_per_container': {'key': 'maxConcurrentRequestsPerContainer', 'type': 'int'},
'num_replicas': {'key': 'numReplicas', 'type': 'int'},
'event_hub_enabled': {'key': 'eventHubEnabled', 'type': 'bool'},
'storage_enabled': {'key': 'storageEnabled', 'type': 'bool'},
'app_insights_enabled': {'key': 'appInsightsEnabled', 'type': 'bool'},
'auto_scale_enabled': {'key': 'autoScaleEnabled', 'type': 'bool'},
'min_replicas': {'key': 'minReplicas', 'type': 'int'},
'max_replicas': {'key': 'maxReplicas', 'type': 'int'},
'target_utilization': {'key': 'targetUtilization', 'type': 'int'},
'refresh_period_in_seconds': {'key': 'refreshPeriodInSeconds', 'type': 'int'},
'scoring_uri': {'key': 'scoringUri', 'type': 'str'},
'deployment_status': {'key': 'deploymentStatus', 'type': 'AKSReplicaStatus'},
'scoring_timeout_ms': {'key': 'scoringTimeoutMs', 'type': 'int'},
'auth_enabled': {'key': 'authEnabled', 'type': 'bool'},
'aad_auth_enabled': {'key': 'aadAuthEnabled', 'type': 'bool'},
'region': {'key': 'region', 'type': 'str'},
'primary_key': {'key': 'primaryKey', 'type': 'str'},
'secondary_key': {'key': 'secondaryKey', 'type': 'str'},
'swagger_uri': {'key': 'swaggerUri', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
'linked_pipeline_run_id': {'key': 'linkedPipelineRunId', 'type': 'str'},
'warning': {'key': 'warning', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'updated_time': {'key': 'updatedTime', 'type': 'iso-8601'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
}
def __init__(
self,
*,
created_by: Optional[str] = None,
kv_tags: Optional[Dict[str, str]] = None,
state: Optional[Union[str, "WebServiceState"]] = None,
error: Optional["ModelManagementErrorResponse"] = None,
compute_type: Optional[Union[str, "ComputeEnvironmentType"]] = None,
image_id: Optional[str] = None,
cpu: Optional[float] = None,
memory_in_gb: Optional[float] = None,
max_concurrent_requests_per_container: Optional[int] = None,
num_replicas: Optional[int] = None,
event_hub_enabled: Optional[bool] = None,
storage_enabled: Optional[bool] = None,
app_insights_enabled: Optional[bool] = None,
auto_scale_enabled: Optional[bool] = None,
min_replicas: Optional[int] = None,
max_replicas: Optional[int] = None,
target_utilization: Optional[int] = None,
refresh_period_in_seconds: Optional[int] = None,
scoring_uri: Optional[str] = None,
deployment_status: Optional["AKSReplicaStatus"] = None,
scoring_timeout_ms: Optional[int] = None,
auth_enabled: Optional[bool] = None,
aad_auth_enabled: Optional[bool] = None,
region: Optional[str] = None,
primary_key: Optional[str] = None,
secondary_key: Optional[str] = None,
swagger_uri: Optional[str] = None,
linked_pipeline_draft_id: Optional[str] = None,
linked_pipeline_run_id: Optional[str] = None,
warning: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
id: Optional[str] = None,
created_time: Optional[datetime.datetime] = None,
updated_time: Optional[datetime.datetime] = None,
compute_name: Optional[str] = None,
updated_by: Optional[str] = None,
**kwargs
):
"""
:keyword created_by:
:paramtype created_by: str
:keyword kv_tags: Dictionary of :code:`<string>`.
:paramtype kv_tags: dict[str, str]
:keyword state: Possible values include: "Transitioning", "Healthy", "Unhealthy", "Failed",
"Unschedulable".
:paramtype state: str or ~flow.models.WebServiceState
:keyword error:
:paramtype error: ~flow.models.ModelManagementErrorResponse
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword image_id:
:paramtype image_id: str
:keyword cpu:
:paramtype cpu: float
:keyword memory_in_gb:
:paramtype memory_in_gb: float
:keyword max_concurrent_requests_per_container:
:paramtype max_concurrent_requests_per_container: int
:keyword num_replicas:
:paramtype num_replicas: int
:keyword event_hub_enabled:
:paramtype event_hub_enabled: bool
:keyword storage_enabled:
:paramtype storage_enabled: bool
:keyword app_insights_enabled:
:paramtype app_insights_enabled: bool
:keyword auto_scale_enabled:
:paramtype auto_scale_enabled: bool
:keyword min_replicas:
:paramtype min_replicas: int
:keyword max_replicas:
:paramtype max_replicas: int
:keyword target_utilization:
:paramtype target_utilization: int
:keyword refresh_period_in_seconds:
:paramtype refresh_period_in_seconds: int
:keyword scoring_uri:
:paramtype scoring_uri: str
:keyword deployment_status:
:paramtype deployment_status: ~flow.models.AKSReplicaStatus
:keyword scoring_timeout_ms:
:paramtype scoring_timeout_ms: int
:keyword auth_enabled:
:paramtype auth_enabled: bool
:keyword aad_auth_enabled:
:paramtype aad_auth_enabled: bool
:keyword region:
:paramtype region: str
:keyword primary_key:
:paramtype primary_key: str
:keyword secondary_key:
:paramtype secondary_key: str
:keyword swagger_uri:
:paramtype swagger_uri: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
:keyword linked_pipeline_run_id:
:paramtype linked_pipeline_run_id: str
:keyword warning:
:paramtype warning: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword id:
:paramtype id: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword updated_time:
:paramtype updated_time: ~datetime.datetime
:keyword compute_name:
:paramtype compute_name: str
:keyword updated_by:
:paramtype updated_by: str
"""
super(RealTimeEndpoint, self).__init__(**kwargs)
self.created_by = created_by
self.kv_tags = kv_tags
self.state = state
self.error = error
self.compute_type = compute_type
self.image_id = image_id
self.cpu = cpu
self.memory_in_gb = memory_in_gb
self.max_concurrent_requests_per_container = max_concurrent_requests_per_container
self.num_replicas = num_replicas
self.event_hub_enabled = event_hub_enabled
self.storage_enabled = storage_enabled
self.app_insights_enabled = app_insights_enabled
self.auto_scale_enabled = auto_scale_enabled
self.min_replicas = min_replicas
self.max_replicas = max_replicas
self.target_utilization = target_utilization
self.refresh_period_in_seconds = refresh_period_in_seconds
self.scoring_uri = scoring_uri
self.deployment_status = deployment_status
self.scoring_timeout_ms = scoring_timeout_ms
self.auth_enabled = auth_enabled
self.aad_auth_enabled = aad_auth_enabled
self.region = region
self.primary_key = primary_key
self.secondary_key = secondary_key
self.swagger_uri = swagger_uri
self.linked_pipeline_draft_id = linked_pipeline_draft_id
self.linked_pipeline_run_id = linked_pipeline_run_id
self.warning = warning
self.name = name
self.description = description
self.id = id
self.created_time = created_time
self.updated_time = updated_time
self.compute_name = compute_name
self.updated_by = updated_by
class RealTimeEndpointInfo(msrest.serialization.Model):
"""RealTimeEndpointInfo.
:ivar web_service_inputs:
:vartype web_service_inputs: list[~flow.models.WebServicePort]
:ivar web_service_outputs:
:vartype web_service_outputs: list[~flow.models.WebServicePort]
:ivar deployments_info:
:vartype deployments_info: list[~flow.models.DeploymentInfo]
"""
_attribute_map = {
'web_service_inputs': {'key': 'webServiceInputs', 'type': '[WebServicePort]'},
'web_service_outputs': {'key': 'webServiceOutputs', 'type': '[WebServicePort]'},
'deployments_info': {'key': 'deploymentsInfo', 'type': '[DeploymentInfo]'},
}
def __init__(
self,
*,
web_service_inputs: Optional[List["WebServicePort"]] = None,
web_service_outputs: Optional[List["WebServicePort"]] = None,
deployments_info: Optional[List["DeploymentInfo"]] = None,
**kwargs
):
"""
:keyword web_service_inputs:
:paramtype web_service_inputs: list[~flow.models.WebServicePort]
:keyword web_service_outputs:
:paramtype web_service_outputs: list[~flow.models.WebServicePort]
:keyword deployments_info:
:paramtype deployments_info: list[~flow.models.DeploymentInfo]
"""
super(RealTimeEndpointInfo, self).__init__(**kwargs)
self.web_service_inputs = web_service_inputs
self.web_service_outputs = web_service_outputs
self.deployments_info = deployments_info
class RealTimeEndpointStatus(msrest.serialization.Model):
"""RealTimeEndpointStatus.
:ivar last_operation: Possible values include: "Create", "Update", "Delete".
:vartype last_operation: str or ~flow.models.RealTimeEndpointOpCode
:ivar last_operation_status: Possible values include: "Ongoing", "Succeeded", "Failed",
"SucceededWithWarning".
:vartype last_operation_status: str or ~flow.models.RealTimeEndpointOpStatusCode
:ivar internal_step: Possible values include: "AboutToDeploy", "WaitAksComputeReady",
"RegisterModels", "CreateServiceFromModels", "UpdateServiceFromModels", "WaitServiceCreating",
"FetchServiceRelatedInfo", "TestWithSampleData", "AboutToDelete", "DeleteDeployment",
"DeleteAsset", "DeleteImage", "DeleteModel", "DeleteServiceRecord".
:vartype internal_step: str or ~flow.models.RealTimeEndpointInternalStepCode
:ivar status_detail:
:vartype status_detail: str
:ivar deployment_state:
:vartype deployment_state: str
:ivar service_id:
:vartype service_id: str
:ivar linked_pipeline_draft_id:
:vartype linked_pipeline_draft_id: str
"""
_attribute_map = {
'last_operation': {'key': 'lastOperation', 'type': 'str'},
'last_operation_status': {'key': 'lastOperationStatus', 'type': 'str'},
'internal_step': {'key': 'internalStep', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'deployment_state': {'key': 'deploymentState', 'type': 'str'},
'service_id': {'key': 'serviceId', 'type': 'str'},
'linked_pipeline_draft_id': {'key': 'linkedPipelineDraftId', 'type': 'str'},
}
def __init__(
self,
*,
last_operation: Optional[Union[str, "RealTimeEndpointOpCode"]] = None,
last_operation_status: Optional[Union[str, "RealTimeEndpointOpStatusCode"]] = None,
internal_step: Optional[Union[str, "RealTimeEndpointInternalStepCode"]] = None,
status_detail: Optional[str] = None,
deployment_state: Optional[str] = None,
service_id: Optional[str] = None,
linked_pipeline_draft_id: Optional[str] = None,
**kwargs
):
"""
:keyword last_operation: Possible values include: "Create", "Update", "Delete".
:paramtype last_operation: str or ~flow.models.RealTimeEndpointOpCode
:keyword last_operation_status: Possible values include: "Ongoing", "Succeeded", "Failed",
"SucceededWithWarning".
:paramtype last_operation_status: str or ~flow.models.RealTimeEndpointOpStatusCode
:keyword internal_step: Possible values include: "AboutToDeploy", "WaitAksComputeReady",
"RegisterModels", "CreateServiceFromModels", "UpdateServiceFromModels", "WaitServiceCreating",
"FetchServiceRelatedInfo", "TestWithSampleData", "AboutToDelete", "DeleteDeployment",
"DeleteAsset", "DeleteImage", "DeleteModel", "DeleteServiceRecord".
:paramtype internal_step: str or ~flow.models.RealTimeEndpointInternalStepCode
:keyword status_detail:
:paramtype status_detail: str
:keyword deployment_state:
:paramtype deployment_state: str
:keyword service_id:
:paramtype service_id: str
:keyword linked_pipeline_draft_id:
:paramtype linked_pipeline_draft_id: str
"""
super(RealTimeEndpointStatus, self).__init__(**kwargs)
self.last_operation = last_operation
self.last_operation_status = last_operation_status
self.internal_step = internal_step
self.status_detail = status_detail
self.deployment_state = deployment_state
self.service_id = service_id
self.linked_pipeline_draft_id = linked_pipeline_draft_id
class RealTimeEndpointSummary(msrest.serialization.Model):
"""RealTimeEndpointSummary.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar id:
:vartype id: str
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar updated_time:
:vartype updated_time: ~datetime.datetime
:ivar compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT", "AKSENDPOINT",
"MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE", "UNKNOWN".
:vartype compute_type: str or ~flow.models.ComputeEnvironmentType
:ivar compute_name:
:vartype compute_name: str
:ivar updated_by:
:vartype updated_by: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'updated_time': {'key': 'updatedTime', 'type': 'iso-8601'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'updated_by': {'key': 'updatedBy', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
id: Optional[str] = None,
created_time: Optional[datetime.datetime] = None,
updated_time: Optional[datetime.datetime] = None,
compute_type: Optional[Union[str, "ComputeEnvironmentType"]] = None,
compute_name: Optional[str] = None,
updated_by: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword id:
:paramtype id: str
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword updated_time:
:paramtype updated_time: ~datetime.datetime
:keyword compute_type: Possible values include: "ACI", "AKS", "AMLCOMPUTE", "IOT",
"AKSENDPOINT", "MIRSINGLEMODEL", "MIRAMLCOMPUTE", "MIRGA", "AMLARC", "BATCHAMLCOMPUTE",
"UNKNOWN".
:paramtype compute_type: str or ~flow.models.ComputeEnvironmentType
:keyword compute_name:
:paramtype compute_name: str
:keyword updated_by:
:paramtype updated_by: str
"""
super(RealTimeEndpointSummary, self).__init__(**kwargs)
self.name = name
self.description = description
self.id = id
self.created_time = created_time
self.updated_time = updated_time
self.compute_type = compute_type
self.compute_name = compute_name
self.updated_by = updated_by
class RealTimeEndpointTestRequest(msrest.serialization.Model):
"""RealTimeEndpointTestRequest.
:ivar end_point:
:vartype end_point: str
:ivar auth_key:
:vartype auth_key: str
:ivar payload:
:vartype payload: str
"""
_attribute_map = {
'end_point': {'key': 'endPoint', 'type': 'str'},
'auth_key': {'key': 'authKey', 'type': 'str'},
'payload': {'key': 'payload', 'type': 'str'},
}
def __init__(
self,
*,
end_point: Optional[str] = None,
auth_key: Optional[str] = None,
payload: Optional[str] = None,
**kwargs
):
"""
:keyword end_point:
:paramtype end_point: str
:keyword auth_key:
:paramtype auth_key: str
:keyword payload:
:paramtype payload: str
"""
super(RealTimeEndpointTestRequest, self).__init__(**kwargs)
self.end_point = end_point
self.auth_key = auth_key
self.payload = payload
class Recurrence(msrest.serialization.Model):
"""Recurrence.
:ivar frequency: Possible values include: "Month", "Week", "Day", "Hour", "Minute".
:vartype frequency: str or ~flow.models.Frequency
:ivar interval:
:vartype interval: int
:ivar schedule:
:vartype schedule: ~flow.models.RecurrenceSchedule
:ivar end_time:
:vartype end_time: str
:ivar start_time:
:vartype start_time: str
:ivar time_zone:
:vartype time_zone: str
"""
_attribute_map = {
'frequency': {'key': 'frequency', 'type': 'str'},
'interval': {'key': 'interval', 'type': 'int'},
'schedule': {'key': 'schedule', 'type': 'RecurrenceSchedule'},
'end_time': {'key': 'endTime', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'str'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
}
def __init__(
self,
*,
frequency: Optional[Union[str, "Frequency"]] = None,
interval: Optional[int] = None,
schedule: Optional["RecurrenceSchedule"] = None,
end_time: Optional[str] = None,
start_time: Optional[str] = None,
time_zone: Optional[str] = None,
**kwargs
):
"""
:keyword frequency: Possible values include: "Month", "Week", "Day", "Hour", "Minute".
:paramtype frequency: str or ~flow.models.Frequency
:keyword interval:
:paramtype interval: int
:keyword schedule:
:paramtype schedule: ~flow.models.RecurrenceSchedule
:keyword end_time:
:paramtype end_time: str
:keyword start_time:
:paramtype start_time: str
:keyword time_zone:
:paramtype time_zone: str
"""
super(Recurrence, self).__init__(**kwargs)
self.frequency = frequency
self.interval = interval
self.schedule = schedule
self.end_time = end_time
self.start_time = start_time
self.time_zone = time_zone
class RecurrencePattern(msrest.serialization.Model):
"""RecurrencePattern.
:ivar hours:
:vartype hours: list[int]
:ivar minutes:
:vartype minutes: list[int]
:ivar weekdays:
:vartype weekdays: list[str or ~flow.models.Weekday]
"""
_attribute_map = {
'hours': {'key': 'hours', 'type': '[int]'},
'minutes': {'key': 'minutes', 'type': '[int]'},
'weekdays': {'key': 'weekdays', 'type': '[str]'},
}
def __init__(
self,
*,
hours: Optional[List[int]] = None,
minutes: Optional[List[int]] = None,
weekdays: Optional[List[Union[str, "Weekday"]]] = None,
**kwargs
):
"""
:keyword hours:
:paramtype hours: list[int]
:keyword minutes:
:paramtype minutes: list[int]
:keyword weekdays:
:paramtype weekdays: list[str or ~flow.models.Weekday]
"""
super(RecurrencePattern, self).__init__(**kwargs)
self.hours = hours
self.minutes = minutes
self.weekdays = weekdays
class RecurrenceSchedule(msrest.serialization.Model):
"""RecurrenceSchedule.
:ivar hours:
:vartype hours: list[int]
:ivar minutes:
:vartype minutes: list[int]
:ivar week_days:
:vartype week_days: list[str or ~flow.models.WeekDays]
:ivar month_days:
:vartype month_days: list[int]
"""
_attribute_map = {
'hours': {'key': 'hours', 'type': '[int]'},
'minutes': {'key': 'minutes', 'type': '[int]'},
'week_days': {'key': 'weekDays', 'type': '[str]'},
'month_days': {'key': 'monthDays', 'type': '[int]'},
}
def __init__(
self,
*,
hours: Optional[List[int]] = None,
minutes: Optional[List[int]] = None,
week_days: Optional[List[Union[str, "WeekDays"]]] = None,
month_days: Optional[List[int]] = None,
**kwargs
):
"""
:keyword hours:
:paramtype hours: list[int]
:keyword minutes:
:paramtype minutes: list[int]
:keyword week_days:
:paramtype week_days: list[str or ~flow.models.WeekDays]
:keyword month_days:
:paramtype month_days: list[int]
"""
super(RecurrenceSchedule, self).__init__(**kwargs)
self.hours = hours
self.minutes = minutes
self.week_days = week_days
self.month_days = month_days
class RegenerateServiceKeysRequest(msrest.serialization.Model):
"""RegenerateServiceKeysRequest.
:ivar key_type: Possible values include: "Primary", "Secondary".
:vartype key_type: str or ~flow.models.KeyType
:ivar key_value:
:vartype key_value: str
"""
_attribute_map = {
'key_type': {'key': 'keyType', 'type': 'str'},
'key_value': {'key': 'keyValue', 'type': 'str'},
}
def __init__(
self,
*,
key_type: Optional[Union[str, "KeyType"]] = None,
key_value: Optional[str] = None,
**kwargs
):
"""
:keyword key_type: Possible values include: "Primary", "Secondary".
:paramtype key_type: str or ~flow.models.KeyType
:keyword key_value:
:paramtype key_value: str
"""
super(RegenerateServiceKeysRequest, self).__init__(**kwargs)
self.key_type = key_type
self.key_value = key_value
class RegisterComponentMetaInfo(msrest.serialization.Model):
"""RegisterComponentMetaInfo.
:ivar aml_module_name:
:vartype aml_module_name: str
:ivar name_only_display_info:
:vartype name_only_display_info: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar module_version_id:
:vartype module_version_id: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:vartype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:ivar module_entity_from_yaml:
:vartype module_entity_from_yaml: ~flow.models.ModuleEntity
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar data_types_from_yaml:
:vartype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:ivar data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:vartype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.RegisterComponentMetaInfoIdentifierHashes
:ivar content_hash:
:vartype content_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.RegisterComponentMetaInfoExtraHashes
:ivar registration:
:vartype registration: bool
:ivar validate_only:
:vartype validate_only: bool
:ivar skip_workspace_related_check:
:vartype skip_workspace_related_check: bool
:ivar intellectual_property_protected_workspace_component_registration_allowed_publisher:
:vartype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:ivar system_managed_registration:
:vartype system_managed_registration: bool
:ivar allow_dup_name_between_input_and_ouput_port:
:vartype allow_dup_name_between_input_and_ouput_port: bool
:ivar module_source:
:vartype module_source: str
:ivar module_scope:
:vartype module_scope: str
:ivar module_additional_includes_count:
:vartype module_additional_includes_count: int
:ivar module_os_type:
:vartype module_os_type: str
:ivar module_codegen_by:
:vartype module_codegen_by: str
:ivar module_client_source:
:vartype module_client_source: str
:ivar module_is_builtin:
:vartype module_is_builtin: bool
:ivar module_register_event_extension_fields: Dictionary of :code:`<string>`.
:vartype module_register_event_extension_fields: dict[str, str]
"""
_attribute_map = {
'aml_module_name': {'key': 'amlModuleName', 'type': 'str'},
'name_only_display_info': {'key': 'nameOnlyDisplayInfo', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'component_registration_type': {'key': 'componentRegistrationType', 'type': 'str'},
'module_entity_from_yaml': {'key': 'moduleEntityFromYaml', 'type': 'ModuleEntity'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'data_types_from_yaml': {'key': 'dataTypesFromYaml', 'type': '[DataTypeCreationInfo]'},
'data_type_mechanism': {'key': 'dataTypeMechanism', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'RegisterComponentMetaInfoIdentifierHashes'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'extra_hashes': {'key': 'extraHashes', 'type': 'RegisterComponentMetaInfoExtraHashes'},
'registration': {'key': 'registration', 'type': 'bool'},
'validate_only': {'key': 'validateOnly', 'type': 'bool'},
'skip_workspace_related_check': {'key': 'skipWorkspaceRelatedCheck', 'type': 'bool'},
'intellectual_property_protected_workspace_component_registration_allowed_publisher': {'key': 'intellectualPropertyProtectedWorkspaceComponentRegistrationAllowedPublisher', 'type': '[str]'},
'system_managed_registration': {'key': 'systemManagedRegistration', 'type': 'bool'},
'allow_dup_name_between_input_and_ouput_port': {'key': 'allowDupNameBetweenInputAndOuputPort', 'type': 'bool'},
'module_source': {'key': 'moduleSource', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_additional_includes_count': {'key': 'moduleAdditionalIncludesCount', 'type': 'int'},
'module_os_type': {'key': 'moduleOSType', 'type': 'str'},
'module_codegen_by': {'key': 'moduleCodegenBy', 'type': 'str'},
'module_client_source': {'key': 'moduleClientSource', 'type': 'str'},
'module_is_builtin': {'key': 'moduleIsBuiltin', 'type': 'bool'},
'module_register_event_extension_fields': {'key': 'moduleRegisterEventExtensionFields', 'type': '{str}'},
}
def __init__(
self,
*,
aml_module_name: Optional[str] = None,
name_only_display_info: Optional[str] = None,
name: Optional[str] = None,
version: Optional[str] = None,
module_version_id: Optional[str] = None,
snapshot_id: Optional[str] = None,
component_registration_type: Optional[Union[str, "ComponentRegistrationTypeEnum"]] = None,
module_entity_from_yaml: Optional["ModuleEntity"] = None,
set_as_default_version: Optional[bool] = None,
data_types_from_yaml: Optional[List["DataTypeCreationInfo"]] = None,
data_type_mechanism: Optional[Union[str, "DataTypeMechanism"]] = None,
identifier_hash: Optional[str] = None,
identifier_hashes: Optional["RegisterComponentMetaInfoIdentifierHashes"] = None,
content_hash: Optional[str] = None,
extra_hash: Optional[str] = None,
extra_hashes: Optional["RegisterComponentMetaInfoExtraHashes"] = None,
registration: Optional[bool] = None,
validate_only: Optional[bool] = None,
skip_workspace_related_check: Optional[bool] = None,
intellectual_property_protected_workspace_component_registration_allowed_publisher: Optional[List[str]] = None,
system_managed_registration: Optional[bool] = None,
allow_dup_name_between_input_and_ouput_port: Optional[bool] = None,
module_source: Optional[str] = None,
module_scope: Optional[str] = None,
module_additional_includes_count: Optional[int] = None,
module_os_type: Optional[str] = None,
module_codegen_by: Optional[str] = None,
module_client_source: Optional[str] = None,
module_is_builtin: Optional[bool] = None,
module_register_event_extension_fields: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword aml_module_name:
:paramtype aml_module_name: str
:keyword name_only_display_info:
:paramtype name_only_display_info: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword module_version_id:
:paramtype module_version_id: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:paramtype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:keyword module_entity_from_yaml:
:paramtype module_entity_from_yaml: ~flow.models.ModuleEntity
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword data_types_from_yaml:
:paramtype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:keyword data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:paramtype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.RegisterComponentMetaInfoIdentifierHashes
:keyword content_hash:
:paramtype content_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.RegisterComponentMetaInfoExtraHashes
:keyword registration:
:paramtype registration: bool
:keyword validate_only:
:paramtype validate_only: bool
:keyword skip_workspace_related_check:
:paramtype skip_workspace_related_check: bool
:keyword intellectual_property_protected_workspace_component_registration_allowed_publisher:
:paramtype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:keyword system_managed_registration:
:paramtype system_managed_registration: bool
:keyword allow_dup_name_between_input_and_ouput_port:
:paramtype allow_dup_name_between_input_and_ouput_port: bool
:keyword module_source:
:paramtype module_source: str
:keyword module_scope:
:paramtype module_scope: str
:keyword module_additional_includes_count:
:paramtype module_additional_includes_count: int
:keyword module_os_type:
:paramtype module_os_type: str
:keyword module_codegen_by:
:paramtype module_codegen_by: str
:keyword module_client_source:
:paramtype module_client_source: str
:keyword module_is_builtin:
:paramtype module_is_builtin: bool
:keyword module_register_event_extension_fields: Dictionary of :code:`<string>`.
:paramtype module_register_event_extension_fields: dict[str, str]
"""
super(RegisterComponentMetaInfo, self).__init__(**kwargs)
self.aml_module_name = aml_module_name
self.name_only_display_info = name_only_display_info
self.name = name
self.version = version
self.module_version_id = module_version_id
self.snapshot_id = snapshot_id
self.component_registration_type = component_registration_type
self.module_entity_from_yaml = module_entity_from_yaml
self.set_as_default_version = set_as_default_version
self.data_types_from_yaml = data_types_from_yaml
self.data_type_mechanism = data_type_mechanism
self.identifier_hash = identifier_hash
self.identifier_hashes = identifier_hashes
self.content_hash = content_hash
self.extra_hash = extra_hash
self.extra_hashes = extra_hashes
self.registration = registration
self.validate_only = validate_only
self.skip_workspace_related_check = skip_workspace_related_check
self.intellectual_property_protected_workspace_component_registration_allowed_publisher = intellectual_property_protected_workspace_component_registration_allowed_publisher
self.system_managed_registration = system_managed_registration
self.allow_dup_name_between_input_and_ouput_port = allow_dup_name_between_input_and_ouput_port
self.module_source = module_source
self.module_scope = module_scope
self.module_additional_includes_count = module_additional_includes_count
self.module_os_type = module_os_type
self.module_codegen_by = module_codegen_by
self.module_client_source = module_client_source
self.module_is_builtin = module_is_builtin
self.module_register_event_extension_fields = module_register_event_extension_fields
class RegisterComponentMetaInfoExtraHashes(msrest.serialization.Model):
"""RegisterComponentMetaInfoExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterComponentMetaInfoExtraHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class RegisterComponentMetaInfoIdentifierHashes(msrest.serialization.Model):
"""RegisterComponentMetaInfoIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterComponentMetaInfoIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class RegisteredDataSetReference(msrest.serialization.Model):
"""RegisteredDataSetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(RegisteredDataSetReference, self).__init__(**kwargs)
self.id = id
self.name = name
self.version = version
class RegisterRegistryComponentMetaInfo(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfo.
:ivar registry_name:
:vartype registry_name: str
:ivar intellectual_property_publisher_information:
:vartype intellectual_property_publisher_information:
~flow.models.IntellectualPropertyPublisherInformation
:ivar blob_reference_data: This is a dictionary.
:vartype blob_reference_data: dict[str, ~flow.models.RegistryBlobReferenceData]
:ivar aml_module_name:
:vartype aml_module_name: str
:ivar name_only_display_info:
:vartype name_only_display_info: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar module_version_id:
:vartype module_version_id: str
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:vartype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:ivar module_entity_from_yaml:
:vartype module_entity_from_yaml: ~flow.models.ModuleEntity
:ivar set_as_default_version:
:vartype set_as_default_version: bool
:ivar data_types_from_yaml:
:vartype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:ivar data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:vartype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.RegisterRegistryComponentMetaInfoIdentifierHashes
:ivar content_hash:
:vartype content_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.RegisterRegistryComponentMetaInfoExtraHashes
:ivar registration:
:vartype registration: bool
:ivar validate_only:
:vartype validate_only: bool
:ivar skip_workspace_related_check:
:vartype skip_workspace_related_check: bool
:ivar intellectual_property_protected_workspace_component_registration_allowed_publisher:
:vartype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:ivar system_managed_registration:
:vartype system_managed_registration: bool
:ivar allow_dup_name_between_input_and_ouput_port:
:vartype allow_dup_name_between_input_and_ouput_port: bool
:ivar module_source:
:vartype module_source: str
:ivar module_scope:
:vartype module_scope: str
:ivar module_additional_includes_count:
:vartype module_additional_includes_count: int
:ivar module_os_type:
:vartype module_os_type: str
:ivar module_codegen_by:
:vartype module_codegen_by: str
:ivar module_client_source:
:vartype module_client_source: str
:ivar module_is_builtin:
:vartype module_is_builtin: bool
:ivar module_register_event_extension_fields: Dictionary of :code:`<string>`.
:vartype module_register_event_extension_fields: dict[str, str]
"""
_attribute_map = {
'registry_name': {'key': 'registryName', 'type': 'str'},
'intellectual_property_publisher_information': {'key': 'intellectualPropertyPublisherInformation', 'type': 'IntellectualPropertyPublisherInformation'},
'blob_reference_data': {'key': 'blobReferenceData', 'type': '{RegistryBlobReferenceData}'},
'aml_module_name': {'key': 'amlModuleName', 'type': 'str'},
'name_only_display_info': {'key': 'nameOnlyDisplayInfo', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'module_version_id': {'key': 'moduleVersionId', 'type': 'str'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'component_registration_type': {'key': 'componentRegistrationType', 'type': 'str'},
'module_entity_from_yaml': {'key': 'moduleEntityFromYaml', 'type': 'ModuleEntity'},
'set_as_default_version': {'key': 'setAsDefaultVersion', 'type': 'bool'},
'data_types_from_yaml': {'key': 'dataTypesFromYaml', 'type': '[DataTypeCreationInfo]'},
'data_type_mechanism': {'key': 'dataTypeMechanism', 'type': 'str'},
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'RegisterRegistryComponentMetaInfoIdentifierHashes'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'extra_hashes': {'key': 'extraHashes', 'type': 'RegisterRegistryComponentMetaInfoExtraHashes'},
'registration': {'key': 'registration', 'type': 'bool'},
'validate_only': {'key': 'validateOnly', 'type': 'bool'},
'skip_workspace_related_check': {'key': 'skipWorkspaceRelatedCheck', 'type': 'bool'},
'intellectual_property_protected_workspace_component_registration_allowed_publisher': {'key': 'intellectualPropertyProtectedWorkspaceComponentRegistrationAllowedPublisher', 'type': '[str]'},
'system_managed_registration': {'key': 'systemManagedRegistration', 'type': 'bool'},
'allow_dup_name_between_input_and_ouput_port': {'key': 'allowDupNameBetweenInputAndOuputPort', 'type': 'bool'},
'module_source': {'key': 'moduleSource', 'type': 'str'},
'module_scope': {'key': 'moduleScope', 'type': 'str'},
'module_additional_includes_count': {'key': 'moduleAdditionalIncludesCount', 'type': 'int'},
'module_os_type': {'key': 'moduleOSType', 'type': 'str'},
'module_codegen_by': {'key': 'moduleCodegenBy', 'type': 'str'},
'module_client_source': {'key': 'moduleClientSource', 'type': 'str'},
'module_is_builtin': {'key': 'moduleIsBuiltin', 'type': 'bool'},
'module_register_event_extension_fields': {'key': 'moduleRegisterEventExtensionFields', 'type': '{str}'},
}
def __init__(
self,
*,
registry_name: Optional[str] = None,
intellectual_property_publisher_information: Optional["IntellectualPropertyPublisherInformation"] = None,
blob_reference_data: Optional[Dict[str, "RegistryBlobReferenceData"]] = None,
aml_module_name: Optional[str] = None,
name_only_display_info: Optional[str] = None,
name: Optional[str] = None,
version: Optional[str] = None,
module_version_id: Optional[str] = None,
snapshot_id: Optional[str] = None,
component_registration_type: Optional[Union[str, "ComponentRegistrationTypeEnum"]] = None,
module_entity_from_yaml: Optional["ModuleEntity"] = None,
set_as_default_version: Optional[bool] = None,
data_types_from_yaml: Optional[List["DataTypeCreationInfo"]] = None,
data_type_mechanism: Optional[Union[str, "DataTypeMechanism"]] = None,
identifier_hash: Optional[str] = None,
identifier_hashes: Optional["RegisterRegistryComponentMetaInfoIdentifierHashes"] = None,
content_hash: Optional[str] = None,
extra_hash: Optional[str] = None,
extra_hashes: Optional["RegisterRegistryComponentMetaInfoExtraHashes"] = None,
registration: Optional[bool] = None,
validate_only: Optional[bool] = None,
skip_workspace_related_check: Optional[bool] = None,
intellectual_property_protected_workspace_component_registration_allowed_publisher: Optional[List[str]] = None,
system_managed_registration: Optional[bool] = None,
allow_dup_name_between_input_and_ouput_port: Optional[bool] = None,
module_source: Optional[str] = None,
module_scope: Optional[str] = None,
module_additional_includes_count: Optional[int] = None,
module_os_type: Optional[str] = None,
module_codegen_by: Optional[str] = None,
module_client_source: Optional[str] = None,
module_is_builtin: Optional[bool] = None,
module_register_event_extension_fields: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword registry_name:
:paramtype registry_name: str
:keyword intellectual_property_publisher_information:
:paramtype intellectual_property_publisher_information:
~flow.models.IntellectualPropertyPublisherInformation
:keyword blob_reference_data: This is a dictionary.
:paramtype blob_reference_data: dict[str, ~flow.models.RegistryBlobReferenceData]
:keyword aml_module_name:
:paramtype aml_module_name: str
:keyword name_only_display_info:
:paramtype name_only_display_info: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword module_version_id:
:paramtype module_version_id: str
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword component_registration_type: Possible values include: "Normal", "AnonymousAmlModule",
"AnonymousAmlModuleVersion", "ModuleEntityOnly".
:paramtype component_registration_type: str or ~flow.models.ComponentRegistrationTypeEnum
:keyword module_entity_from_yaml:
:paramtype module_entity_from_yaml: ~flow.models.ModuleEntity
:keyword set_as_default_version:
:paramtype set_as_default_version: bool
:keyword data_types_from_yaml:
:paramtype data_types_from_yaml: list[~flow.models.DataTypeCreationInfo]
:keyword data_type_mechanism: Possible values include: "ErrorWhenNotExisting",
"RegisterWhenNotExisting", "RegisterBuildinDataTypeOnly".
:paramtype data_type_mechanism: str or ~flow.models.DataTypeMechanism
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.RegisterRegistryComponentMetaInfoIdentifierHashes
:keyword content_hash:
:paramtype content_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.RegisterRegistryComponentMetaInfoExtraHashes
:keyword registration:
:paramtype registration: bool
:keyword validate_only:
:paramtype validate_only: bool
:keyword skip_workspace_related_check:
:paramtype skip_workspace_related_check: bool
:keyword intellectual_property_protected_workspace_component_registration_allowed_publisher:
:paramtype intellectual_property_protected_workspace_component_registration_allowed_publisher:
list[str]
:keyword system_managed_registration:
:paramtype system_managed_registration: bool
:keyword allow_dup_name_between_input_and_ouput_port:
:paramtype allow_dup_name_between_input_and_ouput_port: bool
:keyword module_source:
:paramtype module_source: str
:keyword module_scope:
:paramtype module_scope: str
:keyword module_additional_includes_count:
:paramtype module_additional_includes_count: int
:keyword module_os_type:
:paramtype module_os_type: str
:keyword module_codegen_by:
:paramtype module_codegen_by: str
:keyword module_client_source:
:paramtype module_client_source: str
:keyword module_is_builtin:
:paramtype module_is_builtin: bool
:keyword module_register_event_extension_fields: Dictionary of :code:`<string>`.
:paramtype module_register_event_extension_fields: dict[str, str]
"""
super(RegisterRegistryComponentMetaInfo, self).__init__(**kwargs)
self.registry_name = registry_name
self.intellectual_property_publisher_information = intellectual_property_publisher_information
self.blob_reference_data = blob_reference_data
self.aml_module_name = aml_module_name
self.name_only_display_info = name_only_display_info
self.name = name
self.version = version
self.module_version_id = module_version_id
self.snapshot_id = snapshot_id
self.component_registration_type = component_registration_type
self.module_entity_from_yaml = module_entity_from_yaml
self.set_as_default_version = set_as_default_version
self.data_types_from_yaml = data_types_from_yaml
self.data_type_mechanism = data_type_mechanism
self.identifier_hash = identifier_hash
self.identifier_hashes = identifier_hashes
self.content_hash = content_hash
self.extra_hash = extra_hash
self.extra_hashes = extra_hashes
self.registration = registration
self.validate_only = validate_only
self.skip_workspace_related_check = skip_workspace_related_check
self.intellectual_property_protected_workspace_component_registration_allowed_publisher = intellectual_property_protected_workspace_component_registration_allowed_publisher
self.system_managed_registration = system_managed_registration
self.allow_dup_name_between_input_and_ouput_port = allow_dup_name_between_input_and_ouput_port
self.module_source = module_source
self.module_scope = module_scope
self.module_additional_includes_count = module_additional_includes_count
self.module_os_type = module_os_type
self.module_codegen_by = module_codegen_by
self.module_client_source = module_client_source
self.module_is_builtin = module_is_builtin
self.module_register_event_extension_fields = module_register_event_extension_fields
class RegisterRegistryComponentMetaInfoExtraHashes(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfoExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterRegistryComponentMetaInfoExtraHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class RegisterRegistryComponentMetaInfoIdentifierHashes(msrest.serialization.Model):
"""RegisterRegistryComponentMetaInfoIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(RegisterRegistryComponentMetaInfoIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class RegistrationOptions(msrest.serialization.Model):
"""RegistrationOptions.
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar dataset_registration_options:
:vartype dataset_registration_options: ~flow.models.DatasetRegistrationOptions
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'dataset_registration_options': {'key': 'datasetRegistrationOptions', 'type': 'DatasetRegistrationOptions'},
}
def __init__(
self,
*,
name: Optional[str] = None,
version: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
dataset_registration_options: Optional["DatasetRegistrationOptions"] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword dataset_registration_options:
:paramtype dataset_registration_options: ~flow.models.DatasetRegistrationOptions
"""
super(RegistrationOptions, self).__init__(**kwargs)
self.name = name
self.version = version
self.description = description
self.tags = tags
self.properties = properties
self.dataset_registration_options = dataset_registration_options
class RegistryBlobReferenceData(msrest.serialization.Model):
"""RegistryBlobReferenceData.
:ivar data_reference_id:
:vartype data_reference_id: str
:ivar data:
:vartype data: str
"""
_attribute_map = {
'data_reference_id': {'key': 'dataReferenceId', 'type': 'str'},
'data': {'key': 'data', 'type': 'str'},
}
def __init__(
self,
*,
data_reference_id: Optional[str] = None,
data: Optional[str] = None,
**kwargs
):
"""
:keyword data_reference_id:
:paramtype data_reference_id: str
:keyword data:
:paramtype data: str
"""
super(RegistryBlobReferenceData, self).__init__(**kwargs)
self.data_reference_id = data_reference_id
self.data = data
class RegistryIdentity(msrest.serialization.Model):
"""RegistryIdentity.
:ivar resource_id:
:vartype resource_id: str
:ivar client_id:
:vartype client_id: str
"""
_attribute_map = {
'resource_id': {'key': 'resourceId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
}
def __init__(
self,
*,
resource_id: Optional[str] = None,
client_id: Optional[str] = None,
**kwargs
):
"""
:keyword resource_id:
:paramtype resource_id: str
:keyword client_id:
:paramtype client_id: str
"""
super(RegistryIdentity, self).__init__(**kwargs)
self.resource_id = resource_id
self.client_id = client_id
class Relationship(msrest.serialization.Model):
"""Relationship.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar relation_type:
:vartype relation_type: str
:ivar target_entity_id:
:vartype target_entity_id: str
:ivar asset_id:
:vartype asset_id: str
:ivar entity_type:
:vartype entity_type: str
:ivar direction:
:vartype direction: str
:ivar entity_container_id:
:vartype entity_container_id: str
"""
_validation = {
'entity_type': {'readonly': True},
'entity_container_id': {'readonly': True},
}
_attribute_map = {
'relation_type': {'key': 'relationType', 'type': 'str'},
'target_entity_id': {'key': 'targetEntityId', 'type': 'str'},
'asset_id': {'key': 'assetId', 'type': 'str'},
'entity_type': {'key': 'entityType', 'type': 'str'},
'direction': {'key': 'direction', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
}
def __init__(
self,
*,
relation_type: Optional[str] = None,
target_entity_id: Optional[str] = None,
asset_id: Optional[str] = None,
direction: Optional[str] = None,
**kwargs
):
"""
:keyword relation_type:
:paramtype relation_type: str
:keyword target_entity_id:
:paramtype target_entity_id: str
:keyword asset_id:
:paramtype asset_id: str
:keyword direction:
:paramtype direction: str
"""
super(Relationship, self).__init__(**kwargs)
self.relation_type = relation_type
self.target_entity_id = target_entity_id
self.asset_id = asset_id
self.entity_type = None
self.direction = direction
self.entity_container_id = None
class RemoteDockerComputeInfo(msrest.serialization.Model):
"""RemoteDockerComputeInfo.
:ivar address:
:vartype address: str
:ivar username:
:vartype username: str
:ivar password:
:vartype password: str
:ivar private_key:
:vartype private_key: str
"""
_attribute_map = {
'address': {'key': 'address', 'type': 'str'},
'username': {'key': 'username', 'type': 'str'},
'password': {'key': 'password', 'type': 'str'},
'private_key': {'key': 'privateKey', 'type': 'str'},
}
def __init__(
self,
*,
address: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
private_key: Optional[str] = None,
**kwargs
):
"""
:keyword address:
:paramtype address: str
:keyword username:
:paramtype username: str
:keyword password:
:paramtype password: str
:keyword private_key:
:paramtype private_key: str
"""
super(RemoteDockerComputeInfo, self).__init__(**kwargs)
self.address = address
self.username = username
self.password = password
self.private_key = private_key
class ResourceConfig(msrest.serialization.Model):
"""ResourceConfig.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
*,
gpu_count: Optional[int] = None,
cpu_count: Optional[int] = None,
memory_request_in_gb: Optional[int] = None,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(ResourceConfig, self).__init__(**kwargs)
self.gpu_count = gpu_count
self.cpu_count = cpu_count
self.memory_request_in_gb = memory_request_in_gb
class ResourceConfiguration(msrest.serialization.Model):
"""ResourceConfiguration.
:ivar gpu_count:
:vartype gpu_count: int
:ivar cpu_count:
:vartype cpu_count: int
:ivar memory_request_in_gb:
:vartype memory_request_in_gb: int
"""
_attribute_map = {
'gpu_count': {'key': 'gpuCount', 'type': 'int'},
'cpu_count': {'key': 'cpuCount', 'type': 'int'},
'memory_request_in_gb': {'key': 'memoryRequestInGB', 'type': 'int'},
}
def __init__(
self,
*,
gpu_count: Optional[int] = None,
cpu_count: Optional[int] = None,
memory_request_in_gb: Optional[int] = None,
**kwargs
):
"""
:keyword gpu_count:
:paramtype gpu_count: int
:keyword cpu_count:
:paramtype cpu_count: int
:keyword memory_request_in_gb:
:paramtype memory_request_in_gb: int
"""
super(ResourceConfiguration, self).__init__(**kwargs)
self.gpu_count = gpu_count
self.cpu_count = cpu_count
self.memory_request_in_gb = memory_request_in_gb
class ResourcesSetting(msrest.serialization.Model):
"""ResourcesSetting.
:ivar instance_size:
:vartype instance_size: str
:ivar spark_version:
:vartype spark_version: str
"""
_attribute_map = {
'instance_size': {'key': 'instanceSize', 'type': 'str'},
'spark_version': {'key': 'sparkVersion', 'type': 'str'},
}
def __init__(
self,
*,
instance_size: Optional[str] = None,
spark_version: Optional[str] = None,
**kwargs
):
"""
:keyword instance_size:
:paramtype instance_size: str
:keyword spark_version:
:paramtype spark_version: str
"""
super(ResourcesSetting, self).__init__(**kwargs)
self.instance_size = instance_size
self.spark_version = spark_version
class RetrieveToolFuncResultRequest(msrest.serialization.Model):
"""RetrieveToolFuncResultRequest.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs: This is a dictionary.
:vartype func_kwargs: dict[str, any]
:ivar func_call_scenario: Possible values include: "generated_by", "reverse_generated_by",
"dynamic_list".
:vartype func_call_scenario: str or ~flow.models.ToolFuncCallScenario
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '{object}'},
'func_call_scenario': {'key': 'func_call_scenario', 'type': 'str'},
}
def __init__(
self,
*,
func_path: Optional[str] = None,
func_kwargs: Optional[Dict[str, Any]] = None,
func_call_scenario: Optional[Union[str, "ToolFuncCallScenario"]] = None,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs: This is a dictionary.
:paramtype func_kwargs: dict[str, any]
:keyword func_call_scenario: Possible values include: "generated_by", "reverse_generated_by",
"dynamic_list".
:paramtype func_call_scenario: str or ~flow.models.ToolFuncCallScenario
"""
super(RetrieveToolFuncResultRequest, self).__init__(**kwargs)
self.func_path = func_path
self.func_kwargs = func_kwargs
self.func_call_scenario = func_call_scenario
class RetryConfiguration(msrest.serialization.Model):
"""RetryConfiguration.
:ivar max_retry_count:
:vartype max_retry_count: int
"""
_attribute_map = {
'max_retry_count': {'key': 'maxRetryCount', 'type': 'int'},
}
def __init__(
self,
*,
max_retry_count: Optional[int] = None,
**kwargs
):
"""
:keyword max_retry_count:
:paramtype max_retry_count: int
"""
super(RetryConfiguration, self).__init__(**kwargs)
self.max_retry_count = max_retry_count
class RGitHubPackage(msrest.serialization.Model):
"""RGitHubPackage.
:ivar repository:
:vartype repository: str
:ivar auth_token:
:vartype auth_token: str
"""
_attribute_map = {
'repository': {'key': 'repository', 'type': 'str'},
'auth_token': {'key': 'authToken', 'type': 'str'},
}
def __init__(
self,
*,
repository: Optional[str] = None,
auth_token: Optional[str] = None,
**kwargs
):
"""
:keyword repository:
:paramtype repository: str
:keyword auth_token:
:paramtype auth_token: str
"""
super(RGitHubPackage, self).__init__(**kwargs)
self.repository = repository
self.auth_token = auth_token
class RootError(msrest.serialization.Model):
"""The root error.
:ivar code: The service-defined error code. Supported error codes: ServiceError, UserError,
ValidationError, AzureStorageError, TransientError, RequestThrottled.
:vartype code: str
:ivar severity: The Severity of error.
:vartype severity: int
:ivar message: A human-readable representation of the error.
:vartype message: str
:ivar message_format: An unformatted version of the message with no variable substitution.
:vartype message_format: str
:ivar message_parameters: Value substitutions corresponding to the contents of MessageFormat.
:vartype message_parameters: dict[str, str]
:ivar reference_code: This code can optionally be set by the system generating the error.
It should be used to classify the problem and identify the module and code area where the
failure occured.
:vartype reference_code: str
:ivar details_uri: A URI which points to more details about the context of the error.
:vartype details_uri: str
:ivar target: The target of the error (e.g., the name of the property in error).
:vartype target: str
:ivar details: The related errors that occurred during the request.
:vartype details: list[~flow.models.RootError]
:ivar inner_error: A nested structure of errors.
:vartype inner_error: ~flow.models.InnerErrorResponse
:ivar additional_info: The error additional info.
:vartype additional_info: list[~flow.models.ErrorAdditionalInfo]
"""
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'severity': {'key': 'severity', 'type': 'int'},
'message': {'key': 'message', 'type': 'str'},
'message_format': {'key': 'messageFormat', 'type': 'str'},
'message_parameters': {'key': 'messageParameters', 'type': '{str}'},
'reference_code': {'key': 'referenceCode', 'type': 'str'},
'details_uri': {'key': 'detailsUri', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[RootError]'},
'inner_error': {'key': 'innerError', 'type': 'InnerErrorResponse'},
'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'},
}
def __init__(
self,
*,
code: Optional[str] = None,
severity: Optional[int] = None,
message: Optional[str] = None,
message_format: Optional[str] = None,
message_parameters: Optional[Dict[str, str]] = None,
reference_code: Optional[str] = None,
details_uri: Optional[str] = None,
target: Optional[str] = None,
details: Optional[List["RootError"]] = None,
inner_error: Optional["InnerErrorResponse"] = None,
additional_info: Optional[List["ErrorAdditionalInfo"]] = None,
**kwargs
):
"""
:keyword code: The service-defined error code. Supported error codes: ServiceError, UserError,
ValidationError, AzureStorageError, TransientError, RequestThrottled.
:paramtype code: str
:keyword severity: The Severity of error.
:paramtype severity: int
:keyword message: A human-readable representation of the error.
:paramtype message: str
:keyword message_format: An unformatted version of the message with no variable substitution.
:paramtype message_format: str
:keyword message_parameters: Value substitutions corresponding to the contents of
MessageFormat.
:paramtype message_parameters: dict[str, str]
:keyword reference_code: This code can optionally be set by the system generating the error.
It should be used to classify the problem and identify the module and code area where the
failure occured.
:paramtype reference_code: str
:keyword details_uri: A URI which points to more details about the context of the error.
:paramtype details_uri: str
:keyword target: The target of the error (e.g., the name of the property in error).
:paramtype target: str
:keyword details: The related errors that occurred during the request.
:paramtype details: list[~flow.models.RootError]
:keyword inner_error: A nested structure of errors.
:paramtype inner_error: ~flow.models.InnerErrorResponse
:keyword additional_info: The error additional info.
:paramtype additional_info: list[~flow.models.ErrorAdditionalInfo]
"""
super(RootError, self).__init__(**kwargs)
self.code = code
self.severity = severity
self.message = message
self.message_format = message_format
self.message_parameters = message_parameters
self.reference_code = reference_code
self.details_uri = details_uri
self.target = target
self.details = details
self.inner_error = inner_error
self.additional_info = additional_info
class RSection(msrest.serialization.Model):
"""RSection.
:ivar r_version:
:vartype r_version: str
:ivar user_managed:
:vartype user_managed: bool
:ivar rscript_path:
:vartype rscript_path: str
:ivar snapshot_date:
:vartype snapshot_date: str
:ivar cran_packages:
:vartype cran_packages: list[~flow.models.RCranPackage]
:ivar git_hub_packages:
:vartype git_hub_packages: list[~flow.models.RGitHubPackage]
:ivar custom_url_packages:
:vartype custom_url_packages: list[str]
:ivar bio_conductor_packages:
:vartype bio_conductor_packages: list[str]
"""
_attribute_map = {
'r_version': {'key': 'rVersion', 'type': 'str'},
'user_managed': {'key': 'userManaged', 'type': 'bool'},
'rscript_path': {'key': 'rscriptPath', 'type': 'str'},
'snapshot_date': {'key': 'snapshotDate', 'type': 'str'},
'cran_packages': {'key': 'cranPackages', 'type': '[RCranPackage]'},
'git_hub_packages': {'key': 'gitHubPackages', 'type': '[RGitHubPackage]'},
'custom_url_packages': {'key': 'customUrlPackages', 'type': '[str]'},
'bio_conductor_packages': {'key': 'bioConductorPackages', 'type': '[str]'},
}
def __init__(
self,
*,
r_version: Optional[str] = None,
user_managed: Optional[bool] = None,
rscript_path: Optional[str] = None,
snapshot_date: Optional[str] = None,
cran_packages: Optional[List["RCranPackage"]] = None,
git_hub_packages: Optional[List["RGitHubPackage"]] = None,
custom_url_packages: Optional[List[str]] = None,
bio_conductor_packages: Optional[List[str]] = None,
**kwargs
):
"""
:keyword r_version:
:paramtype r_version: str
:keyword user_managed:
:paramtype user_managed: bool
:keyword rscript_path:
:paramtype rscript_path: str
:keyword snapshot_date:
:paramtype snapshot_date: str
:keyword cran_packages:
:paramtype cran_packages: list[~flow.models.RCranPackage]
:keyword git_hub_packages:
:paramtype git_hub_packages: list[~flow.models.RGitHubPackage]
:keyword custom_url_packages:
:paramtype custom_url_packages: list[str]
:keyword bio_conductor_packages:
:paramtype bio_conductor_packages: list[str]
"""
super(RSection, self).__init__(**kwargs)
self.r_version = r_version
self.user_managed = user_managed
self.rscript_path = rscript_path
self.snapshot_date = snapshot_date
self.cran_packages = cran_packages
self.git_hub_packages = git_hub_packages
self.custom_url_packages = custom_url_packages
self.bio_conductor_packages = bio_conductor_packages
class RunAnnotations(msrest.serialization.Model):
"""RunAnnotations.
:ivar display_name:
:vartype display_name: str
:ivar status:
:vartype status: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar estimated_cost:
:vartype estimated_cost: float
:ivar primary_metric_summary:
:vartype primary_metric_summary: ~flow.models.RunIndexMetricSummary
:ivar metrics: Dictionary of :code:`<RunIndexMetricSummarySystemObject>`.
:vartype metrics: dict[str, ~flow.models.RunIndexMetricSummarySystemObject]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar settings: Dictionary of :code:`<string>`.
:vartype settings: dict[str, str]
:ivar modified_time:
:vartype modified_time: ~datetime.datetime
:ivar retain_for_lifetime_of_workspace:
:vartype retain_for_lifetime_of_workspace: bool
:ivar error:
:vartype error: ~flow.models.IndexedErrorResponse
:ivar resource_metric_summary:
:vartype resource_metric_summary: ~flow.models.RunIndexResourceMetricSummary
:ivar job_cost:
:vartype job_cost: ~flow.models.JobCost
:ivar compute_duration:
:vartype compute_duration: str
:ivar compute_duration_milliseconds:
:vartype compute_duration_milliseconds: float
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar archived:
:vartype archived: bool
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'estimated_cost': {'key': 'estimatedCost', 'type': 'float'},
'primary_metric_summary': {'key': 'primaryMetricSummary', 'type': 'RunIndexMetricSummary'},
'metrics': {'key': 'metrics', 'type': '{RunIndexMetricSummarySystemObject}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'settings': {'key': 'settings', 'type': '{str}'},
'modified_time': {'key': 'modifiedTime', 'type': 'iso-8601'},
'retain_for_lifetime_of_workspace': {'key': 'retainForLifetimeOfWorkspace', 'type': 'bool'},
'error': {'key': 'error', 'type': 'IndexedErrorResponse'},
'resource_metric_summary': {'key': 'resourceMetricSummary', 'type': 'RunIndexResourceMetricSummary'},
'job_cost': {'key': 'jobCost', 'type': 'JobCost'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'compute_duration_milliseconds': {'key': 'computeDurationMilliseconds', 'type': 'float'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'archived': {'key': 'archived', 'type': 'bool'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
*,
display_name: Optional[str] = None,
status: Optional[str] = None,
primary_metric_name: Optional[str] = None,
estimated_cost: Optional[float] = None,
primary_metric_summary: Optional["RunIndexMetricSummary"] = None,
metrics: Optional[Dict[str, "RunIndexMetricSummarySystemObject"]] = None,
parameters: Optional[Dict[str, Any]] = None,
settings: Optional[Dict[str, str]] = None,
modified_time: Optional[datetime.datetime] = None,
retain_for_lifetime_of_workspace: Optional[bool] = None,
error: Optional["IndexedErrorResponse"] = None,
resource_metric_summary: Optional["RunIndexResourceMetricSummary"] = None,
job_cost: Optional["JobCost"] = None,
compute_duration: Optional[str] = None,
compute_duration_milliseconds: Optional[float] = None,
effective_start_time_utc: Optional[datetime.datetime] = None,
name: Optional[str] = None,
description: Optional[str] = None,
archived: Optional[bool] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword status:
:paramtype status: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword estimated_cost:
:paramtype estimated_cost: float
:keyword primary_metric_summary:
:paramtype primary_metric_summary: ~flow.models.RunIndexMetricSummary
:keyword metrics: Dictionary of :code:`<RunIndexMetricSummarySystemObject>`.
:paramtype metrics: dict[str, ~flow.models.RunIndexMetricSummarySystemObject]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword settings: Dictionary of :code:`<string>`.
:paramtype settings: dict[str, str]
:keyword modified_time:
:paramtype modified_time: ~datetime.datetime
:keyword retain_for_lifetime_of_workspace:
:paramtype retain_for_lifetime_of_workspace: bool
:keyword error:
:paramtype error: ~flow.models.IndexedErrorResponse
:keyword resource_metric_summary:
:paramtype resource_metric_summary: ~flow.models.RunIndexResourceMetricSummary
:keyword job_cost:
:paramtype job_cost: ~flow.models.JobCost
:keyword compute_duration:
:paramtype compute_duration: str
:keyword compute_duration_milliseconds:
:paramtype compute_duration_milliseconds: float
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword archived:
:paramtype archived: bool
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(RunAnnotations, self).__init__(**kwargs)
self.display_name = display_name
self.status = status
self.primary_metric_name = primary_metric_name
self.estimated_cost = estimated_cost
self.primary_metric_summary = primary_metric_summary
self.metrics = metrics
self.parameters = parameters
self.settings = settings
self.modified_time = modified_time
self.retain_for_lifetime_of_workspace = retain_for_lifetime_of_workspace
self.error = error
self.resource_metric_summary = resource_metric_summary
self.job_cost = job_cost
self.compute_duration = compute_duration
self.compute_duration_milliseconds = compute_duration_milliseconds
self.effective_start_time_utc = effective_start_time_utc
self.name = name
self.description = description
self.archived = archived
self.tags = tags
class RunCommandsCommandResult(msrest.serialization.Model):
"""RunCommandsCommandResult.
:ivar command:
:vartype command: str
:ivar arguments:
:vartype arguments: list[str]
:ivar exit_code:
:vartype exit_code: int
:ivar stdout:
:vartype stdout: str
:ivar stderr:
:vartype stderr: str
"""
_attribute_map = {
'command': {'key': 'command', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
'exit_code': {'key': 'exit_code', 'type': 'int'},
'stdout': {'key': 'stdout', 'type': 'str'},
'stderr': {'key': 'stderr', 'type': 'str'},
}
def __init__(
self,
*,
command: Optional[str] = None,
arguments: Optional[List[str]] = None,
exit_code: Optional[int] = None,
stdout: Optional[str] = None,
stderr: Optional[str] = None,
**kwargs
):
"""
:keyword command:
:paramtype command: str
:keyword arguments:
:paramtype arguments: list[str]
:keyword exit_code:
:paramtype exit_code: int
:keyword stdout:
:paramtype stdout: str
:keyword stderr:
:paramtype stderr: str
"""
super(RunCommandsCommandResult, self).__init__(**kwargs)
self.command = command
self.arguments = arguments
self.exit_code = exit_code
self.stdout = stdout
self.stderr = stderr
class RunConfiguration(msrest.serialization.Model):
"""RunConfiguration.
:ivar script:
:vartype script: str
:ivar script_type: Possible values include: "Python", "Notebook".
:vartype script_type: str or ~flow.models.ScriptType
:ivar command:
:vartype command: str
:ivar use_absolute_path:
:vartype use_absolute_path: bool
:ivar arguments:
:vartype arguments: list[str]
:ivar framework: Possible values include: "Python", "PySpark", "Cntk", "TensorFlow", "PyTorch",
"PySparkInteractive", "R".
:vartype framework: str or ~flow.models.Framework
:ivar communicator: Possible values include: "None", "ParameterServer", "Gloo", "Mpi", "Nccl",
"ParallelTask".
:vartype communicator: str or ~flow.models.Communicator
:ivar target:
:vartype target: str
:ivar auto_cluster_compute_specification:
:vartype auto_cluster_compute_specification: ~flow.models.AutoClusterComputeSpecification
:ivar data_references: Dictionary of :code:`<DataReferenceConfiguration>`.
:vartype data_references: dict[str, ~flow.models.DataReferenceConfiguration]
:ivar data: Dictionary of :code:`<Data>`.
:vartype data: dict[str, ~flow.models.Data]
:ivar input_assets: Dictionary of :code:`<InputAsset>`.
:vartype input_assets: dict[str, ~flow.models.InputAsset]
:ivar output_data: Dictionary of :code:`<OutputData>`.
:vartype output_data: dict[str, ~flow.models.OutputData]
:ivar datacaches:
:vartype datacaches: list[~flow.models.DatacacheConfiguration]
:ivar job_name:
:vartype job_name: str
:ivar max_run_duration_seconds:
:vartype max_run_duration_seconds: long
:ivar node_count:
:vartype node_count: int
:ivar max_node_count:
:vartype max_node_count: int
:ivar instance_types:
:vartype instance_types: list[str]
:ivar priority:
:vartype priority: int
:ivar credential_passthrough:
:vartype credential_passthrough: bool
:ivar identity:
:vartype identity: ~flow.models.IdentityConfiguration
:ivar environment:
:vartype environment: ~flow.models.EnvironmentDefinition
:ivar history:
:vartype history: ~flow.models.HistoryConfiguration
:ivar spark:
:vartype spark: ~flow.models.SparkConfiguration
:ivar parallel_task:
:vartype parallel_task: ~flow.models.ParallelTaskConfiguration
:ivar tensorflow:
:vartype tensorflow: ~flow.models.TensorflowConfiguration
:ivar mpi:
:vartype mpi: ~flow.models.MpiConfiguration
:ivar py_torch:
:vartype py_torch: ~flow.models.PyTorchConfiguration
:ivar ray:
:vartype ray: ~flow.models.RayConfiguration
:ivar hdi:
:vartype hdi: ~flow.models.HdiConfiguration
:ivar docker:
:vartype docker: ~flow.models.DockerConfiguration
:ivar command_return_code_config:
:vartype command_return_code_config: ~flow.models.CommandReturnCodeConfig
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:vartype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:ivar parameters:
:vartype parameters: list[~flow.models.ParameterDefinition]
:ivar autologger_settings:
:vartype autologger_settings: ~flow.models.AutologgerSettings
:ivar data_bricks:
:vartype data_bricks: ~flow.models.DatabricksConfiguration
:ivar training_diagnostic_config:
:vartype training_diagnostic_config: ~flow.models.TrainingDiagnosticConfiguration
:ivar secrets_configuration: Dictionary of :code:`<SecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.SecretConfiguration]
"""
_attribute_map = {
'script': {'key': 'script', 'type': 'str'},
'script_type': {'key': 'scriptType', 'type': 'str'},
'command': {'key': 'command', 'type': 'str'},
'use_absolute_path': {'key': 'useAbsolutePath', 'type': 'bool'},
'arguments': {'key': 'arguments', 'type': '[str]'},
'framework': {'key': 'framework', 'type': 'str'},
'communicator': {'key': 'communicator', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'auto_cluster_compute_specification': {'key': 'autoClusterComputeSpecification', 'type': 'AutoClusterComputeSpecification'},
'data_references': {'key': 'dataReferences', 'type': '{DataReferenceConfiguration}'},
'data': {'key': 'data', 'type': '{Data}'},
'input_assets': {'key': 'inputAssets', 'type': '{InputAsset}'},
'output_data': {'key': 'outputData', 'type': '{OutputData}'},
'datacaches': {'key': 'datacaches', 'type': '[DatacacheConfiguration]'},
'job_name': {'key': 'jobName', 'type': 'str'},
'max_run_duration_seconds': {'key': 'maxRunDurationSeconds', 'type': 'long'},
'node_count': {'key': 'nodeCount', 'type': 'int'},
'max_node_count': {'key': 'maxNodeCount', 'type': 'int'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'priority': {'key': 'priority', 'type': 'int'},
'credential_passthrough': {'key': 'credentialPassthrough', 'type': 'bool'},
'identity': {'key': 'identity', 'type': 'IdentityConfiguration'},
'environment': {'key': 'environment', 'type': 'EnvironmentDefinition'},
'history': {'key': 'history', 'type': 'HistoryConfiguration'},
'spark': {'key': 'spark', 'type': 'SparkConfiguration'},
'parallel_task': {'key': 'parallelTask', 'type': 'ParallelTaskConfiguration'},
'tensorflow': {'key': 'tensorflow', 'type': 'TensorflowConfiguration'},
'mpi': {'key': 'mpi', 'type': 'MpiConfiguration'},
'py_torch': {'key': 'pyTorch', 'type': 'PyTorchConfiguration'},
'ray': {'key': 'ray', 'type': 'RayConfiguration'},
'hdi': {'key': 'hdi', 'type': 'HdiConfiguration'},
'docker': {'key': 'docker', 'type': 'DockerConfiguration'},
'command_return_code_config': {'key': 'commandReturnCodeConfig', 'type': 'CommandReturnCodeConfig'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'application_endpoints': {'key': 'applicationEndpoints', 'type': '{ApplicationEndpointConfiguration}'},
'parameters': {'key': 'parameters', 'type': '[ParameterDefinition]'},
'autologger_settings': {'key': 'autologgerSettings', 'type': 'AutologgerSettings'},
'data_bricks': {'key': 'dataBricks', 'type': 'DatabricksConfiguration'},
'training_diagnostic_config': {'key': 'trainingDiagnosticConfig', 'type': 'TrainingDiagnosticConfiguration'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{SecretConfiguration}'},
}
def __init__(
self,
*,
script: Optional[str] = None,
script_type: Optional[Union[str, "ScriptType"]] = None,
command: Optional[str] = None,
use_absolute_path: Optional[bool] = None,
arguments: Optional[List[str]] = None,
framework: Optional[Union[str, "Framework"]] = None,
communicator: Optional[Union[str, "Communicator"]] = None,
target: Optional[str] = None,
auto_cluster_compute_specification: Optional["AutoClusterComputeSpecification"] = None,
data_references: Optional[Dict[str, "DataReferenceConfiguration"]] = None,
data: Optional[Dict[str, "Data"]] = None,
input_assets: Optional[Dict[str, "InputAsset"]] = None,
output_data: Optional[Dict[str, "OutputData"]] = None,
datacaches: Optional[List["DatacacheConfiguration"]] = None,
job_name: Optional[str] = None,
max_run_duration_seconds: Optional[int] = None,
node_count: Optional[int] = None,
max_node_count: Optional[int] = None,
instance_types: Optional[List[str]] = None,
priority: Optional[int] = None,
credential_passthrough: Optional[bool] = None,
identity: Optional["IdentityConfiguration"] = None,
environment: Optional["EnvironmentDefinition"] = None,
history: Optional["HistoryConfiguration"] = None,
spark: Optional["SparkConfiguration"] = None,
parallel_task: Optional["ParallelTaskConfiguration"] = None,
tensorflow: Optional["TensorflowConfiguration"] = None,
mpi: Optional["MpiConfiguration"] = None,
py_torch: Optional["PyTorchConfiguration"] = None,
ray: Optional["RayConfiguration"] = None,
hdi: Optional["HdiConfiguration"] = None,
docker: Optional["DockerConfiguration"] = None,
command_return_code_config: Optional["CommandReturnCodeConfig"] = None,
environment_variables: Optional[Dict[str, str]] = None,
application_endpoints: Optional[Dict[str, "ApplicationEndpointConfiguration"]] = None,
parameters: Optional[List["ParameterDefinition"]] = None,
autologger_settings: Optional["AutologgerSettings"] = None,
data_bricks: Optional["DatabricksConfiguration"] = None,
training_diagnostic_config: Optional["TrainingDiagnosticConfiguration"] = None,
secrets_configuration: Optional[Dict[str, "SecretConfiguration"]] = None,
**kwargs
):
"""
:keyword script:
:paramtype script: str
:keyword script_type: Possible values include: "Python", "Notebook".
:paramtype script_type: str or ~flow.models.ScriptType
:keyword command:
:paramtype command: str
:keyword use_absolute_path:
:paramtype use_absolute_path: bool
:keyword arguments:
:paramtype arguments: list[str]
:keyword framework: Possible values include: "Python", "PySpark", "Cntk", "TensorFlow",
"PyTorch", "PySparkInteractive", "R".
:paramtype framework: str or ~flow.models.Framework
:keyword communicator: Possible values include: "None", "ParameterServer", "Gloo", "Mpi",
"Nccl", "ParallelTask".
:paramtype communicator: str or ~flow.models.Communicator
:keyword target:
:paramtype target: str
:keyword auto_cluster_compute_specification:
:paramtype auto_cluster_compute_specification: ~flow.models.AutoClusterComputeSpecification
:keyword data_references: Dictionary of :code:`<DataReferenceConfiguration>`.
:paramtype data_references: dict[str, ~flow.models.DataReferenceConfiguration]
:keyword data: Dictionary of :code:`<Data>`.
:paramtype data: dict[str, ~flow.models.Data]
:keyword input_assets: Dictionary of :code:`<InputAsset>`.
:paramtype input_assets: dict[str, ~flow.models.InputAsset]
:keyword output_data: Dictionary of :code:`<OutputData>`.
:paramtype output_data: dict[str, ~flow.models.OutputData]
:keyword datacaches:
:paramtype datacaches: list[~flow.models.DatacacheConfiguration]
:keyword job_name:
:paramtype job_name: str
:keyword max_run_duration_seconds:
:paramtype max_run_duration_seconds: long
:keyword node_count:
:paramtype node_count: int
:keyword max_node_count:
:paramtype max_node_count: int
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword priority:
:paramtype priority: int
:keyword credential_passthrough:
:paramtype credential_passthrough: bool
:keyword identity:
:paramtype identity: ~flow.models.IdentityConfiguration
:keyword environment:
:paramtype environment: ~flow.models.EnvironmentDefinition
:keyword history:
:paramtype history: ~flow.models.HistoryConfiguration
:keyword spark:
:paramtype spark: ~flow.models.SparkConfiguration
:keyword parallel_task:
:paramtype parallel_task: ~flow.models.ParallelTaskConfiguration
:keyword tensorflow:
:paramtype tensorflow: ~flow.models.TensorflowConfiguration
:keyword mpi:
:paramtype mpi: ~flow.models.MpiConfiguration
:keyword py_torch:
:paramtype py_torch: ~flow.models.PyTorchConfiguration
:keyword ray:
:paramtype ray: ~flow.models.RayConfiguration
:keyword hdi:
:paramtype hdi: ~flow.models.HdiConfiguration
:keyword docker:
:paramtype docker: ~flow.models.DockerConfiguration
:keyword command_return_code_config:
:paramtype command_return_code_config: ~flow.models.CommandReturnCodeConfig
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword application_endpoints: Dictionary of :code:`<ApplicationEndpointConfiguration>`.
:paramtype application_endpoints: dict[str, ~flow.models.ApplicationEndpointConfiguration]
:keyword parameters:
:paramtype parameters: list[~flow.models.ParameterDefinition]
:keyword autologger_settings:
:paramtype autologger_settings: ~flow.models.AutologgerSettings
:keyword data_bricks:
:paramtype data_bricks: ~flow.models.DatabricksConfiguration
:keyword training_diagnostic_config:
:paramtype training_diagnostic_config: ~flow.models.TrainingDiagnosticConfiguration
:keyword secrets_configuration: Dictionary of :code:`<SecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.SecretConfiguration]
"""
super(RunConfiguration, self).__init__(**kwargs)
self.script = script
self.script_type = script_type
self.command = command
self.use_absolute_path = use_absolute_path
self.arguments = arguments
self.framework = framework
self.communicator = communicator
self.target = target
self.auto_cluster_compute_specification = auto_cluster_compute_specification
self.data_references = data_references
self.data = data
self.input_assets = input_assets
self.output_data = output_data
self.datacaches = datacaches
self.job_name = job_name
self.max_run_duration_seconds = max_run_duration_seconds
self.node_count = node_count
self.max_node_count = max_node_count
self.instance_types = instance_types
self.priority = priority
self.credential_passthrough = credential_passthrough
self.identity = identity
self.environment = environment
self.history = history
self.spark = spark
self.parallel_task = parallel_task
self.tensorflow = tensorflow
self.mpi = mpi
self.py_torch = py_torch
self.ray = ray
self.hdi = hdi
self.docker = docker
self.command_return_code_config = command_return_code_config
self.environment_variables = environment_variables
self.application_endpoints = application_endpoints
self.parameters = parameters
self.autologger_settings = autologger_settings
self.data_bricks = data_bricks
self.training_diagnostic_config = training_diagnostic_config
self.secrets_configuration = secrets_configuration
class RunDatasetReference(msrest.serialization.Model):
"""RunDatasetReference.
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword version:
:paramtype version: str
"""
super(RunDatasetReference, self).__init__(**kwargs)
self.id = id
self.name = name
self.version = version
class RunDefinition(msrest.serialization.Model):
"""RunDefinition.
:ivar configuration:
:vartype configuration: ~flow.models.RunConfiguration
:ivar snapshot_id:
:vartype snapshot_id: str
:ivar snapshots:
:vartype snapshots: list[~flow.models.Snapshot]
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar run_type:
:vartype run_type: str
:ivar display_name:
:vartype display_name: str
:ivar environment_asset_id:
:vartype environment_asset_id: str
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar description:
:vartype description: str
:ivar cancel_reason:
:vartype cancel_reason: str
:ivar properties: Dictionary of :code:`<string>`.
:vartype properties: dict[str, str]
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
"""
_attribute_map = {
'configuration': {'key': 'configuration', 'type': 'RunConfiguration'},
'snapshot_id': {'key': 'snapshotId', 'type': 'str'},
'snapshots': {'key': 'snapshots', 'type': '[Snapshot]'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'environment_asset_id': {'key': 'environmentAssetId', 'type': 'str'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'cancel_reason': {'key': 'cancelReason', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'tags': {'key': 'tags', 'type': '{str}'},
}
def __init__(
self,
*,
configuration: Optional["RunConfiguration"] = None,
snapshot_id: Optional[str] = None,
snapshots: Optional[List["Snapshot"]] = None,
parent_run_id: Optional[str] = None,
run_type: Optional[str] = None,
display_name: Optional[str] = None,
environment_asset_id: Optional[str] = None,
primary_metric_name: Optional[str] = None,
description: Optional[str] = None,
cancel_reason: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword configuration:
:paramtype configuration: ~flow.models.RunConfiguration
:keyword snapshot_id:
:paramtype snapshot_id: str
:keyword snapshots:
:paramtype snapshots: list[~flow.models.Snapshot]
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword run_type:
:paramtype run_type: str
:keyword display_name:
:paramtype display_name: str
:keyword environment_asset_id:
:paramtype environment_asset_id: str
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword description:
:paramtype description: str
:keyword cancel_reason:
:paramtype cancel_reason: str
:keyword properties: Dictionary of :code:`<string>`.
:paramtype properties: dict[str, str]
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
"""
super(RunDefinition, self).__init__(**kwargs)
self.configuration = configuration
self.snapshot_id = snapshot_id
self.snapshots = snapshots
self.parent_run_id = parent_run_id
self.run_type = run_type
self.display_name = display_name
self.environment_asset_id = environment_asset_id
self.primary_metric_name = primary_metric_name
self.description = description
self.cancel_reason = cancel_reason
self.properties = properties
self.tags = tags
class RunDetailsDto(msrest.serialization.Model):
"""RunDetailsDto.
:ivar run_id:
:vartype run_id: str
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar root_run_uuid:
:vartype root_run_uuid: str
:ivar target:
:vartype target: str
:ivar status:
:vartype status: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar created_time_utc:
:vartype created_time_utc: ~datetime.datetime
:ivar start_time_utc:
:vartype start_time_utc: ~datetime.datetime
:ivar end_time_utc:
:vartype end_time_utc: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar warnings:
:vartype warnings: list[~flow.models.RunDetailsWarningDto]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar services: This is a dictionary.
:vartype services: dict[str, ~flow.models.EndpointSetting]
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar log_files: This is a dictionary.
:vartype log_files: dict[str, str]
:ivar job_cost:
:vartype job_cost: ~flow.models.JobCost
:ivar revision:
:vartype revision: long
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2
:ivar settings: This is a dictionary.
:vartype settings: dict[str, str]
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar created_by:
:vartype created_by: ~flow.models.User
:ivar compute_duration:
:vartype compute_duration: str
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar run_number:
:vartype run_number: int
:ivar root_run_id:
:vartype root_run_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar user_id:
:vartype user_id: str
:ivar status_revision:
:vartype status_revision: long
:ivar current_compute_time:
:vartype current_compute_time: str
:ivar last_start_time_utc:
:vartype last_start_time_utc: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.User
:ivar last_modified_utc:
:vartype last_modified_utc: ~datetime.datetime
:ivar duration:
:vartype duration: str
:ivar inputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype inputs: dict[str, ~flow.models.TypedAssetReference]
:ivar outputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype outputs: dict[str, ~flow.models.TypedAssetReference]
:ivar current_attempt_id:
:vartype current_attempt_id: int
"""
_validation = {
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'root_run_uuid': {'key': 'rootRunUuid', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'created_time_utc': {'key': 'createdTimeUtc', 'type': 'iso-8601'},
'start_time_utc': {'key': 'startTimeUtc', 'type': 'iso-8601'},
'end_time_utc': {'key': 'endTimeUtc', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'warnings': {'key': 'warnings', 'type': '[RunDetailsWarningDto]'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'services': {'key': 'services', 'type': '{EndpointSetting}'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'log_files': {'key': 'logFiles', 'type': '{str}'},
'job_cost': {'key': 'jobCost', 'type': 'JobCost'},
'revision': {'key': 'revision', 'type': 'long'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2'},
'settings': {'key': 'settings', 'type': '{str}'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'created_by': {'key': 'createdBy', 'type': 'User'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'user_id': {'key': 'userId', 'type': 'str'},
'status_revision': {'key': 'statusRevision', 'type': 'long'},
'current_compute_time': {'key': 'currentComputeTime', 'type': 'str'},
'last_start_time_utc': {'key': 'lastStartTimeUtc', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'User'},
'last_modified_utc': {'key': 'lastModifiedUtc', 'type': 'iso-8601'},
'duration': {'key': 'duration', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{TypedAssetReference}'},
'outputs': {'key': 'outputs', 'type': '{TypedAssetReference}'},
'current_attempt_id': {'key': 'currentAttemptId', 'type': 'int'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
run_uuid: Optional[str] = None,
parent_run_uuid: Optional[str] = None,
root_run_uuid: Optional[str] = None,
target: Optional[str] = None,
status: Optional[str] = None,
parent_run_id: Optional[str] = None,
data_container_id: Optional[str] = None,
created_time_utc: Optional[datetime.datetime] = None,
start_time_utc: Optional[datetime.datetime] = None,
end_time_utc: Optional[datetime.datetime] = None,
error: Optional["ErrorResponse"] = None,
warnings: Optional[List["RunDetailsWarningDto"]] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
parameters: Optional[Dict[str, Any]] = None,
services: Optional[Dict[str, "EndpointSetting"]] = None,
input_datasets: Optional[List["DatasetLineage"]] = None,
output_datasets: Optional[List["OutputDatasetLineage"]] = None,
run_definition: Optional[Any] = None,
log_files: Optional[Dict[str, str]] = None,
job_cost: Optional["JobCost"] = None,
revision: Optional[int] = None,
run_type_v2: Optional["RunTypeV2"] = None,
settings: Optional[Dict[str, str]] = None,
compute_request: Optional["ComputeRequest"] = None,
compute: Optional["Compute"] = None,
created_by: Optional["User"] = None,
compute_duration: Optional[str] = None,
effective_start_time_utc: Optional[datetime.datetime] = None,
run_number: Optional[int] = None,
root_run_id: Optional[str] = None,
experiment_id: Optional[str] = None,
user_id: Optional[str] = None,
status_revision: Optional[int] = None,
current_compute_time: Optional[str] = None,
last_start_time_utc: Optional[datetime.datetime] = None,
last_modified_by: Optional["User"] = None,
last_modified_utc: Optional[datetime.datetime] = None,
duration: Optional[str] = None,
inputs: Optional[Dict[str, "TypedAssetReference"]] = None,
outputs: Optional[Dict[str, "TypedAssetReference"]] = None,
current_attempt_id: Optional[int] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword root_run_uuid:
:paramtype root_run_uuid: str
:keyword target:
:paramtype target: str
:keyword status:
:paramtype status: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword created_time_utc:
:paramtype created_time_utc: ~datetime.datetime
:keyword start_time_utc:
:paramtype start_time_utc: ~datetime.datetime
:keyword end_time_utc:
:paramtype end_time_utc: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword warnings:
:paramtype warnings: list[~flow.models.RunDetailsWarningDto]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword services: This is a dictionary.
:paramtype services: dict[str, ~flow.models.EndpointSetting]
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword log_files: This is a dictionary.
:paramtype log_files: dict[str, str]
:keyword job_cost:
:paramtype job_cost: ~flow.models.JobCost
:keyword revision:
:paramtype revision: long
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2
:keyword settings: This is a dictionary.
:paramtype settings: dict[str, str]
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword created_by:
:paramtype created_by: ~flow.models.User
:keyword compute_duration:
:paramtype compute_duration: str
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword run_number:
:paramtype run_number: int
:keyword root_run_id:
:paramtype root_run_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword user_id:
:paramtype user_id: str
:keyword status_revision:
:paramtype status_revision: long
:keyword current_compute_time:
:paramtype current_compute_time: str
:keyword last_start_time_utc:
:paramtype last_start_time_utc: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.User
:keyword last_modified_utc:
:paramtype last_modified_utc: ~datetime.datetime
:keyword duration:
:paramtype duration: str
:keyword inputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype inputs: dict[str, ~flow.models.TypedAssetReference]
:keyword outputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype outputs: dict[str, ~flow.models.TypedAssetReference]
:keyword current_attempt_id:
:paramtype current_attempt_id: int
"""
super(RunDetailsDto, self).__init__(**kwargs)
self.run_id = run_id
self.run_uuid = run_uuid
self.parent_run_uuid = parent_run_uuid
self.root_run_uuid = root_run_uuid
self.target = target
self.status = status
self.parent_run_id = parent_run_id
self.data_container_id = data_container_id
self.created_time_utc = created_time_utc
self.start_time_utc = start_time_utc
self.end_time_utc = end_time_utc
self.error = error
self.warnings = warnings
self.tags = tags
self.properties = properties
self.parameters = parameters
self.services = services
self.input_datasets = input_datasets
self.output_datasets = output_datasets
self.run_definition = run_definition
self.log_files = log_files
self.job_cost = job_cost
self.revision = revision
self.run_type_v2 = run_type_v2
self.settings = settings
self.compute_request = compute_request
self.compute = compute
self.created_by = created_by
self.compute_duration = compute_duration
self.effective_start_time_utc = effective_start_time_utc
self.run_number = run_number
self.root_run_id = root_run_id
self.experiment_id = experiment_id
self.user_id = user_id
self.status_revision = status_revision
self.current_compute_time = current_compute_time
self.last_start_time_utc = last_start_time_utc
self.last_modified_by = last_modified_by
self.last_modified_utc = last_modified_utc
self.duration = duration
self.inputs = inputs
self.outputs = outputs
self.current_attempt_id = current_attempt_id
class RunDetailsWarningDto(msrest.serialization.Model):
"""RunDetailsWarningDto.
:ivar source:
:vartype source: str
:ivar message:
:vartype message: str
"""
_attribute_map = {
'source': {'key': 'source', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
}
def __init__(
self,
*,
source: Optional[str] = None,
message: Optional[str] = None,
**kwargs
):
"""
:keyword source:
:paramtype source: str
:keyword message:
:paramtype message: str
"""
super(RunDetailsWarningDto, self).__init__(**kwargs)
self.source = source
self.message = message
class RunDto(msrest.serialization.Model):
"""RunDto.
:ivar run_number:
:vartype run_number: int
:ivar root_run_id:
:vartype root_run_id: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar created_by:
:vartype created_by: ~flow.models.User
:ivar user_id:
:vartype user_id: str
:ivar token:
:vartype token: str
:ivar token_expiry_time_utc:
:vartype token_expiry_time_utc: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar warnings:
:vartype warnings: list[~flow.models.RunDetailsWarningDto]
:ivar revision:
:vartype revision: long
:ivar status_revision:
:vartype status_revision: long
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar root_run_uuid:
:vartype root_run_uuid: str
:ivar last_start_time_utc:
:vartype last_start_time_utc: ~datetime.datetime
:ivar current_compute_time:
:vartype current_compute_time: str
:ivar compute_duration:
:vartype compute_duration: str
:ivar effective_start_time_utc:
:vartype effective_start_time_utc: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: ~flow.models.User
:ivar last_modified_utc:
:vartype last_modified_utc: ~datetime.datetime
:ivar duration:
:vartype duration: str
:ivar cancelation_reason:
:vartype cancelation_reason: str
:ivar current_attempt_id:
:vartype current_attempt_id: int
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar status:
:vartype status: str
:ivar start_time_utc:
:vartype start_time_utc: ~datetime.datetime
:ivar end_time_utc:
:vartype end_time_utc: ~datetime.datetime
:ivar schedule_id:
:vartype schedule_id: str
:ivar display_name:
:vartype display_name: str
:ivar name:
:vartype name: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar description:
:vartype description: str
:ivar hidden:
:vartype hidden: bool
:ivar run_type:
:vartype run_type: str
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar parameters: Dictionary of :code:`<any>`.
:vartype parameters: dict[str, any]
:ivar action_uris: Dictionary of :code:`<string>`.
:vartype action_uris: dict[str, str]
:ivar script_name:
:vartype script_name: str
:ivar target:
:vartype target: str
:ivar unique_child_run_compute_targets:
:vartype unique_child_run_compute_targets: list[str]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar settings: Dictionary of :code:`<string>`.
:vartype settings: dict[str, str]
:ivar services: Dictionary of :code:`<EndpointSetting>`.
:vartype services: dict[str, ~flow.models.EndpointSetting]
:ivar input_datasets:
:vartype input_datasets: list[~flow.models.DatasetLineage]
:ivar output_datasets:
:vartype output_datasets: list[~flow.models.OutputDatasetLineage]
:ivar run_definition: Anything.
:vartype run_definition: any
:ivar job_specification: Anything.
:vartype job_specification: any
:ivar primary_metric_name:
:vartype primary_metric_name: str
:ivar created_from:
:vartype created_from: ~flow.models.CreatedFromDto
:ivar cancel_uri:
:vartype cancel_uri: str
:ivar complete_uri:
:vartype complete_uri: str
:ivar diagnostics_uri:
:vartype diagnostics_uri: str
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar retain_for_lifetime_of_workspace:
:vartype retain_for_lifetime_of_workspace: bool
:ivar queueing_info:
:vartype queueing_info: ~flow.models.QueueingInfo
:ivar inputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype inputs: dict[str, ~flow.models.TypedAssetReference]
:ivar outputs: Dictionary of :code:`<TypedAssetReference>`.
:vartype outputs: dict[str, ~flow.models.TypedAssetReference]
"""
_validation = {
'unique_child_run_compute_targets': {'unique': True},
'input_datasets': {'unique': True},
'output_datasets': {'unique': True},
}
_attribute_map = {
'run_number': {'key': 'runNumber', 'type': 'int'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'User'},
'user_id': {'key': 'userId', 'type': 'str'},
'token': {'key': 'token', 'type': 'str'},
'token_expiry_time_utc': {'key': 'tokenExpiryTimeUtc', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'warnings': {'key': 'warnings', 'type': '[RunDetailsWarningDto]'},
'revision': {'key': 'revision', 'type': 'long'},
'status_revision': {'key': 'statusRevision', 'type': 'long'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'root_run_uuid': {'key': 'rootRunUuid', 'type': 'str'},
'last_start_time_utc': {'key': 'lastStartTimeUtc', 'type': 'iso-8601'},
'current_compute_time': {'key': 'currentComputeTime', 'type': 'str'},
'compute_duration': {'key': 'computeDuration', 'type': 'str'},
'effective_start_time_utc': {'key': 'effectiveStartTimeUtc', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'User'},
'last_modified_utc': {'key': 'lastModifiedUtc', 'type': 'iso-8601'},
'duration': {'key': 'duration', 'type': 'str'},
'cancelation_reason': {'key': 'cancelationReason', 'type': 'str'},
'current_attempt_id': {'key': 'currentAttemptId', 'type': 'int'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'start_time_utc': {'key': 'startTimeUtc', 'type': 'iso-8601'},
'end_time_utc': {'key': 'endTimeUtc', 'type': 'iso-8601'},
'schedule_id': {'key': 'scheduleId', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'hidden': {'key': 'hidden', 'type': 'bool'},
'run_type': {'key': 'runType', 'type': 'str'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2'},
'properties': {'key': 'properties', 'type': '{str}'},
'parameters': {'key': 'parameters', 'type': '{object}'},
'action_uris': {'key': 'actionUris', 'type': '{str}'},
'script_name': {'key': 'scriptName', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'unique_child_run_compute_targets': {'key': 'uniqueChildRunComputeTargets', 'type': '[str]'},
'tags': {'key': 'tags', 'type': '{str}'},
'settings': {'key': 'settings', 'type': '{str}'},
'services': {'key': 'services', 'type': '{EndpointSetting}'},
'input_datasets': {'key': 'inputDatasets', 'type': '[DatasetLineage]'},
'output_datasets': {'key': 'outputDatasets', 'type': '[OutputDatasetLineage]'},
'run_definition': {'key': 'runDefinition', 'type': 'object'},
'job_specification': {'key': 'jobSpecification', 'type': 'object'},
'primary_metric_name': {'key': 'primaryMetricName', 'type': 'str'},
'created_from': {'key': 'createdFrom', 'type': 'CreatedFromDto'},
'cancel_uri': {'key': 'cancelUri', 'type': 'str'},
'complete_uri': {'key': 'completeUri', 'type': 'str'},
'diagnostics_uri': {'key': 'diagnosticsUri', 'type': 'str'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'retain_for_lifetime_of_workspace': {'key': 'retainForLifetimeOfWorkspace', 'type': 'bool'},
'queueing_info': {'key': 'queueingInfo', 'type': 'QueueingInfo'},
'inputs': {'key': 'inputs', 'type': '{TypedAssetReference}'},
'outputs': {'key': 'outputs', 'type': '{TypedAssetReference}'},
}
def __init__(
self,
*,
run_number: Optional[int] = None,
root_run_id: Optional[str] = None,
created_utc: Optional[datetime.datetime] = None,
created_by: Optional["User"] = None,
user_id: Optional[str] = None,
token: Optional[str] = None,
token_expiry_time_utc: Optional[datetime.datetime] = None,
error: Optional["ErrorResponse"] = None,
warnings: Optional[List["RunDetailsWarningDto"]] = None,
revision: Optional[int] = None,
status_revision: Optional[int] = None,
run_uuid: Optional[str] = None,
parent_run_uuid: Optional[str] = None,
root_run_uuid: Optional[str] = None,
last_start_time_utc: Optional[datetime.datetime] = None,
current_compute_time: Optional[str] = None,
compute_duration: Optional[str] = None,
effective_start_time_utc: Optional[datetime.datetime] = None,
last_modified_by: Optional["User"] = None,
last_modified_utc: Optional[datetime.datetime] = None,
duration: Optional[str] = None,
cancelation_reason: Optional[str] = None,
current_attempt_id: Optional[int] = None,
run_id: Optional[str] = None,
parent_run_id: Optional[str] = None,
experiment_id: Optional[str] = None,
status: Optional[str] = None,
start_time_utc: Optional[datetime.datetime] = None,
end_time_utc: Optional[datetime.datetime] = None,
schedule_id: Optional[str] = None,
display_name: Optional[str] = None,
name: Optional[str] = None,
data_container_id: Optional[str] = None,
description: Optional[str] = None,
hidden: Optional[bool] = None,
run_type: Optional[str] = None,
run_type_v2: Optional["RunTypeV2"] = None,
properties: Optional[Dict[str, str]] = None,
parameters: Optional[Dict[str, Any]] = None,
action_uris: Optional[Dict[str, str]] = None,
script_name: Optional[str] = None,
target: Optional[str] = None,
unique_child_run_compute_targets: Optional[List[str]] = None,
tags: Optional[Dict[str, str]] = None,
settings: Optional[Dict[str, str]] = None,
services: Optional[Dict[str, "EndpointSetting"]] = None,
input_datasets: Optional[List["DatasetLineage"]] = None,
output_datasets: Optional[List["OutputDatasetLineage"]] = None,
run_definition: Optional[Any] = None,
job_specification: Optional[Any] = None,
primary_metric_name: Optional[str] = None,
created_from: Optional["CreatedFromDto"] = None,
cancel_uri: Optional[str] = None,
complete_uri: Optional[str] = None,
diagnostics_uri: Optional[str] = None,
compute_request: Optional["ComputeRequest"] = None,
compute: Optional["Compute"] = None,
retain_for_lifetime_of_workspace: Optional[bool] = None,
queueing_info: Optional["QueueingInfo"] = None,
inputs: Optional[Dict[str, "TypedAssetReference"]] = None,
outputs: Optional[Dict[str, "TypedAssetReference"]] = None,
**kwargs
):
"""
:keyword run_number:
:paramtype run_number: int
:keyword root_run_id:
:paramtype root_run_id: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword created_by:
:paramtype created_by: ~flow.models.User
:keyword user_id:
:paramtype user_id: str
:keyword token:
:paramtype token: str
:keyword token_expiry_time_utc:
:paramtype token_expiry_time_utc: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword warnings:
:paramtype warnings: list[~flow.models.RunDetailsWarningDto]
:keyword revision:
:paramtype revision: long
:keyword status_revision:
:paramtype status_revision: long
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword root_run_uuid:
:paramtype root_run_uuid: str
:keyword last_start_time_utc:
:paramtype last_start_time_utc: ~datetime.datetime
:keyword current_compute_time:
:paramtype current_compute_time: str
:keyword compute_duration:
:paramtype compute_duration: str
:keyword effective_start_time_utc:
:paramtype effective_start_time_utc: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: ~flow.models.User
:keyword last_modified_utc:
:paramtype last_modified_utc: ~datetime.datetime
:keyword duration:
:paramtype duration: str
:keyword cancelation_reason:
:paramtype cancelation_reason: str
:keyword current_attempt_id:
:paramtype current_attempt_id: int
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword status:
:paramtype status: str
:keyword start_time_utc:
:paramtype start_time_utc: ~datetime.datetime
:keyword end_time_utc:
:paramtype end_time_utc: ~datetime.datetime
:keyword schedule_id:
:paramtype schedule_id: str
:keyword display_name:
:paramtype display_name: str
:keyword name:
:paramtype name: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword description:
:paramtype description: str
:keyword hidden:
:paramtype hidden: bool
:keyword run_type:
:paramtype run_type: str
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword parameters: Dictionary of :code:`<any>`.
:paramtype parameters: dict[str, any]
:keyword action_uris: Dictionary of :code:`<string>`.
:paramtype action_uris: dict[str, str]
:keyword script_name:
:paramtype script_name: str
:keyword target:
:paramtype target: str
:keyword unique_child_run_compute_targets:
:paramtype unique_child_run_compute_targets: list[str]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword settings: Dictionary of :code:`<string>`.
:paramtype settings: dict[str, str]
:keyword services: Dictionary of :code:`<EndpointSetting>`.
:paramtype services: dict[str, ~flow.models.EndpointSetting]
:keyword input_datasets:
:paramtype input_datasets: list[~flow.models.DatasetLineage]
:keyword output_datasets:
:paramtype output_datasets: list[~flow.models.OutputDatasetLineage]
:keyword run_definition: Anything.
:paramtype run_definition: any
:keyword job_specification: Anything.
:paramtype job_specification: any
:keyword primary_metric_name:
:paramtype primary_metric_name: str
:keyword created_from:
:paramtype created_from: ~flow.models.CreatedFromDto
:keyword cancel_uri:
:paramtype cancel_uri: str
:keyword complete_uri:
:paramtype complete_uri: str
:keyword diagnostics_uri:
:paramtype diagnostics_uri: str
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword retain_for_lifetime_of_workspace:
:paramtype retain_for_lifetime_of_workspace: bool
:keyword queueing_info:
:paramtype queueing_info: ~flow.models.QueueingInfo
:keyword inputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype inputs: dict[str, ~flow.models.TypedAssetReference]
:keyword outputs: Dictionary of :code:`<TypedAssetReference>`.
:paramtype outputs: dict[str, ~flow.models.TypedAssetReference]
"""
super(RunDto, self).__init__(**kwargs)
self.run_number = run_number
self.root_run_id = root_run_id
self.created_utc = created_utc
self.created_by = created_by
self.user_id = user_id
self.token = token
self.token_expiry_time_utc = token_expiry_time_utc
self.error = error
self.warnings = warnings
self.revision = revision
self.status_revision = status_revision
self.run_uuid = run_uuid
self.parent_run_uuid = parent_run_uuid
self.root_run_uuid = root_run_uuid
self.last_start_time_utc = last_start_time_utc
self.current_compute_time = current_compute_time
self.compute_duration = compute_duration
self.effective_start_time_utc = effective_start_time_utc
self.last_modified_by = last_modified_by
self.last_modified_utc = last_modified_utc
self.duration = duration
self.cancelation_reason = cancelation_reason
self.current_attempt_id = current_attempt_id
self.run_id = run_id
self.parent_run_id = parent_run_id
self.experiment_id = experiment_id
self.status = status
self.start_time_utc = start_time_utc
self.end_time_utc = end_time_utc
self.schedule_id = schedule_id
self.display_name = display_name
self.name = name
self.data_container_id = data_container_id
self.description = description
self.hidden = hidden
self.run_type = run_type
self.run_type_v2 = run_type_v2
self.properties = properties
self.parameters = parameters
self.action_uris = action_uris
self.script_name = script_name
self.target = target
self.unique_child_run_compute_targets = unique_child_run_compute_targets
self.tags = tags
self.settings = settings
self.services = services
self.input_datasets = input_datasets
self.output_datasets = output_datasets
self.run_definition = run_definition
self.job_specification = job_specification
self.primary_metric_name = primary_metric_name
self.created_from = created_from
self.cancel_uri = cancel_uri
self.complete_uri = complete_uri
self.diagnostics_uri = diagnostics_uri
self.compute_request = compute_request
self.compute = compute
self.retain_for_lifetime_of_workspace = retain_for_lifetime_of_workspace
self.queueing_info = queueing_info
self.inputs = inputs
self.outputs = outputs
class RunIndexEntity(msrest.serialization.Model):
"""RunIndexEntity.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar schema_id:
:vartype schema_id: str
:ivar entity_id:
:vartype entity_id: str
:ivar kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:vartype kind: str or ~flow.models.EntityKind
:ivar annotations:
:vartype annotations: ~flow.models.RunAnnotations
:ivar properties:
:vartype properties: ~flow.models.RunProperties
:ivar internal: Any object.
:vartype internal: any
:ivar update_sequence:
:vartype update_sequence: long
:ivar type:
:vartype type: str
:ivar version:
:vartype version: str
:ivar entity_container_id:
:vartype entity_container_id: str
:ivar entity_object_id:
:vartype entity_object_id: str
:ivar resource_type:
:vartype resource_type: str
:ivar relationships:
:vartype relationships: list[~flow.models.Relationship]
:ivar asset_id:
:vartype asset_id: str
"""
_validation = {
'version': {'readonly': True},
'entity_container_id': {'readonly': True},
'entity_object_id': {'readonly': True},
'resource_type': {'readonly': True},
}
_attribute_map = {
'schema_id': {'key': 'schemaId', 'type': 'str'},
'entity_id': {'key': 'entityId', 'type': 'str'},
'kind': {'key': 'kind', 'type': 'str'},
'annotations': {'key': 'annotations', 'type': 'RunAnnotations'},
'properties': {'key': 'properties', 'type': 'RunProperties'},
'internal': {'key': 'internal', 'type': 'object'},
'update_sequence': {'key': 'updateSequence', 'type': 'long'},
'type': {'key': 'type', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
'entity_container_id': {'key': 'entityContainerId', 'type': 'str'},
'entity_object_id': {'key': 'entityObjectId', 'type': 'str'},
'resource_type': {'key': 'resourceType', 'type': 'str'},
'relationships': {'key': 'relationships', 'type': '[Relationship]'},
'asset_id': {'key': 'assetId', 'type': 'str'},
}
def __init__(
self,
*,
schema_id: Optional[str] = None,
entity_id: Optional[str] = None,
kind: Optional[Union[str, "EntityKind"]] = None,
annotations: Optional["RunAnnotations"] = None,
properties: Optional["RunProperties"] = None,
internal: Optional[Any] = None,
update_sequence: Optional[int] = None,
type: Optional[str] = None,
relationships: Optional[List["Relationship"]] = None,
asset_id: Optional[str] = None,
**kwargs
):
"""
:keyword schema_id:
:paramtype schema_id: str
:keyword entity_id:
:paramtype entity_id: str
:keyword kind: Possible values include: "Invalid", "LineageRoot", "Versioned", "Unversioned".
:paramtype kind: str or ~flow.models.EntityKind
:keyword annotations:
:paramtype annotations: ~flow.models.RunAnnotations
:keyword properties:
:paramtype properties: ~flow.models.RunProperties
:keyword internal: Any object.
:paramtype internal: any
:keyword update_sequence:
:paramtype update_sequence: long
:keyword type:
:paramtype type: str
:keyword relationships:
:paramtype relationships: list[~flow.models.Relationship]
:keyword asset_id:
:paramtype asset_id: str
"""
super(RunIndexEntity, self).__init__(**kwargs)
self.schema_id = schema_id
self.entity_id = entity_id
self.kind = kind
self.annotations = annotations
self.properties = properties
self.internal = internal
self.update_sequence = update_sequence
self.type = type
self.version = None
self.entity_container_id = None
self.entity_object_id = None
self.resource_type = None
self.relationships = relationships
self.asset_id = asset_id
class RunIndexMetricSummary(msrest.serialization.Model):
"""RunIndexMetricSummary.
:ivar count:
:vartype count: long
:ivar last_value: Anything.
:vartype last_value: any
:ivar minimum_value: Anything.
:vartype minimum_value: any
:ivar maximum_value: Anything.
:vartype maximum_value: any
:ivar metric_type:
:vartype metric_type: str
"""
_attribute_map = {
'count': {'key': 'count', 'type': 'long'},
'last_value': {'key': 'lastValue', 'type': 'object'},
'minimum_value': {'key': 'minimumValue', 'type': 'object'},
'maximum_value': {'key': 'maximumValue', 'type': 'object'},
'metric_type': {'key': 'metricType', 'type': 'str'},
}
def __init__(
self,
*,
count: Optional[int] = None,
last_value: Optional[Any] = None,
minimum_value: Optional[Any] = None,
maximum_value: Optional[Any] = None,
metric_type: Optional[str] = None,
**kwargs
):
"""
:keyword count:
:paramtype count: long
:keyword last_value: Anything.
:paramtype last_value: any
:keyword minimum_value: Anything.
:paramtype minimum_value: any
:keyword maximum_value: Anything.
:paramtype maximum_value: any
:keyword metric_type:
:paramtype metric_type: str
"""
super(RunIndexMetricSummary, self).__init__(**kwargs)
self.count = count
self.last_value = last_value
self.minimum_value = minimum_value
self.maximum_value = maximum_value
self.metric_type = metric_type
class RunIndexMetricSummarySystemObject(msrest.serialization.Model):
"""RunIndexMetricSummarySystemObject.
:ivar count:
:vartype count: long
:ivar last_value: Anything.
:vartype last_value: any
:ivar minimum_value: Anything.
:vartype minimum_value: any
:ivar maximum_value: Anything.
:vartype maximum_value: any
:ivar metric_type:
:vartype metric_type: str
"""
_attribute_map = {
'count': {'key': 'count', 'type': 'long'},
'last_value': {'key': 'lastValue', 'type': 'object'},
'minimum_value': {'key': 'minimumValue', 'type': 'object'},
'maximum_value': {'key': 'maximumValue', 'type': 'object'},
'metric_type': {'key': 'metricType', 'type': 'str'},
}
def __init__(
self,
*,
count: Optional[int] = None,
last_value: Optional[Any] = None,
minimum_value: Optional[Any] = None,
maximum_value: Optional[Any] = None,
metric_type: Optional[str] = None,
**kwargs
):
"""
:keyword count:
:paramtype count: long
:keyword last_value: Anything.
:paramtype last_value: any
:keyword minimum_value: Anything.
:paramtype minimum_value: any
:keyword maximum_value: Anything.
:paramtype maximum_value: any
:keyword metric_type:
:paramtype metric_type: str
"""
super(RunIndexMetricSummarySystemObject, self).__init__(**kwargs)
self.count = count
self.last_value = last_value
self.minimum_value = minimum_value
self.maximum_value = maximum_value
self.metric_type = metric_type
class RunIndexResourceMetricSummary(msrest.serialization.Model):
"""RunIndexResourceMetricSummary.
:ivar gpu_utilization_percent_last_hour:
:vartype gpu_utilization_percent_last_hour: float
:ivar gpu_memory_utilization_percent_last_hour:
:vartype gpu_memory_utilization_percent_last_hour: float
:ivar gpu_energy_joules:
:vartype gpu_energy_joules: float
:ivar resource_metric_names:
:vartype resource_metric_names: list[str]
"""
_attribute_map = {
'gpu_utilization_percent_last_hour': {'key': 'gpuUtilizationPercentLastHour', 'type': 'float'},
'gpu_memory_utilization_percent_last_hour': {'key': 'gpuMemoryUtilizationPercentLastHour', 'type': 'float'},
'gpu_energy_joules': {'key': 'gpuEnergyJoules', 'type': 'float'},
'resource_metric_names': {'key': 'resourceMetricNames', 'type': '[str]'},
}
def __init__(
self,
*,
gpu_utilization_percent_last_hour: Optional[float] = None,
gpu_memory_utilization_percent_last_hour: Optional[float] = None,
gpu_energy_joules: Optional[float] = None,
resource_metric_names: Optional[List[str]] = None,
**kwargs
):
"""
:keyword gpu_utilization_percent_last_hour:
:paramtype gpu_utilization_percent_last_hour: float
:keyword gpu_memory_utilization_percent_last_hour:
:paramtype gpu_memory_utilization_percent_last_hour: float
:keyword gpu_energy_joules:
:paramtype gpu_energy_joules: float
:keyword resource_metric_names:
:paramtype resource_metric_names: list[str]
"""
super(RunIndexResourceMetricSummary, self).__init__(**kwargs)
self.gpu_utilization_percent_last_hour = gpu_utilization_percent_last_hour
self.gpu_memory_utilization_percent_last_hour = gpu_memory_utilization_percent_last_hour
self.gpu_energy_joules = gpu_energy_joules
self.resource_metric_names = resource_metric_names
class RunMetricDto(msrest.serialization.Model):
"""RunMetricDto.
:ivar run_id:
:vartype run_id: str
:ivar metric_id:
:vartype metric_id: str
:ivar data_container_id:
:vartype data_container_id: str
:ivar metric_type:
:vartype metric_type: str
:ivar created_utc:
:vartype created_utc: ~datetime.datetime
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar label:
:vartype label: str
:ivar num_cells:
:vartype num_cells: int
:ivar data_location:
:vartype data_location: str
:ivar cells:
:vartype cells: list[dict[str, any]]
:ivar schema:
:vartype schema: ~flow.models.MetricSchemaDto
"""
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
'metric_id': {'key': 'metricId', 'type': 'str'},
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'metric_type': {'key': 'metricType', 'type': 'str'},
'created_utc': {'key': 'createdUtc', 'type': 'iso-8601'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'num_cells': {'key': 'numCells', 'type': 'int'},
'data_location': {'key': 'dataLocation', 'type': 'str'},
'cells': {'key': 'cells', 'type': '[{object}]'},
'schema': {'key': 'schema', 'type': 'MetricSchemaDto'},
}
def __init__(
self,
*,
run_id: Optional[str] = None,
metric_id: Optional[str] = None,
data_container_id: Optional[str] = None,
metric_type: Optional[str] = None,
created_utc: Optional[datetime.datetime] = None,
name: Optional[str] = None,
description: Optional[str] = None,
label: Optional[str] = None,
num_cells: Optional[int] = None,
data_location: Optional[str] = None,
cells: Optional[List[Dict[str, Any]]] = None,
schema: Optional["MetricSchemaDto"] = None,
**kwargs
):
"""
:keyword run_id:
:paramtype run_id: str
:keyword metric_id:
:paramtype metric_id: str
:keyword data_container_id:
:paramtype data_container_id: str
:keyword metric_type:
:paramtype metric_type: str
:keyword created_utc:
:paramtype created_utc: ~datetime.datetime
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword label:
:paramtype label: str
:keyword num_cells:
:paramtype num_cells: int
:keyword data_location:
:paramtype data_location: str
:keyword cells:
:paramtype cells: list[dict[str, any]]
:keyword schema:
:paramtype schema: ~flow.models.MetricSchemaDto
"""
super(RunMetricDto, self).__init__(**kwargs)
self.run_id = run_id
self.metric_id = metric_id
self.data_container_id = data_container_id
self.metric_type = metric_type
self.created_utc = created_utc
self.name = name
self.description = description
self.label = label
self.num_cells = num_cells
self.data_location = data_location
self.cells = cells
self.schema = schema
class RunMetricsTypesDto(msrest.serialization.Model):
"""RunMetricsTypesDto.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
"""
super(RunMetricsTypesDto, self).__init__(**kwargs)
self.name = name
self.type = type
class RunProperties(msrest.serialization.Model):
"""RunProperties.
:ivar data_container_id:
:vartype data_container_id: str
:ivar target_name:
:vartype target_name: str
:ivar run_name:
:vartype run_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar root_run_id:
:vartype root_run_id: str
:ivar run_type:
:vartype run_type: str
:ivar run_type_v2:
:vartype run_type_v2: ~flow.models.RunTypeV2Index
:ivar script_name:
:vartype script_name: str
:ivar experiment_id:
:vartype experiment_id: str
:ivar run_uuid:
:vartype run_uuid: str
:ivar parent_run_uuid:
:vartype parent_run_uuid: str
:ivar run_number:
:vartype run_number: int
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar compute_request:
:vartype compute_request: ~flow.models.ComputeRequest
:ivar compute:
:vartype compute: ~flow.models.Compute
:ivar user_properties: This is a dictionary.
:vartype user_properties: dict[str, str]
:ivar action_uris: This is a dictionary.
:vartype action_uris: dict[str, str]
:ivar duration:
:vartype duration: str
:ivar duration_milliseconds:
:vartype duration_milliseconds: float
:ivar creation_context:
:vartype creation_context: ~flow.models.CreationContext
"""
_attribute_map = {
'data_container_id': {'key': 'dataContainerId', 'type': 'str'},
'target_name': {'key': 'targetName', 'type': 'str'},
'run_name': {'key': 'runName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'root_run_id': {'key': 'rootRunId', 'type': 'str'},
'run_type': {'key': 'runType', 'type': 'str'},
'run_type_v2': {'key': 'runTypeV2', 'type': 'RunTypeV2Index'},
'script_name': {'key': 'scriptName', 'type': 'str'},
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'run_uuid': {'key': 'runUuid', 'type': 'str'},
'parent_run_uuid': {'key': 'parentRunUuid', 'type': 'str'},
'run_number': {'key': 'runNumber', 'type': 'int'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'compute_request': {'key': 'computeRequest', 'type': 'ComputeRequest'},
'compute': {'key': 'compute', 'type': 'Compute'},
'user_properties': {'key': 'userProperties', 'type': '{str}'},
'action_uris': {'key': 'actionUris', 'type': '{str}'},
'duration': {'key': 'duration', 'type': 'str'},
'duration_milliseconds': {'key': 'durationMilliseconds', 'type': 'float'},
'creation_context': {'key': 'creationContext', 'type': 'CreationContext'},
}
def __init__(
self,
*,
data_container_id: Optional[str] = None,
target_name: Optional[str] = None,
run_name: Optional[str] = None,
experiment_name: Optional[str] = None,
run_id: Optional[str] = None,
parent_run_id: Optional[str] = None,
root_run_id: Optional[str] = None,
run_type: Optional[str] = None,
run_type_v2: Optional["RunTypeV2Index"] = None,
script_name: Optional[str] = None,
experiment_id: Optional[str] = None,
run_uuid: Optional[str] = None,
parent_run_uuid: Optional[str] = None,
run_number: Optional[int] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
compute_request: Optional["ComputeRequest"] = None,
compute: Optional["Compute"] = None,
user_properties: Optional[Dict[str, str]] = None,
action_uris: Optional[Dict[str, str]] = None,
duration: Optional[str] = None,
duration_milliseconds: Optional[float] = None,
creation_context: Optional["CreationContext"] = None,
**kwargs
):
"""
:keyword data_container_id:
:paramtype data_container_id: str
:keyword target_name:
:paramtype target_name: str
:keyword run_name:
:paramtype run_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword root_run_id:
:paramtype root_run_id: str
:keyword run_type:
:paramtype run_type: str
:keyword run_type_v2:
:paramtype run_type_v2: ~flow.models.RunTypeV2Index
:keyword script_name:
:paramtype script_name: str
:keyword experiment_id:
:paramtype experiment_id: str
:keyword run_uuid:
:paramtype run_uuid: str
:keyword parent_run_uuid:
:paramtype parent_run_uuid: str
:keyword run_number:
:paramtype run_number: int
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword compute_request:
:paramtype compute_request: ~flow.models.ComputeRequest
:keyword compute:
:paramtype compute: ~flow.models.Compute
:keyword user_properties: This is a dictionary.
:paramtype user_properties: dict[str, str]
:keyword action_uris: This is a dictionary.
:paramtype action_uris: dict[str, str]
:keyword duration:
:paramtype duration: str
:keyword duration_milliseconds:
:paramtype duration_milliseconds: float
:keyword creation_context:
:paramtype creation_context: ~flow.models.CreationContext
"""
super(RunProperties, self).__init__(**kwargs)
self.data_container_id = data_container_id
self.target_name = target_name
self.run_name = run_name
self.experiment_name = experiment_name
self.run_id = run_id
self.parent_run_id = parent_run_id
self.root_run_id = root_run_id
self.run_type = run_type
self.run_type_v2 = run_type_v2
self.script_name = script_name
self.experiment_id = experiment_id
self.run_uuid = run_uuid
self.parent_run_uuid = parent_run_uuid
self.run_number = run_number
self.start_time = start_time
self.end_time = end_time
self.compute_request = compute_request
self.compute = compute
self.user_properties = user_properties
self.action_uris = action_uris
self.duration = duration
self.duration_milliseconds = duration_milliseconds
self.creation_context = creation_context
class RunSettingParameter(msrest.serialization.Model):
"""RunSettingParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Undefined", "Int", "Double", "Bool", "String",
"JsonString", "YamlString", "StringList".
:vartype parameter_type: str or ~flow.models.RunSettingParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar description:
:vartype description: str
:ivar run_setting_ui_hint:
:vartype run_setting_ui_hint: ~flow.models.RunSettingUIParameterHint
:ivar argument_name:
:vartype argument_name: str
:ivar section_name:
:vartype section_name: str
:ivar section_description:
:vartype section_description: str
:ivar section_argument_name:
:vartype section_argument_name: str
:ivar examples:
:vartype examples: list[str]
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar disabled_by_parameters:
:vartype disabled_by_parameters: list[str]
:ivar module_run_setting_type: Possible values include: "Released", "Testing", "Legacy",
"Preview", "Integration", "All", "Default", "Full", "UxIntegration", "UxFull".
:vartype module_run_setting_type: str or ~flow.models.ModuleRunSettingTypes
:ivar linked_parameter_default_value_mapping: Dictionary of :code:`<string>`.
:vartype linked_parameter_default_value_mapping: dict[str, str]
:ivar linked_parameter_key_name:
:vartype linked_parameter_key_name: str
:ivar support_link_setting:
:vartype support_link_setting: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'run_setting_ui_hint': {'key': 'runSettingUIHint', 'type': 'RunSettingUIParameterHint'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
'section_name': {'key': 'sectionName', 'type': 'str'},
'section_description': {'key': 'sectionDescription', 'type': 'str'},
'section_argument_name': {'key': 'sectionArgumentName', 'type': 'str'},
'examples': {'key': 'examples', 'type': '[str]'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'disabled_by_parameters': {'key': 'disabledByParameters', 'type': '[str]'},
'module_run_setting_type': {'key': 'moduleRunSettingType', 'type': 'str'},
'linked_parameter_default_value_mapping': {'key': 'linkedParameterDefaultValueMapping', 'type': '{str}'},
'linked_parameter_key_name': {'key': 'linkedParameterKeyName', 'type': 'str'},
'support_link_setting': {'key': 'supportLinkSetting', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
parameter_type: Optional[Union[str, "RunSettingParameterType"]] = None,
is_optional: Optional[bool] = None,
default_value: Optional[str] = None,
lower_bound: Optional[str] = None,
upper_bound: Optional[str] = None,
description: Optional[str] = None,
run_setting_ui_hint: Optional["RunSettingUIParameterHint"] = None,
argument_name: Optional[str] = None,
section_name: Optional[str] = None,
section_description: Optional[str] = None,
section_argument_name: Optional[str] = None,
examples: Optional[List[str]] = None,
enum_values: Optional[List[str]] = None,
enum_values_to_argument_strings: Optional[Dict[str, str]] = None,
enabled_by_parameter_name: Optional[str] = None,
enabled_by_parameter_values: Optional[List[str]] = None,
disabled_by_parameters: Optional[List[str]] = None,
module_run_setting_type: Optional[Union[str, "ModuleRunSettingTypes"]] = None,
linked_parameter_default_value_mapping: Optional[Dict[str, str]] = None,
linked_parameter_key_name: Optional[str] = None,
support_link_setting: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Undefined", "Int", "Double", "Bool",
"String", "JsonString", "YamlString", "StringList".
:paramtype parameter_type: str or ~flow.models.RunSettingParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword description:
:paramtype description: str
:keyword run_setting_ui_hint:
:paramtype run_setting_ui_hint: ~flow.models.RunSettingUIParameterHint
:keyword argument_name:
:paramtype argument_name: str
:keyword section_name:
:paramtype section_name: str
:keyword section_description:
:paramtype section_description: str
:keyword section_argument_name:
:paramtype section_argument_name: str
:keyword examples:
:paramtype examples: list[str]
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword disabled_by_parameters:
:paramtype disabled_by_parameters: list[str]
:keyword module_run_setting_type: Possible values include: "Released", "Testing", "Legacy",
"Preview", "Integration", "All", "Default", "Full", "UxIntegration", "UxFull".
:paramtype module_run_setting_type: str or ~flow.models.ModuleRunSettingTypes
:keyword linked_parameter_default_value_mapping: Dictionary of :code:`<string>`.
:paramtype linked_parameter_default_value_mapping: dict[str, str]
:keyword linked_parameter_key_name:
:paramtype linked_parameter_key_name: str
:keyword support_link_setting:
:paramtype support_link_setting: bool
"""
super(RunSettingParameter, self).__init__(**kwargs)
self.name = name
self.label = label
self.parameter_type = parameter_type
self.is_optional = is_optional
self.default_value = default_value
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.description = description
self.run_setting_ui_hint = run_setting_ui_hint
self.argument_name = argument_name
self.section_name = section_name
self.section_description = section_description
self.section_argument_name = section_argument_name
self.examples = examples
self.enum_values = enum_values
self.enum_values_to_argument_strings = enum_values_to_argument_strings
self.enabled_by_parameter_name = enabled_by_parameter_name
self.enabled_by_parameter_values = enabled_by_parameter_values
self.disabled_by_parameters = disabled_by_parameters
self.module_run_setting_type = module_run_setting_type
self.linked_parameter_default_value_mapping = linked_parameter_default_value_mapping
self.linked_parameter_key_name = linked_parameter_key_name
self.support_link_setting = support_link_setting
class RunSettingParameterAssignment(msrest.serialization.Model):
"""RunSettingParameterAssignment.
:ivar use_graph_default_compute:
:vartype use_graph_default_compute: bool
:ivar mlc_compute_type:
:vartype mlc_compute_type: str
:ivar compute_run_settings:
:vartype compute_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar linked_parameter_name:
:vartype linked_parameter_name: str
:ivar value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:vartype value_type: str or ~flow.models.ParameterValueType
:ivar assignments_to_concatenate:
:vartype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:ivar data_path_assignment:
:vartype data_path_assignment: ~flow.models.LegacyDataPath
:ivar data_set_definition_value_assignment:
:vartype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
"""
_attribute_map = {
'use_graph_default_compute': {'key': 'useGraphDefaultCompute', 'type': 'bool'},
'mlc_compute_type': {'key': 'mlcComputeType', 'type': 'str'},
'compute_run_settings': {'key': 'computeRunSettings', 'type': '[RunSettingParameterAssignment]'},
'linked_parameter_name': {'key': 'linkedParameterName', 'type': 'str'},
'value_type': {'key': 'valueType', 'type': 'str'},
'assignments_to_concatenate': {'key': 'assignmentsToConcatenate', 'type': '[ParameterAssignment]'},
'data_path_assignment': {'key': 'dataPathAssignment', 'type': 'LegacyDataPath'},
'data_set_definition_value_assignment': {'key': 'dataSetDefinitionValueAssignment', 'type': 'DataSetDefinitionValue'},
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(
self,
*,
use_graph_default_compute: Optional[bool] = None,
mlc_compute_type: Optional[str] = None,
compute_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
linked_parameter_name: Optional[str] = None,
value_type: Optional[Union[str, "ParameterValueType"]] = None,
assignments_to_concatenate: Optional[List["ParameterAssignment"]] = None,
data_path_assignment: Optional["LegacyDataPath"] = None,
data_set_definition_value_assignment: Optional["DataSetDefinitionValue"] = None,
name: Optional[str] = None,
value: Optional[str] = None,
**kwargs
):
"""
:keyword use_graph_default_compute:
:paramtype use_graph_default_compute: bool
:keyword mlc_compute_type:
:paramtype mlc_compute_type: str
:keyword compute_run_settings:
:paramtype compute_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword linked_parameter_name:
:paramtype linked_parameter_name: str
:keyword value_type: Possible values include: "Literal", "GraphParameterName", "Concatenate",
"Input", "DataPath", "DataSetDefinition".
:paramtype value_type: str or ~flow.models.ParameterValueType
:keyword assignments_to_concatenate:
:paramtype assignments_to_concatenate: list[~flow.models.ParameterAssignment]
:keyword data_path_assignment:
:paramtype data_path_assignment: ~flow.models.LegacyDataPath
:keyword data_set_definition_value_assignment:
:paramtype data_set_definition_value_assignment: ~flow.models.DataSetDefinitionValue
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
"""
super(RunSettingParameterAssignment, self).__init__(**kwargs)
self.use_graph_default_compute = use_graph_default_compute
self.mlc_compute_type = mlc_compute_type
self.compute_run_settings = compute_run_settings
self.linked_parameter_name = linked_parameter_name
self.value_type = value_type
self.assignments_to_concatenate = assignments_to_concatenate
self.data_path_assignment = data_path_assignment
self.data_set_definition_value_assignment = data_set_definition_value_assignment
self.name = name
self.value = value
class RunSettingUIParameterHint(msrest.serialization.Model):
"""RunSettingUIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "ComputeSelection", "JsonEditor",
"Mode", "SearchSpaceParameter", "SectionToggle", "YamlEditor", "EnableRuntimeSweep",
"DataStoreSelection", "Checkbox", "MultipleSelection", "HyperparameterConfiguration",
"JsonTextBox", "Connection", "Static".
:vartype ui_widget_type: str or ~flow.models.RunSettingUIWidgetTypeEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.UIJsonEditor
:ivar yaml_editor:
:vartype yaml_editor: ~flow.models.UIYamlEditor
:ivar compute_selection:
:vartype compute_selection: ~flow.models.UIComputeSelection
:ivar hyperparameter_configuration:
:vartype hyperparameter_configuration: ~flow.models.UIHyperparameterConfiguration
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
:ivar support_reset:
:vartype support_reset: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'UIJsonEditor'},
'yaml_editor': {'key': 'yamlEditor', 'type': 'UIYamlEditor'},
'compute_selection': {'key': 'computeSelection', 'type': 'UIComputeSelection'},
'hyperparameter_configuration': {'key': 'hyperparameterConfiguration', 'type': 'UIHyperparameterConfiguration'},
'ux_ignore': {'key': 'uxIgnore', 'type': 'bool'},
'anonymous': {'key': 'anonymous', 'type': 'bool'},
'support_reset': {'key': 'supportReset', 'type': 'bool'},
}
def __init__(
self,
*,
ui_widget_type: Optional[Union[str, "RunSettingUIWidgetTypeEnum"]] = None,
json_editor: Optional["UIJsonEditor"] = None,
yaml_editor: Optional["UIYamlEditor"] = None,
compute_selection: Optional["UIComputeSelection"] = None,
hyperparameter_configuration: Optional["UIHyperparameterConfiguration"] = None,
ux_ignore: Optional[bool] = None,
anonymous: Optional[bool] = None,
support_reset: Optional[bool] = None,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "ComputeSelection", "JsonEditor",
"Mode", "SearchSpaceParameter", "SectionToggle", "YamlEditor", "EnableRuntimeSweep",
"DataStoreSelection", "Checkbox", "MultipleSelection", "HyperparameterConfiguration",
"JsonTextBox", "Connection", "Static".
:paramtype ui_widget_type: str or ~flow.models.RunSettingUIWidgetTypeEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.UIJsonEditor
:keyword yaml_editor:
:paramtype yaml_editor: ~flow.models.UIYamlEditor
:keyword compute_selection:
:paramtype compute_selection: ~flow.models.UIComputeSelection
:keyword hyperparameter_configuration:
:paramtype hyperparameter_configuration: ~flow.models.UIHyperparameterConfiguration
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
:keyword support_reset:
:paramtype support_reset: bool
"""
super(RunSettingUIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = ui_widget_type
self.json_editor = json_editor
self.yaml_editor = yaml_editor
self.compute_selection = compute_selection
self.hyperparameter_configuration = hyperparameter_configuration
self.ux_ignore = ux_ignore
self.anonymous = anonymous
self.support_reset = support_reset
class RunStatusPeriod(msrest.serialization.Model):
"""RunStatusPeriod.
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar sub_periods:
:vartype sub_periods: list[~flow.models.SubStatusPeriod]
:ivar start:
:vartype start: long
:ivar end:
:vartype end: long
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'sub_periods': {'key': 'subPeriods', 'type': '[SubStatusPeriod]'},
'start': {'key': 'start', 'type': 'long'},
'end': {'key': 'end', 'type': 'long'},
}
def __init__(
self,
*,
status: Optional[Union[str, "RunStatus"]] = None,
sub_periods: Optional[List["SubStatusPeriod"]] = None,
start: Optional[int] = None,
end: Optional[int] = None,
**kwargs
):
"""
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword sub_periods:
:paramtype sub_periods: list[~flow.models.SubStatusPeriod]
:keyword start:
:paramtype start: long
:keyword end:
:paramtype end: long
"""
super(RunStatusPeriod, self).__init__(**kwargs)
self.status = status
self.sub_periods = sub_periods
self.start = start
self.end = end
class RuntimeConfiguration(msrest.serialization.Model):
"""RuntimeConfiguration.
:ivar base_image:
:vartype base_image: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'base_image': {'key': 'baseImage', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
base_image: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword base_image:
:paramtype base_image: str
:keyword version:
:paramtype version: str
"""
super(RuntimeConfiguration, self).__init__(**kwargs)
self.base_image = base_image
self.version = version
class RunTypeV2(msrest.serialization.Model):
"""RunTypeV2.
:ivar orchestrator:
:vartype orchestrator: str
:ivar traits:
:vartype traits: list[str]
:ivar attribution:
:vartype attribution: str
:ivar compute_type:
:vartype compute_type: str
"""
_validation = {
'traits': {'unique': True},
}
_attribute_map = {
'orchestrator': {'key': 'orchestrator', 'type': 'str'},
'traits': {'key': 'traits', 'type': '[str]'},
'attribution': {'key': 'attribution', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
*,
orchestrator: Optional[str] = None,
traits: Optional[List[str]] = None,
attribution: Optional[str] = None,
compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword orchestrator:
:paramtype orchestrator: str
:keyword traits:
:paramtype traits: list[str]
:keyword attribution:
:paramtype attribution: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(RunTypeV2, self).__init__(**kwargs)
self.orchestrator = orchestrator
self.traits = traits
self.attribution = attribution
self.compute_type = compute_type
class RunTypeV2Index(msrest.serialization.Model):
"""RunTypeV2Index.
:ivar orchestrator:
:vartype orchestrator: str
:ivar traits: Dictionary of :code:`<string>`.
:vartype traits: dict[str, str]
:ivar attribution:
:vartype attribution: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'orchestrator': {'key': 'orchestrator', 'type': 'str'},
'traits': {'key': 'traits', 'type': '{str}'},
'attribution': {'key': 'attribution', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
*,
orchestrator: Optional[str] = None,
traits: Optional[Dict[str, str]] = None,
attribution: Optional[str] = None,
compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword orchestrator:
:paramtype orchestrator: str
:keyword traits: Dictionary of :code:`<string>`.
:paramtype traits: dict[str, str]
:keyword attribution:
:paramtype attribution: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(RunTypeV2Index, self).__init__(**kwargs)
self.orchestrator = orchestrator
self.traits = traits
self.attribution = attribution
self.compute_type = compute_type
class SampleMeta(msrest.serialization.Model):
"""SampleMeta.
:ivar image:
:vartype image: str
:ivar id:
:vartype id: str
:ivar display_name:
:vartype display_name: str
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar doc_link:
:vartype doc_link: str
:ivar tags: A set of tags.
:vartype tags: list[str]
:ivar created_at:
:vartype created_at: ~datetime.datetime
:ivar updated_at:
:vartype updated_at: ~datetime.datetime
:ivar feed_name:
:vartype feed_name: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'image': {'key': 'image', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'doc_link': {'key': 'docLink', 'type': 'str'},
'tags': {'key': 'tags', 'type': '[str]'},
'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
'updated_at': {'key': 'updatedAt', 'type': 'iso-8601'},
'feed_name': {'key': 'feedName', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
image: Optional[str] = None,
id: Optional[str] = None,
display_name: Optional[str] = None,
name: Optional[str] = None,
description: Optional[str] = None,
doc_link: Optional[str] = None,
tags: Optional[List[str]] = None,
created_at: Optional[datetime.datetime] = None,
updated_at: Optional[datetime.datetime] = None,
feed_name: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword image:
:paramtype image: str
:keyword id:
:paramtype id: str
:keyword display_name:
:paramtype display_name: str
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword doc_link:
:paramtype doc_link: str
:keyword tags: A set of tags.
:paramtype tags: list[str]
:keyword created_at:
:paramtype created_at: ~datetime.datetime
:keyword updated_at:
:paramtype updated_at: ~datetime.datetime
:keyword feed_name:
:paramtype feed_name: str
:keyword version:
:paramtype version: str
"""
super(SampleMeta, self).__init__(**kwargs)
self.image = image
self.id = id
self.display_name = display_name
self.name = name
self.description = description
self.doc_link = doc_link
self.tags = tags
self.created_at = created_at
self.updated_at = updated_at
self.feed_name = feed_name
self.version = version
class SavedDataSetReference(msrest.serialization.Model):
"""SavedDataSetReference.
:ivar id:
:vartype id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
"""
super(SavedDataSetReference, self).__init__(**kwargs)
self.id = id
class SavePipelineDraftRequest(msrest.serialization.Model):
"""SavePipelineDraftRequest.
:ivar ui_widget_meta_infos:
:vartype ui_widget_meta_infos: list[~flow.models.UIWidgetMetaInfo]
:ivar web_service_inputs:
:vartype web_service_inputs: list[~flow.models.WebServicePort]
:ivar web_service_outputs:
:vartype web_service_outputs: list[~flow.models.WebServicePort]
:ivar nodes_in_draft:
:vartype nodes_in_draft: list[str]
:ivar name:
:vartype name: str
:ivar pipeline_type: Possible values include: "TrainingPipeline", "RealTimeInferencePipeline",
"BatchInferencePipeline", "Unknown".
:vartype pipeline_type: str or ~flow.models.PipelineType
:ivar pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:vartype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:ivar graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:vartype graph_components_mode: str or ~flow.models.GraphComponentsMode
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'ui_widget_meta_infos': {'key': 'uiWidgetMetaInfos', 'type': '[UIWidgetMetaInfo]'},
'web_service_inputs': {'key': 'webServiceInputs', 'type': '[WebServicePort]'},
'web_service_outputs': {'key': 'webServiceOutputs', 'type': '[WebServicePort]'},
'nodes_in_draft': {'key': 'nodesInDraft', 'type': '[str]'},
'name': {'key': 'name', 'type': 'str'},
'pipeline_type': {'key': 'pipelineType', 'type': 'str'},
'pipeline_draft_mode': {'key': 'pipelineDraftMode', 'type': 'str'},
'graph_components_mode': {'key': 'graphComponentsMode', 'type': 'str'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
ui_widget_meta_infos: Optional[List["UIWidgetMetaInfo"]] = None,
web_service_inputs: Optional[List["WebServicePort"]] = None,
web_service_outputs: Optional[List["WebServicePort"]] = None,
nodes_in_draft: Optional[List[str]] = None,
name: Optional[str] = None,
pipeline_type: Optional[Union[str, "PipelineType"]] = None,
pipeline_draft_mode: Optional[Union[str, "PipelineDraftMode"]] = None,
graph_components_mode: Optional[Union[str, "GraphComponentsMode"]] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
flattened_sub_graphs: Optional[Dict[str, "PipelineSubDraft"]] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword ui_widget_meta_infos:
:paramtype ui_widget_meta_infos: list[~flow.models.UIWidgetMetaInfo]
:keyword web_service_inputs:
:paramtype web_service_inputs: list[~flow.models.WebServicePort]
:keyword web_service_outputs:
:paramtype web_service_outputs: list[~flow.models.WebServicePort]
:keyword nodes_in_draft:
:paramtype nodes_in_draft: list[str]
:keyword name:
:paramtype name: str
:keyword pipeline_type: Possible values include: "TrainingPipeline",
"RealTimeInferencePipeline", "BatchInferencePipeline", "Unknown".
:paramtype pipeline_type: str or ~flow.models.PipelineType
:keyword pipeline_draft_mode: Possible values include: "None", "Normal", "Custom".
:paramtype pipeline_draft_mode: str or ~flow.models.PipelineDraftMode
:keyword graph_components_mode: Possible values include: "Normal", "AllDesignerBuildin",
"ContainsDesignerBuildin".
:paramtype graph_components_mode: str or ~flow.models.GraphComponentsMode
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(SavePipelineDraftRequest, self).__init__(**kwargs)
self.ui_widget_meta_infos = ui_widget_meta_infos
self.web_service_inputs = web_service_inputs
self.web_service_outputs = web_service_outputs
self.nodes_in_draft = nodes_in_draft
self.name = name
self.pipeline_type = pipeline_type
self.pipeline_draft_mode = pipeline_draft_mode
self.graph_components_mode = graph_components_mode
self.sub_pipelines_info = sub_pipelines_info
self.flattened_sub_graphs = flattened_sub_graphs
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class ScheduleBase(msrest.serialization.Model):
"""ScheduleBase.
:ivar schedule_status: Possible values include: "Enabled", "Disabled".
:vartype schedule_status: str or ~flow.models.MfeInternalScheduleStatus
:ivar schedule_type: Possible values include: "Cron", "Recurrence".
:vartype schedule_type: str or ~flow.models.ScheduleType
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar time_zone:
:vartype time_zone: str
:ivar expression:
:vartype expression: str
:ivar frequency: Possible values include: "Minute", "Hour", "Day", "Week", "Month".
:vartype frequency: str or ~flow.models.RecurrenceFrequency
:ivar interval:
:vartype interval: int
:ivar pattern:
:vartype pattern: ~flow.models.RecurrencePattern
"""
_attribute_map = {
'schedule_status': {'key': 'scheduleStatus', 'type': 'str'},
'schedule_type': {'key': 'scheduleType', 'type': 'str'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'time_zone': {'key': 'timeZone', 'type': 'str'},
'expression': {'key': 'expression', 'type': 'str'},
'frequency': {'key': 'frequency', 'type': 'str'},
'interval': {'key': 'interval', 'type': 'int'},
'pattern': {'key': 'pattern', 'type': 'RecurrencePattern'},
}
def __init__(
self,
*,
schedule_status: Optional[Union[str, "MfeInternalScheduleStatus"]] = None,
schedule_type: Optional[Union[str, "ScheduleType"]] = None,
end_time: Optional[datetime.datetime] = None,
start_time: Optional[datetime.datetime] = None,
time_zone: Optional[str] = None,
expression: Optional[str] = None,
frequency: Optional[Union[str, "RecurrenceFrequency"]] = None,
interval: Optional[int] = None,
pattern: Optional["RecurrencePattern"] = None,
**kwargs
):
"""
:keyword schedule_status: Possible values include: "Enabled", "Disabled".
:paramtype schedule_status: str or ~flow.models.MfeInternalScheduleStatus
:keyword schedule_type: Possible values include: "Cron", "Recurrence".
:paramtype schedule_type: str or ~flow.models.ScheduleType
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword time_zone:
:paramtype time_zone: str
:keyword expression:
:paramtype expression: str
:keyword frequency: Possible values include: "Minute", "Hour", "Day", "Week", "Month".
:paramtype frequency: str or ~flow.models.RecurrenceFrequency
:keyword interval:
:paramtype interval: int
:keyword pattern:
:paramtype pattern: ~flow.models.RecurrencePattern
"""
super(ScheduleBase, self).__init__(**kwargs)
self.schedule_status = schedule_status
self.schedule_type = schedule_type
self.end_time = end_time
self.start_time = start_time
self.time_zone = time_zone
self.expression = expression
self.frequency = frequency
self.interval = interval
self.pattern = pattern
class SchemaContractsCreatedBy(msrest.serialization.Model):
"""SchemaContractsCreatedBy.
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar user_name:
:vartype user_name: str
:ivar user_principal_name:
:vartype user_principal_name: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'user_principal_name': {'key': 'userPrincipalName', 'type': 'str'},
}
def __init__(
self,
*,
user_object_id: Optional[str] = None,
user_tenant_id: Optional[str] = None,
user_name: Optional[str] = None,
user_principal_name: Optional[str] = None,
**kwargs
):
"""
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword user_name:
:paramtype user_name: str
:keyword user_principal_name:
:paramtype user_principal_name: str
"""
super(SchemaContractsCreatedBy, self).__init__(**kwargs)
self.user_object_id = user_object_id
self.user_tenant_id = user_tenant_id
self.user_name = user_name
self.user_principal_name = user_principal_name
class ScopeCloudConfiguration(msrest.serialization.Model):
"""ScopeCloudConfiguration.
:ivar input_path_suffixes: This is a dictionary.
:vartype input_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:ivar output_path_suffixes: This is a dictionary.
:vartype output_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:ivar user_alias:
:vartype user_alias: str
:ivar tokens:
:vartype tokens: int
:ivar auto_token:
:vartype auto_token: int
:ivar vcp:
:vartype vcp: float
"""
_attribute_map = {
'input_path_suffixes': {'key': 'inputPathSuffixes', 'type': '{ArgumentAssignment}'},
'output_path_suffixes': {'key': 'outputPathSuffixes', 'type': '{ArgumentAssignment}'},
'user_alias': {'key': 'userAlias', 'type': 'str'},
'tokens': {'key': 'tokens', 'type': 'int'},
'auto_token': {'key': 'autoToken', 'type': 'int'},
'vcp': {'key': 'vcp', 'type': 'float'},
}
def __init__(
self,
*,
input_path_suffixes: Optional[Dict[str, "ArgumentAssignment"]] = None,
output_path_suffixes: Optional[Dict[str, "ArgumentAssignment"]] = None,
user_alias: Optional[str] = None,
tokens: Optional[int] = None,
auto_token: Optional[int] = None,
vcp: Optional[float] = None,
**kwargs
):
"""
:keyword input_path_suffixes: This is a dictionary.
:paramtype input_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:keyword output_path_suffixes: This is a dictionary.
:paramtype output_path_suffixes: dict[str, ~flow.models.ArgumentAssignment]
:keyword user_alias:
:paramtype user_alias: str
:keyword tokens:
:paramtype tokens: int
:keyword auto_token:
:paramtype auto_token: int
:keyword vcp:
:paramtype vcp: float
"""
super(ScopeCloudConfiguration, self).__init__(**kwargs)
self.input_path_suffixes = input_path_suffixes
self.output_path_suffixes = output_path_suffixes
self.user_alias = user_alias
self.tokens = tokens
self.auto_token = auto_token
self.vcp = vcp
class Seasonality(msrest.serialization.Model):
"""Seasonality.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.SeasonalityMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "SeasonalityMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.SeasonalityMode
:keyword value:
:paramtype value: int
"""
super(Seasonality, self).__init__(**kwargs)
self.mode = mode
self.value = value
class SecretConfiguration(msrest.serialization.Model):
"""SecretConfiguration.
:ivar workspace_secret_name:
:vartype workspace_secret_name: str
:ivar uri:
:vartype uri: str
"""
_attribute_map = {
'workspace_secret_name': {'key': 'workspace_secret_name', 'type': 'str'},
'uri': {'key': 'uri', 'type': 'str'},
}
def __init__(
self,
*,
workspace_secret_name: Optional[str] = None,
uri: Optional[str] = None,
**kwargs
):
"""
:keyword workspace_secret_name:
:paramtype workspace_secret_name: str
:keyword uri:
:paramtype uri: str
"""
super(SecretConfiguration, self).__init__(**kwargs)
self.workspace_secret_name = workspace_secret_name
self.uri = uri
class SegmentedResult1(msrest.serialization.Model):
"""SegmentedResult1.
:ivar value:
:vartype value: list[~flow.models.FlowIndexEntity]
:ivar continuation_token:
:vartype continuation_token: str
:ivar count:
:vartype count: int
:ivar next_link:
:vartype next_link: str
"""
_attribute_map = {
'value': {'key': 'value', 'type': '[FlowIndexEntity]'},
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'count': {'key': 'count', 'type': 'int'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
value: Optional[List["FlowIndexEntity"]] = None,
continuation_token: Optional[str] = None,
count: Optional[int] = None,
next_link: Optional[str] = None,
**kwargs
):
"""
:keyword value:
:paramtype value: list[~flow.models.FlowIndexEntity]
:keyword continuation_token:
:paramtype continuation_token: str
:keyword count:
:paramtype count: int
:keyword next_link:
:paramtype next_link: str
"""
super(SegmentedResult1, self).__init__(**kwargs)
self.value = value
self.continuation_token = continuation_token
self.count = count
self.next_link = next_link
class ServiceLogRequest(msrest.serialization.Model):
"""ServiceLogRequest.
:ivar log_level: Possible values include: "Trace", "Debug", "Information", "Warning", "Error",
"Critical", "None".
:vartype log_level: str or ~flow.models.LogLevel
:ivar message:
:vartype message: str
:ivar timestamp:
:vartype timestamp: ~datetime.datetime
"""
_attribute_map = {
'log_level': {'key': 'logLevel', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},
}
def __init__(
self,
*,
log_level: Optional[Union[str, "LogLevel"]] = None,
message: Optional[str] = None,
timestamp: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword log_level: Possible values include: "Trace", "Debug", "Information", "Warning",
"Error", "Critical", "None".
:paramtype log_level: str or ~flow.models.LogLevel
:keyword message:
:paramtype message: str
:keyword timestamp:
:paramtype timestamp: ~datetime.datetime
"""
super(ServiceLogRequest, self).__init__(**kwargs)
self.log_level = log_level
self.message = message
self.timestamp = timestamp
class SessionApplication(msrest.serialization.Model):
"""SessionApplication.
:ivar name:
:vartype name: str
:ivar type:
:vartype type: str
:ivar image:
:vartype image: str
:ivar env_vars: Dictionary of :code:`<string>`.
:vartype env_vars: dict[str, str]
:ivar python_pip_requirements:
:vartype python_pip_requirements: list[str]
:ivar volumes:
:vartype volumes: list[~flow.models.Volume]
:ivar setup_results:
:vartype setup_results: list[~flow.models.SessionApplicationRunCommandResult]
:ivar port:
:vartype port: int
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'image': {'key': 'image', 'type': 'str'},
'env_vars': {'key': 'envVars', 'type': '{str}'},
'python_pip_requirements': {'key': 'pythonPipRequirements', 'type': '[str]'},
'volumes': {'key': 'volumes', 'type': '[Volume]'},
'setup_results': {'key': 'setupResults', 'type': '[SessionApplicationRunCommandResult]'},
'port': {'key': 'port', 'type': 'int'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[str] = None,
image: Optional[str] = None,
env_vars: Optional[Dict[str, str]] = None,
python_pip_requirements: Optional[List[str]] = None,
volumes: Optional[List["Volume"]] = None,
setup_results: Optional[List["SessionApplicationRunCommandResult"]] = None,
port: Optional[int] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type:
:paramtype type: str
:keyword image:
:paramtype image: str
:keyword env_vars: Dictionary of :code:`<string>`.
:paramtype env_vars: dict[str, str]
:keyword python_pip_requirements:
:paramtype python_pip_requirements: list[str]
:keyword volumes:
:paramtype volumes: list[~flow.models.Volume]
:keyword setup_results:
:paramtype setup_results: list[~flow.models.SessionApplicationRunCommandResult]
:keyword port:
:paramtype port: int
"""
super(SessionApplication, self).__init__(**kwargs)
self.name = name
self.type = type
self.image = image
self.env_vars = env_vars
self.python_pip_requirements = python_pip_requirements
self.volumes = volumes
self.setup_results = setup_results
self.port = port
class SessionApplicationRunCommandResult(msrest.serialization.Model):
"""SessionApplicationRunCommandResult.
:ivar command:
:vartype command: str
:ivar arguments:
:vartype arguments: list[str]
:ivar exit_code:
:vartype exit_code: int
:ivar std_out:
:vartype std_out: str
:ivar std_err:
:vartype std_err: str
"""
_attribute_map = {
'command': {'key': 'command', 'type': 'str'},
'arguments': {'key': 'arguments', 'type': '[str]'},
'exit_code': {'key': 'exitCode', 'type': 'int'},
'std_out': {'key': 'stdOut', 'type': 'str'},
'std_err': {'key': 'stdErr', 'type': 'str'},
}
def __init__(
self,
*,
command: Optional[str] = None,
arguments: Optional[List[str]] = None,
exit_code: Optional[int] = None,
std_out: Optional[str] = None,
std_err: Optional[str] = None,
**kwargs
):
"""
:keyword command:
:paramtype command: str
:keyword arguments:
:paramtype arguments: list[str]
:keyword exit_code:
:paramtype exit_code: int
:keyword std_out:
:paramtype std_out: str
:keyword std_err:
:paramtype std_err: str
"""
super(SessionApplicationRunCommandResult, self).__init__(**kwargs)
self.command = command
self.arguments = arguments
self.exit_code = exit_code
self.std_out = std_out
self.std_err = std_err
class SessionProperties(msrest.serialization.Model):
"""SessionProperties.
:ivar session_id:
:vartype session_id: str
:ivar subscription_id:
:vartype subscription_id: str
:ivar resource_group_name:
:vartype resource_group_name: str
:ivar workspace_name:
:vartype workspace_name: str
:ivar existing_user_compute_instance_name:
:vartype existing_user_compute_instance_name: str
:ivar user_object_id:
:vartype user_object_id: str
:ivar user_tenant_id:
:vartype user_tenant_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar applications:
:vartype applications: list[~flow.models.SessionApplication]
:ivar application:
:vartype application: ~flow.models.SessionApplication
:ivar last_alive_time:
:vartype last_alive_time: ~datetime.datetime
"""
_attribute_map = {
'session_id': {'key': 'sessionId', 'type': 'str'},
'subscription_id': {'key': 'subscriptionId', 'type': 'str'},
'resource_group_name': {'key': 'resourceGroupName', 'type': 'str'},
'workspace_name': {'key': 'workspaceName', 'type': 'str'},
'existing_user_compute_instance_name': {'key': 'existingUserComputeInstanceName', 'type': 'str'},
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'applications': {'key': 'applications', 'type': '[SessionApplication]'},
'application': {'key': 'application', 'type': 'SessionApplication'},
'last_alive_time': {'key': 'lastAliveTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
session_id: Optional[str] = None,
subscription_id: Optional[str] = None,
resource_group_name: Optional[str] = None,
workspace_name: Optional[str] = None,
existing_user_compute_instance_name: Optional[str] = None,
user_object_id: Optional[str] = None,
user_tenant_id: Optional[str] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
applications: Optional[List["SessionApplication"]] = None,
application: Optional["SessionApplication"] = None,
last_alive_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword session_id:
:paramtype session_id: str
:keyword subscription_id:
:paramtype subscription_id: str
:keyword resource_group_name:
:paramtype resource_group_name: str
:keyword workspace_name:
:paramtype workspace_name: str
:keyword existing_user_compute_instance_name:
:paramtype existing_user_compute_instance_name: str
:keyword user_object_id:
:paramtype user_object_id: str
:keyword user_tenant_id:
:paramtype user_tenant_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword applications:
:paramtype applications: list[~flow.models.SessionApplication]
:keyword application:
:paramtype application: ~flow.models.SessionApplication
:keyword last_alive_time:
:paramtype last_alive_time: ~datetime.datetime
"""
super(SessionProperties, self).__init__(**kwargs)
self.session_id = session_id
self.subscription_id = subscription_id
self.resource_group_name = resource_group_name
self.workspace_name = workspace_name
self.existing_user_compute_instance_name = existing_user_compute_instance_name
self.user_object_id = user_object_id
self.user_tenant_id = user_tenant_id
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.applications = applications
self.application = application
self.last_alive_time = last_alive_time
class SetupFlowSessionRequest(msrest.serialization.Model):
"""SetupFlowSessionRequest.
:ivar action: Possible values include: "Install", "Reset", "Update", "Delete".
:vartype action: str or ~flow.models.SetupFlowSessionAction
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
:ivar compute_name:
:vartype compute_name: str
"""
_attribute_map = {
'action': {'key': 'action', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
'compute_name': {'key': 'computeName', 'type': 'str'},
}
def __init__(
self,
*,
action: Optional[Union[str, "SetupFlowSessionAction"]] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
compute_name: Optional[str] = None,
**kwargs
):
"""
:keyword action: Possible values include: "Install", "Reset", "Update", "Delete".
:paramtype action: str or ~flow.models.SetupFlowSessionAction
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
:keyword compute_name:
:paramtype compute_name: str
"""
super(SetupFlowSessionRequest, self).__init__(**kwargs)
self.action = action
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
self.compute_name = compute_name
class SharingScope(msrest.serialization.Model):
"""SharingScope.
:ivar type: Possible values include: "Global", "Tenant", "Subscription", "ResourceGroup",
"Workspace".
:vartype type: str or ~flow.models.ScopeType
:ivar identifier:
:vartype identifier: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'identifier': {'key': 'identifier', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[Union[str, "ScopeType"]] = None,
identifier: Optional[str] = None,
**kwargs
):
"""
:keyword type: Possible values include: "Global", "Tenant", "Subscription", "ResourceGroup",
"Workspace".
:paramtype type: str or ~flow.models.ScopeType
:keyword identifier:
:paramtype identifier: str
"""
super(SharingScope, self).__init__(**kwargs)
self.type = type
self.identifier = identifier
class Snapshot(msrest.serialization.Model):
"""Snapshot.
:ivar id:
:vartype id: str
:ivar directory_name:
:vartype directory_name: str
:ivar snapshot_asset_id:
:vartype snapshot_asset_id: str
:ivar snapshot_entity_id:
:vartype snapshot_entity_id: str
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'directory_name': {'key': 'directoryName', 'type': 'str'},
'snapshot_asset_id': {'key': 'snapshotAssetId', 'type': 'str'},
'snapshot_entity_id': {'key': 'snapshotEntityId', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
directory_name: Optional[str] = None,
snapshot_asset_id: Optional[str] = None,
snapshot_entity_id: Optional[str] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword directory_name:
:paramtype directory_name: str
:keyword snapshot_asset_id:
:paramtype snapshot_asset_id: str
:keyword snapshot_entity_id:
:paramtype snapshot_entity_id: str
"""
super(Snapshot, self).__init__(**kwargs)
self.id = id
self.directory_name = directory_name
self.snapshot_asset_id = snapshot_asset_id
self.snapshot_entity_id = snapshot_entity_id
class SnapshotInfo(msrest.serialization.Model):
"""SnapshotInfo.
:ivar root_download_url:
:vartype root_download_url: str
:ivar snapshots: This is a dictionary.
:vartype snapshots: dict[str, ~flow.models.DownloadResourceInfo]
"""
_attribute_map = {
'root_download_url': {'key': 'rootDownloadUrl', 'type': 'str'},
'snapshots': {'key': 'snapshots', 'type': '{DownloadResourceInfo}'},
}
def __init__(
self,
*,
root_download_url: Optional[str] = None,
snapshots: Optional[Dict[str, "DownloadResourceInfo"]] = None,
**kwargs
):
"""
:keyword root_download_url:
:paramtype root_download_url: str
:keyword snapshots: This is a dictionary.
:paramtype snapshots: dict[str, ~flow.models.DownloadResourceInfo]
"""
super(SnapshotInfo, self).__init__(**kwargs)
self.root_download_url = root_download_url
self.snapshots = snapshots
class SourceCodeDataReference(msrest.serialization.Model):
"""SourceCodeDataReference.
:ivar data_store_name:
:vartype data_store_name: str
:ivar path:
:vartype path: str
"""
_attribute_map = {
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'path': {'key': 'path', 'type': 'str'},
}
def __init__(
self,
*,
data_store_name: Optional[str] = None,
path: Optional[str] = None,
**kwargs
):
"""
:keyword data_store_name:
:paramtype data_store_name: str
:keyword path:
:paramtype path: str
"""
super(SourceCodeDataReference, self).__init__(**kwargs)
self.data_store_name = data_store_name
self.path = path
class SparkConfiguration(msrest.serialization.Model):
"""SparkConfiguration.
:ivar configuration: Dictionary of :code:`<string>`.
:vartype configuration: dict[str, str]
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar py_files:
:vartype py_files: list[str]
:ivar spark_pool_resource_id:
:vartype spark_pool_resource_id: str
"""
_attribute_map = {
'configuration': {'key': 'configuration', 'type': '{str}'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'spark_pool_resource_id': {'key': 'sparkPoolResourceId', 'type': 'str'},
}
def __init__(
self,
*,
configuration: Optional[Dict[str, str]] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
py_files: Optional[List[str]] = None,
spark_pool_resource_id: Optional[str] = None,
**kwargs
):
"""
:keyword configuration: Dictionary of :code:`<string>`.
:paramtype configuration: dict[str, str]
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword py_files:
:paramtype py_files: list[str]
:keyword spark_pool_resource_id:
:paramtype spark_pool_resource_id: str
"""
super(SparkConfiguration, self).__init__(**kwargs)
self.configuration = configuration
self.files = files
self.archives = archives
self.jars = jars
self.py_files = py_files
self.spark_pool_resource_id = spark_pool_resource_id
class SparkJarTaskDto(msrest.serialization.Model):
"""SparkJarTaskDto.
:ivar main_class_name:
:vartype main_class_name: str
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'main_class_name': {'key': 'main_class_name', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
*,
main_class_name: Optional[str] = None,
parameters: Optional[List[str]] = None,
**kwargs
):
"""
:keyword main_class_name:
:paramtype main_class_name: str
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkJarTaskDto, self).__init__(**kwargs)
self.main_class_name = main_class_name
self.parameters = parameters
class SparkJob(msrest.serialization.Model):
"""SparkJob.
:ivar job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:vartype job_type: str or ~flow.models.JobType
:ivar resources:
:vartype resources: ~flow.models.SparkResourceConfiguration
:ivar args:
:vartype args: str
:ivar code_id:
:vartype code_id: str
:ivar entry:
:vartype entry: ~flow.models.SparkJobEntry
:ivar py_files:
:vartype py_files: list[str]
:ivar jars:
:vartype jars: list[str]
:ivar files:
:vartype files: list[str]
:ivar archives:
:vartype archives: list[str]
:ivar environment_id:
:vartype environment_id: str
:ivar input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:vartype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:ivar output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:vartype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:ivar conf: Dictionary of :code:`<string>`.
:vartype conf: dict[str, str]
:ivar environment_variables: Dictionary of :code:`<string>`.
:vartype environment_variables: dict[str, str]
:ivar provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:vartype provisioning_state: str or ~flow.models.JobProvisioningState
:ivar parent_job_name:
:vartype parent_job_name: str
:ivar display_name:
:vartype display_name: str
:ivar experiment_name:
:vartype experiment_name: str
:ivar status: Possible values include: "NotStarted", "Starting", "Provisioning", "Preparing",
"Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed", "Canceled",
"NotResponding", "Paused", "Unknown", "Scheduled".
:vartype status: str or ~flow.models.JobStatus
:ivar interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:vartype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:ivar identity:
:vartype identity: ~flow.models.MfeInternalIdentityConfiguration
:ivar compute:
:vartype compute: ~flow.models.ComputeConfiguration
:ivar priority:
:vartype priority: int
:ivar output:
:vartype output: ~flow.models.JobOutputArtifacts
:ivar is_archived:
:vartype is_archived: bool
:ivar schedule:
:vartype schedule: ~flow.models.ScheduleBase
:ivar component_id:
:vartype component_id: str
:ivar notification_setting:
:vartype notification_setting: ~flow.models.NotificationSetting
:ivar secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:vartype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
"""
_attribute_map = {
'job_type': {'key': 'jobType', 'type': 'str'},
'resources': {'key': 'resources', 'type': 'SparkResourceConfiguration'},
'args': {'key': 'args', 'type': 'str'},
'code_id': {'key': 'codeId', 'type': 'str'},
'entry': {'key': 'entry', 'type': 'SparkJobEntry'},
'py_files': {'key': 'pyFiles', 'type': '[str]'},
'jars': {'key': 'jars', 'type': '[str]'},
'files': {'key': 'files', 'type': '[str]'},
'archives': {'key': 'archives', 'type': '[str]'},
'environment_id': {'key': 'environmentId', 'type': 'str'},
'input_data_bindings': {'key': 'inputDataBindings', 'type': '{InputDataBinding}'},
'output_data_bindings': {'key': 'outputDataBindings', 'type': '{OutputDataBinding}'},
'conf': {'key': 'conf', 'type': '{str}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'parent_job_name': {'key': 'parentJobName', 'type': 'str'},
'display_name': {'key': 'displayName', 'type': 'str'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'status': {'key': 'status', 'type': 'str'},
'interaction_endpoints': {'key': 'interactionEndpoints', 'type': '{JobEndpoint}'},
'identity': {'key': 'identity', 'type': 'MfeInternalIdentityConfiguration'},
'compute': {'key': 'compute', 'type': 'ComputeConfiguration'},
'priority': {'key': 'priority', 'type': 'int'},
'output': {'key': 'output', 'type': 'JobOutputArtifacts'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'schedule': {'key': 'schedule', 'type': 'ScheduleBase'},
'component_id': {'key': 'componentId', 'type': 'str'},
'notification_setting': {'key': 'notificationSetting', 'type': 'NotificationSetting'},
'secrets_configuration': {'key': 'secretsConfiguration', 'type': '{MfeInternalSecretConfiguration}'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
}
def __init__(
self,
*,
job_type: Optional[Union[str, "JobType"]] = None,
resources: Optional["SparkResourceConfiguration"] = None,
args: Optional[str] = None,
code_id: Optional[str] = None,
entry: Optional["SparkJobEntry"] = None,
py_files: Optional[List[str]] = None,
jars: Optional[List[str]] = None,
files: Optional[List[str]] = None,
archives: Optional[List[str]] = None,
environment_id: Optional[str] = None,
input_data_bindings: Optional[Dict[str, "InputDataBinding"]] = None,
output_data_bindings: Optional[Dict[str, "OutputDataBinding"]] = None,
conf: Optional[Dict[str, str]] = None,
environment_variables: Optional[Dict[str, str]] = None,
provisioning_state: Optional[Union[str, "JobProvisioningState"]] = None,
parent_job_name: Optional[str] = None,
display_name: Optional[str] = None,
experiment_name: Optional[str] = None,
status: Optional[Union[str, "JobStatus"]] = None,
interaction_endpoints: Optional[Dict[str, "JobEndpoint"]] = None,
identity: Optional["MfeInternalIdentityConfiguration"] = None,
compute: Optional["ComputeConfiguration"] = None,
priority: Optional[int] = None,
output: Optional["JobOutputArtifacts"] = None,
is_archived: Optional[bool] = None,
schedule: Optional["ScheduleBase"] = None,
component_id: Optional[str] = None,
notification_setting: Optional["NotificationSetting"] = None,
secrets_configuration: Optional[Dict[str, "MfeInternalSecretConfiguration"]] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword job_type: Possible values include: "Command", "Sweep", "Labeling", "Pipeline", "Data",
"AutoML", "Spark", "Base".
:paramtype job_type: str or ~flow.models.JobType
:keyword resources:
:paramtype resources: ~flow.models.SparkResourceConfiguration
:keyword args:
:paramtype args: str
:keyword code_id:
:paramtype code_id: str
:keyword entry:
:paramtype entry: ~flow.models.SparkJobEntry
:keyword py_files:
:paramtype py_files: list[str]
:keyword jars:
:paramtype jars: list[str]
:keyword files:
:paramtype files: list[str]
:keyword archives:
:paramtype archives: list[str]
:keyword environment_id:
:paramtype environment_id: str
:keyword input_data_bindings: Dictionary of :code:`<InputDataBinding>`.
:paramtype input_data_bindings: dict[str, ~flow.models.InputDataBinding]
:keyword output_data_bindings: Dictionary of :code:`<OutputDataBinding>`.
:paramtype output_data_bindings: dict[str, ~flow.models.OutputDataBinding]
:keyword conf: Dictionary of :code:`<string>`.
:paramtype conf: dict[str, str]
:keyword environment_variables: Dictionary of :code:`<string>`.
:paramtype environment_variables: dict[str, str]
:keyword provisioning_state: Possible values include: "Succeeded", "Failed", "Canceled",
"InProgress".
:paramtype provisioning_state: str or ~flow.models.JobProvisioningState
:keyword parent_job_name:
:paramtype parent_job_name: str
:keyword display_name:
:paramtype display_name: str
:keyword experiment_name:
:paramtype experiment_name: str
:keyword status: Possible values include: "NotStarted", "Starting", "Provisioning",
"Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed", "Failed",
"Canceled", "NotResponding", "Paused", "Unknown", "Scheduled".
:paramtype status: str or ~flow.models.JobStatus
:keyword interaction_endpoints: Dictionary of :code:`<JobEndpoint>`.
:paramtype interaction_endpoints: dict[str, ~flow.models.JobEndpoint]
:keyword identity:
:paramtype identity: ~flow.models.MfeInternalIdentityConfiguration
:keyword compute:
:paramtype compute: ~flow.models.ComputeConfiguration
:keyword priority:
:paramtype priority: int
:keyword output:
:paramtype output: ~flow.models.JobOutputArtifacts
:keyword is_archived:
:paramtype is_archived: bool
:keyword schedule:
:paramtype schedule: ~flow.models.ScheduleBase
:keyword component_id:
:paramtype component_id: str
:keyword notification_setting:
:paramtype notification_setting: ~flow.models.NotificationSetting
:keyword secrets_configuration: Dictionary of :code:`<MfeInternalSecretConfiguration>`.
:paramtype secrets_configuration: dict[str, ~flow.models.MfeInternalSecretConfiguration]
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
"""
super(SparkJob, self).__init__(**kwargs)
self.job_type = job_type
self.resources = resources
self.args = args
self.code_id = code_id
self.entry = entry
self.py_files = py_files
self.jars = jars
self.files = files
self.archives = archives
self.environment_id = environment_id
self.input_data_bindings = input_data_bindings
self.output_data_bindings = output_data_bindings
self.conf = conf
self.environment_variables = environment_variables
self.provisioning_state = provisioning_state
self.parent_job_name = parent_job_name
self.display_name = display_name
self.experiment_name = experiment_name
self.status = status
self.interaction_endpoints = interaction_endpoints
self.identity = identity
self.compute = compute
self.priority = priority
self.output = output
self.is_archived = is_archived
self.schedule = schedule
self.component_id = component_id
self.notification_setting = notification_setting
self.secrets_configuration = secrets_configuration
self.description = description
self.tags = tags
self.properties = properties
class SparkJobEntry(msrest.serialization.Model):
"""SparkJobEntry.
:ivar file:
:vartype file: str
:ivar class_name:
:vartype class_name: str
"""
_attribute_map = {
'file': {'key': 'file', 'type': 'str'},
'class_name': {'key': 'className', 'type': 'str'},
}
def __init__(
self,
*,
file: Optional[str] = None,
class_name: Optional[str] = None,
**kwargs
):
"""
:keyword file:
:paramtype file: str
:keyword class_name:
:paramtype class_name: str
"""
super(SparkJobEntry, self).__init__(**kwargs)
self.file = file
self.class_name = class_name
class SparkMavenPackage(msrest.serialization.Model):
"""SparkMavenPackage.
:ivar group:
:vartype group: str
:ivar artifact:
:vartype artifact: str
:ivar version:
:vartype version: str
"""
_attribute_map = {
'group': {'key': 'group', 'type': 'str'},
'artifact': {'key': 'artifact', 'type': 'str'},
'version': {'key': 'version', 'type': 'str'},
}
def __init__(
self,
*,
group: Optional[str] = None,
artifact: Optional[str] = None,
version: Optional[str] = None,
**kwargs
):
"""
:keyword group:
:paramtype group: str
:keyword artifact:
:paramtype artifact: str
:keyword version:
:paramtype version: str
"""
super(SparkMavenPackage, self).__init__(**kwargs)
self.group = group
self.artifact = artifact
self.version = version
class SparkPythonTaskDto(msrest.serialization.Model):
"""SparkPythonTaskDto.
:ivar python_file:
:vartype python_file: str
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'python_file': {'key': 'python_file', 'type': 'str'},
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
*,
python_file: Optional[str] = None,
parameters: Optional[List[str]] = None,
**kwargs
):
"""
:keyword python_file:
:paramtype python_file: str
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkPythonTaskDto, self).__init__(**kwargs)
self.python_file = python_file
self.parameters = parameters
class SparkResourceConfiguration(msrest.serialization.Model):
"""SparkResourceConfiguration.
:ivar instance_type:
:vartype instance_type: str
:ivar runtime_version:
:vartype runtime_version: str
"""
_attribute_map = {
'instance_type': {'key': 'instanceType', 'type': 'str'},
'runtime_version': {'key': 'runtimeVersion', 'type': 'str'},
}
def __init__(
self,
*,
instance_type: Optional[str] = None,
runtime_version: Optional[str] = None,
**kwargs
):
"""
:keyword instance_type:
:paramtype instance_type: str
:keyword runtime_version:
:paramtype runtime_version: str
"""
super(SparkResourceConfiguration, self).__init__(**kwargs)
self.instance_type = instance_type
self.runtime_version = runtime_version
class SparkSection(msrest.serialization.Model):
"""SparkSection.
:ivar repositories:
:vartype repositories: list[str]
:ivar packages:
:vartype packages: list[~flow.models.SparkMavenPackage]
:ivar precache_packages:
:vartype precache_packages: bool
"""
_attribute_map = {
'repositories': {'key': 'repositories', 'type': '[str]'},
'packages': {'key': 'packages', 'type': '[SparkMavenPackage]'},
'precache_packages': {'key': 'precachePackages', 'type': 'bool'},
}
def __init__(
self,
*,
repositories: Optional[List[str]] = None,
packages: Optional[List["SparkMavenPackage"]] = None,
precache_packages: Optional[bool] = None,
**kwargs
):
"""
:keyword repositories:
:paramtype repositories: list[str]
:keyword packages:
:paramtype packages: list[~flow.models.SparkMavenPackage]
:keyword precache_packages:
:paramtype precache_packages: bool
"""
super(SparkSection, self).__init__(**kwargs)
self.repositories = repositories
self.packages = packages
self.precache_packages = precache_packages
class SparkSubmitTaskDto(msrest.serialization.Model):
"""SparkSubmitTaskDto.
:ivar parameters:
:vartype parameters: list[str]
"""
_attribute_map = {
'parameters': {'key': 'parameters', 'type': '[str]'},
}
def __init__(
self,
*,
parameters: Optional[List[str]] = None,
**kwargs
):
"""
:keyword parameters:
:paramtype parameters: list[str]
"""
super(SparkSubmitTaskDto, self).__init__(**kwargs)
self.parameters = parameters
class SqlDataPath(msrest.serialization.Model):
"""SqlDataPath.
:ivar sql_table_name:
:vartype sql_table_name: str
:ivar sql_query:
:vartype sql_query: str
:ivar sql_stored_procedure_name:
:vartype sql_stored_procedure_name: str
:ivar sql_stored_procedure_params:
:vartype sql_stored_procedure_params: list[~flow.models.StoredProcedureParameter]
"""
_attribute_map = {
'sql_table_name': {'key': 'sqlTableName', 'type': 'str'},
'sql_query': {'key': 'sqlQuery', 'type': 'str'},
'sql_stored_procedure_name': {'key': 'sqlStoredProcedureName', 'type': 'str'},
'sql_stored_procedure_params': {'key': 'sqlStoredProcedureParams', 'type': '[StoredProcedureParameter]'},
}
def __init__(
self,
*,
sql_table_name: Optional[str] = None,
sql_query: Optional[str] = None,
sql_stored_procedure_name: Optional[str] = None,
sql_stored_procedure_params: Optional[List["StoredProcedureParameter"]] = None,
**kwargs
):
"""
:keyword sql_table_name:
:paramtype sql_table_name: str
:keyword sql_query:
:paramtype sql_query: str
:keyword sql_stored_procedure_name:
:paramtype sql_stored_procedure_name: str
:keyword sql_stored_procedure_params:
:paramtype sql_stored_procedure_params: list[~flow.models.StoredProcedureParameter]
"""
super(SqlDataPath, self).__init__(**kwargs)
self.sql_table_name = sql_table_name
self.sql_query = sql_query
self.sql_stored_procedure_name = sql_stored_procedure_name
self.sql_stored_procedure_params = sql_stored_procedure_params
class StackEnsembleSettings(msrest.serialization.Model):
"""StackEnsembleSettings.
:ivar stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:vartype stack_meta_learner_type: str or ~flow.models.StackMetaLearnerType
:ivar stack_meta_learner_train_percentage:
:vartype stack_meta_learner_train_percentage: float
:ivar stack_meta_learner_k_wargs: Anything.
:vartype stack_meta_learner_k_wargs: any
"""
_attribute_map = {
'stack_meta_learner_type': {'key': 'stackMetaLearnerType', 'type': 'str'},
'stack_meta_learner_train_percentage': {'key': 'stackMetaLearnerTrainPercentage', 'type': 'float'},
'stack_meta_learner_k_wargs': {'key': 'stackMetaLearnerKWargs', 'type': 'object'},
}
def __init__(
self,
*,
stack_meta_learner_type: Optional[Union[str, "StackMetaLearnerType"]] = None,
stack_meta_learner_train_percentage: Optional[float] = None,
stack_meta_learner_k_wargs: Optional[Any] = None,
**kwargs
):
"""
:keyword stack_meta_learner_type: Possible values include: "None", "LogisticRegression",
"LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
"LightGBMRegressor", "LinearRegression".
:paramtype stack_meta_learner_type: str or ~flow.models.StackMetaLearnerType
:keyword stack_meta_learner_train_percentage:
:paramtype stack_meta_learner_train_percentage: float
:keyword stack_meta_learner_k_wargs: Anything.
:paramtype stack_meta_learner_k_wargs: any
"""
super(StackEnsembleSettings, self).__init__(**kwargs)
self.stack_meta_learner_type = stack_meta_learner_type
self.stack_meta_learner_train_percentage = stack_meta_learner_train_percentage
self.stack_meta_learner_k_wargs = stack_meta_learner_k_wargs
class StandbyPoolProperties(msrest.serialization.Model):
"""StandbyPoolProperties.
:ivar name:
:vartype name: str
:ivar count:
:vartype count: int
:ivar vm_size:
:vartype vm_size: str
:ivar standby_available_instances:
:vartype standby_available_instances: list[~flow.models.StandbyPoolResourceStatus]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'count': {'key': 'count', 'type': 'int'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'standby_available_instances': {'key': 'standbyAvailableInstances', 'type': '[StandbyPoolResourceStatus]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
count: Optional[int] = None,
vm_size: Optional[str] = None,
standby_available_instances: Optional[List["StandbyPoolResourceStatus"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword count:
:paramtype count: int
:keyword vm_size:
:paramtype vm_size: str
:keyword standby_available_instances:
:paramtype standby_available_instances: list[~flow.models.StandbyPoolResourceStatus]
"""
super(StandbyPoolProperties, self).__init__(**kwargs)
self.name = name
self.count = count
self.vm_size = vm_size
self.standby_available_instances = standby_available_instances
class StandbyPoolResourceStatus(msrest.serialization.Model):
"""StandbyPoolResourceStatus.
:ivar status:
:vartype status: str
:ivar error:
:vartype error: ~flow.models.CloudError
"""
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'error': {'key': 'error', 'type': 'CloudError'},
}
def __init__(
self,
*,
status: Optional[str] = None,
error: Optional["CloudError"] = None,
**kwargs
):
"""
:keyword status:
:paramtype status: str
:keyword error:
:paramtype error: ~flow.models.CloudError
"""
super(StandbyPoolResourceStatus, self).__init__(**kwargs)
self.status = status
self.error = error
class StartRunResult(msrest.serialization.Model):
"""StartRunResult.
All required parameters must be populated in order to send to Azure.
:ivar run_id: Required.
:vartype run_id: str
"""
_validation = {
'run_id': {'required': True, 'min_length': 1},
}
_attribute_map = {
'run_id': {'key': 'runId', 'type': 'str'},
}
def __init__(
self,
*,
run_id: str,
**kwargs
):
"""
:keyword run_id: Required.
:paramtype run_id: str
"""
super(StartRunResult, self).__init__(**kwargs)
self.run_id = run_id
class StepRunProfile(msrest.serialization.Model):
"""StepRunProfile.
:ivar step_run_id:
:vartype step_run_id: str
:ivar step_run_number:
:vartype step_run_number: int
:ivar run_url:
:vartype run_url: str
:ivar compute_target:
:vartype compute_target: str
:ivar compute_target_url:
:vartype compute_target_url: str
:ivar node_id:
:vartype node_id: str
:ivar node_name:
:vartype node_name: str
:ivar step_name:
:vartype step_name: str
:ivar create_time:
:vartype create_time: long
:ivar start_time:
:vartype start_time: long
:ivar end_time:
:vartype end_time: long
:ivar status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:vartype status: str or ~flow.models.RunStatus
:ivar status_detail:
:vartype status_detail: str
:ivar is_reused:
:vartype is_reused: bool
:ivar reused_pipeline_run_id:
:vartype reused_pipeline_run_id: str
:ivar reused_step_run_id:
:vartype reused_step_run_id: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar status_timeline:
:vartype status_timeline: list[~flow.models.RunStatusPeriod]
"""
_attribute_map = {
'step_run_id': {'key': 'stepRunId', 'type': 'str'},
'step_run_number': {'key': 'stepRunNumber', 'type': 'int'},
'run_url': {'key': 'runUrl', 'type': 'str'},
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'compute_target_url': {'key': 'computeTargetUrl', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'node_name': {'key': 'nodeName', 'type': 'str'},
'step_name': {'key': 'stepName', 'type': 'str'},
'create_time': {'key': 'createTime', 'type': 'long'},
'start_time': {'key': 'startTime', 'type': 'long'},
'end_time': {'key': 'endTime', 'type': 'long'},
'status': {'key': 'status', 'type': 'str'},
'status_detail': {'key': 'statusDetail', 'type': 'str'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
'reused_pipeline_run_id': {'key': 'reusedPipelineRunId', 'type': 'str'},
'reused_step_run_id': {'key': 'reusedStepRunId', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'status_timeline': {'key': 'statusTimeline', 'type': '[RunStatusPeriod]'},
}
def __init__(
self,
*,
step_run_id: Optional[str] = None,
step_run_number: Optional[int] = None,
run_url: Optional[str] = None,
compute_target: Optional[str] = None,
compute_target_url: Optional[str] = None,
node_id: Optional[str] = None,
node_name: Optional[str] = None,
step_name: Optional[str] = None,
create_time: Optional[int] = None,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
status: Optional[Union[str, "RunStatus"]] = None,
status_detail: Optional[str] = None,
is_reused: Optional[bool] = None,
reused_pipeline_run_id: Optional[str] = None,
reused_step_run_id: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
status_timeline: Optional[List["RunStatusPeriod"]] = None,
**kwargs
):
"""
:keyword step_run_id:
:paramtype step_run_id: str
:keyword step_run_number:
:paramtype step_run_number: int
:keyword run_url:
:paramtype run_url: str
:keyword compute_target:
:paramtype compute_target: str
:keyword compute_target_url:
:paramtype compute_target_url: str
:keyword node_id:
:paramtype node_id: str
:keyword node_name:
:paramtype node_name: str
:keyword step_name:
:paramtype step_name: str
:keyword create_time:
:paramtype create_time: long
:keyword start_time:
:paramtype start_time: long
:keyword end_time:
:paramtype end_time: long
:keyword status: Possible values include: "NotStarted", "Unapproved", "Pausing", "Paused",
"Starting", "Preparing", "Queued", "Running", "Finalizing", "CancelRequested", "Completed",
"Failed", "Canceled".
:paramtype status: str or ~flow.models.RunStatus
:keyword status_detail:
:paramtype status_detail: str
:keyword is_reused:
:paramtype is_reused: bool
:keyword reused_pipeline_run_id:
:paramtype reused_pipeline_run_id: str
:keyword reused_step_run_id:
:paramtype reused_step_run_id: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword status_timeline:
:paramtype status_timeline: list[~flow.models.RunStatusPeriod]
"""
super(StepRunProfile, self).__init__(**kwargs)
self.step_run_id = step_run_id
self.step_run_number = step_run_number
self.run_url = run_url
self.compute_target = compute_target
self.compute_target_url = compute_target_url
self.node_id = node_id
self.node_name = node_name
self.step_name = step_name
self.create_time = create_time
self.start_time = start_time
self.end_time = end_time
self.status = status
self.status_detail = status_detail
self.is_reused = is_reused
self.reused_pipeline_run_id = reused_pipeline_run_id
self.reused_step_run_id = reused_step_run_id
self.tags = tags
self.status_timeline = status_timeline
class StorageInfo(msrest.serialization.Model):
"""StorageInfo.
:ivar storage_auth_type: Possible values include: "MSI", "ConnectionString", "SAS".
:vartype storage_auth_type: str or ~flow.models.StorageAuthType
:ivar connection_string:
:vartype connection_string: str
:ivar sas_token:
:vartype sas_token: str
:ivar account_name:
:vartype account_name: str
"""
_attribute_map = {
'storage_auth_type': {'key': 'storageAuthType', 'type': 'str'},
'connection_string': {'key': 'connectionString', 'type': 'str'},
'sas_token': {'key': 'sasToken', 'type': 'str'},
'account_name': {'key': 'accountName', 'type': 'str'},
}
def __init__(
self,
*,
storage_auth_type: Optional[Union[str, "StorageAuthType"]] = None,
connection_string: Optional[str] = None,
sas_token: Optional[str] = None,
account_name: Optional[str] = None,
**kwargs
):
"""
:keyword storage_auth_type: Possible values include: "MSI", "ConnectionString", "SAS".
:paramtype storage_auth_type: str or ~flow.models.StorageAuthType
:keyword connection_string:
:paramtype connection_string: str
:keyword sas_token:
:paramtype sas_token: str
:keyword account_name:
:paramtype account_name: str
"""
super(StorageInfo, self).__init__(**kwargs)
self.storage_auth_type = storage_auth_type
self.connection_string = connection_string
self.sas_token = sas_token
self.account_name = account_name
class StoredProcedureParameter(msrest.serialization.Model):
"""StoredProcedureParameter.
:ivar name:
:vartype name: str
:ivar value:
:vartype value: str
:ivar type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:vartype type: str or ~flow.models.StoredProcedureParameterType
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
value: Optional[str] = None,
type: Optional[Union[str, "StoredProcedureParameterType"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword value:
:paramtype value: str
:keyword type: Possible values include: "String", "Int", "Decimal", "Guid", "Boolean", "Date".
:paramtype type: str or ~flow.models.StoredProcedureParameterType
"""
super(StoredProcedureParameter, self).__init__(**kwargs)
self.name = name
self.value = value
self.type = type
class Stream(msrest.serialization.Model):
"""Stream.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar can_read:
:vartype can_read: bool
:ivar can_write:
:vartype can_write: bool
:ivar can_seek:
:vartype can_seek: bool
:ivar can_timeout:
:vartype can_timeout: bool
:ivar length:
:vartype length: long
:ivar position:
:vartype position: long
:ivar read_timeout:
:vartype read_timeout: int
:ivar write_timeout:
:vartype write_timeout: int
"""
_validation = {
'can_read': {'readonly': True},
'can_write': {'readonly': True},
'can_seek': {'readonly': True},
'can_timeout': {'readonly': True},
'length': {'readonly': True},
}
_attribute_map = {
'can_read': {'key': 'canRead', 'type': 'bool'},
'can_write': {'key': 'canWrite', 'type': 'bool'},
'can_seek': {'key': 'canSeek', 'type': 'bool'},
'can_timeout': {'key': 'canTimeout', 'type': 'bool'},
'length': {'key': 'length', 'type': 'long'},
'position': {'key': 'position', 'type': 'long'},
'read_timeout': {'key': 'readTimeout', 'type': 'int'},
'write_timeout': {'key': 'writeTimeout', 'type': 'int'},
}
def __init__(
self,
*,
position: Optional[int] = None,
read_timeout: Optional[int] = None,
write_timeout: Optional[int] = None,
**kwargs
):
"""
:keyword position:
:paramtype position: long
:keyword read_timeout:
:paramtype read_timeout: int
:keyword write_timeout:
:paramtype write_timeout: int
"""
super(Stream, self).__init__(**kwargs)
self.can_read = None
self.can_write = None
self.can_seek = None
self.can_timeout = None
self.length = None
self.position = position
self.read_timeout = read_timeout
self.write_timeout = write_timeout
class StructuredInterface(msrest.serialization.Model):
"""StructuredInterface.
:ivar command_line_pattern:
:vartype command_line_pattern: str
:ivar inputs:
:vartype inputs: list[~flow.models.StructuredInterfaceInput]
:ivar outputs:
:vartype outputs: list[~flow.models.StructuredInterfaceOutput]
:ivar control_outputs:
:vartype control_outputs: list[~flow.models.ControlOutput]
:ivar parameters:
:vartype parameters: list[~flow.models.StructuredInterfaceParameter]
:ivar metadata_parameters:
:vartype metadata_parameters: list[~flow.models.StructuredInterfaceParameter]
:ivar arguments:
:vartype arguments: list[~flow.models.ArgumentAssignment]
"""
_attribute_map = {
'command_line_pattern': {'key': 'commandLinePattern', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '[StructuredInterfaceInput]'},
'outputs': {'key': 'outputs', 'type': '[StructuredInterfaceOutput]'},
'control_outputs': {'key': 'controlOutputs', 'type': '[ControlOutput]'},
'parameters': {'key': 'parameters', 'type': '[StructuredInterfaceParameter]'},
'metadata_parameters': {'key': 'metadataParameters', 'type': '[StructuredInterfaceParameter]'},
'arguments': {'key': 'arguments', 'type': '[ArgumentAssignment]'},
}
def __init__(
self,
*,
command_line_pattern: Optional[str] = None,
inputs: Optional[List["StructuredInterfaceInput"]] = None,
outputs: Optional[List["StructuredInterfaceOutput"]] = None,
control_outputs: Optional[List["ControlOutput"]] = None,
parameters: Optional[List["StructuredInterfaceParameter"]] = None,
metadata_parameters: Optional[List["StructuredInterfaceParameter"]] = None,
arguments: Optional[List["ArgumentAssignment"]] = None,
**kwargs
):
"""
:keyword command_line_pattern:
:paramtype command_line_pattern: str
:keyword inputs:
:paramtype inputs: list[~flow.models.StructuredInterfaceInput]
:keyword outputs:
:paramtype outputs: list[~flow.models.StructuredInterfaceOutput]
:keyword control_outputs:
:paramtype control_outputs: list[~flow.models.ControlOutput]
:keyword parameters:
:paramtype parameters: list[~flow.models.StructuredInterfaceParameter]
:keyword metadata_parameters:
:paramtype metadata_parameters: list[~flow.models.StructuredInterfaceParameter]
:keyword arguments:
:paramtype arguments: list[~flow.models.ArgumentAssignment]
"""
super(StructuredInterface, self).__init__(**kwargs)
self.command_line_pattern = command_line_pattern
self.inputs = inputs
self.outputs = outputs
self.control_outputs = control_outputs
self.parameters = parameters
self.metadata_parameters = metadata_parameters
self.arguments = arguments
class StructuredInterfaceInput(msrest.serialization.Model):
"""StructuredInterfaceInput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_ids_list:
:vartype data_type_ids_list: list[str]
:ivar is_optional:
:vartype is_optional: bool
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_resource:
:vartype is_resource: bool
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar dataset_types:
:vartype dataset_types: list[str or ~flow.models.DatasetType]
"""
_validation = {
'dataset_types': {'unique': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_ids_list': {'key': 'dataTypeIdsList', 'type': '[str]'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_resource': {'key': 'isResource', 'type': 'bool'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'dataset_types': {'key': 'datasetTypes', 'type': '[str]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
data_type_ids_list: Optional[List[str]] = None,
is_optional: Optional[bool] = None,
description: Optional[str] = None,
skip_processing: Optional[bool] = None,
is_resource: Optional[bool] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
dataset_types: Optional[List[Union[str, "DatasetType"]]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_ids_list:
:paramtype data_type_ids_list: list[str]
:keyword is_optional:
:paramtype is_optional: bool
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_resource:
:paramtype is_resource: bool
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword dataset_types:
:paramtype dataset_types: list[str or ~flow.models.DatasetType]
"""
super(StructuredInterfaceInput, self).__init__(**kwargs)
self.name = name
self.label = label
self.data_type_ids_list = data_type_ids_list
self.is_optional = is_optional
self.description = description
self.skip_processing = skip_processing
self.is_resource = is_resource
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.dataset_types = dataset_types
class StructuredInterfaceOutput(msrest.serialization.Model):
"""StructuredInterfaceOutput.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar data_type_id:
:vartype data_type_id: str
:ivar pass_through_data_type_input_name:
:vartype pass_through_data_type_input_name: str
:ivar description:
:vartype description: str
:ivar skip_processing:
:vartype skip_processing: bool
:ivar is_artifact:
:vartype is_artifact: bool
:ivar data_store_name:
:vartype data_store_name: str
:ivar data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:vartype data_store_mode: str or ~flow.models.AEVADataStoreMode
:ivar path_on_compute:
:vartype path_on_compute: str
:ivar overwrite:
:vartype overwrite: bool
:ivar data_reference_name:
:vartype data_reference_name: str
:ivar training_output:
:vartype training_output: ~flow.models.TrainingOutput
:ivar dataset_output:
:vartype dataset_output: ~flow.models.DatasetOutput
:ivar asset_output_settings:
:vartype asset_output_settings: ~flow.models.AssetOutputSettings
:ivar early_available:
:vartype early_available: bool
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'data_type_id': {'key': 'dataTypeId', 'type': 'str'},
'pass_through_data_type_input_name': {'key': 'passThroughDataTypeInputName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'skip_processing': {'key': 'skipProcessing', 'type': 'bool'},
'is_artifact': {'key': 'IsArtifact', 'type': 'bool'},
'data_store_name': {'key': 'dataStoreName', 'type': 'str'},
'data_store_mode': {'key': 'dataStoreMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'data_reference_name': {'key': 'dataReferenceName', 'type': 'str'},
'training_output': {'key': 'trainingOutput', 'type': 'TrainingOutput'},
'dataset_output': {'key': 'datasetOutput', 'type': 'DatasetOutput'},
'asset_output_settings': {'key': 'AssetOutputSettings', 'type': 'AssetOutputSettings'},
'early_available': {'key': 'EarlyAvailable', 'type': 'bool'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
data_type_id: Optional[str] = None,
pass_through_data_type_input_name: Optional[str] = None,
description: Optional[str] = None,
skip_processing: Optional[bool] = None,
is_artifact: Optional[bool] = None,
data_store_name: Optional[str] = None,
data_store_mode: Optional[Union[str, "AEVADataStoreMode"]] = None,
path_on_compute: Optional[str] = None,
overwrite: Optional[bool] = None,
data_reference_name: Optional[str] = None,
training_output: Optional["TrainingOutput"] = None,
dataset_output: Optional["DatasetOutput"] = None,
asset_output_settings: Optional["AssetOutputSettings"] = None,
early_available: Optional[bool] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword data_type_id:
:paramtype data_type_id: str
:keyword pass_through_data_type_input_name:
:paramtype pass_through_data_type_input_name: str
:keyword description:
:paramtype description: str
:keyword skip_processing:
:paramtype skip_processing: bool
:keyword is_artifact:
:paramtype is_artifact: bool
:keyword data_store_name:
:paramtype data_store_name: str
:keyword data_store_mode: Possible values include: "None", "Mount", "Download", "Upload",
"Direct", "Hdfs", "Link".
:paramtype data_store_mode: str or ~flow.models.AEVADataStoreMode
:keyword path_on_compute:
:paramtype path_on_compute: str
:keyword overwrite:
:paramtype overwrite: bool
:keyword data_reference_name:
:paramtype data_reference_name: str
:keyword training_output:
:paramtype training_output: ~flow.models.TrainingOutput
:keyword dataset_output:
:paramtype dataset_output: ~flow.models.DatasetOutput
:keyword asset_output_settings:
:paramtype asset_output_settings: ~flow.models.AssetOutputSettings
:keyword early_available:
:paramtype early_available: bool
"""
super(StructuredInterfaceOutput, self).__init__(**kwargs)
self.name = name
self.label = label
self.data_type_id = data_type_id
self.pass_through_data_type_input_name = pass_through_data_type_input_name
self.description = description
self.skip_processing = skip_processing
self.is_artifact = is_artifact
self.data_store_name = data_store_name
self.data_store_mode = data_store_mode
self.path_on_compute = path_on_compute
self.overwrite = overwrite
self.data_reference_name = data_reference_name
self.training_output = training_output
self.dataset_output = dataset_output
self.asset_output_settings = asset_output_settings
self.early_available = early_available
class StructuredInterfaceParameter(msrest.serialization.Model):
"""StructuredInterfaceParameter.
:ivar name:
:vartype name: str
:ivar label:
:vartype label: str
:ivar parameter_type: Possible values include: "Int", "Double", "Bool", "String", "Undefined".
:vartype parameter_type: str or ~flow.models.ParameterType
:ivar is_optional:
:vartype is_optional: bool
:ivar default_value:
:vartype default_value: str
:ivar lower_bound:
:vartype lower_bound: str
:ivar upper_bound:
:vartype upper_bound: str
:ivar enum_values:
:vartype enum_values: list[str]
:ivar enum_values_to_argument_strings: This is a dictionary.
:vartype enum_values_to_argument_strings: dict[str, str]
:ivar description:
:vartype description: str
:ivar set_environment_variable:
:vartype set_environment_variable: bool
:ivar environment_variable_override:
:vartype environment_variable_override: str
:ivar enabled_by_parameter_name:
:vartype enabled_by_parameter_name: str
:ivar enabled_by_parameter_values:
:vartype enabled_by_parameter_values: list[str]
:ivar ui_hint:
:vartype ui_hint: ~flow.models.UIParameterHint
:ivar group_names:
:vartype group_names: list[str]
:ivar argument_name:
:vartype argument_name: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'parameter_type': {'key': 'parameterType', 'type': 'str'},
'is_optional': {'key': 'isOptional', 'type': 'bool'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'lower_bound': {'key': 'lowerBound', 'type': 'str'},
'upper_bound': {'key': 'upperBound', 'type': 'str'},
'enum_values': {'key': 'enumValues', 'type': '[str]'},
'enum_values_to_argument_strings': {'key': 'enumValuesToArgumentStrings', 'type': '{str}'},
'description': {'key': 'description', 'type': 'str'},
'set_environment_variable': {'key': 'setEnvironmentVariable', 'type': 'bool'},
'environment_variable_override': {'key': 'environmentVariableOverride', 'type': 'str'},
'enabled_by_parameter_name': {'key': 'enabledByParameterName', 'type': 'str'},
'enabled_by_parameter_values': {'key': 'enabledByParameterValues', 'type': '[str]'},
'ui_hint': {'key': 'uiHint', 'type': 'UIParameterHint'},
'group_names': {'key': 'groupNames', 'type': '[str]'},
'argument_name': {'key': 'argumentName', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
label: Optional[str] = None,
parameter_type: Optional[Union[str, "ParameterType"]] = None,
is_optional: Optional[bool] = None,
default_value: Optional[str] = None,
lower_bound: Optional[str] = None,
upper_bound: Optional[str] = None,
enum_values: Optional[List[str]] = None,
enum_values_to_argument_strings: Optional[Dict[str, str]] = None,
description: Optional[str] = None,
set_environment_variable: Optional[bool] = None,
environment_variable_override: Optional[str] = None,
enabled_by_parameter_name: Optional[str] = None,
enabled_by_parameter_values: Optional[List[str]] = None,
ui_hint: Optional["UIParameterHint"] = None,
group_names: Optional[List[str]] = None,
argument_name: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword label:
:paramtype label: str
:keyword parameter_type: Possible values include: "Int", "Double", "Bool", "String",
"Undefined".
:paramtype parameter_type: str or ~flow.models.ParameterType
:keyword is_optional:
:paramtype is_optional: bool
:keyword default_value:
:paramtype default_value: str
:keyword lower_bound:
:paramtype lower_bound: str
:keyword upper_bound:
:paramtype upper_bound: str
:keyword enum_values:
:paramtype enum_values: list[str]
:keyword enum_values_to_argument_strings: This is a dictionary.
:paramtype enum_values_to_argument_strings: dict[str, str]
:keyword description:
:paramtype description: str
:keyword set_environment_variable:
:paramtype set_environment_variable: bool
:keyword environment_variable_override:
:paramtype environment_variable_override: str
:keyword enabled_by_parameter_name:
:paramtype enabled_by_parameter_name: str
:keyword enabled_by_parameter_values:
:paramtype enabled_by_parameter_values: list[str]
:keyword ui_hint:
:paramtype ui_hint: ~flow.models.UIParameterHint
:keyword group_names:
:paramtype group_names: list[str]
:keyword argument_name:
:paramtype argument_name: str
"""
super(StructuredInterfaceParameter, self).__init__(**kwargs)
self.name = name
self.label = label
self.parameter_type = parameter_type
self.is_optional = is_optional
self.default_value = default_value
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.enum_values = enum_values
self.enum_values_to_argument_strings = enum_values_to_argument_strings
self.description = description
self.set_environment_variable = set_environment_variable
self.environment_variable_override = environment_variable_override
self.enabled_by_parameter_name = enabled_by_parameter_name
self.enabled_by_parameter_values = enabled_by_parameter_values
self.ui_hint = ui_hint
self.group_names = group_names
self.argument_name = argument_name
class StudioMigrationInfo(msrest.serialization.Model):
"""StudioMigrationInfo.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar source_workspace_id:
:vartype source_workspace_id: str
:ivar source_experiment_id:
:vartype source_experiment_id: str
:ivar source_experiment_link:
:vartype source_experiment_link: str
:ivar failed_node_id_list:
:vartype failed_node_id_list: list[str]
:ivar error_message:
:vartype error_message: str
"""
_validation = {
'error_message': {'readonly': True},
}
_attribute_map = {
'source_workspace_id': {'key': 'sourceWorkspaceId', 'type': 'str'},
'source_experiment_id': {'key': 'sourceExperimentId', 'type': 'str'},
'source_experiment_link': {'key': 'sourceExperimentLink', 'type': 'str'},
'failed_node_id_list': {'key': 'failedNodeIdList', 'type': '[str]'},
'error_message': {'key': 'errorMessage', 'type': 'str'},
}
def __init__(
self,
*,
source_workspace_id: Optional[str] = None,
source_experiment_id: Optional[str] = None,
source_experiment_link: Optional[str] = None,
failed_node_id_list: Optional[List[str]] = None,
**kwargs
):
"""
:keyword source_workspace_id:
:paramtype source_workspace_id: str
:keyword source_experiment_id:
:paramtype source_experiment_id: str
:keyword source_experiment_link:
:paramtype source_experiment_link: str
:keyword failed_node_id_list:
:paramtype failed_node_id_list: list[str]
"""
super(StudioMigrationInfo, self).__init__(**kwargs)
self.source_workspace_id = source_workspace_id
self.source_experiment_id = source_experiment_id
self.source_experiment_link = source_experiment_link
self.failed_node_id_list = failed_node_id_list
self.error_message = None
class SubGraphConcatenateAssignment(msrest.serialization.Model):
"""SubGraphConcatenateAssignment.
:ivar concatenate_parameter:
:vartype concatenate_parameter: list[~flow.models.ParameterAssignment]
:ivar parameter_assignments:
:vartype parameter_assignments: ~flow.models.SubPipelineParameterAssignment
"""
_attribute_map = {
'concatenate_parameter': {'key': 'concatenateParameter', 'type': '[ParameterAssignment]'},
'parameter_assignments': {'key': 'parameterAssignments', 'type': 'SubPipelineParameterAssignment'},
}
def __init__(
self,
*,
concatenate_parameter: Optional[List["ParameterAssignment"]] = None,
parameter_assignments: Optional["SubPipelineParameterAssignment"] = None,
**kwargs
):
"""
:keyword concatenate_parameter:
:paramtype concatenate_parameter: list[~flow.models.ParameterAssignment]
:keyword parameter_assignments:
:paramtype parameter_assignments: ~flow.models.SubPipelineParameterAssignment
"""
super(SubGraphConcatenateAssignment, self).__init__(**kwargs)
self.concatenate_parameter = concatenate_parameter
self.parameter_assignments = parameter_assignments
class SubGraphConfiguration(msrest.serialization.Model):
"""SubGraphConfiguration.
:ivar graph_id:
:vartype graph_id: str
:ivar graph_draft_id:
:vartype graph_draft_id: str
:ivar default_cloud_priority:
:vartype default_cloud_priority: ~flow.models.CloudPrioritySetting
:ivar is_dynamic:
:vartype is_dynamic: bool
"""
_attribute_map = {
'graph_id': {'key': 'graphId', 'type': 'str'},
'graph_draft_id': {'key': 'graphDraftId', 'type': 'str'},
'default_cloud_priority': {'key': 'DefaultCloudPriority', 'type': 'CloudPrioritySetting'},
'is_dynamic': {'key': 'IsDynamic', 'type': 'bool'},
}
def __init__(
self,
*,
graph_id: Optional[str] = None,
graph_draft_id: Optional[str] = None,
default_cloud_priority: Optional["CloudPrioritySetting"] = None,
is_dynamic: Optional[bool] = False,
**kwargs
):
"""
:keyword graph_id:
:paramtype graph_id: str
:keyword graph_draft_id:
:paramtype graph_draft_id: str
:keyword default_cloud_priority:
:paramtype default_cloud_priority: ~flow.models.CloudPrioritySetting
:keyword is_dynamic:
:paramtype is_dynamic: bool
"""
super(SubGraphConfiguration, self).__init__(**kwargs)
self.graph_id = graph_id
self.graph_draft_id = graph_draft_id
self.default_cloud_priority = default_cloud_priority
self.is_dynamic = is_dynamic
class SubGraphConnectionInfo(msrest.serialization.Model):
"""SubGraphConnectionInfo.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
port_name: Optional[str] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
"""
super(SubGraphConnectionInfo, self).__init__(**kwargs)
self.node_id = node_id
self.port_name = port_name
class SubGraphDataPathParameterAssignment(msrest.serialization.Model):
"""SubGraphDataPathParameterAssignment.
:ivar data_set_path_parameter:
:vartype data_set_path_parameter: ~flow.models.DataSetPathParameter
:ivar data_set_path_parameter_assignments:
:vartype data_set_path_parameter_assignments: list[str]
"""
_attribute_map = {
'data_set_path_parameter': {'key': 'dataSetPathParameter', 'type': 'DataSetPathParameter'},
'data_set_path_parameter_assignments': {'key': 'dataSetPathParameterAssignments', 'type': '[str]'},
}
def __init__(
self,
*,
data_set_path_parameter: Optional["DataSetPathParameter"] = None,
data_set_path_parameter_assignments: Optional[List[str]] = None,
**kwargs
):
"""
:keyword data_set_path_parameter:
:paramtype data_set_path_parameter: ~flow.models.DataSetPathParameter
:keyword data_set_path_parameter_assignments:
:paramtype data_set_path_parameter_assignments: list[str]
"""
super(SubGraphDataPathParameterAssignment, self).__init__(**kwargs)
self.data_set_path_parameter = data_set_path_parameter
self.data_set_path_parameter_assignments = data_set_path_parameter_assignments
class SubGraphInfo(msrest.serialization.Model):
"""SubGraphInfo.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar default_compute_target:
:vartype default_compute_target: ~flow.models.ComputeSetting
:ivar default_data_store:
:vartype default_data_store: ~flow.models.DatastoreSetting
:ivar id:
:vartype id: str
:ivar parent_graph_id:
:vartype parent_graph_id: str
:ivar pipeline_definition_id:
:vartype pipeline_definition_id: str
:ivar sub_graph_parameter_assignment:
:vartype sub_graph_parameter_assignment: list[~flow.models.SubGraphParameterAssignment]
:ivar sub_graph_concatenate_assignment:
:vartype sub_graph_concatenate_assignment: list[~flow.models.SubGraphConcatenateAssignment]
:ivar sub_graph_data_path_parameter_assignment:
:vartype sub_graph_data_path_parameter_assignment:
list[~flow.models.SubGraphDataPathParameterAssignment]
:ivar sub_graph_default_compute_target_nodes:
:vartype sub_graph_default_compute_target_nodes: list[str]
:ivar sub_graph_default_data_store_nodes:
:vartype sub_graph_default_data_store_nodes: list[str]
:ivar inputs:
:vartype inputs: list[~flow.models.SubGraphPortInfo]
:ivar outputs:
:vartype outputs: list[~flow.models.SubGraphPortInfo]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_compute_target': {'key': 'defaultComputeTarget', 'type': 'ComputeSetting'},
'default_data_store': {'key': 'defaultDataStore', 'type': 'DatastoreSetting'},
'id': {'key': 'id', 'type': 'str'},
'parent_graph_id': {'key': 'parentGraphId', 'type': 'str'},
'pipeline_definition_id': {'key': 'pipelineDefinitionId', 'type': 'str'},
'sub_graph_parameter_assignment': {'key': 'subGraphParameterAssignment', 'type': '[SubGraphParameterAssignment]'},
'sub_graph_concatenate_assignment': {'key': 'subGraphConcatenateAssignment', 'type': '[SubGraphConcatenateAssignment]'},
'sub_graph_data_path_parameter_assignment': {'key': 'subGraphDataPathParameterAssignment', 'type': '[SubGraphDataPathParameterAssignment]'},
'sub_graph_default_compute_target_nodes': {'key': 'subGraphDefaultComputeTargetNodes', 'type': '[str]'},
'sub_graph_default_data_store_nodes': {'key': 'subGraphDefaultDataStoreNodes', 'type': '[str]'},
'inputs': {'key': 'inputs', 'type': '[SubGraphPortInfo]'},
'outputs': {'key': 'outputs', 'type': '[SubGraphPortInfo]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
default_compute_target: Optional["ComputeSetting"] = None,
default_data_store: Optional["DatastoreSetting"] = None,
id: Optional[str] = None,
parent_graph_id: Optional[str] = None,
pipeline_definition_id: Optional[str] = None,
sub_graph_parameter_assignment: Optional[List["SubGraphParameterAssignment"]] = None,
sub_graph_concatenate_assignment: Optional[List["SubGraphConcatenateAssignment"]] = None,
sub_graph_data_path_parameter_assignment: Optional[List["SubGraphDataPathParameterAssignment"]] = None,
sub_graph_default_compute_target_nodes: Optional[List[str]] = None,
sub_graph_default_data_store_nodes: Optional[List[str]] = None,
inputs: Optional[List["SubGraphPortInfo"]] = None,
outputs: Optional[List["SubGraphPortInfo"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword default_compute_target:
:paramtype default_compute_target: ~flow.models.ComputeSetting
:keyword default_data_store:
:paramtype default_data_store: ~flow.models.DatastoreSetting
:keyword id:
:paramtype id: str
:keyword parent_graph_id:
:paramtype parent_graph_id: str
:keyword pipeline_definition_id:
:paramtype pipeline_definition_id: str
:keyword sub_graph_parameter_assignment:
:paramtype sub_graph_parameter_assignment: list[~flow.models.SubGraphParameterAssignment]
:keyword sub_graph_concatenate_assignment:
:paramtype sub_graph_concatenate_assignment: list[~flow.models.SubGraphConcatenateAssignment]
:keyword sub_graph_data_path_parameter_assignment:
:paramtype sub_graph_data_path_parameter_assignment:
list[~flow.models.SubGraphDataPathParameterAssignment]
:keyword sub_graph_default_compute_target_nodes:
:paramtype sub_graph_default_compute_target_nodes: list[str]
:keyword sub_graph_default_data_store_nodes:
:paramtype sub_graph_default_data_store_nodes: list[str]
:keyword inputs:
:paramtype inputs: list[~flow.models.SubGraphPortInfo]
:keyword outputs:
:paramtype outputs: list[~flow.models.SubGraphPortInfo]
"""
super(SubGraphInfo, self).__init__(**kwargs)
self.name = name
self.description = description
self.default_compute_target = default_compute_target
self.default_data_store = default_data_store
self.id = id
self.parent_graph_id = parent_graph_id
self.pipeline_definition_id = pipeline_definition_id
self.sub_graph_parameter_assignment = sub_graph_parameter_assignment
self.sub_graph_concatenate_assignment = sub_graph_concatenate_assignment
self.sub_graph_data_path_parameter_assignment = sub_graph_data_path_parameter_assignment
self.sub_graph_default_compute_target_nodes = sub_graph_default_compute_target_nodes
self.sub_graph_default_data_store_nodes = sub_graph_default_data_store_nodes
self.inputs = inputs
self.outputs = outputs
class SubGraphParameterAssignment(msrest.serialization.Model):
"""SubGraphParameterAssignment.
:ivar parameter:
:vartype parameter: ~flow.models.Parameter
:ivar parameter_assignments:
:vartype parameter_assignments: list[~flow.models.SubPipelineParameterAssignment]
"""
_attribute_map = {
'parameter': {'key': 'parameter', 'type': 'Parameter'},
'parameter_assignments': {'key': 'parameterAssignments', 'type': '[SubPipelineParameterAssignment]'},
}
def __init__(
self,
*,
parameter: Optional["Parameter"] = None,
parameter_assignments: Optional[List["SubPipelineParameterAssignment"]] = None,
**kwargs
):
"""
:keyword parameter:
:paramtype parameter: ~flow.models.Parameter
:keyword parameter_assignments:
:paramtype parameter_assignments: list[~flow.models.SubPipelineParameterAssignment]
"""
super(SubGraphParameterAssignment, self).__init__(**kwargs)
self.parameter = parameter
self.parameter_assignments = parameter_assignments
class SubGraphPortInfo(msrest.serialization.Model):
"""SubGraphPortInfo.
:ivar name:
:vartype name: str
:ivar internal:
:vartype internal: list[~flow.models.SubGraphConnectionInfo]
:ivar external:
:vartype external: list[~flow.models.SubGraphConnectionInfo]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'internal': {'key': 'internal', 'type': '[SubGraphConnectionInfo]'},
'external': {'key': 'external', 'type': '[SubGraphConnectionInfo]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
internal: Optional[List["SubGraphConnectionInfo"]] = None,
external: Optional[List["SubGraphConnectionInfo"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword internal:
:paramtype internal: list[~flow.models.SubGraphConnectionInfo]
:keyword external:
:paramtype external: list[~flow.models.SubGraphConnectionInfo]
"""
super(SubGraphPortInfo, self).__init__(**kwargs)
self.name = name
self.internal = internal
self.external = external
class SubmitBulkRunRequest(msrest.serialization.Model):
"""SubmitBulkRunRequest.
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_definition_resource_id:
:vartype flow_definition_resource_id: str
:ivar flow_definition_data_store_name:
:vartype flow_definition_data_store_name: str
:ivar flow_definition_blob_path:
:vartype flow_definition_blob_path: str
:ivar flow_definition_data_uri:
:vartype flow_definition_data_uri: str
:ivar run_id:
:vartype run_id: str
:ivar run_display_name:
:vartype run_display_name: str
:ivar run_experiment_name:
:vartype run_experiment_name: str
:ivar node_variant:
:vartype node_variant: str
:ivar variant_run_id:
:vartype variant_run_id: str
:ivar baseline_run_id:
:vartype baseline_run_id: str
:ivar session_id:
:vartype session_id: str
:ivar session_setup_mode: Possible values include: "ClientWait", "SystemWait".
:vartype session_setup_mode: str or ~flow.models.SessionSetupModeEnum
:ivar session_config_mode: Possible values include: "Default", "ForceInstallPackage",
"ForceReset".
:vartype session_config_mode: str or ~flow.models.SessionConfigModeEnum
:ivar flow_lineage_id:
:vartype flow_lineage_id: str
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
:ivar compute_name:
:vartype compute_name: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_definition_resource_id': {'key': 'flowDefinitionResourceId', 'type': 'str'},
'flow_definition_data_store_name': {'key': 'flowDefinitionDataStoreName', 'type': 'str'},
'flow_definition_blob_path': {'key': 'flowDefinitionBlobPath', 'type': 'str'},
'flow_definition_data_uri': {'key': 'flowDefinitionDataUri', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'run_display_name': {'key': 'runDisplayName', 'type': 'str'},
'run_experiment_name': {'key': 'runExperimentName', 'type': 'str'},
'node_variant': {'key': 'nodeVariant', 'type': 'str'},
'variant_run_id': {'key': 'variantRunId', 'type': 'str'},
'baseline_run_id': {'key': 'baselineRunId', 'type': 'str'},
'session_id': {'key': 'sessionId', 'type': 'str'},
'session_setup_mode': {'key': 'sessionSetupMode', 'type': 'str'},
'session_config_mode': {'key': 'sessionConfigMode', 'type': 'str'},
'flow_lineage_id': {'key': 'flowLineageId', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
'compute_name': {'key': 'computeName', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
flow_definition_file_path: Optional[str] = None,
flow_definition_resource_id: Optional[str] = None,
flow_definition_data_store_name: Optional[str] = None,
flow_definition_blob_path: Optional[str] = None,
flow_definition_data_uri: Optional[str] = None,
run_id: Optional[str] = None,
run_display_name: Optional[str] = None,
run_experiment_name: Optional[str] = None,
node_variant: Optional[str] = None,
variant_run_id: Optional[str] = None,
baseline_run_id: Optional[str] = None,
session_id: Optional[str] = None,
session_setup_mode: Optional[Union[str, "SessionSetupModeEnum"]] = None,
session_config_mode: Optional[Union[str, "SessionConfigModeEnum"]] = None,
flow_lineage_id: Optional[str] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
compute_name: Optional[str] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_definition_resource_id:
:paramtype flow_definition_resource_id: str
:keyword flow_definition_data_store_name:
:paramtype flow_definition_data_store_name: str
:keyword flow_definition_blob_path:
:paramtype flow_definition_blob_path: str
:keyword flow_definition_data_uri:
:paramtype flow_definition_data_uri: str
:keyword run_id:
:paramtype run_id: str
:keyword run_display_name:
:paramtype run_display_name: str
:keyword run_experiment_name:
:paramtype run_experiment_name: str
:keyword node_variant:
:paramtype node_variant: str
:keyword variant_run_id:
:paramtype variant_run_id: str
:keyword baseline_run_id:
:paramtype baseline_run_id: str
:keyword session_id:
:paramtype session_id: str
:keyword session_setup_mode: Possible values include: "ClientWait", "SystemWait".
:paramtype session_setup_mode: str or ~flow.models.SessionSetupModeEnum
:keyword session_config_mode: Possible values include: "Default", "ForceInstallPackage",
"ForceReset".
:paramtype session_config_mode: str or ~flow.models.SessionConfigModeEnum
:keyword flow_lineage_id:
:paramtype flow_lineage_id: str
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
:keyword compute_name:
:paramtype compute_name: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(SubmitBulkRunRequest, self).__init__(**kwargs)
self.flow_definition_file_path = flow_definition_file_path
self.flow_definition_resource_id = flow_definition_resource_id
self.flow_definition_data_store_name = flow_definition_data_store_name
self.flow_definition_blob_path = flow_definition_blob_path
self.flow_definition_data_uri = flow_definition_data_uri
self.run_id = run_id
self.run_display_name = run_display_name
self.run_experiment_name = run_experiment_name
self.node_variant = node_variant
self.variant_run_id = variant_run_id
self.baseline_run_id = baseline_run_id
self.session_id = session_id
self.session_setup_mode = session_setup_mode
self.session_config_mode = session_config_mode
self.flow_lineage_id = flow_lineage_id
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
self.compute_name = compute_name
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class SubmitBulkRunResponse(msrest.serialization.Model):
"""SubmitBulkRunResponse.
:ivar next_action_interval_in_seconds:
:vartype next_action_interval_in_seconds: int
:ivar action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:vartype action_type: str or ~flow.models.ActionType
:ivar flow_runs:
:vartype flow_runs: list[any]
:ivar node_runs:
:vartype node_runs: list[any]
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
:ivar flow_name:
:vartype flow_name: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_graph:
:vartype flow_graph: ~flow.models.FlowGraph
:ivar flow_graph_layout:
:vartype flow_graph_layout: ~flow.models.FlowGraphLayout
:ivar flow_run_resource_id:
:vartype flow_run_resource_id: str
:ivar bulk_test_id:
:vartype bulk_test_id: str
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar created_by:
:vartype created_by: ~flow.models.SchemaContractsCreatedBy
:ivar created_on:
:vartype created_on: ~datetime.datetime
:ivar flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:vartype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar runtime_name:
:vartype runtime_name: str
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar flow_run_logs: Dictionary of :code:`<string>`.
:vartype flow_run_logs: dict[str, str]
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar working_directory:
:vartype working_directory: str
:ivar flow_dag_file_relative_path:
:vartype flow_dag_file_relative_path: str
:ivar flow_snapshot_id:
:vartype flow_snapshot_id: str
:ivar variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1mlssi7·schemas·submitbulkrunresponse·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:vartype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
_attribute_map = {
'next_action_interval_in_seconds': {'key': 'nextActionIntervalInSeconds', 'type': 'int'},
'action_type': {'key': 'actionType', 'type': 'str'},
'flow_runs': {'key': 'flow_runs', 'type': '[object]'},
'node_runs': {'key': 'node_runs', 'type': '[object]'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_graph': {'key': 'flowGraph', 'type': 'FlowGraph'},
'flow_graph_layout': {'key': 'flowGraphLayout', 'type': 'FlowGraphLayout'},
'flow_run_resource_id': {'key': 'flowRunResourceId', 'type': 'str'},
'bulk_test_id': {'key': 'bulkTestId', 'type': 'str'},
'batch_inputs': {'key': 'batchInputs', 'type': '[{object}]'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'created_by': {'key': 'createdBy', 'type': 'SchemaContractsCreatedBy'},
'created_on': {'key': 'createdOn', 'type': 'iso-8601'},
'flow_run_type': {'key': 'flowRunType', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'flow_run_logs': {'key': 'flowRunLogs', 'type': '{str}'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'working_directory': {'key': 'workingDirectory', 'type': 'str'},
'flow_dag_file_relative_path': {'key': 'flowDagFileRelativePath', 'type': 'str'},
'flow_snapshot_id': {'key': 'flowSnapshotId', 'type': 'str'},
'variant_run_to_evaluation_runs_id_mapping': {'key': 'variantRunToEvaluationRunsIdMapping', 'type': '{[str]}'},
}
def __init__(
self,
*,
next_action_interval_in_seconds: Optional[int] = None,
action_type: Optional[Union[str, "ActionType"]] = None,
flow_runs: Optional[List[Any]] = None,
node_runs: Optional[List[Any]] = None,
error_response: Optional["ErrorResponse"] = None,
flow_name: Optional[str] = None,
flow_run_display_name: Optional[str] = None,
flow_run_id: Optional[str] = None,
flow_graph: Optional["FlowGraph"] = None,
flow_graph_layout: Optional["FlowGraphLayout"] = None,
flow_run_resource_id: Optional[str] = None,
bulk_test_id: Optional[str] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
batch_data_input: Optional["BatchDataInput"] = None,
created_by: Optional["SchemaContractsCreatedBy"] = None,
created_on: Optional[datetime.datetime] = None,
flow_run_type: Optional[Union[str, "FlowRunTypeEnum"]] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
runtime_name: Optional[str] = None,
aml_compute_name: Optional[str] = None,
flow_run_logs: Optional[Dict[str, str]] = None,
flow_test_mode: Optional[Union[str, "FlowTestMode"]] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
working_directory: Optional[str] = None,
flow_dag_file_relative_path: Optional[str] = None,
flow_snapshot_id: Optional[str] = None,
variant_run_to_evaluation_runs_id_mapping: Optional[Dict[str, List[str]]] = None,
**kwargs
):
"""
:keyword next_action_interval_in_seconds:
:paramtype next_action_interval_in_seconds: int
:keyword action_type: Possible values include: "SendValidationRequest", "GetValidationStatus",
"SubmitBulkRun", "LogRunResult", "LogRunTerminatedEvent", "SubmitFlowRun".
:paramtype action_type: str or ~flow.models.ActionType
:keyword flow_runs:
:paramtype flow_runs: list[any]
:keyword node_runs:
:paramtype node_runs: list[any]
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
:keyword flow_name:
:paramtype flow_name: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_graph:
:paramtype flow_graph: ~flow.models.FlowGraph
:keyword flow_graph_layout:
:paramtype flow_graph_layout: ~flow.models.FlowGraphLayout
:keyword flow_run_resource_id:
:paramtype flow_run_resource_id: str
:keyword bulk_test_id:
:paramtype bulk_test_id: str
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword created_by:
:paramtype created_by: ~flow.models.SchemaContractsCreatedBy
:keyword created_on:
:paramtype created_on: ~datetime.datetime
:keyword flow_run_type: Possible values include: "FlowRun", "EvaluationRun",
"PairwiseEvaluationRun", "SingleNodeRun", "FromNodeRun".
:paramtype flow_run_type: str or ~flow.models.FlowRunTypeEnum
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword runtime_name:
:paramtype runtime_name: str
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword flow_run_logs: Dictionary of :code:`<string>`.
:paramtype flow_run_logs: dict[str, str]
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword working_directory:
:paramtype working_directory: str
:keyword flow_dag_file_relative_path:
:paramtype flow_dag_file_relative_path: str
:keyword flow_snapshot_id:
:paramtype flow_snapshot_id: str
:keyword variant_run_to_evaluation_runs_id_mapping: Dictionary of
<components·1mlssi7·schemas·submitbulkrunresponse·properties·variantruntoevaluationrunsidmapping·additionalproperties>.
:paramtype variant_run_to_evaluation_runs_id_mapping: dict[str, list[str]]
"""
super(SubmitBulkRunResponse, self).__init__(**kwargs)
self.next_action_interval_in_seconds = next_action_interval_in_seconds
self.action_type = action_type
self.flow_runs = flow_runs
self.node_runs = node_runs
self.error_response = error_response
self.flow_name = flow_name
self.flow_run_display_name = flow_run_display_name
self.flow_run_id = flow_run_id
self.flow_graph = flow_graph
self.flow_graph_layout = flow_graph_layout
self.flow_run_resource_id = flow_run_resource_id
self.bulk_test_id = bulk_test_id
self.batch_inputs = batch_inputs
self.batch_data_input = batch_data_input
self.created_by = created_by
self.created_on = created_on
self.flow_run_type = flow_run_type
self.flow_type = flow_type
self.runtime_name = runtime_name
self.aml_compute_name = aml_compute_name
self.flow_run_logs = flow_run_logs
self.flow_test_mode = flow_test_mode
self.flow_test_infos = flow_test_infos
self.working_directory = working_directory
self.flow_dag_file_relative_path = flow_dag_file_relative_path
self.flow_snapshot_id = flow_snapshot_id
self.variant_run_to_evaluation_runs_id_mapping = variant_run_to_evaluation_runs_id_mapping
class SubmitFlowRequest(msrest.serialization.Model):
"""SubmitFlowRequest.
:ivar flow_run_id:
:vartype flow_run_id: str
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar flow_id:
:vartype flow_id: str
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_submit_run_settings:
:vartype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:ivar async_submission:
:vartype async_submission: bool
:ivar use_workspace_connection:
:vartype use_workspace_connection: bool
:ivar enable_async_flow_test:
:vartype enable_async_flow_test: bool
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
_attribute_map = {
'flow_run_id': {'key': 'flowRunId', 'type': 'str'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'flow_id': {'key': 'flowId', 'type': 'str'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_submit_run_settings': {'key': 'flowSubmitRunSettings', 'type': 'FlowSubmitRunSettings'},
'async_submission': {'key': 'asyncSubmission', 'type': 'bool'},
'use_workspace_connection': {'key': 'useWorkspaceConnection', 'type': 'bool'},
'enable_async_flow_test': {'key': 'enableAsyncFlowTest', 'type': 'bool'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
}
def __init__(
self,
*,
flow_run_id: Optional[str] = None,
flow_run_display_name: Optional[str] = None,
flow_id: Optional[str] = None,
flow: Optional["Flow"] = None,
flow_submit_run_settings: Optional["FlowSubmitRunSettings"] = None,
async_submission: Optional[bool] = None,
use_workspace_connection: Optional[bool] = None,
enable_async_flow_test: Optional[bool] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
**kwargs
):
"""
:keyword flow_run_id:
:paramtype flow_run_id: str
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword flow_id:
:paramtype flow_id: str
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_submit_run_settings:
:paramtype flow_submit_run_settings: ~flow.models.FlowSubmitRunSettings
:keyword async_submission:
:paramtype async_submission: bool
:keyword use_workspace_connection:
:paramtype use_workspace_connection: bool
:keyword enable_async_flow_test:
:paramtype enable_async_flow_test: bool
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
"""
super(SubmitFlowRequest, self).__init__(**kwargs)
self.flow_run_id = flow_run_id
self.flow_run_display_name = flow_run_display_name
self.flow_id = flow_id
self.flow = flow
self.flow_submit_run_settings = flow_submit_run_settings
self.async_submission = async_submission
self.use_workspace_connection = use_workspace_connection
self.enable_async_flow_test = enable_async_flow_test
self.run_display_name_generation_type = run_display_name_generation_type
class SubmitPipelineRunRequest(msrest.serialization.Model):
"""SubmitPipelineRunRequest.
:ivar compute_target:
:vartype compute_target: str
:ivar flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:vartype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:ivar step_tags: This is a dictionary.
:vartype step_tags: dict[str, str]
:ivar experiment_name:
:vartype experiment_name: str
:ivar pipeline_parameters: This is a dictionary.
:vartype pipeline_parameters: dict[str, str]
:ivar data_path_assignments: This is a dictionary.
:vartype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:ivar data_set_definition_value_assignments: This is a dictionary.
:vartype data_set_definition_value_assignments: dict[str, ~flow.models.DataSetDefinitionValue]
:ivar asset_output_settings_assignments: This is a dictionary.
:vartype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:ivar enable_notification:
:vartype enable_notification: bool
:ivar sub_pipelines_info:
:vartype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:ivar display_name:
:vartype display_name: str
:ivar run_id:
:vartype run_id: str
:ivar parent_run_id:
:vartype parent_run_id: str
:ivar graph:
:vartype graph: ~flow.models.GraphDraftEntity
:ivar pipeline_run_settings:
:vartype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:ivar module_node_run_settings:
:vartype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:ivar module_node_ui_input_settings:
:vartype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar continue_run_on_step_failure:
:vartype continue_run_on_step_failure: bool
:ivar description:
:vartype description: str
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar enforce_rerun:
:vartype enforce_rerun: bool
:ivar dataset_access_modes: Possible values include: "Default", "DatasetInDpv2", "AssetInDpv2",
"DatasetInDesignerUI", "AssetInDesignerUI", "DatasetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithAssetInDesignerUI",
"DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset", "Asset".
:vartype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
_attribute_map = {
'compute_target': {'key': 'computeTarget', 'type': 'str'},
'flattened_sub_graphs': {'key': 'flattenedSubGraphs', 'type': '{PipelineSubDraft}'},
'step_tags': {'key': 'stepTags', 'type': '{str}'},
'experiment_name': {'key': 'experimentName', 'type': 'str'},
'pipeline_parameters': {'key': 'pipelineParameters', 'type': '{str}'},
'data_path_assignments': {'key': 'dataPathAssignments', 'type': '{LegacyDataPath}'},
'data_set_definition_value_assignments': {'key': 'dataSetDefinitionValueAssignments', 'type': '{DataSetDefinitionValue}'},
'asset_output_settings_assignments': {'key': 'assetOutputSettingsAssignments', 'type': '{AssetOutputSettings}'},
'enable_notification': {'key': 'enableNotification', 'type': 'bool'},
'sub_pipelines_info': {'key': 'subPipelinesInfo', 'type': 'SubPipelinesInfo'},
'display_name': {'key': 'displayName', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'parent_run_id': {'key': 'parentRunId', 'type': 'str'},
'graph': {'key': 'graph', 'type': 'GraphDraftEntity'},
'pipeline_run_settings': {'key': 'pipelineRunSettings', 'type': '[RunSettingParameterAssignment]'},
'module_node_run_settings': {'key': 'moduleNodeRunSettings', 'type': '[GraphModuleNodeRunSetting]'},
'module_node_ui_input_settings': {'key': 'moduleNodeUIInputSettings', 'type': '[GraphModuleNodeUIInputSetting]'},
'tags': {'key': 'tags', 'type': '{str}'},
'continue_run_on_step_failure': {'key': 'continueRunOnStepFailure', 'type': 'bool'},
'description': {'key': 'description', 'type': 'str'},
'properties': {'key': 'properties', 'type': '{str}'},
'enforce_rerun': {'key': 'enforceRerun', 'type': 'bool'},
'dataset_access_modes': {'key': 'datasetAccessModes', 'type': 'str'},
}
def __init__(
self,
*,
compute_target: Optional[str] = None,
flattened_sub_graphs: Optional[Dict[str, "PipelineSubDraft"]] = None,
step_tags: Optional[Dict[str, str]] = None,
experiment_name: Optional[str] = None,
pipeline_parameters: Optional[Dict[str, str]] = None,
data_path_assignments: Optional[Dict[str, "LegacyDataPath"]] = None,
data_set_definition_value_assignments: Optional[Dict[str, "DataSetDefinitionValue"]] = None,
asset_output_settings_assignments: Optional[Dict[str, "AssetOutputSettings"]] = None,
enable_notification: Optional[bool] = None,
sub_pipelines_info: Optional["SubPipelinesInfo"] = None,
display_name: Optional[str] = None,
run_id: Optional[str] = None,
parent_run_id: Optional[str] = None,
graph: Optional["GraphDraftEntity"] = None,
pipeline_run_settings: Optional[List["RunSettingParameterAssignment"]] = None,
module_node_run_settings: Optional[List["GraphModuleNodeRunSetting"]] = None,
module_node_ui_input_settings: Optional[List["GraphModuleNodeUIInputSetting"]] = None,
tags: Optional[Dict[str, str]] = None,
continue_run_on_step_failure: Optional[bool] = None,
description: Optional[str] = None,
properties: Optional[Dict[str, str]] = None,
enforce_rerun: Optional[bool] = None,
dataset_access_modes: Optional[Union[str, "DatasetAccessModes"]] = None,
**kwargs
):
"""
:keyword compute_target:
:paramtype compute_target: str
:keyword flattened_sub_graphs: Dictionary of :code:`<PipelineSubDraft>`.
:paramtype flattened_sub_graphs: dict[str, ~flow.models.PipelineSubDraft]
:keyword step_tags: This is a dictionary.
:paramtype step_tags: dict[str, str]
:keyword experiment_name:
:paramtype experiment_name: str
:keyword pipeline_parameters: This is a dictionary.
:paramtype pipeline_parameters: dict[str, str]
:keyword data_path_assignments: This is a dictionary.
:paramtype data_path_assignments: dict[str, ~flow.models.LegacyDataPath]
:keyword data_set_definition_value_assignments: This is a dictionary.
:paramtype data_set_definition_value_assignments: dict[str,
~flow.models.DataSetDefinitionValue]
:keyword asset_output_settings_assignments: This is a dictionary.
:paramtype asset_output_settings_assignments: dict[str, ~flow.models.AssetOutputSettings]
:keyword enable_notification:
:paramtype enable_notification: bool
:keyword sub_pipelines_info:
:paramtype sub_pipelines_info: ~flow.models.SubPipelinesInfo
:keyword display_name:
:paramtype display_name: str
:keyword run_id:
:paramtype run_id: str
:keyword parent_run_id:
:paramtype parent_run_id: str
:keyword graph:
:paramtype graph: ~flow.models.GraphDraftEntity
:keyword pipeline_run_settings:
:paramtype pipeline_run_settings: list[~flow.models.RunSettingParameterAssignment]
:keyword module_node_run_settings:
:paramtype module_node_run_settings: list[~flow.models.GraphModuleNodeRunSetting]
:keyword module_node_ui_input_settings:
:paramtype module_node_ui_input_settings: list[~flow.models.GraphModuleNodeUIInputSetting]
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword continue_run_on_step_failure:
:paramtype continue_run_on_step_failure: bool
:keyword description:
:paramtype description: str
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword enforce_rerun:
:paramtype enforce_rerun: bool
:keyword dataset_access_modes: Possible values include: "Default", "DatasetInDpv2",
"AssetInDpv2", "DatasetInDesignerUI", "AssetInDesignerUI",
"DatasetInDpv2WithDatasetInDesignerUI", "AssetInDpv2WithDatasetInDesignerUI",
"AssetInDpv2WithAssetInDesignerUI", "DatasetAndAssetInDpv2WithDatasetInDesignerUI", "Dataset",
"Asset".
:paramtype dataset_access_modes: str or ~flow.models.DatasetAccessModes
"""
super(SubmitPipelineRunRequest, self).__init__(**kwargs)
self.compute_target = compute_target
self.flattened_sub_graphs = flattened_sub_graphs
self.step_tags = step_tags
self.experiment_name = experiment_name
self.pipeline_parameters = pipeline_parameters
self.data_path_assignments = data_path_assignments
self.data_set_definition_value_assignments = data_set_definition_value_assignments
self.asset_output_settings_assignments = asset_output_settings_assignments
self.enable_notification = enable_notification
self.sub_pipelines_info = sub_pipelines_info
self.display_name = display_name
self.run_id = run_id
self.parent_run_id = parent_run_id
self.graph = graph
self.pipeline_run_settings = pipeline_run_settings
self.module_node_run_settings = module_node_run_settings
self.module_node_ui_input_settings = module_node_ui_input_settings
self.tags = tags
self.continue_run_on_step_failure = continue_run_on_step_failure
self.description = description
self.properties = properties
self.enforce_rerun = enforce_rerun
self.dataset_access_modes = dataset_access_modes
class SubPipelineDefinition(msrest.serialization.Model):
"""SubPipelineDefinition.
:ivar name:
:vartype name: str
:ivar description:
:vartype description: str
:ivar default_compute_target:
:vartype default_compute_target: ~flow.models.ComputeSetting
:ivar default_data_store:
:vartype default_data_store: ~flow.models.DatastoreSetting
:ivar pipeline_function_name:
:vartype pipeline_function_name: str
:ivar id:
:vartype id: str
:ivar parent_definition_id:
:vartype parent_definition_id: str
:ivar from_module_name:
:vartype from_module_name: str
:ivar parameter_list:
:vartype parameter_list: list[~flow.models.Kwarg]
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'default_compute_target': {'key': 'defaultComputeTarget', 'type': 'ComputeSetting'},
'default_data_store': {'key': 'defaultDataStore', 'type': 'DatastoreSetting'},
'pipeline_function_name': {'key': 'pipelineFunctionName', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'parent_definition_id': {'key': 'parentDefinitionId', 'type': 'str'},
'from_module_name': {'key': 'fromModuleName', 'type': 'str'},
'parameter_list': {'key': 'parameterList', 'type': '[Kwarg]'},
}
def __init__(
self,
*,
name: Optional[str] = None,
description: Optional[str] = None,
default_compute_target: Optional["ComputeSetting"] = None,
default_data_store: Optional["DatastoreSetting"] = None,
pipeline_function_name: Optional[str] = None,
id: Optional[str] = None,
parent_definition_id: Optional[str] = None,
from_module_name: Optional[str] = None,
parameter_list: Optional[List["Kwarg"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword description:
:paramtype description: str
:keyword default_compute_target:
:paramtype default_compute_target: ~flow.models.ComputeSetting
:keyword default_data_store:
:paramtype default_data_store: ~flow.models.DatastoreSetting
:keyword pipeline_function_name:
:paramtype pipeline_function_name: str
:keyword id:
:paramtype id: str
:keyword parent_definition_id:
:paramtype parent_definition_id: str
:keyword from_module_name:
:paramtype from_module_name: str
:keyword parameter_list:
:paramtype parameter_list: list[~flow.models.Kwarg]
"""
super(SubPipelineDefinition, self).__init__(**kwargs)
self.name = name
self.description = description
self.default_compute_target = default_compute_target
self.default_data_store = default_data_store
self.pipeline_function_name = pipeline_function_name
self.id = id
self.parent_definition_id = parent_definition_id
self.from_module_name = from_module_name
self.parameter_list = parameter_list
class SubPipelineParameterAssignment(msrest.serialization.Model):
"""SubPipelineParameterAssignment.
:ivar node_id:
:vartype node_id: str
:ivar parameter_name:
:vartype parameter_name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
parameter_name: Optional[str] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword parameter_name:
:paramtype parameter_name: str
"""
super(SubPipelineParameterAssignment, self).__init__(**kwargs)
self.node_id = node_id
self.parameter_name = parameter_name
class SubPipelinesInfo(msrest.serialization.Model):
"""SubPipelinesInfo.
:ivar sub_graph_info:
:vartype sub_graph_info: list[~flow.models.SubGraphInfo]
:ivar node_id_to_sub_graph_id_mapping: Dictionary of :code:`<string>`.
:vartype node_id_to_sub_graph_id_mapping: dict[str, str]
:ivar sub_pipeline_definition:
:vartype sub_pipeline_definition: list[~flow.models.SubPipelineDefinition]
"""
_attribute_map = {
'sub_graph_info': {'key': 'subGraphInfo', 'type': '[SubGraphInfo]'},
'node_id_to_sub_graph_id_mapping': {'key': 'nodeIdToSubGraphIdMapping', 'type': '{str}'},
'sub_pipeline_definition': {'key': 'subPipelineDefinition', 'type': '[SubPipelineDefinition]'},
}
def __init__(
self,
*,
sub_graph_info: Optional[List["SubGraphInfo"]] = None,
node_id_to_sub_graph_id_mapping: Optional[Dict[str, str]] = None,
sub_pipeline_definition: Optional[List["SubPipelineDefinition"]] = None,
**kwargs
):
"""
:keyword sub_graph_info:
:paramtype sub_graph_info: list[~flow.models.SubGraphInfo]
:keyword node_id_to_sub_graph_id_mapping: Dictionary of :code:`<string>`.
:paramtype node_id_to_sub_graph_id_mapping: dict[str, str]
:keyword sub_pipeline_definition:
:paramtype sub_pipeline_definition: list[~flow.models.SubPipelineDefinition]
"""
super(SubPipelinesInfo, self).__init__(**kwargs)
self.sub_graph_info = sub_graph_info
self.node_id_to_sub_graph_id_mapping = node_id_to_sub_graph_id_mapping
self.sub_pipeline_definition = sub_pipeline_definition
class SubStatusPeriod(msrest.serialization.Model):
"""SubStatusPeriod.
:ivar name:
:vartype name: str
:ivar sub_periods:
:vartype sub_periods: list[~flow.models.SubStatusPeriod]
:ivar start:
:vartype start: long
:ivar end:
:vartype end: long
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'sub_periods': {'key': 'subPeriods', 'type': '[SubStatusPeriod]'},
'start': {'key': 'start', 'type': 'long'},
'end': {'key': 'end', 'type': 'long'},
}
def __init__(
self,
*,
name: Optional[str] = None,
sub_periods: Optional[List["SubStatusPeriod"]] = None,
start: Optional[int] = None,
end: Optional[int] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword sub_periods:
:paramtype sub_periods: list[~flow.models.SubStatusPeriod]
:keyword start:
:paramtype start: long
:keyword end:
:paramtype end: long
"""
super(SubStatusPeriod, self).__init__(**kwargs)
self.name = name
self.sub_periods = sub_periods
self.start = start
self.end = end
class SweepEarlyTerminationPolicy(msrest.serialization.Model):
"""SweepEarlyTerminationPolicy.
:ivar policy_type: Possible values include: "Bandit", "MedianStopping", "TruncationSelection".
:vartype policy_type: str or ~flow.models.EarlyTerminationPolicyType
:ivar evaluation_interval:
:vartype evaluation_interval: int
:ivar delay_evaluation:
:vartype delay_evaluation: int
:ivar slack_factor:
:vartype slack_factor: float
:ivar slack_amount:
:vartype slack_amount: float
:ivar truncation_percentage:
:vartype truncation_percentage: int
"""
_attribute_map = {
'policy_type': {'key': 'policyType', 'type': 'str'},
'evaluation_interval': {'key': 'evaluationInterval', 'type': 'int'},
'delay_evaluation': {'key': 'delayEvaluation', 'type': 'int'},
'slack_factor': {'key': 'slackFactor', 'type': 'float'},
'slack_amount': {'key': 'slackAmount', 'type': 'float'},
'truncation_percentage': {'key': 'truncationPercentage', 'type': 'int'},
}
def __init__(
self,
*,
policy_type: Optional[Union[str, "EarlyTerminationPolicyType"]] = None,
evaluation_interval: Optional[int] = None,
delay_evaluation: Optional[int] = None,
slack_factor: Optional[float] = None,
slack_amount: Optional[float] = None,
truncation_percentage: Optional[int] = None,
**kwargs
):
"""
:keyword policy_type: Possible values include: "Bandit", "MedianStopping",
"TruncationSelection".
:paramtype policy_type: str or ~flow.models.EarlyTerminationPolicyType
:keyword evaluation_interval:
:paramtype evaluation_interval: int
:keyword delay_evaluation:
:paramtype delay_evaluation: int
:keyword slack_factor:
:paramtype slack_factor: float
:keyword slack_amount:
:paramtype slack_amount: float
:keyword truncation_percentage:
:paramtype truncation_percentage: int
"""
super(SweepEarlyTerminationPolicy, self).__init__(**kwargs)
self.policy_type = policy_type
self.evaluation_interval = evaluation_interval
self.delay_evaluation = delay_evaluation
self.slack_factor = slack_factor
self.slack_amount = slack_amount
self.truncation_percentage = truncation_percentage
class SweepSettings(msrest.serialization.Model):
"""SweepSettings.
:ivar limits:
:vartype limits: ~flow.models.SweepSettingsLimits
:ivar search_space:
:vartype search_space: list[dict[str, str]]
:ivar sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:vartype sampling_algorithm: str or ~flow.models.SamplingAlgorithmType
:ivar early_termination:
:vartype early_termination: ~flow.models.SweepEarlyTerminationPolicy
"""
_attribute_map = {
'limits': {'key': 'limits', 'type': 'SweepSettingsLimits'},
'search_space': {'key': 'searchSpace', 'type': '[{str}]'},
'sampling_algorithm': {'key': 'samplingAlgorithm', 'type': 'str'},
'early_termination': {'key': 'earlyTermination', 'type': 'SweepEarlyTerminationPolicy'},
}
def __init__(
self,
*,
limits: Optional["SweepSettingsLimits"] = None,
search_space: Optional[List[Dict[str, str]]] = None,
sampling_algorithm: Optional[Union[str, "SamplingAlgorithmType"]] = None,
early_termination: Optional["SweepEarlyTerminationPolicy"] = None,
**kwargs
):
"""
:keyword limits:
:paramtype limits: ~flow.models.SweepSettingsLimits
:keyword search_space:
:paramtype search_space: list[dict[str, str]]
:keyword sampling_algorithm: Possible values include: "Random", "Grid", "Bayesian".
:paramtype sampling_algorithm: str or ~flow.models.SamplingAlgorithmType
:keyword early_termination:
:paramtype early_termination: ~flow.models.SweepEarlyTerminationPolicy
"""
super(SweepSettings, self).__init__(**kwargs)
self.limits = limits
self.search_space = search_space
self.sampling_algorithm = sampling_algorithm
self.early_termination = early_termination
class SweepSettingsLimits(msrest.serialization.Model):
"""SweepSettingsLimits.
:ivar max_total_trials:
:vartype max_total_trials: int
:ivar max_concurrent_trials:
:vartype max_concurrent_trials: int
"""
_attribute_map = {
'max_total_trials': {'key': 'maxTotalTrials', 'type': 'int'},
'max_concurrent_trials': {'key': 'maxConcurrentTrials', 'type': 'int'},
}
def __init__(
self,
*,
max_total_trials: Optional[int] = None,
max_concurrent_trials: Optional[int] = None,
**kwargs
):
"""
:keyword max_total_trials:
:paramtype max_total_trials: int
:keyword max_concurrent_trials:
:paramtype max_concurrent_trials: int
"""
super(SweepSettingsLimits, self).__init__(**kwargs)
self.max_total_trials = max_total_trials
self.max_concurrent_trials = max_concurrent_trials
class SystemData(msrest.serialization.Model):
"""SystemData.
:ivar created_at:
:vartype created_at: ~datetime.datetime
:ivar created_by:
:vartype created_by: str
:ivar created_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:vartype created_by_type: str or ~flow.models.UserType
:ivar last_modified_at:
:vartype last_modified_at: ~datetime.datetime
:ivar last_modified_by:
:vartype last_modified_by: str
:ivar last_modified_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:vartype last_modified_by_type: str or ~flow.models.UserType
"""
_attribute_map = {
'created_at': {'key': 'createdAt', 'type': 'iso-8601'},
'created_by': {'key': 'createdBy', 'type': 'str'},
'created_by_type': {'key': 'createdByType', 'type': 'str'},
'last_modified_at': {'key': 'lastModifiedAt', 'type': 'iso-8601'},
'last_modified_by': {'key': 'lastModifiedBy', 'type': 'str'},
'last_modified_by_type': {'key': 'lastModifiedByType', 'type': 'str'},
}
def __init__(
self,
*,
created_at: Optional[datetime.datetime] = None,
created_by: Optional[str] = None,
created_by_type: Optional[Union[str, "UserType"]] = None,
last_modified_at: Optional[datetime.datetime] = None,
last_modified_by: Optional[str] = None,
last_modified_by_type: Optional[Union[str, "UserType"]] = None,
**kwargs
):
"""
:keyword created_at:
:paramtype created_at: ~datetime.datetime
:keyword created_by:
:paramtype created_by: str
:keyword created_by_type: Possible values include: "User", "Application", "ManagedIdentity",
"Key".
:paramtype created_by_type: str or ~flow.models.UserType
:keyword last_modified_at:
:paramtype last_modified_at: ~datetime.datetime
:keyword last_modified_by:
:paramtype last_modified_by: str
:keyword last_modified_by_type: Possible values include: "User", "Application",
"ManagedIdentity", "Key".
:paramtype last_modified_by_type: str or ~flow.models.UserType
"""
super(SystemData, self).__init__(**kwargs)
self.created_at = created_at
self.created_by = created_by
self.created_by_type = created_by_type
self.last_modified_at = last_modified_at
self.last_modified_by = last_modified_by
self.last_modified_by_type = last_modified_by_type
class SystemMeta(msrest.serialization.Model):
"""SystemMeta.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar extra_hash:
:vartype extra_hash: str
:ivar content_hash:
:vartype content_hash: str
:ivar identifier_hashes:
:vartype identifier_hashes: ~flow.models.SystemMetaIdentifierHashes
:ivar extra_hashes:
:vartype extra_hashes: ~flow.models.SystemMetaExtraHashes
"""
_attribute_map = {
'identifier_hash': {'key': 'identifierHash', 'type': 'str'},
'extra_hash': {'key': 'extraHash', 'type': 'str'},
'content_hash': {'key': 'contentHash', 'type': 'str'},
'identifier_hashes': {'key': 'identifierHashes', 'type': 'SystemMetaIdentifierHashes'},
'extra_hashes': {'key': 'extraHashes', 'type': 'SystemMetaExtraHashes'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
extra_hash: Optional[str] = None,
content_hash: Optional[str] = None,
identifier_hashes: Optional["SystemMetaIdentifierHashes"] = None,
extra_hashes: Optional["SystemMetaExtraHashes"] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword extra_hash:
:paramtype extra_hash: str
:keyword content_hash:
:paramtype content_hash: str
:keyword identifier_hashes:
:paramtype identifier_hashes: ~flow.models.SystemMetaIdentifierHashes
:keyword extra_hashes:
:paramtype extra_hashes: ~flow.models.SystemMetaExtraHashes
"""
super(SystemMeta, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.extra_hash = extra_hash
self.content_hash = content_hash
self.identifier_hashes = identifier_hashes
self.extra_hashes = extra_hashes
class SystemMetaExtraHashes(msrest.serialization.Model):
"""SystemMetaExtraHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(SystemMetaExtraHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class SystemMetaIdentifierHashes(msrest.serialization.Model):
"""SystemMetaIdentifierHashes.
:ivar identifier_hash:
:vartype identifier_hash: str
:ivar identifier_hash_v2:
:vartype identifier_hash_v2: str
"""
_attribute_map = {
'identifier_hash': {'key': 'IdentifierHash', 'type': 'str'},
'identifier_hash_v2': {'key': 'IdentifierHashV2', 'type': 'str'},
}
def __init__(
self,
*,
identifier_hash: Optional[str] = None,
identifier_hash_v2: Optional[str] = None,
**kwargs
):
"""
:keyword identifier_hash:
:paramtype identifier_hash: str
:keyword identifier_hash_v2:
:paramtype identifier_hash_v2: str
"""
super(SystemMetaIdentifierHashes, self).__init__(**kwargs)
self.identifier_hash = identifier_hash
self.identifier_hash_v2 = identifier_hash_v2
class TargetLags(msrest.serialization.Model):
"""TargetLags.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.TargetLagsMode
:ivar values:
:vartype values: list[int]
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'values': {'key': 'values', 'type': '[int]'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "TargetLagsMode"]] = None,
values: Optional[List[int]] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.TargetLagsMode
:keyword values:
:paramtype values: list[int]
"""
super(TargetLags, self).__init__(**kwargs)
self.mode = mode
self.values = values
class TargetRollingWindowSize(msrest.serialization.Model):
"""TargetRollingWindowSize.
:ivar mode: Possible values include: "Auto", "Custom".
:vartype mode: str or ~flow.models.TargetRollingWindowSizeMode
:ivar value:
:vartype value: int
"""
_attribute_map = {
'mode': {'key': 'mode', 'type': 'str'},
'value': {'key': 'value', 'type': 'int'},
}
def __init__(
self,
*,
mode: Optional[Union[str, "TargetRollingWindowSizeMode"]] = None,
value: Optional[int] = None,
**kwargs
):
"""
:keyword mode: Possible values include: "Auto", "Custom".
:paramtype mode: str or ~flow.models.TargetRollingWindowSizeMode
:keyword value:
:paramtype value: int
"""
super(TargetRollingWindowSize, self).__init__(**kwargs)
self.mode = mode
self.value = value
class TargetSelectorConfiguration(msrest.serialization.Model):
"""TargetSelectorConfiguration.
:ivar low_priority_vm_tolerant:
:vartype low_priority_vm_tolerant: bool
:ivar cluster_block_list:
:vartype cluster_block_list: list[str]
:ivar compute_type:
:vartype compute_type: str
:ivar instance_type:
:vartype instance_type: list[str]
:ivar instance_types:
:vartype instance_types: list[str]
:ivar my_resource_only:
:vartype my_resource_only: bool
:ivar plan_id:
:vartype plan_id: str
:ivar plan_region_id:
:vartype plan_region_id: str
:ivar region:
:vartype region: list[str]
:ivar regions:
:vartype regions: list[str]
:ivar vc_block_list:
:vartype vc_block_list: list[str]
"""
_attribute_map = {
'low_priority_vm_tolerant': {'key': 'lowPriorityVMTolerant', 'type': 'bool'},
'cluster_block_list': {'key': 'clusterBlockList', 'type': '[str]'},
'compute_type': {'key': 'computeType', 'type': 'str'},
'instance_type': {'key': 'instanceType', 'type': '[str]'},
'instance_types': {'key': 'instanceTypes', 'type': '[str]'},
'my_resource_only': {'key': 'myResourceOnly', 'type': 'bool'},
'plan_id': {'key': 'planId', 'type': 'str'},
'plan_region_id': {'key': 'planRegionId', 'type': 'str'},
'region': {'key': 'region', 'type': '[str]'},
'regions': {'key': 'regions', 'type': '[str]'},
'vc_block_list': {'key': 'vcBlockList', 'type': '[str]'},
}
def __init__(
self,
*,
low_priority_vm_tolerant: Optional[bool] = None,
cluster_block_list: Optional[List[str]] = None,
compute_type: Optional[str] = None,
instance_type: Optional[List[str]] = None,
instance_types: Optional[List[str]] = None,
my_resource_only: Optional[bool] = None,
plan_id: Optional[str] = None,
plan_region_id: Optional[str] = None,
region: Optional[List[str]] = None,
regions: Optional[List[str]] = None,
vc_block_list: Optional[List[str]] = None,
**kwargs
):
"""
:keyword low_priority_vm_tolerant:
:paramtype low_priority_vm_tolerant: bool
:keyword cluster_block_list:
:paramtype cluster_block_list: list[str]
:keyword compute_type:
:paramtype compute_type: str
:keyword instance_type:
:paramtype instance_type: list[str]
:keyword instance_types:
:paramtype instance_types: list[str]
:keyword my_resource_only:
:paramtype my_resource_only: bool
:keyword plan_id:
:paramtype plan_id: str
:keyword plan_region_id:
:paramtype plan_region_id: str
:keyword region:
:paramtype region: list[str]
:keyword regions:
:paramtype regions: list[str]
:keyword vc_block_list:
:paramtype vc_block_list: list[str]
"""
super(TargetSelectorConfiguration, self).__init__(**kwargs)
self.low_priority_vm_tolerant = low_priority_vm_tolerant
self.cluster_block_list = cluster_block_list
self.compute_type = compute_type
self.instance_type = instance_type
self.instance_types = instance_types
self.my_resource_only = my_resource_only
self.plan_id = plan_id
self.plan_region_id = plan_region_id
self.region = region
self.regions = regions
self.vc_block_list = vc_block_list
class Task(msrest.serialization.Model):
"""Task.
Variables are only populated by the server, and will be ignored when sending a request.
:ivar id:
:vartype id: int
:ivar exception: Anything.
:vartype exception: any
:ivar status: Possible values include: "Created", "WaitingForActivation", "WaitingToRun",
"Running", "WaitingForChildrenToComplete", "RanToCompletion", "Canceled", "Faulted".
:vartype status: str or ~flow.models.TaskStatus
:ivar is_canceled:
:vartype is_canceled: bool
:ivar is_completed:
:vartype is_completed: bool
:ivar is_completed_successfully:
:vartype is_completed_successfully: bool
:ivar creation_options: Possible values include: "None", "PreferFairness", "LongRunning",
"AttachedToParent", "DenyChildAttach", "HideScheduler", "RunContinuationsAsynchronously".
:vartype creation_options: str or ~flow.models.TaskCreationOptions
:ivar async_state: Anything.
:vartype async_state: any
:ivar is_faulted:
:vartype is_faulted: bool
"""
_validation = {
'id': {'readonly': True},
'exception': {'readonly': True},
'is_canceled': {'readonly': True},
'is_completed': {'readonly': True},
'is_completed_successfully': {'readonly': True},
'async_state': {'readonly': True},
'is_faulted': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'int'},
'exception': {'key': 'exception', 'type': 'object'},
'status': {'key': 'status', 'type': 'str'},
'is_canceled': {'key': 'isCanceled', 'type': 'bool'},
'is_completed': {'key': 'isCompleted', 'type': 'bool'},
'is_completed_successfully': {'key': 'isCompletedSuccessfully', 'type': 'bool'},
'creation_options': {'key': 'creationOptions', 'type': 'str'},
'async_state': {'key': 'asyncState', 'type': 'object'},
'is_faulted': {'key': 'isFaulted', 'type': 'bool'},
}
def __init__(
self,
*,
status: Optional[Union[str, "TaskStatus"]] = None,
creation_options: Optional[Union[str, "TaskCreationOptions"]] = None,
**kwargs
):
"""
:keyword status: Possible values include: "Created", "WaitingForActivation", "WaitingToRun",
"Running", "WaitingForChildrenToComplete", "RanToCompletion", "Canceled", "Faulted".
:paramtype status: str or ~flow.models.TaskStatus
:keyword creation_options: Possible values include: "None", "PreferFairness", "LongRunning",
"AttachedToParent", "DenyChildAttach", "HideScheduler", "RunContinuationsAsynchronously".
:paramtype creation_options: str or ~flow.models.TaskCreationOptions
"""
super(Task, self).__init__(**kwargs)
self.id = None
self.exception = None
self.status = status
self.is_canceled = None
self.is_completed = None
self.is_completed_successfully = None
self.creation_options = creation_options
self.async_state = None
self.is_faulted = None
class TaskControlFlowInfo(msrest.serialization.Model):
"""TaskControlFlowInfo.
:ivar control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:vartype control_flow_type: str or ~flow.models.ControlFlowType
:ivar iteration_index:
:vartype iteration_index: int
:ivar item_name:
:vartype item_name: str
:ivar parameters_overwritten: Dictionary of :code:`<string>`.
:vartype parameters_overwritten: dict[str, str]
:ivar is_reused:
:vartype is_reused: bool
"""
_attribute_map = {
'control_flow_type': {'key': 'controlFlowType', 'type': 'str'},
'iteration_index': {'key': 'iterationIndex', 'type': 'int'},
'item_name': {'key': 'itemName', 'type': 'str'},
'parameters_overwritten': {'key': 'parametersOverwritten', 'type': '{str}'},
'is_reused': {'key': 'isReused', 'type': 'bool'},
}
def __init__(
self,
*,
control_flow_type: Optional[Union[str, "ControlFlowType"]] = None,
iteration_index: Optional[int] = None,
item_name: Optional[str] = None,
parameters_overwritten: Optional[Dict[str, str]] = None,
is_reused: Optional[bool] = None,
**kwargs
):
"""
:keyword control_flow_type: Possible values include: "None", "DoWhile", "ParallelFor".
:paramtype control_flow_type: str or ~flow.models.ControlFlowType
:keyword iteration_index:
:paramtype iteration_index: int
:keyword item_name:
:paramtype item_name: str
:keyword parameters_overwritten: Dictionary of :code:`<string>`.
:paramtype parameters_overwritten: dict[str, str]
:keyword is_reused:
:paramtype is_reused: bool
"""
super(TaskControlFlowInfo, self).__init__(**kwargs)
self.control_flow_type = control_flow_type
self.iteration_index = iteration_index
self.item_name = item_name
self.parameters_overwritten = parameters_overwritten
self.is_reused = is_reused
class TaskReuseInfo(msrest.serialization.Model):
"""TaskReuseInfo.
:ivar experiment_id:
:vartype experiment_id: str
:ivar pipeline_run_id:
:vartype pipeline_run_id: str
:ivar node_id:
:vartype node_id: str
:ivar request_id:
:vartype request_id: str
:ivar run_id:
:vartype run_id: str
:ivar node_start_time:
:vartype node_start_time: ~datetime.datetime
:ivar node_end_time:
:vartype node_end_time: ~datetime.datetime
"""
_attribute_map = {
'experiment_id': {'key': 'experimentId', 'type': 'str'},
'pipeline_run_id': {'key': 'pipelineRunId', 'type': 'str'},
'node_id': {'key': 'nodeId', 'type': 'str'},
'request_id': {'key': 'requestId', 'type': 'str'},
'run_id': {'key': 'runId', 'type': 'str'},
'node_start_time': {'key': 'nodeStartTime', 'type': 'iso-8601'},
'node_end_time': {'key': 'nodeEndTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
experiment_id: Optional[str] = None,
pipeline_run_id: Optional[str] = None,
node_id: Optional[str] = None,
request_id: Optional[str] = None,
run_id: Optional[str] = None,
node_start_time: Optional[datetime.datetime] = None,
node_end_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword experiment_id:
:paramtype experiment_id: str
:keyword pipeline_run_id:
:paramtype pipeline_run_id: str
:keyword node_id:
:paramtype node_id: str
:keyword request_id:
:paramtype request_id: str
:keyword run_id:
:paramtype run_id: str
:keyword node_start_time:
:paramtype node_start_time: ~datetime.datetime
:keyword node_end_time:
:paramtype node_end_time: ~datetime.datetime
"""
super(TaskReuseInfo, self).__init__(**kwargs)
self.experiment_id = experiment_id
self.pipeline_run_id = pipeline_run_id
self.node_id = node_id
self.request_id = request_id
self.run_id = run_id
self.node_start_time = node_start_time
self.node_end_time = node_end_time
class TensorflowConfiguration(msrest.serialization.Model):
"""TensorflowConfiguration.
:ivar worker_count:
:vartype worker_count: int
:ivar parameter_server_count:
:vartype parameter_server_count: int
"""
_attribute_map = {
'worker_count': {'key': 'workerCount', 'type': 'int'},
'parameter_server_count': {'key': 'parameterServerCount', 'type': 'int'},
}
def __init__(
self,
*,
worker_count: Optional[int] = None,
parameter_server_count: Optional[int] = None,
**kwargs
):
"""
:keyword worker_count:
:paramtype worker_count: int
:keyword parameter_server_count:
:paramtype parameter_server_count: int
"""
super(TensorflowConfiguration, self).__init__(**kwargs)
self.worker_count = worker_count
self.parameter_server_count = parameter_server_count
class TestDataSettings(msrest.serialization.Model):
"""TestDataSettings.
:ivar test_data_size:
:vartype test_data_size: float
"""
_attribute_map = {
'test_data_size': {'key': 'testDataSize', 'type': 'float'},
}
def __init__(
self,
*,
test_data_size: Optional[float] = None,
**kwargs
):
"""
:keyword test_data_size:
:paramtype test_data_size: float
"""
super(TestDataSettings, self).__init__(**kwargs)
self.test_data_size = test_data_size
class Tool(msrest.serialization.Model):
"""Tool.
:ivar name:
:vartype name: str
:ivar type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:vartype type: str or ~flow.models.ToolType
:ivar inputs: This is a dictionary.
:vartype inputs: dict[str, ~flow.models.InputDefinition]
:ivar outputs: This is a dictionary.
:vartype outputs: dict[str, ~flow.models.OutputDefinition]
:ivar description:
:vartype description: str
:ivar connection_type:
:vartype connection_type: list[str or ~flow.models.ConnectionType]
:ivar module:
:vartype module: str
:ivar class_name:
:vartype class_name: str
:ivar source:
:vartype source: str
:ivar lkg_code:
:vartype lkg_code: str
:ivar code:
:vartype code: str
:ivar function:
:vartype function: str
:ivar action_type:
:vartype action_type: str
:ivar provider_config: This is a dictionary.
:vartype provider_config: dict[str, ~flow.models.InputDefinition]
:ivar function_config: This is a dictionary.
:vartype function_config: dict[str, ~flow.models.InputDefinition]
:ivar icon: Anything.
:vartype icon: any
:ivar category:
:vartype category: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, any]
:ivar is_builtin:
:vartype is_builtin: bool
:ivar package:
:vartype package: str
:ivar package_version:
:vartype package_version: str
:ivar default_prompt:
:vartype default_prompt: str
:ivar enable_kwargs:
:vartype enable_kwargs: bool
:ivar deprecated_tools:
:vartype deprecated_tools: list[str]
:ivar tool_state: Possible values include: "Stable", "Preview", "Deprecated".
:vartype tool_state: str or ~flow.models.ToolState
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'inputs': {'key': 'inputs', 'type': '{InputDefinition}'},
'outputs': {'key': 'outputs', 'type': '{OutputDefinition}'},
'description': {'key': 'description', 'type': 'str'},
'connection_type': {'key': 'connection_type', 'type': '[str]'},
'module': {'key': 'module', 'type': 'str'},
'class_name': {'key': 'class_name', 'type': 'str'},
'source': {'key': 'source', 'type': 'str'},
'lkg_code': {'key': 'lkgCode', 'type': 'str'},
'code': {'key': 'code', 'type': 'str'},
'function': {'key': 'function', 'type': 'str'},
'action_type': {'key': 'action_type', 'type': 'str'},
'provider_config': {'key': 'provider_config', 'type': '{InputDefinition}'},
'function_config': {'key': 'function_config', 'type': '{InputDefinition}'},
'icon': {'key': 'icon', 'type': 'object'},
'category': {'key': 'category', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{object}'},
'is_builtin': {'key': 'is_builtin', 'type': 'bool'},
'package': {'key': 'package', 'type': 'str'},
'package_version': {'key': 'package_version', 'type': 'str'},
'default_prompt': {'key': 'default_prompt', 'type': 'str'},
'enable_kwargs': {'key': 'enable_kwargs', 'type': 'bool'},
'deprecated_tools': {'key': 'deprecated_tools', 'type': '[str]'},
'tool_state': {'key': 'tool_state', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
type: Optional[Union[str, "ToolType"]] = None,
inputs: Optional[Dict[str, "InputDefinition"]] = None,
outputs: Optional[Dict[str, "OutputDefinition"]] = None,
description: Optional[str] = None,
connection_type: Optional[List[Union[str, "ConnectionType"]]] = None,
module: Optional[str] = None,
class_name: Optional[str] = None,
source: Optional[str] = None,
lkg_code: Optional[str] = None,
code: Optional[str] = None,
function: Optional[str] = None,
action_type: Optional[str] = None,
provider_config: Optional[Dict[str, "InputDefinition"]] = None,
function_config: Optional[Dict[str, "InputDefinition"]] = None,
icon: Optional[Any] = None,
category: Optional[str] = None,
tags: Optional[Dict[str, Any]] = None,
is_builtin: Optional[bool] = None,
package: Optional[str] = None,
package_version: Optional[str] = None,
default_prompt: Optional[str] = None,
enable_kwargs: Optional[bool] = None,
deprecated_tools: Optional[List[str]] = None,
tool_state: Optional[Union[str, "ToolState"]] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword type: Possible values include: "llm", "python", "action", "prompt", "custom_llm",
"csharp", "typescript".
:paramtype type: str or ~flow.models.ToolType
:keyword inputs: This is a dictionary.
:paramtype inputs: dict[str, ~flow.models.InputDefinition]
:keyword outputs: This is a dictionary.
:paramtype outputs: dict[str, ~flow.models.OutputDefinition]
:keyword description:
:paramtype description: str
:keyword connection_type:
:paramtype connection_type: list[str or ~flow.models.ConnectionType]
:keyword module:
:paramtype module: str
:keyword class_name:
:paramtype class_name: str
:keyword source:
:paramtype source: str
:keyword lkg_code:
:paramtype lkg_code: str
:keyword code:
:paramtype code: str
:keyword function:
:paramtype function: str
:keyword action_type:
:paramtype action_type: str
:keyword provider_config: This is a dictionary.
:paramtype provider_config: dict[str, ~flow.models.InputDefinition]
:keyword function_config: This is a dictionary.
:paramtype function_config: dict[str, ~flow.models.InputDefinition]
:keyword icon: Anything.
:paramtype icon: any
:keyword category:
:paramtype category: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, any]
:keyword is_builtin:
:paramtype is_builtin: bool
:keyword package:
:paramtype package: str
:keyword package_version:
:paramtype package_version: str
:keyword default_prompt:
:paramtype default_prompt: str
:keyword enable_kwargs:
:paramtype enable_kwargs: bool
:keyword deprecated_tools:
:paramtype deprecated_tools: list[str]
:keyword tool_state: Possible values include: "Stable", "Preview", "Deprecated".
:paramtype tool_state: str or ~flow.models.ToolState
"""
super(Tool, self).__init__(**kwargs)
self.name = name
self.type = type
self.inputs = inputs
self.outputs = outputs
self.description = description
self.connection_type = connection_type
self.module = module
self.class_name = class_name
self.source = source
self.lkg_code = lkg_code
self.code = code
self.function = function
self.action_type = action_type
self.provider_config = provider_config
self.function_config = function_config
self.icon = icon
self.category = category
self.tags = tags
self.is_builtin = is_builtin
self.package = package
self.package_version = package_version
self.default_prompt = default_prompt
self.enable_kwargs = enable_kwargs
self.deprecated_tools = deprecated_tools
self.tool_state = tool_state
class ToolFuncResponse(msrest.serialization.Model):
"""ToolFuncResponse.
:ivar result: Anything.
:vartype result: any
:ivar logs: This is a dictionary.
:vartype logs: dict[str, str]
"""
_attribute_map = {
'result': {'key': 'result', 'type': 'object'},
'logs': {'key': 'logs', 'type': '{str}'},
}
def __init__(
self,
*,
result: Optional[Any] = None,
logs: Optional[Dict[str, str]] = None,
**kwargs
):
"""
:keyword result: Anything.
:paramtype result: any
:keyword logs: This is a dictionary.
:paramtype logs: dict[str, str]
"""
super(ToolFuncResponse, self).__init__(**kwargs)
self.result = result
self.logs = logs
class ToolInputDynamicList(msrest.serialization.Model):
"""ToolInputDynamicList.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs:
:vartype func_kwargs: list[dict[str, any]]
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '[{object}]'},
}
def __init__(
self,
*,
func_path: Optional[str] = None,
func_kwargs: Optional[List[Dict[str, Any]]] = None,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs:
:paramtype func_kwargs: list[dict[str, any]]
"""
super(ToolInputDynamicList, self).__init__(**kwargs)
self.func_path = func_path
self.func_kwargs = func_kwargs
class ToolInputGeneratedBy(msrest.serialization.Model):
"""ToolInputGeneratedBy.
:ivar func_path:
:vartype func_path: str
:ivar func_kwargs:
:vartype func_kwargs: list[dict[str, any]]
:ivar reverse_func_path:
:vartype reverse_func_path: str
"""
_attribute_map = {
'func_path': {'key': 'func_path', 'type': 'str'},
'func_kwargs': {'key': 'func_kwargs', 'type': '[{object}]'},
'reverse_func_path': {'key': 'reverse_func_path', 'type': 'str'},
}
def __init__(
self,
*,
func_path: Optional[str] = None,
func_kwargs: Optional[List[Dict[str, Any]]] = None,
reverse_func_path: Optional[str] = None,
**kwargs
):
"""
:keyword func_path:
:paramtype func_path: str
:keyword func_kwargs:
:paramtype func_kwargs: list[dict[str, any]]
:keyword reverse_func_path:
:paramtype reverse_func_path: str
"""
super(ToolInputGeneratedBy, self).__init__(**kwargs)
self.func_path = func_path
self.func_kwargs = func_kwargs
self.reverse_func_path = reverse_func_path
class ToolMetaDto(msrest.serialization.Model):
"""ToolMetaDto.
:ivar tools: This is a dictionary.
:vartype tools: dict[str, ~flow.models.Tool]
:ivar errors: This is a dictionary.
:vartype errors: dict[str, ~flow.models.ErrorResponse]
"""
_attribute_map = {
'tools': {'key': 'tools', 'type': '{Tool}'},
'errors': {'key': 'errors', 'type': '{ErrorResponse}'},
}
def __init__(
self,
*,
tools: Optional[Dict[str, "Tool"]] = None,
errors: Optional[Dict[str, "ErrorResponse"]] = None,
**kwargs
):
"""
:keyword tools: This is a dictionary.
:paramtype tools: dict[str, ~flow.models.Tool]
:keyword errors: This is a dictionary.
:paramtype errors: dict[str, ~flow.models.ErrorResponse]
"""
super(ToolMetaDto, self).__init__(**kwargs)
self.tools = tools
self.errors = errors
class ToolSetting(msrest.serialization.Model):
"""ToolSetting.
:ivar providers:
:vartype providers: list[~flow.models.ProviderEntity]
"""
_attribute_map = {
'providers': {'key': 'providers', 'type': '[ProviderEntity]'},
}
def __init__(
self,
*,
providers: Optional[List["ProviderEntity"]] = None,
**kwargs
):
"""
:keyword providers:
:paramtype providers: list[~flow.models.ProviderEntity]
"""
super(ToolSetting, self).__init__(**kwargs)
self.providers = providers
class ToolSourceMeta(msrest.serialization.Model):
"""ToolSourceMeta.
:ivar tool_type:
:vartype tool_type: str
"""
_attribute_map = {
'tool_type': {'key': 'tool_type', 'type': 'str'},
}
def __init__(
self,
*,
tool_type: Optional[str] = None,
**kwargs
):
"""
:keyword tool_type:
:paramtype tool_type: str
"""
super(ToolSourceMeta, self).__init__(**kwargs)
self.tool_type = tool_type
class TorchDistributedConfiguration(msrest.serialization.Model):
"""TorchDistributedConfiguration.
:ivar process_count_per_node:
:vartype process_count_per_node: int
"""
_attribute_map = {
'process_count_per_node': {'key': 'processCountPerNode', 'type': 'int'},
}
def __init__(
self,
*,
process_count_per_node: Optional[int] = None,
**kwargs
):
"""
:keyword process_count_per_node:
:paramtype process_count_per_node: int
"""
super(TorchDistributedConfiguration, self).__init__(**kwargs)
self.process_count_per_node = process_count_per_node
class TrainingDiagnosticConfiguration(msrest.serialization.Model):
"""TrainingDiagnosticConfiguration.
:ivar job_heart_beat_timeout_seconds:
:vartype job_heart_beat_timeout_seconds: int
"""
_attribute_map = {
'job_heart_beat_timeout_seconds': {'key': 'jobHeartBeatTimeoutSeconds', 'type': 'int'},
}
def __init__(
self,
*,
job_heart_beat_timeout_seconds: Optional[int] = None,
**kwargs
):
"""
:keyword job_heart_beat_timeout_seconds:
:paramtype job_heart_beat_timeout_seconds: int
"""
super(TrainingDiagnosticConfiguration, self).__init__(**kwargs)
self.job_heart_beat_timeout_seconds = job_heart_beat_timeout_seconds
class TrainingOutput(msrest.serialization.Model):
"""TrainingOutput.
:ivar training_output_type: Possible values include: "Metrics", "Model".
:vartype training_output_type: str or ~flow.models.TrainingOutputType
:ivar iteration:
:vartype iteration: int
:ivar metric:
:vartype metric: str
:ivar model_file:
:vartype model_file: str
"""
_attribute_map = {
'training_output_type': {'key': 'trainingOutputType', 'type': 'str'},
'iteration': {'key': 'iteration', 'type': 'int'},
'metric': {'key': 'metric', 'type': 'str'},
'model_file': {'key': 'modelFile', 'type': 'str'},
}
def __init__(
self,
*,
training_output_type: Optional[Union[str, "TrainingOutputType"]] = None,
iteration: Optional[int] = None,
metric: Optional[str] = None,
model_file: Optional[str] = None,
**kwargs
):
"""
:keyword training_output_type: Possible values include: "Metrics", "Model".
:paramtype training_output_type: str or ~flow.models.TrainingOutputType
:keyword iteration:
:paramtype iteration: int
:keyword metric:
:paramtype metric: str
:keyword model_file:
:paramtype model_file: str
"""
super(TrainingOutput, self).__init__(**kwargs)
self.training_output_type = training_output_type
self.iteration = iteration
self.metric = metric
self.model_file = model_file
class TrainingSettings(msrest.serialization.Model):
"""TrainingSettings.
:ivar block_list_models:
:vartype block_list_models: list[str]
:ivar allow_list_models:
:vartype allow_list_models: list[str]
:ivar enable_dnn_training:
:vartype enable_dnn_training: bool
:ivar enable_onnx_compatible_models:
:vartype enable_onnx_compatible_models: bool
:ivar stack_ensemble_settings:
:vartype stack_ensemble_settings: ~flow.models.StackEnsembleSettings
:ivar enable_stack_ensemble:
:vartype enable_stack_ensemble: bool
:ivar enable_vote_ensemble:
:vartype enable_vote_ensemble: bool
:ivar ensemble_model_download_timeout:
:vartype ensemble_model_download_timeout: str
:ivar enable_model_explainability:
:vartype enable_model_explainability: bool
:ivar training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:vartype training_mode: str or ~flow.models.TabularTrainingMode
"""
_attribute_map = {
'block_list_models': {'key': 'blockListModels', 'type': '[str]'},
'allow_list_models': {'key': 'allowListModels', 'type': '[str]'},
'enable_dnn_training': {'key': 'enableDnnTraining', 'type': 'bool'},
'enable_onnx_compatible_models': {'key': 'enableOnnxCompatibleModels', 'type': 'bool'},
'stack_ensemble_settings': {'key': 'stackEnsembleSettings', 'type': 'StackEnsembleSettings'},
'enable_stack_ensemble': {'key': 'enableStackEnsemble', 'type': 'bool'},
'enable_vote_ensemble': {'key': 'enableVoteEnsemble', 'type': 'bool'},
'ensemble_model_download_timeout': {'key': 'ensembleModelDownloadTimeout', 'type': 'str'},
'enable_model_explainability': {'key': 'enableModelExplainability', 'type': 'bool'},
'training_mode': {'key': 'trainingMode', 'type': 'str'},
}
def __init__(
self,
*,
block_list_models: Optional[List[str]] = None,
allow_list_models: Optional[List[str]] = None,
enable_dnn_training: Optional[bool] = None,
enable_onnx_compatible_models: Optional[bool] = None,
stack_ensemble_settings: Optional["StackEnsembleSettings"] = None,
enable_stack_ensemble: Optional[bool] = None,
enable_vote_ensemble: Optional[bool] = None,
ensemble_model_download_timeout: Optional[str] = None,
enable_model_explainability: Optional[bool] = None,
training_mode: Optional[Union[str, "TabularTrainingMode"]] = None,
**kwargs
):
"""
:keyword block_list_models:
:paramtype block_list_models: list[str]
:keyword allow_list_models:
:paramtype allow_list_models: list[str]
:keyword enable_dnn_training:
:paramtype enable_dnn_training: bool
:keyword enable_onnx_compatible_models:
:paramtype enable_onnx_compatible_models: bool
:keyword stack_ensemble_settings:
:paramtype stack_ensemble_settings: ~flow.models.StackEnsembleSettings
:keyword enable_stack_ensemble:
:paramtype enable_stack_ensemble: bool
:keyword enable_vote_ensemble:
:paramtype enable_vote_ensemble: bool
:keyword ensemble_model_download_timeout:
:paramtype ensemble_model_download_timeout: str
:keyword enable_model_explainability:
:paramtype enable_model_explainability: bool
:keyword training_mode: Possible values include: "Distributed", "NonDistributed", "Auto".
:paramtype training_mode: str or ~flow.models.TabularTrainingMode
"""
super(TrainingSettings, self).__init__(**kwargs)
self.block_list_models = block_list_models
self.allow_list_models = allow_list_models
self.enable_dnn_training = enable_dnn_training
self.enable_onnx_compatible_models = enable_onnx_compatible_models
self.stack_ensemble_settings = stack_ensemble_settings
self.enable_stack_ensemble = enable_stack_ensemble
self.enable_vote_ensemble = enable_vote_ensemble
self.ensemble_model_download_timeout = ensemble_model_download_timeout
self.enable_model_explainability = enable_model_explainability
self.training_mode = training_mode
class TriggerAsyncOperationStatus(msrest.serialization.Model):
"""TriggerAsyncOperationStatus.
:ivar id:
:vartype id: str
:ivar operation_type: Possible values include: "Create", "Update", "Delete", "CreateOrUpdate".
:vartype operation_type: str or ~flow.models.TriggerOperationType
:ivar provisioning_status: Possible values include: "Creating", "Updating", "Deleting",
"Succeeded", "Failed", "Canceled".
:vartype provisioning_status: str or ~flow.models.ScheduleProvisioningStatus
:ivar created_time:
:vartype created_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
:ivar error: The error response.
:vartype error: ~flow.models.ErrorResponse
:ivar status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing",
"EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent",
"ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed",
"MultipleChoices", "Ambiguous", "MovedPermanently", "Moved", "Found", "Redirect", "SeeOther",
"RedirectMethod", "NotModified", "UseProxy", "Unused", "TemporaryRedirect", "RedirectKeepVerb",
"PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", "Forbidden", "NotFound",
"MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", "RequestTimeout",
"Conflict", "Gone", "LengthRequired", "PreconditionFailed", "RequestEntityTooLarge",
"RequestUriTooLong", "UnsupportedMediaType", "RequestedRangeNotSatisfiable",
"ExpectationFailed", "MisdirectedRequest", "UnprocessableEntity", "Locked", "FailedDependency",
"UpgradeRequired", "PreconditionRequired", "TooManyRequests", "RequestHeaderFieldsTooLarge",
"UnavailableForLegalReasons", "InternalServerError", "NotImplemented", "BadGateway",
"ServiceUnavailable", "GatewayTimeout", "HttpVersionNotSupported", "VariantAlsoNegotiates",
"InsufficientStorage", "LoopDetected", "NotExtended", "NetworkAuthenticationRequired".
:vartype status_code: str or ~flow.models.HttpStatusCode
"""
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'operation_type': {'key': 'operationType', 'type': 'str'},
'provisioning_status': {'key': 'provisioningStatus', 'type': 'str'},
'created_time': {'key': 'createdTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'ErrorResponse'},
'status_code': {'key': 'statusCode', 'type': 'str'},
}
def __init__(
self,
*,
id: Optional[str] = None,
operation_type: Optional[Union[str, "TriggerOperationType"]] = None,
provisioning_status: Optional[Union[str, "ScheduleProvisioningStatus"]] = None,
created_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
error: Optional["ErrorResponse"] = None,
status_code: Optional[Union[str, "HttpStatusCode"]] = None,
**kwargs
):
"""
:keyword id:
:paramtype id: str
:keyword operation_type: Possible values include: "Create", "Update", "Delete",
"CreateOrUpdate".
:paramtype operation_type: str or ~flow.models.TriggerOperationType
:keyword provisioning_status: Possible values include: "Creating", "Updating", "Deleting",
"Succeeded", "Failed", "Canceled".
:paramtype provisioning_status: str or ~flow.models.ScheduleProvisioningStatus
:keyword created_time:
:paramtype created_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
:keyword error: The error response.
:paramtype error: ~flow.models.ErrorResponse
:keyword status_code: Possible values include: "Continue", "SwitchingProtocols", "Processing",
"EarlyHints", "OK", "Created", "Accepted", "NonAuthoritativeInformation", "NoContent",
"ResetContent", "PartialContent", "MultiStatus", "AlreadyReported", "IMUsed",
"MultipleChoices", "Ambiguous", "MovedPermanently", "Moved", "Found", "Redirect", "SeeOther",
"RedirectMethod", "NotModified", "UseProxy", "Unused", "TemporaryRedirect", "RedirectKeepVerb",
"PermanentRedirect", "BadRequest", "Unauthorized", "PaymentRequired", "Forbidden", "NotFound",
"MethodNotAllowed", "NotAcceptable", "ProxyAuthenticationRequired", "RequestTimeout",
"Conflict", "Gone", "LengthRequired", "PreconditionFailed", "RequestEntityTooLarge",
"RequestUriTooLong", "UnsupportedMediaType", "RequestedRangeNotSatisfiable",
"ExpectationFailed", "MisdirectedRequest", "UnprocessableEntity", "Locked", "FailedDependency",
"UpgradeRequired", "PreconditionRequired", "TooManyRequests", "RequestHeaderFieldsTooLarge",
"UnavailableForLegalReasons", "InternalServerError", "NotImplemented", "BadGateway",
"ServiceUnavailable", "GatewayTimeout", "HttpVersionNotSupported", "VariantAlsoNegotiates",
"InsufficientStorage", "LoopDetected", "NotExtended", "NetworkAuthenticationRequired".
:paramtype status_code: str or ~flow.models.HttpStatusCode
"""
super(TriggerAsyncOperationStatus, self).__init__(**kwargs)
self.id = id
self.operation_type = operation_type
self.provisioning_status = provisioning_status
self.created_time = created_time
self.end_time = end_time
self.error = error
self.status_code = status_code
class TuningNodeRunSetting(msrest.serialization.Model):
"""TuningNodeRunSetting.
:ivar simulation_flow:
:vartype simulation_flow: ~flow.models.FlowGraphReference
:ivar simulation_flow_run_setting:
:vartype simulation_flow_run_setting: ~flow.models.FlowRunSettingsBase
:ivar batch_inputs:
:vartype batch_inputs: list[dict[str, any]]
:ivar input_universal_link:
:vartype input_universal_link: str
:ivar data_inputs: This is a dictionary.
:vartype data_inputs: dict[str, str]
:ivar flow_run_output_directory:
:vartype flow_run_output_directory: str
:ivar connection_overrides:
:vartype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:ivar flow_run_display_name:
:vartype flow_run_display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. This is a dictionary.
:vartype tags: dict[str, str]
:ivar properties: This is a dictionary.
:vartype properties: dict[str, str]
:ivar runtime_name:
:vartype runtime_name: str
:ivar batch_data_input:
:vartype batch_data_input: ~flow.models.BatchDataInput
:ivar inputs_mapping: This is a dictionary.
:vartype inputs_mapping: dict[str, str]
:ivar connections: This is a dictionary.
:vartype connections: dict[str, dict[str, str]]
:ivar environment_variables: This is a dictionary.
:vartype environment_variables: dict[str, str]
:ivar output_data_store:
:vartype output_data_store: str
:ivar run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:vartype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:ivar aml_compute_name:
:vartype aml_compute_name: str
:ivar worker_count:
:vartype worker_count: int
:ivar timeout_in_seconds:
:vartype timeout_in_seconds: int
:ivar promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:vartype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
_attribute_map = {
'simulation_flow': {'key': 'simulationFlow', 'type': 'FlowGraphReference'},
'simulation_flow_run_setting': {'key': 'simulationFlowRunSetting', 'type': 'FlowRunSettingsBase'},
'batch_inputs': {'key': 'batch_inputs', 'type': '[{object}]'},
'input_universal_link': {'key': 'inputUniversalLink', 'type': 'str'},
'data_inputs': {'key': 'dataInputs', 'type': '{str}'},
'flow_run_output_directory': {'key': 'flowRunOutputDirectory', 'type': 'str'},
'connection_overrides': {'key': 'connectionOverrides', 'type': '[ConnectionOverrideSetting]'},
'flow_run_display_name': {'key': 'flowRunDisplayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'runtime_name': {'key': 'runtimeName', 'type': 'str'},
'batch_data_input': {'key': 'batchDataInput', 'type': 'BatchDataInput'},
'inputs_mapping': {'key': 'inputsMapping', 'type': '{str}'},
'connections': {'key': 'connections', 'type': '{{str}}'},
'environment_variables': {'key': 'environmentVariables', 'type': '{str}'},
'output_data_store': {'key': 'outputDataStore', 'type': 'str'},
'run_display_name_generation_type': {'key': 'runDisplayNameGenerationType', 'type': 'str'},
'aml_compute_name': {'key': 'amlComputeName', 'type': 'str'},
'worker_count': {'key': 'workerCount', 'type': 'int'},
'timeout_in_seconds': {'key': 'timeoutInSeconds', 'type': 'int'},
'promptflow_engine_type': {'key': 'promptflowEngineType', 'type': 'str'},
}
def __init__(
self,
*,
simulation_flow: Optional["FlowGraphReference"] = None,
simulation_flow_run_setting: Optional["FlowRunSettingsBase"] = None,
batch_inputs: Optional[List[Dict[str, Any]]] = None,
input_universal_link: Optional[str] = None,
data_inputs: Optional[Dict[str, str]] = None,
flow_run_output_directory: Optional[str] = None,
connection_overrides: Optional[List["ConnectionOverrideSetting"]] = None,
flow_run_display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
properties: Optional[Dict[str, str]] = None,
runtime_name: Optional[str] = None,
batch_data_input: Optional["BatchDataInput"] = None,
inputs_mapping: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
output_data_store: Optional[str] = None,
run_display_name_generation_type: Optional[Union[str, "RunDisplayNameGenerationType"]] = None,
aml_compute_name: Optional[str] = None,
worker_count: Optional[int] = None,
timeout_in_seconds: Optional[int] = None,
promptflow_engine_type: Optional[Union[str, "PromptflowEngineType"]] = None,
**kwargs
):
"""
:keyword simulation_flow:
:paramtype simulation_flow: ~flow.models.FlowGraphReference
:keyword simulation_flow_run_setting:
:paramtype simulation_flow_run_setting: ~flow.models.FlowRunSettingsBase
:keyword batch_inputs:
:paramtype batch_inputs: list[dict[str, any]]
:keyword input_universal_link:
:paramtype input_universal_link: str
:keyword data_inputs: This is a dictionary.
:paramtype data_inputs: dict[str, str]
:keyword flow_run_output_directory:
:paramtype flow_run_output_directory: str
:keyword connection_overrides:
:paramtype connection_overrides: list[~flow.models.ConnectionOverrideSetting]
:keyword flow_run_display_name:
:paramtype flow_run_display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. This is a dictionary.
:paramtype tags: dict[str, str]
:keyword properties: This is a dictionary.
:paramtype properties: dict[str, str]
:keyword runtime_name:
:paramtype runtime_name: str
:keyword batch_data_input:
:paramtype batch_data_input: ~flow.models.BatchDataInput
:keyword inputs_mapping: This is a dictionary.
:paramtype inputs_mapping: dict[str, str]
:keyword connections: This is a dictionary.
:paramtype connections: dict[str, dict[str, str]]
:keyword environment_variables: This is a dictionary.
:paramtype environment_variables: dict[str, str]
:keyword output_data_store:
:paramtype output_data_store: str
:keyword run_display_name_generation_type: Possible values include: "AutoAppend",
"UserProvidedMacro".
:paramtype run_display_name_generation_type: str or ~flow.models.RunDisplayNameGenerationType
:keyword aml_compute_name:
:paramtype aml_compute_name: str
:keyword worker_count:
:paramtype worker_count: int
:keyword timeout_in_seconds:
:paramtype timeout_in_seconds: int
:keyword promptflow_engine_type: Possible values include: "FastEngine", "ScalableEngine".
:paramtype promptflow_engine_type: str or ~flow.models.PromptflowEngineType
"""
super(TuningNodeRunSetting, self).__init__(**kwargs)
self.simulation_flow = simulation_flow
self.simulation_flow_run_setting = simulation_flow_run_setting
self.batch_inputs = batch_inputs
self.input_universal_link = input_universal_link
self.data_inputs = data_inputs
self.flow_run_output_directory = flow_run_output_directory
self.connection_overrides = connection_overrides
self.flow_run_display_name = flow_run_display_name
self.description = description
self.tags = tags
self.properties = properties
self.runtime_name = runtime_name
self.batch_data_input = batch_data_input
self.inputs_mapping = inputs_mapping
self.connections = connections
self.environment_variables = environment_variables
self.output_data_store = output_data_store
self.run_display_name_generation_type = run_display_name_generation_type
self.aml_compute_name = aml_compute_name
self.worker_count = worker_count
self.timeout_in_seconds = timeout_in_seconds
self.promptflow_engine_type = promptflow_engine_type
class TuningNodeSetting(msrest.serialization.Model):
"""TuningNodeSetting.
:ivar variant_ids:
:vartype variant_ids: list[str]
:ivar tuning_node_run_settings: This is a dictionary.
:vartype tuning_node_run_settings: dict[str, ~flow.models.TuningNodeRunSetting]
"""
_attribute_map = {
'variant_ids': {'key': 'variantIds', 'type': '[str]'},
'tuning_node_run_settings': {'key': 'tuningNodeRunSettings', 'type': '{TuningNodeRunSetting}'},
}
def __init__(
self,
*,
variant_ids: Optional[List[str]] = None,
tuning_node_run_settings: Optional[Dict[str, "TuningNodeRunSetting"]] = None,
**kwargs
):
"""
:keyword variant_ids:
:paramtype variant_ids: list[str]
:keyword tuning_node_run_settings: This is a dictionary.
:paramtype tuning_node_run_settings: dict[str, ~flow.models.TuningNodeRunSetting]
"""
super(TuningNodeSetting, self).__init__(**kwargs)
self.variant_ids = variant_ids
self.tuning_node_run_settings = tuning_node_run_settings
class TypedAssetReference(msrest.serialization.Model):
"""TypedAssetReference.
:ivar asset_id:
:vartype asset_id: str
:ivar type:
:vartype type: str
"""
_attribute_map = {
'asset_id': {'key': 'assetId', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
}
def __init__(
self,
*,
asset_id: Optional[str] = None,
type: Optional[str] = None,
**kwargs
):
"""
:keyword asset_id:
:paramtype asset_id: str
:keyword type:
:paramtype type: str
"""
super(TypedAssetReference, self).__init__(**kwargs)
self.asset_id = asset_id
self.type = type
class UIAzureOpenAIDeploymentNameSelector(msrest.serialization.Model):
"""UIAzureOpenAIDeploymentNameSelector.
:ivar capabilities:
:vartype capabilities: ~flow.models.UIAzureOpenAIModelCapabilities
"""
_attribute_map = {
'capabilities': {'key': 'Capabilities', 'type': 'UIAzureOpenAIModelCapabilities'},
}
def __init__(
self,
*,
capabilities: Optional["UIAzureOpenAIModelCapabilities"] = None,
**kwargs
):
"""
:keyword capabilities:
:paramtype capabilities: ~flow.models.UIAzureOpenAIModelCapabilities
"""
super(UIAzureOpenAIDeploymentNameSelector, self).__init__(**kwargs)
self.capabilities = capabilities
class UIAzureOpenAIModelCapabilities(msrest.serialization.Model):
"""UIAzureOpenAIModelCapabilities.
:ivar completion:
:vartype completion: bool
:ivar chat_completion:
:vartype chat_completion: bool
:ivar embeddings:
:vartype embeddings: bool
"""
_attribute_map = {
'completion': {'key': 'Completion', 'type': 'bool'},
'chat_completion': {'key': 'ChatCompletion', 'type': 'bool'},
'embeddings': {'key': 'Embeddings', 'type': 'bool'},
}
def __init__(
self,
*,
completion: Optional[bool] = None,
chat_completion: Optional[bool] = None,
embeddings: Optional[bool] = None,
**kwargs
):
"""
:keyword completion:
:paramtype completion: bool
:keyword chat_completion:
:paramtype chat_completion: bool
:keyword embeddings:
:paramtype embeddings: bool
"""
super(UIAzureOpenAIModelCapabilities, self).__init__(**kwargs)
self.completion = completion
self.chat_completion = chat_completion
self.embeddings = embeddings
class UIColumnPicker(msrest.serialization.Model):
"""UIColumnPicker.
:ivar column_picker_for:
:vartype column_picker_for: str
:ivar column_selection_categories:
:vartype column_selection_categories: list[str]
:ivar single_column_selection:
:vartype single_column_selection: bool
"""
_attribute_map = {
'column_picker_for': {'key': 'columnPickerFor', 'type': 'str'},
'column_selection_categories': {'key': 'columnSelectionCategories', 'type': '[str]'},
'single_column_selection': {'key': 'singleColumnSelection', 'type': 'bool'},
}
def __init__(
self,
*,
column_picker_for: Optional[str] = None,
column_selection_categories: Optional[List[str]] = None,
single_column_selection: Optional[bool] = None,
**kwargs
):
"""
:keyword column_picker_for:
:paramtype column_picker_for: str
:keyword column_selection_categories:
:paramtype column_selection_categories: list[str]
:keyword single_column_selection:
:paramtype single_column_selection: bool
"""
super(UIColumnPicker, self).__init__(**kwargs)
self.column_picker_for = column_picker_for
self.column_selection_categories = column_selection_categories
self.single_column_selection = single_column_selection
class UIComputeSelection(msrest.serialization.Model):
"""UIComputeSelection.
:ivar compute_types:
:vartype compute_types: list[str]
:ivar require_gpu:
:vartype require_gpu: bool
:ivar os_types:
:vartype os_types: list[str]
:ivar support_serverless:
:vartype support_serverless: bool
:ivar compute_run_settings_mapping: Dictionary of
<components·10my8oj·schemas·uicomputeselection·properties·computerunsettingsmapping·additionalproperties>.
:vartype compute_run_settings_mapping: dict[str, list[~flow.models.RunSettingParameter]]
"""
_attribute_map = {
'compute_types': {'key': 'computeTypes', 'type': '[str]'},
'require_gpu': {'key': 'requireGpu', 'type': 'bool'},
'os_types': {'key': 'osTypes', 'type': '[str]'},
'support_serverless': {'key': 'supportServerless', 'type': 'bool'},
'compute_run_settings_mapping': {'key': 'computeRunSettingsMapping', 'type': '{[RunSettingParameter]}'},
}
def __init__(
self,
*,
compute_types: Optional[List[str]] = None,
require_gpu: Optional[bool] = None,
os_types: Optional[List[str]] = None,
support_serverless: Optional[bool] = None,
compute_run_settings_mapping: Optional[Dict[str, List["RunSettingParameter"]]] = None,
**kwargs
):
"""
:keyword compute_types:
:paramtype compute_types: list[str]
:keyword require_gpu:
:paramtype require_gpu: bool
:keyword os_types:
:paramtype os_types: list[str]
:keyword support_serverless:
:paramtype support_serverless: bool
:keyword compute_run_settings_mapping: Dictionary of
<components·10my8oj·schemas·uicomputeselection·properties·computerunsettingsmapping·additionalproperties>.
:paramtype compute_run_settings_mapping: dict[str, list[~flow.models.RunSettingParameter]]
"""
super(UIComputeSelection, self).__init__(**kwargs)
self.compute_types = compute_types
self.require_gpu = require_gpu
self.os_types = os_types
self.support_serverless = support_serverless
self.compute_run_settings_mapping = compute_run_settings_mapping
class UIHyperparameterConfiguration(msrest.serialization.Model):
"""UIHyperparameterConfiguration.
:ivar model_name_to_hyper_parameter_and_distribution_mapping: Dictionary of
<components·1nrp69t·schemas·uihyperparameterconfiguration·properties·modelnametohyperparameteranddistributionmapping·additionalproperties>.
:vartype model_name_to_hyper_parameter_and_distribution_mapping: dict[str, dict[str,
list[str]]]
:ivar distribution_parameters_mapping: Dictionary of
<components·d9plq4·schemas·uihyperparameterconfiguration·properties·distributionparametersmapping·additionalproperties>.
:vartype distribution_parameters_mapping: dict[str, list[~flow.models.DistributionParameter]]
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'model_name_to_hyper_parameter_and_distribution_mapping': {'key': 'modelNameToHyperParameterAndDistributionMapping', 'type': '{{[str]}}'},
'distribution_parameters_mapping': {'key': 'distributionParametersMapping', 'type': '{[DistributionParameter]}'},
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
*,
model_name_to_hyper_parameter_and_distribution_mapping: Optional[Dict[str, Dict[str, List[str]]]] = None,
distribution_parameters_mapping: Optional[Dict[str, List["DistributionParameter"]]] = None,
json_schema: Optional[str] = None,
**kwargs
):
"""
:keyword model_name_to_hyper_parameter_and_distribution_mapping: Dictionary of
<components·1nrp69t·schemas·uihyperparameterconfiguration·properties·modelnametohyperparameteranddistributionmapping·additionalproperties>.
:paramtype model_name_to_hyper_parameter_and_distribution_mapping: dict[str, dict[str,
list[str]]]
:keyword distribution_parameters_mapping: Dictionary of
<components·d9plq4·schemas·uihyperparameterconfiguration·properties·distributionparametersmapping·additionalproperties>.
:paramtype distribution_parameters_mapping: dict[str, list[~flow.models.DistributionParameter]]
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIHyperparameterConfiguration, self).__init__(**kwargs)
self.model_name_to_hyper_parameter_and_distribution_mapping = model_name_to_hyper_parameter_and_distribution_mapping
self.distribution_parameters_mapping = distribution_parameters_mapping
self.json_schema = json_schema
class UIInputSetting(msrest.serialization.Model):
"""UIInputSetting.
:ivar name:
:vartype name: str
:ivar data_delivery_mode: Possible values include: "Read-only mount", "Read-write mount",
"Download", "Direct", "Evaluate mount", "Evaluate download", "Hdfs".
:vartype data_delivery_mode: str or ~flow.models.UIInputDataDeliveryMode
:ivar path_on_compute:
:vartype path_on_compute: str
"""
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'data_delivery_mode': {'key': 'dataDeliveryMode', 'type': 'str'},
'path_on_compute': {'key': 'pathOnCompute', 'type': 'str'},
}
def __init__(
self,
*,
name: Optional[str] = None,
data_delivery_mode: Optional[Union[str, "UIInputDataDeliveryMode"]] = None,
path_on_compute: Optional[str] = None,
**kwargs
):
"""
:keyword name:
:paramtype name: str
:keyword data_delivery_mode: Possible values include: "Read-only mount", "Read-write mount",
"Download", "Direct", "Evaluate mount", "Evaluate download", "Hdfs".
:paramtype data_delivery_mode: str or ~flow.models.UIInputDataDeliveryMode
:keyword path_on_compute:
:paramtype path_on_compute: str
"""
super(UIInputSetting, self).__init__(**kwargs)
self.name = name
self.data_delivery_mode = data_delivery_mode
self.path_on_compute = path_on_compute
class UIJsonEditor(msrest.serialization.Model):
"""UIJsonEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
*,
json_schema: Optional[str] = None,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIJsonEditor, self).__init__(**kwargs)
self.json_schema = json_schema
class UIParameterHint(msrest.serialization.Model):
"""UIParameterHint.
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
:ivar column_picker:
:vartype column_picker: ~flow.models.UIColumnPicker
:ivar ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:vartype ui_script_language: str or ~flow.models.UIScriptLanguageEnum
:ivar json_editor:
:vartype json_editor: ~flow.models.UIJsonEditor
:ivar prompt_flow_connection_selector:
:vartype prompt_flow_connection_selector: ~flow.models.UIPromptFlowConnectionSelector
:ivar azure_open_ai_deployment_name_selector:
:vartype azure_open_ai_deployment_name_selector:
~flow.models.UIAzureOpenAIDeploymentNameSelector
:ivar ux_ignore:
:vartype ux_ignore: bool
:ivar anonymous:
:vartype anonymous: bool
"""
_attribute_map = {
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
'column_picker': {'key': 'columnPicker', 'type': 'UIColumnPicker'},
'ui_script_language': {'key': 'uiScriptLanguage', 'type': 'str'},
'json_editor': {'key': 'jsonEditor', 'type': 'UIJsonEditor'},
'prompt_flow_connection_selector': {'key': 'PromptFlowConnectionSelector', 'type': 'UIPromptFlowConnectionSelector'},
'azure_open_ai_deployment_name_selector': {'key': 'AzureOpenAIDeploymentNameSelector', 'type': 'UIAzureOpenAIDeploymentNameSelector'},
'ux_ignore': {'key': 'UxIgnore', 'type': 'bool'},
'anonymous': {'key': 'Anonymous', 'type': 'bool'},
}
def __init__(
self,
*,
ui_widget_type: Optional[Union[str, "UIWidgetTypeEnum"]] = None,
column_picker: Optional["UIColumnPicker"] = None,
ui_script_language: Optional[Union[str, "UIScriptLanguageEnum"]] = None,
json_editor: Optional["UIJsonEditor"] = None,
prompt_flow_connection_selector: Optional["UIPromptFlowConnectionSelector"] = None,
azure_open_ai_deployment_name_selector: Optional["UIAzureOpenAIDeploymentNameSelector"] = None,
ux_ignore: Optional[bool] = None,
anonymous: Optional[bool] = None,
**kwargs
):
"""
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
:keyword column_picker:
:paramtype column_picker: ~flow.models.UIColumnPicker
:keyword ui_script_language: Possible values include: "None", "Python", "R", "Json", "Sql".
:paramtype ui_script_language: str or ~flow.models.UIScriptLanguageEnum
:keyword json_editor:
:paramtype json_editor: ~flow.models.UIJsonEditor
:keyword prompt_flow_connection_selector:
:paramtype prompt_flow_connection_selector: ~flow.models.UIPromptFlowConnectionSelector
:keyword azure_open_ai_deployment_name_selector:
:paramtype azure_open_ai_deployment_name_selector:
~flow.models.UIAzureOpenAIDeploymentNameSelector
:keyword ux_ignore:
:paramtype ux_ignore: bool
:keyword anonymous:
:paramtype anonymous: bool
"""
super(UIParameterHint, self).__init__(**kwargs)
self.ui_widget_type = ui_widget_type
self.column_picker = column_picker
self.ui_script_language = ui_script_language
self.json_editor = json_editor
self.prompt_flow_connection_selector = prompt_flow_connection_selector
self.azure_open_ai_deployment_name_selector = azure_open_ai_deployment_name_selector
self.ux_ignore = ux_ignore
self.anonymous = anonymous
class UIPromptFlowConnectionSelector(msrest.serialization.Model):
"""UIPromptFlowConnectionSelector.
:ivar prompt_flow_connection_type:
:vartype prompt_flow_connection_type: str
"""
_attribute_map = {
'prompt_flow_connection_type': {'key': 'PromptFlowConnectionType', 'type': 'str'},
}
def __init__(
self,
*,
prompt_flow_connection_type: Optional[str] = None,
**kwargs
):
"""
:keyword prompt_flow_connection_type:
:paramtype prompt_flow_connection_type: str
"""
super(UIPromptFlowConnectionSelector, self).__init__(**kwargs)
self.prompt_flow_connection_type = prompt_flow_connection_type
class UIWidgetMetaInfo(msrest.serialization.Model):
"""UIWidgetMetaInfo.
:ivar module_node_id:
:vartype module_node_id: str
:ivar meta_module_id:
:vartype meta_module_id: str
:ivar parameter_name:
:vartype parameter_name: str
:ivar ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker", "Credential",
"Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter", "SectionToggle",
"YamlEditor", "EnableRuntimeSweep", "DataStoreSelection", "InstanceTypeSelection",
"ConnectionSelection", "PromptFlowConnectionSelection", "AzureOpenAIDeploymentNameSelection".
:vartype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
"""
_attribute_map = {
'module_node_id': {'key': 'moduleNodeId', 'type': 'str'},
'meta_module_id': {'key': 'metaModuleId', 'type': 'str'},
'parameter_name': {'key': 'parameterName', 'type': 'str'},
'ui_widget_type': {'key': 'uiWidgetType', 'type': 'str'},
}
def __init__(
self,
*,
module_node_id: Optional[str] = None,
meta_module_id: Optional[str] = None,
parameter_name: Optional[str] = None,
ui_widget_type: Optional[Union[str, "UIWidgetTypeEnum"]] = None,
**kwargs
):
"""
:keyword module_node_id:
:paramtype module_node_id: str
:keyword meta_module_id:
:paramtype meta_module_id: str
:keyword parameter_name:
:paramtype parameter_name: str
:keyword ui_widget_type: Possible values include: "Default", "Mode", "ColumnPicker",
"Credential", "Script", "ComputeSelection", "JsonEditor", "SearchSpaceParameter",
"SectionToggle", "YamlEditor", "EnableRuntimeSweep", "DataStoreSelection",
"InstanceTypeSelection", "ConnectionSelection", "PromptFlowConnectionSelection",
"AzureOpenAIDeploymentNameSelection".
:paramtype ui_widget_type: str or ~flow.models.UIWidgetTypeEnum
"""
super(UIWidgetMetaInfo, self).__init__(**kwargs)
self.module_node_id = module_node_id
self.meta_module_id = meta_module_id
self.parameter_name = parameter_name
self.ui_widget_type = ui_widget_type
class UIYamlEditor(msrest.serialization.Model):
"""UIYamlEditor.
:ivar json_schema:
:vartype json_schema: str
"""
_attribute_map = {
'json_schema': {'key': 'jsonSchema', 'type': 'str'},
}
def __init__(
self,
*,
json_schema: Optional[str] = None,
**kwargs
):
"""
:keyword json_schema:
:paramtype json_schema: str
"""
super(UIYamlEditor, self).__init__(**kwargs)
self.json_schema = json_schema
class UnversionedEntityRequestDto(msrest.serialization.Model):
"""UnversionedEntityRequestDto.
:ivar unversioned_entity_ids:
:vartype unversioned_entity_ids: list[str]
"""
_attribute_map = {
'unversioned_entity_ids': {'key': 'unversionedEntityIds', 'type': '[str]'},
}
def __init__(
self,
*,
unversioned_entity_ids: Optional[List[str]] = None,
**kwargs
):
"""
:keyword unversioned_entity_ids:
:paramtype unversioned_entity_ids: list[str]
"""
super(UnversionedEntityRequestDto, self).__init__(**kwargs)
self.unversioned_entity_ids = unversioned_entity_ids
class UnversionedEntityResponseDto(msrest.serialization.Model):
"""UnversionedEntityResponseDto.
:ivar unversioned_entities:
:vartype unversioned_entities: list[~flow.models.FlowIndexEntity]
:ivar unversioned_entity_json_schema: Anything.
:vartype unversioned_entity_json_schema: any
:ivar normalized_request_charge:
:vartype normalized_request_charge: float
:ivar normalized_request_charge_period:
:vartype normalized_request_charge_period: str
"""
_attribute_map = {
'unversioned_entities': {'key': 'unversionedEntities', 'type': '[FlowIndexEntity]'},
'unversioned_entity_json_schema': {'key': 'unversionedEntityJsonSchema', 'type': 'object'},
'normalized_request_charge': {'key': 'normalizedRequestCharge', 'type': 'float'},
'normalized_request_charge_period': {'key': 'normalizedRequestChargePeriod', 'type': 'str'},
}
def __init__(
self,
*,
unversioned_entities: Optional[List["FlowIndexEntity"]] = None,
unversioned_entity_json_schema: Optional[Any] = None,
normalized_request_charge: Optional[float] = None,
normalized_request_charge_period: Optional[str] = None,
**kwargs
):
"""
:keyword unversioned_entities:
:paramtype unversioned_entities: list[~flow.models.FlowIndexEntity]
:keyword unversioned_entity_json_schema: Anything.
:paramtype unversioned_entity_json_schema: any
:keyword normalized_request_charge:
:paramtype normalized_request_charge: float
:keyword normalized_request_charge_period:
:paramtype normalized_request_charge_period: str
"""
super(UnversionedEntityResponseDto, self).__init__(**kwargs)
self.unversioned_entities = unversioned_entities
self.unversioned_entity_json_schema = unversioned_entity_json_schema
self.normalized_request_charge = normalized_request_charge
self.normalized_request_charge_period = normalized_request_charge_period
class UnversionedRebuildIndexDto(msrest.serialization.Model):
"""UnversionedRebuildIndexDto.
:ivar continuation_token:
:vartype continuation_token: str
:ivar entity_count:
:vartype entity_count: int
:ivar entity_container_type:
:vartype entity_container_type: str
:ivar entity_type:
:vartype entity_type: str
:ivar resource_id:
:vartype resource_id: str
:ivar workspace_id:
:vartype workspace_id: str
:ivar immutable_resource_id:
:vartype immutable_resource_id: str
:ivar start_time:
:vartype start_time: ~datetime.datetime
:ivar end_time:
:vartype end_time: ~datetime.datetime
"""
_attribute_map = {
'continuation_token': {'key': 'continuationToken', 'type': 'str'},
'entity_count': {'key': 'entityCount', 'type': 'int'},
'entity_container_type': {'key': 'entityContainerType', 'type': 'str'},
'entity_type': {'key': 'entityType', 'type': 'str'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'workspace_id': {'key': 'workspaceId', 'type': 'str'},
'immutable_resource_id': {'key': 'immutableResourceId', 'type': 'str'},
'start_time': {'key': 'startTime', 'type': 'iso-8601'},
'end_time': {'key': 'endTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
continuation_token: Optional[str] = None,
entity_count: Optional[int] = None,
entity_container_type: Optional[str] = None,
entity_type: Optional[str] = None,
resource_id: Optional[str] = None,
workspace_id: Optional[str] = None,
immutable_resource_id: Optional[str] = None,
start_time: Optional[datetime.datetime] = None,
end_time: Optional[datetime.datetime] = None,
**kwargs
):
"""
:keyword continuation_token:
:paramtype continuation_token: str
:keyword entity_count:
:paramtype entity_count: int
:keyword entity_container_type:
:paramtype entity_container_type: str
:keyword entity_type:
:paramtype entity_type: str
:keyword resource_id:
:paramtype resource_id: str
:keyword workspace_id:
:paramtype workspace_id: str
:keyword immutable_resource_id:
:paramtype immutable_resource_id: str
:keyword start_time:
:paramtype start_time: ~datetime.datetime
:keyword end_time:
:paramtype end_time: ~datetime.datetime
"""
super(UnversionedRebuildIndexDto, self).__init__(**kwargs)
self.continuation_token = continuation_token
self.entity_count = entity_count
self.entity_container_type = entity_container_type
self.entity_type = entity_type
self.resource_id = resource_id
self.workspace_id = workspace_id
self.immutable_resource_id = immutable_resource_id
self.start_time = start_time
self.end_time = end_time
class UnversionedRebuildResponseDto(msrest.serialization.Model):
"""UnversionedRebuildResponseDto.
:ivar entities:
:vartype entities: ~flow.models.SegmentedResult1
:ivar unversioned_entity_schema: Anything.
:vartype unversioned_entity_schema: any
:ivar normalized_request_charge:
:vartype normalized_request_charge: float
:ivar normalized_request_charge_period:
:vartype normalized_request_charge_period: str
"""
_attribute_map = {
'entities': {'key': 'entities', 'type': 'SegmentedResult1'},
'unversioned_entity_schema': {'key': 'unversionedEntitySchema', 'type': 'object'},
'normalized_request_charge': {'key': 'normalizedRequestCharge', 'type': 'float'},
'normalized_request_charge_period': {'key': 'normalizedRequestChargePeriod', 'type': 'str'},
}
def __init__(
self,
*,
entities: Optional["SegmentedResult1"] = None,
unversioned_entity_schema: Optional[Any] = None,
normalized_request_charge: Optional[float] = None,
normalized_request_charge_period: Optional[str] = None,
**kwargs
):
"""
:keyword entities:
:paramtype entities: ~flow.models.SegmentedResult1
:keyword unversioned_entity_schema: Anything.
:paramtype unversioned_entity_schema: any
:keyword normalized_request_charge:
:paramtype normalized_request_charge: float
:keyword normalized_request_charge_period:
:paramtype normalized_request_charge_period: str
"""
super(UnversionedRebuildResponseDto, self).__init__(**kwargs)
self.entities = entities
self.unversioned_entity_schema = unversioned_entity_schema
self.normalized_request_charge = normalized_request_charge
self.normalized_request_charge_period = normalized_request_charge_period
class UpdateComponentRequest(msrest.serialization.Model):
"""UpdateComponentRequest.
:ivar display_name:
:vartype display_name: str
:ivar description:
:vartype description: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar module_update_operation_type: Possible values include: "SetDefaultVersion",
"EnableModule", "DisableModule", "UpdateDisplayName", "UpdateDescription", "UpdateTags".
:vartype module_update_operation_type: str or ~flow.models.ModuleUpdateOperationType
:ivar module_version:
:vartype module_version: str
"""
_attribute_map = {
'display_name': {'key': 'displayName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'module_update_operation_type': {'key': 'moduleUpdateOperationType', 'type': 'str'},
'module_version': {'key': 'moduleVersion', 'type': 'str'},
}
def __init__(
self,
*,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
module_update_operation_type: Optional[Union[str, "ModuleUpdateOperationType"]] = None,
module_version: Optional[str] = None,
**kwargs
):
"""
:keyword display_name:
:paramtype display_name: str
:keyword description:
:paramtype description: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword module_update_operation_type: Possible values include: "SetDefaultVersion",
"EnableModule", "DisableModule", "UpdateDisplayName", "UpdateDescription", "UpdateTags".
:paramtype module_update_operation_type: str or ~flow.models.ModuleUpdateOperationType
:keyword module_version:
:paramtype module_version: str
"""
super(UpdateComponentRequest, self).__init__(**kwargs)
self.display_name = display_name
self.description = description
self.tags = tags
self.module_update_operation_type = module_update_operation_type
self.module_version = module_version
class UpdateFlowRequest(msrest.serialization.Model):
"""UpdateFlowRequest.
:ivar flow_run_result:
:vartype flow_run_result: ~flow.models.FlowRunResult
:ivar flow_test_mode: Possible values include: "Sync", "Async".
:vartype flow_test_mode: str or ~flow.models.FlowTestMode
:ivar flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:vartype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:ivar flow_name:
:vartype flow_name: str
:ivar description:
:vartype description: str
:ivar details:
:vartype details: str
:ivar tags: A set of tags. Dictionary of :code:`<string>`.
:vartype tags: dict[str, str]
:ivar flow:
:vartype flow: ~flow.models.Flow
:ivar flow_definition_file_path:
:vartype flow_definition_file_path: str
:ivar flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:vartype flow_type: str or ~flow.models.FlowType
:ivar flow_run_settings:
:vartype flow_run_settings: ~flow.models.FlowRunSettings
:ivar is_archived:
:vartype is_archived: bool
:ivar vm_size:
:vartype vm_size: str
:ivar max_idle_time_seconds:
:vartype max_idle_time_seconds: long
:ivar identity:
:vartype identity: str
"""
_attribute_map = {
'flow_run_result': {'key': 'flowRunResult', 'type': 'FlowRunResult'},
'flow_test_mode': {'key': 'flowTestMode', 'type': 'str'},
'flow_test_infos': {'key': 'flowTestInfos', 'type': '{FlowTestInfo}'},
'flow_name': {'key': 'flowName', 'type': 'str'},
'description': {'key': 'description', 'type': 'str'},
'details': {'key': 'details', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'flow': {'key': 'flow', 'type': 'Flow'},
'flow_definition_file_path': {'key': 'flowDefinitionFilePath', 'type': 'str'},
'flow_type': {'key': 'flowType', 'type': 'str'},
'flow_run_settings': {'key': 'flowRunSettings', 'type': 'FlowRunSettings'},
'is_archived': {'key': 'isArchived', 'type': 'bool'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'max_idle_time_seconds': {'key': 'maxIdleTimeSeconds', 'type': 'long'},
'identity': {'key': 'identity', 'type': 'str'},
}
def __init__(
self,
*,
flow_run_result: Optional["FlowRunResult"] = None,
flow_test_mode: Optional[Union[str, "FlowTestMode"]] = None,
flow_test_infos: Optional[Dict[str, "FlowTestInfo"]] = None,
flow_name: Optional[str] = None,
description: Optional[str] = None,
details: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
flow: Optional["Flow"] = None,
flow_definition_file_path: Optional[str] = None,
flow_type: Optional[Union[str, "FlowType"]] = None,
flow_run_settings: Optional["FlowRunSettings"] = None,
is_archived: Optional[bool] = None,
vm_size: Optional[str] = None,
max_idle_time_seconds: Optional[int] = None,
identity: Optional[str] = None,
**kwargs
):
"""
:keyword flow_run_result:
:paramtype flow_run_result: ~flow.models.FlowRunResult
:keyword flow_test_mode: Possible values include: "Sync", "Async".
:paramtype flow_test_mode: str or ~flow.models.FlowTestMode
:keyword flow_test_infos: Dictionary of :code:`<FlowTestInfo>`.
:paramtype flow_test_infos: dict[str, ~flow.models.FlowTestInfo]
:keyword flow_name:
:paramtype flow_name: str
:keyword description:
:paramtype description: str
:keyword details:
:paramtype details: str
:keyword tags: A set of tags. Dictionary of :code:`<string>`.
:paramtype tags: dict[str, str]
:keyword flow:
:paramtype flow: ~flow.models.Flow
:keyword flow_definition_file_path:
:paramtype flow_definition_file_path: str
:keyword flow_type: Possible values include: "Default", "Evaluation", "Chat", "Rag".
:paramtype flow_type: str or ~flow.models.FlowType
:keyword flow_run_settings:
:paramtype flow_run_settings: ~flow.models.FlowRunSettings
:keyword is_archived:
:paramtype is_archived: bool
:keyword vm_size:
:paramtype vm_size: str
:keyword max_idle_time_seconds:
:paramtype max_idle_time_seconds: long
:keyword identity:
:paramtype identity: str
"""
super(UpdateFlowRequest, self).__init__(**kwargs)
self.flow_run_result = flow_run_result
self.flow_test_mode = flow_test_mode
self.flow_test_infos = flow_test_infos
self.flow_name = flow_name
self.description = description
self.details = details
self.tags = tags
self.flow = flow
self.flow_definition_file_path = flow_definition_file_path
self.flow_type = flow_type
self.flow_run_settings = flow_run_settings
self.is_archived = is_archived
self.vm_size = vm_size
self.max_idle_time_seconds = max_idle_time_seconds
self.identity = identity
class UpdateFlowRuntimeRequest(msrest.serialization.Model):
"""UpdateFlowRuntimeRequest.
:ivar runtime_description:
:vartype runtime_description: str
:ivar environment:
:vartype environment: str
:ivar instance_count:
:vartype instance_count: int
"""
_attribute_map = {
'runtime_description': {'key': 'runtimeDescription', 'type': 'str'},
'environment': {'key': 'environment', 'type': 'str'},
'instance_count': {'key': 'instanceCount', 'type': 'int'},
}
def __init__(
self,
*,
runtime_description: Optional[str] = None,
environment: Optional[str] = None,
instance_count: Optional[int] = None,
**kwargs
):
"""
:keyword runtime_description:
:paramtype runtime_description: str
:keyword environment:
:paramtype environment: str
:keyword instance_count:
:paramtype instance_count: int
"""
super(UpdateFlowRuntimeRequest, self).__init__(**kwargs)
self.runtime_description = runtime_description
self.environment = environment
self.instance_count = instance_count
class UpdateFlowStatusRequest(msrest.serialization.Model):
"""UpdateFlowStatusRequest.
:ivar flow_run_status: Possible values include: "Started", "Completed", "Failed", "Cancelled",
"NotStarted", "Running", "Queued", "Paused", "Unapproved", "Starting", "Preparing",
"CancelRequested", "Pausing", "Finalizing", "Canceled", "Bypassed".
:vartype flow_run_status: str or ~flow.models.FlowRunStatusEnum
:ivar error_response: The error response.
:vartype error_response: ~flow.models.ErrorResponse
"""
_attribute_map = {
'flow_run_status': {'key': 'flowRunStatus', 'type': 'str'},
'error_response': {'key': 'errorResponse', 'type': 'ErrorResponse'},
}
def __init__(
self,
*,
flow_run_status: Optional[Union[str, "FlowRunStatusEnum"]] = None,
error_response: Optional["ErrorResponse"] = None,
**kwargs
):
"""
:keyword flow_run_status: Possible values include: "Started", "Completed", "Failed",
"Cancelled", "NotStarted", "Running", "Queued", "Paused", "Unapproved", "Starting",
"Preparing", "CancelRequested", "Pausing", "Finalizing", "Canceled", "Bypassed".
:paramtype flow_run_status: str or ~flow.models.FlowRunStatusEnum
:keyword error_response: The error response.
:paramtype error_response: ~flow.models.ErrorResponse
"""
super(UpdateFlowStatusRequest, self).__init__(**kwargs)
self.flow_run_status = flow_run_status
self.error_response = error_response
class UpdateRegistryComponentRequest(msrest.serialization.Model):
"""UpdateRegistryComponentRequest.
:ivar registry_name:
:vartype registry_name: str
:ivar component_name:
:vartype component_name: str
:ivar component_version:
:vartype component_version: str
:ivar update_type: The only acceptable values to pass in are None and "SetDefaultVersion". The
default value is None.
:vartype update_type: str
"""
_attribute_map = {
'registry_name': {'key': 'registryName', 'type': 'str'},
'component_name': {'key': 'componentName', 'type': 'str'},
'component_version': {'key': 'componentVersion', 'type': 'str'},
'update_type': {'key': 'updateType', 'type': 'str'},
}
def __init__(
self,
*,
registry_name: Optional[str] = None,
component_name: Optional[str] = None,
component_version: Optional[str] = None,
update_type: Optional[str] = None,
**kwargs
):
"""
:keyword registry_name:
:paramtype registry_name: str
:keyword component_name:
:paramtype component_name: str
:keyword component_version:
:paramtype component_version: str
:keyword update_type: The only acceptable values to pass in are None and "SetDefaultVersion".
The default value is None.
:paramtype update_type: str
"""
super(UpdateRegistryComponentRequest, self).__init__(**kwargs)
self.registry_name = registry_name
self.component_name = component_name
self.component_version = component_version
self.update_type = update_type
class UploadOptions(msrest.serialization.Model):
"""UploadOptions.
:ivar overwrite:
:vartype overwrite: bool
:ivar source_globs:
:vartype source_globs: ~flow.models.ExecutionGlobsOptions
"""
_attribute_map = {
'overwrite': {'key': 'overwrite', 'type': 'bool'},
'source_globs': {'key': 'sourceGlobs', 'type': 'ExecutionGlobsOptions'},
}
def __init__(
self,
*,
overwrite: Optional[bool] = None,
source_globs: Optional["ExecutionGlobsOptions"] = None,
**kwargs
):
"""
:keyword overwrite:
:paramtype overwrite: bool
:keyword source_globs:
:paramtype source_globs: ~flow.models.ExecutionGlobsOptions
"""
super(UploadOptions, self).__init__(**kwargs)
self.overwrite = overwrite
self.source_globs = source_globs
class UriReference(msrest.serialization.Model):
"""UriReference.
:ivar path:
:vartype path: str
:ivar is_file:
:vartype is_file: bool
"""
_attribute_map = {
'path': {'key': 'path', 'type': 'str'},
'is_file': {'key': 'isFile', 'type': 'bool'},
}
def __init__(
self,
*,
path: Optional[str] = None,
is_file: Optional[bool] = None,
**kwargs
):
"""
:keyword path:
:paramtype path: str
:keyword is_file:
:paramtype is_file: bool
"""
super(UriReference, self).__init__(**kwargs)
self.path = path
self.is_file = is_file
class User(msrest.serialization.Model):
"""User.
:ivar user_object_id: A user or service principal's object ID.
This is EUPI and may only be logged to warm path telemetry.
:vartype user_object_id: str
:ivar user_pu_id: A user or service principal's PuID.
This is PII and should never be logged.
:vartype user_pu_id: str
:ivar user_idp: A user identity provider. Eg live.com
This is PII and should never be logged.
:vartype user_idp: str
:ivar user_alt_sec_id: A user alternate sec id. This represents the user in a different
identity provider system Eg.1:live.com:puid
This is PII and should never be logged.
:vartype user_alt_sec_id: str
:ivar user_iss: The issuer which issed the token for this user.
This is PII and should never be logged.
:vartype user_iss: str
:ivar user_tenant_id: A user or service principal's tenant ID.
:vartype user_tenant_id: str
:ivar user_name: A user's full name or a service principal's app ID.
This is PII and should never be logged.
:vartype user_name: str
:ivar upn: A user's Principal name (upn)
This is PII andshould never be logged.
:vartype upn: str
"""
_attribute_map = {
'user_object_id': {'key': 'userObjectId', 'type': 'str'},
'user_pu_id': {'key': 'userPuId', 'type': 'str'},
'user_idp': {'key': 'userIdp', 'type': 'str'},
'user_alt_sec_id': {'key': 'userAltSecId', 'type': 'str'},
'user_iss': {'key': 'userIss', 'type': 'str'},
'user_tenant_id': {'key': 'userTenantId', 'type': 'str'},
'user_name': {'key': 'userName', 'type': 'str'},
'upn': {'key': 'upn', 'type': 'str'},
}
def __init__(
self,
*,
user_object_id: Optional[str] = None,
user_pu_id: Optional[str] = None,
user_idp: Optional[str] = None,
user_alt_sec_id: Optional[str] = None,
user_iss: Optional[str] = None,
user_tenant_id: Optional[str] = None,
user_name: Optional[str] = None,
upn: Optional[str] = None,
**kwargs
):
"""
:keyword user_object_id: A user or service principal's object ID.
This is EUPI and may only be logged to warm path telemetry.
:paramtype user_object_id: str
:keyword user_pu_id: A user or service principal's PuID.
This is PII and should never be logged.
:paramtype user_pu_id: str
:keyword user_idp: A user identity provider. Eg live.com
This is PII and should never be logged.
:paramtype user_idp: str
:keyword user_alt_sec_id: A user alternate sec id. This represents the user in a different
identity provider system Eg.1:live.com:puid
This is PII and should never be logged.
:paramtype user_alt_sec_id: str
:keyword user_iss: The issuer which issed the token for this user.
This is PII and should never be logged.
:paramtype user_iss: str
:keyword user_tenant_id: A user or service principal's tenant ID.
:paramtype user_tenant_id: str
:keyword user_name: A user's full name or a service principal's app ID.
This is PII and should never be logged.
:paramtype user_name: str
:keyword upn: A user's Principal name (upn)
This is PII andshould never be logged.
:paramtype upn: str
"""
super(User, self).__init__(**kwargs)
self.user_object_id = user_object_id
self.user_pu_id = user_pu_id
self.user_idp = user_idp
self.user_alt_sec_id = user_alt_sec_id
self.user_iss = user_iss
self.user_tenant_id = user_tenant_id
self.user_name = user_name
self.upn = upn
class UserAssignedIdentity(msrest.serialization.Model):
"""UserAssignedIdentity.
:ivar principal_id:
:vartype principal_id: str
:ivar client_id:
:vartype client_id: str
"""
_attribute_map = {
'principal_id': {'key': 'principalId', 'type': 'str'},
'client_id': {'key': 'clientId', 'type': 'str'},
}
def __init__(
self,
*,
principal_id: Optional[str] = None,
client_id: Optional[str] = None,
**kwargs
):
"""
:keyword principal_id:
:paramtype principal_id: str
:keyword client_id:
:paramtype client_id: str
"""
super(UserAssignedIdentity, self).__init__(**kwargs)
self.principal_id = principal_id
self.client_id = client_id
class ValidationDataSettings(msrest.serialization.Model):
"""ValidationDataSettings.
:ivar n_cross_validations:
:vartype n_cross_validations: ~flow.models.NCrossValidations
:ivar validation_data_size:
:vartype validation_data_size: float
:ivar cv_split_column_names:
:vartype cv_split_column_names: list[str]
:ivar validation_type:
:vartype validation_type: str
"""
_attribute_map = {
'n_cross_validations': {'key': 'nCrossValidations', 'type': 'NCrossValidations'},
'validation_data_size': {'key': 'validationDataSize', 'type': 'float'},
'cv_split_column_names': {'key': 'cvSplitColumnNames', 'type': '[str]'},
'validation_type': {'key': 'validationType', 'type': 'str'},
}
def __init__(
self,
*,
n_cross_validations: Optional["NCrossValidations"] = None,
validation_data_size: Optional[float] = None,
cv_split_column_names: Optional[List[str]] = None,
validation_type: Optional[str] = None,
**kwargs
):
"""
:keyword n_cross_validations:
:paramtype n_cross_validations: ~flow.models.NCrossValidations
:keyword validation_data_size:
:paramtype validation_data_size: float
:keyword cv_split_column_names:
:paramtype cv_split_column_names: list[str]
:keyword validation_type:
:paramtype validation_type: str
"""
super(ValidationDataSettings, self).__init__(**kwargs)
self.n_cross_validations = n_cross_validations
self.validation_data_size = validation_data_size
self.cv_split_column_names = cv_split_column_names
self.validation_type = validation_type
class VariantIdentifier(msrest.serialization.Model):
"""VariantIdentifier.
:ivar variant_id:
:vartype variant_id: str
:ivar tuning_node_name:
:vartype tuning_node_name: str
"""
_attribute_map = {
'variant_id': {'key': 'variantId', 'type': 'str'},
'tuning_node_name': {'key': 'tuningNodeName', 'type': 'str'},
}
def __init__(
self,
*,
variant_id: Optional[str] = None,
tuning_node_name: Optional[str] = None,
**kwargs
):
"""
:keyword variant_id:
:paramtype variant_id: str
:keyword tuning_node_name:
:paramtype tuning_node_name: str
"""
super(VariantIdentifier, self).__init__(**kwargs)
self.variant_id = variant_id
self.tuning_node_name = tuning_node_name
class VariantNode(msrest.serialization.Model):
"""VariantNode.
:ivar node:
:vartype node: ~flow.models.Node
:ivar description:
:vartype description: str
"""
_attribute_map = {
'node': {'key': 'node', 'type': 'Node'},
'description': {'key': 'description', 'type': 'str'},
}
def __init__(
self,
*,
node: Optional["Node"] = None,
description: Optional[str] = None,
**kwargs
):
"""
:keyword node:
:paramtype node: ~flow.models.Node
:keyword description:
:paramtype description: str
"""
super(VariantNode, self).__init__(**kwargs)
self.node = node
self.description = description
class Volume(msrest.serialization.Model):
"""Volume.
:ivar type:
:vartype type: str
:ivar source:
:vartype source: str
:ivar target:
:vartype target: str
"""
_attribute_map = {
'type': {'key': 'type', 'type': 'str'},
'source': {'key': 'source', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
}
def __init__(
self,
*,
type: Optional[str] = None,
source: Optional[str] = None,
target: Optional[str] = None,
**kwargs
):
"""
:keyword type:
:paramtype type: str
:keyword source:
:paramtype source: str
:keyword target:
:paramtype target: str
"""
super(Volume, self).__init__(**kwargs)
self.type = type
self.source = source
self.target = target
class Webhook(msrest.serialization.Model):
"""Webhook.
:ivar webhook_type: The only acceptable values to pass in are None and "AzureDevOps". The
default value is None.
:vartype webhook_type: str
:ivar event_type:
:vartype event_type: str
"""
_attribute_map = {
'webhook_type': {'key': 'webhookType', 'type': 'str'},
'event_type': {'key': 'eventType', 'type': 'str'},
}
def __init__(
self,
*,
webhook_type: Optional[str] = None,
event_type: Optional[str] = None,
**kwargs
):
"""
:keyword webhook_type: The only acceptable values to pass in are None and "AzureDevOps". The
default value is None.
:paramtype webhook_type: str
:keyword event_type:
:paramtype event_type: str
"""
super(Webhook, self).__init__(**kwargs)
self.webhook_type = webhook_type
self.event_type = event_type
class WebServiceComputeMetaInfo(msrest.serialization.Model):
"""WebServiceComputeMetaInfo.
:ivar node_count:
:vartype node_count: int
:ivar is_ssl_enabled:
:vartype is_ssl_enabled: bool
:ivar aks_not_found:
:vartype aks_not_found: bool
:ivar cluster_purpose:
:vartype cluster_purpose: str
:ivar public_ip_address:
:vartype public_ip_address: str
:ivar vm_size:
:vartype vm_size: str
:ivar location:
:vartype location: str
:ivar provisioning_state:
:vartype provisioning_state: str
:ivar state:
:vartype state: str
:ivar os_type:
:vartype os_type: str
:ivar id:
:vartype id: str
:ivar name:
:vartype name: str
:ivar created_by_studio:
:vartype created_by_studio: bool
:ivar is_gpu_type:
:vartype is_gpu_type: bool
:ivar resource_id:
:vartype resource_id: str
:ivar compute_type:
:vartype compute_type: str
"""
_attribute_map = {
'node_count': {'key': 'nodeCount', 'type': 'int'},
'is_ssl_enabled': {'key': 'isSslEnabled', 'type': 'bool'},
'aks_not_found': {'key': 'aksNotFound', 'type': 'bool'},
'cluster_purpose': {'key': 'clusterPurpose', 'type': 'str'},
'public_ip_address': {'key': 'publicIpAddress', 'type': 'str'},
'vm_size': {'key': 'vmSize', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'provisioning_state': {'key': 'provisioningState', 'type': 'str'},
'state': {'key': 'state', 'type': 'str'},
'os_type': {'key': 'osType', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'created_by_studio': {'key': 'createdByStudio', 'type': 'bool'},
'is_gpu_type': {'key': 'isGpuType', 'type': 'bool'},
'resource_id': {'key': 'resourceId', 'type': 'str'},
'compute_type': {'key': 'computeType', 'type': 'str'},
}
def __init__(
self,
*,
node_count: Optional[int] = None,
is_ssl_enabled: Optional[bool] = None,
aks_not_found: Optional[bool] = None,
cluster_purpose: Optional[str] = None,
public_ip_address: Optional[str] = None,
vm_size: Optional[str] = None,
location: Optional[str] = None,
provisioning_state: Optional[str] = None,
state: Optional[str] = None,
os_type: Optional[str] = None,
id: Optional[str] = None,
name: Optional[str] = None,
created_by_studio: Optional[bool] = None,
is_gpu_type: Optional[bool] = None,
resource_id: Optional[str] = None,
compute_type: Optional[str] = None,
**kwargs
):
"""
:keyword node_count:
:paramtype node_count: int
:keyword is_ssl_enabled:
:paramtype is_ssl_enabled: bool
:keyword aks_not_found:
:paramtype aks_not_found: bool
:keyword cluster_purpose:
:paramtype cluster_purpose: str
:keyword public_ip_address:
:paramtype public_ip_address: str
:keyword vm_size:
:paramtype vm_size: str
:keyword location:
:paramtype location: str
:keyword provisioning_state:
:paramtype provisioning_state: str
:keyword state:
:paramtype state: str
:keyword os_type:
:paramtype os_type: str
:keyword id:
:paramtype id: str
:keyword name:
:paramtype name: str
:keyword created_by_studio:
:paramtype created_by_studio: bool
:keyword is_gpu_type:
:paramtype is_gpu_type: bool
:keyword resource_id:
:paramtype resource_id: str
:keyword compute_type:
:paramtype compute_type: str
"""
super(WebServiceComputeMetaInfo, self).__init__(**kwargs)
self.node_count = node_count
self.is_ssl_enabled = is_ssl_enabled
self.aks_not_found = aks_not_found
self.cluster_purpose = cluster_purpose
self.public_ip_address = public_ip_address
self.vm_size = vm_size
self.location = location
self.provisioning_state = provisioning_state
self.state = state
self.os_type = os_type
self.id = id
self.name = name
self.created_by_studio = created_by_studio
self.is_gpu_type = is_gpu_type
self.resource_id = resource_id
self.compute_type = compute_type
class WebServicePort(msrest.serialization.Model):
"""WebServicePort.
:ivar node_id:
:vartype node_id: str
:ivar port_name:
:vartype port_name: str
:ivar name:
:vartype name: str
"""
_attribute_map = {
'node_id': {'key': 'nodeId', 'type': 'str'},
'port_name': {'key': 'portName', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
}
def __init__(
self,
*,
node_id: Optional[str] = None,
port_name: Optional[str] = None,
name: Optional[str] = None,
**kwargs
):
"""
:keyword node_id:
:paramtype node_id: str
:keyword port_name:
:paramtype port_name: str
:keyword name:
:paramtype name: str
"""
super(WebServicePort, self).__init__(**kwargs)
self.node_id = node_id
self.port_name = port_name
self.name = name
class WorkspaceConnectionSpec(msrest.serialization.Model):
"""WorkspaceConnectionSpec.
:ivar connection_category: Possible values include: "PythonFeed", "ACR", "Git", "S3",
"Snowflake", "AzureSqlDb", "AzureSynapseAnalytics", "AzureMySqlDb", "AzurePostgresDb",
"AzureDataLakeGen2", "Redis", "ApiKey", "AzureOpenAI", "CognitiveSearch", "CognitiveService",
"CustomKeys", "AzureBlob", "AzureOneLake", "CosmosDb", "CosmosDbMongoDbApi",
"AzureDataExplorer", "AzureMariaDb", "AzureDatabricksDeltaLake", "AzureSqlMi",
"AzureTableStorage", "AmazonRdsForOracle", "AmazonRdsForSqlServer", "AmazonRedshift", "Db2",
"Drill", "GoogleBigQuery", "Greenplum", "Hbase", "Hive", "Impala", "Informix", "MariaDb",
"MicrosoftAccess", "MySql", "Netezza", "Oracle", "Phoenix", "PostgreSql", "Presto",
"SapOpenHub", "SapBw", "SapHana", "SapTable", "Spark", "SqlServer", "Sybase", "Teradata",
"Vertica", "Cassandra", "Couchbase", "MongoDbV2", "MongoDbAtlas", "AmazonS3Compatible",
"FileServer", "FtpServer", "GoogleCloudStorage", "Hdfs", "OracleCloudStorage", "Sftp",
"GenericHttp", "ODataRest", "Odbc", "GenericRest", "AmazonMws", "Concur", "Dynamics",
"DynamicsAx", "DynamicsCrm", "GoogleAdWords", "Hubspot", "Jira", "Magento", "Marketo",
"Office365", "Eloqua", "Responsys", "OracleServiceCloud", "PayPal", "QuickBooks", "Salesforce",
"SalesforceServiceCloud", "SalesforceMarketingCloud", "SapCloudForCustomer", "SapEcc",
"ServiceNow", "SharePointOnlineList", "Shopify", "Square", "WebTable", "Xero", "Zoho",
"GenericContainerRegistry".
:vartype connection_category: str or ~flow.models.ConnectionCategory
:ivar flow_value_type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:vartype flow_value_type: str or ~flow.models.ValueType
:ivar connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:vartype connection_type: str or ~flow.models.ConnectionType
:ivar connection_type_display_name:
:vartype connection_type_display_name: str
:ivar config_specs:
:vartype config_specs: list[~flow.models.ConnectionConfigSpec]
:ivar module:
:vartype module: str
"""
_attribute_map = {
'connection_category': {'key': 'connectionCategory', 'type': 'str'},
'flow_value_type': {'key': 'flowValueType', 'type': 'str'},
'connection_type': {'key': 'connectionType', 'type': 'str'},
'connection_type_display_name': {'key': 'connectionTypeDisplayName', 'type': 'str'},
'config_specs': {'key': 'configSpecs', 'type': '[ConnectionConfigSpec]'},
'module': {'key': 'module', 'type': 'str'},
}
def __init__(
self,
*,
connection_category: Optional[Union[str, "ConnectionCategory"]] = None,
flow_value_type: Optional[Union[str, "ValueType"]] = None,
connection_type: Optional[Union[str, "ConnectionType"]] = None,
connection_type_display_name: Optional[str] = None,
config_specs: Optional[List["ConnectionConfigSpec"]] = None,
module: Optional[str] = None,
**kwargs
):
"""
:keyword connection_category: Possible values include: "PythonFeed", "ACR", "Git", "S3",
"Snowflake", "AzureSqlDb", "AzureSynapseAnalytics", "AzureMySqlDb", "AzurePostgresDb",
"AzureDataLakeGen2", "Redis", "ApiKey", "AzureOpenAI", "CognitiveSearch", "CognitiveService",
"CustomKeys", "AzureBlob", "AzureOneLake", "CosmosDb", "CosmosDbMongoDbApi",
"AzureDataExplorer", "AzureMariaDb", "AzureDatabricksDeltaLake", "AzureSqlMi",
"AzureTableStorage", "AmazonRdsForOracle", "AmazonRdsForSqlServer", "AmazonRedshift", "Db2",
"Drill", "GoogleBigQuery", "Greenplum", "Hbase", "Hive", "Impala", "Informix", "MariaDb",
"MicrosoftAccess", "MySql", "Netezza", "Oracle", "Phoenix", "PostgreSql", "Presto",
"SapOpenHub", "SapBw", "SapHana", "SapTable", "Spark", "SqlServer", "Sybase", "Teradata",
"Vertica", "Cassandra", "Couchbase", "MongoDbV2", "MongoDbAtlas", "AmazonS3Compatible",
"FileServer", "FtpServer", "GoogleCloudStorage", "Hdfs", "OracleCloudStorage", "Sftp",
"GenericHttp", "ODataRest", "Odbc", "GenericRest", "AmazonMws", "Concur", "Dynamics",
"DynamicsAx", "DynamicsCrm", "GoogleAdWords", "Hubspot", "Jira", "Magento", "Marketo",
"Office365", "Eloqua", "Responsys", "OracleServiceCloud", "PayPal", "QuickBooks", "Salesforce",
"SalesforceServiceCloud", "SalesforceMarketingCloud", "SapCloudForCustomer", "SapEcc",
"ServiceNow", "SharePointOnlineList", "Shopify", "Square", "WebTable", "Xero", "Zoho",
"GenericContainerRegistry".
:paramtype connection_category: str or ~flow.models.ConnectionCategory
:keyword flow_value_type: Possible values include: "int", "double", "bool", "string", "secret",
"prompt_template", "object", "list", "BingConnection", "OpenAIConnection",
"AzureOpenAIConnection", "AzureContentModeratorConnection", "CustomConnection",
"AzureContentSafetyConnection", "SerpConnection", "CognitiveSearchConnection",
"SubstrateLLMConnection", "PineconeConnection", "QdrantConnection", "WeaviateConnection",
"function_list", "function_str", "FormRecognizerConnection", "file_path", "image",
"assistant_definition".
:paramtype flow_value_type: str or ~flow.models.ValueType
:keyword connection_type: Possible values include: "OpenAI", "AzureOpenAI", "Serp", "Bing",
"AzureContentModerator", "Custom", "AzureContentSafety", "CognitiveSearch", "SubstrateLLM",
"Pinecone", "Qdrant", "Weaviate", "FormRecognizer".
:paramtype connection_type: str or ~flow.models.ConnectionType
:keyword connection_type_display_name:
:paramtype connection_type_display_name: str
:keyword config_specs:
:paramtype config_specs: list[~flow.models.ConnectionConfigSpec]
:keyword module:
:paramtype module: str
"""
super(WorkspaceConnectionSpec, self).__init__(**kwargs)
self.connection_category = connection_category
self.flow_value_type = flow_value_type
self.connection_type = connection_type
self.connection_type_display_name = connection_type_display_name
self.config_specs = config_specs
self.module = module
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/models/_models_py3.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/models/_models_py3.py",
"repo_id": "promptflow",
"token_count": 751139
} | 17 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from threading import Lock
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
class _FlowServiceCallerFactory:
caller_cache_by_workspace_id = {}
_instance_lock = Lock()
@classmethod
def get_instance(cls, workspace, credential, operation_scope, region=None, **kwargs) -> FlowServiceCaller:
"""Get instance of flow service caller.
:param workspace: workspace
"""
cache_id = workspace.id if workspace else region
cache = cls.caller_cache_by_workspace_id
if cache_id not in cache:
with _FlowServiceCallerFactory._instance_lock:
if cache_id not in cache:
cache[cache_id] = FlowServiceCaller(
workspace, credential=credential, operation_scope=operation_scope, region=region, **kwargs
)
return cache[cache_id]
| promptflow/src/promptflow/promptflow/azure/_restclient/service_caller_factory.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/service_caller_factory.py",
"repo_id": "promptflow",
"token_count": 390
} | 18 |
$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
# will be changed to flow to support parallelism
type: command
outputs:
output:
# PRS team will always aggregate all the outputs into a single file under this folder for now
type: uri_folder
| promptflow/src/promptflow/promptflow/azure/resources/component_spec_template.yaml/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/resources/component_spec_template.yaml",
"repo_id": "promptflow",
"token_count": 85
} | 19 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Mapping, Optional
from dateutil import parser
class Status(Enum):
"""An enumeration class for different types of run status."""
Running = "Running"
Preparing = "Preparing"
Completed = "Completed"
Failed = "Failed"
Bypassed = "Bypassed"
Canceled = "Canceled"
NotStarted = "NotStarted"
CancelRequested = "CancelRequested"
@staticmethod
def is_terminated(status):
"""Check if a given status is terminated.
:param status: The status to be checked
:type status: str or :class:`Status`
:return: True if the status is terminated, False otherwise
:rtype: bool
"""
if isinstance(status, Status):
status = status.value
return status in {s.value for s in {Status.Completed, Status.Failed, Status.Bypassed, Status.Canceled}}
@dataclass
class RunInfo:
"""A dataclass representing the run information.
:param node: Node name
:type node: str
:param flow_run_id: The id of the flow run
:type flow_run_id: str
:param run_id: The id of the run, which equals ``flow_run_id:step_run_id``
:type run_id: str
:param status: Status of the run
:type status: ~promptflow.contracts.run_info.Status
:param inputs: List of inputs for the run
:type inputs: list
:param output: Output of the run
:type output: object
:param metrics: Metrics of the run
:type metrics: Dict[str, Any]
:param error: Errors occurred during the run
:type error: Dict[str, Any]
:param parent_run_id: Parent run id
:type parent_run_id: str
:param start_time: Start time of the run
:type start_time: datetime
:param end_time: End time of the run
:type end_time: datetime
:param index: Index of the run
:type index: Optional[int]
:param api_calls: API calls made during the run
:type api_calls: Optional[List[Dict[str, Any]]]
:param variant_id: Variant id of the run
:type variant_id: Optional[str]
:param cached_run_id: Cached run id
:type cached_run_id: Optional[str]
:param cached_flow_run_id: Cached flow run id
:type cached_flow_run_id: Optional[str]
:param logs: Logs of the run
:type logs: Optional[Dict[str, str]]
:param system_metrics: System metrics of the run
:type system_metrics: Optional[Dict[str, Any]]
:param result: Result of the run
:type result: Optional[object]
"""
node: str
flow_run_id: str
run_id: str
status: Status
inputs: Mapping[str, Any]
output: object
metrics: Dict[str, Any]
error: Dict[str, Any]
parent_run_id: str
start_time: datetime
end_time: datetime
index: Optional[int] = None
api_calls: Optional[List[Dict[str, Any]]] = None
variant_id: str = ""
cached_run_id: str = None
cached_flow_run_id: str = None
logs: Optional[Dict[str, str]] = None
system_metrics: Dict[str, Any] = None
result: object = None
@staticmethod
def deserialize(data: dict) -> "RunInfo":
"""Deserialize the RunInfo from a dict."""
run_info = RunInfo(
node=data.get("node"),
flow_run_id=data.get("flow_run_id"),
run_id=data.get("run_id"),
status=Status(data.get("status")),
inputs=data.get("inputs", None),
output=data.get("output", None),
metrics=data.get("metrics", None),
error=data.get("error", None),
parent_run_id=data.get("parent_run_id", None),
start_time=parser.parse(data.get("start_time")).replace(tzinfo=None),
end_time=parser.parse(data.get("end_time")).replace(tzinfo=None),
index=data.get("index", None),
api_calls=data.get("api_calls", None),
variant_id=data.get("variant_id", ""),
cached_run_id=data.get("cached_run_id", None),
cached_flow_run_id=data.get("cached_flow_run_id", None),
logs=data.get("logs", None),
system_metrics=data.get("system_metrics", None),
result=data.get("result", None),
)
return run_info
@dataclass
class FlowRunInfo:
"""A dataclass representing the run information.
:param run_id: The id of the run, which equals ``flow_run_id:child_flow_run_id``
:type run_id: str
:param status: Status of the flow run
:type status: ~promptflow.contracts.run_info.Status
:param error: Errors occurred during the flow run
:type error: Dict[str, Any]
:param inputs: Inputs for the flow run
:type inputs: object
:param output: Output of the flow run
:type output: object
:param metrics: Metrics of the flow run
:type metrics: Dict[str, Any]
:param request: Request made for the flow run
:type request: object
:param parent_run_id: Parent run id of the flow run
:type parent_run_id: str
:param root_run_id: Root run id of the flow run
:type root_run_id: str
:param source_run_id: The run id of the run that triggered the flow run
:type source_run_id: str
:param flow_id: Flow id of the flow run
:type flow_id: str
:param start_time: Start time of the flow run
:type start_time: datetime
:param end_time: End time of the flow run
:type end_time: datetime
:param index: Index of the flow run (used for bulk test mode)
:type index: Optional[int]
:param api_calls: API calls made during the flow run
:type api_calls: Optional[List[Dict[str, Any]]]
:param variant_id: Variant id of the flow run
:type variant_id: Optional[str]
:param name: Name of the flow run
:type name: Optional[str]
:param description: Description of the flow run
:type description: Optional[str]
:param tags: Tags of the flow run
:type tags: Optional[Dict[str, str]]
:param system_metrics: System metrics of the flow run
:type system_metrics: Optional[Dict[str, Any]]
:param result: Result of the flow run
:type result: Optional[object]
:param upload_metrics: Flag indicating whether to upload metrics for the flow run
:type upload_metrics: Optional[bool]
"""
run_id: str
status: Status
error: object
inputs: object
output: object
metrics: Dict[str, Any]
request: object
parent_run_id: str
root_run_id: str
source_run_id: str
flow_id: str
start_time: datetime
end_time: datetime
index: Optional[int] = None
api_calls: Optional[List[Dict[str, Any]]] = None
variant_id: str = ""
name: str = ""
description: str = ""
tags: Optional[Mapping[str, str]] = None
system_metrics: Dict[str, Any] = None
result: object = None
upload_metrics: bool = False # only set as true for root runs in bulk test mode and evaluation mode
@staticmethod
def deserialize(data: dict) -> "FlowRunInfo":
"""Deserialize the FlowRunInfo from a dict."""
flow_run_info = FlowRunInfo(
run_id=data.get("run_id"),
status=Status(data.get("status")),
error=data.get("error", None),
inputs=data.get("inputs", None),
output=data.get("output", None),
metrics=data.get("metrics", None),
request=data.get("request", None),
parent_run_id=data.get("parent_run_id", None),
root_run_id=data.get("root_run_id", None),
source_run_id=data.get("source_run_id", None),
flow_id=data.get("flow_id"),
start_time=parser.parse(data.get("start_time")).replace(tzinfo=None),
end_time=parser.parse(data.get("end_time")).replace(tzinfo=None),
index=data.get("index", None),
api_calls=data.get("api_calls", None),
variant_id=data.get("variant_id", ""),
name=data.get("name", ""),
description=data.get("description", ""),
tags=data.get("tags", None),
system_metrics=data.get("system_metrics", None),
result=data.get("result", None),
upload_metrics=data.get("upload_metrics", False),
)
return flow_run_info
@staticmethod
def create_with_error(start_time, inputs, index, run_id, error):
return FlowRunInfo(
run_id=run_id,
status=Status.Failed,
error=error,
inputs=inputs,
output=None,
metrics=None,
request=None,
parent_run_id=run_id,
root_run_id=run_id,
source_run_id=run_id,
flow_id="default_flow_id",
start_time=start_time,
end_time=datetime.utcnow(),
index=index,
)
| promptflow/src/promptflow/promptflow/contracts/run_info.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/contracts/run_info.py",
"repo_id": "promptflow",
"token_count": 3703
} | 20 |
import multiprocessing
import queue
import signal
from dataclasses import dataclass
from enum import Enum
from functools import partial
from multiprocessing import Queue
from typing import List
import psutil
from promptflow._core.operation_context import OperationContext
from promptflow._utils.logger_utils import LogContext, bulk_logger
from promptflow.executor._errors import SpawnedForkProcessManagerStartFailure
from promptflow.executor.flow_executor import FlowExecutor
@dataclass
class ProcessInfo:
index: int
process_id: str
process_name: str
class ProcessControlSignal(str, Enum):
START = "start"
RESTART = "restart"
END = "end"
class AbstractProcessManager:
"""
AbstractProcessManager is a base class for managing processes.
:param input_queues: Queues for providing input data to the processes.
:type input_queues: List[multiprocessing.Queue]
:param output_queues: Queues for receiving execution results of the processes.
:type output_queues: List[multiprocessing.Queue]
:param process_info: Dictionary to store information about the processes.
:type process_info: dict
:param process_target_func: The target function that the processes will execute.
:param raise_ex: Flag to determine whether to raise exceptions or not.
:type raise_ex: bool
"""
def __init__(
self,
input_queues: List[Queue],
output_queues: List[Queue],
process_info: dict,
process_target_func,
*args,
**kwargs,
) -> None:
self._input_queues = input_queues
self._output_queues = output_queues
self._process_info = process_info
self._process_target_func = process_target_func
current_log_context = LogContext.get_current()
self._log_context_initialization_func = current_log_context.get_initializer() if current_log_context else None
self._current_operation_context = OperationContext.get_instance().get_context_dict()
def new_process(self, i):
"""
Create and start a new process.
:param i: Index of the new process to start.
:type i: int
"""
raise NotImplementedError("AbstractProcessManager is an abstract class, no implementation for new_process.")
def restart_process(self, i):
"""
Restarts a specified process
:param i: Index of the process to restart.
:type i: int
"""
raise NotImplementedError("AbstractProcessManager is an abstract class, no implementation for restart_process.")
def end_process(self, i):
"""
Terminates a specified process.
:param i: Index of the process to terminate.
:type i: int
"""
raise NotImplementedError("AbstractProcessManager is an abstract class, no implementation for end_process.")
def ensure_healthy(self):
"""
Checks the health of the managed processes.
This method should be implemented in subclasses to provide specific health check mechanisms.
"""
raise NotImplementedError("AbstractProcessManager is an abstract class, no implementation for end_process.")
class SpawnProcessManager(AbstractProcessManager):
"""
SpawnProcessManager extends AbstractProcessManager to specifically manage processes using the 'spawn' start method.
:param executor_creation_func: Function to create an executor for each process.
:param args: Additional positional arguments for the AbstractProcessManager.
:param kwargs: Additional keyword arguments for the AbstractProcessManager.
"""
def __init__(self, executor_creation_func, *args, **kwargs):
super().__init__(*args, **kwargs)
self._executor_creation_func = executor_creation_func
self.context = multiprocessing.get_context("spawn")
def start_processes(self):
"""
Initiates processes.
"""
for i in range(len(self._input_queues)):
self.new_process(i)
def new_process(self, i):
"""
Create and start a new process using the 'spawn' context.
:param i: Index of the input and output queue for the new process.
:type i: int
"""
process = self.context.Process(
target=self._process_target_func,
args=(
self._executor_creation_func,
self._input_queues[i],
self._output_queues[i],
self._log_context_initialization_func,
self._current_operation_context,
),
# Set the process as a daemon process to automatically terminated and release system resources
# when the main process exits.
daemon=True,
)
process.start()
try:
self._process_info[i] = ProcessInfo(
index=i,
process_id=process.pid,
process_name=process.name,
)
except Exception as e:
bulk_logger.warning(
f"Unexpected error occurred while creating ProcessInfo for index {i} and process id {process.pid}. "
f"Exception: {e}"
)
return process
def restart_process(self, i):
"""
Restarts a specified process by first terminating it then creating a new one.
:param i: Index of the process to restart.
:type i: int
"""
self.end_process(i)
self.new_process(i)
def end_process(self, i):
"""
Terminates a specified process.
:param i: Index of the process to terminate.
:type i: int
"""
try:
pid = self._process_info[i].process_id
process = psutil.Process(pid)
process.terminate()
process.wait()
self._process_info.pop(i)
except psutil.NoSuchProcess:
bulk_logger.warning(f"Process {pid} had been terminated")
except Exception as e:
bulk_logger.warning(
f"Unexpected error occurred while end process for index {i} and process id {process.pid}. "
f"Exception: {e}"
)
def ensure_healthy(self):
"""
Checks the health of the managed processes.
Note:
Health checks for spawn mode processes are currently not performed.
Add detailed checks in this function if needed in the future.
"""
pass
class ForkProcessManager(AbstractProcessManager):
'''
ForkProcessManager extends AbstractProcessManager to manage processes using the 'fork' method
in a spawned process.
:param control_signal_queue: A queue for controlling signals to manage process operations.
:type control_signal_queue: multiprocessing.Queue
:param flow_file: The path to the flow file.
:type flow_file: Path
:param connections: The connections to be used for the flow.
:type connections: dict
:param working_dir: The working directory to be used for the flow.
:type working_dir: str
:param args: Additional positional arguments for the AbstractProcessManager.
:param kwargs: Additional keyword arguments for the AbstractProcessManager.
"""
'''
def __init__(self, control_signal_queue: Queue, flow_create_kwargs, *args, **kwargs):
super().__init__(*args, **kwargs)
self._control_signal_queue = control_signal_queue
self._flow_create_kwargs = flow_create_kwargs
def start_processes(self):
"""
Initiates a process with "spawn" method to establish a clean environment.
"""
context = multiprocessing.get_context("spawn")
process = context.Process(
target=create_spawned_fork_process_manager,
args=(
self._log_context_initialization_func,
self._current_operation_context,
self._input_queues,
self._output_queues,
self._control_signal_queue,
self._flow_create_kwargs,
self._process_info,
self._process_target_func,
),
)
process.start()
self._spawned_fork_process_manager_pid = process.pid
def restart_process(self, i):
"""
Sends a signal to restart a specific process.
:param i: Index of the process to restart.
:type i: int
"""
self._control_signal_queue.put((ProcessControlSignal.RESTART, i))
def end_process(self, i):
"""
Sends a signal to terminate a specific process.
:param i: Index of the process to terminate.
:type i: int
"""
self._control_signal_queue.put((ProcessControlSignal.END, i))
def new_process(self, i):
"""
Sends a signal to start a new process.
:param i: Index of the new process to start.
:type i: int
"""
self._control_signal_queue.put((ProcessControlSignal.START, i))
def ensure_healthy(self):
# A 'zombie' process is a process that has finished running but still remains in
# the process table, waiting for its parent process to collect and handle its exit status.
# The normal state of the spawned process is 'running'. If the process does not start successfully
# or exit unexpectedly, its state will be 'zombie'.
if psutil.Process(self._spawned_fork_process_manager_pid).status() == "zombie":
bulk_logger.error("The spawned fork process manager failed to start.")
ex = SpawnedForkProcessManagerStartFailure()
raise ex
class SpawnedForkProcessManager(AbstractProcessManager):
"""
SpawnedForkProcessManager extends AbstractProcessManager to manage processes using 'fork' method
in a spawned process.
:param control_signal_queue: A queue for controlling signals to manage process operations.
:type control_signal_queue: multiprocessing.Queue
:param executor_creation_func: Function to create an executor for each process.
:type executor_creation_func: Callable
:param args: Additional positional arguments for the AbstractProcessManager.
:param kwargs: Additional keyword arguments for the AbstractProcessManager.
"""
def __init__(
self,
log_context_initialization_func,
current_operation_context,
control_signal_queue,
executor_creation_func,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self._log_context_initialization_func = log_context_initialization_func
self._current_operation_context = current_operation_context
self._control_signal_queue = control_signal_queue
self._executor_creation_func = executor_creation_func
self.context = multiprocessing.get_context("fork")
def new_process(self, i):
"""
Create and start a new process using the 'fork' context.
:param i: Index of the input and output queue for the new process.
:type i: int
"""
process = self.context.Process(
target=self._process_target_func,
args=(
self._executor_creation_func,
self._input_queues[i],
self._output_queues[i],
self._log_context_initialization_func,
self._current_operation_context,
),
daemon=True,
)
process.start()
try:
self._process_info[i] = ProcessInfo(
index=i,
process_id=process.pid,
process_name=process.name,
)
except Exception as e:
bulk_logger.warning(
f"Unexpected error occurred while creating ProcessInfo for index {i} and process id {process.pid}. "
f"Exception: {e}"
)
return process
def end_process(self, i):
"""
Terminates a specified process.
:param i: Index of the process to terminate.
:type i: int
"""
try:
pid = self._process_info[i].process_id
process = psutil.Process(pid)
process.terminate()
process.wait()
self._process_info.pop(i)
except psutil.NoSuchProcess:
bulk_logger.warning(f"Process {pid} had been terminated")
except Exception as e:
bulk_logger.warning(
f"Unexpected error occurred while end process for index {i} and process id {process.pid}. "
f"Exception: {e}"
)
def restart_process(self, i):
"""
Restarts a specified process by first terminating it then creating a new one.
:param i: Index of the process to restart.
:type i: int
"""
self.end_process(i)
self.new_process(i)
def handle_signals(self, control_signal, i):
"""
Handles control signals for processes, performing actions such as starting, ending,
or restarting them based on the received signal.
:param control_signal: The control signal indicating the desired action. It can be 'start', 'end', or 'restart'.
:type control_signal: str
:param i: Index of the process to control.
:type i: int
"""
if control_signal == ProcessControlSignal.END:
self.end_process(i)
elif control_signal == ProcessControlSignal.RESTART:
self.restart_process(i)
elif control_signal == ProcessControlSignal.START:
self.new_process(i)
def create_spawned_fork_process_manager(
log_context_initialization_func,
current_operation_context,
input_queues,
output_queues,
control_signal_queue,
flow_create_kwargs,
process_info,
process_target_func,
):
"""
Manages the creation, termination, and signaling of processes using the 'fork' context.
"""
# Set up signal handling for process interruption.
from promptflow.executor._line_execution_process_pool import create_executor_fork, signal_handler
signal.signal(signal.SIGINT, signal_handler)
# Create flow executor.
executor = FlowExecutor.create(**flow_create_kwargs)
# When using fork, we use this method to create the executor to avoid reloading the flow
# which will introduce a lot more memory.
executor_creation_func = partial(create_executor_fork, flow_executor=executor)
manager = SpawnedForkProcessManager(
log_context_initialization_func,
current_operation_context,
control_signal_queue,
executor_creation_func,
input_queues,
output_queues,
process_info,
process_target_func,
)
# Initialize processes.
for i in range(len(input_queues)):
manager.new_process(i)
# Main loop to handle control signals and manage process lifecycle.
while True:
all_processes_stopped = True
try:
process_info_list = process_info.items()
except Exception as e:
bulk_logger.warning(f"Unexpected error occurred while get process info list. Exception: {e}")
break
for _, info in list(process_info_list):
pid = info.process_id
# Check if at least one process is alive.
if psutil.pid_exists(pid):
process = psutil.Process(pid)
if process.status() != "zombie":
all_processes_stopped = False
else:
# If do not call wait(), the child process may become a zombie process,
# and psutil.pid_exists(pid) is always true, which will cause spawn proces
# never exit.
process.wait()
# If all fork child processes exit, exit the loop.
if all_processes_stopped:
break
try:
control_signal, i = control_signal_queue.get(timeout=1)
manager.handle_signals(control_signal, i)
except queue.Empty:
# Do nothing until the process_queue have not content or process is killed
pass
| promptflow/src/promptflow/promptflow/executor/_process_manager.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_process_manager.py",
"repo_id": "promptflow",
"token_count": 6562
} | 21 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
from json import JSONDecodeError
from typing import Any, List, Mapping, Optional
from promptflow._utils.logger_utils import logger
from promptflow.contracts.flow import Flow, InputValueType, Node
from promptflow.contracts.tool import ValueType
from promptflow.executor._errors import (
DuplicateNodeName,
EmptyOutputReference,
InputNotFound,
InputParseError,
InputReferenceNotFound,
InputTypeError,
InvalidAggregationInput,
InvalidNodeReference,
NodeCircularDependency,
NodeReferenceNotFound,
OutputReferenceNotFound,
)
class FlowValidator:
"""This is a validation class designed to verify the integrity and validity of flow definitions and input data."""
@staticmethod
def _ensure_nodes_order(flow: Flow):
dependencies = {n.name: set() for n in flow.nodes}
aggregation_nodes = set(node.name for node in flow.nodes if node.aggregation)
for n in flow.nodes:
inputs_list = [i for i in n.inputs.values()]
if n.activate:
if (
n.aggregation
and n.activate.condition.value_type == InputValueType.NODE_REFERENCE
and n.activate.condition.value not in aggregation_nodes
):
msg_format = (
"Invalid node definitions found in the flow graph. Non-aggregation node '{invalid_reference}' "
"cannot be referenced in the activate config of the aggregation node '{node_name}'. Please "
"review and rectify the node reference."
)
raise InvalidNodeReference(
message_format=msg_format, invalid_reference=n.activate.condition.value, node_name=n.name
)
inputs_list.extend([n.activate.condition])
for i in inputs_list:
if i.value_type != InputValueType.NODE_REFERENCE:
continue
if i.value not in dependencies:
msg_format = (
"Invalid node definitions found in the flow graph. Node '{node_name}' references "
"a non-existent node '{reference_node_name}' in your flow. Please review your flow to "
"ensure that the node name is accurately specified."
)
raise NodeReferenceNotFound(
message_format=msg_format, node_name=n.name, reference_node_name=i.value
)
dependencies[n.name].add(i.value)
if not n.aggregation:
invalid_reference = dependencies[n.name].intersection(aggregation_nodes)
if invalid_reference:
msg_format = (
"Invalid node definitions found in the flow graph. Non-aggregate node '{node_name}' "
"cannot reference aggregate nodes {invalid_reference}. Please review and rectify "
"the node reference."
)
raise InvalidNodeReference(
message_format=msg_format, node_name=n.name, invalid_reference=invalid_reference
)
sorted_nodes = []
picked = set()
for _ in range(len(flow.nodes)):
available_nodes_iterator = (
n for n in flow.nodes if n.name not in picked and all(d in picked for d in dependencies[n.name])
)
node_to_pick = next(available_nodes_iterator, None)
if not node_to_pick:
# Figure out the nodes names with circular dependency problem alphabetically
remaining_nodes = sorted(list(set(dependencies.keys()) - picked))
raise NodeCircularDependency(
message_format=(
"Invalid node definitions found in the flow graph. Node circular dependency has been detected "
"among the nodes in your flow. Kindly review the reference relationships for the nodes "
"{remaining_nodes} and resolve the circular reference issue in the flow."
),
remaining_nodes=remaining_nodes,
)
sorted_nodes.append(node_to_pick)
picked.add(node_to_pick.name)
if any(n1.name != n2.name for n1, n2 in zip(flow.nodes, sorted_nodes)):
return Flow(
id=flow.id,
name=flow.name,
nodes=sorted_nodes,
inputs=flow.inputs,
outputs=flow.outputs,
tools=flow.tools,
)
return copy.copy(flow)
@staticmethod
def _validate_nodes_topology(flow: Flow) -> Flow:
node_names = set()
for node in flow.nodes:
if node.name in node_names:
raise DuplicateNodeName(
message_format=(
"Invalid node definitions found in the flow graph. Node with name '{node_name}' appears "
"more than once in the node definitions in your flow, which is not allowed. To address "
"this issue, please review your flow and either rename or remove nodes with identical names."
),
node_name=node.name,
)
node_names.add(node.name)
for node in flow.nodes:
for v in node.inputs.values():
if v.value_type != InputValueType.FLOW_INPUT:
continue
if v.value not in flow.inputs:
msg_format = (
"Invalid node definitions found in the flow graph. Node '{node_name}' references flow input "
"'{flow_input_name}' which is not defined in your flow. To resolve this issue, "
"please review your flow, ensuring that you either add the missing flow inputs "
"or adjust node reference to the correct flow input."
)
raise InputReferenceNotFound(
message_format=msg_format, node_name=node.name, flow_input_name=v.value
)
return FlowValidator._ensure_nodes_order(flow)
@staticmethod
def _parse_input_value(input_key: str, input_value: Any, expected_type: ValueType, idx=None):
try:
return expected_type.parse(input_value)
except JSONDecodeError as e:
line_info = "" if idx is None else f" in line {idx} of input data"
flow_input_info = f"'{input_key}'{line_info}"
error_type_and_message = f"({e.__class__.__name__}) {e}"
msg_format = (
"Failed to parse the flow input. The value for flow input {flow_input_info} "
"was interpreted as JSON string since its type is '{value_type}'. However, the value "
"'{input_value}' is invalid for JSON parsing. Error details: {error_type_and_message}. "
"Please make sure your inputs are properly formatted."
)
raise InputParseError(
message_format=msg_format,
flow_input_info=flow_input_info,
input_value=input_value,
value_type=expected_type.value if hasattr(expected_type, "value") else expected_type,
error_type_and_message=error_type_and_message,
) from e
except Exception as e:
line_info = "" if idx is None else f" in line {idx} of input data"
flow_input_info = f"'{input_key}'{line_info}"
msg_format = (
"The input for flow is incorrect. The value for flow input {flow_input_info} "
"does not match the expected type '{expected_type}'. Please change flow input type "
"or adjust the input value in your input data."
)
expected_type_value = expected_type.value if hasattr(expected_type, "value") else expected_type
raise InputTypeError(
message_format=msg_format, flow_input_info=flow_input_info, expected_type=expected_type_value
) from e
@staticmethod
def resolve_aggregated_flow_inputs_type(flow: Flow, inputs: Mapping[str, List[Any]]) -> Mapping[str, Any]:
updated_inputs = {}
for input_key, input_def in flow.inputs.items():
if input_key in inputs:
input_value_list = inputs[input_key]
updated_inputs[input_key] = [
FlowValidator._parse_input_value(input_key, each_line_item, input_def.type, idx)
for idx, each_line_item in enumerate(input_value_list)
]
return updated_inputs
@staticmethod
def resolve_flow_inputs_type(flow: Flow, inputs: Mapping[str, Any], idx: Optional[int] = None) -> Mapping[str, Any]:
"""Resolve inputs by type if existing. Ignore missing inputs.
:param flow: The `flow` parameter is of type `Flow` and represents a flow object
:type flow: ~promptflow.contracts.flow.Flow
:param inputs: A dictionary containing the input values for the flow. The keys are the names of the
flow inputs, and the values are the corresponding input values
:type inputs: Mapping[str, Any]
:param idx: The `idx` parameter is an optional integer that represents the line index of the input
data. It is used to provide additional information in case there is an error with the input data
:type idx: Optional[int]
:return: The updated inputs with values are type-converted based on the expected type specified
in the `flow` object.
:rtype: Mapping[str, Any]
"""
updated_inputs = {k: v for k, v in inputs.items()}
for k, v in flow.inputs.items():
if k in inputs:
updated_inputs[k] = FlowValidator._parse_input_value(k, inputs[k], v.type, idx)
return updated_inputs
@staticmethod
def ensure_flow_inputs_type(flow: Flow, inputs: Mapping[str, Any], idx: Optional[int] = None) -> Mapping[str, Any]:
"""Make sure the inputs are completed and in the correct type. Raise Exception if not valid.
:param flow: The `flow` parameter is of type `Flow` and represents a flow object
:type flow: ~promptflow.contracts.flow.Flow
:param inputs: A dictionary containing the input values for the flow. The keys are the names of the
flow inputs, and the values are the corresponding input values
:type inputs: Mapping[str, Any]
:param idx: The `idx` parameter is an optional integer that represents the line index of the input
data. It is used to provide additional information in case there is an error with the input data
:type idx: Optional[int]
:return: The updated inputs, where the values are type-converted based on the expected
type specified in the `flow` object.
:rtype: Mapping[str, Any]
"""
for k, v in flow.inputs.items():
if k not in inputs:
line_info = "in input data" if idx is None else f"in line {idx} of input data"
msg_format = (
"The input for flow is incorrect. The value for flow input '{input_name}' is not "
"provided {line_info}. Please review your input data or remove this input in your flow "
"if it's no longer needed."
)
raise InputNotFound(message_format=msg_format, input_name=k, line_info=line_info)
return FlowValidator.resolve_flow_inputs_type(flow, inputs, idx)
@staticmethod
def convert_flow_inputs_for_node(flow: Flow, node: Node, inputs: Mapping[str, Any]) -> Mapping[str, Any]:
"""Filter the flow inputs for node and resolve the value by type.
:param flow: The `flow` parameter is an instance of the `Flow` class. It represents the flow or
workflow that contains the node and inputs
:type flow: ~promptflow.contracts.flow.Flow
:param node: The `node` parameter is an instance of the `Node` class
:type node: ~promptflow.contracts.flow.Node
:param inputs: A dictionary containing the input values for the node. The keys are the names of the
input variables, and the values are the corresponding input values
:type inputs: Mapping[str, Any]
:return: the resolved flow inputs which are needed by the node only by the node only.
:rtype: Mapping[str, Any]
"""
updated_inputs = {}
inputs = inputs or {}
for k, v in node.inputs.items():
if v.value_type == InputValueType.FLOW_INPUT:
if v.value not in flow.inputs:
raise InputNotFound(
message_format=(
"The input for node is incorrect. Node input '{node_input_name}' is not found "
"from flow inputs of node '{node_name}'. Please review the node definition in your flow."
),
node_input_name=v.value,
node_name=node.name,
)
if v.value not in inputs:
raise InputNotFound(
message_format=(
"The input for node is incorrect. Node input '{node_input_name}' is not found "
"in input data for node '{node_name}'. Please verify the inputs data for the node."
),
node_input_name=v.value,
node_name=node.name,
)
try:
updated_inputs[v.value] = flow.inputs[v.value].type.parse(inputs[v.value])
except Exception as e:
msg_format = (
"The input for node is incorrect. Value for input '{input_name}' of node '{node_name}' "
"is not type '{expected_type}'. Please review and rectify the input data."
)
raise InputTypeError(
message_format=msg_format,
input_name=k,
node_name=node.name,
expected_type=flow.inputs[v.value].type.value,
) from e
return updated_inputs
@staticmethod
def _validate_aggregation_inputs(aggregated_flow_inputs: Mapping[str, Any], aggregation_inputs: Mapping[str, Any]):
"""Validate the aggregation inputs according to the flow inputs."""
for key, value in aggregated_flow_inputs.items():
if key in aggregation_inputs:
raise InvalidAggregationInput(
message_format=(
"The input for aggregation is incorrect. The input '{input_key}' appears in both "
"aggregated flow input and aggregated reference input. "
"Please remove one of them and try the operation again."
),
input_key=key,
)
if not isinstance(value, list):
raise InvalidAggregationInput(
message_format=(
"The input for aggregation is incorrect. "
"The value for aggregated flow input '{input_key}' should be a list, "
"but received {value_type}. Please adjust the input value to match the expected format."
),
input_key=key,
value_type=type(value).__name__,
)
for key, value in aggregation_inputs.items():
if not isinstance(value, list):
raise InvalidAggregationInput(
message_format=(
"The input for aggregation is incorrect. "
"The value for aggregated reference input '{input_key}' should be a list, "
"but received {value_type}. Please adjust the input value to match the expected format."
),
input_key=key,
value_type=type(value).__name__,
)
inputs_len = {key: len(value) for key, value in aggregated_flow_inputs.items()}
inputs_len.update({key: len(value) for key, value in aggregation_inputs.items()})
if len(set(inputs_len.values())) > 1:
raise InvalidAggregationInput(
message_format=(
"The input for aggregation is incorrect. "
"The length of all aggregated inputs should be the same. Current input lengths are: "
"{key_len}. Please adjust the input value in your input data."
),
key_len=inputs_len,
)
@staticmethod
def _ensure_outputs_valid(flow: Flow):
updated_outputs = {}
for k, v in flow.outputs.items():
if v.reference.value_type == InputValueType.LITERAL and v.reference.value == "":
msg_format = (
"The output '{output_name}' for flow is incorrect. The reference is not specified for "
"the output '{output_name}' in the flow. To rectify this, "
"ensure that you accurately specify the reference in the flow."
)
raise EmptyOutputReference(message_format=msg_format, output_name=k)
if v.reference.value_type == InputValueType.FLOW_INPUT and v.reference.value not in flow.inputs:
msg_format = (
"The output '{output_name}' for flow is incorrect. The output '{output_name}' references "
"non-existent flow input '{flow_input_name}' in your flow. Please carefully review your flow and "
"correct the reference definition for the output in question."
)
raise OutputReferenceNotFound(
message_format=msg_format, output_name=k, flow_input_name=v.reference.value
)
if v.reference.value_type == InputValueType.NODE_REFERENCE:
node = flow.get_node(v.reference.value)
if node is None:
msg_format = (
"The output '{output_name}' for flow is incorrect. The output '{output_name}' references "
"non-existent node '{node_name}' in your flow. To resolve this issue, please carefully review "
"your flow and correct the reference definition for the output in question."
)
raise OutputReferenceNotFound(message_format=msg_format, output_name=k, node_name=v.reference.value)
if node.aggregation:
msg = f"Output '{k}' references a reduce node '{v.reference.value}', will not take effect."
logger.warning(msg)
# We will not add this output to the flow outputs, so we simply ignore it here
continue
updated_outputs[k] = v
return updated_outputs
@staticmethod
def ensure_flow_valid_in_batch_mode(flow: Flow):
if not flow.inputs:
message = (
"The input for flow cannot be empty in batch mode. Please review your flow and provide valid inputs."
)
raise InputNotFound(message=message)
| promptflow/src/promptflow/promptflow/executor/flow_validator.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/flow_validator.py",
"repo_id": "promptflow",
"token_count": 9197
} | 22 |
# Frequency asked questions (FAQ)
## Troubleshooting ##
### Token expired when run pfazure cmd
If hit error "AADSTS700082: The refresh token has expired due to inactivity." when running pfazure cmd, it's caused by local cached token expired. Please clear the cached token under "%LOCALAPPDATA%/.IdentityService/msal.cache". Then run below command to login again:
```sh
az login
``` | promptflow/docs/cloud/azureai/faq.md/0 | {
"file_path": "promptflow/docs/cloud/azureai/faq.md",
"repo_id": "promptflow",
"token_count": 108
} | 0 |
# Add conditional control to a flow
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](faq.md#stable-vs-experimental).
:::
In prompt flow, we support control logic by activate config, like if-else, switch. Activate config enables conditional execution of nodes within your flow, ensuring that specific actions are taken only when the specified conditions are met.
This guide will help you learn how to use activate config to add conditional control to your flow.
## Prerequisites
Please ensure that your promptflow version is greater than `0.1.0b5`.
## Usage
Each node in your flow can have an associated activate config, specifying when it should execute and when it should bypass. If a node has activate config, it will only be executed when the activate condition is met. The configuration consists of two essential components:
- `activate.when`: The condition that triggers the execution of the node. It can be based on the outputs of a previous node, or the inputs of the flow.
- `activate.is`: The condition's value, which can be a constant value of string, boolean, integer, double.
You can manually change the flow.dag.yaml in the flow folder or use the visual editor in VS Code Extension to add activate config to nodes in the flow.
::::{tab-set}
:::{tab-item} YAML
:sync: YAML
You can add activate config in the node section of flow yaml.
```yaml
activate:
when: ${node.output}
is: true
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
- Click `Visual editor` in the flow.dag.yaml to enter the flow interface.
- Click on the `Activation config` section in the node you want to add and fill in the values for "when" and "is".
:::
::::
### Further details and important notes
1. If the node using the python tool has an input that references a node that may be bypassed, please provide a default value for this input whenever possible. If there is no default value for input, the output of the bypassed node will be set to None.
2. It is not recommended to directly connect nodes that might be bypassed to the flow's outputs. If it is connected, the output will be None and a warning will be raised.
3. In a conditional flow, if a node has activate config, we will always use this config to determine whether the node should be bypassed. If a node is bypassed, its status will be marked as "Bypassed", as shown in the figure below Show. There are three situations in which a node is bypassed.
(1) If a node has activate config and the value of `activate.when` is not equals to `activate.is`, it will be bypassed. If you want to fore a node to always be executed, you can set the activate config to `when dummy is dummy` which always meets the activate condition.
(2) If a node has activate config and the node pointed to by `activate.when` is bypassed, it will be bypassed.
(3) If a node does not have activate config but depends on other nodes that have been bypassed, it will be bypassed.
## Example flow
Let's illustrate how to use activate config with practical examples.
- If-Else scenario: Learn how to develop a conditional flow for if-else scenarios. [View Example](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/conditional-flow-for-if-else)
- Switch scenario: Explore conditional flow for switch scenarios. [View Example](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/conditional-flow-for-switch)
## Next steps
- [Run and evaluate a flow](./run-and-evaluate-a-flow/index.md)
| promptflow/docs/how-to-guides/add-conditional-control-to-a-flow.md/0 | {
"file_path": "promptflow/docs/how-to-guides/add-conditional-control-to-a-flow.md",
"repo_id": "promptflow",
"token_count": 1231
} | 1 |
# Create and Use Your Own Custom Strong Type Connection
Connections provide a secure method for managing credentials for external APIs and data sources in prompt flow. This guide explains how to create and use a custom strong type connection.
## What is a Custom Strong Type Connection?
A custom strong type connection in prompt flow allows you to define a custom connection class with strongly typed keys. This provides the following benefits:
* Enhanced user experience - no need to manually enter connection keys.
* Rich intellisense experience - defining key types enables real-time suggestions and auto-completion of available keys as you work in VS Code.
* Central location to view available keys and data types.
For other connections types, please refer to [Connections](https://microsoft.github.io/promptflow/concepts/concept-connections.html).
## Prerequisites
- Please ensure that your [Prompt flow for VS Code](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow) is updated to at least version 1.2.1.
- Please install promptflow package and ensure that its version is 0.1.0b8 or later.
```
pip install promptflow>=0.1.0b8
```
## Create a custom strong type connection
Follow these steps to create a custom strong type connection:
1. Define a Python class inheriting from `CustomStrongTypeConnection`.
> [!Note] Please avoid using the `CustomStrongTypeConnection` class directly.
2. Use the Secret type to indicate secure keys. This enhances security by scrubbing secret keys.
3. Document with docstrings explaining each key.
For example:
```python
from promptflow.connections import CustomStrongTypeConnection
from promptflow.contracts.types import Secret
class MyCustomConnection(CustomStrongTypeConnection):
"""My custom strong type connection.
:param api_key: The api key.
:type api_key: Secret
:param api_base: The api base.
:type api_base: String
"""
api_key: Secret
api_base: str = "This is a fake api base."
```
See [this example](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_custom_strong_type_connection.py) for a complete implementation.
## Use the connection in a flow
Once you create a custom strong type connection, here are two ways to use it in your flows:
### With Package Tools:
1. Refer to the [Create and Use Tool Package](create-and-use-tool-package.md#create-custom-tool-package) to build and install your tool package containing the connection.
2. Develop a flow with custom tools. Please take [this folder](https://github.com/microsoft/promptflow/tree/main/examples/tools/use-cases/custom-strong-type-connection-package-tool-showcase) as an example.
3. Create a custom strong type connection using one of the following methods:
- If the connection type hasn't been created previously, click the 'Add connection' button to create the connection.
- Click the 'Create connection' plus sign in the CONNECTIONS section.
- Click 'Create connection' plus sign in the Custom category.
4. Fill in the `values` starting with `to-replace-with` in the connection template.
5. Run the flow with the created custom strong type connection.
### With Script Tools:
1. Develop a flow with python script tools. Please take [this folder](https://github.com/microsoft/promptflow/tree/main/examples/tools/use-cases/custom-strong-type-connection-script-tool-showcase) as an example.
2. Create a `CustomConnection`. Fill in the `keys` and `values` in the connection template.
3. Run the flow with the created custom connection.
## Local to cloud
When creating the necessary connections in Azure AI, you will need to create a `CustomConnection`. In the node interface of your flow, this connection will be displayed as the `CustomConnection` type.
Please refer to [Run prompt flow in Azure AI](../../cloud/azureai/quick-start/index.md) for more details.
Here is an example command:
```
pfazure run create --subscription 96aede12-2f73-41cb-b983-6d11a904839b -g promptflow -w my-pf-eus --flow D:\proj\github\ms\promptflow\examples\flows\standard\flow-with-package-tool-using-custom-strong-type-connection --data D:\proj\github\ms\promptflow\examples\flows\standard\flow-with-package-tool-using-custom-strong-type-connection\data.jsonl --runtime test-compute
```
## FAQs
### I followed the steps to create a custom strong type connection, but it's not showing up. What could be the issue?
Once the new tool package is installed in your local environment, a window reload is necessary. This action ensures that the new tools and custom strong type connections become visible and accessible.
| promptflow/docs/how-to-guides/develop-a-tool/create-your-own-custom-strong-type-connection.md/0 | {
"file_path": "promptflow/docs/how-to-guides/develop-a-tool/create-your-own-custom-strong-type-connection.md",
"repo_id": "promptflow",
"token_count": 1583
} | 2 |
# Tune prompts using variants
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](faq.md#stable-vs-experimental).
:::
To better understand this part, please read [Quick start](./quick-start.md) and [Run and evaluate a flow](./run-and-evaluate-a-flow/index.md) first.
## What is variant and why should we care
In order to help users tune the prompts in a more efficient way, we introduce [the concept of variants](../../concepts/concept-variants.md) which can help you test the model’s behavior under different conditions, such as different wording, formatting, context, temperature, or top-k, compare and find the best prompt and configuration that maximizes the model’s accuracy, diversity, or coherence.
## Create a run with different variant node
In this example, we use the flow [web-classification](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification), its node `summarize_text_content` has two variants: `variant_0` and `variant_1`. The difference between them is the inputs parameters:
```yaml
...
nodes:
- name: summarize_text_content
use_variants: true
...
node_variants:
summarize_text_content:
default_variant_id: variant_0
variants:
variant_0:
node:
type: llm
source:
type: code
path: summarize_text_content.jinja2
inputs:
deployment_name: text-davinci-003
max_tokens: '128'
temperature: '0.2'
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: open_ai_connection
api: completion
module: promptflow.tools.aoai
variant_1:
node:
type: llm
source:
type: code
path: summarize_text_content__variant_1.jinja2
inputs:
deployment_name: text-davinci-003
max_tokens: '256'
temperature: '0.3'
text: ${fetch_text_content_from_url.output}
provider: AzureOpenAI
connection: open_ai_connection
api: completion
module: promptflow.tools.aoai
```
You can check the whole flow definition in [flow.dag.yaml](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification/flow.dag.yaml).
Now we will create a variant run which uses node `summarize_text_content`'s variant `variant_1`.
Assuming you are in working directory `<path-to-the-sample-repo>/examples/flows/standard`
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
Note we pass `--variant` to specify which variant of the node should be running.
```sh
pf run create --flow web-classification --data web-classification/data.jsonl --variant '${summarize_text_content.variant_1}' --column-mapping url='${data.url}' --stream --name my_first_variant_run
```
:::
:::{tab-item} SDK
:sync: SDK
```python
from promptflow import PFClient
pf = PFClient() # get a promptflow client
flow = "web-classification"
data= "web-classification/data.jsonl"
# use the variant1 of the summarize_text_content node.
variant_run = pf.run(
flow=flow,
data=data,
variant="${summarize_text_content.variant_1}", # use variant 1.
column_mapping={"url": "${data.url}"},
)
pf.stream(variant_run)
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
:::
::::
After the variant run is created, you can evaluate the variant run with a evaluation flow, just like you evalute a standard flow run.
## Next steps
Learn more about:
- [Run and evaluate a flow](./run-and-evaluate-a-flow/index.md)
- [Deploy a flow](./deploy-a-flow/index.md)
- [Prompt flow in Azure AI](../cloud/azureai/quick-start/index.md) | promptflow/docs/how-to-guides/tune-prompts-with-variants.md/0 | {
"file_path": "promptflow/docs/how-to-guides/tune-prompts-with-variants.md",
"repo_id": "promptflow",
"token_count": 1456
} | 3 |
# Open Model LLM
## Introduction
The Open Model LLM tool enables the utilization of a variety of Open Model and Foundational Models, such as [Falcon](https://ml.azure.com/models/tiiuae-falcon-7b/version/4/catalog/registry/azureml) and [Llama 2](https://ml.azure.com/models/Llama-2-7b-chat/version/14/catalog/registry/azureml-meta), for natural language processing in Azure ML Prompt Flow.
Here's how it looks in action on the Visual Studio Code prompt flow extension. In this example, the tool is being used to call a LlaMa-2 chat endpoint and asking "What is CI?".
This prompt flow tool supports two different LLM API types:
- **Chat**: Shown in the example above. The chat API type facilitates interactive conversations with text-based inputs and responses.
- **Completion**: The Completion API type is used to generate single response text completions based on provided prompt input.
## Quick Overview: How do I use Open Model LLM Tool?
1. Choose a Model from the AzureML Model Catalog and get it deployed.
2. Connect to the model deployment.
3. Configure the open model llm tool settings.
4. Prepare the Prompt with [guidance](./prompt-tool.md#how-to-write-prompt).
5. Run the flow.
## Prerequisites: Model Deployment
1. Pick the model which matched your scenario from the [Azure Machine Learning model catalog](https://ml.azure.com/model/catalog).
2. Use the "Deploy" button to deploy the model to a AzureML Online Inference endpoint.
2.1. Use one of the Pay as you go deployment options.
More detailed instructions can be found here [Deploying foundation models to endpoints for inferencing.](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-foundation-models?view=azureml-api-2#deploying-foundation-models-to-endpoints-for-inferencing)
## Prerequisites: Connect to the Model
In order for prompt flow to use your deployed model, you will need to connect to it. There are several ways to connect.
### 1. Endpoint Connections
Once associated to a AzureML or Azure AI Studio workspace, the Open Model LLM tool can use the endpoints on that workspace.
1. **Using AzureML or Azure AI Studio workspaces**: If you are using prompt flow in one of the web page based browsers workspaces, the online endpoints available on that workspace will automatically who up.
2. **Using VScode or Code First**: If you are using prompt flow in VScode or one of the Code First offerings, you will need to connect to the workspace. The Open Model LLM tool uses the azure.identity DefaultAzureCredential client for authorization. One way is through [setting environment credential values](https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.environmentcredential?view=azure-python).
### 2. Custom Connections
The Open Model LLM tool uses the CustomConnection. Prompt flow supports two types of connections:
1. **Workspace Connections** - These are connections which are stored as secrets on an Azure Machine Learning workspace. While these can be used, in many places, the are commonly created and maintained in the Studio UI.
2. **Local Connections** - These are connections which are stored locally on your machine. These connections are not available in the Studio UX's, but can be used with the VScode extension.
Instructions on how to create a workspace or local Custom Connection [can be found here.](../../how-to-guides/manage-connections.md#create-a-connection)
The required keys to set are:
1. **endpoint_url**
- This value can be found at the previously created Inferencing endpoint.
2. **endpoint_api_key**
- Ensure to set this as a secret value.
- This value can be found at the previously created Inferencing endpoint.
3. **model_family**
- Supported values: LLAMA, DOLLY, GPT2, or FALCON
- This value is dependent on the type of deployment you are targeting.
## Running the Tool: Inputs
The Open Model LLM tool has a number of parameters, some of which are required. Please see the below table for details, you can match these to the screen shot above for visual clarity.
| Name | Type | Description | Required |
|------|------|-------------|----------|
| api | string | This is the API mode and will depend on the model used and the scenario selected. *Supported values: (Completion \| Chat)* | Yes |
| endpoint_name | string | Name of an Online Inferencing Endpoint with a supported model deployed on it. Takes priority over connection. | No |
| temperature | float | The randomness of the generated text. Default is 1. | No |
| max_new_tokens | integer | The maximum number of tokens to generate in the completion. Default is 500. | No |
| top_p | float | The probability of using the top choice from the generated tokens. Default is 1. | No |
| model_kwargs | dictionary | This input is used to provide configuration specific to the model used. For example, the Llama-02 model may use {\"temperature\":0.4}. *Default: {}* | No |
| deployment_name | string | The name of the deployment to target on the Online Inferencing endpoint. If no value is passed, the Inferencing load balancer traffic settings will be used. | No |
| prompt | string | The text prompt that the language model will use to generate it's response. | Yes |
## Outputs
| API | Return Type | Description |
|------------|-------------|------------------------------------------|
| Completion | string | The text of one predicted completion |
| Chat | string | The text of one response int the conversation |
## Deploying to an Online Endpoint
When deploying a flow containing the Open Model LLM tool to an online endpoint, there is an additional step to setup permissions. During deployment through the web pages, there is a choice between System-assigned and User-assigned Identity types. Either way, using the Azure Portal (or a similar functionality), add the "Reader" Job function role to the identity on the Azure Machine Learning workspace or Ai Studio project which is hosting the endpoint. The prompt flow deployment may need to be refreshed.
| promptflow/docs/reference/tools-reference/open_model_llm_tool.md/0 | {
"file_path": "promptflow/docs/reference/tools-reference/open_model_llm_tool.md",
"repo_id": "promptflow",
"token_count": 1634
} | 4 |
try:
from openai import AzureOpenAI as AzureOpenAIClient
except Exception:
raise Exception(
"Please upgrade your OpenAI package to version 1.0.0 or later using the command: pip install --upgrade openai.")
from promptflow._internal import ToolProvider, tool
from promptflow.connections import AzureOpenAIConnection
from promptflow.contracts.types import PromptTemplate
from promptflow.exceptions import ErrorTarget, UserErrorException
from typing import List, Dict
from promptflow.tools.common import render_jinja_template, handle_openai_error, parse_chat, \
preprocess_template_string, find_referenced_image_set, convert_to_chat_list, normalize_connection_config, \
post_process_chat_api_response
GPT4V_VERSION = "vision-preview"
def _get_credential():
from azure.identity import DefaultAzureCredential
from azure.ai.ml._azure_environments import _get_default_cloud_name, EndpointURLS, _get_cloud, AzureEnvironments
# Support sovereign cloud cases, like mooncake, fairfax.
cloud_name = _get_default_cloud_name()
if cloud_name != AzureEnvironments.ENV_DEFAULT:
cloud = _get_cloud(cloud=cloud_name)
authority = cloud.get(EndpointURLS.ACTIVE_DIRECTORY_ENDPOINT)
credential = DefaultAzureCredential(authority=authority, exclude_shared_token_cache_credential=True)
else:
credential = DefaultAzureCredential()
return credential
def _parse_resource_id(resource_id):
# Resource id is connection's id in following format:
# "/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}"
split_parts = resource_id.split("/")
if len(split_parts) != 9:
raise ParseConnectionError(
f"Connection resourceId format invalid, cur resourceId is {resource_id}."
)
sub, rg, account = split_parts[2], split_parts[4], split_parts[-1]
return sub, rg, account
class Deployment:
def __init__(
self,
name: str,
model_name: str,
version: str
):
self.name = name
self.model_name = model_name
self.version = version
class ListDeploymentsError(UserErrorException):
def __init__(self, msg, **kwargs):
super().__init__(msg, target=ErrorTarget.TOOL, **kwargs)
class ParseConnectionError(ListDeploymentsError):
def __init__(self, msg, **kwargs):
super().__init__(msg, **kwargs)
def _build_deployment_dict(item) -> Deployment:
model = item.properties.model
return Deployment(item.name, model.name, model.version)
def list_deployment_names(
subscription_id,
resource_group_name,
workspace_name,
connection: AzureOpenAIConnection = None
) -> List[Dict[str, str]]:
res = []
try:
# Does not support dynamic list for local.
from azure.mgmt.cognitiveservices import CognitiveServicesManagementClient
from promptflow.azure.operations._arm_connection_operations import \
ArmConnectionOperations, OpenURLFailedUserError
except ImportError:
return res
# For local, subscription_id is None. Does not suppot dynamic list for local.
if not subscription_id:
return res
try:
credential = _get_credential()
try:
conn = ArmConnectionOperations._build_connection_dict(
name=connection,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
credential=credential
)
resource_id = conn.get("value").get('resource_id', "")
if not resource_id:
return res
conn_sub, conn_rg, conn_account = _parse_resource_id(resource_id)
except OpenURLFailedUserError:
return res
except ListDeploymentsError as e:
raise e
except Exception as e:
msg = f"Parsing connection with exception: {e}"
raise ListDeploymentsError(msg=msg) from e
client = CognitiveServicesManagementClient(
credential=credential,
subscription_id=conn_sub,
)
deployment_collection = client.deployments.list(
resource_group_name=conn_rg,
account_name=conn_account,
)
for item in deployment_collection:
deployment = _build_deployment_dict(item)
if deployment.version == GPT4V_VERSION:
cur_item = {
"value": deployment.name,
"display_value": deployment.name,
}
res.append(cur_item)
except Exception as e:
if hasattr(e, 'status_code') and e.status_code == 403:
msg = f"Failed to list deployments due to permission issue: {e}"
raise ListDeploymentsError(msg=msg) from e
else:
msg = f"Failed to list deployments with exception: {e}"
raise ListDeploymentsError(msg=msg) from e
return res
class AzureOpenAI(ToolProvider):
def __init__(self, connection: AzureOpenAIConnection):
super().__init__()
self.connection = connection
self._connection_dict = normalize_connection_config(self.connection)
azure_endpoint = self._connection_dict.get("azure_endpoint")
api_version = self._connection_dict.get("api_version")
api_key = self._connection_dict.get("api_key")
self._client = AzureOpenAIClient(azure_endpoint=azure_endpoint, api_version=api_version, api_key=api_key)
@tool(streaming_option_parameter="stream")
@handle_openai_error()
def chat(
self,
prompt: PromptTemplate,
deployment_name: str,
temperature: float = 1.0,
top_p: float = 1.0,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
**kwargs,
) -> str:
# keep_trailing_newline=True is to keep the last \n in the prompt to avoid converting "user:\t\n" to "user:".
prompt = preprocess_template_string(prompt)
referenced_images = find_referenced_image_set(kwargs)
# convert list type into ChatInputList type
converted_kwargs = convert_to_chat_list(kwargs)
chat_str = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **converted_kwargs)
messages = parse_chat(chat_str, list(referenced_images))
headers = {
"Content-Type": "application/json",
"ms-azure-ai-promptflow-called-from": "aoai-gpt4v-tool"
}
params = {
"messages": messages,
"temperature": temperature,
"top_p": top_p,
"n": 1,
"stream": stream,
"presence_penalty": presence_penalty,
"frequency_penalty": frequency_penalty,
"extra_headers": headers,
"model": deployment_name,
}
if stop:
params["stop"] = stop
if max_tokens is not None:
params["max_tokens"] = max_tokens
completion = self._client.chat.completions.create(**params)
return post_process_chat_api_response(completion, stream, None)
| promptflow/src/promptflow-tools/promptflow/tools/aoai_gpt4v.py/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/aoai_gpt4v.py",
"repo_id": "promptflow",
"token_count": 3075
} | 5 |
promptflow.tools.serpapi.SerpAPI.search:
name: Serp API
description: Use Serp API to obtain search results from a specific search engine.
inputs:
connection:
type:
- SerpConnection
engine:
default: google
enum:
- google
- bing
type:
- string
location:
default: ''
type:
- string
num:
default: '10'
type:
- int
query:
type:
- string
safe:
default: 'off'
enum:
- active
- 'off'
type:
- string
type: python
module: promptflow.tools.serpapi
class_name: SerpAPI
function: search
| promptflow/src/promptflow-tools/promptflow/tools/yamls/serpapi.yaml/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/yamls/serpapi.yaml",
"repo_id": "promptflow",
"token_count": 302
} | 6 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# pylint: disable=wrong-import-position
import json
import time
from promptflow._cli._pf.help import show_privacy_statement, show_welcome_message
from promptflow._cli._user_agent import USER_AGENT
from promptflow._cli._utils import _get_cli_activity_name, get_client_info_for_cli
from promptflow._sdk._telemetry import ActivityType, get_telemetry_logger, log_activity
# Log the start time
start_time = time.perf_counter()
# E402 module level import not at top of file
import argparse # noqa: E402
import logging # noqa: E402
import sys # noqa: E402
from promptflow._cli._pf_azure._flow import add_parser_flow, dispatch_flow_commands # noqa: E402
from promptflow._cli._pf_azure._run import add_parser_run, dispatch_run_commands # noqa: E402
from promptflow._sdk._utils import ( # noqa: E402
get_promptflow_sdk_version,
print_pf_version,
setup_user_agent_to_operation_context,
)
from promptflow._utils.logger_utils import get_cli_sdk_logger # noqa: E402
# get logger for CLI
logger = get_cli_sdk_logger()
def run_command(args):
# Log the init finish time
init_finish_time = time.perf_counter()
try:
# --verbose, enable info logging
if hasattr(args, "verbose") and args.verbose:
for handler in logger.handlers:
handler.setLevel(logging.INFO)
# --debug, enable debug logging
if hasattr(args, "debug") and args.debug:
for handler in logger.handlers:
handler.setLevel(logging.DEBUG)
if args.version:
print_pf_version()
elif args.action == "run":
dispatch_run_commands(args)
elif args.action == "flow":
dispatch_flow_commands(args)
except KeyboardInterrupt as ex:
logger.debug("Keyboard interrupt is captured.")
raise ex
except SystemExit as ex: # some code directly call sys.exit, this is to make sure command metadata is logged
exit_code = ex.code if ex.code is not None else 1
logger.debug(f"Code directly call sys.exit with code {exit_code}")
raise ex
except Exception as ex:
logger.debug(f"Command {args} execute failed. {str(ex)}")
raise ex
finally:
# Log the invoke finish time
invoke_finish_time = time.perf_counter()
logger.info(
"Command ran in %.3f seconds (init: %.3f, invoke: %.3f)",
invoke_finish_time - start_time,
init_finish_time - start_time,
invoke_finish_time - init_finish_time,
)
def get_parser_args(argv):
parser = argparse.ArgumentParser(
prog="pfazure",
formatter_class=argparse.RawDescriptionHelpFormatter,
description="pfazure: manage prompt flow assets in azure. Learn more: https://microsoft.github.io/promptflow.",
)
parser.add_argument(
"-v", "--version", dest="version", action="store_true", help="show current CLI version and exit"
)
subparsers = parser.add_subparsers()
add_parser_run(subparsers)
add_parser_flow(subparsers)
return parser.prog, parser.parse_args(argv)
def _get_workspace_info(args):
try:
subscription_id, resource_group_name, workspace_name = get_client_info_for_cli(
subscription_id=args.subscription,
resource_group_name=args.resource_group,
workspace_name=args.workspace_name,
)
return {
"subscription_id": subscription_id,
"resource_group_name": resource_group_name,
"workspace_name": workspace_name,
}
except Exception:
# fall back to empty dict if workspace info is not available
return {}
def entry(argv):
"""
Control plane CLI tools for promptflow cloud version.
"""
prog, args = get_parser_args(argv)
if hasattr(args, "user_agent"):
setup_user_agent_to_operation_context(args.user_agent)
logger = get_telemetry_logger()
custom_dimensions = _get_workspace_info(args)
with log_activity(
logger,
_get_cli_activity_name(cli=prog, args=args),
activity_type=ActivityType.PUBLICAPI,
custom_dimensions=custom_dimensions,
):
run_command(args)
def main():
"""Entrance of pf CLI."""
command_args = sys.argv[1:]
if len(command_args) == 1 and command_args[0] == "version":
version_dict = {"promptflow": get_promptflow_sdk_version()}
return json.dumps(version_dict, ensure_ascii=False, indent=2, sort_keys=True, separators=(",", ": ")) + "\n"
if len(command_args) == 0:
# print privacy statement & welcome message like azure-cli
show_privacy_statement()
show_welcome_message()
command_args.append("-h")
elif len(command_args) == 1:
# pfazure only has "pf --version" with 1 layer
if command_args[0] not in ["--version", "-v"]:
command_args.append("-h")
setup_user_agent_to_operation_context(USER_AGENT)
entry(command_args)
if __name__ == "__main__":
main()
| promptflow/src/promptflow/promptflow/_cli/_pf_azure/entry.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf_azure/entry.py",
"repo_id": "promptflow",
"token_count": 2094
} | 7 |
{"groundtruth": "App", "prediction": "App"}
| promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/data.jsonl/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/data.jsonl",
"repo_id": "promptflow",
"token_count": 15
} | 8 |
from traceback import TracebackException
from promptflow._utils.exception_utils import (
ADDITIONAL_INFO_USER_EXECUTION_ERROR,
is_pf_core_frame,
last_frame_info,
remove_suffix,
)
from promptflow.exceptions import ErrorTarget, SystemErrorException, UserErrorException, ValidationException
class UnexpectedError(SystemErrorException):
"""Exception raised for unexpected errors that should not occur under normal circumstances."""
pass
class NotSupported(UserErrorException):
"""This exception should be raised when a feature is not supported by the package or product.
Customers should take action, such as upgrading the package or using the product in the correct way, to resolve it.
"""
pass
class PackageToolNotFoundError(ValidationException):
"""Exception raised when package tool is not found in the current runtime environment."""
pass
class MissingRequiredInputs(ValidationException):
pass
class InputTypeMismatch(ValidationException):
pass
class ToolCanceledError(UserErrorException):
"""Exception raised when tool execution is canceled."""
pass
class InvalidSource(ValidationException):
pass
class ToolLoadError(UserErrorException):
"""Exception raised when tool load failed."""
def __init__(self, module: str = None, **kwargs):
super().__init__(target=ErrorTarget.TOOL, module=module, **kwargs)
class ToolExecutionError(UserErrorException):
"""Exception raised when tool execution failed."""
def __init__(self, *, node_name: str, module: str = None):
self._node_name = node_name
super().__init__(target=ErrorTarget.TOOL, module=module)
@property
def message(self):
if self.inner_exception:
error_type_and_message = f"({self.inner_exception.__class__.__name__}) {self.inner_exception}"
return remove_suffix(self._message, ".") + f": {error_type_and_message}"
else:
return self._message
@property
def message_format(self):
return "Execution failure in '{node_name}'."
@property
def message_parameters(self):
return {"node_name": self._node_name}
@property
def tool_last_frame_info(self):
"""Return the line number inside the tool where the error occurred."""
return last_frame_info(self.inner_exception)
@property
def tool_traceback(self):
"""Return the traceback inside the tool's source code scope.
The traceback inside the promptflow's internal code will be taken off.
"""
exc = self.inner_exception
if exc and exc.__traceback__ is not None:
tb = exc.__traceback__.tb_next
if tb is not None:
# The first frames are always our code invoking the tool.
# We do not want to dump it to user code's traceback.
# So, skip these frames from pf core module.
while is_pf_core_frame(tb.tb_frame) and tb.tb_next is not None:
tb = tb.tb_next
# We don't use traceback.format_exception since its interface differs between 3.8 and 3.10.
# Use this internal class to adapt to different python versions.
te = TracebackException(type(exc), exc, tb)
formatted_tb = "".join(te.format())
return formatted_tb
return None
@property
def additional_info(self):
"""Set the tool exception details as additional info."""
if not self.inner_exception:
# Only populate additional info when inner exception is present.
return None
info = {
"type": self.inner_exception.__class__.__name__,
"message": str(self.inner_exception),
"traceback": self.tool_traceback,
}
info.update(self.tool_last_frame_info)
return {
ADDITIONAL_INFO_USER_EXECUTION_ERROR: info,
}
class GenerateMetaUserError(UserErrorException):
"""Base exception raised when failed to validate tool."""
def __init__(self, **kwargs):
super().__init__(target=ErrorTarget.EXECUTOR, **kwargs)
class MetaFileNotFound(GenerateMetaUserError):
pass
class MetaFileReadError(GenerateMetaUserError):
pass
class RunRecordNotFound(SystemErrorException):
pass
class FlowOutputUnserializable(UserErrorException):
pass
class ProcessPoolError(SystemErrorException):
pass
class DuplicateToolMappingError(ValidationException):
"""Exception raised when multiple tools are linked to the same deprecated tool id."""
pass
| promptflow/src/promptflow/promptflow/_core/_errors.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/_errors.py",
"repo_id": "promptflow",
"token_count": 1699
} | 9 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
# flake8: noqa
"""Put some imports here for internal packages to minimize the effort of refactoring."""
from promptflow._constants import PROMPTFLOW_CONNECTIONS
from promptflow._core._errors import GenerateMetaUserError, PackageToolNotFoundError, ToolExecutionError
from promptflow._core.cache_manager import AbstractCacheManager, CacheManager, enable_cache
from promptflow._core.connection_manager import ConnectionManager
from promptflow._core.flow_execution_context import FlowExecutionContext
from promptflow._core.log_manager import NodeLogManager, NodeLogWriter
from promptflow._core.metric_logger import add_metric_logger
from promptflow._core.openai_injector import inject_openai_api
from promptflow._core.operation_context import OperationContext
from promptflow._core.run_tracker import RunRecordNotFound, RunTracker
from promptflow._core.tool import ToolInvoker, ToolProvider, tool
from promptflow._core.tool_meta_generator import (
JinjaParsingError,
MultipleToolsDefined,
NoToolDefined,
PythonParsingError,
ReservedVariableCannotBeUsed,
generate_prompt_meta,
generate_python_meta,
generate_tool_meta_dict_by_file,
is_tool,
)
from promptflow._core.tools_manager import (
BuiltinsManager,
CustomPythonToolLoadError,
EmptyCodeInCustomTool,
MissingTargetFunction,
ToolsManager,
builtins,
collect_package_tools,
gen_dynamic_list,
register_apis,
register_builtins,
register_connections,
retrieve_tool_func_result,
)
from promptflow._core.tracer import Tracer
from promptflow._sdk._constants import LOCAL_MGMT_DB_PATH
from promptflow._sdk._serving.response_creator import ResponseCreator
from promptflow._sdk._serving.swagger import generate_swagger
from promptflow._sdk._serving.utils import (
get_output_fields_to_remove,
get_sample_json,
handle_error_to_response,
load_request_data,
streaming_response_required,
validate_request_data,
)
from promptflow._sdk._utils import (
get_used_connection_names_from_environment_variables,
setup_user_agent_to_operation_context,
update_environment_variables_with_connections,
)
from promptflow._utils.context_utils import _change_working_dir, inject_sys_path
from promptflow._utils.credential_scrubber import CredentialScrubber
from promptflow._utils.dataclass_serializer import deserialize_dataclass, serialize
from promptflow._utils.exception_utils import (
ErrorResponse,
ExceptionPresenter,
JsonSerializedPromptflowException,
RootErrorCode,
infer_error_code_from_class,
)
from promptflow._utils.execution_utils import handle_line_failures
from promptflow._utils.feature_utils import Feature, FeatureState, get_feature_list
from promptflow._utils.inputs_mapping_utils import apply_inputs_mapping
from promptflow._utils.logger_utils import (
DATETIME_FORMAT,
LOG_FORMAT,
CredentialScrubberFormatter,
FileHandler,
FileHandlerConcurrentWrapper,
LogContext,
bulk_logger,
flow_logger,
get_logger,
logger,
update_log_path,
)
from promptflow._utils.multimedia_data_converter import (
AbstractMultimediaInfoConverter,
MultimediaConverter,
MultimediaInfo,
ResourceType,
)
from promptflow._utils.multimedia_utils import (
_create_image_from_file,
convert_multimedia_data_to_base64,
is_multimedia_dict,
persist_multimedia_data,
resolve_multimedia_data_recursively,
)
from promptflow._utils.utils import (
AttrDict,
camel_to_snake,
count_and_log_progress,
load_json,
reverse_transpose,
set_context,
transpose,
)
from promptflow._version import VERSION
from promptflow.batch._csharp_base_executor_proxy import CSharpBaseExecutorProxy
from promptflow.executor._errors import InputNotFound
from promptflow.executor._tool_invoker import DefaultToolInvoker
from promptflow.storage._run_storage import DefaultRunStorage
| promptflow/src/promptflow/promptflow/_internal/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_internal/__init__.py",
"repo_id": "promptflow",
"token_count": 1330
} | 10 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Union
from .._utils.logger_utils import get_cli_sdk_logger
from ._configuration import Configuration
from ._constants import MAX_SHOW_DETAILS_RESULTS
from ._load_functions import load_flow
from ._user_agent import USER_AGENT
from ._utils import ClientUserAgentUtil, get_connection_operation, setup_user_agent_to_operation_context
from .entities import Run
from .entities._eager_flow import EagerFlow
from .operations import RunOperations
from .operations._connection_operations import ConnectionOperations
from .operations._experiment_operations import ExperimentOperations
from .operations._flow_operations import FlowOperations
from .operations._tool_operations import ToolOperations
from .operations._trace_operations import TraceOperations
logger = get_cli_sdk_logger()
def _create_run(run: Run, **kwargs):
client = PFClient()
return client.runs.create_or_update(run=run, **kwargs)
class PFClient:
"""A client class to interact with prompt flow entities."""
def __init__(self, **kwargs):
logger.debug("PFClient init with kwargs: %s", kwargs)
self._runs = RunOperations(self)
self._connection_provider = kwargs.pop("connection_provider", None)
self._config = kwargs.get("config", None) or {}
# The credential is used as an option to override
# DefaultAzureCredential when using workspace connection provider
self._credential = kwargs.get("credential", None)
# Lazy init to avoid azure credential requires too early
self._connections = None
self._flows = FlowOperations(client=self)
self._tools = ToolOperations()
# add user agent from kwargs if any
if isinstance(kwargs.get("user_agent"), str):
ClientUserAgentUtil.append_user_agent(kwargs["user_agent"])
self._experiments = ExperimentOperations(self)
self._traces = TraceOperations()
setup_user_agent_to_operation_context(USER_AGENT)
def run(
self,
flow: Union[str, PathLike],
*,
data: Union[str, PathLike] = None,
run: Union[str, Run] = None,
column_mapping: dict = None,
variant: str = None,
connections: dict = None,
environment_variables: dict = None,
name: str = None,
display_name: str = None,
tags: Dict[str, str] = None,
**kwargs,
) -> Run:
"""Run flow against provided data or run.
.. note::
At least one of the ``data`` or ``run`` parameters must be provided.
.. admonition:: Column_mapping
Column mapping is a mapping from flow input name to specified values.
If specified, the flow will be executed with provided value for specified inputs.
The value can be:
- from data:
- ``data.col1``
- from run:
- ``run.inputs.col1``: if need reference run's inputs
- ``run.output.col1``: if need reference run's outputs
- Example:
- ``{"ground_truth": "${data.answer}", "prediction": "${run.outputs.answer}"}``
:param flow: Path to the flow directory to run evaluation.
:type flow: Union[str, PathLike]
:param data: Pointer to the test data (of variant bulk runs) for eval runs.
:type data: Union[str, PathLike]
:param run: Flow run ID or flow run. This parameter helps keep lineage between
the current run and variant runs. Batch outputs can be
referenced as ``${run.outputs.col_name}`` in inputs_mapping.
:type run: Union[str, ~promptflow.entities.Run]
:param column_mapping: Define a data flow logic to map input data.
:type column_mapping: Dict[str, str]
:param variant: Node & variant name in the format of ``${node_name.variant_name}``.
The default variant will be used if not specified.
:type variant: str
:param connections: Overwrite node level connections with provided values.
Example: ``{"node1": {"connection": "new_connection", "deployment_name": "gpt-35-turbo"}}``
:type connections: Dict[str, Dict[str, str]]
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: ``{"key1": "${my_connection.api_key}", "key2"="value2"}``
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: Dict[str, str]
:param name: Name of the run.
:type name: str
:param display_name: Display name of the run.
:type display_name: str
:param tags: Tags of the run.
:type tags: Dict[str, str]
:return: Flow run info.
:rtype: ~promptflow.entities.Run
"""
if not os.path.exists(flow):
raise FileNotFoundError(f"flow path {flow} does not exist")
if data and not os.path.exists(data):
raise FileNotFoundError(f"data path {data} does not exist")
if not run and not data:
raise ValueError("at least one of data or run must be provided")
# load flow object for validation and early failure
flow_obj = load_flow(source=flow)
# validate param conflicts
if isinstance(flow_obj, EagerFlow):
if variant or connections:
logger.warning("variant and connections are not supported for eager flow, will be ignored")
variant, connections = None, None
run = Run(
name=name,
display_name=display_name,
tags=tags,
data=data,
column_mapping=column_mapping,
run=run,
variant=variant,
flow=Path(flow),
connections=connections,
environment_variables=environment_variables,
config=Configuration(overrides=self._config),
)
return self.runs.create_or_update(run=run, **kwargs)
def stream(self, run: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream run logs to the console.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: flow run info.
:rtype: ~promptflow.sdk.entities.Run
"""
return self.runs.stream(run, raise_on_error)
def get_details(
self, run: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False
) -> "DataFrame":
"""Get the details from the run including inputs and outputs.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param run: The run name or run object
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
return self.runs.get_details(name=run, max_results=max_results, all_results=all_results)
def get_metrics(self, run: Union[str, Run]) -> Dict[str, Any]:
"""Get run metrics.
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
:return: Run metrics.
:rtype: Dict[str, Any]
"""
return self.runs.get_metrics(run)
def visualize(self, runs: Union[List[str], List[Run]]) -> None:
"""Visualize run(s).
:param run: Run object or name of the run.
:type run: Union[str, ~promptflow.sdk.entities.Run]
"""
self.runs.visualize(runs)
@property
def runs(self) -> RunOperations:
"""Run operations that can manage runs."""
return self._runs
@property
def tools(self) -> ToolOperations:
"""Tool operations that can manage tools."""
return self._tools
def _ensure_connection_provider(self) -> str:
if not self._connection_provider:
# Get a copy with config override instead of the config instance
self._connection_provider = Configuration(overrides=self._config).get_connection_provider()
logger.debug("PFClient connection provider: %s", self._connection_provider)
return self._connection_provider
@property
def connections(self) -> ConnectionOperations:
"""Connection operations that can manage connections."""
if not self._connections:
self._ensure_connection_provider()
self._connections = get_connection_operation(self._connection_provider, self._credential)
return self._connections
@property
def flows(self) -> FlowOperations:
"""Operations on the flow that can manage flows."""
return self._flows
def test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
) -> dict:
"""Test flow or node.
:param flow: path to flow directory to test
:type flow: Union[str, PathLike]
:param inputs: Input data for the flow test
:type inputs: dict
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:type variant: str
:param node: If specified it will only test this node, else it will test the flow.
:type node: str
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: dict
:return: The result of flow or node
:rtype: dict
"""
return self.flows.test(
flow=flow, inputs=inputs, variant=variant, environment_variables=environment_variables, node=node
)
| promptflow/src/promptflow/promptflow/_sdk/_pf_client.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_pf_client.py",
"repo_id": "promptflow",
"token_count": 4254
} | 11 |
{
"swagger": "2.0",
"basePath": "/v1.0",
"paths": {
"/Connections/": {
"get": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
},
"200": {
"description": "Success",
"schema": {
"type": "array",
"items": {
"$ref": "#/definitions/Connection"
}
}
}
},
"description": "List all connection",
"operationId": "get_connection_list",
"parameters": [
{
"name": "working_directory",
"in": "query",
"type": "string"
}
],
"tags": [
"Connections"
]
}
},
"/Connections/specs": {
"get": {
"responses": {
"200": {
"description": "List connection spec",
"schema": {
"$ref": "#/definitions/ConnectionSpec"
}
}
},
"description": "List connection spec",
"operationId": "get_connection_specs",
"tags": [
"Connections"
]
}
},
"/Connections/{name}": {
"parameters": [
{
"in": "path",
"description": "The connection name.",
"name": "name",
"required": true,
"type": "string"
}
],
"put": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
},
"200": {
"description": "Connection details",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
},
"description": "Update connection",
"operationId": "put_connection",
"parameters": [
{
"name": "payload",
"required": true,
"in": "body",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
],
"tags": [
"Connections"
]
},
"get": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
},
"200": {
"description": "Connection details",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
},
"description": "Get connection",
"operationId": "get_connection",
"parameters": [
{
"name": "working_directory",
"in": "query",
"type": "string"
}
],
"tags": [
"Connections"
]
},
"delete": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
}
},
"description": "Delete connection",
"operationId": "delete_connection",
"tags": [
"Connections"
]
},
"post": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
},
"200": {
"description": "Connection details",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
},
"description": "Create connection",
"operationId": "post_connection",
"parameters": [
{
"name": "payload",
"required": true,
"in": "body",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
],
"tags": [
"Connections"
]
}
},
"/Connections/{name}/listsecrets": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"403": {
"description": "This service is available for local user only, please specify X-Remote-User in headers."
},
"200": {
"description": "Connection details with secret",
"schema": {
"$ref": "#/definitions/ConnectionDict"
}
}
},
"description": "Get connection with secret",
"operationId": "get_connection_with_secret",
"parameters": [
{
"name": "working_directory",
"in": "query",
"type": "string"
}
],
"tags": [
"Connections"
]
}
},
"/Runs/": {
"get": {
"responses": {
"200": {
"description": "Runs",
"schema": {
"$ref": "#/definitions/RunList"
}
}
},
"description": "List all runs",
"operationId": "get_run_list",
"tags": [
"Runs"
]
}
},
"/Runs/submit": {
"post": {
"responses": {
"200": {
"description": "Submit run info",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Submit run",
"operationId": "post_run_submit",
"parameters": [
{
"name": "payload",
"required": true,
"in": "body",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
],
"tags": [
"Runs"
]
}
},
"/Runs/{name}": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"put": {
"responses": {
"200": {
"description": "Update run info",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Update run",
"operationId": "put_run",
"parameters": [
{
"name": "display_name",
"in": "formData",
"type": "string"
},
{
"name": "description",
"in": "formData",
"type": "string"
},
{
"name": "tags",
"in": "formData",
"type": "string"
}
],
"consumes": [
"application/x-www-form-urlencoded",
"multipart/form-data"
],
"tags": [
"Runs"
]
},
"get": {
"responses": {
"200": {
"description": "Get run info",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Get run",
"operationId": "get_run",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/archive": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Archived run",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Archive run",
"operationId": "get_archive_run",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/childRuns": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Child runs",
"schema": {
"$ref": "#/definitions/RunList"
}
}
},
"description": "Get child runs",
"operationId": "get_flow_child_runs",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/logContent": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Log content",
"schema": {
"type": "string"
}
}
},
"description": "Get run log content",
"operationId": "get_log_content",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/metaData": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Run metadata",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Get metadata of run",
"operationId": "get_meta_data",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/metrics": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Run metrics",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Get run metrics",
"operationId": "get_metrics",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/nodeRuns/{node_name}": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
},
{
"name": "node_name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Node runs",
"schema": {
"$ref": "#/definitions/RunList"
}
}
},
"description": "Get node runs info",
"operationId": "get_flow_node_runs",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/restore": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Restored run",
"schema": {
"$ref": "#/definitions/RunDict"
}
}
},
"description": "Restore run",
"operationId": "get_restore_run",
"tags": [
"Runs"
]
}
},
"/Runs/{name}/visualize": {
"parameters": [
{
"name": "name",
"in": "path",
"required": true,
"type": "string"
}
],
"get": {
"responses": {
"200": {
"description": "Visualize run",
"schema": {
"type": "string"
}
}
},
"description": "Visualize run",
"operationId": "get_visualize_run",
"produces": [
"text/html"
],
"tags": [
"Runs"
]
}
},
"/Telemetries/": {
"post": {
"responses": {
"403": {
"description": "Telemetry is disabled or X-Remote-User is not set.",
"headers": {
"x-ms-promptflow-request-id": {
"type": "string"
}
}
},
"400": {
"description": "Input payload validation failed",
"headers": {
"x-ms-promptflow-request-id": {
"type": "string"
}
}
},
"200": {
"description": "Create telemetry record",
"headers": {
"x-ms-promptflow-request-id": {
"type": "string"
}
}
}
},
"description": "Create telemetry record",
"operationId": "post_telemetry",
"parameters": [
{
"name": "payload",
"required": true,
"in": "body",
"schema": {
"$ref": "#/definitions/Telemetry"
}
}
],
"tags": [
"Telemetries"
]
}
}
},
"info": {
"title": "Prompt Flow Service",
"version": "1.0"
},
"produces": [
"application/json"
],
"consumes": [
"application/json"
],
"tags": [
{
"name": "Connections",
"description": "Connections Management"
},
{
"name": "Runs",
"description": "Runs Management"
},
{
"name": "Telemetries",
"description": "Telemetry Management"
}
],
"definitions": {
"Connection": {
"properties": {
"name": {
"type": "string"
},
"type": {
"type": "string"
},
"module": {
"type": "string"
},
"expiry_time": {
"type": "string"
},
"created_date": {
"type": "string"
},
"last_modified_date": {
"type": "string"
}
},
"type": "object"
},
"ConnectionDict": {
"additionalProperties": true,
"type": "object"
},
"ConnectionSpec": {
"properties": {
"connection_type": {
"type": "string"
},
"config_spec": {
"type": "array",
"items": {
"$ref": "#/definitions/ConnectionConfigSpec"
}
}
},
"type": "object"
},
"ConnectionConfigSpec": {
"properties": {
"name": {
"type": "string"
},
"optional": {
"type": "boolean"
},
"default": {
"type": "string"
}
},
"type": "object"
},
"RunList": {
"type": "array",
"items": {
"$ref": "#/definitions/RunDict"
}
},
"RunDict": {
"additionalProperties": true,
"type": "object"
},
"Telemetry": {
"required": [
"eventType",
"timestamp"
],
"properties": {
"eventType": {
"type": "string",
"description": "The event type of the telemetry.",
"example": "Start",
"enum": [
"Start",
"End"
]
},
"timestamp": {
"type": "string",
"format": "date-time",
"description": "The timestamp of the telemetry."
},
"firstCall": {
"type": "boolean",
"description": "Whether current activity is the first activity in the call chain.",
"default": true
},
"metadata": {
"$ref": "#/definitions/Metadata"
}
},
"type": "object"
},
"Metadata": {
"required": [
"activityName",
"activityType"
],
"properties": {
"activityName": {
"type": "string",
"description": "The name of the activity.",
"example": "pf.flow.test",
"enum": [
"pf.flow.test",
"pf.flow.node_test",
"pf.flow._generate_tools_meta"
]
},
"activityType": {
"type": "string",
"description": "The type of the activity."
},
"completionStatus": {
"type": "string",
"description": "The completion status of the activity.",
"example": "Success",
"enum": [
"Success",
"Failure"
]
},
"durationMs": {
"type": "integer",
"description": "The duration of the activity in milliseconds."
},
"errorCategory": {
"type": "string",
"description": "The error category of the activity."
},
"errorType": {
"type": "string",
"description": "The error type of the activity."
},
"errorTarget": {
"type": "string",
"description": "The error target of the activity."
},
"errorMessage": {
"type": "string",
"description": "The error message of the activity."
},
"errorDetails": {
"type": "string",
"description": "The error details of the activity."
}
},
"type": "object"
}
},
"responses": {
"ParseError": {
"description": "When a mask can't be parsed"
},
"MaskError": {
"description": "When any error occurs on mask"
},
"Exception": {
"description": "When any error occurs on the server, return a formatted error message"
}
}
}
| promptflow/src/promptflow/promptflow/_sdk/_service/swagger.json/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/swagger.json",
"repo_id": "promptflow",
"token_count": 16309
} | 12 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import functools
import threading
import uuid
from contextvars import ContextVar
from datetime import datetime
from typing import Any, Dict
from promptflow._sdk._telemetry.telemetry import TelemetryMixin
from promptflow._sdk._utils import ClientUserAgentUtil
from promptflow.exceptions import _ErrorInfo
class ActivityType(object):
"""The type of activity (code) monitored.
The default type is "PublicAPI".
"""
PUBLICAPI = "PublicApi" # incoming public API call (default)
INTERNALCALL = "InternalCall" # internal (function) call
CLIENTPROXY = "ClientProxy" # an outgoing service API call
class ActivityCompletionStatus(object):
"""The activity (code) completion status, success, or failure."""
SUCCESS = "Success"
FAILURE = "Failure"
request_id_context = ContextVar("request_id_context", default=None)
def log_activity_start(activity_info: Dict[str, Any], logger) -> None:
"""Log activity start.
Sample activity_info:
{
"request_id": "request_id",
"first_call": True,
"activity_name": "activity_name",
"activity_type": "activity_type",
"user_agent": "user_agent",
}
:param activity_info: The custom properties of the activity to record.
:type activity_info: dict
:param logger: The logger adapter.
:type logger: logging.LoggerAdapter
"""
message = f"{activity_info['activity_name']}.start"
logger.info(message, extra={"custom_dimensions": activity_info})
pass
def log_activity_end(activity_info: Dict[str, Any], logger) -> None:
"""Log activity end.
Sample activity_info for success (start info plus run info):
{
...,
"duration_ms": 1000
"completion_status": "Success",
}
Sample activity_info for failure (start info plus error info):
{
...,
"duration_ms": 1000
"completion_status": "Failure",
"error_category": "error_category",
"error_type": "error_type",
"error_target": "error_target",
"error_message": "error_message",
"error_detail": "error_detail"
}
Error target & error type can be found in the following link:
https://github.com/microsoft/promptflow/blob/main/src/promptflow/promptflow/exceptions.py
:param activity_info: The custom properties of the activity.
:type activity_info: dict
:param logger: The logger adapter.
:type logger: logging.LoggerAdapter
"""
try:
# we will fail this log if activity_name/completion_status is not in activity_info, so directly use get()
message = f"{activity_info['activity_name']}.complete"
if activity_info["completion_status"] == ActivityCompletionStatus.FAILURE:
logger.error(message, extra={"custom_dimensions": activity_info})
else:
logger.info(message, extra={"custom_dimensions": activity_info})
except Exception: # pylint: disable=broad-except
# skip if logger failed to log
pass # pylint: disable=lost-exception
def generate_request_id():
return str(uuid.uuid4())
@contextlib.contextmanager
def log_activity(
logger,
activity_name,
activity_type=ActivityType.INTERNALCALL,
custom_dimensions=None,
):
"""Log an activity.
An activity is a logical block of code that consumers want to monitor.
To monitor, wrap the logical block of code with the ``log_activity()`` method. As an alternative, you can
also use the ``@monitor_with_activity`` decorator.
:param logger: The logger adapter.
:type logger: logging.LoggerAdapter
:param activity_name: The name of the activity. The name should be unique per the wrapped logical code block.
:type activity_name: str
:param activity_type: One of PUBLICAPI, INTERNALCALL, or CLIENTPROXY which represent an incoming API call,
an internal (function) call, or an outgoing API call. If not specified, INTERNALCALL is used.
:type activity_type: str
:param custom_dimensions: The custom properties of the activity.
:type custom_dimensions: dict
:return: None
"""
if not custom_dimensions:
custom_dimensions = {}
user_agent = ClientUserAgentUtil.get_user_agent()
request_id = request_id_context.get()
if not request_id:
# public function call
first_call = True
request_id = generate_request_id()
request_id_context.set(request_id)
else:
first_call = False
activity_info = {
"request_id": request_id,
"first_call": first_call,
"activity_name": activity_name,
"activity_type": activity_type,
"user_agent": user_agent,
}
activity_info.update(custom_dimensions)
start_time = datetime.utcnow()
completion_status = ActivityCompletionStatus.SUCCESS
log_activity_start(activity_info, logger)
exception = None
try:
yield logger
except BaseException as e: # pylint: disable=broad-except
exception = e
completion_status = ActivityCompletionStatus.FAILURE
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(exception)
activity_info["error_category"] = error_category
activity_info["error_type"] = error_type
activity_info["error_target"] = error_target
activity_info["error_message"] = error_message
activity_info["error_detail"] = error_detail
finally:
if first_call:
# recover request id in global storage
request_id_context.set(None)
end_time = datetime.utcnow()
duration_ms = round((end_time - start_time).total_seconds() * 1000, 2)
activity_info["completion_status"] = completion_status
activity_info["duration_ms"] = duration_ms
log_activity_end(activity_info, logger)
# raise the exception to align with the behavior of the with statement
if exception:
raise exception
def extract_telemetry_info(self):
"""Extract pf telemetry info from given telemetry mix-in instance."""
result = {}
try:
if isinstance(self, TelemetryMixin):
return self._get_telemetry_values()
except Exception:
pass
return result
def update_activity_name(activity_name, kwargs=None, args=None):
"""Update activity name according to kwargs. For flow test, we want to know if it's node test."""
if activity_name == "pf.flows.test":
# SDK
if kwargs.get("node", None):
activity_name = "pf.flows.node_test"
elif activity_name == "pf.flow.test":
# CLI
if getattr(args, "node", None):
activity_name = "pf.flow.node_test"
return activity_name
def monitor_operation(
activity_name,
activity_type=ActivityType.INTERNALCALL,
custom_dimensions=None,
):
"""A wrapper for monitoring an activity in operations class.
To monitor, use the ``@monitor_operation`` decorator.
Note: this decorator should only use in operations class methods.
:param activity_name: The name of the activity. The name should be unique per the wrapped logical code block.
:type activity_name: str
:param activity_type: One of PUBLICAPI, INTERNALCALL, or CLIENTPROXY which represent an incoming API call,
an internal (function) call, or an outgoing API call. If not specified, INTERNALCALL is used.
:type activity_type: str
:param custom_dimensions: The custom properties of the activity.
:type custom_dimensions: dict
:return:
"""
if not custom_dimensions:
custom_dimensions = {}
def monitor(f):
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
from promptflow._sdk._telemetry.telemetry import get_telemetry_logger
from promptflow._utils.version_hint_utils import HINT_ACTIVITY_NAME, check_latest_version, hint_for_update
logger = get_telemetry_logger()
custom_dimensions.update(extract_telemetry_info(self))
# update activity name according to kwargs.
_activity_name = update_activity_name(activity_name, kwargs=kwargs)
with log_activity(logger, _activity_name, activity_type, custom_dimensions):
if _activity_name in HINT_ACTIVITY_NAME:
hint_for_update()
# set check_latest_version as deamon thread to avoid blocking main thread
thread = threading.Thread(target=check_latest_version, daemon=True)
thread.start()
return f(self, *args, **kwargs)
return wrapper
return monitor
| promptflow/src/promptflow/promptflow/_sdk/_telemetry/activity.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_telemetry/activity.py",
"repo_id": "promptflow",
"token_count": 3256
} | 13 |
Exported Dockerfile & its dependencies are located in the same folder. The structure is as below:
- flow: the folder contains all the flow files
- ...
- connections: the folder contains yaml files to create all related connections
- ...
- Dockerfile: the dockerfile to build the image
- settings.json: a json file to store the settings of the docker image
- README.md: the readme file to describe how to use the dockerfile
Please refer to [official doc](https://microsoft.github.io/promptflow/how-to-guides/deploy-and-export-a-flow.html#export-a-flow)
for more details about how to use the exported dockerfile and scripts.
| promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/README.md/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/README.md",
"repo_id": "promptflow",
"token_count": 165
} | 14 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import datetime
import json
import shutil
import uuid
from os import PathLike
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from marshmallow import Schema
from promptflow._sdk._constants import (
BASE_PATH_CONTEXT_KEY,
PARAMS_OVERRIDE_KEY,
PROMPT_FLOW_DIR_NAME,
PROMPT_FLOW_EXP_DIR_NAME,
ExperimentNodeType,
ExperimentStatus,
)
from promptflow._sdk._errors import ExperimentValidationError, ExperimentValueError
from promptflow._sdk._orm.experiment import Experiment as ORMExperiment
from promptflow._sdk._utils import _merge_local_code_and_additional_includes, _sanitize_python_variable_name
from promptflow._sdk.entities import Run
from promptflow._sdk.entities._validation import MutableValidationResult, SchemaValidatableMixin
from promptflow._sdk.entities._yaml_translatable import YAMLTranslatableMixin
from promptflow._sdk.schemas._experiment import (
CommandNodeSchema,
ExperimentDataSchema,
ExperimentInputSchema,
ExperimentSchema,
ExperimentTemplateSchema,
FlowNodeSchema,
)
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.contracts.tool import ValueType
logger = get_cli_sdk_logger()
class ExperimentData(YAMLTranslatableMixin):
def __init__(self, name, path, **kwargs):
self.name = name
self.path = path
@classmethod
def _get_schema_cls(cls):
return ExperimentDataSchema
class ExperimentInput(YAMLTranslatableMixin):
def __init__(self, name, default, type, **kwargs):
self.name = name
self.type, self.default = self._resolve_type_and_default(type, default)
@classmethod
def _get_schema_cls(cls):
return ExperimentInputSchema
def _resolve_type_and_default(self, typ, default):
supported_types = [
ValueType.INT,
ValueType.STRING,
ValueType.DOUBLE,
ValueType.LIST,
ValueType.OBJECT,
ValueType.BOOL,
]
value_type: ValueType = next((i for i in supported_types if typ.lower() == i.value.lower()), None)
if value_type is None:
raise ExperimentValueError(f"Unknown experiment input type {typ!r}, supported are {supported_types}.")
return value_type.value, value_type.parse(default) if default is not None else None
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
# Override this to avoid 'type' got pop out
schema_cls = cls._get_schema_cls()
try:
loaded_data = schema_cls(context=context).load(data, **kwargs)
except Exception as e:
raise Exception(f"Load experiment input failed with {str(e)}. f{(additional_message or '')}.")
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
class FlowNode(YAMLTranslatableMixin):
def __init__(
self,
path: Union[Path, str],
name: str,
# input fields are optional since it's not stored in DB
data: Optional[str] = None,
variant: Optional[str] = None,
run: Optional[Union["Run", str]] = None,
inputs: Optional[dict] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[List[Dict[str, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
connections: Optional[Dict[str, Dict]] = None,
properties: Optional[Dict[str, Any]] = None,
**kwargs,
):
self.type = ExperimentNodeType.FLOW
self.data = data
self.inputs = inputs
self.display_name = display_name
self.description = description
self.tags = tags
self.variant = variant
self.run = run
self.environment_variables = environment_variables or {}
self.connections = connections or {}
self._properties = properties or {}
# init here to make sure those fields initialized in all branches.
self.path = path
# default run name: flow directory name + timestamp
self.name = name
self._runtime = kwargs.get("runtime", None)
self._resources = kwargs.get("resources", None)
@classmethod
def _get_schema_cls(cls):
return FlowNodeSchema
def _save_snapshot(self, target):
"""Save flow source to experiment snapshot."""
# Resolve additional includes in flow
from .._load_functions import load_flow
from .._submitter import remove_additional_includes
Path(target).mkdir(parents=True, exist_ok=True)
flow = load_flow(source=self.path)
saved_flow_path = Path(target) / self.name
with _merge_local_code_and_additional_includes(code_path=flow.code) as resolved_flow_dir:
remove_additional_includes(Path(resolved_flow_dir))
shutil.copytree(src=resolved_flow_dir, dst=saved_flow_path)
logger.debug(f"Flow source saved to {saved_flow_path}.")
self.path = saved_flow_path.resolve().absolute().as_posix()
class CommandNode(YAMLTranslatableMixin):
def __init__(
self,
command,
name,
inputs=None,
outputs=None,
runtime=None,
environment_variables=None,
code=None,
display_name=None,
**kwargs,
):
self.type = ExperimentNodeType.COMMAND
self.name = name
self.display_name = display_name
self.code = code
self.command = command
self.inputs = inputs or {}
self.outputs = outputs or {}
self.runtime = runtime
self.environment_variables = environment_variables or {}
@classmethod
def _get_schema_cls(cls):
return CommandNodeSchema
def _save_snapshot(self, target):
"""Save command source to experiment snapshot."""
Path(target).mkdir(parents=True, exist_ok=True)
saved_path = Path(target) / self.name
if not self.code:
# Create an empty folder
saved_path.mkdir(parents=True, exist_ok=True)
self.code = saved_path.resolve().absolute().as_posix()
return
code = Path(self.code)
if not code.exists():
raise ExperimentValueError(f"Command node code {code} does not exist.")
if code.is_dir():
shutil.copytree(src=self.code, dst=saved_path)
else:
saved_path.mkdir(parents=True, exist_ok=True)
shutil.copy(src=self.code, dst=saved_path)
logger.debug(f"Command node source saved to {saved_path}.")
self.code = saved_path.resolve().absolute().as_posix()
class ExperimentTemplate(YAMLTranslatableMixin, SchemaValidatableMixin):
def __init__(self, nodes, description=None, data=None, inputs=None, **kwargs):
self._base_path = kwargs.get(BASE_PATH_CONTEXT_KEY, Path("."))
self.dir_name = self._get_directory_name()
self.description = description
self.nodes = nodes
self.data = data or []
self.inputs = inputs or []
self._source_path = None
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, **kwargs):
return cls, "experiment_template"
@classmethod
def _get_schema_cls(cls):
return ExperimentTemplateSchema
@classmethod
def _load(
cls,
data: Optional[Dict] = None,
yaml_path: Optional[Union[PathLike, str]] = None,
params_override: Optional[list] = None,
**kwargs,
):
data = data or {}
params_override = params_override or []
context = {
BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
PARAMS_OVERRIDE_KEY: params_override,
}
logger.debug(f"Loading class object with data {data}, params_override {params_override}, context {context}.")
exp = cls._load_from_dict(
data=data,
context=context,
additional_message="Failed to load experiment",
**kwargs,
)
if yaml_path:
exp._source_path = yaml_path
return exp
def _get_directory_name(self) -> str:
"""Get experiment template directory name."""
try:
folder_name = Path(self._base_path).resolve().absolute().name
return folder_name
except Exception as e:
logger.debug(f"Failed to generate template name, error: {e}, use uuid.")
return str(uuid.uuid4())
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str = None, **kwargs):
schema_cls = cls._get_schema_cls()
try:
loaded_data = schema_cls(context=context).load(data, **kwargs)
except Exception as e:
raise Exception(f"Load experiment template failed with {str(e)}. f{(additional_message or '')}.")
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
@classmethod
def _create_schema_for_validation(cls, context) -> Schema:
return cls._get_schema_cls()(context=context)
def _default_context(self) -> dict:
return {BASE_PATH_CONTEXT_KEY: self._base_path}
@classmethod
def _create_validation_error(cls, message: str, no_personal_data_message: str) -> Exception:
return ExperimentValidationError(
message=message,
no_personal_data_message=no_personal_data_message,
)
def _customized_validate(self) -> MutableValidationResult:
"""Validate the resource with customized logic.
Override this method to add customized validation logic.
:return: The customized validation result
:rtype: MutableValidationResult
"""
pass
class Experiment(ExperimentTemplate):
def __init__(
self,
nodes,
name=None,
data=None,
inputs=None,
status=ExperimentStatus.NOT_STARTED,
node_runs=None,
properties=None,
**kwargs,
):
self.name = name or self._generate_name()
self.status = status
self.node_runs = node_runs or {}
self.properties = properties or {}
self.created_on = kwargs.get("created_on", datetime.datetime.now().isoformat())
self.last_start_time = kwargs.get("last_start_time", None)
self.last_end_time = kwargs.get("last_end_time", None)
self.is_archived = kwargs.get("is_archived", False)
self._output_dir = Path.home() / PROMPT_FLOW_DIR_NAME / PROMPT_FLOW_EXP_DIR_NAME / self.name
super().__init__(nodes, name=self.name, data=data, inputs=inputs, **kwargs)
@classmethod
def _get_schema_cls(cls):
return ExperimentSchema
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, **kwargs):
return cls, "experiment"
def _generate_name(self) -> str:
"""Generate a experiment name."""
try:
folder_name = Path(self._base_path).resolve().absolute().name
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
exp_name = f"{folder_name}_{timestamp}"
return _sanitize_python_variable_name(exp_name)
except Exception as e:
logger.debug(f"Failed to generate experiment name, error: {e}, use uuid.")
return str(uuid.uuid4())
def _save_snapshot_and_update_node(
self,
):
"""Save node source to experiment snapshot, update node path."""
snapshot_dir = self._output_dir / "snapshots"
for node in self.nodes:
node._save_snapshot(snapshot_dir)
def _append_node_run(self, node_name, run: Run):
"""Append node run to experiment."""
if node_name not in self.node_runs or not isinstance(self.node_runs[node_name], list):
self.node_runs[node_name] = []
# TODO: Review this
self.node_runs[node_name].append({"name": run.name, "status": run.status})
def _to_orm_object(self):
"""Convert to ORM object."""
result = ORMExperiment(
name=self.name,
description=self.description,
status=self.status,
created_on=self.created_on,
archived=self.is_archived,
last_start_time=self.last_start_time,
last_end_time=self.last_end_time,
properties=json.dumps(self.properties),
data=json.dumps([item._to_dict() for item in self.data]),
inputs=json.dumps([input._to_dict() for input in self.inputs]),
nodes=json.dumps([node._to_dict() for node in self.nodes]),
node_runs=json.dumps(self.node_runs),
)
logger.debug(f"Experiment to ORM object: {result.__dict__}")
return result
@classmethod
def _from_orm_object(cls, obj: ORMExperiment) -> "Experiment":
"""Create a experiment object from ORM object."""
nodes = []
context = {BASE_PATH_CONTEXT_KEY: "./"}
for node_dict in json.loads(obj.nodes):
if node_dict["type"] == ExperimentNodeType.FLOW:
nodes.append(
FlowNode._load_from_dict(node_dict, context=context, additional_message="Failed to load node.")
)
elif node_dict["type"] == ExperimentNodeType.COMMAND:
nodes.append(
CommandNode._load_from_dict(node_dict, context=context, additional_message="Failed to load node.")
)
else:
raise Exception(f"Unknown node type {node_dict['type']}")
data = [
ExperimentData._load_from_dict(item, context=context, additional_message="Failed to load experiment data")
for item in json.loads(obj.data)
]
inputs = [
ExperimentInput._load_from_dict(
item, context=context, additional_message="Failed to load experiment inputs"
)
for item in json.loads(obj.inputs)
]
return cls(
name=obj.name,
description=obj.description,
status=obj.status,
created_on=obj.created_on,
last_start_time=obj.last_start_time,
last_end_time=obj.last_end_time,
is_archived=obj.archived,
properties=json.loads(obj.properties),
data=data,
inputs=inputs,
nodes=nodes,
node_runs=json.loads(obj.node_runs),
)
@classmethod
def from_template(cls, template: ExperimentTemplate, name=None):
"""Create a experiment object from template."""
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
exp_name = name or f"{template.dir_name}_{timestamp}"
experiment = cls(
name=exp_name,
description=template.description,
data=copy.deepcopy(template.data),
inputs=copy.deepcopy(template.inputs),
nodes=copy.deepcopy(template.nodes),
base_path=template._base_path,
)
logger.debug("Start saving snapshot and update node.")
experiment._save_snapshot_and_update_node()
return experiment
| promptflow/src/promptflow/promptflow/_sdk/entities/_experiment.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/entities/_experiment.py",
"repo_id": "promptflow",
"token_count": 6671
} | 15 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import os.path
import sys
import time
from dataclasses import asdict
from typing import Any, Dict, List, Optional, Union
from promptflow._constants import LANGUAGE_KEY, AvailableIDE, FlowLanguage
from promptflow._sdk._constants import (
MAX_RUN_LIST_RESULTS,
MAX_SHOW_DETAILS_RESULTS,
FlowRunProperties,
ListViewType,
RunInfoSources,
RunStatus,
)
from promptflow._sdk._errors import InvalidRunStatusError, RunExistsError, RunNotFoundError, RunOperationParameterError
from promptflow._sdk._orm import RunInfo as ORMRun
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import incremental_print, print_red_error, safe_parse_object_list
from promptflow._sdk._visualize_functions import dump_html, generate_html_string
from promptflow._sdk.entities import Run
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import load_yaml_string
from promptflow.contracts._run_management import RunDetail, RunMetadata, RunVisualization, VisualizationConfig
from promptflow.exceptions import UserErrorException
RUNNING_STATUSES = RunStatus.get_running_statuses()
logger = get_cli_sdk_logger()
class RunOperations(TelemetryMixin):
"""RunOperations."""
def __init__(self, client, **kwargs):
super().__init__(**kwargs)
self._client = client
@monitor_operation(activity_name="pf.runs.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: Optional[int] = MAX_RUN_LIST_RESULTS,
*,
list_view_type: ListViewType = ListViewType.ACTIVE_ONLY,
) -> List[Run]:
"""List runs.
:param max_results: Max number of results to return. Default: MAX_RUN_LIST_RESULTS.
:type max_results: Optional[int]
:param list_view_type: View type for including/excluding (for example) archived runs. Default: ACTIVE_ONLY.
:type include_archived: Optional[ListViewType]
:return: List of run objects.
:rtype: List[~promptflow.entities.Run]
"""
orm_runs = ORMRun.list(max_results=max_results, list_view_type=list_view_type)
return safe_parse_object_list(
obj_list=orm_runs,
parser=Run._from_orm_object,
message_generator=lambda x: f"Error parsing run {x.name!r}, skipped.",
)
@monitor_operation(activity_name="pf.runs.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str) -> Run:
"""Get a run entity.
:param name: Name of the run.
:type name: str
:return: run object retrieved from the database.
:rtype: ~promptflow.entities.Run
"""
return self._get(name)
def _get(self, name: str) -> Run:
name = Run._validate_and_return_run_name(name)
try:
return Run._from_orm_object(ORMRun.get(name))
except RunNotFoundError as e:
raise e
@monitor_operation(activity_name="pf.runs.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, run: Run, **kwargs) -> Run:
"""Create or update a run.
:param run: Run object to create or update.
:type run: ~promptflow.entities.Run
:return: Run object created or updated.
:rtype: ~promptflow.entities.Run
"""
# create run from an existing run folder
if run._run_source == RunInfoSources.EXISTING_RUN:
return self._create_run_from_existing_run_folder(run=run, **kwargs)
# TODO: change to async
stream = kwargs.pop("stream", False)
try:
from promptflow._sdk._submitter import RunSubmitter
created_run = RunSubmitter(client=self._client).submit(run=run, **kwargs)
if stream:
self.stream(created_run)
return created_run
except RunExistsError:
raise RunExistsError(f"Run {run.name!r} already exists.")
def _create_run_from_existing_run_folder(self, run: Run, **kwargs) -> Run:
"""Create run from existing run folder."""
try:
self.get(run.name)
except RunNotFoundError:
pass
else:
raise RunExistsError(f"Run {run.name!r} already exists.")
try:
run._dump()
return run
except Exception as e:
raise UserErrorException(
f"Failed to create run {run.name!r} from existing run folder {run.source!r}: {str(e)}"
) from e
def _print_run_summary(self, run: Run) -> None:
print("======= Run Summary =======\n")
duration = str(run._end_time - run._created_on)
print(
f'Run name: "{run.name}"\n'
f'Run status: "{run.status}"\n'
f'Start time: "{run._created_on}"\n'
f'Duration: "{duration}"\n'
f'Output path: "{run._output_path}"\n'
)
@monitor_operation(activity_name="pf.runs.stream", activity_type=ActivityType.PUBLICAPI)
def stream(self, name: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream run logs to the console.
:param name: Name of the run, or run object.
:type name: Union[str, ~promptflow.sdk.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: Run object.
:rtype: ~promptflow.entities.Run
"""
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
local_storage = LocalStorageOperations(run=run)
file_handler = sys.stdout
try:
printed = 0
run = self.get(run.name)
while run.status in RUNNING_STATUSES or run.status == RunStatus.FINALIZING:
file_handler.flush()
available_logs = local_storage.logger.get_logs()
printed = incremental_print(available_logs, printed, file_handler)
time.sleep(10)
run = self.get(run.name)
# ensure all logs are printed
file_handler.flush()
available_logs = local_storage.logger.get_logs()
incremental_print(available_logs, printed, file_handler)
self._print_run_summary(run)
except KeyboardInterrupt:
error_message = "The output streaming for the run was interrupted, but the run is still executing."
print(error_message)
if run.status == RunStatus.FAILED or run.status == RunStatus.CANCELED:
if run.status == RunStatus.FAILED:
error_message = local_storage.load_exception().get("message", "Run fails with unknown error.")
else:
error_message = "Run is canceled."
if raise_on_error:
raise InvalidRunStatusError(error_message)
else:
print_red_error(error_message)
return run
@monitor_operation(activity_name="pf.runs.archive", activity_type=ActivityType.PUBLICAPI)
def archive(self, name: Union[str, Run]) -> Run:
"""Archive a run.
:param name: Name of the run.
:type name: str
:return: archived run object.
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
ORMRun.get(name).archive()
return self.get(name)
@monitor_operation(activity_name="pf.runs.restore", activity_type=ActivityType.PUBLICAPI)
def restore(self, name: Union[str, Run]) -> Run:
"""Restore a run.
:param name: Name of the run.
:type name: str
:return: restored run object.
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
ORMRun.get(name).restore()
return self.get(name)
@monitor_operation(activity_name="pf.runs.update", activity_type=ActivityType.PUBLICAPI)
def update(
self,
name: Union[str, Run],
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
**kwargs,
) -> Run:
"""Update run status.
:param name: run name
:param display_name: display name to update
:param description: description to update
:param tags: tags to update
:param kwargs: other fields to update, fields not supported will be directly dropped.
:return: updated run object
:rtype: ~promptflow._sdk.entities._run.Run
"""
name = Run._validate_and_return_run_name(name)
# the kwargs is to support update run status scenario but keep it private
ORMRun.get(name).update(display_name=display_name, description=description, tags=tags, **kwargs)
return self.get(name)
@monitor_operation(activity_name="pf.runs.delete", activity_type=ActivityType.PUBLICAPI)
def delete(
self,
name: str,
) -> None:
"""Delete run permanently.
Caution: This operation will delete the run permanently from your local disk.
Both run entity and output data will be deleted.
:param name: run name to delete
:return: None
"""
valid_run = self.get(name)
LocalStorageOperations(valid_run).delete()
ORMRun.delete(name)
@monitor_operation(activity_name="pf.runs.get_details", activity_type=ActivityType.PUBLICAPI)
def get_details(
self, name: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False
) -> "DataFrame":
"""Get the details from the run.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param name: The run name or run object
:type name: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
from pandas import DataFrame
# if all_results is True, set max_results to sys.maxsize
if all_results:
max_results = sys.maxsize
if not isinstance(max_results, int) or max_results < 1:
raise RunOperationParameterError(f"'max_results' must be a positive integer, got {max_results!r}")
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
local_storage = LocalStorageOperations(run=run)
inputs, outputs = local_storage.load_inputs_and_outputs()
inputs = inputs.to_dict("list")
outputs = outputs.to_dict("list")
data = {}
columns = []
for k in inputs:
new_k = f"inputs.{k}"
data[new_k] = copy.deepcopy(inputs[k])
columns.append(new_k)
for k in outputs:
new_k = f"outputs.{k}"
data[new_k] = copy.deepcopy(outputs[k])
columns.append(new_k)
df = DataFrame(data).head(max_results).reindex(columns=columns)
return df
@monitor_operation(activity_name="pf.runs.get_metrics", activity_type=ActivityType.PUBLICAPI)
def get_metrics(self, name: Union[str, Run]) -> Dict[str, Any]:
"""Get run metrics.
:param name: name of the run.
:type name: str
:return: Run metrics.
:rtype: Dict[str, Any]
"""
name = Run._validate_and_return_run_name(name)
run = self.get(name=name)
run._check_run_status_is_completed()
local_storage = LocalStorageOperations(run=run)
return local_storage.load_metrics()
def _visualize(self, runs: List[Run], html_path: Optional[str] = None) -> None:
details: List[RunDetail] = []
metadatas: List[RunMetadata] = []
configs: List[VisualizationConfig] = []
for run in runs:
# check run status first
# if run status is not compeleted, there might be unexpected error during parse data
# so we directly raise error if there is any incomplete run
run._check_run_status_is_completed()
local_storage = LocalStorageOperations(run)
# nan, inf and -inf are not JSON serializable, which will lead to JavaScript parse error
# so specify `parse_const_as_str` as True to parse them as string
detail = local_storage.load_detail(parse_const_as_str=True)
# ad-hoc step: make logs field empty to avoid too big HTML file
# we don't provide logs view in visualization page for now
# when we enable, we will save those big data (e.g. logs) in separate file(s)
# JS load can be faster than static HTML
for i in range(len(detail["node_runs"])):
detail["node_runs"][i]["logs"] = {"stdout": "", "stderr": ""}
metadata = RunMetadata(
name=run.name,
display_name=run.display_name,
create_time=run.created_on,
flow_path=run.properties.get(FlowRunProperties.FLOW_PATH, None),
output_path=run.properties[FlowRunProperties.OUTPUT_PATH],
tags=run.tags,
lineage=run.run,
metrics=local_storage.load_metrics(parse_const_as_str=True),
dag=local_storage.load_dag_as_string(),
flow_tools_json=local_storage.load_flow_tools_json(),
mode="eager" if local_storage.eager_mode else "",
)
details.append(copy.deepcopy(detail))
metadatas.append(asdict(metadata))
# TODO: add language to run metadata
flow_dag = load_yaml_string(metadata.dag) or {}
configs.append(
VisualizationConfig(
[AvailableIDE.VS_CODE]
if flow_dag.get(LANGUAGE_KEY, FlowLanguage.Python) == FlowLanguage.Python
else [AvailableIDE.VS]
)
)
data_for_visualize = RunVisualization(
detail=details,
metadata=metadatas,
config=configs,
)
html_string = generate_html_string(asdict(data_for_visualize))
# if html_path is specified, not open it in webbrowser(as it comes from VSC)
dump_html(html_string, html_path=html_path, open_html=html_path is None)
@monitor_operation(activity_name="pf.runs.visualize", activity_type=ActivityType.PUBLICAPI)
def visualize(self, runs: Union[str, Run, List[str], List[Run]], **kwargs) -> None:
"""Visualize run(s).
:param runs: List of run objects, or names of the runs.
:type runs: Union[str, ~promptflow.sdk.entities.Run, List[str], List[~promptflow.sdk.entities.Run]]
"""
if not isinstance(runs, list):
runs = [runs]
validated_runs = []
for run in runs:
run_name = Run._validate_and_return_run_name(run)
validated_runs.append(self.get(name=run_name))
html_path = kwargs.pop("html_path", None)
try:
self._visualize(validated_runs, html_path=html_path)
except InvalidRunStatusError as e:
error_message = f"Cannot visualize non-completed run. {str(e)}"
logger.error(error_message)
def _get_outputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]:
"""Get the outputs of the run, load from local storage."""
local_storage = self._get_local_storage(run)
return local_storage.load_outputs()
def _get_inputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]:
"""Get the outputs of the run, load from local storage."""
local_storage = self._get_local_storage(run)
return local_storage.load_inputs()
def _get_outputs_path(self, run: Union[str, Run]) -> str:
"""Get the outputs file path of the run."""
local_storage = self._get_local_storage(run)
return local_storage._outputs_path if local_storage.load_outputs() else None
def _get_data_path(self, run: Union[str, Run]) -> str:
"""Get the outputs file path of the run."""
local_storage = self._get_local_storage(run)
# TODO: what if the data is deleted?
if local_storage._data_path and not os.path.exists(local_storage._data_path):
raise UserErrorException(
f"Data path {local_storage._data_path} for run {run.name} does not exist. "
"Please make sure it exists and not deleted."
)
return local_storage._data_path
def _get_local_storage(self, run: Union[str, Run]) -> LocalStorageOperations:
"""Get the local storage of the run."""
if isinstance(run, str):
run = self.get(name=run)
return LocalStorageOperations(run)
| promptflow/src/promptflow/promptflow/_sdk/operations/_run_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/operations/_run_operations.py",
"repo_id": "promptflow",
"token_count": 7472
} | 16 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""!!!Note: context in this file only used for command line related logics,
please avoid using them in service code!!!"""
import contextlib
import os
import sys
@contextlib.contextmanager
def _change_working_dir(path, mkdir=True):
"""Context manager for changing the current working directory"""
saved_path = os.getcwd()
if mkdir:
os.makedirs(path, exist_ok=True)
os.chdir(str(path))
try:
yield
finally:
os.chdir(saved_path)
@contextlib.contextmanager
def inject_sys_path(path):
original_sys_path = sys.path.copy()
sys.path.insert(0, str(path))
try:
yield
finally:
sys.path = original_sys_path
| promptflow/src/promptflow/promptflow/_utils/context_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/context_utils.py",
"repo_id": "promptflow",
"token_count": 285
} | 17 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import importlib
import inspect
import logging
import re
from enum import Enum, EnumMeta
from typing import Any, Callable, Dict, List, Union, get_args, get_origin
from jinja2 import Environment, meta
from promptflow._core._errors import DuplicateToolMappingError
from promptflow._utils.utils import is_json_serializable
from promptflow.exceptions import ErrorTarget, UserErrorException
from ..contracts.tool import ConnectionType, InputDefinition, Tool, ToolFuncCallScenario, ToolType, ValueType
from ..contracts.types import PromptTemplate
module_logger = logging.getLogger(__name__)
_DEPRECATED_TOOLS = "deprecated_tools"
UI_HINTS = "ui_hints"
def value_to_str(val):
if val is inspect.Parameter.empty:
# For empty case, default field will be skipped when dumping to json
return None
if val is None:
# Dump default: "" in json to avoid UI validation error
return ""
if isinstance(val, Enum):
return val.value
return str(val)
def resolve_annotation(anno) -> Union[str, list]:
"""Resolve the union annotation to type list."""
origin = get_origin(anno)
if origin != Union:
return anno
# Optional[Type] is Union[Type, NoneType], filter NoneType out
args = [arg for arg in get_args(anno) if arg != type(None)] # noqa: E721
return args[0] if len(args) == 1 else args
def param_to_definition(param, gen_custom_type_conn=False) -> (InputDefinition, bool):
default_value = param.default
# Get value type and enum from annotation
value_type = resolve_annotation(param.annotation)
enum = None
custom_type_conn = None
# Get value type and enum from default if no annotation
if default_value is not inspect.Parameter.empty and value_type == inspect.Parameter.empty:
value_type = default_value.__class__ if isinstance(default_value, Enum) else type(default_value)
# Extract enum for enum class
if isinstance(value_type, EnumMeta):
enum = [str(option.value) for option in value_type]
value_type = str
is_connection = False
if ConnectionType.is_connection_value(value_type):
if ConnectionType.is_custom_strong_type(value_type):
typ = ["CustomConnection"]
custom_type_conn = [value_type.__name__]
else:
typ = [value_type.__name__]
is_connection = True
elif isinstance(value_type, list):
if not all(ConnectionType.is_connection_value(t) for t in value_type):
typ = [ValueType.OBJECT]
else:
custom_connection_added = False
typ = []
custom_type_conn = []
for t in value_type:
# Add 'CustomConnection' to typ list when custom strong type connection exists. Collect all custom types
if ConnectionType.is_custom_strong_type(t):
if not custom_connection_added:
custom_connection_added = True
typ.append("CustomConnection")
custom_type_conn.append(t.__name__)
else:
if t.__name__ != "CustomConnection":
typ.append(t.__name__)
elif not custom_connection_added:
custom_connection_added = True
typ.append(t.__name__)
is_connection = True
else:
typ = [ValueType.from_type(value_type)]
# 1. Do not generate custom type when generating flow.tools.json for script tool.
# Extension would show custom type if it exists. While for script tool with custom strong type connection,
# we still want to show 'CustomConnection' type.
# 2. Generate custom connection type when resolving tool in _tool_resolver, since we rely on it to convert the
# custom connection to custom strong type connection.
if not gen_custom_type_conn:
custom_type_conn = None
return (
InputDefinition(
type=typ,
default=value_to_str(default_value),
description=None,
enum=enum,
custom_type=custom_type_conn,
),
is_connection,
)
def function_to_interface(
f: Callable, initialize_inputs=None, gen_custom_type_conn=False, skip_prompt_template=False
) -> tuple:
sign = inspect.signature(f)
all_inputs = {}
input_defs = {}
connection_types = []
# Collect all inputs from class and func
if initialize_inputs:
if any(k for k in initialize_inputs if k in sign.parameters):
raise Exception(f'Duplicate inputs found from {f.__name__!r} and "__init__()"!')
all_inputs = {**initialize_inputs}
enable_kwargs = any([param.kind == inspect.Parameter.VAR_KEYWORD for _, param in sign.parameters.items()])
all_inputs.update(
{
k: v
for k, v in sign.parameters.items()
if k != "self" and v.kind != v.VAR_KEYWORD and v.kind != v.VAR_POSITIONAL # TODO: Handle these cases
}
)
# Resolve inputs to definitions.
for k, v in all_inputs.items():
# Get value type from annotation
value_type = resolve_annotation(v.annotation)
if skip_prompt_template and value_type is PromptTemplate:
# custom llm tool has prompt template as input, skip it
continue
input_def, is_connection = param_to_definition(v, gen_custom_type_conn=gen_custom_type_conn)
input_defs[k] = input_def
if is_connection:
connection_types.append(input_def.type)
outputs = {}
# Note: We don't have output definition now
return input_defs, outputs, connection_types, enable_kwargs
def function_to_tool_definition(f: Callable, type=None, initialize_inputs=None) -> Tool:
"""Translate a function to tool definition.
:param f: Function to be translated.
:param type: Tool type
:param initialize_inputs: The initialize() func inputs get by get_initialize_inputs() when function
defined in class. We will merge those inputs with f() inputs.
:return: The tool definition.
"""
if hasattr(f, "__original_function"):
f = f.__original_function
inputs, outputs, _, _ = function_to_interface(f, initialize_inputs)
# Hack to get class name
class_name = None
if "." in f.__qualname__:
class_name = f.__qualname__.replace(f".{f.__name__}", "")
meta_dict = {
"name": f.__qualname__,
"description": inspect.getdoc(f) or None,
"inputs": inputs,
"outputs": outputs,
"class_name": class_name,
"function": f.__name__,
}
return Tool(type=type, module=f.__module__, **meta_dict, is_builtin=True, stage="test")
def get_inputs_for_prompt_template(template_str):
"""Get all input variable names and definitions from a jinja2 template string.
: param template_str: template string
: type t: str
: return: the input name to InputDefinition dict
: rtype t: Dict[str, ~promptflow.contracts.tool.InputDefinition]
Example:
>>> get_inputs_for_prompt_template(
template_str="A simple prompt with no variables"
)
{}
>>> get_inputs_for_prompt_template(
template_str="Prompt with only one string input {{str_input}}"
)
{"str_input": InputDefinition(type=[ValueType.STRING])}
>>> get_inputs_for_prompt_template(
template_str="Prompt with image input  and string input {{str_input}}"
)
{"image_input": InputDefinition(type=[ValueType.IMAGE]), "str_input": InputDefinition(type=[ValueType.STRING])
"""
env = Environment()
template = env.parse(template_str)
inputs = sorted(meta.find_undeclared_variables(template), key=lambda x: template_str.find(x))
result_dict = {i: InputDefinition(type=[ValueType.STRING]) for i in inputs}
# currently we only support image type
pattern = r"\!\[(\s*image\s*)\]\(\{\{\s*([^{}]+)\s*\}\}\)"
matches = re.finditer(pattern, template_str)
for match in matches:
input_name = match.group(2).strip()
result_dict[input_name] = InputDefinition([ValueType(match.group(1).strip())])
return result_dict
def get_prompt_param_name_from_func(f):
"""Get the param name of prompt template on provider."""
return next((k for k, annotation in f.__annotations__.items() if annotation == PromptTemplate), None)
def validate_dynamic_list_func_response_type(response: Any, f: str):
"""Verify response type is correct.
The response is a list of items. Each item is a dict with the following keys:
- value: for backend use. Required.
- display_value: for UI display. Optional.
- hyperlink: external link. Optional.
- description: information icon tip. Optional.
The response can not be None.
"""
if response is None:
raise ListFunctionResponseError(f"{f} response can not be None.")
if not isinstance(response, List):
raise ListFunctionResponseError(f"{f} response must be a list.")
for item in response:
if not isinstance(item, Dict):
raise ListFunctionResponseError(f"{f} response must be a list of dict. {item} is not a dict.")
if "value" not in item:
raise ListFunctionResponseError(f"{f} response dict must have 'value' key.")
for key, value in item.items():
if not isinstance(key, str):
raise ListFunctionResponseError(f"{f} response dict key must be a string. {key} is not a string.")
if not is_json_serializable(value):
raise ListFunctionResponseError(f"{f} response dict value {value} is not json serializable.")
if not isinstance(value, (str, int, float, list, Dict)):
raise ListFunctionResponseError(
f"{f} response dict value must be a string, int, float, list or dict. {value} is not supported."
)
def validate_tool_func_result(func_call_scenario: str, result):
if func_call_scenario == ToolFuncCallScenario.REVERSE_GENERATED_BY:
if not isinstance(result, Dict):
raise RetrieveToolFuncResultValidationError(
f"ToolFuncCallScenario {func_call_scenario} response must be a dict. " f"{result} is not a dict."
)
elif func_call_scenario == ToolFuncCallScenario.DYNAMIC_LIST:
validate_dynamic_list_func_response_type(result, f"ToolFuncCallScenario {func_call_scenario}")
def append_workspace_triple_to_func_input_params(
func_sig_params: Dict, func_input_params_dict: Dict, ws_triple_dict: Dict[str, str]
):
"""Append workspace triple to func input params.
:param func_sig_params: function signature parameters, full params.
:param func_input_params_dict: user input param key-values for dynamic list function.
:param ws_triple_dict: workspace triple dict, including subscription_id, resource_group_name, workspace_name.
:return: combined func input params.
"""
# append workspace triple to func input params if any below condition are met:
# 1. func signature has kwargs param.
# 2. func signature has param named 'subscription_id','resource_group_name','workspace_name'.
ws_triple_dict = ws_triple_dict if ws_triple_dict is not None else {}
func_input_params_dict = func_input_params_dict if func_input_params_dict is not None else {}
has_kwargs_param = any([param.kind == inspect.Parameter.VAR_KEYWORD for _, param in func_sig_params.items()])
if has_kwargs_param is False:
# keep only params that are in func signature. Or run into error when calling func.
avail_ws_info_dict = {k: v for k, v in ws_triple_dict.items() if k in set(func_sig_params.keys())}
else:
avail_ws_info_dict = ws_triple_dict
# if ws triple key is in func input params, it means user has provided value for it,
# do not expect implicit override.
combined_func_input_params = dict(avail_ws_info_dict, **func_input_params_dict)
return combined_func_input_params
def load_function_from_function_path(func_path: str):
"""Load a function from a function path.
The function path should be in the format of "module_name.function_name".
"""
try:
module_name, func_name = func_path.rsplit(".", 1)
module = importlib.import_module(module_name)
f = getattr(module, func_name)
if callable(f):
return f
else:
raise FunctionPathValidationError(f"'{f}' is not callable.")
except Exception as e:
raise FunctionPathValidationError(
f"Failed to parse function from function path: '{func_path}'. Expected format: format 'my_module.my_func'. "
f"Detailed error: {e}"
)
def assign_tool_input_index_for_ux_order_if_needed(tool):
"""
Automatically adds an index to the inputs of a tool based on their order in the tool's YAML.
This function directly modifies the tool without returning any value.
Example:
- tool (dict): A dictionary representing a tool configuration. Inputs do not contain 'ui_hints':
{
'name': 'My Custom LLM Tool',
'type': 'custom_llm',
'inputs':
{
'input1': {'type': 'string'},
'input2': {'type': 'string'},
'input3': {'type': 'string'}
}
}
>>> assign_tool_input_index_for_ux_order_if_needed(tool)
- tool (dict): Tool inputs are modified to include 'ui_hints' with an 'index', indicating the order.
{
'name': 'My Custom LLM Tool',
'type': 'custom_llm',
'inputs':
{
'input1': {'type': 'string', 'ui_hints': {'index': 0}},
'input2': {'type': 'string', 'ui_hints': {'index': 1}},
'input3': {'type': 'string', 'ui_hints': {'index': 2}}
}
}
"""
tool_type = tool.get("type")
if should_preserve_tool_inputs_order(tool_type) and "inputs" in tool:
inputs_dict = tool["inputs"]
input_index = 0
# The keys can keep order because the tool YAML is loaded by ruamel.yaml and
# ruamel.yaml has the feature of preserving the order of keys.
# For more information on ruamel.yaml's feature, please
# visit https://yaml.readthedocs.io/en/latest/overview/#overview.
for input_name, settings in inputs_dict.items():
# 'uionly_hidden' indicates that the inputs are not the tool's inputs.
# They are not displayed on the main interface but appear in a popup window.
# These inputs are passed to UX as a list, maintaining the same order as generated by func parameters.
# Skip the 'uionly_hidden' input type because the 'ui_hints: index' is not needed.
if "input_type" in settings.keys() and settings["input_type"] == "uionly_hidden":
continue
settings.setdefault(UI_HINTS, {})
settings[UI_HINTS]["index"] = input_index
input_index += 1
def should_preserve_tool_inputs_order(tool_type):
"""
Currently, we only automatically add input indexes for the custom_llm tool,
following the order specified in the tool interface or YAML.
As of now, only the custom_llm tool requires the order of its inputs displayed on the UI
to be consistent with the order in the YAML, because its inputs are shown in parameter style.
To avoid extensive changes, other types of tools will remain as they are.
"""
return tool_type == ToolType.CUSTOM_LLM
# Handling backward compatibility and generating a mapping between the previous and new tool IDs.
def _find_deprecated_tools(package_tools) -> Dict[str, str]:
_deprecated_tools = {}
for tool_id, tool in package_tools.items():
# a list of old tool IDs that are mapped to the current tool ID.
if tool and _DEPRECATED_TOOLS in tool:
for old_tool_id in tool[_DEPRECATED_TOOLS]:
# throw error to prompt user for manual resolution of this conflict, ensuring secure operation.
if old_tool_id in _deprecated_tools:
raise DuplicateToolMappingError(
message_format=(
"The tools '{first_tool_id}', '{second_tool_id}' are both linked to the deprecated "
"tool ID '{deprecated_tool_id}'. To ensure secure operation, please either "
"remove or adjust one of these tools in your environment and fix this conflict."
),
first_tool_id=_deprecated_tools[old_tool_id],
second_tool_id=tool_id,
deprecated_tool_id=old_tool_id,
target=ErrorTarget.TOOL,
)
_deprecated_tools[old_tool_id] = tool_id
return _deprecated_tools
def _get_function_path(function):
# Validate function exist
if isinstance(function, str):
module_name, func_name = function.rsplit(".", 1)
module = importlib.import_module(module_name)
func = getattr(module, func_name)
func_path = function
elif isinstance(function, Callable):
func = function
func_path = f"{function.__module__}.{function.__name__}"
else:
raise UserErrorException("Function has invalid type, please provide callable or function name for function.")
return func, func_path
class RetrieveToolFuncResultError(UserErrorException):
"""Base exception raised for retreive tool func result errors."""
def __init__(self, message):
msg = (
f"Unable to retreive tool func result due to '{message}'. \nPlease contact the tool author/support team "
f"for troubleshooting assistance."
)
super().__init__(msg, target=ErrorTarget.FUNCTION_PATH)
class RetrieveToolFuncResultValidationError(RetrieveToolFuncResultError):
pass
class DynamicListError(UserErrorException):
"""Base exception raised for dynamic list errors."""
def __init__(self, message):
msg = (
f"Unable to display list of items due to '{message}'. \nPlease contact the tool author/support team "
f"for troubleshooting assistance."
)
super().__init__(msg, target=ErrorTarget.FUNCTION_PATH)
class ListFunctionResponseError(DynamicListError):
pass
class FunctionPathValidationError(DynamicListError):
pass
| promptflow/src/promptflow/promptflow/_utils/tool_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/tool_utils.py",
"repo_id": "promptflow",
"token_count": 7341
} | 18 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._flow_runtimes_workspace_independent_operations import build_get_runtime_latest_config_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowRuntimesWorkspaceIndependentOperations:
"""FlowRuntimesWorkspaceIndependentOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def get_runtime_latest_config(
self,
**kwargs: Any
) -> "_models.RuntimeConfiguration":
"""get_runtime_latest_config.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/runtimes/latestConfig'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_workspace_independent_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_workspace_independent_operations.py",
"repo_id": "promptflow",
"token_count": 1237
} | 19 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_update_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_ci_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
compute_instance_name = kwargs.pop('compute_instance_name') # type: str
custom_app_name = kwargs.pop('custom_app_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['computeInstanceName'] = _SERIALIZER.query("compute_instance_name", compute_instance_name, 'str')
query_parameters['customAppName'] = _SERIALIZER.query("custom_app_name", custom_app_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_mir_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
endpoint_name = kwargs.pop('endpoint_name') # type: str
deployment_name = kwargs.pop('deployment_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
query_parameters['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str')
query_parameters['deploymentName'] = _SERIALIZER.query("deployment_name", deployment_name, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_runtime_upgrade_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_runtime_capability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_runtime_latest_config_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_runtimes_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class FlowRuntimesOperations(object):
"""FlowRuntimesOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.CreateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""create_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.CreateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateFlowRuntimeRequest')
else:
_json = None
request = build_create_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.create_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def update_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.UpdateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""update_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.UpdateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'UpdateFlowRuntimeRequest')
else:
_json = None
request = build_update_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.update_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def get_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""get_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def delete_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""delete_runtime.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
async_call=async_call,
msi_token=msi_token,
template_url=self.delete_runtime.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def check_ci_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
compute_instance_name, # type: str
custom_app_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_ci_availability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param compute_instance_name:
:type compute_instance_name: str
:param custom_app_name:
:type custom_app_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_ci_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
compute_instance_name=compute_instance_name,
custom_app_name=custom_app_name,
template_url=self.check_ci_availability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_ci_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability'} # type: ignore
@distributed_trace
def check_mir_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
endpoint_name, # type: str
deployment_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_mir_availability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param endpoint_name:
:type endpoint_name: str
:param deployment_name:
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_mir_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
endpoint_name=endpoint_name,
deployment_name=deployment_name,
template_url=self.check_mir_availability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_mir_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability'} # type: ignore
@distributed_trace
def check_runtime_upgrade(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""check_runtime_upgrade.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[bool]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_runtime_upgrade_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.check_runtime_upgrade.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('bool', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_runtime_upgrade.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade'} # type: ignore
@distributed_trace
def get_runtime_capability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeCapability"
"""get_runtime_capability.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeCapability, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeCapability
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeCapability"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_capability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime_capability.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('FlowRuntimeCapability', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_capability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability'} # type: ignore
@distributed_trace
def get_runtime_latest_config(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.RuntimeConfiguration"
"""get_runtime_latest_config.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_runtime_latest_config.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig'} # type: ignore
@distributed_trace
def list_runtimes(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.FlowRuntimeDto"]
"""list_runtimes.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of FlowRuntimeDto, or the result of cls(response)
:rtype: list[~flow.models.FlowRuntimeDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowRuntimeDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_runtimes_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_runtimes.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[FlowRuntimeDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_runtimes.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_operations.py",
"repo_id": "promptflow",
"token_count": 17964
} | 20 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import jwt
from promptflow.exceptions import ValidationException
def is_arm_id(obj) -> bool:
return isinstance(obj, str) and obj.startswith("azureml://")
def get_token(credential, resource) -> str:
from azure.ai.ml._azure_environments import _resource_to_scopes
azure_ml_scopes = _resource_to_scopes(resource)
token = credential.get_token(*azure_ml_scopes).token
# validate token has aml audience
decoded_token = jwt.decode(
token,
options={"verify_signature": False, "verify_aud": False},
)
if decoded_token.get("aud") != resource:
msg = """AAD token with aml scope could not be fetched using the credentials being used.
Please validate if token with {0} scope can be fetched using credentials provided to PFClient.
Token with {0} scope can be fetched using credentials.get_token({0})
"""
raise ValidationException(
message=msg.format(*azure_ml_scopes),
)
return token
def get_aml_token(credential) -> str:
from azure.ai.ml._azure_environments import _get_aml_resource_id_from_metadata
resource = _get_aml_resource_id_from_metadata()
return get_token(credential, resource)
def get_arm_token(credential) -> str:
from azure.ai.ml._azure_environments import _get_base_url_from_metadata
resource = _get_base_url_from_metadata()
return get_token(credential, resource)
def get_authorization(credential=None) -> str:
token = get_arm_token(credential=credential)
return "Bearer " + token
def get_user_alias_from_credential(credential):
token = get_arm_token(credential=credential)
decode_json = jwt.decode(token, options={"verify_signature": False, "verify_aud": False})
try:
email = decode_json.get("upn", decode_json.get("email", None))
return email.split("@")[0]
except Exception:
# use oid when failed to get upn, e.g. service principal
return decode_json["oid"]
| promptflow/src/promptflow/promptflow/azure/_utils/gerneral.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_utils/gerneral.py",
"repo_id": "promptflow",
"token_count": 783
} | 21 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import socket
import subprocess
import uuid
from pathlib import Path
from typing import Optional
from promptflow._core._errors import UnexpectedError
from promptflow.batch._csharp_base_executor_proxy import CSharpBaseExecutorProxy
from promptflow.storage._run_storage import AbstractRunStorage
EXECUTOR_SERVICE_DOMAIN = "http://localhost:"
EXECUTOR_SERVICE_DLL = "Promptflow.dll"
class CSharpExecutorProxy(CSharpBaseExecutorProxy):
def __init__(
self,
*,
process,
port: str,
working_dir: Optional[Path] = None,
chat_output_name: Optional[str] = None,
enable_stream_output: bool = False,
):
self._process = process
self._port = port
self._chat_output_name = chat_output_name
super().__init__(
working_dir=working_dir,
enable_stream_output=enable_stream_output,
)
@property
def api_endpoint(self) -> str:
return EXECUTOR_SERVICE_DOMAIN + self.port
@property
def port(self) -> str:
return self._port
@property
def chat_output_name(self) -> Optional[str]:
return self._chat_output_name
@classmethod
def generate_metadata(cls, flow_file: Path, assembly_folder: Path):
"""Generate metadata for the flow and save them to files under .promptflow folder.
including flow.json and flow.tools.json.
"""
command = [
"dotnet",
EXECUTOR_SERVICE_DLL,
"--flow_meta",
"--yaml_path",
flow_file.absolute().as_posix(),
"--assembly_folder",
".",
]
try:
subprocess.check_output(
command,
cwd=assembly_folder,
)
except subprocess.CalledProcessError as e:
raise UnexpectedError(
message_format=f"Failed to generate flow meta for csharp flow.\n"
f"Command: {' '.join(command)}\n"
f"Working directory: {assembly_folder.as_posix()}\n"
f"Return code: {e.returncode}\n"
f"Output: {e.output}",
)
@classmethod
def get_outputs_definition(cls, flow_file: Path, working_dir: Path) -> dict:
from promptflow._utils.yaml_utils import load_yaml
flow_data = load_yaml(flow_file)
# TODO: no outputs definition for eager flow for now
if flow_data.get("entry", None) is not None:
return {}
# TODO: get this from self._get_flow_meta for both eager flow and non-eager flow then remove
# dependency on flow_file and working_dir
from promptflow.contracts.flow import Flow as DataplaneFlow
dataplane_flow = DataplaneFlow.from_yaml(flow_file, working_dir=working_dir)
return dataplane_flow.outputs
@classmethod
async def create(
cls,
flow_file: Path,
working_dir: Optional[Path] = None,
*,
connections: Optional[dict] = None,
storage: Optional[AbstractRunStorage] = None,
**kwargs,
) -> "CSharpExecutorProxy":
"""Create a new executor"""
port = kwargs.get("port", None)
log_path = kwargs.get("log_path", "")
init_error_file = Path(working_dir) / f"init_error_{str(uuid.uuid4())}.json"
init_error_file.touch()
if port is None:
# if port is not provided, find an available port and start a new execution service
port = cls.find_available_port()
process = subprocess.Popen(
cls._construct_service_startup_command(
port=port,
log_path=log_path,
error_file_path=init_error_file,
yaml_path=flow_file.as_posix(),
)
)
else:
# if port is provided, assume the execution service is already started
process = None
outputs_definition = cls.get_outputs_definition(flow_file, working_dir=working_dir)
chat_output_name = next(
filter(
lambda key: outputs_definition[key].is_chat_output,
outputs_definition.keys(),
),
None,
)
executor_proxy = cls(
process=process,
port=port,
working_dir=working_dir,
# TODO: remove this from the constructor after can always be inferred from flow meta?
chat_output_name=chat_output_name,
enable_stream_output=kwargs.get("enable_stream_output", False),
)
try:
await executor_proxy.ensure_executor_startup(init_error_file)
finally:
Path(init_error_file).unlink()
return executor_proxy
async def destroy(self):
"""Destroy the executor"""
# process is not None, it means the executor service is started by the current executor proxy
# and should be terminated when the executor proxy is destroyed if the service is still active
if self._process and self._is_executor_active():
self._process.terminate()
try:
self._process.wait(timeout=5)
except subprocess.TimeoutExpired:
self._process.kill()
def _is_executor_active(self):
"""Check if the process is still running and return False if it has exited"""
# if prot is provided on creation, assume the execution service is already started and keeps active within
# the lifetime of current executor proxy
if self._process is None:
return True
# get the exit code of the process by poll() and if it is None, it means the process is still running
return self._process.poll() is None
@classmethod
def find_available_port(cls) -> str:
"""Find an available port on localhost"""
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(("localhost", 0))
_, port = s.getsockname()
return str(port)
| promptflow/src/promptflow/promptflow/batch/_csharp_executor_proxy.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/batch/_csharp_executor_proxy.py",
"repo_id": "promptflow",
"token_count": 2729
} | 22 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import inspect
import string
import traceback
from enum import Enum
from functools import cached_property
from typing import Dict
from azure.core.exceptions import HttpResponseError
class ErrorCategory(str, Enum):
USER_ERROR = "UserError"
SYSTEM_ERROR = "SystemError"
UNKNOWN = "Unknown"
class ErrorTarget(str, Enum):
"""The target of the error, indicates which part of the system the error occurs."""
EXECUTOR = "Executor"
BATCH = "Batch"
FLOW_EXECUTOR = "FlowExecutor"
NODE_EXECUTOR = "NodeExecutor"
TOOL = "Tool"
AZURE_RUN_STORAGE = "AzureRunStorage"
RUNTIME = "Runtime"
UNKNOWN = "Unknown"
RUN_TRACKER = "RunTracker"
RUN_STORAGE = "RunStorage"
CONTROL_PLANE_SDK = "ControlPlaneSDK"
SERVING_APP = "ServingApp"
FLOW_INVOKER = "FlowInvoker"
FUNCTION_PATH = "FunctionPath"
class PromptflowException(Exception):
"""Base exception for all errors.
:param message: A message describing the error. This is the error message the user will see.
:type message: str
:param target: The name of the element that caused the exception to be thrown.
:type target: ~promptflow.exceptions.ErrorTarget
:param error: The original exception if any.
:type error: Exception
"""
def __init__(
self,
message="",
message_format="",
target: ErrorTarget = ErrorTarget.UNKNOWN,
module=None,
**kwargs,
):
self._inner_exception = kwargs.get("error")
self._target = target
self._module = module
self._message_format = message_format
self._kwargs = kwargs
if message:
self._message = str(message)
elif self.message_format:
self._message = self.message_format.format(**self.message_parameters)
else:
self._message = self.__class__.__name__
super().__init__(self._message)
@property
def message(self):
"""The error message."""
return self._message
@property
def message_format(self):
"""The error message format."""
return self._message_format
@cached_property
def message_parameters(self):
"""The error message parameters."""
if not self._kwargs:
return {}
required_arguments = self.get_arguments_from_message_format(self.message_format)
parameters = {}
for argument in required_arguments:
if argument not in self._kwargs:
parameters[argument] = f"<{argument}>"
else:
parameters[argument] = self._kwargs[argument]
return parameters
@cached_property
def serializable_message_parameters(self):
"""The serializable error message parameters."""
return {k: str(v) for k, v in self.message_parameters.items()}
@property
def target(self):
"""The error target.
:return: The error target.
:rtype: ~promptflow.exceptions.ErrorTarget
"""
return self._target
@target.setter
def target(self, value):
"""Set the error target."""
self._target = value
@property
def module(self):
"""The module of the error that occurs.
It is similar to `target` but is more specific.
It is meant to store the Python module name of the code that raises the exception.
"""
return self._module
@module.setter
def module(self, value):
"""Set the module of the error that occurs."""
self._module = value
@property
def reference_code(self):
"""The reference code of the error."""
# In Python 3.11, the __str__ method of the Enum type returns the name of the enumeration member.
# However, in earlier Python versions, the __str__ method returns the value of the enumeration member.
# Therefore, when dealing with this situation, we need to make some additional adjustments.
target = self.target.value if isinstance(self.target, ErrorTarget) else self.target
if self.module:
return f"{target}/{self.module}"
else:
return target
@property
def inner_exception(self):
"""Get the inner exception.
The inner exception can be set via either style:
1) Set via the error parameter in the constructor.
raise PromptflowException("message", error=inner_exception)
2) Set via raise from statement.
raise PromptflowException("message") from inner_exception
"""
return self._inner_exception or self.__cause__
@property
def additional_info(self):
"""Return a dict of the additional info of the exception.
By default, this information could usually be empty.
However, we can still define additional info for some specific exception.
i.e. For ToolExcutionError, we may add the tool's line number, stacktrace to the additional info.
"""
return None
@property
def error_codes(self):
"""Returns a list of the error codes for this exception.
The error codes is defined the same as the class inheritance.
i.e. For ToolExcutionError which inherits from UserErrorException,
The result would be ["UserErrorException", "ToolExecutionError"].
"""
if getattr(self, "_error_codes", None):
return self._error_codes
from promptflow._utils.exception_utils import infer_error_code_from_class
def reversed_error_codes():
for clz in self.__class__.__mro__:
if clz is PromptflowException:
break
yield infer_error_code_from_class(clz)
self._error_codes = list(reversed_error_codes())
self._error_codes.reverse()
return self._error_codes
def get_arguments_from_message_format(self, message_format):
"""Get the arguments from the message format."""
def iter_field_name():
if not message_format:
return
for _, field_name, _, _ in string.Formatter().parse(message_format):
if field_name is not None:
yield field_name
return set(iter_field_name())
def __str__(self):
"""Return the error message.
Some child classes may override this method to return a more detailed error message."""
return self.message
class UserErrorException(PromptflowException):
"""Exception raised when invalid or unsupported inputs are provided."""
pass
class SystemErrorException(PromptflowException):
"""Exception raised when service error is triggered."""
pass
class ValidationException(UserErrorException):
"""Exception raised when validation fails."""
pass
class _ErrorInfo:
@classmethod
def get_error_info(cls, e: BaseException):
if not isinstance(e, BaseException):
return ErrorCategory.UNKNOWN, type(e).__name__, ErrorTarget.UNKNOWN, "", ""
if cls._is_user_error(e):
return (
ErrorCategory.USER_ERROR,
cls._error_type(e),
cls._error_target(e),
cls._error_message(e),
cls._error_detail(e),
)
return (
ErrorCategory.SYSTEM_ERROR,
cls._error_type(e),
cls._error_target(e),
cls._error_message(e),
cls._error_detail(e),
)
@classmethod
def _is_system_error(cls, e: BaseException):
if isinstance(e, SystemErrorException):
return True
if isinstance(e, HttpResponseError):
status_code = str(e.status_code)
# Except for 429, 400-499 are all client errors.
if not status_code.startswith("4") and status_code != "429":
return True
return False
@classmethod
def _is_user_error(cls, e: BaseException):
if isinstance(e, UserErrorException):
return True
return False
@classmethod
def _error_type(cls, e: BaseException):
"""Return exception type.
Note:
For PromptflowException(error=ValueError(message="xxx")) or
UserErrorException(error=ValueError(message="xxx")) or
SystemErrorException(error=ValueError(message="xxx")),
the desired return type is ValueError,
not PromptflowException, UserErrorException and SystemErrorException.
"""
error_type = type(e).__name__
if type(e) in (PromptflowException, UserErrorException, SystemErrorException):
if e.inner_exception:
error_type = type(e.inner_exception).__name__
return error_type
@classmethod
def _error_target(cls, e: BaseException):
error_target = getattr(e, "target", ErrorTarget.UNKNOWN)
if error_target != ErrorTarget.UNKNOWN:
return error_target
module_target_map = cls._module_target_map()
exception_codes = cls._get_exception_codes(e)
for exception_code in exception_codes[::-1]:
for module_name, target in module_target_map.items():
# For example: "promptflow.executor" in "promptflow.executor._errors"
if module_name in exception_code["module"]:
return target
return ErrorTarget.EXECUTOR
@classmethod
def _module_target_map(cls) -> Dict[str, ErrorTarget]:
return {
"promptflow._sdk": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow._cli": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow.azure": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow.connections": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow.entities": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow.operations": ErrorTarget.CONTROL_PLANE_SDK,
"promptflow.executor": ErrorTarget.EXECUTOR,
"promptflow._core": ErrorTarget.EXECUTOR,
"promptflow.batch": ErrorTarget.EXECUTOR,
"promptflow.contracts": ErrorTarget.EXECUTOR,
"promptflow._utils": ErrorTarget.EXECUTOR,
"promptflow._internal": ErrorTarget.EXECUTOR,
"promptflow.integrations": ErrorTarget.EXECUTOR,
"promptflow.storage": ErrorTarget.EXECUTOR,
"promptflow.tools": ErrorTarget.TOOL,
}
@classmethod
def _error_message(cls, e: BaseException):
return getattr(e, "message_format", "")
@classmethod
def _error_detail(cls, e: BaseException):
promptflow_codes = cls._promptflow_error_traceback(e)
inner_exception = e.inner_exception if isinstance(e, PromptflowException) else e.__cause__
if inner_exception:
promptflow_codes += "The above exception was the direct cause of the following exception:\n"
promptflow_codes += cls._promptflow_error_traceback(inner_exception)
return promptflow_codes
@classmethod
def _promptflow_error_traceback(cls, e: BaseException):
exception_codes = cls._get_exception_codes(e)
promptflow_codes = ""
for item in exception_codes:
if "promptflow" in item["module"]: # Only record the promptflow package and code.
promptflow_codes += f"{item['module']}, line {item['lineno']}, {item['exception_code']}\n"
return promptflow_codes
@classmethod
def _get_exception_codes(cls, e: BaseException) -> list:
"""
Obtain information on each line of the traceback, including the module name,
exception code and lineno where the error occurred.
:param e: Exception object
:return: A list, each item contains information for each row of the traceback, which format is like this:
{
'module': 'promptflow.executor.errors',
'exception_code': 'return self.inner_exception.additional_info',
'lineno': 223
}
"""
exception_codes = []
traceback_info = traceback.extract_tb(e.__traceback__)
for item in traceback_info:
lineno = item.lineno
filename = item.filename
line_code = item.line
module = inspect.getmodule(None, _filename=filename)
exception_code = {"module": "", "exception_code": line_code, "lineno": lineno}
if module is not None:
exception_code["module"] = module.__name__
exception_codes.append(exception_code)
return exception_codes
| promptflow/src/promptflow/promptflow/exceptions.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/exceptions.py",
"repo_id": "promptflow",
"token_count": 5250
} | 23 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
from fastapi import APIRouter
from fastapi.responses import PlainTextResponse
from promptflow._utils.feature_utils import get_feature_list
from promptflow._version import VERSION
router = APIRouter()
@router.get("/health")
async def health_check():
return PlainTextResponse("healthy")
@router.get("/version")
async def version():
build_info = os.environ.get("BUILD_INFO", "")
try:
build_info_dict = json.loads(build_info)
version = build_info_dict["build_number"]
except Exception:
version = VERSION
return {
"status": "healthy",
"build_info": build_info,
"version": version,
"feature_list": get_feature_list(),
}
| promptflow/src/promptflow/promptflow/executor/_service/apis/common.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_service/apis/common.py",
"repo_id": "promptflow",
"token_count": 295
} | 24 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class DuplicatedPrimaryKeyException(Exception):
pass
class NotFoundException(Exception):
pass
| promptflow/src/promptflow/promptflow/storage/_errors.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/storage/_errors.py",
"repo_id": "promptflow",
"token_count": 53
} | 25 |
# Contributing to Prompt Flow
You can contribute to prompt flow with issues and pull requests (PRs). Simply
filing issues for problems you encounter is a great way to contribute. Contributing
code is greatly appreciated.
## Reporting Issues
We always welcome bug reports, API proposals and overall feedback. Here are a few
tips on how you can make reporting your issue as effective as possible.
### Where to Report
New issues can be reported in our [list of issues](https://github.com/microsoft/promptflow/issues).
Before filing a new issue, please search the list of issues to make sure it does
not already exist.
If you do find an existing issue for what you wanted to report, please include
your own feedback in the discussion. Do consider upvoting (👍 reaction) the original
post, as this helps us prioritize popular issues in our backlog.
### Writing a Good Bug Report
Good bug reports make it easier for maintainers to verify and root cause the
underlying problem.
The better a bug report, the faster the problem will be resolved. Ideally, a bug
report should contain the following information:
- A high-level description of the problem.
- A _minimal reproduction_, i.e. the smallest size of code/configuration required
to reproduce the wrong behavior.
- A description of the _expected behavior_, contrasted with the _actual behavior_ observed.
- Information on the environment: OS/distribution, CPU architecture, SDK version, etc.
- Additional information, e.g. Is it a regression from previous versions? Are there
any known workarounds?
## Contributing Changes
Project maintainers will merge accepted code changes from contributors.
### DOs and DON'Ts
DO's:
- **DO** follow the standard coding conventions: [Python](https://pypi.org/project/black/)
- **DO** give priority to the current style of the project or file you're changing
if it diverges from the general guidelines.
- **DO** include tests when adding new features. When fixing bugs, start with
adding a test that highlights how the current behavior is broken.
- **DO** add proper docstring for functions and classes following [API Documentation Guidelines](./docs/dev/documentation_guidelines.md).
- **DO** keep the discussions focused. When a new or related topic comes up
it's often better to create new issue than to side track the discussion.
- **DO** clearly state on an issue that you are going to take on implementing it.
- **DO** blog and tweet (or whatever) about your contributions, frequently!
DON'Ts:
- **DON'T** surprise us with big pull requests. Instead, file an issue and start
a discussion so we can agree on a direction before you invest a large amount of time.
- **DON'T** commit code that you didn't write. If you find code that you think is a good
fit to add to prompt flow, file an issue and start a discussion before proceeding.
- **DON'T** submit PRs that alter licensing related files or headers. If you believe
there's a problem with them, file an issue and we'll be happy to discuss it.
- **DON'T** make new APIs without filing an issue and discussing with us first.
### Breaking Changes
Contributions must maintain API signature and behavioral compatibility. Contributions
that include breaking changes will be rejected. Please file an issue to discuss
your idea or change if you believe that a breaking change is warranted.
### Suggested Workflow
We use and recommend the following workflow:
1. Create an issue for your work, or reuse an existing issue on the same topic.
- Get agreement from the team and the community that your proposed change is
a good one.
- Clearly state that you are going to take on implementing it, if that's the case.
You can request that the issue be assigned to you. Note: The issue filer and
the implementer don't have to be the same person.
2. Create a personal fork of the repository on GitHub (if you don't already have one).
3. In your fork, create a branch off of main (`git checkout -b my_branch`).
- Name the branch so that it clearly communicates your intentions, such as
"issue-123" or "githubhandle-issue".
4. Make and commit your changes to your branch.
5. Add new tests corresponding to your change, if applicable.
6. Run the relevant scripts in [the section below](https://github.com/microsoft/promptflow/blob/main/CONTRIBUTING.md#dev-scripts) to ensure that your build is clean and all tests are passing.
7. Create a PR against the repository's **main** branch.
- State in the description what issue or improvement your change is addressing.
- Link the PR to the issue in step 1.
- Verify that all the Continuous Integration checks are passing.
8. Wait for feedback or approval of your changes from the code maintainers.
- If there is no response for a few days, you can create a new issue to raise awareness.
Promptflow team has triage process toward issues without assignee,
then you can directly contact the issue owner to follow up (e.g. loop related internal reviewer).
9. When area owners have signed off, and all checks are green, your PR will be merged.
### Development scripts
The scripts below are used to build, test, and lint within the project.
- see [doc/dev/dev_setup.md](https://github.com/microsoft/promptflow/blob/main/docs/dev/dev_setup.md).
### PR - CI Process
The continuous integration (CI) system will automatically perform the required
builds and run tests (including the ones you are expected to run) for PRs. Builds
and test runs must be clean.
If the CI build fails for any reason, the PR issue will be updated with a link
that can be used to determine the cause of the failure.
| promptflow/CONTRIBUTING.md/0 | {
"file_path": "promptflow/CONTRIBUTING.md",
"repo_id": "promptflow",
"token_count": 1394
} | 0 |
While how LLMs work may be elusive to many developers, how LLM apps work is not - they essentially involve a series of calls to external services such as LLMs/databases/search engines, or intermediate data processing, all glued together. Thus LLM apps are merely Directed Acyclic Graphs (DAGs) of function calls. These DAGs are flows in prompt flow.
# Flows
A flow in prompt flow is a DAG of functions (we call them [tools](./concept-tools.md)). These functions/tools connected via input/output dependencies and executed based on the topology by prompt flow executor.
A flow is represented as a YAML file and can be visualized with our [Prompt flow for VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow). Here is an example:
## Flow types
Prompt flow has three flow types:
- **Standard flow** and **Chat flow**: these two are for you to develop your LLM application. The primary difference between the two lies in the additional support provided by the "Chat Flow" for chat applications. For instance, you can define chat_history, chat_input, and chat_output for your flow. The prompt flow, in turn, will offer a chat-like experience (including conversation history) during the development of the flow. Moreover, it also provides a sample chat application for deployment purposes.
- **Evaluation flow** is for you to test/evaluate the quality of your LLM application (standard/chat flow). It usually run on the outputs of standard/chat flow, and compute some metrics that can be used to determine whether the standard/chat flow performs well. E.g. is the answer accurate? is the answer fact-based?
## When to use standard flow vs. chat flow?
As a general guideline, if you are building a chatbot that needs to maintain conversation history, try chat flow. In most other cases, standard flow should serve your needs.
Our examples should also give you an idea when to use what:
- [examples/flows/standard](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard)
- [examples/flows/chat](https://github.com/microsoft/promptflow/tree/main/examples/flows/chat)
## Next steps
- [Quick start](../how-to-guides/quick-start.md)
- [Initialize and test a flow](../how-to-guides/init-and-test-a-flow.md)
- [Run and evaluate a flow](../how-to-guides/run-and-evaluate-a-flow/index.md)
- [Tune prompts using variants](../how-to-guides/tune-prompts-with-variants.md) | promptflow/docs/concepts/concept-flows.md/0 | {
"file_path": "promptflow/docs/concepts/concept-flows.md",
"repo_id": "promptflow",
"token_count": 673
} | 1 |
# Develop standard flow
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](../faq.md#stable-vs-experimental).
:::
From this document, you can learn how to develop a standard flow by writing a flow yaml from scratch. You can
find additional information about flow yaml schema in [Flow YAML Schema](../../reference/flow-yaml-schema-reference.md).
## Flow input data
The flow input data is the data that you want to process in your flow.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can add a flow input in inputs section of flow yaml.
```yaml
inputs:
url:
type: string
default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
When unfolding Inputs section in the authoring page, you can set and view your flow inputs, including input schema (name and type),
and the input value.
:::
::::
For Web Classification sample as shown the screenshot above, the flow input is an url of string type.
For more input types in a python tool, please refer to [Input types](../../reference/tools-reference/python-tool.md#types).
## Develop the flow using different tools
In one flow, you can consume different kinds of tools. We now support built-in tool like
[LLM](../../reference/tools-reference/llm-tool.md), [Python](../../reference/tools-reference/python-tool.md) and
[Prompt](../../reference/tools-reference/prompt-tool.md) and
third-party tool like [Serp API](../../reference/tools-reference/serp-api-tool.md),
[Vector Search](../../reference/tools-reference/vector_db_lookup_tool.md), etc.
### Add tool as your need
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can add a tool node in nodes section of flow yaml. For example, yaml below shows how to add a Python tool node in the flow.
```yaml
nodes:
- name: fetch_text_content_from_url
type: python
source:
type: code
path: fetch_text_content_from_url.py
inputs:
url: ${inputs.url}
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
By selecting the tool card on the very top, you'll add a new tool node to flow.
:::
::::
### Edit tool
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can edit the tool by simply opening the source file and making edits. For example, we provide a simple Python tool code below.
```python
from promptflow import tool
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signature per need
@tool
def my_python_tool(input1: str) -> str:
return 'hello ' + input1
```
We also provide an LLM tool prompt below.
```jinja
Please summarize the following text in one paragraph. 100 words.
Do not add any information that is not in the text.
Text: {{text}}
Summary:
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
When a new tool node is added to flow, it will be appended at the bottom of flatten view with a random name by default.
At the top of each tool node card, there's a toolbar for adjusting the tool node. You can move it up or down, you can delete or rename it too.
For a python tool node, you can edit the tool code by clicking the code file. For a LLM tool node, you can edit the
tool prompt by clicking the prompt file and adjust input parameters like connection, api and etc.
:::
::::
### Create connection
Please refer to the [Create necessary connections](../quick-start.md#create-necessary-connections) for details.
## Chain your flow - link nodes together
Before linking nodes together, you need to define and expose an interface.
### Define LLM node interface
LLM node has only one output, the completion given by LLM provider.
As for inputs, we offer a templating strategy that can help you create parametric prompts that accept different input
values. Instead of fixed text, enclose your input name in `{{}}`, so it can be replaced on the fly. We use Jinja as our
templating language. For example:
```jinja
Your task is to classify a given url into one of the following types:
Movie, App, Academic, Channel, Profile, PDF or None based on the text content information.
The classification will be based on the url, the webpage text content summary, or both.
Here are a few examples:
{% for ex in examples %}
URL: {{ex.url}}
Text content: {{ex.text_content}}
OUTPUT:
{"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"}
{% endfor %}
For a given URL : {{url}}, and text content: {{text_content}}.
Classify above url to complete the category and indicate evidence.
OUTPUT:
```
### Define Python node interface
Python node might have multiple inputs and outputs. Define inputs and outputs as shown below.
If you have multiple outputs, remember to make it a dictionary so that the downstream node can call each key separately.
For example:
```python
import json
from promptflow import tool
@tool
def convert_to_dict(input_str: str, input_str2: str) -> dict:
try:
print(input_str2)
return json.loads(input_str)
except Exception as e:
print("input is not valid, error: {}".format(e))
return {"category": "None", "evidence": "None"}
```
### Link nodes together
After the interface is defined, you can use:
- ${inputs.key} to link with flow input.
- ${upstream_node_name.output} to link with single-output upstream node.
- ${upstream_node_name.output.key} to link with multi-output upstream node.
Below are common scenarios for linking nodes together.
### Scenario 1 - Link LLM node with flow input and single-output upstream node
After you add a new LLM node and edit the prompt file like [Define LLM node interface](#define-llm-node-interface),
three inputs called `url`, `examples` and `text_content` are created in inputs section.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can link the LLM node input with flow input by `${inputs.url}`.
And you can link `examples` to the upstream `prepare_examples` node and `text_content` to the `summarize_text_content` node
by `${prepare_examples.output}` and `${summarize_text_content.output}`.
```yaml
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: text-davinci-003
suffix: ""
max_tokens: 128
temperature: 0.2
top_p: 1
echo: false
presence_penalty: 0
frequency_penalty: 0
best_of: 1
url: ${inputs.url} # Link with flow input
examples: ${prepare_examples.output} # Link LLM node with single-output upstream node
text_content: ${summarize_text_content.output} # Link LLM node with single-output upstream node
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
In the value drop-down, select `${inputs.url}`, `${prepare_examples.output}` and `${summarize_text_content.output}`, then
you'll see in the graph view that the newly created LLM node is linked to the flow input, upstream `prepare_examples` and `summarize_text_content` node.
:::
::::
When running the flow, the `url` input of the node will be replaced by flow input on the fly, and the `examples` and
`text_content` input of the node will be replaced by `prepare_examples` and `summarize_text_content` node output on the fly.
### Scenario 2 - Link LLM node with multi-output upstream node
Suppose we want to link the newly created LLM node with `covert_to_dict` Python node whose output is a dictionary with two keys: `category` and `evidence`.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can link `examples` to the `evidence` output of upstream `covert_to_dict` node by `${convert_to_dict.output.evidence}` like below:
```yaml
- name: classify_with_llm
type: llm
source:
type: code
path: classify_with_llm.jinja2
inputs:
deployment_name: text-davinci-003
suffix: ""
max_tokens: 128
temperature: 0.2
top_p: 1
echo: false
presence_penalty: 0
frequency_penalty: 0
best_of: 1
text_content: ${convert_to_dict.output.evidence} # Link LLM node with multi-output upstream node
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
In the value drop-down, select `${convert_to_dict.output}`, then manually append `evidence`, then you'll see in the graph
view that the newly created LLM node is linked to the upstream `convert_to_dict node`.
:::
::::
When running the flow, the `text_content` input of the node will be replaced by `evidence` value from `convert_to_dict node` output dictionary on the fly.
### Scenario 3 - Link Python node with upstream node/flow input
After you add a new Python node and edit the code file like [Define Python node interface](#define-python-node-interface)],
two inputs called `input_str` and `input_str2` are created in inputs section. The linkage is the same as LLM node,
using `${flow.input_name}` to link with flow input or `${upstream_node_name.output}` to link with upstream node.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
```yaml
- name: prepare_examples
type: python
source:
type: code
path: prepare_examples.py
inputs:
input_str: ${inputs.url} # Link Python node with flow input
input_str2: ${fetch_text_content_from_url.output} # Link Python node with single-output upstream node
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
:::
::::
When running the flow, the `input_str` input of the node will be replaced by flow input on the fly and the `input_str2`
input of the node will be replaced by `fetch_text_content_from_url` node output dictionary on the fly.
## Set flow output
When the flow is complicated, instead of checking outputs on each node, you can set flow output and check outputs of
multiple nodes in one place. Moreover, flow output helps:
- Check bulk test results in one single table.
- Define evaluation interface mapping.
- Set deployment response schema.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
You can add flow outputs in outputs section of flow yaml . The linkage is the same as LLM node,
using `${convert_to_dict.output.category}` to link `category` flow output with with `category` value of upstream node
`convert_to_dict`.
```yaml
outputs:
category:
type: string
reference: ${convert_to_dict.output.category}
evidence:
type: string
reference: ${convert_to_dict.output.evidence}
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
First define flow output schema, then select in drop-down the node whose output you want to set as flow output.
Since `convert_to_dict` has a dictionary output with two keys: `category` and `evidence`, you need to manually append
`category` and `evidence` to each. Then run flow, after a while, you can check flow output in a table.
:::
:::: | promptflow/docs/how-to-guides/develop-a-flow/develop-standard-flow.md/0 | {
"file_path": "promptflow/docs/how-to-guides/develop-a-flow/develop-standard-flow.md",
"repo_id": "promptflow",
"token_count": 3588
} | 2 |
# Initialize and test a flow
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](faq.md#stable-vs-experimental).
:::
From this document, customer can initialize a flow and test it.
## Initialize flow
Creating a flow folder with code/prompts and yaml definitions of the flow.
### Initialize flow from scratch
Promptflow can [create three types of flow folder](https://promptflow.azurewebsites.net/concepts/concept-flows.html#flow-types):
- standard: Basic structure of flow folder.
- chat: Chat flow is designed for conversational application development, building upon the capabilities of standard flow and providing enhanced support for chat inputs/outputs and chat history management.
- evaluation: Evaluation flows are special types of flows that assess how well the outputs of a flow align with specific criteria and goals.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
```bash
# Create a flow
pf flow init --flow <flow-name>
# Create a chat flow
pf flow init --flow <flow-name> --type chat
```
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
Use VS Code explorer pane > directory icon > right click > the "New flow in this directory" action. Follow the popped out dialog to initialize your flow in the target folder.
Alternatively, you can use the "Create new flow" action on the prompt flow pane > quick access section to create a new flow
:::
::::
Structure of flow folder:
- **flow.dag.yaml**: The flow definition with inputs/outputs, nodes, tools and variants for authoring purpose.
- **.promptflow/flow.tools.json**: It contains tools meta referenced in `flow.dag.yaml`.
- **Source code files (.py, .jinja2)**: User managed, the code scripts referenced by tools.
- **requirements.txt**: Python package dependencies for this flow.
### Create from existing code
Customer needs to pass the path of tool script to `entry`, and also needs to pass in the promptflow template dict to `prompt-template`, which the key is the input name of the tool and the value is the path to the promptflow template.
Promptflow CLI can generate the yaml definitions needed for prompt flow from the existing folder, using the tools script and prompt templates.
```bash
# Create a flow in existing folder
pf flow init --flow <flow-name> --entry <tool-script-path> --function <tool-function-name> --prompt-template <prompt-param-name>=<prompt-tempate-path>
```
Take [customer-intent-extraction](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/customer-intent-extraction) for example, which demonstrating how to convert a langchain code into a prompt flow.
In this case, promptflow CLI generates `flow.dag.yaml`, `.promptflow/flow.tools.json` and `extract_intent_tool.py`, it is a python tool in the flow.
## Test a flow
:::{admonition} Note
Testing flow will NOT create a batch run record, therefore it's unable to use commands like `pf run show-details` to get the run information. If you want to persist the run record, see [Run and evaluate a flow](./run-and-evaluate-a-flow/index.md)
:::
Promptflow also provides ways to test the initialized flow or flow node. It will help you quickly test your flow.
### Visual editor on the VS Code for prompt flow.
::::{tab-set}
:::{tab-item} VS Code Extension
:sync: VS Code Extension
Open the flow.dag.yaml file of your flow. On the top of the yaml editor you can find the "Visual editor" action. Use it to open the Visual editor with GUI support.
:::
::::
### Test flow
Customer can use CLI or VS Code extension to test the flow.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
```bash
# Test flow
pf flow test --flow <flow-name>
# Test flow with specified variant
pf flow test --flow <flow-name> --variant '${<node-name>.<variant-name>}'
```
The log and result of flow test will be displayed in the terminal.
Promptflow CLI will generate test logs and outputs in `.promptflow`:
- **flow.detail.json**: Defails info of flow test, include the result of each node.
- **flow.log**: The log of flow test.
- **flow.output.json**: The result of flow test.
:::
:::{tab-item} SDK
:sync: SDK
The return value of `test` function is the flow outputs.
```python
from promptflow import PFClient
pf_client = PFClient()
# Test flow
inputs = {"<flow_input_name>": "<flow_input_value>"} # The inputs of the flow.
flow_result = pf_client.test(flow="<flow_folder_path>", inputs=inputs)
print(f"Flow outputs: {flow_result}")
```
The log and result of flow test will be displayed in the terminal.
Promptflow CLI will generate test logs and outputs in `.promptflow`:
- **flow.detail.json**: Defails info of flow test, include the result of each node.
- **flow.log**: The log of flow test.
- **flow.output.json**: The result of flow test.
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
You can use the action either on the default yaml editor or the visual editor to trigger flow test. See the snapshots below:
:::
::::
### Test a single node in the flow
Customer can test a single python node in the flow. It will use customer provides date or the default value of the node as input. It will only use customer specified node to execute with the input.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
Customer can execute this command to test the flow.
```bash
# Test flow node
pf flow test --flow <flow-name> --node <node-name>
```
The log and result of flow node test will be displayed in the terminal. And the details of node test will generated to `.promptflow/flow-<node-name>.node.detail.json`.
:::
:::{tab-item} SDK
:sync: SDK
Customer can execute this command to test the flow. The return value of `test` function is the node outputs.
```python
from promptflow import PFClient
pf_client = PFClient()
# Test not iun the flow
inputs = {<node_input_name>: <node_input_value>} # The inputs of the node.
node_result = pf_client.test(flow=<flow_folder_path>, inputs=inputs, node=<node_name>)
print(f"Node outputs: {node_result}")
```
The log and result of flow node test will be displayed in the terminal. And the details of node test will generated to `.promptflow/flow-<node-name>.node.detail.json`.
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
The prompt flow extension provides inline actions in both default yaml editor and visual editor to trigger single node runs.
:::
::::
### Test with interactive mode
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
Promptflow CLI provides a way to start an interactive chat session for chat flow. Customer can use below command to start an interactive chat session:
```bash
# Chat in the flow
pf flow test --flow <flow-name> --interactive
```
After executing this command, customer can interact with the chat flow in the terminal. Customer can press **Enter** to send the message to chat flow. And customer can quit with **ctrl+C**.
Promptflow CLI will distinguish the output of different roles by color, <span style="color:Green">User input</span>, <span style="color:Gold">Bot output</span>, <span style="color:Blue">Flow script output</span>, <span style="color:Cyan">Node output</span>.
Using this [chat flow](https://github.com/microsoft/promptflow/tree/main/examples/flows/chat/basic-chat) to show how to use interactive mode.
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
If a flow contains chat inputs or chat outputs in the flow interface, there will be a selection when triggering flow test. You can select the interactive mode if you want to.
:::
::::
When the [LLM node](https://promptflow.azurewebsites.net/tools-reference/llm-tool.html) in the chat flow that is connected to the flow output, Promptflow SDK streams the results of the LLM node.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
The flow result will be streamed in the terminal as shown below.
:::
:::{tab-item} SDK
:sync: SDK
The LLM node return value of `test` function is a generator, you can consume the result by this way:
```python
from promptflow import PFClient
pf_client = PFClient()
# Test flow
inputs = {"<flow_input_name>": "<flow_input_value>"} # The inputs of the flow.
flow_result = pf_client.test(flow="<flow_folder_path>", inputs=inputs)
for item in flow_result["<LLM_node_output_name>"]:
print(item)
```
:::
::::
### Debug a single node in the flow
Customer can debug a single python node in VScode by the extension.
::::{tab-set}
:::{tab-item} VS Code Extension
:sync: VS Code Extension
Break points and debugging functionalities for the Python steps in your flow. Just set the break points and use the debug actions on either default yaml editor or visual editor.
:::
::::
## Next steps
- [Add conditional control to a flow](./add-conditional-control-to-a-flow.md) | promptflow/docs/how-to-guides/init-and-test-a-flow.md/0 | {
"file_path": "promptflow/docs/how-to-guides/init-and-test-a-flow.md",
"repo_id": "promptflow",
"token_count": 3178
} | 3 |
# pfazure
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](../how-to-guides/faq.md#stable-vs-experimental).
:::
Manage prompt flow resources on Azure with the prompt flow CLI.
| Command | Description |
| --- | --- |
| [pfazure flow](#pfazure-flow) | Manage flows. |
| [pfazure run](#pfazure-run) | Manage runs. |
## pfazure flow
Manage flows.
| Command | Description |
| --- | --- |
| [pfazure flow create](#pfazure-flow-create) | Create a flow. |
| [pfazure flow list](#pfazure-flow-list) | List flows in a workspace. |
### pfazure flow create
Create a flow in Azure AI from a local flow folder.
```bash
pfazure flow create [--flow]
[--set]
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--flow`
Local path to the flow directory.
`--set`
Update an object by specifying a property path and value to set.
- `display_name`: Flow display name that will be created in remote. Default to be flow folder name + timestamp if not specified.
- `type`: Flow type. Default to be "standard" if not specified. Available types are: "standard", "evaluation", "chat".
- `description`: Flow description. e.g. "--set description=\<description\>."
- `tags`: Flow tags. e.g. "--set tags.key1=value1 tags.key2=value2."
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure flow list
List remote flows on Azure AI.
```bash
pfazure flow list [--max-results]
[--include-others]
[--type]
[--output]
[--archived-only]
[--include-archived]
[--subscription]
[--resource-group]
[--workspace-name]
[--output]
```
#### Parameters
`--max-results -r`
Max number of results to return. Default is 50, upper bound is 100.
`--include-others`
Include flows created by other owners. By default only flows created by the current user are returned.
`--type`
Filter flows by type. Available types are: "standard", "evaluation", "chat".
`--archived-only`
List archived flows only.
`--include-archived`
List archived flows and active flows.
`--output -o`
Output format. Allowed values: `json`, `table`. Default: `json`.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
## pfazure run
Manage prompt flow runs.
| Command | Description |
| --- | --- |
| [pfazure run create](#pfazure-run-create) | Create a run. |
| [pfazure run list](#pfazure-run-list) | List runs in a workspace. |
| [pfazure run show](#pfazure-run-show) | Show details for a run. |
| [pfazure run stream](#pfazure-run-stream) | Stream run logs to the console. |
| [pfazure run show-details](#pfazure-run-show-details) | Show a run details. |
| [pfazure run show-metrics](#pfazure-run-show-metrics) | Show run metrics. |
| [pfazure run visualize](#pfazure-run-visualize) | Visualize a run. |
| [pfazure run archive](#pfazure-run-archive) | Archive a run. |
| [pfazure run restore](#pfazure-run-restore) | Restore a run. |
| [pfazure run update](#pfazure-run-update) | Update a run. |
| [pfazure run download](#pfazure-run-download) | Download a run. |
### pfazure run create
Create a run.
```bash
pfazure run create [--file]
[--flow]
[--data]
[--column-mapping]
[--run]
[--variant]
[--stream]
[--environment-variables]
[--connections]
[--set]
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--file -f`
Local path to the YAML file containing the prompt flow run specification; can be overwritten by other parameters. Reference [here](https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json) for YAML schema.
`--flow`
Local path to the flow directory.
`--data`
Local path to the data file or remote data. e.g. azureml:name:version.
`--column-mapping`
Inputs column mapping, use `${data.xx}` to refer to data columns, use `${run.inputs.xx}` to refer to referenced run's data columns, and `${run.outputs.xx}` to refer to run outputs columns.
`--run`
Referenced flow run name. For example, you can run an evaluation flow against an existing run. For example, "pfazure run create --flow evaluation_flow_dir --run existing_bulk_run --column-mapping url='${data.url}'".
`--variant`
Node & variant name in format of `${node_name.variant_name}`.
`--stream -s`
Indicates whether to stream the run's logs to the console.
default value: False
`--environment-variables`
Environment variables to set by specifying a property path and value. Example:
`--environment-variable key1='${my_connection.api_key}' key2='value2'`. The value reference
to connection keys will be resolved to the actual value, and all environment variables
specified will be set into os.environ.
`--connections`
Overwrite node level connections with provided value.
Example: `--connections node1.connection=test_llm_connection node1.deployment_name=gpt-35-turbo`
`--set`
Update an object by specifying a property path and value to set.
Example: `--set property1.property2=<value>`.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run list
List runs in a workspace.
```bash
pfazure run list [--archived-only]
[--include-archived]
[--max-results]
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--archived-only`
List archived runs only.
default value: False
`--include-archived`
List archived runs and active runs.
default value: False
`--max-results -r`
Max number of results to return. Default is 50, upper bound is 100.
default value: 50
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run show
Show details for a run.
```bash
pfazure run show --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run stream
Stream run logs to the console.
```bash
pfazure run stream --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run show-details
Show a run details.
```bash
pfazure run show-details --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run show-metrics
Show run metrics.
```bash
pfazure run show-metrics --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run visualize
Visualize a run.
```bash
pfazure run visualize --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run archive
Archive a run.
```bash
pfazure run archive --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run restore
Restore a run.
```bash
pfazure run restore --name
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Parameters
`--name -n`
Name of the run.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run update
Update a run's metadata, such as `display name`, `description` and `tags`.
```bash
pfazure run update --name
[--set display_name="<value>" description="<value>" tags.key="<value>"]
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Examples
Set `display name`, `description` and `tags`:
```bash
pfazure run update --name <run_name> --set display_name="<value>" description="<value>" tags.key="<value>"
```
#### Parameters
`--name -n`
Name of the run.
`--set`
Set meta information of the run, like `display_name`, `description` or `tags`. Example: --set <key>=<value>.
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
### pfazure run download
Download a run's metadata, such as `input`, `output`, `snapshot` and `artifact`. After the download is finished, you can use `pf run create --source <run-info-local-folder>` to register this run as a local run record, then you can use commands like `pf run show/visualize` to inspect the run just like a run that was created from local flow.
```bash
pfazure run download --name
[--output]
[--overwrite]
[--subscription]
[--resource-group]
[--workspace-name]
```
#### Examples
Download a run data to local:
```bash
pfazure run download --name <name> --output <output-folder-path>
```
#### Parameters
`--name -n`
Name of the run.
`--output -o`
Output folder path to store the downloaded run data. Default to be `~/.promptflow/.runs` if not specified
`--overwrite`
Overwrite the existing run data if the output folder already exists. Default to be `False` if not specified
`--subscription`
Subscription id, required when there is no default value from `az configure`.
`--resource-group -g`
Resource group name, required when there is no default value from `az configure`.
`--workspace-name -w`
Workspace name, required when there is no default value from `az configure`.
| promptflow/docs/reference/pfazure-command-reference.md/0 | {
"file_path": "promptflow/docs/reference/pfazure-command-reference.md",
"repo_id": "promptflow",
"token_count": 4975
} | 4 |
# Release History
## 1.0.0 (2023.11.30)
### Features Added
- Support openai 1.x in promptflow-tools
- Add new tool "OpenAI GPT-4V"
| promptflow/src/promptflow-tools/CHANGELOG.md/0 | {
"file_path": "promptflow/src/promptflow-tools/CHANGELOG.md",
"repo_id": "promptflow",
"token_count": 52
} | 5 |
try:
from openai import OpenAI as OpenAIClient
except Exception:
raise Exception(
"Please upgrade your OpenAI package to version 1.0.0 or later using the command: pip install --upgrade openai.")
from promptflow.connections import OpenAIConnection
from promptflow.contracts.types import PromptTemplate
from promptflow._internal import ToolProvider, tool
from promptflow.tools.common import render_jinja_template, handle_openai_error, \
parse_chat, post_process_chat_api_response, preprocess_template_string, \
find_referenced_image_set, convert_to_chat_list, normalize_connection_config
class OpenAI(ToolProvider):
def __init__(self, connection: OpenAIConnection):
super().__init__()
self._connection_dict = normalize_connection_config(connection)
self._client = OpenAIClient(**self._connection_dict)
@tool(streaming_option_parameter="stream")
@handle_openai_error()
def chat(
self,
prompt: PromptTemplate,
model: str = "gpt-4-vision-preview",
temperature: float = 1.0,
top_p: float = 1.0,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
**kwargs,
) -> [str, dict]:
# keep_trailing_newline=True is to keep the last \n in the prompt to avoid converting "user:\t\n" to "user:".
prompt = preprocess_template_string(prompt)
referenced_images = find_referenced_image_set(kwargs)
# convert list type into ChatInputList type
converted_kwargs = convert_to_chat_list(kwargs)
chat_str = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **converted_kwargs)
messages = parse_chat(chat_str, list(referenced_images))
params = {
"model": model,
"messages": messages,
"temperature": temperature,
"top_p": top_p,
"n": 1,
"stream": stream,
"presence_penalty": presence_penalty,
"frequency_penalty": frequency_penalty,
}
if stop:
params["stop"] = stop
if max_tokens is not None:
params["max_tokens"] = max_tokens
completion = self._client.chat.completions.create(**params)
return post_process_chat_api_response(completion, stream, None)
| promptflow/src/promptflow-tools/promptflow/tools/openai_gpt4v.py/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/openai_gpt4v.py",
"repo_id": "promptflow",
"token_count": 1021
} | 6 |
# Prompt flow
[](https://pypi.org/project/promptflow/)
[](https://pypi.python.org/pypi/promptflow/)
[](https://pypi.org/project/promptflow/)
[](https://microsoft.github.io/promptflow/reference/pf-command-reference.html)
[](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow)
[](https://microsoft.github.io/promptflow/index.html)
[](https://github.com/microsoft/promptflow/issues/new/choose)
[](https://github.com/microsoft/promptflow/issues/new/choose)
[](https://github.com/microsoft/promptflow/blob/main/CONTRIBUTING.md)
[](https://github.com/microsoft/promptflow/blob/main/LICENSE)
> Welcome to join us to make prompt flow better by
> participating [discussions](https://github.com/microsoft/promptflow/discussions),
> opening [issues](https://github.com/microsoft/promptflow/issues/new/choose),
> submitting [PRs](https://github.com/microsoft/promptflow/pulls).
**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
With prompt flow, you will be able to:
- **Create and iteratively develop flow**
- Create executable [flows](https://microsoft.github.io/promptflow/concepts/concept-flows.html) that link LLMs, prompts, Python code and other [tools](https://microsoft.github.io/promptflow/concepts/concept-tools.html) together.
- Debug and iterate your flows, especially the [interaction with LLMs](https://microsoft.github.io/promptflow/concepts/concept-connections.html) with ease.
- **Evaluate flow quality and performance**
- Evaluate your flow's quality and performance with larger datasets.
- Integrate the testing and evaluation into your CI/CD system to ensure quality of your flow.
- **Streamlined development cycle for production**
- Deploy your flow to the serving platform you choose or integrate into your app's code base easily.
- (Optional but highly recommended) Collaborate with your team by leveraging the cloud version of [prompt flow in Azure AI](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2).
------
## Installation
Ensure you have a python environment, `python=3.9` is recommended.
```sh
pip install promptflow promptflow-tools
```
## Quick Start ⚡
**Create a chatbot with prompt flow**
Run the command to initiate a prompt flow from a chat template, it creates folder named `my_chatbot` and generates required files within it:
```sh
pf flow init --flow ./my_chatbot --type chat
```
**Setup a connection for your API key**
For OpenAI key, establish a connection by running the command, using the `openai.yaml` file in the `my_chatbot` folder, which stores your OpenAI key:
```sh
# Override keys with --set to avoid yaml file changes
pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection
```
For Azure OpenAI key, establish the connection by running the command, using the `azure_openai.yaml` file:
```sh
pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
```
**Chat with your flow**
In the `my_chatbot` folder, there's a `flow.dag.yaml` file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc
> Note that in the `chat` node, we're using a connection named `open_ai_connection` (specified in `connection` field) and the `gpt-35-turbo` model (specified in `deployment_name` field). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource.
Interact with your chatbot by running: (press `Ctrl + C` to end the session)
```sh
pf flow test --flow ./my_chatbot --interactive
```
#### Continue to delve deeper into [prompt flow](https://github.com/microsoft/promptflow).
| promptflow/src/promptflow/README.md/0 | {
"file_path": "promptflow/src/promptflow/README.md",
"repo_id": "promptflow",
"token_count": 1489
} | 7 |
import os
from promptflow._cli._params import add_param_yes, base_params
from promptflow._cli._utils import activate_action, get_cli_sdk_logger
from promptflow._utils.utils import prompt_y_n
from promptflow.exceptions import UserErrorException
logger = get_cli_sdk_logger()
UPGRADE_MSG = "Not able to upgrade automatically"
def add_upgrade_parser(subparsers):
"""Add upgrade parser to the pf subparsers."""
epilog = """
Examples:
# Upgrade prompt flow without prompt and run non-interactively:
pf upgrade --yes
""" # noqa: E501
add_params = [
add_param_yes,
] + base_params
activate_action(
name="upgrade",
description="Upgrade prompt flow CLI.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pf upgrade",
action_param_name="action",
)
def upgrade_version(args):
import platform
import subprocess
import sys
from packaging.version import parse
from promptflow._constants import _ENV_PF_INSTALLER, CLI_PACKAGE_NAME
from promptflow._utils.version_hint_utils import get_latest_version_from_pypi
from promptflow._version import VERSION as local_version
latest_version = get_latest_version_from_pypi(CLI_PACKAGE_NAME)
if not latest_version:
logger.warning("Failed to get the latest prompt flow version.")
return
elif parse(latest_version) <= parse(local_version):
logger.warning("You already have the latest prompt flow version: %s", local_version)
return
yes = args.yes
exit_code = 0
installer = os.getenv(_ENV_PF_INSTALLER) or ""
installer = installer.upper()
print(f"installer: {installer}")
latest_version_msg = (
"Upgrading prompt flow CLI version to {}.".format(latest_version)
if yes
else "Latest version available is {}.".format(latest_version)
)
logger.warning("Your current prompt flow CLI version is %s. %s", local_version, latest_version_msg)
if not yes:
logger.warning("Please check the release notes first")
if not sys.stdin.isatty():
logger.debug("No tty available.")
raise UserErrorException("No tty available. Please run command with --yes.")
confirmation = prompt_y_n("Do you want to continue?", default="y")
if not confirmation:
logger.debug("Upgrade stopped by user")
return
if installer == "MSI":
_upgrade_on_windows(yes)
elif installer == "PIP":
pip_args = [
sys.executable,
"-m",
"pip",
"install",
"--upgrade",
"promptflow[azure,executable,azureml-serving]",
"-vv",
"--disable-pip-version-check",
"--no-cache-dir",
]
logger.debug("Update prompt flow with '%s'", " ".join(pip_args))
exit_code = subprocess.call(pip_args, shell=platform.system() == "Windows")
elif installer == "SCRIPT":
command = "curl https://promptflowartifact.blob.core.windows.net/linux-install-scripts/install | bash"
logger.warning(f"{UPGRADE_MSG}, you can try to run {command} in your terminal directly to upgrade package.")
return
else:
logger.warning(UPGRADE_MSG)
return
if exit_code:
err_msg = "CLI upgrade failed."
logger.warning(err_msg)
sys.exit(exit_code)
import importlib
import json
importlib.reload(subprocess)
importlib.reload(json)
version_result = subprocess.check_output(["pf", "version"], shell=platform.system() == "Windows")
version_json = json.loads(version_result)
new_version = version_json["promptflow"]
if new_version == local_version:
err_msg = f"CLI upgrade to version {latest_version} failed or aborted."
logger.warning(err_msg)
sys.exit(1)
logger.warning("Upgrade finished.")
def _upgrade_on_windows(yes):
"""Download MSI to a temp folder and install it with msiexec.exe.
Directly installing from URL may be blocked by policy: https://github.com/Azure/azure-cli/issues/19171
This also gives the user a chance to manually install the MSI in case of msiexec.exe failure.
"""
import subprocess
import sys
import tempfile
msi_url = "https://aka.ms/installpromptflowwindowsx64"
logger.warning("Updating prompt flow CLI with MSI from %s", msi_url)
# Save MSI to ~\AppData\Local\Temp\promptflow-msi, clean up the folder first
msi_dir = os.path.join(tempfile.gettempdir(), "promptflow-msi")
try:
import shutil
shutil.rmtree(msi_dir)
except FileNotFoundError:
# The folder has already been deleted. No further retry is needed.
# errno: 2, winerror: 3, strerror: 'The system cannot find the path specified'
pass
except OSError as err:
logger.warning("Failed to delete '%s': %s. You may try to delete it manually.", msi_dir, err)
os.makedirs(msi_dir, exist_ok=True)
msi_path = _download_from_url(msi_url, msi_dir)
if yes:
subprocess.Popen(["msiexec.exe", "/i", msi_path, "/qn"])
else:
subprocess.call(["msiexec.exe", "/i", msi_path])
logger.warning("Installation started. Please complete the upgrade in the opened window.")
sys.exit(0)
def _download_from_url(url, target_dir):
import requests
r = requests.get(url, stream=True)
if r.status_code != 200:
raise UserErrorException("Request to {} failed with {}".format(url, r.status_code))
# r.url is the real path of the msi, like
# 'https://promptflowartifact.blob.core.windows.net/msi-installer/promptflow.msi'
file_name = r.url.rsplit("/")[-1]
msi_path = os.path.join(target_dir, file_name)
logger.warning("Downloading MSI to %s", msi_path)
with open(msi_path, "wb") as f:
for chunk in r.iter_content(chunk_size=1024):
f.write(chunk)
return msi_path
| promptflow/src/promptflow/promptflow/_cli/_pf/_upgrade.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf/_upgrade.py",
"repo_id": "promptflow",
"token_count": 2397
} | 8 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/OpenAIConnection.schema.json
name: {{ connection }}
type: open_ai
api_key: "<user-input>"
| promptflow/src/promptflow/promptflow/_cli/data/chat_flow/template/openai.yaml.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/chat_flow/template/openai.yaml.jinja2",
"repo_id": "promptflow",
"token_count": 59
} | 9 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
from contextvars import ContextVar
from typing import Dict, Mapping
from promptflow._version import VERSION
class OperationContext(Dict):
"""The OperationContext class.
This class is used to store the context information for the current operation. It is a dictionary-like class
that can be used to store any primitive context information. The object is a context variable that can be
accessed from anywhere in the current context. The context information is used to provide additional information
to the service for logging and telemetry purposes.
"""
_CONTEXT_KEY = "operation_context"
_OTEL_ATTRIBUTES = "_otel_attributes"
_current_context = ContextVar(_CONTEXT_KEY, default=None)
USER_AGENT_KEY = "user_agent"
_DEFAULT_TRACKING_KEYS = {"run_mode", "root_run_id", "flow_id", "batch_input_source"}
_TRACKING_KEYS = "_tracking_keys"
def _add_otel_attributes(self, key, value):
attributes = self.get(OperationContext._OTEL_ATTRIBUTES, {})
attributes[key] = value
self[OperationContext._OTEL_ATTRIBUTES] = attributes
def _remove_otel_attributes(self, keys: list):
if isinstance(keys, str):
keys = [keys]
attributes = self.get(OperationContext._OTEL_ATTRIBUTES, {})
for key in keys:
attributes.pop(key, None)
self[OperationContext._OTEL_ATTRIBUTES] = attributes
def _get_otel_attributes(self):
return self.get(OperationContext._OTEL_ATTRIBUTES, {})
@classmethod
def get_instance(cls):
"""Get the OperationContext instance.
This method returns the OperationContext instance from the current context.
If there is no instance in the current context, it creates a new one and sets it in the current context.
Returns:
OperationContext: The OperationContext instance.
"""
# get the OperationContext instance from the current context
instance = cls._current_context.get()
if instance is None:
# create a new instance and set it in the current context
instance = OperationContext()
cls._current_context.set(instance)
if cls._TRACKING_KEYS not in instance:
instance[cls._TRACKING_KEYS] = copy.copy(cls._DEFAULT_TRACKING_KEYS)
return instance
def __setattr__(self, name, value):
"""Set the attribute.
This method sets an attribute with the given name and value in the OperationContext instance.
The name must be a string and the value must be a primitive.
Args:
name (str): The name of the attribute.
value (int, float, str, bool, or None): The value of the attribute.
Raises:
TypeError: If name is not a string or value is not a primitive.
"""
# check that name is a string
if not isinstance(name, str):
raise TypeError("Name must be a string")
# set the item in the data attribute
self[name] = value
def __getattr__(self, name):
"""Get the attribute.
This method returns the attribute with the given name from the OperationContext instance.
If there is no such attribute, it returns the default attribute from the super class.
Args:
name (str): The name of the attribute.
Returns:
int, float, str, bool, or None: The value of the attribute.
"""
if name in self:
return self[name]
else:
super().__getattribute__(name)
def __delattr__(self, name):
"""Delete the attribute.
This method deletes the attribute with the given name from the OperationContext instance.
If there is no such attribute, it deletes the default attribute from the super class.
Args:
name (str): The name of the attribute.
"""
if name in self:
del self[name]
else:
super().__delattr__(name)
def get_user_agent(self):
"""Get the user agent string.
This method returns the user agent string for the OperationContext instance.
The user agent string consists of the promptflow-sdk version and any additional user agent information stored in
the user_agent attribute.
Returns:
str: The user agent string.
"""
def parts():
if OperationContext.USER_AGENT_KEY in self:
yield self.get(OperationContext.USER_AGENT_KEY)
yield f"promptflow/{VERSION}"
# strip to avoid leading or trailing spaces, which may cause error when sending request
ua = " ".join(parts()).strip()
return ua
def append_user_agent(self, user_agent: str):
"""Append the user agent string.
This method appends user agent information to the user_agent attribute of the OperationContext instance.
If there is no user_agent attribute, it creates one with the given user agent information.
Args:
user_agent (str): The user agent information to append.
"""
if OperationContext.USER_AGENT_KEY in self:
if user_agent not in self.user_agent:
self.user_agent = f"{self.user_agent.strip()} {user_agent.strip()}"
else:
self.user_agent = user_agent
def set_batch_input_source_from_inputs_mapping(self, inputs_mapping: Mapping[str, str]):
"""Infer the batch input source from the input mapping and set it in the OperationContext instance.
This method analyzes the `inputs_mapping` to ascertain the origin of the inputs for a batch operation.
The `inputs_mapping` should be a dictionary with keys representing input names and values specifying the sources
of these inputs. Inputs can originate from direct data or from the outputs of a previous run.
The `inputs_mapping` is dictated entirely by the external caller. For more details on column mapping, refer to
https://aka.ms/pf/column-mapping. The mapping can include references to both the inputs and outputs of previous
runs, using a reserved source name 'run' to indicate such references. However, this method specifically checks
for references to outputs of previous runs, which are denoted by values starting with "${run.outputs". When such
a reference is found, the `batch_input_source` attribute of the OperationContext instance is set to "Run" to
reflect that the batch operation is utilizing outputs from a prior run.
If no values in the `inputs_mapping` start with "${run.outputs", it is inferred that the inputs do not derive
from a previous run, and the `batch_input_source` is set to "Data".
Examples of `inputs_mapping`:
- Referencing a previous run's output:
{'input1': '${run.outputs.some_output}', 'input2': 'direct_data'}
In this case, 'input1' is sourced from a prior run's output, and 'input2' is from direct data.
The `batch_input_source` would be set to "Run".
- Sourcing directly from data:
{'input1': 'data_source1', 'input2': 'data_source2'}
Since no values start with "${run.outputs", the `batch_input_source` is set to "Data".
Args:
inputs_mapping (Mapping[str, str]): A dictionary mapping input names to their sources, where the sources
can be either direct data or outputs from a previous run. The structure and content of this mapping are
entirely under the control of the external caller.
Returns:
None
"""
if inputs_mapping and any(
isinstance(value, str) and value.startswith("${run.outputs") for value in inputs_mapping.values()
):
self.batch_input_source = "Run"
else:
self.batch_input_source = "Data"
def get_context_dict(self):
"""Get the context dictionary.
This method returns the context dictionary for the OperationContext instance.
The context dictionary is a dictionary that contains all the context information stored in the OperationContext
instance.
Returns:
dict: The context dictionary.
"""
return dict(self)
def _get_tracking_info(self):
keys = getattr(self, self._TRACKING_KEYS, self._DEFAULT_TRACKING_KEYS)
return {k: v for k, v in self.items() if k in keys}
| promptflow/src/promptflow/promptflow/_core/operation_context.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/operation_context.py",
"repo_id": "promptflow",
"token_count": 3185
} | 10 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import shlex
import subprocess
import sys
import tempfile
from dataclasses import asdict
from pathlib import Path
from flask import Response, jsonify, make_response, request
from promptflow._sdk._constants import FlowRunProperties, get_list_view_type
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._service import Namespace, Resource, fields
from promptflow._sdk._service.utils.utils import build_pfs_user_agent, get_client_from_request, make_response_no_content
from promptflow._sdk.entities import Run as RunEntity
from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations
from promptflow._utils.yaml_utils import dump_yaml
from promptflow.contracts._run_management import RunMetadata
api = Namespace("Runs", description="Runs Management")
# Define update run request parsing
update_run_parser = api.parser()
update_run_parser.add_argument("display_name", type=str, location="form", required=False)
update_run_parser.add_argument("description", type=str, location="form", required=False)
update_run_parser.add_argument("tags", type=str, location="form", required=False)
# Define visualize request parsing
visualize_parser = api.parser()
visualize_parser.add_argument("html", type=str, location="form", required=False)
# Response model of run operation
dict_field = api.schema_model("RunDict", {"additionalProperties": True, "type": "object"})
list_field = api.schema_model("RunList", {"type": "array", "items": {"$ref": "#/definitions/RunDict"}})
@api.route("/")
class RunList(Resource):
@api.response(code=200, description="Runs", model=list_field)
@api.doc(description="List all runs")
def get(self):
# parse query parameters
max_results = request.args.get("max_results", default=50, type=int)
all_results = request.args.get("all_results", default=False, type=bool)
archived_only = request.args.get("archived_only", default=False, type=bool)
include_archived = request.args.get("include_archived", default=False, type=bool)
# align with CLI behavior
if all_results:
max_results = None
list_view_type = get_list_view_type(archived_only=archived_only, include_archived=include_archived)
runs = get_client_from_request().runs.list(max_results=max_results, list_view_type=list_view_type)
runs_dict = [run._to_dict() for run in runs]
return jsonify(runs_dict)
@api.route("/submit")
class RunSubmit(Resource):
@api.response(code=200, description="Submit run info", model=dict_field)
@api.doc(body=dict_field, description="Submit run")
def post(self):
run_dict = request.get_json(force=True)
run_name = run_dict.get("name", None)
if not run_name:
run = RunEntity(**run_dict)
run_name = run._generate_run_name()
run_dict["name"] = run_name
with tempfile.TemporaryDirectory() as temp_dir:
run_file = Path(temp_dir) / "batch_run.yaml"
with open(run_file, "w", encoding="utf-8") as f:
dump_yaml(run_dict, f)
cmd = [
"pf",
"run",
"create",
"--file",
str(run_file),
"--user-agent",
build_pfs_user_agent(),
]
if sys.executable.endswith("pfcli.exe"):
cmd = ["pfcli"] + cmd
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
stdout, _ = process.communicate()
if process.returncode == 0:
try:
run = get_client_from_request().runs._get(name=run_name)
return jsonify(run._to_dict())
except RunNotFoundError as e:
raise RunNotFoundError(
f"Failed to get the submitted run: {e}\n"
f"Used command: {' '.join(shlex.quote(arg) for arg in cmd)}\n"
f"Output: {stdout.decode('utf-8')}"
)
else:
raise Exception(f"Create batch run failed: {stdout.decode('utf-8')}")
@api.route("/<string:name>")
class Run(Resource):
@api.response(code=200, description="Update run info", model=dict_field)
@api.doc(parser=update_run_parser, description="Update run")
def put(self, name: str):
args = update_run_parser.parse_args()
tags = json.loads(args.tags) if args.tags else None
run = get_client_from_request().runs.update(
name=name, display_name=args.display_name, description=args.description, tags=tags
)
return jsonify(run._to_dict())
@api.response(code=200, description="Get run info", model=dict_field)
@api.doc(description="Get run")
def get(self, name: str):
run = get_client_from_request().runs.get(name=name)
return jsonify(run._to_dict())
@api.response(code=204, description="Delete run", model=dict_field)
@api.doc(description="Delete run")
def delete(self, name: str):
get_client_from_request().runs.delete(name=name)
return make_response_no_content()
@api.route("/<string:name>/childRuns")
class FlowChildRuns(Resource):
@api.response(code=200, description="Child runs", model=list_field)
@api.doc(description="Get child runs")
def get(self, name: str):
run = get_client_from_request().runs.get(name=name)
local_storage_op = LocalStorageOperations(run=run)
detail_dict = local_storage_op.load_detail()
return jsonify(detail_dict["flow_runs"])
@api.route("/<string:name>/nodeRuns/<string:node_name>")
class FlowNodeRuns(Resource):
@api.response(code=200, description="Node runs", model=list_field)
@api.doc(description="Get node runs info")
def get(self, name: str, node_name: str):
run = get_client_from_request().runs.get(name=name)
local_storage_op = LocalStorageOperations(run=run)
detail_dict = local_storage_op.load_detail()
node_runs = [item for item in detail_dict["node_runs"] if item["node"] == node_name]
return jsonify(node_runs)
@api.route("/<string:name>/metaData")
class MetaData(Resource):
@api.doc(description="Get metadata of run")
@api.response(code=200, description="Run metadata", model=dict_field)
def get(self, name: str):
run = get_client_from_request().runs.get(name=name)
local_storage_op = LocalStorageOperations(run=run)
metadata = RunMetadata(
name=run.name,
display_name=run.display_name,
create_time=run.created_on,
flow_path=run.properties[FlowRunProperties.FLOW_PATH],
output_path=run.properties[FlowRunProperties.OUTPUT_PATH],
tags=run.tags,
lineage=run.run,
metrics=local_storage_op.load_metrics(),
dag=local_storage_op.load_dag_as_string(),
flow_tools_json=local_storage_op.load_flow_tools_json(),
)
return jsonify(asdict(metadata))
@api.route("/<string:name>/logContent")
class LogContent(Resource):
@api.doc(description="Get run log content")
@api.response(code=200, description="Log content", model=fields.String)
def get(self, name: str):
run = get_client_from_request().runs.get(name=name)
local_storage_op = LocalStorageOperations(run=run)
log_content = local_storage_op.logger.get_logs()
return make_response(log_content)
@api.route("/<string:name>/metrics")
class Metrics(Resource):
@api.doc(description="Get run metrics")
@api.response(code=200, description="Run metrics", model=dict_field)
def get(self, name: str):
run = get_client_from_request().runs.get(name=name)
local_storage_op = LocalStorageOperations(run=run)
metrics = local_storage_op.load_metrics()
return jsonify(metrics)
@api.route("/<string:name>/visualize")
class VisualizeRun(Resource):
@api.doc(description="Visualize run")
@api.response(code=200, description="Visualize run", model=fields.String)
@api.produces(["text/html"])
def get(self, name: str):
with tempfile.TemporaryDirectory() as temp_dir:
from promptflow._sdk.operations import RunOperations
run_op: RunOperations = get_client_from_request().runs
html_path = Path(temp_dir) / "visualize_run.html"
# visualize operation may accept name in string
run_op.visualize(name, html_path=html_path)
with open(html_path, "r") as f:
return Response(f.read(), mimetype="text/html")
@api.route("/<string:name>/archive")
class ArchiveRun(Resource):
@api.doc(description="Archive run")
@api.response(code=200, description="Archived run", model=dict_field)
def get(self, name: str):
run = get_client_from_request().runs.archive(name=name)
return jsonify(run._to_dict())
@api.route("/<string:name>/restore")
class RestoreRun(Resource):
@api.doc(description="Restore run")
@api.response(code=200, description="Restored run", model=dict_field)
def get(self, name: str):
run = get_client_from_request().runs.restore(name=name)
return jsonify(run._to_dict())
| promptflow/src/promptflow/promptflow/_sdk/_service/apis/run.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/apis/run.py",
"repo_id": "promptflow",
"token_count": 3864
} | 11 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from flask import Blueprint, current_app as app, request
from promptflow._sdk._serving.monitor.flow_monitor import FlowMonitor
def is_monitoring_enabled() -> bool:
enabled = False
if request.endpoint in app.view_functions:
view_func = app.view_functions[request.endpoint]
enabled = hasattr(view_func, "_enable_monitoring")
return enabled
def construct_monitor_blueprint(flow_monitor: FlowMonitor):
"""Construct monitor blueprint."""
monitor_blueprint = Blueprint("monitor_blueprint", __name__)
@monitor_blueprint.before_app_request
def start_monitoring():
if not is_monitoring_enabled():
return
flow_monitor.start_monitoring()
@monitor_blueprint.after_app_request
def finish_monitoring(response):
if not is_monitoring_enabled():
return response
flow_monitor.finish_monitoring(response.status_code)
return response
return monitor_blueprint
| promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/monitor_blueprint.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/monitor_blueprint.py",
"repo_id": "promptflow",
"token_count": 365
} | 12 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
from promptflow.contracts.flow import Flow, FlowInputDefinition, FlowOutputDefinition
from promptflow.contracts.tool import ValueType
type_mapping = {
ValueType.INT: "integer",
ValueType.DOUBLE: "number",
ValueType.BOOL: "boolean",
ValueType.STRING: "string",
ValueType.LIST: "array",
ValueType.OBJECT: "object",
ValueType.IMAGE: "object", # Dump as object as portal test page can't handle image now
}
def generate_input_field_schema(input: FlowInputDefinition) -> dict:
field_schema = {"type": type_mapping[input.type]}
if input.description:
field_schema["description"] = input.description
if input.default:
field_schema["default"] = input.default
if input.type == ValueType.OBJECT:
field_schema["additionalProperties"] = {}
if input.type == ValueType.LIST:
field_schema["items"] = {"type": "object", "additionalProperties": {}}
return field_schema
def generate_output_field_schema(output: FlowOutputDefinition) -> dict:
field_schema = {"type": type_mapping[output.type]}
if output.description:
field_schema["description"] = output.description
if output.type == ValueType.OBJECT:
field_schema["additionalProperties"] = {}
if output.type == ValueType.LIST:
field_schema["items"] = {"type": "object", "additionalProperties": {}}
return field_schema
def generate_swagger(flow: Flow, samples, outputs_to_remove: list) -> dict:
"""convert a flow to swagger object."""
swagger = {"openapi": "3.0.0"}
swagger["info"] = {
"title": f"Promptflow[{flow.name}] API",
"version": "1.0.0",
"x-flow-name": str(flow.name),
}
swagger["components"] = {
"securitySchemes": {
"bearerAuth": {
"type": "http",
"scheme": "bearer",
}
}
}
swagger["security"] = [{"bearerAuth": []}]
input_schema = {"type": "object"}
request_body_required = False
if len(flow.inputs) > 0:
input_schema["properties"] = {}
input_schema["required"] = []
request_body_required = True
for name, input in flow.inputs.items():
if input.is_chat_input:
swagger["info"]["x-chat-input"] = name
swagger["info"]["x-flow-type"] = "chat"
if input.is_chat_history:
swagger["info"]["x-chat-history"] = name
input_schema["properties"][name] = generate_input_field_schema(input)
input_schema["required"].append(name)
output_schema = {"type": "object"}
if len(flow.outputs) > 0:
output_schema["properties"] = {}
for name, output in flow.outputs.items():
# skip evaluation only outputs in swagger
# TODO remove this if sdk removed this evaluation_only field
if output.evaluation_only:
continue
if output.is_chat_output:
swagger["info"]["x-chat-output"] = name
if outputs_to_remove and name in outputs_to_remove:
continue
output_schema["properties"][name] = generate_output_field_schema(output)
example = {}
if samples:
if isinstance(samples, list):
example = samples[0]
else:
logging.warning("samples should be a list of dict, but got %s, skipped.", type(samples))
swagger["paths"] = {
"/score": {
"post": {
"summary": f"run promptflow: {flow.name} with an given input",
"requestBody": {
"description": "promptflow input data",
"required": request_body_required,
"content": {
"application/json": {
"schema": input_schema,
"example": example, # need to check this based on the sample data
}
},
},
"responses": {
"200": {
"description": "successful operation",
"content": {
"application/json": {
"schema": output_schema,
}
},
},
"400": {
"description": "Invalid input",
},
"default": {
"description": "unexpected error",
},
},
}
}
}
return swagger
| promptflow/src/promptflow/promptflow/_sdk/_serving/swagger.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/swagger.py",
"repo_id": "promptflow",
"token_count": 2285
} | 13 |
import json
import os
import time
from copy import copy
from pathlib import Path
from types import GeneratorType
import streamlit as st
from PIL import Image
from streamlit_quill import st_quill
from utils import dict_iter_render_message, parse_image_content, parse_list_from_html, render_single_dict_message
from promptflow import load_flow
from promptflow._constants import STREAMING_ANIMATION_TIME
from promptflow._sdk._submitter.utils import resolve_generator, resolve_generator_output_with_cache
from promptflow._sdk._utils import dump_flow_result
from promptflow._utils.multimedia_utils import convert_multimedia_data_to_base64, persist_multimedia_data
invoker = None
def start():
def clear_chat() -> None:
st.session_state.messages = []
def render_message(role, message_items):
with st.chat_message(role):
if is_chat_flow:
render_single_dict_message(message_items)
else:
dict_iter_render_message(message_items)
def show_conversation() -> None:
if "messages" not in st.session_state:
st.session_state.messages = []
st.session_state.history = []
if st.session_state.messages:
for role, message_items in st.session_state.messages:
render_message(role, message_items)
def get_chat_history_from_session():
if "history" in st.session_state:
return st.session_state.history
return []
def post_process_dump_result(response, session_state_history, *, generator_record):
response = resolve_generator(response, generator_record)
# Get base64 for multi modal object
resolved_outputs = {
k: convert_multimedia_data_to_base64(v, with_type=True, dict_type=True) for k, v in response.output.items()
}
st.session_state.messages.append(("assistant", resolved_outputs))
session_state_history.update({"outputs": response.output})
st.session_state.history.append(session_state_history)
if is_chat_flow:
dump_path = Path(flow_path).parent
response.output = persist_multimedia_data(
response.output, base_dir=dump_path, sub_dir=Path(".promptflow/output")
)
dump_flow_result(flow_folder=dump_path, flow_result=response, prefix="chat")
return resolved_outputs
def submit(**kwargs) -> None:
# generator record should be reset for each submit
generator_record = {}
st.session_state.messages.append(("user", kwargs))
session_state_history = dict()
session_state_history.update({"inputs": kwargs})
with container:
render_message("user", kwargs)
# Force append chat history to kwargs
if is_chat_flow:
response = run_flow({chat_history_input_name: get_chat_history_from_session(), **kwargs})
else:
response = run_flow(kwargs)
if response.run_info.status.value == "Failed":
raise Exception(response.run_info.error)
if is_streaming:
# Display assistant response in chat message container
with container:
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = f"{chat_output_name}: "
prefix_length = len(full_response)
chat_output = response.output[chat_output_name]
if isinstance(chat_output, GeneratorType):
# Simulate stream of response with milliseconds delay
for chunk in resolve_generator_output_with_cache(
chat_output, generator_record, generator_key=f"run.outputs.{chat_output_name}"
):
# there should be no extra spaces between adjacent chunks?
full_response += chunk
time.sleep(STREAMING_ANIMATION_TIME)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
response.output[chat_output_name] = full_response[prefix_length:]
post_process_dump_result(response, session_state_history, generator_record=generator_record)
return
resolved_outputs = post_process_dump_result(response, session_state_history, generator_record=generator_record)
with container:
render_message("assistant", resolved_outputs)
def run_flow(data: dict) -> dict:
global invoker
if not invoker:
if flow_path:
flow = Path(flow_path)
else:
flow = Path(__file__).parent / "flow"
if flow.is_dir():
os.chdir(flow)
else:
os.chdir(flow.parent)
invoker = load_flow(flow)
invoker.context.streaming = is_streaming
result = invoker.invoke(data)
return result
image = Image.open(Path(__file__).parent / "logo.png")
st.set_page_config(
layout="wide",
page_title=f"{flow_name} - Promptflow App",
page_icon=image,
menu_items={
"About": """
# This is a Promptflow App.
You can refer to [promptflow](https://github.com/microsoft/promptflow) for more information.
"""
},
)
# Set primary button color here since button color of the same form need to be identical in streamlit, but we only
# need Run/Chat button to be blue.
st.config.set_option("theme.primaryColor", "#0F6CBD")
st.title(flow_name)
st.divider()
st.chat_message("assistant").write("Hello, please input following flow inputs.")
container = st.container()
with container:
show_conversation()
with st.form(key="input_form", clear_on_submit=True):
settings_path = os.path.join(os.path.dirname(__file__), "settings.json")
if os.path.exists(settings_path):
with open(settings_path, "r", encoding="utf-8") as file:
json_data = json.load(file)
environment_variables = list(json_data.keys())
for environment_variable in environment_variables:
secret_input = st.sidebar.text_input(
label=environment_variable,
type="password",
placeholder=f"Please input {environment_variable} here. "
f"If you input before, you can leave it blank.",
)
if secret_input != "":
os.environ[environment_variable] = secret_input
flow_inputs_params = {}
for flow_input, (default_value, value_type) in flow_inputs.items():
if value_type == "list":
st.text(flow_input)
input = st_quill(
html=True,
toolbar=["image"],
key=flow_input,
placeholder="Please enter the list values and use the image icon to upload a picture. "
"Make sure to format each list item correctly with line breaks",
)
elif value_type == "image":
input = st.file_uploader(label=flow_input)
elif value_type == "string":
input = st.text_input(label=flow_input, placeholder=default_value)
else:
input = st.text_input(label=flow_input, placeholder=default_value)
flow_inputs_params.update({flow_input: copy(input)})
cols = st.columns(7)
submit_bt = cols[0].form_submit_button(label=label, type="primary")
clear_bt = cols[1].form_submit_button(label="Clear")
if submit_bt:
with st.spinner("Loading..."):
for flow_input, (default_value, value_type) in flow_inputs.items():
if value_type == "list":
input = parse_list_from_html(flow_inputs_params[flow_input])
flow_inputs_params.update({flow_input: copy(input)})
elif value_type == "image":
input = parse_image_content(
flow_inputs_params[flow_input],
flow_inputs_params[flow_input].type if flow_inputs_params[flow_input] else None,
)
flow_inputs_params.update({flow_input: copy(input)})
submit(**flow_inputs_params)
if clear_bt:
with st.spinner("Cleaning..."):
clear_chat()
st.rerun()
if __name__ == "__main__":
with open(Path(__file__).parent / "config.json", "r") as f:
config = json.load(f)
is_chat_flow = config["is_chat_flow"]
chat_history_input_name = config["chat_history_input_name"]
flow_path = config["flow_path"]
flow_name = config["flow_name"]
flow_inputs = config["flow_inputs"]
label = config["label"]
is_streaming = config["is_streaming"]
chat_output_name = config["chat_output_name"]
start()
| promptflow/src/promptflow/promptflow/_sdk/data/executable/main.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/executable/main.py",
"repo_id": "promptflow",
"token_count": 4323
} | 14 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import abc
from typing import Dict, Optional
from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY, CommonYamlFields
from promptflow._sdk._utils import load_from_dict
from promptflow._utils.yaml_utils import dump_yaml
class YAMLTranslatableMixin(abc.ABC):
@classmethod
# pylint: disable=unused-argument
def _resolve_cls_and_type(cls, data, params_override: Optional[list]):
"""Resolve the class to use for deserializing the data. Return current class if no override is provided.
:param data: Data to deserialize.
:type data: dict
:param params_override: Parameters to override, defaults to None
:type params_override: typing.Optional[list]
:return: Class to use for deserializing the data & its "type". Type will be None if no override is provided.
:rtype: tuple[class, typing.Optional[str]]
"""
@classmethod
def _get_schema_cls(self):
pass
def _to_dict(self) -> Dict:
schema_cls = self._get_schema_cls()
return schema_cls(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
def _to_yaml(self) -> str:
return dump_yaml(self._to_dict())
def __str__(self):
try:
return self._to_yaml()
except BaseException: # pylint: disable=broad-except
return super(YAMLTranslatableMixin, self).__str__()
@classmethod
def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs):
schema_cls = cls._get_schema_cls()
loaded_data = load_from_dict(schema_cls, data, context, additional_message, **kwargs)
# pop the type field since it already exists in class init
loaded_data.pop(CommonYamlFields.TYPE, None)
return cls(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
| promptflow/src/promptflow/promptflow/_sdk/entities/_yaml_translatable.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/entities/_yaml_translatable.py",
"repo_id": "promptflow",
"token_count": 744
} | 15 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import re
from marshmallow import ValidationError, fields, validate, validates_schema
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import FlowType
from promptflow._sdk.schemas._base import PatchedSchemaMeta, YamlFileSchema
from promptflow._sdk.schemas._fields import NestedField
class FlowInputSchema(metaclass=PatchedSchemaMeta):
"""Schema for flow input."""
type = fields.Str(required=True)
description = fields.Str()
# Note: default attribute default can be various types, so we use Raw type here,
# but when transforming to json schema, there is no equivalent type, it will become string type
# may need to delete the default type in the generated json schema to avoid false alarm
default = fields.Raw()
is_chat_input = fields.Bool()
is_chat_history = fields.Bool()
class FlowOutputSchema(metaclass=PatchedSchemaMeta):
"""Schema for flow output."""
type = fields.Str(required=True)
reference = fields.Str()
description = fields.Str()
is_chat_output = fields.Bool()
class BaseFlowSchema(YamlFileSchema):
"""Base schema for flow."""
additional_includes = fields.List(fields.Str())
environment = fields.Dict()
# metadata
type = fields.Str(validate=validate.OneOf(FlowType.get_all_values()))
language = fields.Str(
default=FlowLanguage.Python,
validate=validate.OneOf([FlowLanguage.Python, FlowLanguage.CSharp]),
)
description = fields.Str()
display_name = fields.Str()
tags = fields.Dict(keys=fields.Str(), values=fields.Str())
class FlowSchema(BaseFlowSchema):
"""Schema for flow dag."""
inputs = fields.Dict(keys=fields.Str(), values=NestedField(FlowInputSchema))
outputs = fields.Dict(keys=fields.Str(), values=NestedField(FlowOutputSchema))
nodes = fields.List(fields.Dict())
node_variants = fields.Dict(keys=fields.Str(), values=fields.Dict())
class PythonEagerFlowEntry(fields.Str):
"""Entry point for eager flow. For example: pkg.module:func"""
default_error_messages = {
"invalid_entry": "Provided entry {entry} has incorrect format. "
"Python eager flow only support pkg.module:func format.",
}
def _validate(self, value):
super()._validate(value)
if not re.match(r"^[a-zA-Z0-9_.]+:[a-zA-Z0-9_]+$", value):
raise self.make_error("invalid_entry", entry=value)
class EagerFlowSchema(BaseFlowSchema):
"""Schema for eager flow."""
# entry point for eager flow
entry = fields.Str(required=True)
@validates_schema(skip_on_field_errors=False)
def validate_entry(self, data, **kwargs):
"""Validate entry."""
language = data.get(LANGUAGE_KEY, FlowLanguage.Python)
entry_regex = None
if language == FlowLanguage.CSharp:
entry_regex = r"\((.+)\)[a-zA-Z0-9]+(\.[a-zA-Z0-9]+)+"
elif language == FlowLanguage.Python:
entry_regex = r"^[a-zA-Z0-9_.]+:[a-zA-Z0-9_]+$"
if entry_regex is not None and not re.match(entry_regex, data["entry"]):
raise ValidationError(field_name="entry", message=f"Entry function {data['entry']} is not valid.")
| promptflow/src/promptflow/promptflow/_sdk/schemas/_flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/schemas/_flow.py",
"repo_id": "promptflow",
"token_count": 1215
} | 16 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import os
from pathlib import Path
from typing import Any, Dict, List, Tuple, Union
from promptflow.exceptions import ErrorTarget, UserErrorException
module_logger = logging.getLogger(__name__)
def _pd_read_file(local_path: str, logger: logging.Logger = None, max_rows_count: int = None) -> "DataFrame":
import pandas as pd
local_path = str(local_path)
# if file is empty, return empty DataFrame directly
if (
os.path.getsize(local_path) == 0
): # CodeQL [SM01305] Safe use per local_path is set by PRT service not by end user
return pd.DataFrame()
# load different file formats
# set dtype to object to avoid auto type conversion
# executor will apply type conversion based on flow definition, so no conversion should be acceptable
# note that for csv and tsv format, this will make integer and float columns to be string;
# for rest, integer will be int and float will be float
dtype = object
if local_path.endswith(".csv"):
df = pd.read_csv(local_path, dtype=dtype, keep_default_na=False, nrows=max_rows_count)
elif local_path.endswith(".json"):
df = pd.read_json(local_path, dtype=dtype)
elif local_path.endswith(".jsonl"):
df = pd.read_json(local_path, dtype=dtype, lines=True, nrows=max_rows_count)
elif local_path.endswith(".tsv"):
df = pd.read_table(local_path, dtype=dtype, keep_default_na=False, nrows=max_rows_count)
elif local_path.endswith(".parquet"):
df = pd.read_parquet(local_path) # read_parquet has no parameter dtype
else:
# parse file as jsonl when extension is not known (including unavailable)
# ignore and logging if failed to load file content.
try:
df = pd.read_json(local_path, dtype=dtype, lines=True, nrows=max_rows_count)
except: # noqa: E722
if logger is None:
logger = module_logger
logger.warning(
f"File {Path(local_path).name} is not supported format: "
f"csv, tsv, json, jsonl, parquet. Ignoring it."
)
return pd.DataFrame()
return df
def _bfs_dir(dir_path: List[str]) -> Tuple[List[str], List[str]]:
"""BFS traverse directory with depth 1, returns files and directories"""
files, dirs = [], []
for path in dir_path:
for filename in os.listdir(path):
file = Path(path, filename).resolve()
if file.is_file():
files.append(str(file))
else:
dirs.append(str(file))
return files, dirs
def _handle_dir(dir_path: str, max_rows_count: int, logger: logging.Logger = None) -> "DataFrame":
"""load data from directory"""
import pandas as pd
df = pd.DataFrame()
# BFS traverse directory to collect files to load
target_dir = [str(dir_path)]
while len(target_dir) > 0:
files, dirs = _bfs_dir(target_dir)
for file in files:
current_df = _pd_read_file(file, logger=logger, max_rows_count=max_rows_count)
df = pd.concat([df, current_df])
length = len(df)
if max_rows_count and length >= max_rows_count:
df = df.head(max_rows_count)
return df
# no readable data in current level, dive into next level
target_dir = dirs
return df
def load_data(
local_path: Union[str, Path], *, logger: logging.Logger = None, max_rows_count: int = None
) -> List[Dict[str, Any]]:
"""load data from local file"""
df = load_df(local_path, logger, max_rows_count=max_rows_count)
# convert dataframe to list of dict
result = []
for _, row in df.iterrows():
result.append(row.to_dict())
return result
def load_df(local_path: Union[str, Path], logger: logging.Logger = None, max_rows_count: int = None) -> "DataFrame":
"""load data from local file to df. For the usage of PRS."""
lp = local_path if isinstance(local_path, Path) else Path(local_path)
try:
if lp.is_file():
df = _pd_read_file(local_path, logger=logger, max_rows_count=max_rows_count)
# honor max_rows_count if it is specified
if max_rows_count and len(df) > max_rows_count:
df = df.head(max_rows_count)
else:
df = _handle_dir(local_path, max_rows_count=max_rows_count, logger=logger)
except ValueError as e:
raise InvalidUserData(
message_format="Fail to load invalid data. We support file formats: csv, tsv, json, jsonl, parquet. "
"Please check input data."
) from e
return df
class InvalidUserData(UserErrorException):
def __init__(self, **kwargs):
super().__init__(target=ErrorTarget.RUNTIME, **kwargs)
| promptflow/src/promptflow/promptflow/_utils/load_data.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/load_data.py",
"repo_id": "promptflow",
"token_count": 2029
} | 17 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
class FlowType:
STANDARD = "standard"
CHAT = "chat"
EVALUATION = "evaluate"
class FlowJobType:
STANDARD = "azureml.promptflow.FlowRun"
EVALUATION = "azureml.promptflow.EvaluationRun"
# Use this storage since it's the storage used by notebook
DEFAULT_STORAGE = "workspaceworkingdirectory"
PROMPTFLOW_FILE_SHARE_DIR = "promptflow"
CLOUD_RUNS_PAGE_SIZE = 25 # align with UX
SESSION_CREATION_TIMEOUT_SECONDS = 10 * 60 # 10 minutes
SESSION_CREATION_TIMEOUT_ENV_VAR = "PROMPTFLOW_SESSION_CREATION_TIMEOUT_SECONDS"
ENVIRONMENT = "environment"
PYTHON_REQUIREMENTS_TXT = "python_requirements_txt"
ADDITIONAL_INCLUDES = "additional_includes"
BASE_IMAGE = "image"
AUTOMATIC_RUNTIME_NAME = "automatic"
AUTOMATIC_RUNTIME = "automatic runtime"
| promptflow/src/promptflow/promptflow/azure/_constants/_flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_constants/_flow.py",
"repo_id": "promptflow",
"token_count": 322
} | 18 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Optional
from azure.core.configuration import Configuration
from azure.core.pipeline import policies
VERSION = "unknown"
class AzureMachineLearningDesignerServiceClientConfiguration(Configuration):
"""Configuration for AzureMachineLearningDesignerServiceClient.
Note that all parameters used to create this instance are saved as instance
attributes.
:param api_version: Api Version. The default value is "1.0.0".
:type api_version: str
"""
def __init__(
self,
api_version: Optional[str] = "1.0.0",
**kwargs: Any
) -> None:
super(AzureMachineLearningDesignerServiceClientConfiguration, self).__init__(**kwargs)
self.api_version = api_version
kwargs.setdefault('sdk_moniker', 'azuremachinelearningdesignerserviceclient/{}'.format(VERSION))
self._configure(**kwargs)
def _configure(
self,
**kwargs: Any
) -> None:
self.user_agent_policy = kwargs.get('user_agent_policy') or policies.UserAgentPolicy(**kwargs)
self.headers_policy = kwargs.get('headers_policy') or policies.HeadersPolicy(**kwargs)
self.proxy_policy = kwargs.get('proxy_policy') or policies.ProxyPolicy(**kwargs)
self.logging_policy = kwargs.get('logging_policy') or policies.NetworkTraceLoggingPolicy(**kwargs)
self.http_logging_policy = kwargs.get('http_logging_policy') or policies.HttpLoggingPolicy(**kwargs)
self.retry_policy = kwargs.get('retry_policy') or policies.AsyncRetryPolicy(**kwargs)
self.custom_hook_policy = kwargs.get('custom_hook_policy') or policies.CustomHookPolicy(**kwargs)
self.redirect_policy = kwargs.get('redirect_policy') or policies.AsyncRedirectPolicy(**kwargs)
self.authentication_policy = kwargs.get('authentication_policy')
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_configuration.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/_configuration.py",
"repo_id": "promptflow",
"token_count": 741
} | 19 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import concurrent
import copy
import hashlib
import json
import os
import shutil
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from functools import cached_property
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import requests
from azure.ai.ml._artifacts._artifact_utilities import _upload_and_generate_remote_uri
from azure.ai.ml._scope_dependent_operations import (
OperationConfig,
OperationsContainer,
OperationScope,
_ScopeDependentOperations,
)
from azure.ai.ml.constants._common import AssetTypes, AzureMLResourceType
from azure.ai.ml.entities import Workspace
from azure.ai.ml.operations import DataOperations
from azure.ai.ml.operations._operation_orchestrator import OperationOrchestrator
from promptflow._constants import LANGUAGE_KEY, FlowLanguage
from promptflow._sdk._constants import (
LINE_NUMBER,
MAX_RUN_LIST_RESULTS,
MAX_SHOW_DETAILS_RESULTS,
PROMPT_FLOW_DIR_NAME,
PROMPT_FLOW_RUNS_DIR_NAME,
REGISTRY_URI_PREFIX,
VIS_PORTAL_URL_TMPL,
AzureRunTypes,
ListViewType,
RunDataKeys,
RunHistoryKeys,
RunStatus,
)
from promptflow._sdk._errors import InvalidRunStatusError, RunNotFoundError, RunOperationParameterError
from promptflow._sdk._telemetry import ActivityType, WorkspaceTelemetryMixin, monitor_operation
from promptflow._sdk._utils import in_jupyter_notebook, incremental_print, is_remote_uri, print_red_error
from promptflow._sdk.entities import Run
from promptflow._utils.async_utils import async_run_allowing_running_loop
from promptflow._utils.flow_utils import get_flow_lineage_id
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow.azure._constants._flow import AUTOMATIC_RUNTIME, AUTOMATIC_RUNTIME_NAME, CLOUD_RUNS_PAGE_SIZE
from promptflow.azure._load_functions import load_flow
from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller
from promptflow.azure._utils.gerneral import get_authorization, get_user_alias_from_credential
from promptflow.azure.operations._flow_operations import FlowOperations
from promptflow.exceptions import UserErrorException
RUNNING_STATUSES = RunStatus.get_running_statuses()
logger = get_cli_sdk_logger()
class RunRequestException(Exception):
"""RunRequestException."""
def __init__(self, message):
super().__init__(message)
class RunOperations(WorkspaceTelemetryMixin, _ScopeDependentOperations):
"""RunOperations that can manage runs.
You should not instantiate this class directly. Instead, you should
create an :class:`~promptflow.azure.PFClient` instance and this operation is available as the instance's attribute.
"""
def __init__(
self,
operation_scope: OperationScope,
operation_config: OperationConfig,
all_operations: OperationsContainer,
flow_operations: FlowOperations,
credential,
service_caller: FlowServiceCaller,
workspace: Workspace,
**kwargs: Dict,
):
super().__init__(
operation_scope=operation_scope,
operation_config=operation_config,
workspace_name=operation_scope.workspace_name,
subscription_id=operation_scope.subscription_id,
resource_group_name=operation_scope.resource_group_name,
)
self._operation_scope = operation_scope
self._all_operations = all_operations
self._service_caller = service_caller
self._workspace = workspace
self._credential = credential
self._flow_operations = flow_operations
self._orchestrators = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config)
self._workspace_default_datastore = self._datastore_operations.get_default()
@property
def _data_operations(self):
return self._all_operations.get_operation(AzureMLResourceType.DATA, lambda x: isinstance(x, DataOperations))
@property
def _datastore_operations(self) -> "DatastoreOperations":
return self._all_operations.all_operations[AzureMLResourceType.DATASTORE]
@cached_property
def _run_history_endpoint_url(self):
"""Get the endpoint url for the workspace."""
endpoint = self._service_caller._service_endpoint
return endpoint + "history/v1.0" + self._service_caller._common_azure_url_pattern
def _get_run_portal_url(self, run_id: str):
"""Get the portal url for the run."""
portal_url, run_info = None, None
try:
run_info = self._get_run_from_pfs(run_id=run_id)
except Exception as e:
logger.warning(f"Failed to get run portal url from pfs for run {run_id!r}: {str(e)}")
if run_info and hasattr(run_info, "studio_portal_endpoint"):
portal_url = run_info.studio_portal_endpoint
return portal_url
def _get_headers(self):
custom_header = {
"Authorization": get_authorization(credential=self._credential),
"Content-Type": "application/json",
}
return custom_header
@monitor_operation(activity_name="pfazure.runs.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, run: Run, **kwargs) -> Run:
"""Create or update a run.
:param run: Run object to create or update.
:type run: ~promptflow.entities.Run
:return: Run object created or updated.
:rtype: ~promptflow.entities.Run
"""
stream = kwargs.pop("stream", False)
reset = kwargs.pop("reset_runtime", False)
# validate the run object
run._validate_for_run_create_operation()
rest_obj = self._resolve_dependencies_in_parallel(run=run, runtime=kwargs.get("runtime"), reset=reset)
self._service_caller.submit_bulk_run(
subscription_id=self._operation_scope.subscription_id,
resource_group_name=self._operation_scope.resource_group_name,
workspace_name=self._operation_scope.workspace_name,
body=rest_obj,
)
if in_jupyter_notebook():
print(f"Portal url: {self._get_run_portal_url(run_id=run.name)}")
if stream:
self.stream(run=run.name)
return self.get(run=run.name)
@monitor_operation(activity_name="pfazure.runs.list", activity_type=ActivityType.PUBLICAPI)
def list(
self, max_results: int = MAX_RUN_LIST_RESULTS, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY, **kwargs
) -> List[Run]:
"""List runs in the workspace.
:param max_results: The max number of runs to return, defaults to 50, max is 100
:type max_results: int
:param list_view_type: The list view type, defaults to ListViewType.ACTIVE_ONLY
:type list_view_type: ListViewType
:return: The list of runs.
:rtype: List[~promptflow.entities.Run]
"""
if not isinstance(max_results, int) or max_results < 0:
raise RunOperationParameterError(f"'max_results' must be a positive integer, got {max_results!r}")
headers = self._get_headers()
filter_archived = []
if list_view_type == ListViewType.ACTIVE_ONLY:
filter_archived = ["false"]
elif list_view_type == ListViewType.ARCHIVED_ONLY:
filter_archived = ["true"]
elif list_view_type == ListViewType.ALL:
filter_archived = ["true", "false"]
else:
raise RunOperationParameterError(
f"Invalid list view type: {list_view_type!r}, expecting one of ['ActiveOnly', 'ArchivedOnly', 'All']"
)
pay_load = {
"filters": [
{"field": "type", "operator": "eq", "values": ["runs"]},
{"field": "annotations/archived", "operator": "eq", "values": filter_archived},
{
"field": "properties/runType",
"operator": "contains",
"values": [
AzureRunTypes.BATCH,
AzureRunTypes.EVALUATION,
AzureRunTypes.PAIRWISE_EVALUATE,
],
},
],
"freeTextSearch": "",
"order": [{"direction": "Desc", "field": "properties/creationContext/createdTime"}],
# index service can return 100 results at most
"pageSize": min(max_results, 100),
"skip": 0,
"includeTotalResultCount": True,
"searchBuilder": "AppendPrefix",
}
endpoint = self._run_history_endpoint_url.replace("/history", "/index")
url = endpoint + "/entities"
response = requests.post(url, headers=headers, json=pay_load)
if response.status_code == 200:
entities = json.loads(response.text)
runs = entities["value"]
else:
raise RunRequestException(
f"Failed to get runs from service. Code: {response.status_code}, text: {response.text}"
)
refined_runs = []
for run in runs:
refined_runs.append(Run._from_index_service_entity(run))
return refined_runs
@monitor_operation(activity_name="pfazure.runs.get_metrics", activity_type=ActivityType.PUBLICAPI)
def get_metrics(self, run: Union[str, Run], **kwargs) -> dict:
"""Get the metrics from the run.
:param run: The run or the run object
:type run: Union[str, ~promptflow.entities.Run]
:return: The metrics
:rtype: dict
"""
run = Run._validate_and_return_run_name(run)
self._check_cloud_run_completed(run_name=run)
metrics = self._get_metrics_from_metric_service(run)
return metrics
@monitor_operation(activity_name="pfazure.runs.get_details", activity_type=ActivityType.PUBLICAPI)
def get_details(
self, run: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False, **kwargs
) -> "DataFrame":
"""Get the details from the run.
.. note::
If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize.
:param run: The run name or run object
:type run: Union[str, ~promptflow.sdk.entities.Run]
:param max_results: The max number of runs to return, defaults to 100
:type max_results: int
:param all_results: Whether to return all results, defaults to False
:type all_results: bool
:raises RunOperationParameterError: If `max_results` is not a positive integer.
:return: The details data frame.
:rtype: pandas.DataFrame
"""
from pandas import DataFrame
# if all_results is True, set max_results to sys.maxsize
if all_results:
max_results = sys.maxsize
if not isinstance(max_results, int) or max_results < 1:
raise RunOperationParameterError(f"'max_results' must be a positive integer, got {max_results!r}")
run = Run._validate_and_return_run_name(run)
self._check_cloud_run_completed(run_name=run)
child_runs = self._get_flow_runs_pagination(run, max_results=max_results)
inputs, outputs = self._get_inputs_outputs_from_child_runs(child_runs)
# if there is any line run failed, the number of inputs and outputs will be different
# this will result in pandas raising ValueError, so we need to handle mismatched case
# if all line runs are failed, no need to fill the outputs
if len(outputs) > 0:
# get total number of line runs from inputs
num_line_runs = len(list(inputs.values())[0])
num_outputs = len(list(outputs.values())[0])
if num_line_runs > num_outputs:
# build full set with None as placeholder
filled_outputs = {}
output_keys = list(outputs.keys())
for k in output_keys:
filled_outputs[k] = [None] * num_line_runs
filled_outputs[LINE_NUMBER] = list(range(num_line_runs))
for i in range(num_outputs):
line_number = outputs[LINE_NUMBER][i]
for k in output_keys:
filled_outputs[k][line_number] = outputs[k][i]
# replace defective outputs with full set
outputs = copy.deepcopy(filled_outputs)
data = {}
columns = []
for k in inputs:
new_k = f"inputs.{k}"
data[new_k] = copy.deepcopy(inputs[k])
columns.append(new_k)
for k in outputs:
new_k = f"outputs.{k}"
data[new_k] = copy.deepcopy(outputs[k])
columns.append(new_k)
df = DataFrame(data).reindex(columns=columns)
if f"outputs.{LINE_NUMBER}" in columns:
df = df.set_index(f"outputs.{LINE_NUMBER}")
return df
def _check_cloud_run_completed(self, run_name: str) -> bool:
"""Check if the cloud run is completed."""
run = self.get(run=run_name)
run._check_run_status_is_completed()
def _get_flow_runs_pagination(self, name: str, max_results: int) -> List[dict]:
# call childRuns API with pagination to avoid PFS OOM
# different from UX, run status should be completed here
flow_runs = []
start_index, end_index = 0, CLOUD_RUNS_PAGE_SIZE - 1
while start_index < max_results:
current_flow_runs = self._service_caller.get_child_runs(
subscription_id=self._operation_scope.subscription_id,
resource_group_name=self._operation_scope.resource_group_name,
workspace_name=self._operation_scope.workspace_name,
flow_run_id=name,
start_index=start_index,
end_index=end_index,
)
# no data in current page
if len(current_flow_runs) == 0:
break
start_index, end_index = start_index + CLOUD_RUNS_PAGE_SIZE, end_index + CLOUD_RUNS_PAGE_SIZE
flow_runs += current_flow_runs
return flow_runs[0:max_results]
def _extract_metrics_from_metric_service_response(self, values) -> dict:
"""Get metrics from the metric service response."""
refined_metrics = {}
metric_list = values.get("value", [])
if not metric_list:
return refined_metrics
for metric in metric_list:
metric_name = metric["name"]
if self._is_system_metric(metric_name):
continue
refined_metrics[metric_name] = metric["value"][0]["data"][metric_name]
return refined_metrics
def _get_metrics_from_metric_service(self, run_id) -> dict:
"""Get the metrics from metric service."""
headers = self._get_headers()
# refer to MetricController: https://msdata.visualstudio.com/Vienna/_git/vienna?path=/src/azureml-api/src/Metric/EntryPoints/Api/Controllers/MetricController.cs&version=GBmaster # noqa: E501
endpoint = self._run_history_endpoint_url.replace("/history/v1.0", "/metric/v2.0")
url = endpoint + f"/runs/{run_id}/lastvalues"
response = requests.post(url, headers=headers, json={})
if response.status_code == 200:
values = response.json()
return self._extract_metrics_from_metric_service_response(values)
else:
raise RunRequestException(
f"Failed to get metrics from service. Code: {response.status_code}, text: {response.text}"
)
@staticmethod
def _is_system_metric(metric: str) -> bool:
"""Check if the metric is system metric.
Current we have some system metrics like: __pf__.lines.completed, __pf__.lines.bypassed,
__pf__.lines.failed, __pf__.nodes.xx.completed
"""
return (
metric.endswith(".completed")
or metric.endswith(".bypassed")
or metric.endswith(".failed")
or metric.endswith(".is_completed")
)
@monitor_operation(activity_name="pfazure.runs.get", activity_type=ActivityType.PUBLICAPI)
def get(self, run: Union[str, Run], **kwargs) -> Run:
"""Get a run.
:param run: The run name
:type run: Union[str, ~promptflow.entities.Run]
:return: The run object
:rtype: ~promptflow.entities.Run
"""
run = Run._validate_and_return_run_name(run)
return self._get_run_from_run_history(flow_run_id=run, **kwargs)
def _get_run_from_run_history(self, flow_run_id, original_form=False, **kwargs):
"""Get run info from run history"""
headers = self._get_headers()
url = self._run_history_endpoint_url + "/rundata"
payload = {
"runId": flow_run_id,
"selectRunMetadata": True,
"selectRunDefinition": True,
"selectJobSpecification": True,
}
response = requests.post(url, headers=headers, json=payload)
if response.status_code == 200:
run = response.json()
# if original_form is True, return the original run data from run history, mainly for test use
if original_form:
return run
run_data = self._refine_run_data_from_run_history(run)
run = Run._from_run_history_entity(run_data)
return run
elif response.status_code == 404:
raise RunNotFoundError(f"Run {flow_run_id!r} not found.")
else:
raise RunRequestException(
f"Failed to get run from service. Code: {response.status_code}, text: {response.text}"
)
def _refine_run_data_from_run_history(self, run_data: dict) -> dict:
"""Refine the run data from run history.
Generate the portal url, input and output value from run history data.
"""
run_data = run_data[RunHistoryKeys.RunMetaData]
# add cloud run url
run_data[RunDataKeys.PORTAL_URL] = self._get_run_portal_url(run_id=run_data["runId"])
# get input and output value
# TODO: Unify below values to the same pattern - azureml://xx
properties = run_data["properties"]
input_data = properties.pop("azureml.promptflow.input_data", None)
input_run_id = properties.pop("azureml.promptflow.input_run_id", None)
output_data = run_data["outputs"]
if output_data:
output_data = output_data.get("flow_outputs", {}).get("assetId", None)
run_data[RunDataKeys.DATA] = input_data
run_data[RunDataKeys.RUN] = input_run_id
run_data[RunDataKeys.OUTPUT] = output_data
return run_data
def _get_run_from_index_service(self, flow_run_id, **kwargs):
"""Get run info from index service"""
headers = self._get_headers()
payload = {
"filters": [
{"field": "type", "operator": "eq", "values": ["runs"]},
{"field": "annotations/archived", "operator": "eq", "values": ["false"]},
{"field": "properties/runId", "operator": "eq", "values": [flow_run_id]},
],
"order": [{"direction": "Desc", "field": "properties/startTime"}],
"pageSize": 50,
}
endpoint = self._run_history_endpoint_url.replace("/history", "/index")
url = endpoint + "/entities"
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
runs = response.json().get("value", None)
if not runs:
raise RunRequestException(
f"Could not found run with run id {flow_run_id!r}, please double check the run id and try again."
)
run = runs[0]
return Run._from_index_service_entity(run)
else:
raise RunRequestException(
f"Failed to get run metrics from service. Code: {response.status_code}, text: {response.text}"
)
def _get_run_from_pfs(self, run_id, **kwargs):
"""Get run info from pfs"""
return self._service_caller.get_flow_run(
subscription_id=self._operation_scope.subscription_id,
resource_group_name=self._operation_scope.resource_group_name,
workspace_name=self._operation_scope.workspace_name,
flow_run_id=run_id,
)
@monitor_operation(activity_name="pfazure.runs.archive", activity_type=ActivityType.PUBLICAPI)
def archive(self, run: Union[str, Run]) -> Run:
"""Archive a run.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
:return: The run object
:rtype: ~promptflow.entities.Run
"""
run = Run._validate_and_return_run_name(run)
payload = {
RunHistoryKeys.HIDDEN: True,
}
return self._modify_run_in_run_history(run_id=run, payload=payload)
@monitor_operation(activity_name="pfazure.runs.restore", activity_type=ActivityType.PUBLICAPI)
def restore(self, run: Union[str, Run]) -> Run:
"""Restore a run.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
:return: The run object
:rtype: ~promptflow.entities.Run
"""
run = Run._validate_and_return_run_name(run)
payload = {
RunHistoryKeys.HIDDEN: False,
}
return self._modify_run_in_run_history(run_id=run, payload=payload)
def _get_log(self, flow_run_id: str) -> str:
return self._service_caller.caller.bulk_runs.get_flow_run_log_content(
subscription_id=self._operation_scope.subscription_id,
resource_group_name=self._operation_scope.resource_group_name,
workspace_name=self._operation_scope.workspace_name,
flow_run_id=flow_run_id,
headers=self._get_headers(),
)
@monitor_operation(activity_name="pfazure.runs.update", activity_type=ActivityType.PUBLICAPI)
def update(
self,
run: Union[str, Run],
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
) -> Optional[Run]:
"""Update a run. May update the display name, description or tags.
.. note::
- Display name and description are strings, and tags is a dictionary of key-value pairs, both key and value
are also strings.
- Tags is a dictionary of key-value pairs. Updating tags will overwrite the existing key-value pair,
but will not delete the existing key-value pairs.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
:param display_name: The display name
:type display_name: Optional[str]
:param description: The description
:type description: Optional[str]
:param tags: The tags
:type tags: Optional[Dict[str, str]]
:raises UpdateRunError: If nothing or wrong type values provided to update the run.
:return: The run object
:rtype: Optional[~promptflow.entities.Run]
"""
run = Run._validate_and_return_run_name(run)
if display_name is None and description is None and tags is None:
logger.warning("Nothing provided to update the run.")
return None
payload = {}
if isinstance(display_name, str):
payload["displayName"] = display_name
elif display_name is not None:
logger.warning(f"Display name must be a string, got {type(display_name)!r}: {display_name!r}.")
if isinstance(description, str):
payload["description"] = description
elif description is not None:
logger.warning(f"Description must be a string, got {type(description)!r}: {description!r}.")
# check if the tags type is Dict[str, str]
if isinstance(tags, dict) and all(
isinstance(key, str) and isinstance(value, str) for key, value in tags.items()
):
payload["tags"] = tags
elif tags is not None:
logger.warning(f"Tags type must be 'Dict[str, str]', got non-dict or non-string key/value in tags: {tags}.")
return self._modify_run_in_run_history(run_id=run, payload=payload)
@monitor_operation(activity_name="pfazure.runs.stream", activity_type=ActivityType.PUBLICAPI)
def stream(self, run: Union[str, Run], raise_on_error: bool = True) -> Run:
"""Stream the logs of a run.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
:param raise_on_error: Raises an exception if a run fails or canceled.
:type raise_on_error: bool
:return: The run object
:rtype: ~promptflow.entities.Run
"""
run = self.get(run=run)
# TODO: maybe we need to make this configurable
file_handler = sys.stdout
# different from Azure ML job, flow job can run very fast, so it might not print anything;
# use below variable to track this behavior, and at least print something to the user.
try:
printed = 0
stream_count = 0
start = time.time()
while run.status in RUNNING_STATUSES or run.status == RunStatus.FINALIZING:
file_handler.flush()
stream_count += 1
# print prompt every 3 times, in case there is no log printed
if stream_count % 3 == 0:
# print prompt every 3 times
file_handler.write(f"(Run status is {run.status!r}, continue streaming...)\n")
# if the run is not started for 5 minutes, print an error message and break the loop
if run.status == RunStatus.NOT_STARTED:
current = time.time()
if current - start > 300:
file_handler.write(
f"The run {run.name!r} is in status 'NotStarted' for 5 minutes, streaming is stopped."
"Please make sure you are using the latest runtime.\n"
)
break
available_logs = self._get_log(flow_run_id=run.name)
printed = incremental_print(available_logs, printed, file_handler)
time.sleep(10)
run = self.get(run=run.name)
# ensure all logs are printed
file_handler.flush()
available_logs = self._get_log(flow_run_id=run.name)
incremental_print(available_logs, printed, file_handler)
file_handler.write("======= Run Summary =======\n")
duration = None
if run._start_time and run._end_time:
duration = str(run._end_time - run._start_time)
file_handler.write(
f'Run name: "{run.name}"\n'
f'Run status: "{run.status}"\n'
f'Start time: "{run._start_time}"\n'
f'Duration: "{duration}"\n'
f'Run url: "{self._get_run_portal_url(run_id=run.name)}"'
)
except KeyboardInterrupt:
error_message = (
"The output streaming for the flow run was interrupted.\n"
"But the run is still executing on the cloud.\n"
)
print(error_message)
if run.status == RunStatus.FAILED or run.status == RunStatus.CANCELED:
if run.status == RunStatus.FAILED:
try:
error_message = run._error["error"]["message"]
except Exception: # pylint: disable=broad-except
error_message = "Run fails with unknown error."
else:
error_message = "Run is canceled."
if raise_on_error:
raise InvalidRunStatusError(error_message)
else:
print_red_error(error_message)
return run
def _resolve_data_to_asset_id(self, run: Run):
# Skip if no data provided
if run.data is None:
return
test_data = run.data
def _get_data_type(_data):
if os.path.isdir(_data):
return AssetTypes.URI_FOLDER
else:
return AssetTypes.URI_FILE
if is_remote_uri(test_data):
# Pass through ARM id or remote url
return test_data
if os.path.exists(test_data): # absolute local path, upload, transform to remote url
data_type = _get_data_type(test_data)
test_data = _upload_and_generate_remote_uri(
self._operation_scope,
self._datastore_operations,
test_data,
datastore_name=self._workspace_default_datastore.name,
show_progress=self._show_progress,
)
if data_type == AssetTypes.URI_FOLDER and test_data and not test_data.endswith("/"):
test_data = test_data + "/"
else:
raise ValueError(
f"Local path {test_data!r} not exist. "
"If it's remote data, only data with azureml prefix or remote url is supported."
)
return test_data
def _resolve_flow(self, run: Run):
if run._use_remote_flow:
return self._resolve_flow_definition_resource_id(run=run)
flow = load_flow(run.flow)
self._flow_operations._resolve_arm_id_or_upload_dependencies(
flow=flow,
# ignore .promptflow/dag.tools.json only for run submission scenario in python
ignore_tools_json=flow._flow_dict.get(LANGUAGE_KEY, None) != FlowLanguage.CSharp,
)
return flow.path
def _get_session_id(self, flow):
try:
user_alias = get_user_alias_from_credential(self._credential)
except Exception:
# fall back to unknown user when failed to get credential.
user_alias = "unknown_user"
flow_id = get_flow_lineage_id(flow_dir=flow)
session_id = f"{user_alias}_{flow_id}"
# hash and truncate to avoid the session id getting too long
# backend has a 64 bit limit for session id.
# use hexdigest to avoid non-ascii characters in session id
session_id = str(hashlib.sha256(session_id.encode()).hexdigest())[:48]
return session_id
def _get_inputs_outputs_from_child_runs(self, runs: List[Dict[str, Any]]):
"""Get the inputs and outputs from the child runs."""
inputs = {}
outputs = {}
outputs[LINE_NUMBER] = []
runs.sort(key=lambda x: x["index"])
# 1st loop, until have all outputs keys
outputs_keys = []
for run in runs:
run_outputs = run["output"]
if isinstance(run_outputs, dict):
for k in run_outputs:
outputs_keys.append(k)
break
# 2nd complete loop, get values
for run in runs:
index, run_inputs, run_outputs = run["index"], run["inputs"], run["output"]
# input should always available as a dict
for k, v in run_inputs.items():
if k not in inputs:
inputs[k] = []
inputs[k].append(v)
# output
outputs[LINE_NUMBER].append(index)
# for failed line run, output is None, instead of a dict
# in this case, we append an empty line
if not isinstance(run_outputs, dict):
for k in outputs_keys:
if k == LINE_NUMBER:
continue
if k not in outputs:
outputs[k] = []
outputs[k].append(None)
else:
for k, v in run_outputs.items():
if k not in outputs:
outputs[k] = []
outputs[k].append(v)
return inputs, outputs
@monitor_operation(activity_name="pfazure.runs.visualize", activity_type=ActivityType.PUBLICAPI)
def visualize(self, runs: Union[str, Run, List[str], List[Run]], **kwargs) -> None:
"""Visualize run(s) using Azure AI portal.
:param runs: Names of the runs, or list of run objects.
:type runs: Union[str, ~promptflow.sdk.entities.Run, List[str], List[~promptflow.sdk.entities.Run]]
"""
if not isinstance(runs, list):
runs = [runs]
validated_runs = []
for run in runs:
run_name = Run._validate_and_return_run_name(run)
validated_runs.append(run_name)
subscription_id = self._operation_scope.subscription_id
resource_group_name = self._operation_scope.resource_group_name
workspace_name = self._operation_scope.workspace_name
names = ",".join(validated_runs)
portal_url = VIS_PORTAL_URL_TMPL.format(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
names=names,
)
print(f"Web View: {portal_url}")
def _resolve_automatic_runtime(self):
logger.warning(
f"You're using {AUTOMATIC_RUNTIME}, if it's first time you're using it, "
"it may take a while to build runtime and you may see 'NotStarted' status for a while. "
)
runtime_name = AUTOMATIC_RUNTIME_NAME
return runtime_name
def _resolve_runtime(self, run, flow_path, runtime):
runtime = run._runtime or runtime
# for remote flow case, leave session id to None and let service side resolve
# for local flow case, use flow path to calculate session id
session_id = None if run._use_remote_flow else self._get_session_id(flow=flow_path)
if runtime is None or runtime == AUTOMATIC_RUNTIME_NAME:
runtime = self._resolve_automatic_runtime()
elif not isinstance(runtime, str):
raise TypeError(f"runtime should be a string, got {type(runtime)} for {runtime}")
return runtime, session_id
def _resolve_dependencies_in_parallel(self, run, runtime, reset=None):
flow_path = run.flow
with ThreadPoolExecutor() as pool:
tasks = [
pool.submit(self._resolve_data_to_asset_id, run=run),
pool.submit(self._resolve_flow, run=run),
]
concurrent.futures.wait(tasks, return_when=concurrent.futures.ALL_COMPLETED)
task_results = [task.result() for task in tasks]
run.data = task_results[0]
run.flow = task_results[1]
runtime, session_id = self._resolve_runtime(run=run, flow_path=flow_path, runtime=runtime)
rest_obj = run._to_rest_object()
rest_obj.runtime_name = runtime
rest_obj.session_id = session_id
# TODO(2884482): support force reset & force install
if runtime == "None":
# HARD CODE for office scenario, use workspace default runtime when specified None
rest_obj.runtime_name = None
return rest_obj
def _refine_payload_for_run_update(self, payload: dict, key: str, value, expected_type: type) -> dict:
"""Refine the payload for run update."""
if value is not None:
payload[key] = value
return payload
def _modify_run_in_run_history(self, run_id: str, payload: dict) -> Run:
"""Modify run info in run history."""
headers = self._get_headers()
url = self._run_history_endpoint_url + f"/runs/{run_id}/modify"
response = requests.patch(url, headers=headers, json=payload)
if response.status_code == 200:
# the modify api returns different data format compared with get api, so we use get api here to
# return standard Run object
return self.get(run=run_id)
else:
raise RunRequestException(
f"Failed to modify run in run history. Code: {response.status_code}, text: {response.text}"
)
def _resolve_flow_definition_resource_id(self, run: Run):
"""Resolve the flow definition resource id."""
# for registry flow pattern, the flow uri can be passed as flow definition resource id directly
if run.flow.startswith(REGISTRY_URI_PREFIX):
return run.flow
# for workspace flow pattern, generate the flow definition resource id
workspace_id = self._workspace._workspace_id
location = self._workspace.location
return f"azureml://locations/{location}/workspaces/{workspace_id}/flows/{run._flow_name}"
@monitor_operation(activity_name="pfazure.runs.download", activity_type=ActivityType.PUBLICAPI)
def download(
self, run: Union[str, Run], output: Optional[Union[str, Path]] = None, overwrite: Optional[bool] = False
) -> str:
"""Download the data of a run, including input, output, snapshot and other run information.
.. note::
After the download is finished, you can use ``pf run create --source <run-info-local-folder>``
to register this run as a local run record, then you can use commands like ``pf run show/visualize``
to inspect the run just like a run that was created from local flow.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
:param output: The output directory. Default to be default to be "~/.promptflow/.runs" folder.
:type output: Optional[str]
:param overwrite: Whether to overwrite the existing run folder. Default to be False.
:type overwrite: Optional[bool]
:return: The run directory path
:rtype: str
"""
import platform
from promptflow.azure.operations._async_run_downloader import AsyncRunDownloader
run = Run._validate_and_return_run_name(run)
run_folder = self._validate_for_run_download(run=run, output=output, overwrite=overwrite)
run_downloader = AsyncRunDownloader._from_run_operations(run_ops=self, run=run, output_folder=run_folder)
if platform.system().lower() == "windows":
# Reference: https://stackoverflow.com/questions/45600579/asyncio-event-loop-is-closed-when-getting-loop
# On Windows seems to be a problem with EventLoopPolicy, use this snippet to work around it
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
async_run_allowing_running_loop(run_downloader.download)
result_path = run_folder.resolve().as_posix()
logger.info(f"Successfully downloaded run {run!r} to {result_path!r}.")
return result_path
def _validate_for_run_download(self, run: Union[str, Run], output: Optional[Union[str, Path]], overwrite):
"""Validate the run download parameters."""
run = Run._validate_and_return_run_name(run)
# process the output path
if output is None:
# default to be "~/.promptflow/.runs" folder
output_directory = Path.home() / PROMPT_FLOW_DIR_NAME / PROMPT_FLOW_RUNS_DIR_NAME
else:
output_directory = Path(output)
# validate the run folder
run_folder = output_directory / run
if run_folder.exists():
if overwrite is True:
logger.warning("Removing existing run folder %r.", run_folder.resolve().as_posix())
shutil.rmtree(run_folder)
else:
raise UserErrorException(
f"Run folder {run_folder.resolve().as_posix()!r} already exists, please specify a new output path "
f"or set the overwrite flag to be true."
)
# check the run status, only download the completed run
run = self.get(run=run)
if run.status != RunStatus.COMPLETED:
raise UserErrorException(
f"Can only download the run with status {RunStatus.COMPLETED!r} "
f"while {run.name!r}'s status is {run.status!r}."
)
run_folder.mkdir(parents=True)
return run_folder
@monitor_operation(activity_name="pfazure.runs.cancel", activity_type=ActivityType.PUBLICAPI)
def cancel(self, run: Union[str, Run], **kwargs) -> None:
"""Cancel a run.
:param run: The run name or run object
:type run: Union[str, ~promptflow.entities.Run]
"""
run = Run._validate_and_return_run_name(run)
self._service_caller.cancel_flow_run(
subscription_id=self._operation_scope.subscription_id,
resource_group_name=self._operation_scope.resource_group_name,
workspace_name=self._operation_scope.workspace_name,
flow_run_id=run,
)
| promptflow/src/promptflow/promptflow/azure/operations/_run_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/operations/_run_operations.py",
"repo_id": "promptflow",
"token_count": 18051
} | 20 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import logging
import sys
from dataclasses import asdict, dataclass
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional
from promptflow._utils.yaml_utils import load_yaml
from promptflow.contracts._errors import FlowDefinitionError
from promptflow.exceptions import ErrorTarget
from .._constants import LANGUAGE_KEY, FlowLanguage
from .._sdk._constants import DEFAULT_ENCODING
from .._utils.dataclass_serializer import serialize
from .._utils.utils import try_import, _sanitize_python_variable_name
from ._errors import FailedToImportModule
from .tool import ConnectionType, Tool, ToolType, ValueType
logger = logging.getLogger(__name__)
class InputValueType(Enum):
"""The enum of input value type."""
LITERAL = "Literal"
FLOW_INPUT = "FlowInput"
NODE_REFERENCE = "NodeReference"
FLOW_INPUT_PREFIX = "flow."
FLOW_INPUT_PREFIXES = [FLOW_INPUT_PREFIX, "inputs."] # Use a list for backward compatibility
@dataclass
class InputAssignment:
"""This class represents the assignment of an input value.
:param value: The value of the input assignment.
:type value: Any
:param value_type: The type of the input assignment.
:type value_type: ~promptflow.contracts.flow.InputValueType
:param section: The section of the input assignment, usually the output.
:type section: str
:param property: The property of the input assignment that exists in the section.
:type property: str
"""
value: Any
value_type: InputValueType = InputValueType.LITERAL
section: str = ""
property: str = ""
def serialize(self):
"""Serialize the input assignment to a string."""
if self.value_type == InputValueType.FLOW_INPUT:
return f"${{{FLOW_INPUT_PREFIX}{self.value}}}"
elif self.value_type == InputValueType.NODE_REFERENCE:
if self.property:
return f"${{{self.value}.{self.section}.{self.property}}}"
return f"${{{self.value}.{self.section}}}"
elif ConnectionType.is_connection_value(self.value):
return ConnectionType.serialize_conn(self.value)
return self.value
@staticmethod
def deserialize(value: str) -> "InputAssignment":
"""Deserialize the input assignment from a string.
:param value: The string to be deserialized.
:type value: str
:return: The input assignment constructed from the string.
:rtype: ~promptflow.contracts.flow.InputAssignment
"""
literal_value = InputAssignment(value, InputValueType.LITERAL)
if isinstance(value, str) and value.startswith("$") and len(value) > 2:
value = value[1:]
if value[0] != "{" or value[-1] != "}":
return literal_value
value = value[1:-1]
return InputAssignment.deserialize_reference(value)
return literal_value
@staticmethod
def deserialize_reference(value: str) -> "InputAssignment":
"""Deserialize the reference(including node/flow reference) part of an input assignment.
:param value: The string to be deserialized.
:type value: str
:return: The input assignment of reference types.
:rtype: ~promptflow.contracts.flow.InputAssignment
"""
if FlowInputAssignment.is_flow_input(value):
return FlowInputAssignment.deserialize(value)
return InputAssignment.deserialize_node_reference(value)
@staticmethod
def deserialize_node_reference(data: str) -> "InputAssignment":
"""Deserialize the node reference part of an input assignment.
:param data: The string to be deserialized.
:type data: str
:return: Input assignment of node reference type.
:rtype: ~promptflow.contracts.flow.InputAssignment
"""
value_type = InputValueType.NODE_REFERENCE
if "." not in data:
return InputAssignment(data, value_type, "output")
node_name, port_name = data.split(".", 1)
if "." not in port_name:
return InputAssignment(node_name, value_type, port_name)
section, property = port_name.split(".", 1)
return InputAssignment(node_name, value_type, section, property)
@dataclass
class FlowInputAssignment(InputAssignment):
"""This class represents the assignment of a flow input value.
:param prefix: The prefix of the flow input.
:type prefix: str
"""
prefix: str = FLOW_INPUT_PREFIX
@staticmethod
def is_flow_input(input_value: str) -> bool:
"""Check whether the input value is a flow input.
:param input_value: The input value to be checked.
:type input_value: str
:return: Whether the input value is a flow input.
:rtype: bool
"""
for prefix in FLOW_INPUT_PREFIXES:
if input_value.startswith(prefix):
return True
return False
@staticmethod
def deserialize(value: str) -> "FlowInputAssignment":
"""Deserialize the flow input assignment from a string.
:param value: The string to be deserialized.
:type value: str
:return: The flow input assignment constructed from the string.
:rtype: ~promptflow.contracts.flow.FlowInputAssignment
"""
for prefix in FLOW_INPUT_PREFIXES:
if value.startswith(prefix):
return FlowInputAssignment(
value=value[len(prefix) :], value_type=InputValueType.FLOW_INPUT, prefix=prefix
)
raise ValueError(f"Unexpected flow input value {value}")
class ToolSourceType(str, Enum):
"""The enum of tool source type."""
Code = "code"
Package = "package"
PackageWithPrompt = "package_with_prompt"
@dataclass
class ToolSource:
"""This class represents the source of a tool.
:param type: The type of the tool source.
:type type: ~promptflow.contracts.flow.ToolSourceType
:param tool: The tool of the tool source.
:type tool: str
:param path: The path of the tool source.
:type path: str
"""
type: ToolSourceType = ToolSourceType.Code
tool: Optional[str] = None
path: Optional[str] = None
@staticmethod
def deserialize(data: dict) -> "ToolSource":
"""Deserialize the tool source from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The tool source constructed from the dict.
:rtype: ~promptflow.contracts.flow.ToolSource
"""
result = ToolSource(data.get("type", ToolSourceType.Code.value))
if "tool" in data:
result.tool = data["tool"]
if "path" in data:
result.path = data["path"]
return result
@dataclass
class ActivateCondition:
"""This class represents the activate condition of a node.
:param condition: The condition of the activate condition.
:type condition: ~promptflow.contracts.flow.InputAssignment
:param condition_value: The value of the condition.
:type condition_value: Any
"""
condition: InputAssignment
condition_value: Any
@staticmethod
def deserialize(data: dict, node_name: str = None) -> "ActivateCondition":
"""Deserialize the activate condition from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The activate condition constructed from the dict.
:rtype: ~promptflow.contracts.flow.ActivateCondition
"""
node_name = node_name if node_name else ""
if "when" in data and "is" in data:
if data["when"] is None and data["is"] is None:
logger.warning(
f"The activate config for node {node_name} has empty 'when' and 'is'. "
"Please check your flow yaml to ensure it aligns with your expectations."
)
return ActivateCondition(
condition=InputAssignment.deserialize(data["when"]),
condition_value=data["is"],
)
else:
raise FlowDefinitionError(
message_format=(
"The definition of activate config for node {node_name} "
"is incorrect. Please check your flow yaml and resubmit."
),
node_name=node_name,
)
@dataclass
class Node:
"""This class represents a node in a flow.
:param name: The name of the node.
:type name: str
:param tool: The tool of the node.
:type tool: str
:param inputs: The inputs of the node.
:type inputs: Dict[str, InputAssignment]
:param comment: The comment of the node.
:type comment: str
:param api: The api of the node.
:type api: str
:param provider: The provider of the node.
:type provider: str
:param module: The module of the node.
:type module: str
:param connection: The connection of the node.
:type connection: str
:param aggregation: Whether the node is an aggregation node.
:type aggregation: bool
:param enable_cache: Whether the node enable cache.
:type enable_cache: bool
:param use_variants: Whether the node use variants.
:type use_variants: bool
:param source: The source of the node.
:type source: ~promptflow.contracts.flow.ToolSource
:param type: The tool type of the node.
:type type: ~promptflow.contracts.tool.ToolType
:param activate: The activate condition of the node.
:type activate: ~promptflow.contracts.flow.ActivateCondition
"""
name: str
tool: str
inputs: Dict[str, InputAssignment]
comment: str = ""
api: str = None
provider: str = None
module: str = None # The module of provider to import
connection: str = None
aggregation: bool = False
enable_cache: bool = False
use_variants: bool = False
source: Optional[ToolSource] = None
type: Optional[ToolType] = None
activate: Optional[ActivateCondition] = None
def serialize(self):
"""Serialize the node to a dict.
:return: The dict of the node.
:rtype: dict
"""
data = asdict(self, dict_factory=lambda x: {k: v for (k, v) in x if v})
self.inputs = self.inputs or {}
data.update({"inputs": {name: i.serialize() for name, i in self.inputs.items()}})
if self.aggregation:
data["aggregation"] = True
data["reduce"] = True # TODO: Remove this fallback.
if self.type:
data["type"] = self.type.value
return data
@staticmethod
def deserialize(data: dict) -> "Node":
"""Deserialize the node from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The node constructed from the dict.
:rtype: ~promptflow.contracts.flow.Node
"""
node = Node(
name=data.get("name"),
tool=data.get("tool"),
inputs={name: InputAssignment.deserialize(v) for name, v in (data.get("inputs") or {}).items()},
comment=data.get("comment", ""),
api=data.get("api", None),
provider=data.get("provider", None),
module=data.get("module", None),
connection=data.get("connection", None),
aggregation=data.get("aggregation", False) or data.get("reduce", False), # TODO: Remove this fallback.
enable_cache=data.get("enable_cache", False),
use_variants=data.get("use_variants", False),
)
if "source" in data:
node.source = ToolSource.deserialize(data["source"])
if "type" in data:
node.type = ToolType(data["type"])
if "activate" in data:
node.activate = ActivateCondition.deserialize(data["activate"], node.name)
return node
@dataclass
class FlowInputDefinition:
"""This class represents the definition of a flow input.
:param type: The type of the flow input.
:type type: ~promptflow.contracts.tool.ValueType
:param default: The default value of the flow input.
:type default: str
:param description: The description of the flow input.
:type description: str
:param enum: The enum of the flow input.
:type enum: List[str]
:param is_chat_input: Whether the flow input is a chat input.
:type is_chat_input: bool
:param is_chat_history: Whether the flow input is a chat history.
:type is_chat_history: bool
"""
type: ValueType
default: str = None
description: str = None
enum: List[str] = None
is_chat_input: bool = False
is_chat_history: bool = None
def serialize(self):
"""Serialize the flow input definition to a dict.
:return: The dict of the flow input definition.
:rtype: dict
"""
data = {}
data["type"] = self.type.value
if self.default:
data["default"] = str(self.default)
if self.description:
data["description"] = self.description
if self.enum:
data["enum"] = self.enum
if self.is_chat_input:
data["is_chat_input"] = True
if self.is_chat_history:
data["is_chat_history"] = True
return data
@staticmethod
def deserialize(data: dict) -> "FlowInputDefinition":
"""Deserialize the flow input definition from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The flow input definition constructed from the dict.
:rtype: ~promptflow.contracts.flow.FlowInputDefinition
"""
return FlowInputDefinition(
ValueType(data["type"]),
data.get("default", None),
data.get("description", ""),
data.get("enum", []),
data.get("is_chat_input", False),
data.get("is_chat_history", None),
)
@dataclass
class FlowOutputDefinition:
"""This class represents the definition of a flow output.
:param type: The type of the flow output.
:type type: ~promptflow.contracts.tool.ValueType
:param reference: The reference of the flow output.
:type reference: ~promptflow.contracts.flow.InputAssignment
:param description: The description of the flow output.
:type description: str
:param evaluation_only: Whether the flow output is for evaluation only.
:type evaluation_only: bool
:param is_chat_output: Whether the flow output is a chat output.
:type is_chat_output: bool
"""
type: ValueType
reference: InputAssignment
description: str = ""
evaluation_only: bool = False
is_chat_output: bool = False
def serialize(self):
"""Serialize the flow output definition to a dict.
:return: The dict of the flow output definition.
:rtype: dict
"""
data = {}
data["type"] = self.type.value
if self.reference:
data["reference"] = self.reference.serialize()
if self.description:
data["description"] = self.description
if self.evaluation_only:
data["evaluation_only"] = True
if self.is_chat_output:
data["is_chat_output"] = True
return data
@staticmethod
def deserialize(data: dict):
"""Deserialize the flow output definition from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The flow output definition constructed from the dict.
:rtype: ~promptflow.contracts.flow.FlowOutputDefinition
"""
return FlowOutputDefinition(
ValueType(data["type"]),
InputAssignment.deserialize(data.get("reference", "")),
data.get("description", ""),
data.get("evaluation_only", False),
data.get("is_chat_output", False),
)
@dataclass
class NodeVariant:
"""This class represents a node variant.
:param node: The node of the node variant.
:type node: ~promptflow.contracts.flow.Node
:param description: The description of the node variant.
:type description: str
"""
node: Node
description: str = ""
@staticmethod
def deserialize(data: dict) -> "NodeVariant":
"""Deserialize the node variant from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The node variant constructed from the dict.
:rtype: ~promptflow.contracts.flow.NodeVariant
"""
return NodeVariant(
Node.deserialize(data["node"]),
data.get("description", ""),
)
@dataclass
class NodeVariants:
"""This class represents the variants of a node.
:param default_variant_id: The default variant id of the node.
:type default_variant_id: str
:param variants: The variants of the node.
:type variants: Dict[str, NodeVariant]
"""
default_variant_id: str # The default variant id of the node
variants: Dict[str, NodeVariant] # The variants of the node
@staticmethod
def deserialize(data: dict) -> "NodeVariants":
"""Deserialize the node variants from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The node variants constructed from the dict.
:rtype: ~promptflow.contracts.flow.NodeVariants
"""
variants = {}
for variant_id, node in data["variants"].items():
variants[variant_id] = NodeVariant.deserialize(node)
return NodeVariants(default_variant_id=data.get("default_variant_id", ""), variants=variants)
@dataclass
class Flow:
"""This class represents a flow.
:param id: The id of the flow.
:type id: str
:param name: The name of the flow.
:type name: str
:param nodes: The nodes of the flow.
:type nodes: List[Node]
:param inputs: The inputs of the flow.
:type inputs: Dict[str, FlowInputDefinition]
:param outputs: The outputs of the flow.
:type outputs: Dict[str, FlowOutputDefinition]
:param tools: The tools of the flow.
:type tools: List[Tool]
:param node_variants: The node variants of the flow.
:type node_variants: Dict[str, NodeVariants]
:param program_language: The program language of the flow.
:type program_language: str
:param environment_variables: The default environment variables of the flow.
:type environment_variables: Dict[str, object]
"""
id: str
name: str
nodes: List[Node]
inputs: Dict[str, FlowInputDefinition]
outputs: Dict[str, FlowOutputDefinition]
tools: List[Tool]
node_variants: Dict[str, NodeVariants] = None
program_language: str = FlowLanguage.Python
environment_variables: Dict[str, object] = None
def serialize(self):
"""Serialize the flow to a dict.
:return: The dict of the flow.
:rtype: dict
"""
data = {
"id": self.id,
"name": self.name,
"nodes": [n.serialize() for n in self.nodes],
"inputs": {name: i.serialize() for name, i in self.inputs.items()},
"outputs": {name: o.serialize() for name, o in self.outputs.items()},
"tools": [serialize(t) for t in self.tools],
"language": self.program_language,
}
return data
@staticmethod
def _import_requisites(tools, nodes):
"""This function will import tools/nodes required modules to ensure type exists so flow can be executed."""
try:
# Import tool modules to ensure register_builtins & registered_connections executed
for tool in tools:
if tool.module:
try_import(tool.module, f"Import tool {tool.name!r} module {tool.module!r} failed.")
# Import node provider to ensure register_apis executed so that provider & connection exists.
for node in nodes:
if node.module:
try_import(node.module, f"Import node {node.name!r} provider module {node.module!r} failed.")
except Exception as e:
logger.warning("Failed to import modules...")
raise FailedToImportModule(
message=f"Failed to import modules with error: {str(e)}.", target=ErrorTarget.RUNTIME
) from e
@staticmethod
def deserialize(data: dict) -> "Flow":
"""Deserialize the flow from a dict.
:param data: The dict to be deserialized.
:type data: dict
:return: The flow constructed from the dict.
:rtype: ~promptflow.contracts.flow.Flow
"""
tools = [Tool.deserialize(t) for t in data.get("tools") or []]
nodes = [Node.deserialize(n) for n in data.get("nodes") or []]
Flow._import_requisites(tools, nodes)
inputs = data.get("inputs") or {}
outputs = data.get("outputs") or {}
return Flow(
# TODO: Remove this fallback.
data.get("id", "default_flow_id"),
data.get("name", "default_flow"),
nodes,
{name: FlowInputDefinition.deserialize(i) for name, i in inputs.items()},
{name: FlowOutputDefinition.deserialize(o) for name, o in outputs.items()},
tools=tools,
node_variants={name: NodeVariants.deserialize(v) for name, v in (data.get("node_variants") or {}).items()},
program_language=data.get(LANGUAGE_KEY, FlowLanguage.Python),
environment_variables=data.get("environment_variables") or {},
)
def _apply_default_node_variants(self: "Flow"):
self.nodes = [
self._apply_default_node_variant(node, self.node_variants) if node.use_variants else node
for node in self.nodes
]
return self
@staticmethod
def _apply_default_node_variant(node: Node, node_variants: Dict[str, NodeVariants]) -> Node:
if not node_variants:
return node
node_variant = node_variants.get(node.name)
if not node_variant:
return node
default_variant = node_variant.variants.get(node_variant.default_variant_id)
if not default_variant:
return node
default_variant.node.name = node.name
return default_variant.node
@classmethod
def _resolve_working_dir(cls, flow_file: Path, working_dir=None) -> Path:
working_dir = cls._parse_working_dir(flow_file, working_dir)
cls._update_working_dir(working_dir)
return working_dir
@classmethod
def _parse_working_dir(cls, flow_file: Path, working_dir=None) -> Path:
if working_dir is None:
working_dir = Path(flow_file).resolve().parent
working_dir = Path(working_dir).absolute()
return working_dir
@classmethod
def _update_working_dir(cls, working_dir: Path):
sys.path.insert(0, str(working_dir))
@classmethod
def from_yaml(cls, flow_file: Path, working_dir=None) -> "Flow":
"""Load flow from yaml file."""
working_dir = cls._parse_working_dir(flow_file, working_dir)
with open(working_dir / flow_file, "r", encoding=DEFAULT_ENCODING) as fin:
flow_dag = load_yaml(fin)
flow_dag["name"] = flow_dag.get("name", _sanitize_python_variable_name(working_dir.stem))
return Flow._from_dict(flow_dag=flow_dag, working_dir=working_dir)
@classmethod
def _from_dict(cls, flow_dag: dict, working_dir: Path) -> "Flow":
"""Load flow from dict."""
cls._update_working_dir(working_dir)
flow = Flow.deserialize(flow_dag)
flow._set_tool_loader(working_dir)
return flow
@classmethod
def load_env_variables(
cls, flow_file: Path, working_dir=None, environment_variables_overrides: Dict[str, str] = None
) -> Dict[str, str]:
"""
Read flow_environment_variables from flow yaml.
If environment_variables_overrides exists, override yaml level configuration.
Returns the merged environment variables dict.
"""
if Path(flow_file).suffix.lower() != ".yaml":
# The flow_file type of eager flow is .py
return environment_variables_overrides or {}
working_dir = cls._parse_working_dir(flow_file, working_dir)
with open(working_dir / flow_file, "r", encoding=DEFAULT_ENCODING) as fin:
flow_dag = load_yaml(fin)
flow = Flow.deserialize(flow_dag)
return flow.get_environment_variables_with_overrides(
environment_variables_overrides=environment_variables_overrides
)
def get_environment_variables_with_overrides(
self, environment_variables_overrides: Dict[str, str] = None
) -> Dict[str, str]:
environment_variables = {
k: (json.dumps(v) if isinstance(v, (dict, list)) else str(v)) for k, v in self.environment_variables.items()
}
if environment_variables_overrides is not None:
for k, v in environment_variables_overrides.items():
environment_variables[k] = v
return environment_variables
def _set_tool_loader(self, working_dir):
package_tool_keys = [node.source.tool for node in self.nodes if node.source and node.source.tool]
from promptflow._core.tools_manager import ToolLoader
# TODO: consider refactor this. It will raise an error if promptflow-tools
# is not installed even for csharp flow.
self._tool_loader = ToolLoader(working_dir, package_tool_keys)
def _apply_node_overrides(self, node_overrides):
"""Apply node overrides to update the nodes in the flow.
Example:
node_overrides = {
"llm_node1.connection": "some_connection",
"python_node1.some_key": "some_value",
}
We will update the connection field of llm_node1 and the input value of python_node1.some_key.
"""
if not node_overrides:
return self
# We don't do detailed error handling here, since it should never fail
for key, value in node_overrides.items():
node_name, input_name = key.split(".")
node = self.get_node(node_name)
if node is None:
raise ValueError(f"Cannot find node {node_name} in flow {self.name}")
# For LLM node, here we override the connection field in node
if node.connection and input_name == "connection":
node.connection = value
# Other scenarios we override the input value of the inputs
else:
node.inputs[input_name] = InputAssignment(value=value)
return self
def has_aggregation_node(self):
"""Return whether the flow has aggregation node."""
return any(n.aggregation for n in self.nodes)
def get_node(self, node_name):
"""Return the node with the given name."""
return next((n for n in self.nodes if n.name == node_name), None)
def get_tool(self, tool_name):
"""Return the tool with the given name."""
return next((t for t in self.tools if t.name == tool_name), None)
def is_reduce_node(self, node_name):
"""Return whether the node is a reduce node."""
node = next((n for n in self.nodes if n.name == node_name), None)
return node is not None and node.aggregation
def is_normal_node(self, node_name):
"""Return whether the node is a normal node."""
node = next((n for n in self.nodes if n.name == node_name), None)
return node is not None and not node.aggregation
def is_llm_node(self, node):
"""Given a node, return whether it uses LLM tool."""
return node.type == ToolType.LLM
def is_referenced_by_flow_output(self, node):
"""Given a node, return whether it is referenced by output."""
return any(
output
for output in self.outputs.values()
if all(
(
output.reference.value_type == InputValueType.NODE_REFERENCE,
output.reference.value == node.name,
)
)
)
def is_node_referenced_by(self, node: Node, other_node: Node):
"""Given two nodes, return whether the first node is referenced by the second node."""
return other_node.inputs and any(
input
for input in other_node.inputs.values()
if input.value_type == InputValueType.NODE_REFERENCE and input.value == node.name
)
def is_referenced_by_other_node(self, node):
"""Given a node, return whether it is referenced by other node."""
return any(flow_node for flow_node in self.nodes if self.is_node_referenced_by(node, flow_node))
def is_chat_flow(self):
"""Return whether the flow is a chat flow."""
chat_input_name = self.get_chat_input_name()
return chat_input_name is not None
def get_chat_input_name(self):
"""Return the name of the chat input."""
return next((name for name, i in self.inputs.items() if i.is_chat_input), None)
def get_chat_output_name(self):
"""Return the name of the chat output."""
return next((name for name, o in self.outputs.items() if o.is_chat_output), None)
def _get_connection_name_from_tool(self, tool: Tool, node: Node):
connection_names = {}
value_types = set({v.value for v in ValueType.__members__.values()})
for k, v in tool.inputs.items():
input_type = [typ.value if isinstance(typ, Enum) else typ for typ in v.type]
if all(typ.lower() in value_types for typ in input_type):
# All type is value type, the key is not a possible connection key.
continue
input_assignment = node.inputs.get(k)
# Add literal node assignment values to results, skip node reference
if isinstance(input_assignment, InputAssignment) and input_assignment.value_type == InputValueType.LITERAL:
connection_names[k] = input_assignment.value
return connection_names
def get_connection_names(self):
"""Return connection names."""
connection_names = set({})
nodes = [
self._apply_default_node_variant(node, self.node_variants) if node.use_variants else node
for node in self.nodes
]
for node in nodes:
if node.connection:
connection_names.add(node.connection)
continue
if node.type == ToolType.PROMPT or node.type == ToolType.LLM:
continue
logger.debug(f"Try loading connection names for node {node.name}.")
tool = self.get_tool(node.tool) or self._tool_loader.load_tool_for_node(node)
if tool:
node_connection_names = list(self._get_connection_name_from_tool(tool, node).values())
else:
node_connection_names = []
if node_connection_names:
logger.debug(f"Connection names of node {node.name}: {node_connection_names}")
else:
logger.debug(f"Node {node.name} doesn't reference any connection.")
connection_names.update(node_connection_names)
return set({item for item in connection_names if item})
def get_connection_input_names_for_node(self, node_name):
"""Return connection input names."""
node = self.get_node(node_name)
if node and node.use_variants:
node = self._apply_default_node_variant(node, self.node_variants)
# Ignore Prompt node and LLM node, due to they do not have connection inputs.
if not node or node.type == ToolType.PROMPT or node.type == ToolType.LLM:
return []
tool = self.get_tool(node.tool) or self._tool_loader.load_tool_for_node(node)
if tool:
return list(self._get_connection_name_from_tool(tool, node).keys())
return []
def _replace_with_variant(self, variant_node: Node, variant_tools: list):
for index, node in enumerate(self.nodes):
if node.name == variant_node.name:
self.nodes[index] = variant_node
break
self.tools = self.tools + variant_tools
| promptflow/src/promptflow/promptflow/contracts/flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/contracts/flow.py",
"repo_id": "promptflow",
"token_count": 13269
} | 21 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import re
from promptflow._core._errors import NotSupported
from promptflow.contracts.flow import InputAssignment, InputValueType
from promptflow.executor._errors import (
InputNotFound,
InputNotFoundFromAncestorNodeOutput,
InvalidReferenceProperty,
UnsupportedReference,
)
def parse_value(i: InputAssignment, nodes_outputs: dict, flow_inputs: dict):
if i.value_type == InputValueType.LITERAL:
return i.value
if i.value_type == InputValueType.FLOW_INPUT:
if i.value not in flow_inputs:
flow_input_keys = ", ".join(flow_inputs.keys()) if flow_inputs is not None else None
raise InputNotFound(
message_format=(
"Flow execution failed. "
"The input '{input_name}' is not found from flow inputs '{flow_input_keys}'. "
"Please check the input name and try again."
),
input_name=i.value,
flow_input_keys=flow_input_keys,
)
return flow_inputs[i.value]
if i.value_type == InputValueType.NODE_REFERENCE:
if i.section != "output":
raise UnsupportedReference(
message_format=(
"Flow execution failed. "
"The section '{reference_section}' of reference is currently unsupported. "
"Please specify the output part of the node '{reference_node_name}'."
),
reference_section=i.section,
reference_node_name=i.value,
)
if i.value not in nodes_outputs:
node_output_keys = [output_keys for output_keys in nodes_outputs.keys() if nodes_outputs]
raise InputNotFoundFromAncestorNodeOutput(
message_format=(
"Flow execution failed. "
"The input '{input_name}' is not found from ancestor node outputs {node_output_keys}. "
"Please check the node name and try again."
),
input_name=i.value,
node_output_keys=node_output_keys,
)
return parse_node_property(i.value, nodes_outputs[i.value], i.property)
raise NotSupported(
message_format=(
"Flow execution failed. "
"The type '{input_type}' is currently unsupported. "
"Please choose from available types: {supported_output_type} and try again."
),
input_type=i.value_type.value if hasattr(i.value_type, "value") else i.value_type,
supported_output_type=[value_type.value for value_type in InputValueType],
)
property_pattern = r"(\w+)|(\['.*?'\])|(\[\d+\])"
def parse_node_property(node_name, node_val, property=""):
val = node_val
property_parts = re.findall(property_pattern, property)
try:
for part in property_parts:
part = [p for p in part if p][0]
if part.startswith("[") and part.endswith("]"):
index = part[1:-1]
if index.startswith("'") and index.endswith("'") or index.startswith('"') and index.endswith('"'):
index = index[1:-1]
elif index.isdigit():
index = int(index)
else:
raise InvalidReferenceProperty(
message_format=(
"Flow execution failed. "
"Invalid index '{index}' when accessing property '{property}' of the node '{node_name}'. "
"Please check the index and try again."
),
index=index,
property=property,
node_name=node_name,
)
val = val[index]
else:
if isinstance(val, dict):
val = val[part]
else:
val = getattr(val, part)
except (KeyError, IndexError, AttributeError) as e:
message_format = (
"Flow execution failed. "
"Invalid property '{property}' when accessing the node '{node_name}'. "
"Please check the property and try again."
)
raise InvalidReferenceProperty(message_format=message_format, property=property, node_name=node_name) from e
return val
| promptflow/src/promptflow/promptflow/executor/_input_assignment_parser.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_input_assignment_parser.py",
"repo_id": "promptflow",
"token_count": 2155
} | 22 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import inspect
import types
from dataclasses import dataclass
from functools import partial
from pathlib import Path
from typing import Callable, List, Optional
from promptflow._core._errors import InvalidSource
from promptflow._core.connection_manager import ConnectionManager
from promptflow._core.tool import STREAMING_OPTION_PARAMETER_ATTR
from promptflow._core.tools_manager import BuiltinsManager, ToolLoader, connection_type_to_api_mapping
from promptflow._utils.multimedia_utils import create_image, load_multimedia_data_recursively
from promptflow._utils.tool_utils import get_inputs_for_prompt_template, get_prompt_param_name_from_func
from promptflow._utils.yaml_utils import load_yaml
from promptflow.contracts.flow import InputAssignment, InputValueType, Node, ToolSourceType
from promptflow.contracts.tool import ConnectionType, Tool, ToolType, ValueType
from promptflow.contracts.types import AssistantDefinition, PromptTemplate
from promptflow.exceptions import ErrorTarget, PromptflowException, UserErrorException
from promptflow.executor._errors import (
ConnectionNotFound,
EmptyLLMApiMapping,
InvalidConnectionType,
InvalidCustomLLMTool,
NodeInputValidationError,
ResolveToolError,
ValueTypeUnresolved,
)
@dataclass
class ResolvedTool:
node: Node
definition: Tool
callable: Callable
init_args: dict
class ToolResolver:
def __init__(
self,
working_dir: Path,
connections: Optional[dict] = None,
package_tool_keys: Optional[List[str]] = None,
):
try:
# Import openai and aoai for llm tool
from promptflow.tools import aoai, openai # noqa: F401
except ImportError:
pass
self._tool_loader = ToolLoader(working_dir, package_tool_keys=package_tool_keys)
self._working_dir = working_dir
self._connection_manager = ConnectionManager(connections)
@classmethod
def start_resolver(
cls, working_dir: Path, connections: Optional[dict] = None, package_tool_keys: Optional[List[str]] = None
):
resolver = cls(working_dir, connections, package_tool_keys)
resolver._activate_in_context(force=True)
return resolver
def _convert_to_connection_value(self, k: str, v: InputAssignment, node: Node, conn_types: List[ValueType]):
connection_value = self._connection_manager.get(v.value)
if not connection_value:
raise ConnectionNotFound(f"Connection {v.value} not found for node {node.name!r} input {k!r}.")
# Check if type matched
if not any(type(connection_value).__name__ == typ for typ in conn_types):
msg = (
f"Input '{k}' for node '{node.name}' of type {type(connection_value).__name__!r}"
f" is not supported, valid types {conn_types}."
)
raise NodeInputValidationError(message=msg)
return connection_value
def _convert_to_custom_strong_type_connection_value(
self, k: str, v: InputAssignment, node: Node, tool: Tool, conn_types: List[str], module: types.ModuleType
):
if not conn_types:
msg = f"Input '{k}' for node '{node.name}' has invalid types: {conn_types}."
raise NodeInputValidationError(message=msg)
connection_value = self._connection_manager.get(v.value)
if not connection_value:
raise ConnectionNotFound(f"Connection {v.value} not found for node {node.name!r} input {k!r}.")
custom_defined_connection_class_name = conn_types[0]
if node.source.type == ToolSourceType.Package:
module = tool.module
return connection_value._convert_to_custom_strong_type(
module=module, to_class=custom_defined_connection_class_name
)
def _convert_to_assistant_definition(self, assistant_definition_path: str, input_name: str, node_name: str):
if assistant_definition_path is None or not (self._working_dir / assistant_definition_path).is_file():
raise InvalidSource(
target=ErrorTarget.EXECUTOR,
message_format="Input '{input_name}' for node '{node_name}' of value '{source_path}' "
"is not a valid path.",
input_name=input_name,
source_path=assistant_definition_path,
node_name=node_name,
)
file = self._working_dir / assistant_definition_path
with open(file, "r", encoding="utf-8") as file:
assistant_definition = load_yaml(file)
return AssistantDefinition.deserialize(assistant_definition)
def _convert_node_literal_input_types(self, node: Node, tool: Tool, module: types.ModuleType = None):
updated_inputs = {
k: v
for k, v in node.inputs.items()
if (v.value is not None and v.value != "") or v.value_type != InputValueType.LITERAL
}
for k, v in updated_inputs.items():
if v.value_type != InputValueType.LITERAL:
continue
tool_input = tool.inputs.get(k)
if tool_input is None: # For kwargs input, tool_input is None.
continue
value_type = tool_input.type[0]
updated_inputs[k] = InputAssignment(value=v.value, value_type=InputValueType.LITERAL)
if ConnectionType.is_connection_class_name(value_type):
if tool_input.custom_type:
updated_inputs[k].value = self._convert_to_custom_strong_type_connection_value(
k, v, node, tool, tool_input.custom_type, module=module
)
else:
updated_inputs[k].value = self._convert_to_connection_value(k, v, node, tool_input.type)
elif value_type == ValueType.IMAGE:
try:
updated_inputs[k].value = create_image(v.value)
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise NodeInputValidationError(
message_format="Failed to load image for input '{key}': {error_type_and_message}",
key=k,
error_type_and_message=error_type_and_message,
target=ErrorTarget.EXECUTOR,
) from e
elif value_type == ValueType.ASSISTANT_DEFINITION:
try:
updated_inputs[k].value = self._convert_to_assistant_definition(v.value, k, node.name)
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise NodeInputValidationError(
message_format="Failed to load assistant definition from input '{key}': "
"{error_type_and_message}",
key=k,
error_type_and_message=error_type_and_message,
target=ErrorTarget.EXECUTOR,
) from e
elif isinstance(value_type, ValueType):
try:
updated_inputs[k].value = value_type.parse(v.value)
except Exception as e:
raise NodeInputValidationError(
message_format="Input '{key}' for node '{node_name}' of value '{value}' is not "
"type {value_type}.",
key=k,
node_name=node.name,
value=v.value,
value_type=value_type.value,
target=ErrorTarget.EXECUTOR,
) from e
try:
updated_inputs[k].value = load_multimedia_data_recursively(updated_inputs[k].value)
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise NodeInputValidationError(
message_format="Failed to load image for input '{key}': {error_type_and_message}",
key=k,
error_type_and_message=error_type_and_message,
target=ErrorTarget.EXECUTOR,
) from e
else:
# The value type is in ValueType enum or is connection type. null connection has been handled before.
raise ValueTypeUnresolved(
f"Unresolved input type {value_type!r}, please check if it is supported in current version.",
target=ErrorTarget.EXECUTOR,
)
updated_node = copy.deepcopy(node)
updated_node.inputs = updated_inputs
return updated_node
def resolve_tool_by_node(self, node: Node, convert_input_types=True) -> ResolvedTool:
try:
if node.source is None:
raise UserErrorException(f"Node {node.name} does not have source defined.")
if node.type is ToolType.PYTHON:
if node.source.type == ToolSourceType.Package:
return self._resolve_package_node(node, convert_input_types=convert_input_types)
elif node.source.type == ToolSourceType.Code:
return self._resolve_script_node(node, convert_input_types=convert_input_types)
raise NotImplementedError(f"Tool source type {node.source.type} for python tool is not supported yet.")
elif node.type is ToolType.PROMPT:
return self._resolve_prompt_node(node)
elif node.type is ToolType.LLM:
return self._resolve_llm_node(node, convert_input_types=convert_input_types)
elif node.type is ToolType.CUSTOM_LLM:
if node.source.type == ToolSourceType.PackageWithPrompt:
resolved_tool = self._resolve_package_node(node, convert_input_types=convert_input_types)
return self._integrate_prompt_in_package_node(resolved_tool)
raise NotImplementedError(
f"Tool source type {node.source.type} for custom_llm tool is not supported yet."
)
else:
raise NotImplementedError(f"Tool type {node.type} is not supported yet.")
except Exception as e:
if isinstance(e, PromptflowException) and e.target != ErrorTarget.UNKNOWN:
raise ResolveToolError(node_name=node.name, target=e.target, module=e.module) from e
raise ResolveToolError(node_name=node.name) from e
def _load_source_content(self, node: Node) -> str:
source = node.source
# If is_file returns True, the path points to a existing file, so we don't need to check if exists.
if source is None or source.path is None or not (self._working_dir / source.path).is_file():
raise InvalidSource(
target=ErrorTarget.EXECUTOR,
message_format="Node source path '{source_path}' is invalid on node '{node_name}'.",
source_path=source.path if source is not None else None,
node_name=node.name,
)
file = self._working_dir / source.path
return file.read_text(encoding="utf-8")
def _validate_duplicated_inputs(self, prompt_tpl_inputs: list, tool_params: list, msg: str):
duplicated_inputs = set(prompt_tpl_inputs) & set(tool_params)
if duplicated_inputs:
raise NodeInputValidationError(
message=msg.format(duplicated_inputs=duplicated_inputs),
target=ErrorTarget.EXECUTOR,
)
def _load_images_for_prompt_tpl(self, prompt_tpl_inputs_mapping: dict, node_inputs: dict):
for input_name, input in prompt_tpl_inputs_mapping.items():
if ValueType.IMAGE in input.type and input_name in node_inputs:
if node_inputs[input_name].value_type == InputValueType.LITERAL:
node_inputs[input_name].value = create_image(node_inputs[input_name].value)
return node_inputs
def _resolve_prompt_node(self, node: Node) -> ResolvedTool:
prompt_tpl = self._load_source_content(node)
prompt_tpl_inputs_mapping = get_inputs_for_prompt_template(prompt_tpl)
from promptflow.tools.template_rendering import render_template_jinja2
params = inspect.signature(render_template_jinja2).parameters
param_names = [name for name, param in params.items() if param.kind != inspect.Parameter.VAR_KEYWORD]
msg = (
f"Invalid inputs {{duplicated_inputs}} in prompt template of node {node.name}. "
f"These inputs are duplicated with the reserved parameters of prompt tool."
)
self._validate_duplicated_inputs(prompt_tpl_inputs_mapping.keys(), param_names, msg)
node.inputs = self._load_images_for_prompt_tpl(prompt_tpl_inputs_mapping, node.inputs)
callable = partial(render_template_jinja2, template=prompt_tpl)
return ResolvedTool(node=node, definition=None, callable=callable, init_args={})
@staticmethod
def _remove_init_args(node_inputs: dict, init_args: dict):
for k in init_args:
if k in node_inputs:
del node_inputs[k]
def _get_node_connection(self, node: Node):
connection = self._connection_manager.get(node.connection)
if connection is None:
raise ConnectionNotFound(
message=f"Connection {node.connection!r} not found, available connection keys "
f"{self._connection_manager._connections.keys()}.",
target=ErrorTarget.EXECUTOR,
)
return connection
def _resolve_llm_node(self, node: Node, convert_input_types=False) -> ResolvedTool:
connection = self._get_node_connection(node)
if not node.provider:
if not connection_type_to_api_mapping:
raise EmptyLLMApiMapping()
# If provider is not specified, try to resolve it from connection type
connection_type = type(connection).__name__
if connection_type not in connection_type_to_api_mapping:
raise InvalidConnectionType(
message_format="Connection type {conn_type} is not supported for LLM.",
conn_type=connection_type,
)
node.provider = connection_type_to_api_mapping[connection_type]
tool: Tool = self._tool_loader.load_tool_for_llm_node(node)
key, connection = self._resolve_llm_connection_to_inputs(node, tool)
updated_node = copy.deepcopy(node)
updated_node.inputs[key] = InputAssignment(value=connection, value_type=InputValueType.LITERAL)
if convert_input_types:
updated_node = self._convert_node_literal_input_types(updated_node, tool)
prompt_tpl = self._load_source_content(node)
prompt_tpl_inputs_mapping = get_inputs_for_prompt_template(prompt_tpl)
msg = (
f"Invalid inputs {{duplicated_inputs}} in prompt template of node {node.name}. "
f"These inputs are duplicated with the parameters of {node.provider}.{node.api}."
)
self._validate_duplicated_inputs(prompt_tpl_inputs_mapping.keys(), tool.inputs.keys(), msg)
updated_node.inputs = self._load_images_for_prompt_tpl(prompt_tpl_inputs_mapping, updated_node.inputs)
api_func, init_args = BuiltinsManager._load_package_tool(
tool.name, tool.module, tool.class_name, tool.function, updated_node.inputs
)
self._remove_init_args(updated_node.inputs, init_args)
prompt_tpl_param_name = get_prompt_param_name_from_func(api_func)
api_func = partial(api_func, **{prompt_tpl_param_name: prompt_tpl}) if prompt_tpl_param_name else api_func
return ResolvedTool(updated_node, tool, api_func, init_args)
def _resolve_llm_connection_to_inputs(self, node: Node, tool: Tool) -> Node:
connection = self._get_node_connection(node)
for key, input in tool.inputs.items():
if ConnectionType.is_connection_class_name(input.type[0]):
if type(connection).__name__ not in input.type:
msg = (
f"Invalid connection '{node.connection}' type {type(connection).__name__!r} "
f"for node '{node.name}', valid types {input.type}."
)
raise InvalidConnectionType(message=msg)
return key, connection
raise InvalidConnectionType(
message_format="Connection type can not be resolved for tool {tool_name}", tool_name=tool.name
)
def _resolve_script_node(self, node: Node, convert_input_types=False) -> ResolvedTool:
m, tool = self._tool_loader.load_tool_for_script_node(node)
# We only want to load script tool module once.
# Reloading the same module changes the ID of the class, which can cause issues with isinstance() checks.
# This is important when working with connection class checks. For instance, in user tool script it writes:
# isinstance(conn, MyCustomConnection)
# Custom defined script tool and custom defined strong type connection are in the same module.
# The first time to load the module is in above line when loading a tool.
# We need the module again when converting the custom connection to strong type when converting input types.
# To avoid reloading, pass the loaded module to _convert_node_literal_input_types as an arg.
if convert_input_types:
node = self._convert_node_literal_input_types(node, tool, m)
callable, init_args = BuiltinsManager._load_tool_from_module(
m, tool.name, tool.module, tool.class_name, tool.function, node.inputs
)
self._remove_init_args(node.inputs, init_args)
return ResolvedTool(node=node, definition=tool, callable=callable, init_args=init_args)
def _resolve_package_node(self, node: Node, convert_input_types=False) -> ResolvedTool:
tool: Tool = self._tool_loader.load_tool_for_package_node(node)
updated_node = copy.deepcopy(node)
if convert_input_types:
updated_node = self._convert_node_literal_input_types(updated_node, tool)
callable, init_args = BuiltinsManager._load_package_tool(
tool.name, tool.module, tool.class_name, tool.function, updated_node.inputs
)
self._remove_init_args(updated_node.inputs, init_args)
return ResolvedTool(node=updated_node, definition=tool, callable=callable, init_args=init_args)
def _integrate_prompt_in_package_node(self, resolved_tool: ResolvedTool):
node = resolved_tool.node
prompt_tpl = PromptTemplate(self._load_source_content(node))
prompt_tpl_inputs_mapping = get_inputs_for_prompt_template(prompt_tpl)
msg = (
f"Invalid inputs {{duplicated_inputs}} in prompt template of node {node.name}. "
f"These inputs are duplicated with the inputs of custom llm tool."
)
self._validate_duplicated_inputs(prompt_tpl_inputs_mapping.keys(), resolved_tool.definition.inputs.keys(), msg)
node.inputs = self._load_images_for_prompt_tpl(prompt_tpl_inputs_mapping, node.inputs)
callable = resolved_tool.callable
prompt_tpl_param_name = get_prompt_param_name_from_func(callable)
if prompt_tpl_param_name is None:
raise InvalidCustomLLMTool(
f"Invalid Custom LLM tool {resolved_tool.definition.name}: "
f"function {callable.__name__} is missing a prompt template argument.",
target=ErrorTarget.EXECUTOR,
)
resolved_tool.callable = partial(callable, **{prompt_tpl_param_name: prompt_tpl})
# Copy the attributes to make sure they are still available after partial.
attributes_to_set = [STREAMING_OPTION_PARAMETER_ATTR]
for attr in attributes_to_set:
attr_val = getattr(callable, attr, None)
if attr_val is not None:
setattr(resolved_tool.callable, attr, attr_val)
return resolved_tool
| promptflow/src/promptflow/promptflow/executor/_tool_resolver.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_tool_resolver.py",
"repo_id": "promptflow",
"token_count": 8974
} | 23 |
# Devcontainer for promptflow
To facilitate your promptflow project development and empower you to work on LLM projects using promptflow more effectively,
we've configured the necessary environment for developing promptflow projects and utilizing flows through the dev container feature.
You can seamlessly initiate your promptflow project development and start leveraging flows by simply using the dev container feature via VS Code or Codespaces.
## Use Github Codespaces
Use codespaces to open promptflow repo, it will automatically build the dev containers environment and open promptflow with dev containers. You can just click: [](https://codespaces.new/microsoft/promptflow?quickstart=1)
## Use local devcontainer
Use vscode to open promptflow repo, and install vscode extension: Dev Containers and then open promptflow with dev containers.
**About dev containers please refer to: [dev containers](https://code.visualstudio.com/docs/devcontainers/containers)**
| promptflow/.devcontainer/README.md/0 | {
"file_path": "promptflow/.devcontainer/README.md",
"repo_id": "promptflow",
"token_count": 248
} | 0 |
# Promptflow documentation contribute guidelines
This folder contains the source code for [prompt flow documentation site](https://microsoft.github.io/promptflow/).
This readme file will not be included in above doc site. It keeps a guide for promptflow documentation contributors.
## Content
Below is a table of important doc pages.
| Category | Article |
|----------------|----------------|
|Quick start|[Getting started with prompt flow](./how-to-guides/quick-start.md)|
|Concepts|[Flows](./concepts/concept-flows.md)<br> [Tools](./concepts/concept-tools.md)<br> [Connections](./concepts/concept-connections.md)<br> [Variants](./concepts/concept-variants.md)<br> |
|How-to guides|[How to initialize and test a flow](./how-to-guides/init-and-test-a-flow.md) <br>[How to run and evaluate a flow](./how-to-guides/run-and-evaluate-a-flow/index.md)<br> [How to tune prompts using variants](./how-to-guides/tune-prompts-with-variants.md)<br>[How to deploy a flow](./how-to-guides/deploy-a-flow/index.md)<br>[How to create and use your own tool package](./how-to-guides/develop-a-tool/create-and-use-tool-package.md)|
|Tools reference|[LLM tool](./reference/tools-reference/llm-tool.md)<br> [Prompt tool](./reference/tools-reference/prompt-tool.md)<br> [Python tool](./reference/tools-reference/python-tool.md)<br> [Embedding tool](./reference/tools-reference/embedding_tool.md)<br>[SERP API tool](./reference/tools-reference/serp-api-tool.md) ||
## Writing tips
0. Reach the doc source repository by clicking `Edit this page` on any page.
1. Please use :::{admonition} for experimental feature or notes, and admonition with dropdown for the Limitation Part.
2. Please use ::::{tab-set} to group your sdk/cli example, and put the cli at first. Use :sync: to sync multiple tables .
3. If you are unclear with the above lines, refer to [get started](./how-to-guides/quick-start.md) to see the usage.
4. Add gif: Use [ScreenToGif](https://www.screentogif.com/) to record your screen, edit and save as a gif.
5. Reach more element style at [Sphinx Design Components](https://pydata-sphinx-theme.readthedocs.io/en/latest/user_guide/web-components.html).
## Preview your changes
**Local build**: We suggest using local build at the beginning, as it's fast and efficiency.
Please refer to [How to build doc site locally](./dev/documentation_guidelines.md#how-to-build-doc-site-locally).
## FAQ
### Adding image in doc
Please use markdown syntax `` to reference image, because the relative path of image will be changed after sphinx build, and image placed in html tags can not be referenced when build.
### Draw flow chart in doc
We recommend using the mermaid, learn more from the [mermaid syntax doc](https://mermaid-js.github.io/mermaid/#/./flowchart?id=flowcharts-basic-syntax)
- Recommend to install [vscode extension](https://marketplace.visualstudio.com/items?itemName=bierner.markdown-mermaid) to preview graph in vscode.
## Reference
- [md-and-rst](https://coderefinery.github.io/sphinx-lesson/md-and-rst/)
- [sphinx-quickstart](https://www.sphinx-doc.org/en/master/usage/quickstart.html) | promptflow/docs/README.md/0 | {
"file_path": "promptflow/docs/README.md",
"repo_id": "promptflow",
"token_count": 1034
} | 1 |
# Dev Setup
## Set up process
- First create a new [conda](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html) environment. Please specify python version as 3.9.
`conda create -n <env_name> python=3.9`.
- Activate the env you created.
- Set environment variable `PYTHONPATH` in your new conda environment.
`conda env config vars set PYTHONPATH=<path-to-src>\promptflow`.
Once you have set the environment variable, you have to reactivate your environment.
`conda activate <env_name>`.
- In root folder, run `python scripts/building/dev_setup.py --promptflow-extra-deps azure` to install the package and dependencies.
## How to run tests
### Set up your secrets
`dev-connections.json.example` is a template about connections provided in `src/promptflow`. You can follow these steps to refer to this template to configure your connection for the test cases:
1. `cd ./src/promptflow`
2. Run the command `cp dev-connections.json.example connections.json`;
3. Replace the values in the json file with your connection info;
4. Set the environment `PROMPTFLOW_CONNECTIONS='connections.json'`;
After above setup process is finished. You can use `pytest` command to run test, for example in root folder you can:
### Run tests via command
- Run all tests under a folder: `pytest src/promptflow/tests -v`
- Run a single test: ` pytest src/promptflow/tests/promptflow_test/e2etests/test_executor.py::TestExecutor::test_executor_basic_flow -v`
### Run tests in VSCode
1. Set up your python interperter
- Open the Command Palette (Ctrl+Shift+P) and select `Python: Select Interpreter`.
- Select existing conda env which you created previously.
2. Set up your test framework and directory
- Open the Command Palette (Ctrl+Shift+P) and select `Python: Configure Tests`.
- Select `pytest` as test framework.
- Select `Root directory` as test directory.
3. Exclude specific test folders.
You can exclude specific test folders if you don't have some extra dependency to avoid VS Code's test discovery fail.
For example, if you don't have azure dependency, you can exclude `sdk_cli_azure_test`.
Open `.vscode/settings.json`, write `"--ignore=src/promptflow/tests/sdk_cli_azure_test"` to `"python.testing.pytestArgs"`.
4. Click the `Run Test` button on the left
### Run tests in pycharm
1. Set up your pycharm python interpreter
2. Select existing conda env which you created previously
3. Run test, right-click the test name to run, or click the green arrow button on the left.
### Record and replay tests
Please refer to [Replay End-to-End Tests](./replay-e2e-test.md) to learn how to record and replay tests.
## How to write docstring.
A clear and consistent API documentation is crucial for the usability and maintainability of our codebase. Please refer to [API Documentation Guidelines](./documentation_guidelines.md) to learn how to write docstring when developing the project.
## How to write tests
- Put all test data/configs under `src/promptflow/tests/test_configs`.
- Write unit tests:
- Flow run: `src/promptflow/tests/sdk_cli_test/unittest/`
- Flow run in azure: `src/promptflow/tests/sdk_cli_azure_test/unittest/`
- Write e2e tests:
- Flow run: `src/promptflow/tests/sdk_cli_test/e2etests/`
- Flow run in azure: `src/promptflow/tests/sdk_cli_azure_test/e2etests/`
- Test file name and the test case name all start with `test_`.
- A basic test example, see [test_connection.py](../../src/promptflow/tests/sdk_cli_test/e2etests/test_connection.py).
### Test structure
Currently all tests are under `src/promptflow/tests/` folder:
- tests/
- promptflow/
- sdk_cli_test/
- e2etests/
- unittests/
- sdk_cli_azure_test/
- e2etests/
- unittests/
- test_configs/
- connections/
- datas/
- flows/
- runs/
- wrong_flows/
- wrong_tools/
When you want to add tests for a new feature, you can add new test file let's say a e2e test file `test_construction.py`
under `tests/promptflow/**/e2etests/`.
Once the project gets more complicated or anytime you find it necessary to add new test folder and test configs for
a specific feature, feel free to split the `promptflow` to more folders, for example:
- tests/
- (Test folder name)/
- e2etests/
- test_xxx.py
- unittests/
- test_xxx.py
- test_configs/
- (Data or config folder name)/
| promptflow/docs/dev/dev_setup.md/0 | {
"file_path": "promptflow/docs/dev/dev_setup.md",
"repo_id": "promptflow",
"token_count": 1669
} | 2 |
# Create and Use Tool Package
In this document, we will guide you through the process of developing your own tool package, offering detailed steps and advice on how to utilize your creation.
The custom tool is the prompt flow tool developed by yourself. If you find it useful, you can follow this guidance to make it a tool package. This will enable you to conveniently reuse it, share it with your team, or distribute it to anyone in the world.
After successful installation of the package, your custom "tool" will show up in VSCode extension as below:
## Create your own tool package
Your tool package should be a python package. To try it quickly, just use [my-tools-package 0.0.1](https://pypi.org/project/my-tools-package/) and skip this section.
### Prerequisites
Create a new conda environment using python 3.9 or 3.10. Run below command to install PromptFlow dependencies:
```
pip install promptflow
```
Install Pytest packages for running tests:
```
pip install pytest pytest-mock
```
Clone the PromptFlow repository from GitHub using the following command:
```
git clone https://github.com/microsoft/promptflow.git
```
### Create custom tool package
Run below command under the root folder to create your tool project quickly:
```
python <promptflow github repo>\scripts\tool\generate_tool_package_template.py --destination <your-tool-project> --package-name <your-package-name> --tool-name <your-tool-name> --function-name <your-tool-function-name>
```
For example:
```
python D:\proj\github\promptflow\scripts\tool\generate_tool_package_template.py --destination hello-world-proj --package-name hello-world --tool-name hello_world_tool --function-name get_greeting_message
```
This auto-generated script will create one tool for you. The parameters _destination_ and _package-name_ are mandatory. The parameters _tool-name_ and _function-name_ are optional. If left unfilled, the _tool-name_ will default to _hello_world_tool_, and the _function-name_ will default to _tool-name_.
The command will generate the tool project as follows with one tool `hello_world_tool.py` in it:
```
hello-world-proj/
│
├── hello_world/
│ ├── tools/
│ │ ├── __init__.py
│ │ ├── hello_world_tool.py
│ │ └── utils.py
│ ├── yamls/
│ │ └── hello_world_tool.yaml
│ └── __init__.py
│
├── tests/
│ ├── __init__.py
│ └── test_hello_world_tool.py
│
├── MANIFEST.in
│
└── setup.py
```
```The points outlined below explain the purpose of each folder/file in the package. If your aim is to develop multiple tools within your package, please make sure to closely examine point 2 and 5.```
1. **hello-world-proj**: This is the source directory. All of your project's source code should be placed in this directory.
2. **hello-world/tools**: This directory contains the individual tools for your project. Your tool package can contain either one tool or many tools. When adding a new tool, you should create another *_tool.py under the `tools` folder.
3. **hello-world/tools/hello_world_tool.py**: Develop your tool within the def function. Use the `@tool` decorator to identify the function as a tool.
> [!Note] There are two ways to write a tool. The default and recommended way is the function implemented way. You can also use the class implementation way, referring to [my_tool_2.py](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/tools/my_tool_2.py) as an example.
4. **hello-world/tools/utils.py**: This file implements the tool list method, which collects all the tools defined. It is required to have this tool list method, as it allows the User Interface (UI) to retrieve your tools and display them within the UI.
> [!Note] There's no need to create your own list method if you maintain the existing folder structure. You can simply use the auto-generated list method provided in the `utils.py` file.
5. **hello_world/yamls/hello_world_tool.yaml**: Tool YAMLs defines the metadata of the tool. The tool list method, as outlined in the `utils.py`, fetches these tool YAMLs.
> [!Note] If you create a new tool, don't forget to also create the corresponding tool YAML. You can run below command under your tool project to auto generate your tool YAML. You may want to specify `-n` for `name` and `-d` for `description`, which would be displayed as the tool name and tooltip in prompt flow UI.
```
python <promptflow github repo>\scripts\tool\generate_package_tool_meta.py -m <tool_module> -o <tool_yaml_path> -n <tool_name> -d <tool_description>
```
For example:
```
python D:\proj\github\promptflow\scripts\tool\generate_package_tool_meta.py -m hello_world.tools.hello_world_tool -o hello_world\yamls\hello_world_tool.yaml -n "Hello World Tool" -d "This is my hello world tool."
```
To populate your tool module, adhere to the pattern \<package_name\>.tools.\<tool_name\>, which represents the folder path to your tool within the package.
6. **tests**: This directory contains all your tests, though they are not required for creating your custom tool package. When adding a new tool, you can also create corresponding tests and place them in this directory. Run below command under your tool project:
```
pytest tests
```
7. **MANIFEST.in**: This file is used to determine which files to include in the distribution of the project. Tool YAML files should be included in MANIFEST.in so that your tool YAMLs would be packaged and your tools can show in the UI.
> [!Note] There's no need to update this file if you maintain the existing folder structure.
8. **setup.py**: This file contains metadata about your project like the name, version, author, and more. Additionally, the entry point is automatically configured for you in the `generate_tool_package_template.py` script. In Python, configuring the entry point in `setup.py` helps establish the primary execution point for a package, streamlining its integration with other software.
The `package_tools` entry point together with the tool list method are used to retrieve all the tools and display them in the UI.
```python
entry_points={
"package_tools": ["<your_tool_name> = <list_module>:<list_method>"],
},
```
> [!Note] There's no need to update this file if you maintain the existing folder structure.
## Build and share the tool package
Execute the following command in the tool package root directory to build your tool package:
```
python setup.py sdist bdist_wheel
```
This will generate a tool package `<your-package>-0.0.1.tar.gz` and corresponding `whl file` inside the `dist` folder.
Create an account on PyPI if you don't already have one, and install `twine` package by running `pip install twine`.
Upload your package to PyPI by running `twine upload dist/*`, this will prompt you for your Pypi username and password, and then upload your package on PyPI. Once your package is uploaded to PyPI, others can install it using pip by running `pip install your-package-name`. Make sure to replace `your-package-name` with the name of your package as it appears on PyPI.
If you only want to put it on Test PyPI, upload your package by running `twine upload --repository-url https://test.pypi.org/legacy/ dist/*`. Once your package is uploaded to Test PyPI, others can install it using pip by running `pip install --index-url https://test.pypi.org/simple/ your-package-name`.
## Use your tool from VSCode Extension
* Step1: Install [Prompt flow for VS Code extension](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow).
* Step2: Go to terminal and install your tool package in conda environment of the extension. Assume your conda env name is `prompt-flow`.
```
(local_test) PS D:\projects\promptflow\tool-package-quickstart> conda activate prompt-flow
(prompt-flow) PS D:\projects\promptflow\tool-package-quickstart> pip install .\dist\my_tools_package-0.0.1-py3-none-any.whl
```
* Step3: Go to the extension and open one flow folder. Click 'flow.dag.yaml' and preview the flow. Next, click `+` button and you will see your tools. You may need to reload the windows to clean previous cache if you don't see your tool in the list.
## FAQs
### Why is my custom tool not showing up in the UI?
Confirm that the tool YAML files are included in your custom tool package. You can add the YAML files to [MANIFEST.in](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/MANIFEST.in) and include the package data in [setup.py](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/setup.py).
Alternatively, you can test your tool package using the script below to ensure that you've packaged your tool YAML files and configured the package tool entry point correctly.
1. Make sure to install the tool package in your conda environment before executing this script.
2. Create a python file anywhere and copy the content below into it.
```python
import importlib
import importlib.metadata
def test():
"""List all package tools information using the `package-tools` entry point.
This function iterates through all entry points registered under the group "package_tools."
For each tool, it imports the associated module to ensure its validity and then prints
information about the tool.
Note:
- Make sure your package is correctly packed to appear in the list.
- The module is imported to validate its presence and correctness.
Example of tool information printed:
----identifier
{'module': 'module_name', 'package': 'package_name', 'package_version': 'package_version', ...}
"""
entry_points = importlib.metadata.entry_points()
if isinstance(entry_points, list):
entry_points = entry_points.select(group=PACKAGE_TOOLS_ENTRY)
else:
entry_points = entry_points.get(PACKAGE_TOOLS_ENTRY, [])
for entry_point in entry_points:
list_tool_func = entry_point.load()
package_tools = list_tool_func()
for identifier, tool in package_tools.items():
importlib.import_module(tool["module"]) # Import the module to ensure its validity
print(f"----{identifier}\n{tool}")
if __name__ == "__main__":
test()
```
3. Run this script in your conda environment. This will return the metadata of all tools installed in your local environment, and you should verify that your tools are listed.
### Why am I unable to upload package to PyPI?
* Make sure that the entered username and password of your PyPI account are accurate.
* If you encounter a `403 Forbidden Error`, it's likely due to a naming conflict with an existing package. You will need to choose a different name. Package names must be unique on PyPI to avoid confusion and conflicts among users. Before creating a new package, it's recommended to search PyPI (https://pypi.org/) to verify that your chosen name is not already taken. If the name you want is unavailable, consider selecting an alternative name or a variation that clearly differentiates your package from the existing one.
## Advanced features
- [Add a Tool Icon](add-a-tool-icon.md)
- [Add Category and Tags for Tool](add-category-and-tags-for-tool.md)
- [Create and Use Your Own Custom Strong Type Connection](create-your-own-custom-strong-type-connection.md)
- [Customize an LLM Tool](customize_an_llm_tool.md)
- [Use File Path as Tool Input](use-file-path-as-tool-input.md)
- [Create a Dynamic List Tool Input](create-dynamic-list-tool-input.md)
- [Create Cascading Tool Inputs](create-cascading-tool-inputs.md)
| promptflow/docs/how-to-guides/develop-a-tool/create-and-use-tool-package.md/0 | {
"file_path": "promptflow/docs/how-to-guides/develop-a-tool/create-and-use-tool-package.md",
"repo_id": "promptflow",
"token_count": 3697
} | 3 |
# Run and evaluate a flow
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](../faq.md#stable-vs-experimental).
:::
After you have developed and tested the flow in [init and test a flow](../init-and-test-a-flow.md), this guide will help you learn how to run a flow with a larger dataset and then evaluate the flow you have created.
## Create a batch run
Since you have run your flow successfully with a small set of data, you might want to test if it performs well in large set of data, you can run a batch test and check the outputs.
A bulk test allows you to run your flow with a large dataset and generate outputs for each data row, and the run results will be recorded in local db so you can use [pf commands](../../reference/pf-command-reference.md) to view the run results at anytime. (e.g. `pf run list`)
Let's create a run with flow [web-classification](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification). It is a flow demonstrating multi-class classification with LLM. Given an url, it will classify the url into one web category with just a few shots, simple summarization and classification prompts.
To begin with the guide, you need:
- Git clone the sample repository(above flow link) and set the working directory to `<path-to-the-sample-repo>/examples/flows/`.
- Make sure you have already created the necessary connection following [Create necessary connections](../quick-start.md#create-necessary-connections).
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
Create the run with flow and data, can add `--stream` to stream the run.
```sh
pf run create --flow standard/web-classification --data standard/web-classification/data.jsonl --column-mapping url='${data.url}' --stream
```
Note `column-mapping` is a mapping from flow input name to specified values, see more details in [Use column mapping](https://aka.ms/pf/column-mapping).
You can also name the run by specifying `--name my_first_run` in above command, otherwise the run name will be generated in a certain pattern which has timestamp inside.
With a run name, you can easily view or visualize the run details using below commands:
```sh
pf run show-details -n my_first_run
```
```sh
pf run visualize -n my_first_run
```
More details can be found with `pf run --help`
:::
:::{tab-item} SDK
:sync: SDK
```python
from promptflow import PFClient
# Please protect the entry point by using `if __name__ == '__main__':`,
# otherwise it would cause unintended side effect when promptflow spawn worker processes.
# Ref: https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
# PFClient can help manage your runs and connections.
pf = PFClient()
# Set flow path and run input data
flow = "standard/web-classification" # set the flow directory
data= "standard/web-classification/data.jsonl" # set the data file
# create a run, stream it until it's finished
base_run = pf.run(
flow=flow,
data=data,
stream=True,
# map the url field from the data to the url input of the flow
column_mapping={"url": "${data.url}"},
)
```
```python
# get the inputs/outputs details of a finished run.
details = pf.get_details(base_run)
details.head(10)
```
```python
# visualize the run in a web browser
pf.visualize(base_run)
```
Feel free to check [Promptflow Python Library Reference](../../reference/python-library-reference/promptflow.md) for all SDK public interfaces.
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
Use the code lens action on the top of the yaml editor to trigger batch run
Click the bulk test button on the top of the visual editor to trigger flow test.
:::
::::
We also have a more detailed documentation [Manage runs](../manage-runs.md) demonstrating how to manage your finished runs with CLI, SDK and VS Code Extension.
## Evaluate your flow
You can use an evaluation method to evaluate your flow. The evaluation methods are also flows which use Python or LLM etc., to calculate metrics like accuracy, relevance score. Please refer to [Develop evaluation flow](../develop-a-flow/develop-evaluation-flow.md) to learn how to develop an evaluation flow.
In this guide, we use [eval-classification-accuracy](https://github.com/microsoft/promptflow/tree/main/examples/flows/evaluation/eval-classification-accuracy) flow to evaluate. This is a flow illustrating how to evaluate the performance of a classification system. It involves comparing each prediction to the groundtruth and assigns a `Correct` or `Incorrect` grade, and aggregating the results to produce metrics such as `accuracy`, which reflects how good the system is at classifying the data.
### Run evaluation flow against run
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
**Evaluate the finished flow run**
After the run is finished, you can evaluate the run with below command, compared with the normal run create command, note there are two extra arguments:
- `column-mapping`: A mapping from flow input name to specified data values. Reference [here](https://aka.ms/pf/column-mapping) for detailed information.
- `run`: The run name of the flow run to be evaluated.
More details can be found in [Use column mapping](https://aka.ms/pf/column-mapping).
```sh
pf run create --flow evaluation/eval-classification-accuracy --data standard/web-classification/data.jsonl --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run my_first_run --stream
```
Same as the previous run, you can specify the evaluation run name with `--name my_first_eval_run` in above command.
You can also stream or view the run details with:
```sh
pf run stream -n my_first_eval_run # same as "--stream" in command "run create"
pf run show-details -n my_first_eval_run
pf run show-metrics -n my_first_eval_run
```
Since now you have two different runs `my_first_run` and `my_first_eval_run`, you can visualize the two runs at the same time with below command.
```sh
pf run visualize -n "my_first_run,my_first_eval_run"
```
A web browser will be opened to show the visualization result.
:::
:::{tab-item} SDK
:sync: SDK
**Evaluate the finished flow run**
After the run is finished, you can evaluate the run with below command, compared with the normal run create command, note there are two extra arguments:
- `column-mapping`: A dictionary represents sources of the input data that are needed for the evaluation method. The sources can be from the flow run output or from your test dataset.
- If the data column is in your test dataset, then it is specified as `${data.<column_name>}`.
- If the data column is from your flow output, then it is specified as `${run.outputs.<output_name>}`.
- `run`: The run name or run instance of the flow run to be evaluated.
More details can be found in [Use column mapping](https://aka.ms/pf/column-mapping).
```python
from promptflow import PFClient
# PFClient can help manage your runs and connections.
pf = PFClient()
# set eval flow path
eval_flow = "evaluation/eval-classification-accuracy"
data= "standard/web-classification/data.jsonl"
# run the flow with existing run
eval_run = pf.run(
flow=eval_flow,
data=data,
run=base_run,
column_mapping={ # map the url field from the data to the url input of the flow
"groundtruth": "${data.answer}",
"prediction": "${run.outputs.category}",
}
)
# stream the run until it's finished
pf.stream(eval_run)
# get the inputs/outputs details of a finished run.
details = pf.get_details(eval_run)
details.head(10)
# view the metrics of the eval run
metrics = pf.get_metrics(eval_run)
print(json.dumps(metrics, indent=4))
# visualize both the base run and the eval run
pf.visualize([base_run, eval_run])
```
A web browser will be opened to show the visualization result.
:::
:::{tab-item} VS Code Extension
:sync: VS Code Extension
There are actions to trigger local batch runs. To perform an evaluation you can use the run against "existing runs" actions.
:::
::::
## Next steps
Learn more about:
- [Tune prompts with variants](../tune-prompts-with-variants.md)
- [Deploy a flow](../deploy-a-flow/index.md)
- [Manage runs](../manage-runs.md)
- [Python library reference](../../reference/python-library-reference/promptflow.md)
```{toctree}
:maxdepth: 1
:hidden:
use-column-mapping
```
| promptflow/docs/how-to-guides/run-and-evaluate-a-flow/index.md/0 | {
"file_path": "promptflow/docs/how-to-guides/run-and-evaluate-a-flow/index.md",
"repo_id": "promptflow",
"token_count": 2884
} | 4 |
# Embedding
## Introduction
OpenAI's embedding models convert text into dense vector representations for various NLP tasks. See the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) for more information.
## Prerequisite
Create OpenAI resources:
- **OpenAI**
Sign up account [OpenAI website](https://openai.com/)
Login and [Find personal API key](https://platform.openai.com/account/api-keys)
- **Azure OpenAI (AOAI)**
Create Azure OpenAI resources with [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal)
## **Connections**
Setup connections to provide resources in embedding tool.
| Type | Name | API KEY | API Type | API Version |
|-------------|----------|----------|----------|-------------|
| OpenAI | Required | Required | - | - |
| AzureOpenAI | Required | Required | Required | Required |
## Inputs
| Name | Type | Description | Required |
|------------------------|-------------|-----------------------------------------------------------------------|----------|
| input | string | the input text to embed | Yes |
| connection | string | the connection for the embedding tool use to provide resources | Yes |
| model/deployment_name | string | instance of the text-embedding engine to use. Fill in model name if you use OpenAI connection, or deployment name if use Azure OpenAI connection. | Yes |
## Outputs
| Return Type | Description |
|-------------|------------------------------------------|
| list | The vector representations for inputs |
The following is an example response returned by the embedding tool:
<details>
<summary>Output</summary>
```
[-0.005744616035372019,
-0.007096089422702789,
-0.00563855143263936,
-0.005272455979138613,
-0.02355326898396015,
0.03955197334289551,
-0.014260607771575451,
-0.011810848489403725,
-0.023170066997408867,
-0.014739611186087132,
...]
```
</details> | promptflow/docs/reference/tools-reference/embedding_tool.md/0 | {
"file_path": "promptflow/docs/reference/tools-reference/embedding_tool.md",
"repo_id": "promptflow",
"token_count": 851
} | 5 |
{
"azure_open_ai_connection": {
"type": "AzureOpenAIConnection",
"value": {
"api_key": "aoai-api-key",
"api_base": "aoai-api-endpoint",
"api_type": "azure",
"api_version": "2023-07-01-preview"
},
"module": "promptflow.connections"
},
"serp_connection": {
"type": "SerpConnection",
"value": {
"api_key": "serpapi-api-key"
},
"module": "promptflow.connections"
},
"custom_connection": {
"type": "CustomConnection",
"value": {
"key1": "hey",
"key2": "val2"
},
"module": "promptflow.connections",
"secret_keys": [
"key1"
]
},
"gpt2_connection": {
"type": "CustomConnection",
"value": {
"endpoint_url": "custom-endpoint-url",
"model_family": "GPT2",
"endpoint_api_key": "custom-endpoint-api-key"
},
"module": "promptflow.connections",
"secret_keys": [
"endpoint_api_key"
]
},
"open_source_llm_ws_service_connection": {
"type": "CustomConnection",
"value": {
"service_credential": "service-credential"
},
"module": "promptflow.connections",
"secret_keys": [
"service_credential"
]
},
"open_ai_connection": {
"type": "OpenAIConnection",
"value": {
"api_key": "openai-api-key",
"organization": "openai-api-org"
},
"module": "promptflow.connections"
},
"azure_content_safety_connection": {
"type": "AzureContentSafetyConnection",
"value": {
"api_key": "azure-content-safety-api-key",
"endpoint": "azure-content-safety-endpoint-url",
"api_version": "2023-10-01",
"api_type": "Content Safety",
"name": "prompt-flow-acs-tool-test"
},
"module": "promptflow.connections"
}
}
| promptflow/src/promptflow-tools/connections.json.example/0 | {
"file_path": "promptflow/src/promptflow-tools/connections.json.example",
"repo_id": "promptflow",
"token_count": 815
} | 6 |
promptflow.tools.embedding.embedding:
name: Embedding
description: Use Open AI's embedding model to create an embedding vector representing the input text.
type: python
module: promptflow.tools.embedding
function: embedding
inputs:
connection:
type: [AzureOpenAIConnection, OpenAIConnection]
deployment_name:
type:
- string
enabled_by: connection
enabled_by_type: [AzureOpenAIConnection]
capabilities:
completion: false
chat_completion: false
embeddings: true
model_list:
- text-embedding-ada-002
- text-search-ada-doc-001
- text-search-ada-query-001
model:
type:
- string
enabled_by: connection
enabled_by_type: [OpenAIConnection]
enum:
- text-embedding-ada-002
- text-search-ada-doc-001
- text-search-ada-query-001
allow_manual_entry: true
input:
type:
- string
| promptflow/src/promptflow-tools/promptflow/tools/yamls/embedding.yaml/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/yamls/embedding.yaml",
"repo_id": "promptflow",
"token_count": 403
} | 7 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from promptflow._core.metric_logger import log_metric
# flake8: noqa
from promptflow._core.tool import ToolProvider, tool
from promptflow._core.tracer import trace
# control plane sdk functions
from promptflow._sdk._load_functions import load_flow, load_run
from ._sdk._pf_client import PFClient
from ._version import VERSION
# backward compatibility
log_flow_metric = log_metric
__version__ = VERSION
__all__ = ["PFClient", "load_flow", "load_run", "log_metric", "ToolProvider", "tool", "trace"]
| promptflow/src/promptflow/promptflow/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/__init__.py",
"repo_id": "promptflow",
"token_count": 214
} | 8 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from typing import Dict, List
from promptflow._cli._params import (
add_param_archived_only,
add_param_flow_name,
add_param_flow_type,
add_param_include_archived,
add_param_include_others,
add_param_max_results,
add_param_output_format,
add_param_set,
base_params,
)
from promptflow._cli._pf_azure._utils import _get_azure_pf_client
from promptflow._cli._utils import (
_output_result_list_with_format,
_set_workspace_argument_for_subparsers,
activate_action,
exception_handler,
)
from promptflow._sdk._constants import get_list_view_type
def add_parser_flow(subparsers):
"""Add flow parser to the pf subparsers."""
flow_parser = subparsers.add_parser(
"flow",
description="Manage flows for prompt flow.",
help="Manage prompt flows.",
)
flow_subparsers = flow_parser.add_subparsers()
add_parser_flow_create(flow_subparsers)
add_parser_flow_show(flow_subparsers)
add_parser_flow_list(flow_subparsers)
flow_parser.set_defaults(action="flow")
def add_parser_flow_create(subparsers):
"""Add flow create parser to the pf flow subparsers."""
epilog = """
Use "--set" to set flow properties like:
display_name: Flow display name that will be created in remote. Default to be flow folder name + timestamp if not specified.
type: Flow type. Default to be "standard" if not specified. Available types are: "standard", "evaluation", "chat".
description: Flow description. e.g. "--set description=<description>."
tags: Flow tags. e.g. "--set tags.key1=value1 tags.key2=value2."
Note:
In "--set" parameter, if the key name consists of multiple words, use snake-case instead of kebab-case. e.g. "--set display_name=<flow-display-name>"
Examples:
# Create a flow to azure portal with local flow folder.
pfazure flow create --flow <flow-folder-path> --set display_name=<flow-display-name> type=<flow-type>
# Create a flow with more properties
pfazure flow create --flow <flow-folder-path> --set display_name=<flow-display-name> type=<flow-type> description=<flow-description> tags.key1=value1 tags.key2=value2
""" # noqa: E501
add_param_source = lambda parser: parser.add_argument( # noqa: E731
"--flow", type=str, help="Source folder of the flow."
)
add_params = [
_set_workspace_argument_for_subparsers,
add_param_source,
add_param_set,
] + base_params
activate_action(
name="create",
description="A CLI tool to create a flow to Azure.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="Create a flow to Azure with local flow folder.",
action_param_name="sub_action",
)
def add_parser_flow_list(subparsers):
"""Add flow list parser to the pf flow subparsers."""
epilog = """
Examples:
# List flows:
pfazure flow list
# List most recent 10 runs status:
pfazure flow list --max-results 10
# List active and archived flows:
pfazure flow list --include-archived
# List archived flow only:
pfazure flow list --archived-only
# List all flows as table:
pfazure flow list --output table
# List flows with specific type:
pfazure flow list --type standard
# List flows that are owned by all users:
pfazure flow list --include-others
"""
add_params = [
add_param_max_results,
add_param_include_others,
add_param_flow_type,
add_param_archived_only,
add_param_include_archived,
add_param_output_format,
_set_workspace_argument_for_subparsers,
] + base_params
activate_action(
name="list",
description="List flows for promptflow.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pfazure flow list",
action_param_name="sub_action",
)
def add_parser_flow_show(subparsers):
"""Add flow get parser to the pf flow subparsers."""
epilog = """
Examples:
# Get flow:
pfazure flow show --name <flow-name>
"""
add_params = [add_param_flow_name, _set_workspace_argument_for_subparsers] + base_params
activate_action(
name="show",
description="Show a flow from Azure.",
epilog=epilog,
add_params=add_params,
subparsers=subparsers,
help_message="pfazure flow show",
action_param_name="sub_action",
)
def add_parser_flow_download(subparsers):
"""Add flow download parser to the pf flow subparsers."""
add_param_source = lambda parser: parser.add_argument( # noqa: E731
"--source", type=str, help="The flow folder path on file share to download."
)
add_param_destination = lambda parser: parser.add_argument( # noqa: E731
"--destination", "-d", type=str, help="The destination folder path to download."
)
add_params = [
_set_workspace_argument_for_subparsers,
add_param_source,
add_param_destination,
] + base_params
activate_action(
name="download",
description="Download a flow from file share to local.",
epilog=None,
add_params=add_params,
subparsers=subparsers,
help_message="pf flow download",
action_param_name="sub_action",
)
def dispatch_flow_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_flow(args)
elif args.sub_action == "show":
show_flow(args)
elif args.sub_action == "list":
list_flows(args)
def _get_flow_operation(subscription_id, resource_group, workspace_name):
pf_client = _get_azure_pf_client(subscription_id, resource_group, workspace_name)
return pf_client._flows
@exception_handler("Create flow")
def create_flow(args: argparse.Namespace):
"""Create a flow for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
params = _parse_flow_metadata_args(args.params_override)
pf.flows.create_or_update(flow=args.flow, **params)
@exception_handler("Show flow")
def show_flow(args: argparse.Namespace):
"""Get a flow for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
flow = pf.flows.get(args.name)
print(json.dumps(flow._to_dict(), indent=4))
def list_flows(args: argparse.Namespace):
"""List flows for promptflow."""
pf = _get_azure_pf_client(args.subscription, args.resource_group, args.workspace_name, debug=args.debug)
flows = pf.flows.list(
max_results=args.max_results,
include_others=args.include_others,
flow_type=args.type,
list_view_type=get_list_view_type(args.archived_only, args.include_archived),
)
flow_list = [flow._to_dict() for flow in flows]
_output_result_list_with_format(flow_list, args.output)
def _parse_flow_metadata_args(params: List[Dict[str, str]]) -> Dict:
result, tags = {}, {}
if not params:
return result
for param in params:
for k, v in param.items():
if k.startswith("tags."):
tag_key = k.replace("tags.", "")
tags[tag_key] = v
continue
result[k] = v
if tags:
result["tags"] = tags
return result
| promptflow/src/promptflow/promptflow/_cli/_pf_azure/_flow.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf_azure/_flow.py",
"repo_id": "promptflow",
"token_count": 2954
} | 9 |
import os
from promptflow import tool
from promptflow.connections import CustomConnection
{{ function_import }}
@tool
def {{ tool_function }}(
{% for arg in tool_arg_list %}
{{ arg.name }},
{% endfor %}
connection: CustomConnection) -> str:
# set environment variables
for key, value in dict(connection).items():
os.environ[key] = value
# call the entry function
return {{ entry_function }}(
{% for arg in tool_arg_list %}
{{ arg.name }}={{ arg.name }},
{% endfor %}
)
| promptflow/src/promptflow/promptflow/_cli/data/entry_flow/tool.py.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/entry_flow/tool.py.jinja2",
"repo_id": "promptflow",
"token_count": 192
} | 10 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._cli._pf.entry import main
# this is a compatibility layer for the old CLI which is used for vscode extension
if __name__ == "__main__":
main()
| promptflow/src/promptflow/promptflow/_cli/pf.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/pf.py",
"repo_id": "promptflow",
"token_count": 74
} | 11 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""
This file can generate a meta file for the given prompt template or a python file.
"""
import importlib.util
import inspect
import json
import re
import types
from dataclasses import asdict
from pathlib import Path
from traceback import TracebackException
from jinja2 import TemplateSyntaxError
from jinja2.environment import COMMENT_END_STRING, COMMENT_START_STRING
from promptflow._core._errors import MetaFileNotFound, MetaFileReadError, NotSupported
from promptflow._core.tool import ToolProvider
from promptflow._utils.exception_utils import ADDITIONAL_INFO_USER_CODE_STACKTRACE, get_tb_next, last_frame_info
from promptflow._utils.tool_utils import function_to_interface, get_inputs_for_prompt_template
from promptflow.contracts.tool import Tool, ToolType
from promptflow.exceptions import ErrorTarget, UserErrorException
PF_MAIN_MODULE_NAME = "__pf_main__"
def asdict_without_none(obj):
return asdict(obj, dict_factory=lambda x: {k: v for (k, v) in x if v})
def generate_prompt_tool(name, content, prompt_only=False, source=None):
"""Generate meta for a single jinja template file."""
# Get all the variable name from a jinja template
tool_type = ToolType.PROMPT if prompt_only else ToolType.LLM
try:
inputs = get_inputs_for_prompt_template(content)
except TemplateSyntaxError as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise JinjaParsingError(
message_format=(
"Generate tool meta failed for {tool_type} tool. Jinja parsing failed at line {line_number}: "
"{error_type_and_message}"
),
tool_type=tool_type.value,
line_number=e.lineno,
error_type_and_message=error_type_and_message,
) from e
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise JinjaParsingError(
message_format=(
"Generate tool meta failed for {tool_type} tool. Jinja parsing failed: {error_type_and_message}"
),
tool_type=tool_type.value,
error_type_and_message=error_type_and_message,
) from e
pattern = f"{COMMENT_START_STRING}(((?!{COMMENT_END_STRING}).)*){COMMENT_END_STRING}"
match_result = re.match(pattern, content)
description = match_result.groups()[0].strip() if match_result else None
# Construct the Tool structure
tool = Tool(
name=name,
description=description,
type=tool_type,
inputs=inputs,
outputs={},
)
if source is None:
tool.code = content
else:
tool.source = source
return tool
def generate_prompt_meta_dict(name, content, prompt_only=False, source=None):
return asdict_without_none(generate_prompt_tool(name, content, prompt_only, source))
def is_tool(f):
if not isinstance(f, types.FunctionType):
return False
if not hasattr(f, "__tool"):
return False
return True
def collect_tool_functions_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if is_tool(obj):
# Note that the tool should be in defined in exec but not imported in exec,
# so it should also have the same module with the current function.
if getattr(obj, "__module__", "") != m.__name__:
continue
tools.append(obj)
return tools
def collect_tool_methods_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if isinstance(obj, type) and issubclass(obj, ToolProvider) and obj.__module__ == m.__name__:
for _, method in inspect.getmembers(obj):
if is_tool(method):
tools.append(method)
return tools
def collect_tool_methods_with_init_inputs_in_module(m):
tools = []
for _, obj in inspect.getmembers(m):
if isinstance(obj, type) and issubclass(obj, ToolProvider) and obj.__module__ == m.__name__:
for _, method in inspect.getmembers(obj):
if is_tool(method):
tools.append((method, obj.get_initialize_inputs()))
return tools
def _parse_tool_from_function(f, initialize_inputs=None, gen_custom_type_conn=False, skip_prompt_template=False):
try:
tool_type = getattr(f, "__type", None) or ToolType.PYTHON
except Exception as e:
raise e
tool_name = getattr(f, "__name", None)
description = getattr(f, "__description", None)
if hasattr(f, "__tool") and isinstance(f.__tool, Tool):
return f.__tool
if hasattr(f, "__original_function"):
f = f.__original_function
try:
inputs, _, _, enable_kwargs = function_to_interface(
f,
initialize_inputs=initialize_inputs,
gen_custom_type_conn=gen_custom_type_conn,
skip_prompt_template=skip_prompt_template,
)
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise BadFunctionInterface(
message_format="Parse interface for tool '{tool_name}' failed: {error_type_and_message}",
tool_name=f.__name__,
error_type_and_message=error_type_and_message,
) from e
class_name = None
if "." in f.__qualname__:
class_name = f.__qualname__.replace(f".{f.__name__}", "")
# Construct the Tool structure
return Tool(
name=tool_name or f.__qualname__,
description=description or inspect.getdoc(f),
inputs=inputs,
type=tool_type,
class_name=class_name,
function=f.__name__,
module=f.__module__,
enable_kwargs=enable_kwargs,
)
def generate_python_tools_in_module(module):
tool_functions = collect_tool_functions_in_module(module)
tool_methods = collect_tool_methods_in_module(module)
return [_parse_tool_from_function(f) for f in tool_functions + tool_methods]
def generate_python_tools_in_module_as_dict(module):
tools = generate_python_tools_in_module(module)
return {f"{t.module}.{t.name}": asdict_without_none(t) for t in tools}
def load_python_module_from_file(src_file: Path):
# Here we hard code the module name as __pf_main__ since it is invoked as a main script in pf.
src_file = Path(src_file).resolve() # Make sure the path is absolute to align with python import behavior.
spec = importlib.util.spec_from_file_location("__pf_main__", location=src_file)
if spec is None or spec.loader is None:
raise PythonLoaderNotFound(
message_format="Failed to load python file '{src_file}'. Please make sure it is a valid .py file.",
src_file=src_file,
)
m = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(m)
except Exception as e:
# TODO: add stacktrace to additional info
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise PythonLoadError(
message_format="Failed to load python module from file '{src_file}': {error_type_and_message}",
src_file=src_file,
error_type_and_message=error_type_and_message,
) from e
return m
def load_python_module(content, source=None):
# Source represents code first experience.
if source is not None and Path(source).exists():
return load_python_module_from_file(Path(source))
try:
m = types.ModuleType(PF_MAIN_MODULE_NAME)
exec(content, m.__dict__)
return m
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise PythonParsingError(
message_format="Failed to load python module. Python parsing failed: {error_type_and_message}",
error_type_and_message=error_type_and_message,
) from e
def collect_tool_function_in_module(m):
tool_functions = collect_tool_functions_in_module(m)
tool_methods = collect_tool_methods_with_init_inputs_in_module(m)
num_tools = len(tool_functions) + len(tool_methods)
if num_tools == 0:
raise NoToolDefined(
message_format=(
"No tool found in the python script. "
"Please make sure you have one and only one tool definition in your script."
)
)
elif num_tools > 1:
tool_names = ", ".join(t.__name__ for t in tool_functions + tool_methods)
raise MultipleToolsDefined(
message_format=(
"Expected 1 but collected {tool_count} tools: {tool_names}. "
"Please make sure you have one and only one tool definition in your script."
),
tool_count=num_tools,
tool_names=tool_names,
)
if tool_functions:
return tool_functions[0], None
else:
return tool_methods[0]
def generate_python_tool(name, content, source=None):
m = load_python_module(content, source)
f, initialize_inputs = collect_tool_function_in_module(m)
tool = _parse_tool_from_function(f, initialize_inputs=initialize_inputs)
tool.module = None
if name is not None:
tool.name = name
if source is None:
tool.code = content
else:
tool.source = source
return tool
def generate_python_meta_dict(name, content, source=None):
return asdict_without_none(generate_python_tool(name, content, source))
# Only used in non-code first experience.
def generate_python_meta(name, content, source=None):
return json.dumps(generate_python_meta_dict(name, content, source), indent=2)
def generate_prompt_meta(name, content, prompt_only=False, source=None):
return json.dumps(generate_prompt_meta_dict(name, content, prompt_only, source), indent=2)
def generate_tool_meta_dict_by_file(path: str, tool_type: ToolType):
"""Generate meta for a single tool file, which can be a python file or a jinja template file,
note that if a python file is passed, correct working directory must be set and should be added to sys.path.
"""
tool_type = ToolType(tool_type)
file = Path(path)
if not file.is_file():
raise MetaFileNotFound(
message_format="Generate tool meta failed for {tool_type} tool. Meta file '{file_path}' can not be found.",
tool_type=tool_type.value,
file_path=path, # Use a relative path here to make the error message more readable.
)
try:
content = file.read_text(encoding="utf-8")
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise MetaFileReadError(
message_format=(
"Generate tool meta failed for {tool_type} tool. "
"Read meta file '{file_path}' failed: {error_type_and_message}"
),
tool_type=tool_type.value,
file_path=path,
error_type_and_message=error_type_and_message,
) from e
name = file.stem
if tool_type == ToolType.PYTHON:
return generate_python_meta_dict(name, content, path)
elif tool_type == ToolType.LLM:
return generate_prompt_meta_dict(name, content, source=path)
elif tool_type == ToolType.PROMPT:
return generate_prompt_meta_dict(name, content, prompt_only=True, source=path)
else:
raise NotSupported(
message_format=(
"Generate tool meta failed. "
"The type '{tool_type}' is currently unsupported. "
"Please choose from available types: {supported_tool_types} and try again."
),
tool_type=tool_type.value,
supported_tool_types=",".join([ToolType.PYTHON, ToolType.LLM, ToolType.PROMPT]),
)
class ToolValidationError(UserErrorException):
"""Base exception raised when failed to validate tool."""
def __init__(self, **kwargs):
super().__init__(target=ErrorTarget.TOOL, **kwargs)
class JinjaParsingError(ToolValidationError):
pass
class ReservedVariableCannotBeUsed(JinjaParsingError):
pass
class PythonParsingError(ToolValidationError):
pass
class PythonLoaderNotFound(ToolValidationError):
pass
class NoToolDefined(PythonParsingError):
pass
class MultipleToolsDefined(PythonParsingError):
pass
class BadFunctionInterface(PythonParsingError):
pass
class PythonLoadError(PythonParsingError):
@property
def python_load_traceback(self):
"""Return the traceback inside user's source code scope.
The traceback inside the promptflow's internal code will be taken off.
"""
exc = self.inner_exception
if exc and exc.__traceback__ is not None:
tb = exc.__traceback__
# The first three frames are always the code in tool.py who invokes the tool.
# We do not want to dump it to user code's traceback.
tb = get_tb_next(tb, next_cnt=3)
if tb is not None:
te = TracebackException(type(exc), exc, tb)
formatted_tb = "".join(te.format())
return formatted_tb
return None
@property
def additional_info(self):
"""Set the python load exception details as additional info."""
if not self.inner_exception:
return None
info = {
"type": self.inner_exception.__class__.__name__,
"message": str(self.inner_exception),
"traceback": self.python_load_traceback,
}
info.update(last_frame_info(self.inner_exception))
return {
ADDITIONAL_INFO_USER_CODE_STACKTRACE: info,
}
| promptflow/src/promptflow/promptflow/_core/tool_meta_generator.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/tool_meta_generator.py",
"repo_id": "promptflow",
"token_count": 5685
} | 12 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import datetime
import json
from enum import Enum
from typing import Dict, List, Optional, Union
from sqlalchemy import TEXT, Boolean, Column, Index
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import declarative_base
from promptflow._sdk._constants import (
RUN_INFO_CREATED_ON_INDEX_NAME,
RUN_INFO_TABLENAME,
FlowRunProperties,
ListViewType,
)
from promptflow._sdk._errors import RunExistsError, RunNotFoundError
from .retry import sqlite_retry
from .session import mgmt_db_session
Base = declarative_base()
class RunInfo(Base):
__tablename__ = RUN_INFO_TABLENAME
name = Column(TEXT, primary_key=True)
type = Column(TEXT) # deprecated field
created_on = Column(TEXT, nullable=False) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
status = Column(TEXT, nullable=False)
display_name = Column(TEXT) # can be edited by users
description = Column(TEXT) # updated by users
tags = Column(TEXT) # updated by users, json(list of json) string
# properties: flow path, output path..., json string
# as we can parse and get all information from parsing the YAML in memory,
# we don't need to store any extra information in the database at all;
# however, if there is any hot fields, we can store them here additionally.
properties = Column(TEXT)
archived = Column(Boolean, default=False)
# NOTE: please always add columns to the tail, so that we can easily handle schema changes;
# also don't forget to update `__pf_schema_version__` when you change the schema
# NOTE: keep in mind that we need to well handle runs with legacy schema;
# normally new fields will be `None`, remember to handle them properly
start_time = Column(TEXT) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
end_time = Column(TEXT) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string
data = Column(TEXT) # local path of original run data, string
run_source = Column(TEXT) # run source, string
__table_args__ = (Index(RUN_INFO_CREATED_ON_INDEX_NAME, "created_on"),)
# schema version, increase the version number when you change the schema
__pf_schema_version__ = "3"
@sqlite_retry
def dump(self) -> None:
with mgmt_db_session() as session:
try:
session.add(self)
session.commit()
except IntegrityError as e:
# catch "sqlite3.IntegrityError: UNIQUE constraint failed: run_info.name" to raise RunExistsError
# otherwise raise the original error
if "UNIQUE constraint failed" not in str(e):
raise
raise RunExistsError(f"Run name {self.name!r} already exists.")
@sqlite_retry
def archive(self) -> None:
if self.archived is True:
return
self.archived = True
with mgmt_db_session() as session:
session.query(RunInfo).filter(RunInfo.name == self.name).update({"archived": self.archived})
session.commit()
@sqlite_retry
def restore(self) -> None:
if self.archived is False:
return
self.archived = False
with mgmt_db_session() as session:
session.query(RunInfo).filter(RunInfo.name == self.name).update({"archived": self.archived})
session.commit()
@sqlite_retry
def update(
self,
*,
status: Optional[str] = None,
display_name: Optional[str] = None,
description: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
start_time: Optional[Union[str, datetime.datetime]] = None,
end_time: Optional[Union[str, datetime.datetime]] = None,
system_metrics: Optional[Dict[str, int]] = None,
) -> None:
update_dict = {}
if status is not None:
self.status = status
update_dict["status"] = self.status
if display_name is not None:
self.display_name = display_name
update_dict["display_name"] = self.display_name
if description is not None:
self.description = description
update_dict["description"] = self.description
if tags is not None:
self.tags = json.dumps(tags)
update_dict["tags"] = self.tags
if start_time is not None:
self.start_time = start_time if isinstance(start_time, str) else start_time.isoformat()
update_dict["start_time"] = self.start_time
if end_time is not None:
self.end_time = end_time if isinstance(end_time, str) else end_time.isoformat()
update_dict["end_time"] = self.end_time
with mgmt_db_session() as session:
# if not update system metrics, we can directly update the row;
# otherwise, we need to get properties first, update the dict and finally update the row
if system_metrics is None:
session.query(RunInfo).filter(RunInfo.name == self.name).update(update_dict)
else:
# with high concurrency on same row, we may lose the earlier commit
# we regard it acceptable as it should be an edge case to update properties
# on same row with high concurrency;
# if it's a concern, we can move those properties to an extra column
run_info = session.query(RunInfo).filter(RunInfo.name == self.name).first()
props = json.loads(run_info.properties)
props[FlowRunProperties.SYSTEM_METRICS] = system_metrics.copy()
update_dict["properties"] = json.dumps(props)
session.query(RunInfo).filter(RunInfo.name == self.name).update(update_dict)
session.commit()
@staticmethod
@sqlite_retry
def get(name: str) -> "RunInfo":
with mgmt_db_session() as session:
run_info = session.query(RunInfo).filter(RunInfo.name == name).first()
if run_info is None:
raise RunNotFoundError(f"Run name {name!r} cannot be found.")
return run_info
@staticmethod
@sqlite_retry
def list(max_results: Optional[int], list_view_type: ListViewType) -> List["RunInfo"]:
with mgmt_db_session() as session:
basic_statement = session.query(RunInfo)
# filter by archived
list_view_type = list_view_type.value if isinstance(list_view_type, Enum) else list_view_type
if list_view_type == ListViewType.ACTIVE_ONLY.value:
basic_statement = basic_statement.filter(RunInfo.archived == False) # noqa: E712
elif list_view_type == ListViewType.ARCHIVED_ONLY.value:
basic_statement = basic_statement.filter(RunInfo.archived == True) # noqa: E712
basic_statement = basic_statement.order_by(RunInfo.created_on.desc())
if isinstance(max_results, int):
return [run_info for run_info in basic_statement.limit(max_results)]
else:
return [run_info for run_info in basic_statement.all()]
@staticmethod
@sqlite_retry
def delete(name: str) -> None:
with mgmt_db_session() as session:
run_info = session.query(RunInfo).filter(RunInfo.name == name).first()
if run_info is not None:
session.delete(run_info)
session.commit()
else:
raise RunNotFoundError(f"Run name {name!r} cannot be found.")
| promptflow/src/promptflow/promptflow/_sdk/_orm/run_info.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_orm/run_info.py",
"repo_id": "promptflow",
"token_count": 3138
} | 13 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
import logging
import os
import platform
import subprocess
import sys
import waitress
from promptflow._cli._utils import _get_cli_activity_name
from promptflow._constants import PF_NO_INTERACTIVE_LOGIN
from promptflow._sdk._constants import LOGGER_NAME
from promptflow._sdk._service.app import create_app
from promptflow._sdk._service.utils.utils import (
check_pfs_service_status,
dump_port_to_config,
get_port_from_config,
get_started_service_info,
is_port_in_use,
kill_exist_service,
)
from promptflow._sdk._telemetry import ActivityType, get_telemetry_logger, log_activity
from promptflow._sdk._utils import get_promptflow_sdk_version, print_pf_version
from promptflow.exceptions import UserErrorException
app = None
def get_app():
global app
if app is None:
app, _ = create_app()
return app
def add_start_service_action(subparsers):
"""Add action to start pfs."""
start_pfs_parser = subparsers.add_parser(
"start",
description="Start promptflow service.",
help="pfs start",
)
start_pfs_parser.add_argument("-p", "--port", type=int, help="port of the promptflow service")
start_pfs_parser.add_argument(
"--force",
action="store_true",
help="If the port is used, the existing service will be terminated and restart a new service.",
)
start_pfs_parser.add_argument(
"--synchronous",
action="store_true",
help=argparse.SUPPRESS,
)
start_pfs_parser.set_defaults(action="start")
def add_show_status_action(subparsers):
"""Add action to show pfs status."""
show_status_parser = subparsers.add_parser(
"show-status",
description="Display the started promptflow service info.",
help="pfs show-status",
)
show_status_parser.set_defaults(action="show-status")
def start_service(args):
# User Agent will be set based on header in request, so not set globally here.
os.environ[PF_NO_INTERACTIVE_LOGIN] = "true"
port = args.port
app, _ = create_app()
def validate_port(port, force_start):
if is_port_in_use(port):
if force_start:
app.logger.warning(f"Force restart the service on the port {port}.")
kill_exist_service(port)
else:
app.logger.warning(f"Service port {port} is used.")
raise UserErrorException(f"Service port {port} is used.")
if port:
dump_port_to_config(port)
validate_port(port, args.force)
else:
port = get_port_from_config(create_if_not_exists=True)
validate_port(port, args.force)
# Set host to localhost, only allow request from localhost.
if sys.executable.endswith("pfcli.exe"):
# For msi installer, use sdk api to start pfs since it's not supported to invoke waitress by cli directly
# after packaged by Pyinstaller.
app.logger.info(
f"Start Prompt Flow Service on http://localhost:{port}, version: {get_promptflow_sdk_version()}"
)
waitress.serve(app, host="127.0.0.1", port=port)
else:
cmd = [
sys.executable,
"-m",
"waitress",
"--host",
"127.0.0.1",
f"--port={port}",
"--call",
"promptflow._sdk._service.entry:get_app",
]
if args.synchronous:
subprocess.call(cmd)
else:
# Start a pfs process using detach mode
if platform.system() == "Windows":
os.spawnv(os.P_DETACH, sys.executable, cmd)
else:
os.system(" ".join(["nohup"] + cmd + ["&"]))
is_healthy = check_pfs_service_status(port)
if is_healthy:
app.logger.info(
f"Start Prompt Flow Service on http://localhost:{port}, version: {get_promptflow_sdk_version()}"
)
else:
app.logger.warning(f"Pfs service start failed in {port}.")
def main():
command_args = sys.argv[1:]
if len(command_args) == 1 and command_args[0] == "version":
version_dict = {"promptflow": get_promptflow_sdk_version()}
return json.dumps(version_dict, ensure_ascii=False, indent=2, sort_keys=True, separators=(",", ": ")) + "\n"
if len(command_args) == 0:
command_args.append("-h")
entry(command_args)
def entry(command_args):
parser = argparse.ArgumentParser(
prog="pfs",
formatter_class=argparse.RawDescriptionHelpFormatter,
description="Prompt Flow Service",
)
parser.add_argument(
"-v", "--version", dest="version", action="store_true", help="show current PromptflowService version and exit"
)
subparsers = parser.add_subparsers()
add_start_service_action(subparsers)
add_show_status_action(subparsers)
args = parser.parse_args(command_args)
activity_name = _get_cli_activity_name(cli=parser.prog, args=args)
logger = get_telemetry_logger()
with log_activity(logger, activity_name, activity_type=ActivityType.PUBLICAPI):
run_command(args)
def run_command(args):
if args.version:
print_pf_version()
return
elif args.action == "show-status":
port = get_port_from_config()
status = get_started_service_info(port)
if status:
print(status)
return
else:
logger = logging.getLogger(LOGGER_NAME)
logger.warning("Promptflow service is not started.")
exit(1)
elif args.action == "start":
start_service(args)
if __name__ == "__main__":
main()
| promptflow/src/promptflow/promptflow/_sdk/_service/entry.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/entry.py",
"repo_id": "promptflow",
"token_count": 2461
} | 14 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from enum import Enum
from promptflow._sdk._serving.extension.default_extension import AppExtension
class ExtensionType(Enum):
"""Extension type used to identify which extension to load in serving app."""
Default = "local"
AzureML = "azureml"
class ExtensionFactory:
"""ExtensionFactory is used to create extension based on extension type."""
@staticmethod
def create_extension(logger, **kwargs) -> AppExtension:
"""Create extension based on extension type."""
extension_type_str = kwargs.get("extension_type", ExtensionType.Default.value)
if not extension_type_str:
extension_type_str = ExtensionType.Default.value
extension_type = ExtensionType(extension_type_str.lower())
if extension_type == ExtensionType.AzureML:
logger.info("Enable AzureML extension.")
from promptflow._sdk._serving.extension.azureml_extension import AzureMLExtension
return AzureMLExtension(logger=logger, **kwargs)
else:
from promptflow._sdk._serving.extension.default_extension import DefaultAppExtension
return DefaultAppExtension(logger=logger, **kwargs)
| promptflow/src/promptflow/promptflow/_sdk/_serving/extension/extension_factory.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/extension/extension_factory.py",
"repo_id": "promptflow",
"token_count": 445
} | 15 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# this file is a middle layer between the local SDK and executor.
import contextlib
import logging
from pathlib import Path
from types import GeneratorType
from typing import Any, Mapping, Optional, Tuple, Union
from colorama import Fore, init
from promptflow._internal import ConnectionManager
from promptflow._sdk._constants import PROMPT_FLOW_DIR_NAME
from promptflow._sdk._utils import dump_flow_result, parse_variant
from promptflow._sdk.entities._flow import FlowBase, FlowContext, ProtectedFlow
from promptflow._sdk.operations._local_storage_operations import LoggerOperations
from promptflow._utils.context_utils import _change_working_dir
from promptflow._utils.exception_utils import ErrorResponse
from promptflow.contracts.flow import Flow as ExecutableFlow
from promptflow.contracts.run_info import RunInfo, Status
from promptflow.exceptions import UserErrorException
from promptflow.executor._result import LineResult
from promptflow.storage._run_storage import DefaultRunStorage
from ..._constants import LINE_NUMBER_KEY, FlowLanguage
from ..._core._errors import NotSupported
from ..._utils.async_utils import async_run_allowing_running_loop
from ..._utils.logger_utils import get_cli_sdk_logger
from ...batch import APIBasedExecutorProxy, CSharpExecutorProxy
from .._configuration import Configuration
from ..entities._eager_flow import EagerFlow
from .utils import (
SubmitterHelper,
print_chat_output,
resolve_generator,
show_node_log_and_output,
variant_overwrite_context,
)
logger = get_cli_sdk_logger()
class TestSubmitter:
"""
Submitter for testing flow/node.
A submitter will be bonded to a test run (including whether this is a node test or a flow test) after __init__,
and will be bonded to a specific executor proxy within an init context:
1) we will occupy some resources like a temporary folder to save flow with variant resolved, or an execution
service process if applicable;
2) output path will also be fixed within an init context;
Dependent resources like execution service will be created and released within the init context:
with TestSubmitter(...).init(...) as submitter:
# dependent resources are created, e.g., we may assume that an execution service is started here if applicable
...
# dependent resources are released
...
"""
def __init__(
self,
flow: Union[ProtectedFlow, EagerFlow],
flow_context: FlowContext,
client=None,
):
self._flow = flow
self.entry = flow.entry if isinstance(flow, EagerFlow) else None
self._origin_flow = flow
self._dataplane_flow = None
self.flow_context = flow_context
# TODO: remove this
self._variant = flow_context.variant
from .._pf_client import PFClient
self._client = client if client else PFClient()
# below attributes will be set within init context
# TODO: try to minimize the attribute count
self._output_base: Optional[Path] = None
self._relative_flow_output_path: Optional[Path] = None
self._connections: Optional[dict] = None
self._target_node = None
self._storage = None
self._enable_stream_output = None
self._executor_proxy: Optional[APIBasedExecutorProxy] = None
self._within_init_context = False
@property
def executor_proxy(self) -> APIBasedExecutorProxy:
self._raise_if_not_within_init_context()
return self._executor_proxy
def _raise_if_not_within_init_context(self):
if not self._within_init_context:
raise UserErrorException("This method should be called within the init context.")
@property
def enable_stream_output(self) -> bool:
self._raise_if_not_within_init_context()
return self._enable_stream_output
@property
def flow(self):
self._raise_if_not_within_init_context()
return self._flow
@property
def dataplane_flow(self):
# TODO: test submitter shouldn't interact with dataplane flow directly
if not self._dataplane_flow:
self._dataplane_flow = ExecutableFlow.from_yaml(flow_file=self.flow.path, working_dir=self.flow.code)
return self._dataplane_flow
@property
def output_base(self) -> Path:
self._raise_if_not_within_init_context()
return self._output_base
@property
def relative_flow_output_path(self) -> Path:
self._raise_if_not_within_init_context()
return self._relative_flow_output_path
@property
def target_node(self) -> Optional[str]:
self._raise_if_not_within_init_context()
return self._target_node
@contextlib.contextmanager
def _resolve_variant(self):
# TODO(2901096): validate invalid configs like variant & connections
# no variant overwrite for eager flow
# no connection overwrite for eager flow
if self.flow_context.variant:
tuning_node, node_variant = parse_variant(self.flow_context.variant)
else:
tuning_node, node_variant = None, None
with variant_overwrite_context(
flow=self._origin_flow,
tuning_node=tuning_node,
variant=node_variant,
connections=self.flow_context.connections,
overrides=self.flow_context.overrides,
) as temp_flow:
# TODO execute flow test in a separate process.
with _change_working_dir(temp_flow.code):
self._flow = temp_flow
self._tuning_node = tuning_node
self._node_variant = node_variant
yield self
self._flow = None
self._dataplane_flow = None
self._tuning_node = None
self._node_variant = None
@classmethod
def _resolve_connections(cls, flow: FlowBase, client):
if flow.language == FlowLanguage.CSharp:
# TODO: check if this is a shared logic
if isinstance(flow, EagerFlow):
# connection overrides are not supported for eager flow for now
return {}
# TODO: is it possible that we resolve connections after executor proxy is created?
from promptflow.batch import CSharpExecutorProxy
return SubmitterHelper.resolve_used_connections(
flow=flow,
tools_meta=CSharpExecutorProxy.get_tool_metadata(
flow_file=flow.flow_dag_path,
working_dir=flow.code,
),
client=client,
)
if flow.language == FlowLanguage.Python:
# TODO: test submitter should not interact with dataplane flow directly
return SubmitterHelper.resolve_connections(flow=flow, client=client)
raise UserErrorException(f"Unsupported flow language {flow.language}")
@classmethod
def _resolve_environment_variables(cls, environment_variable_overrides, flow: ProtectedFlow, client):
return SubmitterHelper.load_and_resolve_environment_variables(
flow=flow, environment_variable_overrides=environment_variable_overrides, client=client
)
@classmethod
def _resolve_output_path(
cls, *, output_base: Optional[str], default: Path, target_node: str
) -> Tuple[Path, Path, Path]:
if output_base:
output_base, output_sub = Path(output_base), Path(".")
else:
output_base, output_sub = Path(default), Path(PROMPT_FLOW_DIR_NAME)
output_base.mkdir(parents=True, exist_ok=True)
log_path = output_base / output_sub / (f"{target_node}.node.log" if target_node else "flow.log")
return output_base, log_path, output_sub
@contextlib.contextmanager
def init(
self,
*,
connections: Optional[dict] = None,
target_node: Optional[str] = None,
environment_variables: Optional[dict] = None,
stream_log: bool = True,
output_path: Optional[str] = None,
session: Optional[str] = None,
stream_output: bool = True,
):
"""
Create/Occupy dependent resources to execute the test within the context.
Resources will be released after exiting the context.
:param connections: connection overrides.
:type connections: dict
:param target_node: target node name for node test, may only do node_test if specified.
:type target_node: str
:param environment_variables: environment variable overrides.
:type environment_variables: dict
:param stream_log: whether to stream log to stdout.
:type stream_log: bool
:param output_path: output path.
:type output_path: str
:param session: session id. If None, a new session id will be generated with _provision_session.
:type session: str
:param stream_output: whether to return a generator for streaming output.
:type stream_output: bool
:return: TestSubmitter instance.
:rtype: TestSubmitter
"""
from promptflow._trace._start_trace import start_trace
with self._resolve_variant():
# temp flow is generated, will use self.flow instead of self._origin_flow in the following context
self._within_init_context = True
if self.flow.language == FlowLanguage.CSharp:
# TODO: consider move this to Operations
CSharpExecutorProxy.generate_metadata(self.flow.path, self.flow.code)
self._target_node = target_node
self._enable_stream_output = stream_output
SubmitterHelper.init_env(
environment_variables=self._resolve_environment_variables(
environment_variable_overrides=environment_variables,
flow=self.flow,
client=self._client,
)
or {},
)
if Configuration(overrides=self._client._config).is_internal_features_enabled():
logger.debug("Starting trace for flow test...")
start_trace(session=session)
self._output_base, log_path, output_sub = self._resolve_output_path(
output_base=output_path,
default=self.flow.code,
target_node=target_node,
)
self._relative_flow_output_path = output_sub / "output"
# use flow instead of origin_flow here, as flow can be incomplete before resolving additional includes
self._connections = connections or self._resolve_connections(
self.flow,
self._client,
)
credential_list = ConnectionManager(self._connections).get_secret_list()
with LoggerOperations(
file_path=log_path.as_posix(),
stream=stream_log,
credential_list=credential_list,
):
# storage must be created within the LoggerOperations context to shadow credentials
self._storage = DefaultRunStorage(
base_dir=self.output_base,
sub_dir=output_sub / "intermediate",
)
# TODO: set up executor proxy for all languages
if self.flow.language == FlowLanguage.CSharp:
self._executor_proxy = async_run_allowing_running_loop(
CSharpExecutorProxy.create,
self.flow.path,
self.flow.code,
connections=self._connections,
storage=self._storage,
log_path=log_path,
enable_stream_output=stream_output,
)
try:
yield self
finally:
if self.executor_proxy:
async_run_allowing_running_loop(self.executor_proxy.destroy_if_all_generators_exhausted)
self._within_init_context = False
def resolve_data(
self, node_name: str = None, inputs: dict = None, chat_history_name: str = None, dataplane_flow=None
):
"""
Resolve input to flow/node test inputs.
Raise user error when missing required inputs. And log warning when unknown inputs appeared.
:param node_name: Node name.
:type node_name: str
:param inputs: Inputs of flow/node test.
:type inputs: dict
:param chat_history_name: Chat history name.
:type chat_history_name: str
:return: Dict of flow inputs, Dict of reference node output.
:rtype: dict, dict
"""
from promptflow.contracts.flow import InputValueType
# TODO: only store dataplane flow in context resolver
dataplane_flow = dataplane_flow or self.dataplane_flow
inputs = (inputs or {}).copy()
flow_inputs, dependency_nodes_outputs, merged_inputs = {}, {}, {}
missing_inputs = []
# Using default value of inputs as flow input
if node_name:
node = next(filter(lambda item: item.name == node_name, dataplane_flow.nodes), None)
if not node:
raise UserErrorException(f"Cannot find {node_name} in the flow.")
for name, value in node.inputs.items():
if value.value_type == InputValueType.NODE_REFERENCE:
input_name = (
f"{value.value}.{value.section}.{value.property}"
if value.property
else f"{value.value}.{value.section}"
)
if input_name in inputs:
dependency_input = inputs.pop(input_name)
elif name in inputs:
dependency_input = inputs.pop(name)
else:
missing_inputs.append(name)
continue
if value.property:
dependency_nodes_outputs[value.value] = dependency_nodes_outputs.get(value.value, {})
if isinstance(dependency_input, dict) and value.property in dependency_input:
dependency_nodes_outputs[value.value][value.property] = dependency_input[value.property]
elif dependency_input:
dependency_nodes_outputs[value.value][value.property] = dependency_input
else:
dependency_nodes_outputs[value.value] = dependency_input
merged_inputs[name] = dependency_input
elif value.value_type == InputValueType.FLOW_INPUT:
input_name = f"{value.prefix}{value.value}"
if input_name in inputs:
flow_input = inputs.pop(input_name)
elif name in inputs:
flow_input = inputs.pop(name)
else:
flow_input = dataplane_flow.inputs[value.value].default
if flow_input is None:
missing_inputs.append(name)
continue
flow_inputs[value.value] = flow_input
merged_inputs[name] = flow_input
else:
flow_inputs[name] = inputs.pop(name) if name in inputs else value.value
merged_inputs[name] = flow_inputs[name]
else:
for name, value in dataplane_flow.inputs.items():
if name in inputs:
flow_inputs[name] = inputs.pop(name)
merged_inputs[name] = flow_inputs[name]
else:
if value.default is None:
# When the flow is a chat flow and chat_history has no default value, set an empty list for it
if chat_history_name and name == chat_history_name:
flow_inputs[name] = []
else:
missing_inputs.append(name)
else:
flow_inputs[name] = value.default
merged_inputs[name] = flow_inputs[name]
prefix = node_name or "flow"
if missing_inputs:
raise UserErrorException(f'Required input(s) {missing_inputs} are missing for "{prefix}".')
if inputs:
logger.warning(f"Unknown input(s) of {prefix}: {inputs}")
flow_inputs.update(inputs)
merged_inputs.update(inputs)
logger.info(f"{prefix} input(s): {merged_inputs}")
return flow_inputs, dependency_nodes_outputs
def _get_output_path(self, kwargs) -> Tuple[Path, Path]:
"""Return the output path and sub dir path of the output."""
# Note that the different relative path in LocalRunStorage will lead to different image reference
if kwargs.get("output_path"):
return Path(kwargs["output_path"]), Path(".")
return Path(self.flow.code), Path(PROMPT_FLOW_DIR_NAME)
def flow_test(
self,
inputs: Mapping[str, Any],
allow_generator_output: bool = False, # TODO: remove this
run_id: str = None,
) -> LineResult:
"""
Submit a flow test.
Note that you will get an error if you call this method with target_node specified in the init context.
We have separate interface for flow test and node test as they have different input and output.
However, target node will determine log path, which should be specified in the init context, e.g.,
it is required for starting an execution service.
:param inputs: Inputs of the flow.
:type inputs: dict
:param allow_generator_output: Allow generator output.
:type allow_generator_output: bool
:param stream_output: Stream output.
:type stream_output: bool
:param run_id: Run id will be set in operation context and used for session
:type run_id: str
"""
self._raise_if_not_within_init_context()
if self.target_node:
raise UserErrorException("target_node is not allowed for flow test.")
if self.flow.language == FlowLanguage.Python:
# TODO: replace with implementation based on PythonExecutorProxy
from promptflow.executor.flow_executor import execute_flow
line_result = execute_flow(
flow_file=self.flow.path,
working_dir=self.flow.code,
output_dir=self.output_base / self.relative_flow_output_path,
connections=self._connections,
inputs=inputs,
enable_stream_output=self.enable_stream_output,
allow_generator_output=allow_generator_output,
entry=self.entry,
storage=self._storage,
run_id=run_id,
)
else:
from promptflow._utils.multimedia_utils import persist_multimedia_data
# TODO: support run_id for non-python
# TODO: most of below code is duplicate to flow_executor.execute_flow
line_result: LineResult = self.executor_proxy.exec_line(inputs, index=0)
line_result.output = persist_multimedia_data(
line_result.output, base_dir=self.output_base, sub_dir=self.relative_flow_output_path
)
if line_result.aggregation_inputs:
# Convert inputs of aggregation to list type
flow_inputs = {k: [v] for k, v in inputs.items()}
aggregation_inputs = {k: [v] for k, v in line_result.aggregation_inputs.items()}
aggregation_results = async_run_allowing_running_loop(
self.executor_proxy.exec_aggregation_async, flow_inputs, aggregation_inputs
)
line_result.node_run_infos.update(aggregation_results.node_run_infos)
line_result.run_info.metrics = aggregation_results.metrics
if isinstance(line_result.output, dict):
# remove line_number from output
line_result.output.pop(LINE_NUMBER_KEY, None)
if isinstance(line_result.output, dict):
generator_outputs = self._get_generator_outputs(line_result.output)
if generator_outputs:
logger.info(f"Some streaming outputs in the result, {generator_outputs.keys()}")
return line_result
def node_test(
self,
flow_inputs: Mapping[str, Any],
dependency_nodes_outputs: Mapping[str, Any],
) -> RunInfo:
self._raise_if_not_within_init_context()
if self.target_node is None:
raise UserErrorException("target_node is required for node test.")
if self.flow.language == FlowLanguage.CSharp:
raise NotSupported("Node test is not supported for CSharp flow for now.")
from promptflow.executor.flow_executor import FlowExecutor
return FlowExecutor.load_and_exec_node(
self.flow.path,
self.target_node,
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
connections=self._connections,
working_dir=self.flow.code,
storage=self._storage,
)
def _chat_flow(self, inputs, chat_history_name, show_step_output=False):
"""
Interact with Chat Flow. Do the following:
1. Combine chat_history and user input as the input for each round of the chat flow.
2. Each round of chat is executed once flow test.
3. Prefix the output for distinction.
"""
@contextlib.contextmanager
def change_logger_level(level):
origin_level = logger.level
logger.setLevel(level)
yield
logger.setLevel(origin_level)
init(autoreset=True)
chat_history = []
# TODO: test submitter should not interact with dataplane flow directly
input_name = next(
filter(lambda key: self.dataplane_flow.inputs[key].is_chat_input, self.dataplane_flow.inputs.keys())
)
output_name = next(
filter(
lambda key: self.dataplane_flow.outputs[key].is_chat_output,
self.dataplane_flow.outputs.keys(),
)
)
while True:
# generator record should be reset for each round of chat
generator_record = {}
try:
print(f"{Fore.GREEN}User: ", end="")
input_value = input()
if not input_value.strip():
continue
except (KeyboardInterrupt, EOFError):
print("Terminate the chat.")
break
inputs = inputs or {}
inputs[input_name] = input_value
inputs[chat_history_name] = chat_history
with change_logger_level(level=logging.WARNING):
chat_inputs, _ = self.resolve_data(inputs=inputs)
flow_result = self.flow_test(
inputs=chat_inputs,
allow_generator_output=True,
)
self._raise_error_when_test_failed(flow_result, show_trace=True)
show_node_log_and_output(flow_result.node_run_infos, show_step_output, generator_record)
print(f"{Fore.YELLOW}Bot: ", end="")
print_chat_output(
flow_result.output[output_name],
generator_record,
generator_key=f"run.outputs.{output_name}",
)
flow_result = resolve_generator(flow_result, generator_record)
flow_outputs = {k: v for k, v in flow_result.output.items()}
history = {"inputs": {input_name: input_value}, "outputs": flow_outputs}
chat_history.append(history)
dump_flow_result(flow_folder=self._origin_flow.code, flow_result=flow_result, prefix="chat")
@staticmethod
def _raise_error_when_test_failed(test_result, show_trace=False):
from promptflow.executor._result import LineResult
test_status = test_result.run_info.status if isinstance(test_result, LineResult) else test_result.status
if test_status == Status.Failed:
error_dict = test_result.run_info.error if isinstance(test_result, LineResult) else test_result.error
error_response = ErrorResponse.from_error_dict(error_dict)
user_execution_error = error_response.get_user_execution_error_info()
error_message = error_response.message
stack_trace = user_execution_error.get("traceback", "")
error_type = user_execution_error.get("type", "Exception")
if show_trace:
print(stack_trace)
raise UserErrorException(f"{error_type}: {error_message}")
@staticmethod
def _get_generator_outputs(outputs):
outputs = outputs or {}
return {key: outputs for key, output in outputs.items() if isinstance(output, GeneratorType)}
| promptflow/src/promptflow/promptflow/_sdk/_submitter/test_submitter.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_submitter/test_submitter.py",
"repo_id": "promptflow",
"token_count": 11239
} | 16 |
#! /bin/bash
CONDA_ENV_PATH="$(conda info --base)/envs/{{env.conda_env_name}}"
export PATH="$CONDA_ENV_PATH/bin:$PATH"
{% if connection_yaml_paths %}
{% if show_comment %}
# hack: for some unknown reason, without this ls, the connection creation will be failed
{% endif %}
ls
ls /connections
{% endif %}
{% for connection_yaml_path in connection_yaml_paths %}
pf connection create --file /{{ connection_yaml_path }}
{% endfor %}
echo "start promptflow serving with worker_num: 8, worker_threads: 1"
cd /flow
gunicorn -w 8 --threads 1 -b "0.0.0.0:8080" --timeout 300 "promptflow._sdk._serving.app:create_app()" | promptflow/src/promptflow/promptflow/_sdk/data/docker/runit/promptflow-serve/run.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/docker/runit/promptflow-serve/run.jinja2",
"repo_id": "promptflow",
"token_count": 230
} | 17 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# isort: skip_file
# skip to avoid circular import
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from ._connection import (
AzureContentSafetyConnection,
AzureOpenAIConnection,
CognitiveSearchConnection,
CustomConnection,
OpenAIConnection,
SerpConnection,
QdrantConnection,
WeaviateConnection,
FormRecognizerConnection,
CustomStrongTypeConnection,
)
from ._run import Run
from ._validation import ValidationResult
from ._flow import FlowContext
__all__ = [
# region: Connection
"AzureContentSafetyConnection",
"AzureOpenAIConnection",
"OpenAIConnection",
"CustomConnection",
"CustomStrongTypeConnection",
"CognitiveSearchConnection",
"SerpConnection",
"QdrantConnection",
"WeaviateConnection",
"FormRecognizerConnection",
# endregion
# region Run
"Run",
"ValidationResult",
# endregion
# region Flow
"FlowContext",
# endregion
]
| promptflow/src/promptflow/promptflow/_sdk/entities/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/entities/__init__.py",
"repo_id": "promptflow",
"token_count": 372
} | 18 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextlib
import glob
import json
import os
import subprocess
import sys
import uuid
from importlib.metadata import version
from os import PathLike
from pathlib import Path
from typing import Dict, Iterable, List, Tuple, Union
from promptflow._constants import FlowLanguage
from promptflow._sdk._configuration import Configuration
from promptflow._sdk._constants import CHAT_HISTORY, DEFAULT_ENCODING, FLOW_TOOLS_JSON_GEN_TIMEOUT, LOCAL_MGMT_DB_PATH
from promptflow._sdk._load_functions import load_flow
from promptflow._sdk._submitter import TestSubmitter
from promptflow._sdk._submitter.utils import SubmitterHelper
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import (
_get_additional_includes,
_merge_local_code_and_additional_includes,
copy_tree_respect_template_and_ignore_file,
dump_flow_result,
generate_flow_tools_json,
generate_random_string,
logger,
parse_variant,
)
from promptflow._sdk.entities._eager_flow import EagerFlow
from promptflow._sdk.entities._flow import Flow, FlowBase, ProtectedFlow
from promptflow._sdk.entities._validation import ValidationResult
from promptflow._utils.context_utils import _change_working_dir
from promptflow._utils.yaml_utils import dump_yaml, load_yaml
from promptflow.exceptions import UserErrorException
class FlowOperations(TelemetryMixin):
"""FlowOperations."""
def __init__(self, client):
self._client = client
super().__init__()
@monitor_operation(activity_name="pf.flows.test", activity_type=ActivityType.PUBLICAPI)
def test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
entry: str = None,
**kwargs,
) -> dict:
"""Test flow or node.
:param flow: path to flow directory to test
:type flow: Union[str, PathLike]
:param inputs: Input data for the flow test
:type inputs: dict
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:type variant: str
:param node: If specified it will only test this node, else it will test the flow.
:type node: str
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:type environment_variables: dict
:return: The result of flow or node
:rtype: dict
"""
experiment = kwargs.pop("experiment", None)
output_path = kwargs.get("output_path", None)
if Configuration.get_instance().is_internal_features_enabled() and experiment:
return self._client._experiments._test(
flow=flow,
inputs=inputs,
environment_variables=environment_variables,
experiment=experiment,
**kwargs,
)
result = self._test(
flow=flow,
inputs=inputs,
variant=variant,
node=node,
environment_variables=environment_variables,
**kwargs,
)
dump_test_result = kwargs.get("dump_test_result", False)
if dump_test_result:
# Dump flow/node test info
flow = load_flow(flow)
if node:
dump_flow_result(
flow_folder=flow.code, node_result=result, prefix=f"flow-{node}.node", custom_path=output_path
)
else:
if variant:
tuning_node, node_variant = parse_variant(variant)
prefix = f"flow-{tuning_node}-{node_variant}"
else:
prefix = "flow"
dump_flow_result(
flow_folder=flow.code,
flow_result=result,
prefix=prefix,
custom_path=output_path,
)
TestSubmitter._raise_error_when_test_failed(result, show_trace=node is not None)
return result.output
def _test(
self,
flow: Union[str, PathLike],
*,
inputs: dict = None,
variant: str = None,
node: str = None,
environment_variables: dict = None,
stream_log: bool = True,
stream_output: bool = True,
allow_generator_output: bool = True,
**kwargs,
):
"""Test flow or node.
:param flow: path to flow directory to test
:param inputs: Input data for the flow test
:param variant: Node & variant name in format of ${node_name.variant_name}, will use default variant
if not specified.
:param node: If specified it will only test this node, else it will test the flow.
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
:param stream_log: Whether streaming the log.
:param stream_output: Whether streaming the outputs.
:param allow_generator_output: Whether return streaming output when flow has streaming output.
:return: Executor result
"""
from promptflow._sdk._load_functions import load_flow
inputs = inputs or {}
output_path = kwargs.get("output_path", None)
session = kwargs.pop("session", None)
# Run id will be set in operation context and used for session
run_id = kwargs.get("run_id", str(uuid.uuid4()))
flow: FlowBase = load_flow(flow)
if isinstance(flow, EagerFlow):
if variant or node:
logger.warning("variant and node are not supported for eager flow, will be ignored")
variant, node = None, None
flow.context.variant = variant
with TestSubmitter(flow=flow, flow_context=flow.context, client=self._client).init(
target_node=node,
environment_variables=environment_variables,
stream_log=stream_log,
output_path=output_path,
stream_output=stream_output,
session=session,
) as submitter:
if isinstance(flow, EagerFlow):
# TODO(2897153): support chat eager flow
is_chat_flow, chat_history_input_name = False, None
flow_inputs, dependency_nodes_outputs = inputs, None
else:
is_chat_flow, chat_history_input_name, _ = self._is_chat_flow(submitter.dataplane_flow)
flow_inputs, dependency_nodes_outputs = submitter.resolve_data(
node_name=node, inputs=inputs, chat_history_name=chat_history_input_name
)
if node:
return submitter.node_test(
flow_inputs=flow_inputs,
dependency_nodes_outputs=dependency_nodes_outputs,
)
else:
return submitter.flow_test(
inputs=flow_inputs,
allow_generator_output=allow_generator_output and is_chat_flow,
run_id=run_id,
)
@staticmethod
def _is_chat_flow(flow):
"""
Check if the flow is chat flow.
Check if chat_history in the flow input and only one chat input and
one chat output to determine if it is a chat flow.
"""
chat_inputs = [item for item in flow.inputs.values() if item.is_chat_input]
chat_outputs = [item for item in flow.outputs.values() if item.is_chat_output]
chat_history_input_name = next(
iter([input_name for input_name, value in flow.inputs.items() if value.is_chat_history]), None
)
if (
not chat_history_input_name
and CHAT_HISTORY in flow.inputs
and flow.inputs[CHAT_HISTORY].is_chat_history is not False
):
chat_history_input_name = CHAT_HISTORY
is_chat_flow, error_msg = True, ""
if len(chat_inputs) != 1:
is_chat_flow = False
error_msg = "chat flow does not support multiple chat inputs"
elif len(chat_outputs) != 1:
is_chat_flow = False
error_msg = "chat flow does not support multiple chat outputs"
elif not chat_history_input_name:
is_chat_flow = False
error_msg = "chat_history is required in the inputs of chat flow"
return is_chat_flow, chat_history_input_name, error_msg
@monitor_operation(activity_name="pf.flows._chat", activity_type=ActivityType.INTERNALCALL)
def _chat(
self,
flow,
*,
inputs: dict = None,
variant: str = None,
environment_variables: dict = None,
**kwargs,
) -> List:
"""Interact with Chat Flow. Only chat flow supported.
:param flow: path to flow directory to chat
:param inputs: Input data for the flow to chat
:param environment_variables: Environment variables to set by specifying a property path and value.
Example: {"key1": "${my_connection.api_key}", "key2"="value2"}
The value reference to connection keys will be resolved to the actual value,
and all environment variables specified will be set into os.environ.
"""
from promptflow._sdk._load_functions import load_flow
flow: FlowBase = load_flow(flow)
flow.context.variant = variant
with TestSubmitter(flow=flow, flow_context=flow.context, client=self._client).init(
environment_variables=environment_variables,
stream_log=False, # no need to stream log in chat mode
) as submitter:
is_chat_flow, chat_history_input_name, error_msg = self._is_chat_flow(submitter.dataplane_flow)
if not is_chat_flow:
raise UserErrorException(f"Only support chat flow in interactive mode, {error_msg}.")
info_msg = f"Welcome to chat flow, {submitter.dataplane_flow.name}."
print("=" * len(info_msg))
print(info_msg)
print("Press Enter to send your message.")
print("You can quit with ctrl+C.")
print("=" * len(info_msg))
submitter._chat_flow(
inputs=inputs,
chat_history_name=chat_history_input_name,
show_step_output=kwargs.get("show_step_output", False),
)
@monitor_operation(activity_name="pf.flows._chat_with_ui", activity_type=ActivityType.INTERNALCALL)
def _chat_with_ui(self, script):
try:
import bs4 # noqa: F401
import streamlit_quill # noqa: F401
from streamlit.web import cli as st_cli
except ImportError as ex:
raise UserErrorException(
f"Please try 'pip install promptflow[executable]' to install dependency, {ex.msg}."
)
sys.argv = [
"streamlit",
"run",
script,
"--global.developmentMode=false",
"--client.toolbarMode=viewer",
"--browser.gatherUsageStats=false",
]
st_cli.main()
def _build_environment_config(self, flow_dag_path: Path):
flow_info = load_yaml(flow_dag_path)
# standard env object:
# environment:
# image: xxx
# conda_file: xxx
# python_requirements_txt: xxx
# setup_sh: xxx
# TODO: deserialize dag with structured class here to avoid using so many magic strings
env_obj = flow_info.get("environment", {})
env_obj["sdk_version"] = version("promptflow")
# version 0.0.1 is the dev version of promptflow
if env_obj["sdk_version"] == "0.0.1":
del env_obj["sdk_version"]
if not env_obj.get("python_requirements_txt", None) and (flow_dag_path.parent / "requirements.txt").is_file():
env_obj["python_requirements_txt"] = "requirements.txt"
env_obj["conda_env_name"] = "promptflow-serve"
if "conda_file" in env_obj:
conda_file = flow_dag_path.parent / env_obj["conda_file"]
if conda_file.is_file():
conda_obj = load_yaml(conda_file)
if "name" in conda_obj:
env_obj["conda_env_name"] = conda_obj["name"]
return env_obj
@classmethod
def _refine_connection_name(cls, connection_name: str):
return connection_name.replace(" ", "_")
def _dump_connection(self, connection, output_path: Path):
# connection yaml should be a dict instead of ordered dict
connection_dict = connection._to_dict()
connection_yaml = {
"$schema": f"https://azuremlschemas.azureedge.net/promptflow/"
f"latest/{connection.__class__.__name__}.schema.json",
**connection_dict,
}
if connection.type == "Custom":
secret_dict = connection_yaml["secrets"]
else:
secret_dict = connection_yaml
connection_var_name = self._refine_connection_name(connection.name)
env_var_names = [f"{connection_var_name}_{secret_key}".upper() for secret_key in connection.secrets]
for secret_key, secret_env in zip(connection.secrets, env_var_names):
secret_dict[secret_key] = "${env:" + secret_env + "}"
for key in ["created_date", "last_modified_date"]:
if key in connection_yaml:
del connection_yaml[key]
key_order = ["$schema", "type", "name", "configs", "secrets", "module"]
sorted_connection_dict = {
key: connection_yaml[key]
for key in sorted(
connection_yaml.keys(),
key=lambda x: (0, key_order.index(x)) if x in key_order else (1, x),
)
}
with open(output_path, "w", encoding="utf-8") as f:
f.write(dump_yaml(sorted_connection_dict))
return env_var_names
def _migrate_connections(self, connection_names: List[str], output_dir: Path):
from promptflow._sdk._pf_client import PFClient
output_dir.mkdir(parents=True, exist_ok=True)
local_client = PFClient()
connection_paths, env_var_names = [], {}
for connection_name in connection_names:
connection = local_client.connections.get(name=connection_name, with_secrets=True)
connection_var_name = self._refine_connection_name(connection_name)
connection_paths.append(output_dir / f"{connection_var_name}.yaml")
for env_var_name in self._dump_connection(
connection,
connection_paths[-1],
):
if env_var_name in env_var_names:
raise RuntimeError(
f"environment variable name conflict: connection {connection_name} and "
f"{env_var_names[env_var_name]} on {env_var_name}"
)
env_var_names[env_var_name] = connection_name
return connection_paths, list(env_var_names.keys())
def _export_flow_connections(
self,
built_flow_dag_path: Path,
*,
output_dir: Path,
):
"""Export flow connections to yaml files.
:param built_flow_dag_path: path to built flow dag yaml file. Given this is a built flow, we can assume
that the flow involves no additional includes, symlink, or variant.
:param output_dir: output directory to export connections
"""
flow: FlowBase = load_flow(built_flow_dag_path)
with _change_working_dir(flow.code):
if flow.language == FlowLanguage.CSharp:
from promptflow.batch import CSharpExecutorProxy
return self._migrate_connections(
connection_names=SubmitterHelper.get_used_connection_names(
tools_meta=CSharpExecutorProxy.get_tool_metadata(
flow_file=flow.flow_dag_path,
working_dir=flow.code,
),
flow_dag=flow._data,
),
output_dir=output_dir,
)
else:
# TODO: avoid using executable here
from promptflow.contracts.flow import Flow as ExecutableFlow
executable = ExecutableFlow.from_yaml(flow_file=flow.path, working_dir=flow.code)
return self._migrate_connections(
connection_names=executable.get_connection_names(),
output_dir=output_dir,
)
def _build_flow(
self,
flow: Flow,
*,
output: Union[str, PathLike],
tuning_node: str = None,
node_variant: str = None,
update_flow_tools_json: bool = True,
):
# TODO: confirm if we need to import this
from promptflow._sdk._submitter import variant_overwrite_context
flow_copy_target = Path(output)
flow_copy_target.mkdir(parents=True, exist_ok=True)
# resolve additional includes and copy flow directory first to guarantee there is a final flow directory
# TODO: shall we pop "node_variants" unless keep-variants is specified?
with variant_overwrite_context(
flow=flow,
tuning_node=tuning_node,
variant=node_variant,
drop_node_variants=True,
) as temp_flow:
# TODO: avoid copy for twice
copy_tree_respect_template_and_ignore_file(temp_flow.code, flow_copy_target)
if update_flow_tools_json:
generate_flow_tools_json(flow_copy_target)
return flow_copy_target / flow.path.name
def _export_to_docker(
self,
flow_dag_path: Path,
output_dir: Path,
*,
env_var_names: List[str],
connection_paths: List[Path],
flow_name: str,
is_csharp_flow: bool = False,
):
(output_dir / "settings.json").write_text(
data=json.dumps({env_var_name: "" for env_var_name in env_var_names}, indent=2),
encoding="utf-8",
)
environment_config = self._build_environment_config(flow_dag_path)
# TODO: make below strings constants
if is_csharp_flow:
source = Path(__file__).parent.parent / "data" / "docker_csharp"
else:
source = Path(__file__).parent.parent / "data" / "docker"
copy_tree_respect_template_and_ignore_file(
source=source,
target=output_dir,
render_context={
"env": environment_config,
"flow_name": f"{flow_name}-{generate_random_string(6)}",
"local_db_rel_path": LOCAL_MGMT_DB_PATH.relative_to(Path.home()).as_posix(),
"connection_yaml_paths": list(map(lambda x: x.relative_to(output_dir).as_posix(), connection_paths)),
},
)
def _build_as_executable(
self,
flow_dag_path: Path,
output_dir: Path,
*,
flow_name: str,
env_var_names: List[str],
):
try:
import bs4 # noqa: F401
import PyInstaller # noqa: F401
import streamlit
import streamlit_quill # noqa: F401
except ImportError as ex:
raise UserErrorException(
f"Please try 'pip install promptflow[executable]' to install dependency, {ex.msg}."
)
from promptflow.contracts.flow import Flow as ExecutableFlow
(output_dir / "settings.json").write_text(
data=json.dumps({env_var_name: "" for env_var_name in env_var_names}, indent=2),
encoding="utf-8",
)
environment_config = self._build_environment_config(flow_dag_path)
hidden_imports = []
if (
environment_config.get("python_requirements_txt", None)
and (flow_dag_path.parent / "requirements.txt").is_file()
):
with open(flow_dag_path.parent / "requirements.txt", "r", encoding="utf-8") as file:
file_content = file.read()
hidden_imports = file_content.splitlines()
runtime_interpreter_path = (Path(streamlit.__file__).parent / "runtime").as_posix()
executable = ExecutableFlow.from_yaml(flow_file=Path(flow_dag_path.name), working_dir=flow_dag_path.parent)
flow_inputs = {
flow_input: (value.default, value.type.value)
for flow_input, value in executable.inputs.items()
if not value.is_chat_history
}
flow_inputs_params = ["=".join([flow_input, flow_input]) for flow_input, _ in flow_inputs.items()]
flow_inputs_params = ",".join(flow_inputs_params)
is_chat_flow, chat_history_input_name, _ = self._is_chat_flow(executable)
label = "Chat" if is_chat_flow else "Run"
copy_tree_respect_template_and_ignore_file(
source=Path(__file__).parent.parent / "data" / "executable",
target=output_dir,
render_context={
"hidden_imports": hidden_imports,
"flow_name": flow_name,
"runtime_interpreter_path": runtime_interpreter_path,
"flow_inputs": flow_inputs,
"flow_inputs_params": flow_inputs_params,
"flow_path": None,
"is_chat_flow": is_chat_flow,
"chat_history_input_name": chat_history_input_name,
"label": label,
},
)
self._run_pyinstaller(output_dir)
def _run_pyinstaller(self, output_dir):
with _change_working_dir(output_dir, mkdir=False):
subprocess.run(["pyinstaller", "app.spec"], check=True)
print("PyInstaller command executed successfully.")
@monitor_operation(activity_name="pf.flows.build", activity_type=ActivityType.PUBLICAPI)
def build(
self,
flow: Union[str, PathLike],
*,
output: Union[str, PathLike],
format: str = "docker",
variant: str = None,
**kwargs,
):
"""
Build flow to other format.
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param format: export format, support "docker" and "executable" only for now
:type format: str
:param output: output directory
:type output: Union[str, PathLike]
:param variant: node variant in format of {node_name}.{variant_name},
will use default variant if not specified.
:type variant: str
:return: no return
:rtype: None
"""
output_dir = Path(output).absolute()
output_dir.mkdir(parents=True, exist_ok=True)
flow: FlowBase = load_flow(flow)
is_csharp_flow = flow.language == FlowLanguage.CSharp
if format not in ["docker", "executable"]:
raise ValueError(f"Unsupported export format: {format}")
if variant:
tuning_node, node_variant = parse_variant(variant)
else:
tuning_node, node_variant = None, None
flow_only = kwargs.pop("flow_only", False)
if flow_only:
output_flow_dir = output_dir
else:
output_flow_dir = output_dir / "flow"
new_flow_dag_path = self._build_flow(
flow=flow,
output=output_flow_dir,
tuning_node=tuning_node,
node_variant=node_variant,
update_flow_tools_json=False if is_csharp_flow else True,
)
if flow_only:
return
# use new flow dag path below as origin one may miss additional includes
connection_paths, env_var_names = self._export_flow_connections(
built_flow_dag_path=new_flow_dag_path,
output_dir=output_dir / "connections",
)
if format == "docker":
self._export_to_docker(
flow_dag_path=new_flow_dag_path,
output_dir=output_dir,
connection_paths=connection_paths,
flow_name=flow.name,
env_var_names=env_var_names,
is_csharp_flow=is_csharp_flow,
)
elif format == "executable":
self._build_as_executable(
flow_dag_path=new_flow_dag_path,
output_dir=output_dir,
flow_name=flow.name,
env_var_names=env_var_names,
)
@classmethod
@contextlib.contextmanager
def _resolve_additional_includes(cls, flow_dag_path: Path) -> Iterable[Path]:
# TODO: confirm if we need to import this
from promptflow._sdk._submitter import remove_additional_includes
# Eager flow may not contain a yaml file, skip resolving additional includes
def is_yaml_file(file_path):
_, file_extension = os.path.splitext(file_path)
return file_extension.lower() in (".yaml", ".yml")
if is_yaml_file(flow_dag_path) and _get_additional_includes(flow_dag_path):
# Merge the flow folder and additional includes to temp folder.
# TODO: support a flow_dag_path with a name different from flow.dag.yaml
with _merge_local_code_and_additional_includes(code_path=flow_dag_path.parent) as temp_dir:
remove_additional_includes(Path(temp_dir))
yield Path(temp_dir) / flow_dag_path.name
else:
yield flow_dag_path
@monitor_operation(activity_name="pf.flows.validate", activity_type=ActivityType.PUBLICAPI)
def validate(self, flow: Union[str, PathLike], *, raise_error: bool = False, **kwargs) -> ValidationResult:
"""
Validate flow.
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param raise_error: whether raise error when validation failed
:type raise_error: bool
:return: a validation result object
:rtype: ValidationResult
"""
flow_entity: ProtectedFlow = load_flow(source=flow)
# TODO: put off this if we do path existence check in FlowSchema on fields other than additional_includes
validation_result = flow_entity._validate()
source_path_mapping = {}
flow_tools, tools_errors = self._generate_tools_meta(
flow=flow_entity.flow_dag_path,
source_path_mapping=source_path_mapping,
)
flow_entity.tools_meta_path.write_text(
data=json.dumps(flow_tools, indent=4),
encoding=DEFAULT_ENCODING,
)
if tools_errors:
for source_name, message in tools_errors.items():
for yaml_path in source_path_mapping.get(source_name, []):
validation_result.append_error(
yaml_path=yaml_path,
message=message,
)
# flow in control plane is read-only, so resolve location makes sense even in SDK experience
validation_result.resolve_location_for_diagnostics(flow_entity.flow_dag_path.as_posix())
flow_entity._try_raise(
validation_result,
raise_error=raise_error,
)
return validation_result
@monitor_operation(activity_name="pf.flows._generate_tools_meta", activity_type=ActivityType.INTERNALCALL)
def _generate_tools_meta(
self,
flow: Union[str, PathLike],
*,
source_name: str = None,
source_path_mapping: Dict[str, List[str]] = None,
timeout: int = FLOW_TOOLS_JSON_GEN_TIMEOUT,
) -> Tuple[dict, dict]:
"""Generate flow tools meta for a specific flow or a specific node in the flow.
This is a private interface for vscode extension, so do not change the interface unless necessary.
Usage:
from promptflow import PFClient
PFClient().flows._generate_tools_meta(flow="flow.dag.yaml", source_name="convert_to_dict.py")
:param flow: path to the flow directory or flow dag to export
:type flow: Union[str, PathLike]
:param source_name: source name to generate tools meta. If not specified, generate tools meta for all sources.
:type source_name: str
:param source_path_mapping: If passed in None, do nothing; if passed in a dict, will record all reference yaml
paths for each source in the dict passed in.
:type source_path_mapping: Dict[str, List[str]]
:param timeout: timeout for generating tools meta
:type timeout: int
:return: dict of tools meta and dict of tools errors
:rtype: Tuple[dict, dict]
"""
flow: FlowBase = load_flow(source=flow)
if not isinstance(flow, ProtectedFlow):
# No tools meta for eager flow
return {}, {}
with self._resolve_additional_includes(flow.flow_dag_path) as new_flow_dag_path:
flow_tools = generate_flow_tools_json(
flow_directory=new_flow_dag_path.parent,
dump=False,
raise_error=False,
include_errors_in_output=True,
target_source=source_name,
used_packages_only=True,
source_path_mapping=source_path_mapping,
timeout=timeout,
)
flow_tools_meta = flow_tools.pop("code", {})
tools_errors = {}
nodes_with_error = [node_name for node_name, message in flow_tools_meta.items() if isinstance(message, str)]
for node_name in nodes_with_error:
tools_errors[node_name] = flow_tools_meta.pop(node_name)
additional_includes = _get_additional_includes(flow.flow_dag_path)
if additional_includes:
additional_files = {}
for include in additional_includes:
include_path = Path(include) if Path(include).is_absolute() else flow.code / include
if include_path.is_file():
file_name = Path(include).name
additional_files[Path(file_name)] = os.path.relpath(include_path, flow.code)
else:
if not Path(include).is_absolute():
include = flow.code / include
files = glob.glob(os.path.join(include, "**"), recursive=True)
additional_files.update(
{
Path(os.path.relpath(path, include.parent)): os.path.relpath(path, flow.code)
for path in files
}
)
for tool in flow_tools_meta.values():
source = tool.get("source", None)
if source and Path(source) in additional_files:
tool["source"] = additional_files[Path(source)]
flow_tools["code"] = flow_tools_meta
return flow_tools, tools_errors
| promptflow/src/promptflow/promptflow/_sdk/operations/_flow_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/operations/_flow_operations.py",
"repo_id": "promptflow",
"token_count": 14515
} | 19 |
from promptflow.exceptions import SystemErrorException, UserErrorException, ValidationException
class InvalidImageInput(ValidationException):
pass
class LoadMultimediaDataError(UserErrorException):
pass
class YamlParseError(SystemErrorException):
"""Exception raised when yaml parse failed."""
pass
| promptflow/src/promptflow/promptflow/_utils/_errors.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/_errors.py",
"repo_id": "promptflow",
"token_count": 85
} | 20 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import time
from functools import wraps
from typing import Tuple, Type, Union
from requests import Response
from promptflow._utils.logger_utils import LoggerFactory
logger = LoggerFactory.get_logger(__name__)
def retry(exception_to_check: Union[Type[Exception], Tuple[Type[Exception], ...]], tries=4, delay=3, backoff=2):
"""
From https://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/
Retry calling the decorated function using an exponential backoff.
http://www.saltycrane.com/blog/2009/11/trying-out-retry-decorator-python/
original from: http://wiki.python.org/moin/PythonDecoratorLibrary#Retry
:param exception_to_check: the exception to check. may be a tuple of
exceptions to check
:type exception_to_check: Exception or tuple
:param tries: number of times to try (not retry) before giving up
:type tries: int
:param delay: initial delay between retries in seconds
:type delay: int
:param backoff: backoff multiplier e.g. value of 2 will double the delay
each retry
:type backoff: int
:param logger: log the retry action if specified
:type logger: logging.Logger
"""
def deco_retry(f):
@wraps(f)
def f_retry(*args, **kwargs):
retry_times, delay_seconds = tries, delay
while retry_times > 1:
try:
logger.debug("Running %s, %d more tries to go.", str(f), retry_times)
return f(*args, **kwargs)
except exception_to_check:
time.sleep(delay_seconds)
retry_times -= 1
delay_seconds *= backoff
logger.warning("%s, Retrying in %d seconds...", str(exception_to_check), delay_seconds)
return f(*args, **kwargs)
return f_retry # true decorator
return deco_retry
HTTP_SAFE_CODES = set(range(506)) - {408, 429, 500, 502, 503, 504}
HTTP_RETRY_CODES = set(range(999)) - HTTP_SAFE_CODES
def http_retry_wrapper(f, tries=4, delay=3, backoff=2):
"""
:param f: function to be retried, should return a Response object.
:type f: Callable
:param tries: number of times to try (not retry) before giving up
:type tries: int
:param delay: initial delay between retries in seconds
:type delay: int
:param backoff: backoff multiplier e.g. value of 2 will double the delay
each retry
:type backoff: int
"""
@wraps(f)
def f_retry(*args, **kwargs):
retry_times, delay_seconds = tries, delay
while retry_times > 1:
result = f(*args, **kwargs)
if not isinstance(result, Response):
logger.debug(f"Not a retryable function, expected return type {Response}, got {type(result)}.")
return result
if result.status_code not in HTTP_RETRY_CODES:
return result
logger.warning(
f"Retryable error code {result.status_code} returned, retrying in {delay_seconds} seconds. "
f"Function {f.__name__}, Reason: {result.reason}"
)
time.sleep(delay_seconds)
retry_times -= 1
delay_seconds *= backoff
return f(*args, **kwargs)
return f_retry
| promptflow/src/promptflow/promptflow/_utils/retry_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/retry_utils.py",
"repo_id": "promptflow",
"token_count": 1424
} | 21 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""
This file stores functions and objects that will be used in prompt-flow sdk.
DO NOT change the module names in "all" list, add new modules if needed.
"""
class _DummyCallableClassForLazyImportError:
"""This class is used to put off ImportError until the imported class or function is called."""
@classmethod
def _get_message(cls):
return "azure-ai-ml is not installed. Please install azure-ai-ml to use this feature."
def __init__(self, *args, **kwargs):
raise ImportError(self._get_message())
def __call__(self, *args, **kwargs):
raise ImportError(self._get_message())
# TODO: avoid import azure.ai.ml if promptflow.azure.configure is not called
try:
from azure.ai.ml import MLClient, load_component
from azure.ai.ml.entities import Component
from azure.ai.ml.entities._assets import Code
from azure.ai.ml.entities._component._additional_includes import AdditionalIncludesMixin
from azure.ai.ml.entities._load_functions import load_common
except ImportError:
class load_component(_DummyCallableClassForLazyImportError):
pass
class Component(_DummyCallableClassForLazyImportError):
pass
class MLClient(_DummyCallableClassForLazyImportError):
pass
class load_common(_DummyCallableClassForLazyImportError):
pass
class Code(_DummyCallableClassForLazyImportError):
pass
class AdditionalIncludesMixin(_DummyCallableClassForLazyImportError):
pass
__all__ = [
"load_component",
"Component",
"MLClient",
"load_common",
"Code",
"AdditionalIncludesMixin",
]
| promptflow/src/promptflow/promptflow/azure/_ml/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_ml/__init__.py",
"repo_id": "promptflow",
"token_count": 593
} | 22 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._connections_operations import build_create_connection_request, build_delete_connection_request, build_get_connection_request, build_get_connection_with_secrets_request, build_list_azure_open_ai_deployments_request, build_list_connection_specs_request, build_list_connections_request, build_update_connection_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ConnectionsOperations:
"""ConnectionsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace_async
async def create_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
body: Optional["_models.CreateOrUpdateConnectionRequestDto"] = None,
**kwargs: Any
) -> "_models.ConnectionDto":
"""create_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequestDto')
else:
_json = None
request = build_create_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.create_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace_async
async def update_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
body: Optional["_models.CreateOrUpdateConnectionRequestDto"] = None,
**kwargs: Any
) -> "_models.ConnectionDto":
"""update_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequestDto
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequestDto')
else:
_json = None
request = build_update_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.update_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace_async
async def get_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
**kwargs: Any
) -> "_models.ConnectionDto":
"""get_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace_async
async def delete_connection(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
**kwargs: Any
) -> "_models.ConnectionDto":
"""delete_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.delete_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}'} # type: ignore
@distributed_trace_async
async def get_connection_with_secrets(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
**kwargs: Any
) -> "_models.ConnectionDto":
"""get_connection_with_secrets.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionDto, or the result of cls(response)
:rtype: ~flow.models.ConnectionDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_with_secrets_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection_with_secrets.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection_with_secrets.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/listsecrets'} # type: ignore
@distributed_trace_async
async def list_connections(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.ConnectionDto"]:
"""list_connections.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionDto, or the result of cls(response)
:rtype: list[~flow.models.ConnectionDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connections_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connections.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connections.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections'} # type: ignore
@distributed_trace_async
async def list_connection_specs(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.WorkspaceConnectionSpec"]:
"""list_connection_specs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of WorkspaceConnectionSpec, or the result of cls(response)
:rtype: list[~flow.models.WorkspaceConnectionSpec]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.WorkspaceConnectionSpec"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connection_specs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connection_specs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[WorkspaceConnectionSpec]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connection_specs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/specs'} # type: ignore
@distributed_trace_async
async def list_azure_open_ai_deployments(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
connection_name: str,
**kwargs: Any
) -> List["_models.AzureOpenAIDeploymentDto"]:
"""list_azure_open_ai_deployments.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of AzureOpenAIDeploymentDto, or the result of cls(response)
:rtype: list[~flow.models.AzureOpenAIDeploymentDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.AzureOpenAIDeploymentDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_azure_open_ai_deployments_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.list_azure_open_ai_deployments.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[AzureOpenAIDeploymentDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_azure_open_ai_deployments.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connections/{connectionName}/AzureOpenAIDeployments'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_connections_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_connections_operations.py",
"repo_id": "promptflow",
"token_count": 9090
} | 23 |
# coding=utf-8
# --------------------------------------------------------------------------
# Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.9.2, generator: @autorest/python@5.12.2)
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from msrest import Serializer
from .. import models as _models
from .._vendor import _convert_request, _format_url_section
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar, Union
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off
def build_create_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="POST",
url=url,
headers=header_parameters,
**kwargs
)
def build_update_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
if content_type is not None:
header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="PUT",
url=url,
headers=header_parameters,
**kwargs
)
def build_get_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_delete_connection_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
connection_scope = kwargs.pop('connection_scope', None) # type: Optional[Union[str, "_models.ConnectionScope"]]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
"connectionName": _SERIALIZER.url("connection_name", connection_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if connection_scope is not None:
query_parameters['connectionScope'] = _SERIALIZER.query("connection_scope", connection_scope, 'str')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_list_connections_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
def build_list_connection_specs_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs')
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'),
"resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'),
"workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="GET",
url=url,
headers=header_parameters,
**kwargs
)
# fmt: on
class ConnectionOperations(object):
"""ConnectionOperations operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~flow.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
@distributed_trace
def create_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""create_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_create_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.create_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def update_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
body=None, # type: Optional["_models.CreateOrUpdateConnectionRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""update_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param body:
:type body: ~flow.models.CreateOrUpdateConnectionRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
content_type = kwargs.pop('content_type', "application/json") # type: Optional[str]
if body is not None:
_json = self._serialize.body(body, 'CreateOrUpdateConnectionRequest')
else:
_json = None
request = build_update_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
content_type=content_type,
json=_json,
template_url=self.update_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def get_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""get_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
template_url=self.get_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def delete_connection(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
connection_name, # type: str
connection_scope=None, # type: Optional[Union[str, "_models.ConnectionScope"]]
**kwargs # type: Any
):
# type: (...) -> "_models.ConnectionEntity"
"""delete_connection.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:param connection_name:
:type connection_name: str
:param connection_scope:
:type connection_scope: str or ~flow.models.ConnectionScope
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ConnectionEntity, or the result of cls(response)
:rtype: ~flow.models.ConnectionEntity
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ConnectionEntity"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_connection_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
connection_name=connection_name,
connection_scope=connection_scope,
template_url=self.delete_connection.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('ConnectionEntity', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_connection.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/{connectionName}'} # type: ignore
@distributed_trace
def list_connections(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionEntity"]
"""list_connections.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionEntity, or the result of cls(response)
:rtype: list[~flow.models.ConnectionEntity]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionEntity"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connections_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connections.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionEntity]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connections.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection'} # type: ignore
@distributed_trace
def list_connection_specs(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.ConnectionSpec"]
"""list_connection_specs.
:param subscription_id: The Azure Subscription ID.
:type subscription_id: str
:param resource_group_name: The Name of the resource group in which the workspace is located.
:type resource_group_name: str
:param workspace_name: The name of the workspace.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: list of ConnectionSpec, or the result of cls(response)
:rtype: list[~flow.models.ConnectionSpec]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.ConnectionSpec"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_connection_specs_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_connection_specs.metadata['url'],
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
raise HttpResponseError(response=response, model=error)
deserialized = self._deserialize('[ConnectionSpec]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_connection_specs.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Connection/specs'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_connection_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_connection_operations.py",
"repo_id": "promptflow",
"token_count": 10177
} | 24 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
from pathlib import Path
from azure.ai.ml._schema import UnionField, YamlFileSchema
from azure.ai.ml._schema.core.fields import LocalPathField
from marshmallow import fields, post_load
from promptflow._utils.logger_utils import LoggerFactory
module_logger = LoggerFactory.get_logger(__name__)
class FlowSchema(YamlFileSchema):
name = fields.Str(attribute="name")
id = fields.Str(attribute="id")
description = fields.Str(attribute="description")
tags = fields.Dict(keys=fields.Str, attribute="tags")
path = UnionField(
[
LocalPathField(),
fields.Str(),
],
)
display_name = fields.Str(attribute="display_name")
type = fields.Str(attribute="type")
properties = fields.Dict(keys=fields.Str, attribute="properties")
@post_load
def update_properties(self, dct, **kwargs):
folder = Path(self.context["base_path"])
flow_type = dct.get("type")
if flow_type:
mapping = {
"standard": "default",
"evaluate": "evaluation",
}
dct["type"] = mapping[flow_type]
properties = dct.get("properties")
if properties and "promptflow.batch_inputs" in properties:
input_path = properties["promptflow.batch_inputs"]
samples_file = folder / input_path
if samples_file.exists():
with open(samples_file, "r", encoding="utf-8") as fp:
properties["promptflow.batch_inputs"] = json.loads(fp.read())
return dct
| promptflow/src/promptflow/promptflow/azure/_schemas/_flow_schema.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_schemas/_flow_schema.py",
"repo_id": "promptflow",
"token_count": 684
} | 25 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import signal
import threading
import uuid
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional
from promptflow._constants import LANGUAGE_KEY, LINE_NUMBER_KEY, LINE_TIMEOUT_SEC, FlowLanguage
from promptflow._core._errors import UnexpectedError
from promptflow._core.operation_context import OperationContext
from promptflow._utils.async_utils import async_run_allowing_running_loop
from promptflow._utils.context_utils import _change_working_dir
from promptflow._utils.execution_utils import (
apply_default_value_for_input,
collect_lines,
get_aggregation_inputs_properties,
handle_line_failures,
)
from promptflow._utils.logger_utils import bulk_logger
from promptflow._utils.utils import (
dump_list_to_jsonl,
get_int_env_var,
log_progress,
resolve_dir_to_absolute,
transpose,
)
from promptflow._utils.yaml_utils import load_yaml
from promptflow.batch._base_executor_proxy import AbstractExecutorProxy
from promptflow.batch._batch_inputs_processor import BatchInputsProcessor
from promptflow.batch._csharp_executor_proxy import CSharpExecutorProxy
from promptflow.batch._errors import BatchRunTimeoutError
from promptflow.batch._python_executor_proxy import PythonExecutorProxy
from promptflow.batch._result import BatchResult
from promptflow.contracts.flow import Flow
from promptflow.contracts.run_info import Status
from promptflow.exceptions import ErrorTarget, PromptflowException
from promptflow.executor._line_execution_process_pool import signal_handler
from promptflow.executor._result import AggregationResult, LineResult
from promptflow.executor.flow_validator import FlowValidator
from promptflow.storage._run_storage import AbstractRunStorage
OUTPUT_FILE_NAME = "output.jsonl"
DEFAULT_CONCURRENCY = 10
class BatchEngine:
"""This class is used to execute flows in batch mode"""
executor_proxy_classes: Mapping[str, AbstractExecutorProxy] = {
FlowLanguage.Python: PythonExecutorProxy,
FlowLanguage.CSharp: CSharpExecutorProxy,
}
@classmethod
def register_executor(cls, type: str, executor_proxy_cls: AbstractExecutorProxy):
"""Register a executor proxy class for a specific program language.
This method allows users to register a executor proxy class for a particular
programming language. The executor proxy class will be used when creating an instance
of the BatchEngine for flows written in the specified language.
:param type: The flow program language of the executor proxy,
:type type: str
:param executor_proxy_cls: The executor proxy class to be registered.
:type executor_proxy_cls: ~promptflow.batch.AbstractExecutorProxy
"""
cls.executor_proxy_classes[type] = executor_proxy_cls
def __init__(
self,
flow_file: Path,
working_dir: Optional[Path] = None,
*,
connections: Optional[dict] = None,
storage: Optional[AbstractRunStorage] = None,
batch_timeout_sec: Optional[int] = None,
worker_count: Optional[int] = None,
**kwargs,
):
"""Create a new batch engine instance
:param flow_file: The flow file path
:type flow_file: Path
:param working_dir: The flow working directory path
:type working_dir: Optional[Path]
:param connections: The connections used in the flow
:type connections: Optional[dict]
:param storage: The storage to store execution results
:type storage: Optional[~promptflow.storage._run_storage.AbstractRunStorage]
:param batch_timeout: The timeout of batch run in seconds
:type batch_timeout: Optional[int]
:param worker_count: The concurrency limit of batch run
:type worker_count: Optional[int]
:param kwargs: The keyword arguments related to creating the executor proxy class
:type kwargs: Any
"""
self._flow_file = flow_file
self._working_dir = Flow._resolve_working_dir(flow_file, working_dir)
self._is_eager_flow, self._program_language = self._check_eager_flow_and_language_from_yaml()
# TODO: why self._flow is not initialized for eager flow?
if not self._is_eager_flow:
self._flow = Flow.from_yaml(flow_file, working_dir=self._working_dir)
FlowValidator.ensure_flow_valid_in_batch_mode(self._flow)
self._connections = connections
self._storage = storage
self._kwargs = kwargs
self._batch_timeout_sec = batch_timeout_sec or get_int_env_var("PF_BATCH_TIMEOUT_SEC")
self._line_timeout_sec = get_int_env_var("PF_LINE_TIMEOUT_SEC", LINE_TIMEOUT_SEC)
self._worker_count = worker_count or get_int_env_var("PF_WORKER_COUNT")
# set it to True when the batch run is canceled
self._is_canceled = False
def run(
self,
input_dirs: Dict[str, str],
inputs_mapping: Dict[str, str],
output_dir: Path,
run_id: Optional[str] = None,
max_lines_count: Optional[int] = None,
raise_on_line_failure: Optional[bool] = False,
) -> BatchResult:
"""Run flow in batch mode
:param input_dirs: The directories path of input files
:type input_dirs: Dict[str, str]
:param inputs_mapping: The mapping of input names to their corresponding values.
:type inputs_mapping: Dict[str, str]
:param output_dir: output dir
:type output_dir: The directory path of output files
:param run_id: The run id of this run
:type run_id: Optional[str]
:param max_lines_count: The max count of inputs. If it is None, all inputs will be used.
:type max_lines_count: Optional[int]
:param raise_on_line_failure: Whether to raise exception when a line fails.
:type raise_on_line_failure: Optional[bool]
:return: The result of this batch run
:rtype: ~promptflow.batch._result.BatchResult
"""
try:
self._start_time = datetime.utcnow()
with _change_working_dir(self._working_dir):
# create executor proxy instance according to the flow program language
executor_proxy_cls = self.executor_proxy_classes[self._program_language]
self._executor_proxy: AbstractExecutorProxy = async_run_allowing_running_loop(
executor_proxy_cls.create,
self._flow_file,
self._working_dir,
connections=self._connections,
storage=self._storage,
**self._kwargs,
)
try:
# register signal handler for python flow in the main thread
# TODO: For all executor proxies that are executed locally, it might be necessary to
# register a signal for Ctrl+C in order to customize some actions beyond just killing
# the process, such as terminating the executor service.
if isinstance(self._executor_proxy, PythonExecutorProxy):
if threading.current_thread() is threading.main_thread():
signal.signal(signal.SIGINT, signal_handler)
else:
bulk_logger.info(
"Current thread is not main thread, skip signal handler registration in BatchEngine."
)
# set batch input source from input mapping
OperationContext.get_instance().set_batch_input_source_from_inputs_mapping(inputs_mapping)
# if using eager flow, the self._flow is none, so we need to get inputs definition from executor
inputs = self._executor_proxy.get_inputs_definition() if self._is_eager_flow else self._flow.inputs
# resolve input data from input dirs and apply inputs mapping
batch_input_processor = BatchInputsProcessor(self._working_dir, inputs, max_lines_count)
batch_inputs = batch_input_processor.process_batch_inputs(input_dirs, inputs_mapping)
# resolve output dir
output_dir = resolve_dir_to_absolute(self._working_dir, output_dir)
# run flow in batch mode
return async_run_allowing_running_loop(
self._exec_in_task, batch_inputs, run_id, output_dir, raise_on_line_failure
)
finally:
async_run_allowing_running_loop(self._executor_proxy.destroy)
except Exception as e:
bulk_logger.error(f"Error occurred while executing batch run. Exception: {str(e)}")
if isinstance(e, PromptflowException):
raise e
else:
# for unexpected error, we need to wrap it to SystemErrorException to allow us to see the stack trace.
unexpected_error = UnexpectedError(
target=ErrorTarget.BATCH,
message_format=(
"Unexpected error occurred while executing the batch run. Error: {error_type_and_message}."
),
error_type_and_message=f"({e.__class__.__name__}) {e}",
)
raise unexpected_error from e
def cancel(self):
"""Cancel the batch run"""
self._is_canceled = True
async def _exec_in_task(
self,
batch_inputs: List[Dict[str, Any]],
run_id: str = None,
output_dir: Path = None,
raise_on_line_failure: bool = False,
) -> BatchResult:
# if the batch run is canceled, asyncio.CancelledError will be raised and no results will be returned,
# so we pass empty line results list and aggr results and update them in _exec so that when the batch
# run is canceled we can get the current completed line results and aggr results.
line_results: List[LineResult] = []
aggr_result = AggregationResult({}, {}, {})
task = asyncio.create_task(
self._exec(line_results, aggr_result, batch_inputs, run_id, output_dir, raise_on_line_failure)
)
while not task.done():
# check whether the task is completed or canceled every 1s
await asyncio.sleep(1)
if self._is_canceled:
task.cancel()
# use current completed line results and aggregation results to create a BatchResult
return BatchResult.create(
self._start_time, datetime.utcnow(), line_results, aggr_result, status=Status.Canceled
)
return task.result()
async def _exec(
self,
line_results: List[LineResult],
aggr_result: AggregationResult,
batch_inputs: List[Dict[str, Any]],
run_id: str = None,
output_dir: Path = None,
raise_on_line_failure: bool = False,
) -> BatchResult:
# ensure executor health before execution
await self._executor_proxy.ensure_executor_health()
# apply default value in early stage, so we can use it both in line and aggregation nodes execution.
# if the flow is None, we don't need to apply default value for inputs.
if not self._is_eager_flow:
batch_inputs = [
apply_default_value_for_input(self._flow.inputs, each_line_input) for each_line_input in batch_inputs
]
run_id = run_id or str(uuid.uuid4())
# execute lines
is_timeout = False
if isinstance(self._executor_proxy, PythonExecutorProxy):
results, is_timeout = self._executor_proxy._exec_batch(
batch_inputs,
output_dir,
run_id,
batch_timeout_sec=self._batch_timeout_sec,
line_timeout_sec=self._line_timeout_sec,
worker_count=self._worker_count,
)
line_results.extend(results)
else:
# TODO: Enable batch timeout for other api based executor proxy
await self._exec_batch(line_results, batch_inputs, run_id)
handle_line_failures([r.run_info for r in line_results], raise_on_line_failure)
# persist outputs to output dir
outputs = [
{LINE_NUMBER_KEY: r.run_info.index, **r.output}
for r in line_results
if r.run_info.status == Status.Completed
]
outputs.sort(key=lambda x: x[LINE_NUMBER_KEY])
self._persist_outputs(outputs, output_dir)
# if the batch runs with errors, we should update the errors to ex
ex = None
if not is_timeout:
# execute aggregation nodes
aggr_exec_result = await self._exec_aggregation(batch_inputs, line_results, run_id)
# use the execution result to update aggr_result to make sure we can get the aggr_result in _exec_in_task
self._update_aggr_result(aggr_result, aggr_exec_result)
else:
ex = BatchRunTimeoutError(
message="The batch run failed due to timeout. Please adjust the timeout settings to a higher value.",
target=ErrorTarget.BATCH,
)
# summary some infos from line results and aggr results to batch result
return BatchResult.create(self._start_time, datetime.utcnow(), line_results, aggr_result, exception=ex)
async def _exec_batch(
self,
line_results: List[LineResult],
batch_inputs: List[Mapping[str, Any]],
run_id: Optional[str] = None,
) -> List[LineResult]:
worker_count = self._worker_count or DEFAULT_CONCURRENCY
semaphore = asyncio.Semaphore(worker_count)
pending = [
asyncio.create_task(self._exec_line_under_semaphore(semaphore, line_inputs, i, run_id))
for i, line_inputs in enumerate(batch_inputs)
]
total_lines = len(batch_inputs)
completed_line = 0
while completed_line < total_lines:
done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
completed_line_results = [task.result() for task in done]
self._persist_run_info(completed_line_results)
line_results.extend(completed_line_results)
log_progress(
self._start_time,
bulk_logger,
len(line_results),
total_lines,
last_log_count=completed_line,
)
completed_line = len(line_results)
async def _exec_line_under_semaphore(
self,
semaphore,
inputs: Mapping[str, Any],
index: Optional[int] = None,
run_id: Optional[str] = None,
):
async with semaphore:
return await self._executor_proxy.exec_line_async(inputs, index, run_id)
async def _exec_aggregation(
self,
batch_inputs: List[dict],
line_results: List[LineResult],
run_id: Optional[str] = None,
) -> AggregationResult:
if self._is_eager_flow:
return AggregationResult({}, {}, {})
aggregation_nodes = {node.name for node in self._flow.nodes if node.aggregation}
if not aggregation_nodes:
return AggregationResult({}, {}, {})
bulk_logger.info("Executing aggregation nodes...")
run_infos = [r.run_info for r in line_results]
succeeded = [i for i, r in enumerate(run_infos) if r.status == Status.Completed]
succeeded_batch_inputs = [batch_inputs[i] for i in succeeded]
resolved_succeeded_batch_inputs = [
FlowValidator.ensure_flow_inputs_type(flow=self._flow, inputs=input) for input in succeeded_batch_inputs
]
succeeded_inputs = transpose(resolved_succeeded_batch_inputs, keys=list(self._flow.inputs.keys()))
aggregation_inputs = transpose(
[result.aggregation_inputs for result in line_results],
keys=get_aggregation_inputs_properties(self._flow),
)
succeeded_aggregation_inputs = collect_lines(succeeded, aggregation_inputs)
try:
aggr_result = await self._executor_proxy.exec_aggregation_async(
succeeded_inputs, succeeded_aggregation_inputs, run_id
)
# if the flow language is python, we have already persisted node run infos during execution.
# so we should persist node run infos in aggr_result for other languages.
if not isinstance(self._executor_proxy, PythonExecutorProxy):
for node_run in aggr_result.node_run_infos.values():
self._storage.persist_node_run(node_run)
bulk_logger.info("Finish executing aggregation nodes.")
return aggr_result
except PromptflowException as e:
# for PromptflowException, we already do classification, so throw directly.
raise e
except Exception as e:
error_type_and_message = f"({e.__class__.__name__}) {e}"
raise UnexpectedError(
message_format=(
"Unexpected error occurred while executing the aggregated nodes. "
"Please fix or contact support for assistance. The error details: {error_type_and_message}."
),
error_type_and_message=error_type_and_message,
) from e
def _persist_run_info(self, line_results: List[LineResult]):
"""Persist node run infos and flow run info in line result to storage"""
for line_result in line_results:
for node_run in line_result.node_run_infos.values():
self._storage.persist_node_run(node_run)
self._storage.persist_flow_run(line_result.run_info)
def _persist_outputs(self, outputs: List[Mapping[str, Any]], output_dir: Path):
"""Persist outputs to json line file in output directory"""
output_file = output_dir / OUTPUT_FILE_NAME
dump_list_to_jsonl(output_file, outputs)
def _update_aggr_result(self, aggr_result: AggregationResult, aggr_exec_result: AggregationResult):
"""Update aggregation result with the aggregation execution result"""
aggr_result.metrics = aggr_exec_result.metrics
aggr_result.node_run_infos = aggr_exec_result.node_run_infos
aggr_result.output = aggr_exec_result.output
def _check_eager_flow_and_language_from_yaml(self):
flow_file = self._working_dir / self._flow_file if self._working_dir else self._flow_file
# TODO: remove this after path is removed
if flow_file.suffix.lower() == ".dll":
return True, FlowLanguage.CSharp
with open(flow_file, "r", encoding="utf-8") as fin:
flow_dag = load_yaml(fin)
is_eager_flow = "entry" in flow_dag
language = flow_dag.get(LANGUAGE_KEY, FlowLanguage.Python)
return is_eager_flow, language
| promptflow/src/promptflow/promptflow/batch/_batch_engine.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/batch/_batch_engine.py",
"repo_id": "promptflow",
"token_count": 8156
} | 26 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, List, Optional
class TraceType(str, Enum):
"""An enumeration class to represent different types of traces."""
LLM = "LLM"
TOOL = "Tool"
FUNCTION = "Function"
LANGCHAIN = "LangChain"
FLOW = "Flow"
@dataclass
class Trace:
"""A dataclass that represents a trace of a program execution.
:param name: The name of the trace.
:type name: str
:param type: The type of the trace.
:type type: ~promptflow.contracts.trace.TraceType
:param inputs: The inputs of the trace.
:type inputs: Dict[str, Any]
:param output: The output of the trace, or None if not available.
:type output: Optional[Any]
:param start_time: The timestamp of the start time, or None if not available.
:type start_time: Optional[float]
:param end_time: The timestamp of the end time, or None if not available.
:type end_time: Optional[float]
:param error: The error message of the trace, or None if no error occurred.
:type error: Optional[str]
:param children: The list of child traces, or None if no children.
:type children: Optional[List[Trace]]
:param node_name: The node name of the trace, used for flow level trace, or None if not applicable.
:type node_name: Optional[str]
"""
name: str
type: TraceType
inputs: Dict[str, Any]
output: Optional[Any] = None
start_time: Optional[float] = None # The timestamp of the start time
end_time: Optional[float] = None # The timestamp of the end time
error: Optional[str] = None
children: Optional[List["Trace"]] = None
node_name: Optional[str] = None # The node name of the trace, used for flow level trace
parent_id: str = "" # The parent trace id of the trace
id: str = "" # The trace id
| promptflow/src/promptflow/promptflow/contracts/trace.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/contracts/trace.py",
"repo_id": "promptflow",
"token_count": 647
} | 27 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from promptflow._sdk.operations._connection_operations import ConnectionOperations
from promptflow._sdk.operations._flow_operations import FlowOperations
from promptflow._sdk.operations._run_operations import RunOperations
__all__ = ["ConnectionOperations", "FlowOperations", "RunOperations"]
| promptflow/src/promptflow/promptflow/operations/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/operations/__init__.py",
"repo_id": "promptflow",
"token_count": 133
} | 28 |
# Deploy to Azure App Service
[Azure App Service](https://learn.microsoft.com/azure/app-service/) is an HTTP-based service for hosting web applications, REST APIs, and mobile back ends.
The scripts (`deploy.sh` for bash and `deploy.ps1` for powershell) under [this folder](https://github.com/microsoft/promptflow/tree/main/examples/tutorials/flow-deploy/azure-app-service) are here to help deploy the docker image to Azure App Service.
This example demos how to deploy [web-classification](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification/) deploy a flow using Azure App Service.
## Build a flow as docker format app
Use the command below to build a flow as docker format app:
```bash
pf flow build --source ../../flows/standard/web-classification --output dist --format docker
```
Note that all dependent connections must be created before building as docker.
## Deploy with Azure App Service
The two scripts will do the following things:
1. Create a resource group if not exists.
2. Build and push the image to docker registry.
3. Create an app service plan with the given sku.
4. Create an app with specified name, set the deployment container image to the pushed docker image.
5. Set up the environment variables for the app.
::::{tab-set}
:::{tab-item} Bash
Example command to use bash script:
```shell
bash deploy.sh --path dist -i <image_tag> --name my-app-23d8m -r <docker registry> -g <resource_group>
```
See the full parameters by `bash deploy.sh -h`.
:::
:::{tab-item} PowerShell
Example command to use powershell script:
```powershell
.\deploy.ps1 -Path dist -i <image_tag> -n my-app-23d8m -r <docker registry> -g <resource_group>
```
See the full parameters by `.\deploy.ps1 -h`.
:::
::::
Note that the `name` will produce a unique FQDN as AppName.azurewebsites.net.
## View and test the web app
The web app can be found via [azure portal](https://portal.azure.com/)
After the app created, you will need to go to https://portal.azure.com/ find the app and set up the environment variables
at (Settings>Configuration) or (Settings>Environment variables), then restart the app.
The app can be tested by sending a POST request to the endpoint or browse the test page.
::::{tab-set}
:::{tab-item} Bash
```bash
curl https://<name>.azurewebsites.net/score --data '{"url":"https://play.google.com/store/apps/details?id=com.twitter.android"}' -X POST -H "Content-Type: application/json"
```
:::
:::{tab-item} PowerShell
```powershell
Invoke-WebRequest -URI https://<name>.azurewebsites.net/score -Body '{"url":"https://play.google.com/store/apps/details?id=com.twitter.android"}' -Method POST -ContentType "application/json"
```
:::
:::{tab-item} Test Page
Browse the app at Overview and see the test page:
:::
::::
Tips:
- Reach deployment logs at (Deployment>Deployment Central) and app logs at (Monitoring>Log stream).
- Reach advanced deployment tools at (Development Tools>Advanced Tools).
- Reach more details about app service at [Azure App Service](https://learn.microsoft.com/azure/app-service/).
## Next steps
- Try the example [here](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/flow-deploy/azure-app-service). | promptflow/docs/cloud/azureai/deploy-to-azure-appservice.md/0 | {
"file_path": "promptflow/docs/cloud/azureai/deploy-to-azure-appservice.md",
"repo_id": "promptflow",
"token_count": 1049
} | 0 |
# Replay end-to-end tests
* This document introduces replay tests for those located in [sdk_cli_azure_test](../../src/promptflow/tests/sdk_cli_azure_test/e2etests/) and [sdk_cli_test](../../src/promptflow/tests/sdk_cli_test/e2etests/).
* The primary purpose of replay tests is to avoid the need for credentials, Azure workspaces, OpenAI tokens, and to directly test prompt flow behavior.
* Although there are different techniques behind recording/replaying, there are some common steps to run the tests in replay mode.
* The key handle of replay tests is the environment variable `PROMPT_FLOW_TEST_MODE`.
## How to run tests in replay mode
After cloning the full repo and setting up the proper test environment following [dev_setup.md](./dev_setup.md), run the following command in the root directory of the repo:
1. If you have changed/affected tests in __sdk_cli_test__ : Copy or rename the file [dev-connections.json.example](../../src/promptflow/dev-connections.json.example) to `connections.json` in the same folder.
2. In your Python environment, set the environment variable `PROMPT_FLOW_TEST_MODE` to `'replay'` and run the test(s).
These tests should work properly without any real connection settings.
## Test modes
There are 3 representative values of the environment variable `PROMPT_FLOW_TEST_MODE`
- `live`: Tests run against the real backend, which is the way traditional end-to-end tests do.
- `record`: Tests run against the real backend, and network traffic will be sanitized (filter sensitive and unnecessary requests/responses) and recorded to local files (recordings).
- `replay`: There is no real network traffic between SDK/CLI and the backend, tests run against local recordings.
## Update test recordings
To record a test, don’t forget to clone the full repo and set up the proper test environment following [dev_setup.md](./dev_setup.md):
1. Prepare some data.
* If you have changed/affected tests in __sdk_cli_test__: Copy or rename the file [dev-connections.json.example](../../src/promptflow/dev-connections.json.example) to `connections.json` in the same folder.
* If you have changed/affected tests in __sdk_cli_azure_test__: prepare your Azure ML workspace, make sure your Azure CLI logged in, and set the environment variable `PROMPT_FLOW_SUBSCRIPTION_ID`, `PROMPT_FLOW_RESOURCE_GROUP_NAME`, `PROMPT_FLOW_WORKSPACE_NAME` and `PROMPT_FLOW_RUNTIME_NAME` (if needed) pointing to your workspace.
2. Record the test.
* Specify the environment variable `PROMPT_FLOW_TEST_MODE` to `'record'`. If you have a `.env` file, we recommend specifying it there. Here is an example [.env file](../../src/promptflow/.env.example). Then, just run the test that you want to record.
3. Once the test completed.
* If you have changed/affected tests in __sdk_cli_azure_test__: There should be one new YAML file located in `src/promptflow/tests/test_configs/recordings/`, containing the network traffic of the test.
* If you have changed/affected tests in __sdk_cli_test__: There may be changes in the folder `src/promptflow/tests/test_configs/node_recordings/`. Don’t worry if there are no changes, because similar LLM calls may have been recorded before.
## Techniques behind replay test
### Sdk_cli_azure_test
End-to-end tests for pfazure aim to test the behavior of the PromptFlow SDK/CLI as it interacts with the service. This process can be time-consuming, error-prone, and require credentials (which are unavailable to pull requests from forked repositories); all of these go against our intention for a smooth development experience.
Therefore, we introduce replay tests, which leverage [VCR.py](https://pypi.org/project/vcrpy/) to record all required network traffic to local files and replay during tests. In this way, we avoid the need for credentials, speed up, and stabilize the test process.
### Sdk_cli_test
sdk_cli_test often doesn’t use a real backend. It will directly invokes LLM calls from localhost. Thus the key target of replay tests is to avoid the need for OpenAI tokens. If you have OpenAI / Azure OpenAI tokens yourself, you can try recording the tests. Record Storage will not record your own LLM connection, but only the inputs and outputs of the LLM calls.
There are also limitations. Currently, recorded calls are:
* AzureOpenAI calls
* OpenAI calls
* tool name "fetch_text_content_from_url" and tool name "my_python_tool" | promptflow/docs/dev/replay-e2e-test.md/0 | {
"file_path": "promptflow/docs/dev/replay-e2e-test.md",
"repo_id": "promptflow",
"token_count": 1217
} | 1 |
# Creating a Dynamic List Tool Input
Tool input options can be generated on the fly using a dynamic list. Instead of having predefined static options, the tool author defines a request function that queries backends like APIs to retrieve real-time options. This enables flexible integration with various data sources to populate dynamic options. For instance, the function could call a storage API to list current files. Rather than a hardcoded list, the user sees up-to-date options when running the tool.
## Prerequisites
- Please make sure you have the latest version of [Prompt flow for VS Code](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow) installed (v1.3.1+).
- Please install promptflow package and ensure that its version is 1.0.0 or later.
```
pip install promptflow>=1.0.0
```
## Create a tool input with dynamic listing
### Create a list function
To enable dynamic listing, the tool author defines a request function with the following structure:
- Type: Regular Python function, can be in tool file or separate file
- Input: Accepts parameters needed to fetch options
- Output: Returns a list of option objects as `List[Dict[str, Union[str, int, float, list, Dict]]]`:
- Required key:
- `value`: Internal option value passed to tool function
- Optional keys:
- `display_value`: Display text shown in dropdown (defaults to `value`)
- `hyperlink`: URL to open when option clicked
- `description`: Tooltip text on hover
This function can make backend calls to retrieve the latest options, returning them in a standardized dictionary structure for the dynamic list. The required and optional keys enable configuring how each option appears and behaves in the tool input dropdown. See [my_list_func](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_dynamic_list_input.py) as an example.
```python
def my_list_func(prefix: str = "", size: int = 10, **kwargs) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
"""This is a dummy function to generate a list of items.
:param prefix: prefix to add to each item.
:param size: number of items to generate.
:param kwargs: other parameters.
:return: a list of items. Each item is a dict with the following keys:
- value: for backend use. Required.
- display_value: for UI display. Optional.
- hyperlink: external link. Optional.
- description: information icon tip. Optional.
"""
import random
words = ["apple", "banana", "cherry", "date", "elderberry", "fig", "grape", "honeydew", "kiwi", "lemon"]
result = []
for i in range(size):
random_word = f"{random.choice(words)}{i}"
cur_item = {
"value": random_word,
"display_value": f"{prefix}_{random_word}",
"hyperlink": f'https://www.bing.com/search?q={random_word}',
"description": f"this is {i} item",
}
result.append(cur_item)
return result
```
### Configure a tool input with the list function
In `inputs` section of tool YAML, add following properties to the input that you want to make dynamic:
- `dynamic_list`:
- `func_path`: Path to the list function (module_name.function_name).
- `func_kwargs`: Parameters to pass to the function, can reference other input values.
- `allow_manual_entry`: Allow user to enter input value manually. Default to false.
- `is_multi_select`: Allow user to select multiple values. Default to false.
See [tool_with_dynamic_list_input.yaml](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_dynamic_list_input.yaml) as an example.
```yaml
my_tool_package.tools.tool_with_dynamic_list_input.my_tool:
function: my_tool
inputs:
input_text:
type:
- list
dynamic_list:
func_path: my_tool_package.tools.tool_with_dynamic_list_input.my_list_func
func_kwargs:
- name: prefix # argument name to be passed to the function
type:
- string
# if optional is not specified, default to false.
# this is for UX pre-validaton. If optional is false, but no input. UX can throw error in advanced.
optional: true
reference: ${inputs.input_prefix} # dynamic reference to another input parameter
- name: size # another argument name to be passed to the function
type:
- int
optional: true
default: 10
# enum and dynamic list may need below setting.
# allow user to enter input value manually, default false.
allow_manual_entry: true
# allow user to select multiple values, default false.
is_multi_select: true
# used to filter
input_prefix:
type:
- string
module: my_tool_package.tools.tool_with_dynamic_list_input
name: My Tool with Dynamic List Input
description: This is my tool with dynamic list input
type: python
```
## Use the tool in VS Code
Once you package and share your tool, you can use it in VS Code per the [tool package guide](create-and-use-tool-package.md#use-your-tool-from-vscode-extension). You could try `my-tools-package` for a quick test.
```sh
pip install my-tools-package>=0.0.8
```
> Note: If your dynamic list function call Azure APIs, you need to login to Azure and set default workspace. Otherwise, the tool input will be empty and you can't select anything. See [FAQs](#im-a-tool-author-and-want-to-dynamically-list-azure-resources-in-my-tool-input-what-should-i-pay-attention-to) for more details.
## FAQs
### I'm a tool author, and want to dynamically list Azure resources in my tool input. What should I pay attention to?
1. Clarify azure workspace triple "subscription_id", "resource_group_name", "workspace_name" in the list function signature. System helps append workspace triple to function input parameters if they are in function signature. See [list_endpoint_names](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_dynamic_list_input.py) as an example.
```python
def list_endpoint_names(subscription_id, resource_group_name, workspace_name, prefix: str = "") -> List[Dict[str, str]]:
"""This is an example to show how to get Azure ML resource in tool input list function.
:param subscription_id: Azure subscription id.
:param resource_group_name: Azure resource group name.
:param workspace_name: Azure ML workspace name.
:param prefix: prefix to add to each item.
"""
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
credential.get_token("https://management.azure.com/.default")
ml_client = MLClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name)
result = []
for ep in ml_client.online_endpoints.list():
hyperlink = (
f"https://ml.azure.com/endpoints/realtime/{ep.name}/detail?wsid=/subscriptions/"
f"{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft."
f"MachineLearningServices/workspaces/{workspace_name}"
)
cur_item = {
"value": ep.name,
"display_value": f"{prefix}_{ep.name}",
# external link to jump to the endpoint page.
"hyperlink": hyperlink,
"description": f"this is endpoint: {ep.name}",
}
result.append(cur_item)
return result
```
2. Note in your tool doc that if your tool user want to use the tool at local, they should login to azure and set ws triple as default. Or the tool input will be empty and user can't select anything.
```sh
az login
az account set --subscription <subscription_id>
az configure --defaults group=<resource_group_name> workspace=<workspace_name>
```
Install azure dependencies.
```sh
pip install azure-ai-ml
```
```sh
pip install my-tools-package[azure]>=0.0.8
```
### I'm a tool user, and cannot see any options in dynamic list tool input. What should I do?
If you are unable to see any options in a dynamic list tool input, you may see an error message below the input field stating:
"Unable to display list of items due to XXX. Please contact the tool author/support team for troubleshooting assistance."
If this occurs, follow these troubleshooting steps:
- Note the exact error message shown. This provides details on why the dynamic list failed to populate.
- Contact the tool author/support team and report the issue. Provide the error message so they can investigate the root cause.
| promptflow/docs/how-to-guides/develop-a-tool/create-dynamic-list-tool-input.md/0 | {
"file_path": "promptflow/docs/how-to-guides/develop-a-tool/create-dynamic-list-tool-input.md",
"repo_id": "promptflow",
"token_count": 3003
} | 2 |
# Set global configs
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](faq.md#stable-vs-experimental).
:::
Promptflow supports setting global configs to avoid passing the same parameters to each command. The global configs are stored in a yaml file, which is located at `~/.promptflow/pf.yaml` by default.
The config file is shared between promptflow extension and sdk/cli. Promptflow extension controls each config through UI, so the following sections will show how to set global configs using promptflow cli.
## Set config
```shell
pf config set <config_name>=<config_value>
```
For example:
```shell
pf config set connection.provider="azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>"
```
## Show config
The following command will get all configs and show them as json format:
```shell
pf config show
```
After running the above config set command, show command will return the following result:
```json
{
"connection": {
"provider": "azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>"
}
}
```
## Supported configs
### connection.provider
The connection provider, default to "local". There are 3 possible provider values.
#### local
Set connection provider to local with `connection.provider=local`.
Connections will be saved locally. `PFClient`(or `pf connection` commands) will [manage local connections](manage-connections.md). Consequently, the flow will be executed using these local connections.
#### full azure machine learning workspace resource id
Set connection provider to a specific workspace with:
```
connection.provider=azureml://subscriptions/<your-subscription>/resourceGroups/<your-resourcegroup>/providers/Microsoft.MachineLearningServices/workspaces/<your-workspace>
```
When `get` or `list` connections, `PFClient`(or `pf connection` commands) will return workspace connections, and flow will be executed using these workspace connections.
_Secrets for workspace connection will not be shown by those commands, which means you may see empty dict `{}` for custom connections._
:::{note}
Command `create`, `update` and `delete` are not supported for workspace connections, please manage it in workspace portal, az ml cli or AzureML SDK.
:::
#### azureml
In addition to the full resource id, you can designate the connection provider as "azureml" with `connection.provider=azureml`. In this case,
promptflow will attempt to retrieve the workspace configuration by searching `.azureml/config.json` from the current directory, then progressively from its parent folders. So it's possible to set the workspace configuration for different flow by placing the config file in the project folder.
The expected format of the config file is as follows:
```json
{
"workspace_name": "<your-workspace-name>",
"resource_group": "<your-resource-group>",
"subscription_id": "<your-subscription-id>"
}
```
> 💡 Tips
> In addition to the CLI command line setting approach, we also support setting this connection provider through the VS Code extension UI. [Click here to learn more](../cloud/azureai/consume-connections-from-azure-ai.md). | promptflow/docs/how-to-guides/set-global-configs.md/0 | {
"file_path": "promptflow/docs/how-to-guides/set-global-configs.md",
"repo_id": "promptflow",
"token_count": 881
} | 3 |
# LLM
## Introduction
Prompt flow LLM tool enables you to leverage widely used large language models like [OpenAI](https://platform.openai.com/) or [Azure OpenAI (AOAI)](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/overview) for natural language processing.
Prompt flow provides a few different LLM APIs:
- **[Completion](https://platform.openai.com/docs/api-reference/completions)**: OpenAI's completion models generate text based on provided prompts.
- **[Chat](https://platform.openai.com/docs/api-reference/chat)**: OpenAI's chat models facilitate interactive conversations with text-based inputs and responses.
> [!NOTE]
> We now remove the `embedding` option from LLM tool api setting. You can use embedding api with [Embedding tool](https://github.com/microsoft/promptflow/blob/main/docs/reference/tools-reference/embedding_tool.md).
## Prerequisite
Create OpenAI resources:
- **OpenAI**
Sign up account [OpenAI website](https://openai.com/)
Login and [Find personal API key](https://platform.openai.com/account/api-keys)
- **Azure OpenAI (AOAI)**
Create Azure OpenAI resources with [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal)
## **Connections**
Setup connections to provisioned resources in prompt flow.
| Type | Name | API KEY | API Type | API Version |
|-------------|----------|----------|----------|-------------|
| OpenAI | Required | Required | - | - |
| AzureOpenAI | Required | Required | Required | Required |
## Inputs
### Text Completion
| Name | Type | Description | Required |
|------------------------|-------------|-----------------------------------------------------------------------------------------|----------|
| prompt | string | text prompt that the language model will complete | Yes |
| model, deployment_name | string | the language model to use | Yes |
| max\_tokens | integer | the maximum number of tokens to generate in the completion. Default is 16. | No |
| temperature | float | the randomness of the generated text. Default is 1. | No |
| stop | list | the stopping sequence for the generated text. Default is null. | No |
| suffix | string | text appended to the end of the completion | No |
| top_p | float | the probability of using the top choice from the generated tokens. Default is 1. | No |
| logprobs | integer | the number of log probabilities to generate. Default is null. | No |
| echo | boolean | value that indicates whether to echo back the prompt in the response. Default is false. | No |
| presence\_penalty | float | value that controls the model's behavior with regards to repeating phrases. Default is 0. | No |
| frequency\_penalty | float | value that controls the model's behavior with regards to generating rare phrases. Default is 0. | No |
| best\_of | integer | the number of best completions to generate. Default is 1. | No |
| logit\_bias | dictionary | the logit bias for the language model. Default is empty dictionary. | No |
### Chat
| Name | Type | Description | Required |
|------------------------|-------------|------------------------------------------------------------------------------------------------|----------|
| prompt | string | text prompt that the language model will response | Yes |
| model, deployment_name | string | the language model to use | Yes |
| max\_tokens | integer | the maximum number of tokens to generate in the response. Default is inf. | No |
| temperature | float | the randomness of the generated text. Default is 1. | No |
| stop | list | the stopping sequence for the generated text. Default is null. | No |
| top_p | float | the probability of using the top choice from the generated tokens. Default is 1. | No |
| presence\_penalty | float | value that controls the model's behavior with regards to repeating phrases. Default is 0. | No |
| frequency\_penalty | float | value that controls the model's behavior with regards to generating rare phrases. Default is 0.| No |
| logit\_bias | dictionary | the logit bias for the language model. Default is empty dictionary. | No |
| function\_call | object | value that controls which function is called by the model. Default is null. | No |
| functions | list | a list of functions the model may generate JSON inputs for. Default is null. | No |
| response_format | object | an object specifying the format that the model must output. Default is null. | No |
## Outputs
| API | Return Type | Description |
|------------|-------------|------------------------------------------|
| Completion | string | The text of one predicted completion |
| Chat | string | The text of one response of conversation |
## How to use LLM Tool?
1. Setup and select the connections to OpenAI resources
2. Configure LLM model api and its parameters
3. Prepare the Prompt with [guidance](./prompt-tool.md#how-to-write-prompt).
| promptflow/docs/reference/tools-reference/llm-tool.md/0 | {
"file_path": "promptflow/docs/reference/tools-reference/llm-tool.md",
"repo_id": "promptflow",
"token_count": 2760
} | 4 |
import json
try:
from openai import AzureOpenAI as AzureOpenAIClient
except Exception:
raise Exception(
"Please upgrade your OpenAI package to version 1.0.0 or later using the command: pip install --upgrade openai.")
from promptflow.tools.common import render_jinja_template, handle_openai_error, parse_chat, to_bool, \
validate_functions, process_function_call, post_process_chat_api_response, normalize_connection_config
# Avoid circular dependencies: Use import 'from promptflow._internal' instead of 'from promptflow'
# since the code here is in promptflow namespace as well
from promptflow._internal import enable_cache, ToolProvider, tool, register_apis
from promptflow.connections import AzureOpenAIConnection
from promptflow.contracts.types import PromptTemplate
class AzureOpenAI(ToolProvider):
def __init__(self, connection: AzureOpenAIConnection):
super().__init__()
self.connection = connection
self._connection_dict = normalize_connection_config(self.connection)
self._client = AzureOpenAIClient(**self._connection_dict)
def calculate_cache_string_for_completion(
self,
**kwargs,
) -> str:
d = dict(self.connection)
d.pop("api_key")
d.update({**kwargs})
return json.dumps(d)
@tool
@handle_openai_error()
@enable_cache(calculate_cache_string_for_completion)
def completion(
self,
prompt: PromptTemplate,
# for AOAI, deployment name is customized by user, not model name.
deployment_name: str,
suffix: str = None,
max_tokens: int = 16,
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
logprobs: int = None,
echo: bool = False,
stop: list = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
**kwargs,
) -> str:
prompt = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs)
# TODO: remove below type conversion after client can pass json rather than string.
echo = to_bool(echo)
stream = to_bool(stream)
response = self._client.completions.create(
prompt=prompt,
model=deployment_name,
# empty string suffix should be treated as None.
suffix=suffix if suffix else None,
max_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
n=int(n),
stream=stream,
# TODO: remove below type conversion after client pass json rather than string.
# empty string will go to else branch, but original api cannot accept empty
# string, must be None.
logprobs=int(logprobs) if logprobs else None,
echo=echo,
# fix bug "[] is not valid under any of the given schemas-'stop'"
stop=stop if stop else None,
presence_penalty=float(presence_penalty),
frequency_penalty=float(frequency_penalty),
best_of=int(best_of),
# Logit bias must be a dict if we passed it to openai api.
logit_bias=logit_bias if logit_bias else {},
user=user,
extra_headers={"ms-azure-ai-promptflow-called-from": "aoai-tool"})
if stream:
def generator():
for chunk in response:
if chunk.choices:
yield chunk.choices[0].text if hasattr(chunk.choices[0], 'text') and \
chunk.choices[0].text is not None else ""
# We must return the generator object, not using yield directly here.
# Otherwise, the function itself will become a generator, despite whether stream is True or False.
return generator()
else:
# get first element because prompt is single.
return response.choices[0].text
@tool
@handle_openai_error()
def chat(
self,
prompt: PromptTemplate,
# for AOAI, deployment name is customized by user, not model name.
deployment_name: str,
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
# stream is a hidden to the end user, it is only supposed to be set by the executor.
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
logit_bias: dict = {},
user: str = "",
# function_call can be of type str or dict.
function_call: object = None,
functions: list = None,
response_format: object = None,
**kwargs,
) -> [str, dict]:
# keep_trailing_newline=True is to keep the last \n in the prompt to avoid converting "user:\t\n" to "user:".
chat_str = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs)
messages = parse_chat(chat_str)
# TODO: remove below type conversion after client can pass json rather than string.
stream = to_bool(stream)
params = {
"model": deployment_name,
"messages": messages,
"temperature": float(temperature),
"top_p": float(top_p),
"n": int(n),
"stream": stream,
"stop": stop if stop else None,
"max_tokens": int(max_tokens) if max_tokens is not None and str(max_tokens).lower() != "inf" else None,
"presence_penalty": float(presence_penalty),
"frequency_penalty": float(frequency_penalty),
"logit_bias": logit_bias,
"user": user,
"response_format": response_format,
"extra_headers": {"ms-azure-ai-promptflow-called-from": "aoai-tool"}
}
if functions is not None:
validate_functions(functions)
params["functions"] = functions
params["function_call"] = process_function_call(function_call)
completion = self._client.chat.completions.create(**params)
return post_process_chat_api_response(completion, stream, functions)
register_apis(AzureOpenAI)
@tool
def completion(
connection: AzureOpenAIConnection,
prompt: PromptTemplate,
deployment_name: str,
suffix: str = None,
max_tokens: int = 16,
temperature: float = 1.0,
top_p: float = 1,
n: int = 1,
stream: bool = False,
logprobs: int = None,
echo: bool = False,
stop: list = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
**kwargs,
) -> str:
return AzureOpenAI(connection).completion(
prompt=prompt,
deployment_name=deployment_name,
suffix=suffix,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream,
logprobs=logprobs,
echo=echo,
stop=stop if stop else None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
best_of=best_of,
logit_bias=logit_bias,
user=user,
**kwargs,
)
@tool
def chat(
connection: AzureOpenAIConnection,
prompt: PromptTemplate,
deployment_name: str,
temperature: float = 1,
top_p: float = 1,
n: int = 1,
stream: bool = False,
stop: list = None,
max_tokens: int = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
logit_bias: dict = {},
user: str = "",
function_call: object = None,
functions: list = None,
response_format: object = None,
**kwargs,
) -> str:
# chat model is not available in azure openai, so need to set the environment variable.
return AzureOpenAI(connection).chat(
prompt=prompt,
deployment_name=deployment_name,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream,
stop=stop if stop else None,
max_tokens=max_tokens,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
user=user,
function_call=function_call,
functions=functions,
response_format=response_format,
**kwargs,
)
| promptflow/src/promptflow-tools/promptflow/tools/aoai.py/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/aoai.py",
"repo_id": "promptflow",
"token_count": 3768
} | 5 |
promptflow.tools.openai_gpt4v.OpenAI.chat:
name: OpenAI GPT-4V
description: Use OpenAI GPT-4V to leverage vision ability.
type: custom_llm
module: promptflow.tools.openai_gpt4v
class_name: OpenAI
function: chat
tool_state: preview
icon:
light: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAx0lEQVR4nJWSwQ2CQBBFX0jAcjgqXUgPJNiIsQQrIVCIFy8GC6ABDcGDX7Mus9n1Xz7zZ+fPsLPwH4bUg0dD2wMPcbR48Uxq4AKU4iSTDwZ1LhWXipN/B3V0J6hjBTvgLHZNonewBXrgDpzEvXSIjN0BE3AACmmF4kl5F6tNzcCoLpW0SvGovFvsb4oZ2AANcAOu4ka6axCcINN3rg654sww+CYsPD0OwjcozFNh/Qcd78tqVbCIW+n+Fky472Bh/Q6SYb1EEy8tDzd+9IsVPAAAAABJRU5ErkJggg==
dark: data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA2ElEQVR4nJXSzW3CQBAF4DUSTjk+Al1AD0ikESslpBIEheRALhEpgAYSWV8OGUublf/yLuP3PPNmdndS+gdwXZrYDmh7fGE/W+wXbaYd8IYm4rxJPnZ0boI3wZcdJxs/n+AwV7DFK7aFyfQdYIMLPvES8YJNf5yp4jMeeEYdWh38gXOR35YGHe5xabvQdsHv6PLi8qV6gycc8YH3iMfQu6Lh4ASr+F5Hh3XwVWnQYzUkVlX1nccplAb1SN6Y/sfgmlK64VS8wimldIv/0yj2QLkHizG0iWP4AVAfQ34DVQONAAAAAElFTkSuQmCC
default_prompt: |
# system:
As an AI assistant, your task involves interpreting images and responding to questions about the image.
Remember to provide accurate answers based on the information present in the image.
# user:
Can you tell me what the image depicts?
inputs:
connection:
type:
- OpenAIConnection
model:
enum:
- gpt-4-vision-preview
allow_manual_entry: true
type:
- string
temperature:
default: 1
type:
- double
top_p:
default: 1
type:
- double
max_tokens:
default: 512
type:
- int
stop:
default: ""
type:
- list
presence_penalty:
default: 0
type:
- double
frequency_penalty:
default: 0
type:
- double | promptflow/src/promptflow-tools/promptflow/tools/yamls/openai_gpt4v.yaml/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/yamls/openai_gpt4v.yaml",
"repo_id": "promptflow",
"token_count": 1040
} | 6 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
from promptflow._sdk._constants import PROMPT_FLOW_DIR_NAME, PROMPT_FLOW_RUNS_DIR_NAME, CLIListOutputFormat, FlowType
# TODO: avoid azure dependency here
MAX_LIST_CLI_RESULTS = 50
class AppendToDictAction(argparse._AppendAction): # pylint: disable=protected-access
def __call__(self, parser, namespace, values, option_string=None):
action = self.get_action(values, option_string)
super(AppendToDictAction, self).__call__(parser, namespace, action, option_string)
def get_action(self, values, option_string): # pylint: disable=no-self-use
from promptflow._sdk._utils import strip_quotation
kwargs = {}
for item in values:
try:
key, value = strip_quotation(item).split("=", 1)
kwargs[key] = strip_quotation(value)
except ValueError:
raise Exception("Usage error: {} KEY=VALUE [KEY=VALUE ...]".format(option_string))
return kwargs
class FlowTestInputAction(AppendToDictAction): # pylint: disable=protected-access
def get_action(self, values, option_string): # pylint: disable=no-self-use
if len(values) == 1 and "=" not in values[0]:
from promptflow._utils.load_data import load_data
if not values[0].endswith(".jsonl"):
raise ValueError("Only support jsonl file as input.")
return load_data(local_path=values[0])[0]
else:
return super().get_action(values, option_string)
def add_param_yes(parser):
parser.add_argument(
"-y",
"--yes",
"--assume-yes",
action="store_true",
help="Automatic yes to all prompts; assume 'yes' as answer to all prompts and run non-interactively.",
)
def add_param_ua(parser):
# suppress user agent for now since it's only used in vscode extension
parser.add_argument("--user-agent", help=argparse.SUPPRESS)
def add_param_flow_display_name(parser):
parser.add_argument("--flow", type=str, required=True, help="The flow name to create.")
def add_param_entry(parser):
parser.add_argument("--entry", type=str, help="The entry file.")
def add_param_function(parser):
parser.add_argument("--function", type=str, help="The function name in entry file.")
def add_param_prompt_template(parser):
parser.add_argument(
"--prompt-template", action=AppendToDictAction, help="The prompt template parameter and assignment.", nargs="+"
)
def add_param_set(parser):
parser.add_argument(
"--set",
dest="params_override",
action=AppendToDictAction,
help="Update an object by specifying a property path and value to set. Example: --set "
"property1.property2=<value>.",
nargs="+",
)
def add_param_set_positional(parser):
parser.add_argument(
"params_override",
action=AppendToDictAction,
help="Set an object by specifying a property path and value to set. Example: set "
"property1.property2=<value>.",
nargs="+",
)
def add_param_environment_variables(parser):
parser.add_argument(
"--environment-variables",
action=AppendToDictAction,
help="Environment variables to set by specifying a property path and value. Example: --environment-variable "
"key1='${my_connection.api_key}' key2='value2'. The value reference to connection keys will be resolved "
"to the actual value, and all environment variables specified will be set into os.environ.",
nargs="+",
)
def add_param_connections(parser):
parser.add_argument(
"--connections",
action=AppendToDictAction,
help="Overwrite node level connections with provided value. Example: --connections "
"node1.connection=test_llm_connection node1.deployment_name=gpt-35-turbo",
nargs="+",
)
def add_param_columns_mapping(parser):
parser.add_argument(
"--column-mapping",
action=AppendToDictAction,
help="Inputs column mapping, use ${data.xx} to refer to data columns, "
"use ${run.inputs.xx} to refer to referenced run's data columns. "
"and use ${run.outputs.xx} to refer to referenced run's output columns."
"Example: --column-mapping data1='${data.data1}' data2='${run.inputs.data2}' data3='${run.outputs.data3}'",
nargs="+",
)
def add_param_set_tool_extra_info(parser):
parser.add_argument(
"--set",
dest="extra_info",
action=AppendToDictAction,
help="Set extra information about the tool. Example: --set <key>=<value>.",
nargs="+",
)
def add_param_inputs(parser):
parser.add_argument(
"--inputs",
action=FlowTestInputAction,
help="Input datas of file for the flow. Example: --inputs data1=data1_val data2=data2_val",
nargs="+",
)
def add_param_env(parser):
parser.add_argument(
"--env",
type=str,
default=None,
help="The dotenv file path containing the environment variables to be used in the flow.",
)
def add_param_output(parser):
parser.add_argument(
"-o",
"--output",
type=str,
help=(
f"The output directory to store the results. "
f"Default to be ~/{PROMPT_FLOW_DIR_NAME}/{PROMPT_FLOW_RUNS_DIR_NAME} if not specified."
),
)
def add_param_overwrite(parser):
parser.add_argument("--overwrite", action="store_true", help="Overwrite the existing results.")
def add_param_source(parser):
parser.add_argument("--source", type=str, required=True, help="The flow or run source to be used.")
def add_param_run_name(parser):
parser.add_argument("-n", "--name", required=True, type=str, help="Name of the run.")
def add_param_connection_name(parser):
parser.add_argument("-n", "--name", type=str, help="Name of the connection to create.")
def add_param_max_results(parser):
parser.add_argument( # noqa: E731
"-r",
"--max-results",
dest="max_results",
type=int,
default=MAX_LIST_CLI_RESULTS,
help=f"Max number of results to return. Default is {MAX_LIST_CLI_RESULTS}.",
)
def add_param_all_results(parser):
parser.add_argument( # noqa: E731
"--all-results",
action="store_true",
dest="all_results",
default=False,
help="Returns all results. Default to False.",
)
def add_param_variant(parser):
parser.add_argument(
"--variant",
"-v",
type=str,
help="The variant to be used in flow, will use default variant if not specified.",
)
def add_parser_build(subparsers, entity_name: str):
add_param_build_output = lambda parser: parser.add_argument( # noqa: E731
"--output", "-o", required=True, type=str, help="The destination folder path."
)
add_param_format = lambda parser: parser.add_argument( # noqa: E731
"--format", "-f", type=str, help="The format to build with.", choices=["docker", "executable"]
)
# this is a hidden parameter for `mldesigner compile` command
add_param_flow_only = lambda parser: parser.add_argument( # noqa: E731
"--flow-only",
action="store_true",
help=argparse.SUPPRESS,
)
add_params = [
add_param_source,
add_param_build_output,
add_param_format,
add_param_flow_only,
add_param_variant,
] + base_params
from promptflow._cli._utils import activate_action
description = f"Build a {entity_name} for further sharing or deployment."
activate_action(
name="build",
description=description,
epilog=f"pf {entity_name} build --source <source> --output <output> --format " f"docker|package",
add_params=add_params,
subparsers=subparsers,
action_param_name="sub_action",
help_message=description,
)
def add_param_debug(parser):
parser.add_argument(
"-d",
"--debug",
action="store_true",
help="The flag to turn on debug mode for cli.",
)
def add_param_verbose(parser):
parser.add_argument(
"--verbose",
action="store_true",
help="Increase logging verbosity. Use --debug for full debug logs.",
)
def add_param_config(parser):
parser.add_argument(
"--config",
nargs="+",
action=AppendToDictAction,
help=argparse.SUPPRESS,
)
logging_params = [add_param_verbose, add_param_debug]
base_params = logging_params + [
add_param_ua,
]
def add_param_archived_only(parser):
parser.add_argument(
"--archived-only",
action="store_true",
help="Only list archived records.",
)
def add_param_include_archived(parser):
parser.add_argument(
"--include-archived",
action="store_true",
help="List both archived records and active records.",
)
def add_param_output_format(parser):
parser.add_argument(
"-o",
"--output",
type=str,
default=CLIListOutputFormat.JSON,
help="Output format, accepted values are 'json' and 'table'. Default is 'json'.",
choices=[CLIListOutputFormat.TABLE, CLIListOutputFormat.JSON],
)
def add_param_include_others(parser):
parser.add_argument(
"--include-others",
action="store_true",
help="Get records that are owned by all users.",
)
def add_param_flow_type(parser):
parser.add_argument(
"--type",
type=str,
help=(
f"The type of the flow. Available values are {FlowType.get_all_values()}. "
f"Default to be None, which means all types included."
),
)
def add_param_flow_name(parser):
parser.add_argument(
"-n",
"--name",
type=str,
required=True,
help="The name of the flow.",
)
| promptflow/src/promptflow/promptflow/_cli/_params.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_params.py",
"repo_id": "promptflow",
"token_count": 4120
} | 7 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._cli._utils import get_client_for_cli
from promptflow.azure import PFClient
def _get_azure_pf_client(subscription_id, resource_group, workspace_name, debug=False):
ml_client = get_client_for_cli(
subscription_id=subscription_id, resource_group_name=resource_group, workspace_name=workspace_name
)
client = PFClient(ml_client=ml_client, logging_enable=debug)
return client
| promptflow/src/promptflow/promptflow/_cli/_pf_azure/_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf_azure/_utils.py",
"repo_id": "promptflow",
"token_count": 165
} | 8 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from typing import List
from promptflow import log_metric, tool
@tool
def aggregate(processed_results: List[str]):
"""
This tool aggregates the processed result of all lines and calculate the accuracy. Then log metric for the accuracy.
:param processed_results: List of the output of line_process node.
"""
# Add your aggregation logic here
# Aggregate the results of all lines and calculate the accuracy
aggregated_result = round((processed_results.count("Correct") / len(processed_results)), 2)
# Log metric the aggregate result
log_metric(key="accuracy", value=aggregated_result)
return aggregated_result
| promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/aggregate.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/aggregate.py",
"repo_id": "promptflow",
"token_count": 212
} | 9 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import functools
import inspect
import json
import logging
import uuid
from collections.abc import Iterator
from contextvars import ContextVar
from datetime import datetime
from threading import Lock
from typing import Callable, Dict, List, Optional
import opentelemetry.trace as otel_trace
from opentelemetry.trace.status import StatusCode
from promptflow._core.generator_proxy import GeneratorProxy, generate_from_proxy
from promptflow._core.operation_context import OperationContext
from promptflow._utils.dataclass_serializer import serialize
from promptflow._utils.multimedia_utils import default_json_encoder
from promptflow._utils.tool_utils import get_inputs_for_prompt_template, get_prompt_param_name_from_func
from promptflow.contracts.tool import ConnectionType
from promptflow.contracts.trace import Trace, TraceType
from .thread_local_singleton import ThreadLocalSingleton
open_telemetry_tracer = otel_trace.get_tracer("promptflow")
class Tracer(ThreadLocalSingleton):
CONTEXT_VAR_NAME = "Tracer"
context_var = ContextVar(CONTEXT_VAR_NAME, default=None)
def __init__(self, run_id, node_name: Optional[str] = None):
self._run_id = run_id
self._node_name = node_name
self._traces = []
self._current_trace_id = ContextVar("current_trace_id", default="")
self._id_to_trace: Dict[str, Trace] = {}
@classmethod
def start_tracing(cls, run_id, node_name: Optional[str] = None):
current_run_id = cls.current_run_id()
if current_run_id is not None:
msg = f"Try to start tracing for run {run_id} but {current_run_id} is already active."
logging.warning(msg)
return
tracer = cls(run_id, node_name)
tracer._activate_in_context()
@classmethod
def current_run_id(cls):
tracer = cls.active_instance()
if not tracer:
return None
return tracer._run_id
@classmethod
def end_tracing(cls, run_id: Optional[str] = None):
tracer = cls.active_instance()
if not tracer:
return []
if run_id is not None and tracer._run_id != run_id:
return []
tracer._deactivate_in_context()
return tracer.to_json()
@classmethod
def push(cls, trace: Trace):
obj = cls.active_instance()
if not obj:
return
obj._push(trace)
@staticmethod
def to_serializable(obj):
if isinstance(obj, dict) and all(isinstance(k, str) for k in obj.keys()):
return {k: Tracer.to_serializable(v) for k, v in obj.items()}
if isinstance(obj, GeneratorProxy):
return obj
try:
obj = serialize(obj)
json.dumps(obj, default=default_json_encoder)
except Exception:
# We don't want to fail the whole function call because of a serialization error,
# so we simply convert it to str if it cannot be serialized.
obj = str(obj)
return obj
def _get_current_trace(self):
trace_id = self._current_trace_id.get()
if not trace_id:
return None
return self._id_to_trace[trace_id]
def _push(self, trace: Trace):
if not trace.id:
trace.id = str(uuid.uuid4())
if trace.inputs:
trace.inputs = self.to_serializable(trace.inputs)
trace.children = []
if not trace.start_time:
trace.start_time = datetime.utcnow().timestamp()
parent_trace = self._get_current_trace()
if not parent_trace:
self._traces.append(trace)
trace.node_name = self._node_name
else:
parent_trace.children.append(trace)
trace.parent_id = parent_trace.id
self._current_trace_id.set(trace.id)
self._id_to_trace[trace.id] = trace
@classmethod
def pop(cls, output=None, error: Optional[Exception] = None):
obj = cls.active_instance()
return obj._pop(output, error) if obj else output
def _pop(self, output=None, error: Optional[Exception] = None):
last_trace = self._get_current_trace()
if not last_trace:
logging.warning("Try to pop trace but no active trace in current context.")
return output
if isinstance(output, Iterator):
output = GeneratorProxy(output)
if output is not None:
last_trace.output = self.to_serializable(output)
if error is not None:
last_trace.error = self._format_error(error)
last_trace.end_time = datetime.utcnow().timestamp()
self._current_trace_id.set(last_trace.parent_id)
if isinstance(output, GeneratorProxy):
return generate_from_proxy(output)
else:
return output
def to_json(self) -> list:
return serialize(self._traces)
@staticmethod
def _format_error(error: Exception) -> dict:
return {
"message": str(error),
"type": type(error).__qualname__,
}
class TokenCollector():
_lock = Lock()
def __init__(self):
self._span_id_to_tokens = {}
def collect_openai_tokens(self, span, output):
span_id = span.get_span_context().span_id
if not inspect.isgenerator(output) and hasattr(output, "usage") and output.usage is not None:
tokens = output.usage.dict()
if tokens:
with self._lock:
self._span_id_to_tokens[span_id] = tokens
def collect_openai_tokens_for_parent_span(self, span):
tokens = self.try_get_openai_tokens(span.get_span_context().span_id)
if tokens:
if not hasattr(span, "parent") or span.parent is None:
return
parent_span_id = span.parent.span_id
with self._lock:
if parent_span_id in self._span_id_to_tokens:
merged_tokens = {
key: self._span_id_to_tokens[parent_span_id].get(key, 0) + tokens.get(key, 0)
for key in set(self._span_id_to_tokens[parent_span_id]) | set(tokens)
}
self._span_id_to_tokens[parent_span_id] = merged_tokens
else:
self._span_id_to_tokens[parent_span_id] = tokens
def try_get_openai_tokens(self, span_id):
with self._lock:
return self._span_id_to_tokens.get(span_id, None)
token_collector = TokenCollector()
def _create_trace_from_function_call(
f, *, args=None, kwargs=None, args_to_ignore: Optional[List[str]] = None, trace_type=TraceType.FUNCTION
):
"""
Creates a trace object from a function call.
Args:
f (Callable): The function to be traced.
args (list, optional): The positional arguments to the function. Defaults to None.
kwargs (dict, optional): The keyword arguments to the function. Defaults to None.
args_to_ignore (Optional[List[str]], optional): A list of argument names to be ignored in the trace.
Defaults to None.
trace_type (TraceType, optional): The type of the trace. Defaults to TraceType.FUNCTION.
Returns:
Trace: The created trace object.
"""
args = args or []
kwargs = kwargs or {}
args_to_ignore = set(args_to_ignore or [])
sig = inspect.signature(f).parameters
all_kwargs = {**{k: v for k, v in zip(sig.keys(), args)}, **kwargs}
all_kwargs = {
k: ConnectionType.serialize_conn(v) if ConnectionType.is_connection_value(v) else v
for k, v in all_kwargs.items()
}
# TODO: put parameters in self to inputs for builtin tools
all_kwargs.pop("self", None)
for key in args_to_ignore:
all_kwargs.pop(key, None)
name = f.__qualname__
if trace_type == TraceType.LLM and f.__module__:
name = f"{f.__module__}.{name}"
return Trace(
name=name,
type=trace_type,
start_time=datetime.utcnow().timestamp(),
inputs=all_kwargs,
children=[],
)
def get_node_name_from_context():
tracer = Tracer.active_instance()
if tracer is not None:
return tracer._node_name
return None
def enrich_span_with_context(span):
try:
attrs_from_context = OperationContext.get_instance()._get_otel_attributes()
span.set_attributes(attrs_from_context)
except Exception as e:
logging.warning(f"Failed to enrich span with context: {e}")
def enrich_span_with_trace(span, trace):
try:
span.set_attributes(
{
"framework": "promptflow",
"span_type": trace.type.value,
"function": trace.name,
}
)
node_name = get_node_name_from_context()
if node_name:
span.set_attribute("node_name", node_name)
enrich_span_with_context(span)
except Exception as e:
logging.warning(f"Failed to enrich span with trace: {e}")
def enrich_span_with_prompt_info(span, func, kwargs):
try:
# Assume there is only one prompt template parameter in the function,
# we use the first one by default if there are multiple.
prompt_tpl_param_name = get_prompt_param_name_from_func(func)
if prompt_tpl_param_name is not None:
prompt_tpl = kwargs.get(prompt_tpl_param_name)
prompt_vars = {
key: kwargs.get(key) for key in get_inputs_for_prompt_template(prompt_tpl) if key in kwargs
}
prompt_info = {"prompt.template": prompt_tpl, "prompt.variables": serialize_attribute(prompt_vars)}
span.set_attributes(prompt_info)
except Exception as e:
logging.warning(f"Failed to enrich span with prompt info: {e}")
def enrich_span_with_input(span, input):
try:
serialized_input = serialize_attribute(input)
span.set_attribute("inputs", serialized_input)
except Exception as e:
logging.warning(f"Failed to enrich span with input: {e}")
return input
def enrich_span_with_output(span, output):
try:
serialized_output = serialize_attribute(output)
span.set_attribute("output", serialized_output)
except Exception as e:
logging.warning(f"Failed to enrich span with output: {e}")
return output
def enrich_span_with_openai_tokens(span, trace_type):
tokens = token_collector.try_get_openai_tokens(span.get_span_context().span_id)
if tokens:
span_tokens = {f"__computed__.cumulative_token_count.{k.split('_')[0]}": v for k, v in tokens.items()}
if trace_type == TraceType.LLM:
llm_tokens = {f"{trace_type.value.lower()}.token_count.{k.split('_')[0]}": v for k, v in tokens.items()}
span_tokens.update(llm_tokens)
span.set_attributes(span_tokens)
def serialize_attribute(value):
"""Serialize values that can be used as attributes in span."""
try:
serializable = Tracer.to_serializable(value)
serialized_value = serialize(serializable)
return json.dumps(serialized_value, indent=2, default=default_json_encoder)
except Exception as e:
logging.warning(f"Failed to serialize attribute: {e}")
return None
def _traced(
func: Callable = None, *, args_to_ignore: Optional[List[str]] = None, trace_type=TraceType.FUNCTION
) -> Callable:
"""
Decorator that adds tracing to a function.
Args:
func (Callable): The function to be traced.
args_to_ignore (Optional[List[str]], optional): A list of argument names to be ignored in the trace.
Defaults to None.
trace_type (TraceType, optional): The type of the trace. Defaults to TraceType.FUNCTION.
Returns:
Callable: The traced function.
"""
wrapped_method = _traced_async if inspect.iscoroutinefunction(func) else _traced_sync
return wrapped_method(func, args_to_ignore=args_to_ignore, trace_type=trace_type)
def _traced_async(
func: Callable = None, *, args_to_ignore: Optional[List[str]] = None, trace_type=TraceType.FUNCTION
) -> Callable:
"""
Decorator that adds tracing to an asynchronous function.
Args:
func (Callable): The function to be traced.
args_to_ignore (Optional[List[str]], optional): A list of argument names to be ignored in the trace.
Defaults to None.
trace_type (TraceType, optional): The type of the trace. Defaults to TraceType.FUNCTION.
Returns:
Callable: The traced function.
"""
def create_trace(func, args, kwargs):
return _create_trace_from_function_call(
func, args=args, kwargs=kwargs, args_to_ignore=args_to_ignore, trace_type=trace_type
)
@functools.wraps(func)
async def wrapped(*args, **kwargs):
trace = create_trace(func, args, kwargs)
span_name = get_node_name_from_context() if trace_type == TraceType.TOOL else trace.name
with open_telemetry_tracer.start_as_current_span(span_name) as span:
enrich_span_with_trace(span, trace)
enrich_span_with_prompt_info(span, func, kwargs)
# Should not extract these codes to a separate function here.
# We directly call func instead of calling Tracer.invoke,
# because we want to avoid long stack trace when hitting an exception.
try:
Tracer.push(trace)
enrich_span_with_input(span, trace.inputs)
output = await func(*args, **kwargs)
if trace_type == TraceType.LLM:
token_collector.collect_openai_tokens(span, output)
enrich_span_with_output(span, output)
enrich_span_with_openai_tokens(span, trace_type)
span.set_status(StatusCode.OK)
output = Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
token_collector.collect_openai_tokens_for_parent_span(span)
return output
wrapped.__original_function = func
return wrapped
def _traced_sync(func: Callable = None, *, args_to_ignore=None, trace_type=TraceType.FUNCTION) -> Callable:
"""
Decorator that adds tracing to a synchronous function.
Args:
func (Callable): The function to be traced.
args_to_ignore (Optional[List[str]], optional): A list of argument names to be ignored in the trace.
Defaults to None.
trace_type (TraceType, optional): The type of the trace. Defaults to TraceType.FUNCTION.
Returns:
Callable: The traced function.
"""
def create_trace(func, args, kwargs):
return _create_trace_from_function_call(
func, args=args, kwargs=kwargs, args_to_ignore=args_to_ignore, trace_type=trace_type
)
@functools.wraps(func)
def wrapped(*args, **kwargs):
trace = create_trace(func, args, kwargs)
span_name = get_node_name_from_context() if trace_type == TraceType.TOOL else trace.name
with open_telemetry_tracer.start_as_current_span(span_name) as span:
enrich_span_with_trace(span, trace)
enrich_span_with_prompt_info(span, func, kwargs)
# Should not extract these codes to a separate function here.
# We directly call func instead of calling Tracer.invoke,
# because we want to avoid long stack trace when hitting an exception.
try:
Tracer.push(trace)
enrich_span_with_input(span, trace.inputs)
output = func(*args, **kwargs)
if trace_type == TraceType.LLM:
token_collector.collect_openai_tokens(span, output)
enrich_span_with_output(span, output)
enrich_span_with_openai_tokens(span, trace_type)
span.set_status(StatusCode.OK)
output = Tracer.pop(output)
except Exception as e:
Tracer.pop(None, e)
raise
token_collector.collect_openai_tokens_for_parent_span(span)
return output
wrapped.__original_function = func
return wrapped
def trace(func: Callable = None) -> Callable:
"""A decorator to add trace to a function.
When a function is wrapped by this decorator, the function name,
inputs, outputs, start time, end time, and error (if any) will be recorded.
It can be used for both sync and async functions.
For sync functions, it will return a sync function.
For async functions, it will return an async function.
:param func: The function to be traced.
:type func: Callable
:return: The wrapped function with trace enabled.
:rtype: Callable
:Examples:
Synchronous function usage:
.. code-block:: python
@trace
def greetings(user_id):
name = get_name(user_id)
return f"Hello, {name}"
Asynchronous function usage:
.. code-block:: python
@trace
async def greetings_async(user_id):
name = await get_name_async(user_id)
return f"Hello, {name}"
"""
return _traced(func, trace_type=TraceType.FUNCTION)
| promptflow/src/promptflow/promptflow/_core/tracer.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/tracer.py",
"repo_id": "promptflow",
"token_count": 7614
} | 10 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import typing
from sqlalchemy import TEXT, Column, Index, text
from sqlalchemy.exc import IntegrityError
from sqlalchemy.orm import declarative_base
from promptflow._sdk._constants import SPAN_TABLENAME
from .retry import sqlite_retry
from .session import trace_mgmt_db_session
Base = declarative_base()
class Span(Base):
__tablename__ = SPAN_TABLENAME
name = Column(TEXT, nullable=False)
trace_id = Column(TEXT, nullable=False)
span_id = Column(TEXT, primary_key=True)
parent_span_id = Column(TEXT, nullable=True)
span_type = Column(TEXT, nullable=False) # Function/Tool/Flow/LLM/LangChain...
session_id = Column(TEXT, nullable=False)
content = Column(TEXT) # JSON string
# prompt flow concepts
path = Column(TEXT, nullable=True)
run = Column(TEXT, nullable=True)
experiment = Column(TEXT, nullable=True)
__table_args__ = (
Index("idx_span_name", "name"),
Index("idx_span_span_type", "span_type"),
Index("idx_span_session_id", "session_id"),
Index("idx_span_run", "run"),
Index("idx_span_experiment", "experiment"),
)
@sqlite_retry
def persist(self) -> None:
with trace_mgmt_db_session() as session:
try:
session.add(self)
session.commit()
except IntegrityError as e:
# ignore "sqlite3.IntegrityError: UNIQUE constraint failed"
# according to OTLP 1.1.0: https://opentelemetry.io/docs/specs/otlp/#duplicate-data
# there might be duplicate data, we silently ignore it here
if "UNIQUE constraint failed" not in str(e):
raise
@staticmethod
@sqlite_retry
def list(
session_id: typing.Optional[str] = None,
) -> typing.List["Span"]:
with trace_mgmt_db_session() as session:
stmt = session.query(Span)
if session_id is not None:
stmt = stmt.filter(
text(f"trace_id in (select distinct trace_id from span where session_id = '{session_id}')")
)
stmt = stmt.order_by(text("json_extract(span.content, '$.start_time') asc"))
return [span for span in stmt.all()]
class LineRun:
"""Line run is an abstraction of spans, which is not persisted in the database."""
@staticmethod
def list(
session_id: typing.Optional[str] = None,
) -> typing.List[typing.List[Span]]:
with trace_mgmt_db_session() as session:
stmt = session.query(Span)
if session_id is not None:
stmt = stmt.filter(
text(f"trace_id in (select distinct trace_id from span where session_id = '{session_id}')")
)
else:
# TODO: fully support query
raise NotImplementedError
stmt = stmt.order_by(
Span.trace_id,
text("json_extract(span.content, '$.start_time') asc"),
)
line_runs = []
current_spans: typing.List[Span] = []
span: Span
for span in stmt.all():
if len(current_spans) == 0:
current_spans.append(span)
continue
current_trace_id = current_spans[0].trace_id
if span.trace_id == current_trace_id:
current_spans.append(span)
continue
line_runs.append(copy.deepcopy(current_spans))
current_spans = [span]
if len(current_spans) > 0:
line_runs.append(copy.deepcopy(current_spans))
return line_runs
| promptflow/src/promptflow/promptflow/_sdk/_orm/trace.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_orm/trace.py",
"repo_id": "promptflow",
"token_count": 1791
} | 11 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow._sdk._service.entry import main
import sys
import win32serviceutil # ServiceFramework and commandline helper
import win32service # Events
import servicemanager # Simple setup and logging
class PromptFlowService:
"""Silly little application stub"""
def stop(self):
"""Stop the service"""
self.running = False
def run(self):
"""Main service loop. This is where work is done!"""
self.running = True
while self.running:
main() # Important work
servicemanager.LogInfoMsg("Service running...")
class PromptFlowServiceFramework(win32serviceutil.ServiceFramework):
_svc_name_ = "PromptFlowService"
_svc_display_name_ = "Prompt Flow Service"
def SvcStop(self):
"""Stop the service"""
self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)
self.service_impl.stop()
self.ReportServiceStatus(win32service.SERVICE_STOPPED)
def SvcDoRun(self):
"""Start the service; does not return until stopped"""
self.ReportServiceStatus(win32service.SERVICE_START_PENDING)
self.service_impl = PromptFlowService()
self.ReportServiceStatus(win32service.SERVICE_RUNNING)
# Run the service
self.service_impl.run()
def init():
if len(sys.argv) == 1:
servicemanager.Initialize()
servicemanager.PrepareToHostSingle(PromptFlowServiceFramework)
servicemanager.StartServiceCtrlDispatcher()
else:
win32serviceutil.HandleCommandLine(PromptFlowServiceFramework)
if __name__ == "__main__":
init()
| promptflow/src/promptflow/promptflow/_sdk/_service/pfsvc.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/pfsvc.py",
"repo_id": "promptflow",
"token_count": 630
} | 12 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from typing import Mapping, Any
from promptflow.contracts.run_info import FlowRunInfo
from promptflow.contracts.run_info import RunInfo as NodeRunInfo
@dataclass
class FlowResult:
"""The result of a flow call."""
output: Mapping[str, Any]
# trace info of the flow run.
run_info: FlowRunInfo
node_run_infos: Mapping[str, NodeRunInfo]
| promptflow/src/promptflow/promptflow/_sdk/_serving/flow_result.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/flow_result.py",
"repo_id": "promptflow",
"token_count": 154
} | 13 |
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