Saravanan666/pypi-org huggingface dataset:
Package: azureml-fsspec
Filesystem interface to Azure Machine Learning defined URIsGetting startedThis package can be installed using:pipinstallazureml-fsspecExamplesAccepted uri format is Azure Machcine Learning defined datastore uri: azureml://subscriptions/([^/]+)/resourcegroups/([^/]+)/workspaces/([^/]+)/datastores/([^/]+)/paths/([^/]+)# load parquet file to pandasimportpandasdf=pandas.read_parquet('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder/mydata.parquet')# load csv file to pandasimportpandasdf=pandas.read_csv('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder/mydata.csv')# load parquet file to daskimportdask.dataframeasdddf=dd.read_parquet('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder/mydata.parquet')# load csv file to daskimportdask.dataframeasdddf=dd.read_csv('azureml://subscriptions/{sub_id}/resourcegroups/{rs_group}/workspaces/{ws}/datastores/workspaceblobstore/paths/myfolder/mydata.csv')Release History1.3.1 (02-05-2024)Features Addedadded support for using mlclient for authorization1.3.0 (01-17-2024)Features Addedadded registry support for fsspec.1.2.0 (08-14-2023)Features Addedadded auth dict support (SP and managedIdentity) for fsspec.added user exception mapping1.1.0 (06-01-2023)Features Addedupload/download performance improvement1.0.0 (04-07-2023)Features AddedAdded upload/download APIbugfix(root behavior)0.1.0b1Features AddedInitial public preview release of FileSystem for Azure Machine Learning defined datastore uri0.1.0b2Features AddedFix release notes0.1.0b3Features AddedAdd mode in open APIAdd validationUpgrade dataprep to 4.8
Package: azuremlftk
Azuremlftk is the Azure ML Package for Forecasting, a library of models, data transforms and evaluators common in forecasting scenarios.Some of the things it provides:
* data imputation transforms
* rolling window and lagging/leading transforms
* timestamp featurization
* validators for doing cross-validation correctly with time series
* standard “univariate” models” like ARIMA, ETS and Seasonal Naive
* forecasting models that seamlessly convert forecasting to regression and accept external regressors
* grouping multiple time series to learn a single modelAll models and transforms are presented with uniform scikit-like interfaces.Reference documentationhttps://azuremlpackages.blob.core.windows.net/forecasting/release1902/documentation/index.htmlSample notebookshttps://github.com/Azure-Samples/MachineLearningSamples-Notebooks/tree/master/domain-packages/forecasting
Package: azureml-interpret
Microsoft Azure Machine Learning Interpret API for PythonThis package has been tested with Python 3.6 and 3.7.The SDK is released with backwards compatibility guarantees.Machine learning (ML) interpret package is used to interpret black box ML models.The azureml-interpret package interfaces with explainers to allow users to upload and download explanations from Azure.The explainers come from the interpret-community package.The TabularExplainer can be used to give local and global feature importancesThe best explainer is automatically chosen for the user based on the modelLocal feature importances are for each evaluation rowGlobal feature importances summarize the most importance features at the model-levelThe API supports both dense (numpy or pandas) and sparse (scipy) datasetsFor more advanced users, individual explainers can be usedThe KernelExplainer and MimicExplainer are for BlackBox modelsThe MimicExplainer is faster but less accurate than the KernelExplainerThe TreeExplainer is for tree-based modelsThe DeepExplainer is for DNN tensorflow or pytorch modelsSetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
Once you have set up your environment, install the AzureML Interpret package:pipinstallazureml-interpret
Package: azureml-metrics
The azureml-metrics package can be used for computing supported machine learning metrics for
classification, regression, forecasting, translation, summarization, question answering,
image segmentation and object detection task types.Items marked (preview) in this article are currently in public preview. The preview version is
provided without a service level agreement, and it’s not recommended for production workloads.
Certain features might not be supported or might have constrained capabilities. For more information,
see Supplemental Terms of Use for Microsoft Azure Previews.
Package: azureml-mlflow
The azureml-mlflow package contains the integration code of AzureML with MLflow.
MLflow (https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models.
You can use MLflow logging APIs with Azure Machine Learning so that metrics and artifacts are logged to your Azure
machine learning workspace.UsageWithin anAzureML Workspace,
add the code below to use MLflow.importmlflowfromazureml.coreimportWorkspaceworkspace=Workspace.from_config()mlflow.set_tracking_uri(workspace.get_mlflow_tracking_uri())More examples can be found athttps://aka.ms/azureml-mlflow-examples.
Package: azureml-monitoring
Microsoft Azure Machine Learning Data Collection API for PythonThis package has been tested with Python 2.7 and 3.5.This is a preview releaseThe SDK is being released as a preview, for gathering feedback.Future releases are subject to breaking changes.Release History0.1.0a15 (2019-05-14)change ‘identifier’ keyword param to ‘designation’, Supported designations: ‘inputs’, ‘predictions’, ‘labels’, ‘signals, ‘general’add ‘collection_name’ paramter to intialization paramscollect method for ‘inputs’ designation now returns a dictionary with correlation dataadd_correlations method added. This method will take input data and correlation data, and augment input data with correlations0.1.0a1 (2017-05-30)Preview release.
Package: azureml-opendatasets
This package provides a set of APIs to consume Azure Open Datasets.In general, it allows users to turn the open datasets into both SPARK and Pandas dataframe,
with filters that are commonly applied to each specific dataset.For some of the open datasets, it provides enricher capability to join with other data. For example,
you can join your data with weather data by lat/long/zipcode + time quite easily.This package also contains open datasets with the following third-party notices.[1]:ftp://ftp.ncdc.noaa.gov/pub/data/noaa/readme.txt[2]:https://www.ncei.noaa.gov/thredds/catalog/gfs-004-files/catalog.html?dataset=gfs-g4-files[3]:http://opendefinition.org/licenses/odc-pddl/[4]:https://data.seattle.gov/stories/s/Data-Policy/6ukr-wvup/[5]:https://datasf.org/opendata/terms-of-use/[6]:https://www.chicago.gov/city/en/narr/foia/data_disclaimer.html[7]:https://www1.nyc.gov/home/terms-of-use.page
Package: azureml-pipeline
Machine learning (ML) pipelines are used by data scientists to build, optimize, and manage their machine learning workflows. A typical pipeline involves a sequence of steps that cover the following areas:Data preparation, such as normalizations and transformationsModel training, such as hyper parameter tuning and validationModel deployment and evaluationThe Azure Machine Learning SDK for Python can be used to create ML pipelines as well as to submit and track individual pipeline runs.Module and ModuleVersion classes are added to manage reusable compute units in pipelines.
Package: azureml-rai-utils
# Azure Machine Learning Responsible AI Utils Python SDKTheazureml-rai-utilspackage provides the SDK interface that works along with the AzureML Responsible AI Dashboard experience.The [Responsible AI dashboard](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-dashboard) includes a robust, rich set of visualizations and functionality to help you analyze your machine learning model or make data-driven business decisions:Some of the highlights of this package includes the functionality to:
- handle model dependencies
- pre-download images for the RAI Vision Dashboard and AutoML integration# Change LogInitial package release
Package: azureml-responsibleai
Microsoft Azure Machine Learning Responsible AI for PythonThis package has been tested with Python 3.6 and 3.7.The azureml-responsibleai package provides classes for Responsible AI in Azure Machine Learning and includes the following:Model explanations including aggregate and individual feature importance.Causal inference, estimates the causal effect each feature has on the outcome and recommends a policy for when to apply treatments in order to optimize the outcome.Counterfactual analysis, generates diverse counterfactual examples of existing datapoints to achieve a desired model output.Error analysis, a more robust analysis of a model’s performance by identifying which cohorts of your data have disproportionately higher error rate.  For example, a cohort of your data specifying gender or race.This package is experimental and may be subject to future change or removal.SetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
Once you have set up your environment, install the AzureML Responsible AI package:pipinstallazureml-responsibleai
Package: azureml-sdk
The Azure Machine Learning SDK for Python is used by data scientists and AI developers
to build and run machine learning workflows upon the Azure Machine Learning service.
You can interact with the service in any Python environment, including Jupyter Notebooks
or your favorite Python IDE.Use this SDK to quickly build, train, and deploy your machine learning and deep learning
models for various domains.Azure Machine Learning service stores free form text that the customer provides (e.g. names
for workspaces, resource groups, experiments, files, and images) or experiment parameters
in the United States.Getting Started:https://docs.microsoft.com/en-us/python/api/overview/azure/ml/introExamples:https://github.com/Azure/MachineLearningNotebooksRelease updates:https://docs.microsoft.com/en-us/azure/machine-learning/service/azure-machine-learning-release-notes
Package: azureml-telemetry
The azureml-telemetry package is used to collect telemetry data and includes the following:Provides functionality for logging, capturing events and metrics, and monitoring code activity.Collect different types of telemetry using free text or structured logging.Log messages, metrics, events, and activity messages can written to Application Insights.SetupInstall the Azure ML telemetry package:pipinstallazureml-telemetry
Package: azureml-tensorboard
Microsoft Azure Machine Learning TensorBoard API for PythonThis package has been tested with Python 3.6 and 3.7.The SDK is released with backwards compatibility guarantees.The Azure Machine Learningazureml-tensorboardpackage combines the AzureML SDK with TensorBoard visualization.It can be used to:Export run history to TensorBoard logs directory. You can run TensorBoard against the directory to view metrics.Launch TensorBoard from run history. TensorFlow logs can be written to a specified logs directory and then automatically loaded in real-time from Azure Blob Storage.SetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
Once you have set up your environment, install the AzureML TensorBoard package:pipinstallazureml-tensorboard
Package: azureml-train-core
The azureml-train-core package provides estimators for training models and includes the following:Base estimator classes and the generic estimator class in Azure Machine Learning.Estimators used in Deep Neural Network (DNN) training.Modules and classes supporting hyperparameter tuning.Estimator for training with Scikit-Learn.SetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.Once you have set up your environment, install the Azure ML train core package:pipinstallazureml-train-core
Package: azureml-train-restclients-hyperdrive
The azureml-train-restclients-hyperdrive contains classes needed to create HyperDriveRuns with azureml-train-core.SetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
Once you have set up your environment, install the AzureML train rest clients hyperdrive package:
.. code-block:: python
pip install azureml-train-restclients-hyperdrive
Package: azureml-widgets
Project descriptionAzureML Widgetsare fully supported, with interactivity, async auto-updates, and non-blocking cell execution. These widgets will be a way to monitor jobs as they run, monitor logs, and stream useful charts.Within a Jupyter notebook, widgets should be used to display the main highlights of the current run, for more run information the user will switch to the full Run Details page.SetupFollow theseinstructionsto install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.Once you have set up your environment, install the Azure ML Widget SDK:pip install azureml-widgetsazureml-widget supported runsThe following types of runs are supported:*StepRun: Shows run properties, output logs, metrics.*HyperDriveRun: Shows parent run properties, logs, child runs, primary metric chart, and parallel coordinate chart of hyperparameters.*AutoMLRun: Shows child runs and primary metric chart with option to select individual metrics.*PipelineRun: Shows running and non-running nodes of a pipeline along with graphical representation of nodes and edges.*ReinforcementLearningRun: Shows status of runs in real time. Azure Machine Learning Reinforcement Learning is currently a preview feature. For more information, see Reinforcement learning with Azure Marchine Learning.ResourcesMonitor and view ML run logs and metricsAzureML Widgets SDK Docs
Package: azure-monitor-events-extension
Azure Monitor Events ExtensionThe Azure Monitor Events Extension allows users to send custom events to Application Insights in conjunction with theAzure Monitor OpenTelemetry Distro.
This distro automatically installs the following libraries:Additional documentation[Azure Portal][https://portal.azure.com][Official Azure monitor docs][https://learn.microsoft.com/azure/azure-monitor/app/opentelemetry-enable?tabs=python][OpenTelemetry Python Official Docs][https://opentelemetry.io/docs/instrumentation/python/]
Package: azure-monitor-nspkg
# Microsoft Azure SDK for PythonThis is the Microsoft Azure monitor Services namespace package.This package is not intended to be installed directly by the end user.Since version 3.0, this is Python 2 package only, Python 3.x SDKs will usePEP420 <https://www.python.org/dev/peps/pep-0420/>as namespace package strategy.
To avoid issues with package servers that does not supportpython_requires, a Python 3 package is installed but is empty.It provides the necessary files for other packages to extend the azure.ai namespace.If you are looking to install the Azure client libraries, see theazurebundle package.![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fsdk%2Ftextanalytics%2Fazure-ai-nspkg%2FREADME.png)
Package: azure-nspkg
This is the Microsoft Azure namespace package.This package is not intended to be installed directly by the end user.Since version 3.0, this is Python 2 package only, Python 3.x SDKs will usePEP420as namespace package strategy.
To avoid issues with package servers that does not supportpython_requires, a Python 3 package is installed but is empty.It provides the necessary files for other packages to extend the azure namespace.If you are looking to install the Azure client libraries, see theazurebundle package.
Package: azure-openai-tr
azure-openai-trTranslate via Azure OpenaiInstall itpipinstallazure-openai-tr# pip install git+https://github.com/ffreemt/azure-openai-tr# poetry add git+https://github.com/ffreemt/azure-openai-tr# git clone https://github.com/ffreemt/azure-openai-tr && cd azure-openai-trUse itCopy.env.exampleto.envand edit.envaccordingly# .env, e.g.
OPENAI_API_BASE = https://dattw.openai.azure.com
OPENAI_API_KEY = ...
DEPLOYMENT_NAME = chator set those environ variables from the command line.fromazure_openai_trimportazure_openai_trprint(azure_openai_tr("test this and that")# '测试这个和那个。'print(azure_openai_tr("Hey ya",temperature=.2))# 嘿，你好print(azure_openai_tr("Hey ya",temperature=.8))# 嗨啊print(azure_openai_tr("Hey ya",temperature=.8))# 嘿 yaprint(azure_openai_tr("test this and that",to_lang='German',temperature=.8))# Teste dies und jenes.print(azure_openai_tr("test this and that",to_lang='German',temperature=.8))# Teste dies und das.print(azure_openai_tr("test this and that",to_lang='German',temperature=.1))# Teste dies und das.print(azure_openai_tr("test this and that",to_lang='中文',temperature=.8,template='翻成{to_lang}, 列出3个版本\n{text}'))# 1. 测试这个和那个# 2. 检验这个和那个# 3. 试验这个和那个Check source code for more details.
Package: azure-query
This is a CLI utility and pythong library for interacting with the Microsfot Graph REST API v1.0.It provides a thin wrapper, that essentially makes REST calls to the Graph API and returns the JSON result.It can be used as a library to create your own custom python tools that query the Graph API, and it alsocomes with the executable `aq` which allows you to run queries from the command line.It requires OAuth for access to the API, and will utilize a local web browser to obtain an access token.Details on the Graph API v1.0 can be found on Microsoft's sitehttps://docs.microsoft.com/en-us/graph/api/overview?view=graph-rest-1.0
Package: azure-requests
Azure requestsJust a wrapper around Python requests module for communicating with Azure DevOps.DRY (don't repeat yourself) featuresAuthenticationReplace organization, project, and team in URL, so URLs can be copy-pasted from the documentationHandle rate limitHandle ADO temporary server errorsSet appropriate Content-Type headersParse JSON automaticallyRaise exception for wrong HTTPS statusesRationaleAzure DevOps has an excellent HTTPS API with an excellent documentation. It is easy to understand and easy to use. For smaller scripts and projects it is easier to use them as is. Every existing API implementations have many documentation issues.Examplefromazure_requestsimportAzureRequestsazure_requests=AzureRequests(pat="<YOUR PAT>",organization="<YOUR ORGANIZATION>",)WORK_ITEM_ID=12345work_item_fields=azure_requests.call(# Copy-pasted from https://learn.microsoft.com/en-us/rest/api/azure/devops/wit/work-items/update?view=azure-devops-rest-7.0&tabs=HTTP"PATCH https://dev.azure.com/{organization}/{project}/_apis/wit/workitems/{id}?api-version=7.0",# custom URL parametersparams={"id":WORK_ITEM_ID,},# JSON datadata=[{"op":"test","path":"/id","value":WORK_ITEM_ID,}],# syntactic sugar for azure_requests.call(...)[result_key]result_key="fields")print(work_item_fields)For a more detailed example seeexample.py.
Package: azure-saving
Azure saving package is implemented for deploying a function that you can invoke for stopping, deleting or starting each … instance.The goal is to implement this package for each Azure service can stop, delete and start its instances.It is part of theeducational repositoriesto learn how to write stardard code and common uses of the TDD.PrerequisitesYou can useServerless frameworkfor deploying the function:
if you want to use the guide below, you have to install Serverless framework before.If you want to use another Azure tool, you can see the repositoryazure-tool-comparisonbefore to implement your version.InstallationComing soon.Change LogSeeCHANGELOG.mdfor details.LicenseThis package is released under the MIT license.  SeeLICENSEfor details.
Package: azure-schemaregistry-avroserializer
Microsoft Azure SDK for PythonThis package is no longer being maintained. Use theazure-schemaregistry-avroencoderpackage instead.For migration instructions, see themigration guide.Release History1.0.0b4.post1 (2023-08-15)This package is no longer being maintained. Use theazure-schemaregistry-avroencoderpackage instead.For migration instructions, see themigration guide.1.0.0b4 (2021-11-11)Features AddedAsync version ofAvroSerializerhas been added underazure.schemaregistry.serializer.avroserializer.aio.Depends onazure-schemaregistry>=1.0.0,<2.0.0.Breaking ChangesSchemaParseError,SchemaSerializationError, andSchemaDeserializationErrorhave been introduced underazure.schemaregistry.serializer.avroserializer.exceptionsand will be raised for corresponding operations.SchemaParseErrorandSchemaSerializationErrormay be raised for errors when callingserializeonAvroSerializer.SchemaParseErrorandSchemaDeserializationErrormay be raised for errors when callingdeserializeonAvroSerializer.1.0.0b3 (2021-10-06)Features Addedauto_register_schemaskeyword argument has been added toAvroSerializer, which will allow for automatically registering schemas passed in to theserialize, when set toTrue, otherwiseFalseby default.valueparameter inserializeonAvroSerializertakes typeMappingrather thanDict.Depends onazure-schemaregistry==1.0.0b3.Breaking ChangesSchemaRegistryAvroSerializerhas been renamedAvroSerializer.schema_registryparameter in theAvroSerializerconstructor has been renamedclient.schema_groupparameter in theAvroSerializerconstructor has been renamedgroup_name.dataparameter in theserializeanddeserializemethods onAvroSerializerhas been renamedvalue.schemaparameter in theserializemethod onAvroSerializerno longer accepts argument of typebytes.AvroSerializerconstructor no longer takes in thecodeckeyword argument.The following positional arguments are now required keyword arguments:clientandgroup_nameinAvroSerializerconstructorschemainserializeonAvroSerializer1.0.0b2 (2021-08-18)This version and all future versions will require Python 2.7 or Python 3.6+, Python 3.5 is no longer supported.Features AddedDepends onazure-schemaregistry==1.0.0b2which supports client-level caching.1.0.0b1 (2020-09-09)Version 1.0.0b1 is the first preview of our efforts to create a user-friendly and Pythonic client library for Azure Schema Registry Avro Serializer.New featuresSchemaRegistryAvroSerializeris the top-level client class that provides the functionality to encode and decode avro data utilizing the avro library. It will automatically register schema and retrieve schema from Azure Schema Registry Service. It provides two methods:serialize: Serialize dict data into bytes according to the given schema and register schema if needed.deserialize: Deserialize bytes data into dict data by automatically retrieving schema from the service.
Package: azure-security-nspkg
# Microsoft Azure SDK for PythonThis is the Microsoft Azure Security Services namespace package.This package is not intended to be installed directly by the end user.Since version 3.0, this is Python 2 package only, Python 3.x SDKs will usePEP420 <https://www.python.org/dev/peps/pep-0420/>as namespace package strategy.It provides the necessary files for other packages to extend the azure.security namespace.![Impressions](https://azure-sdk-impressions.azurewebsites.net/api/impressions/azure-sdk-for-python%2Fsdk%2Fsecurity%2Fazure-security-nspkg%2FREADME.png)