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https://api.github.com/repos/kubeflow/pipelines/issues/6428 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6428/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6428/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6428/events | https://github.com/kubeflow/pipelines/issues/6428 | 978,966,541 | MDU6SXNzdWU5Nzg5NjY1NDE= | 6,428 | [frontend] logs and artifacts stored in different Minio locations and can't be accessed by frontent | {
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"might be related to https://github.com/kubeflow/pipelines/issues/3818\r\n/assign @zijianjoy ",
"Not sure if related to that issue @Bobgy but looking in `workflow-controller-configmap-patch.yaml`\r\nthe keyFormat is completely different to where the UI looks for logs/metrics:\r\n```\r\nkeyFormat: \"artifacts/{{workflow.name}}/{{workflow.creationTimestamp.Y}}/{{workflow.creationTimestamp.m}}/{{workflow.creationTimestamp.d}}/{{pod.name}}\"\r\n```\r\n\r\nI tried changing this to \r\n```\r\nkeyFormat: \"v2/artifacts/pipeline/{{workflow.annotations.pipelines.kubeflow.org/run_name}}/{{workflow.labels.pipeline/runid}}\"\r\n```\r\n\r\nbut the object path wrong as UI looks for e.g. `{{component display name}}/metrics` whereas this saves as `{{component display name}}-metrics.tgz`",
"@jonasdebeukelaer any workaround for this particular issue? \r\nThis is what I am seeing in the ml-pipeline-ui pod logs after pod deletion:\r\n```\r\nGET /k8s/pod/logs?podname=podname&runid=runid&podnamespace=kubeflow\r\nGetting logs for pod:podname from mlpipeline/artifacts/workflow/podname/main.log.\r\n```\r\nwhich produces:\r\n`Could not get main container logs: S3Error: The specified key does not exist.`\r\n\r\nI tried changing the key format in the `workflow-controller-configmap-patch.yaml` to match the key which ml-pipeline-ui is expecting by removing the timestamp fields but did not seem to fix the issue:\r\n\r\nFrom: \r\n`keyFormat: \"artifacts/{{workflow.name}}/{{workflow.creationTimestamp.Y}}/{{workflow.creationTimestamp.m}}/{{workflow.creationTimestamp.d}}/{{pod.name}}\"`\r\n\r\nTo:\r\n`keyFormat: \"artifacts/{{workflow.name}}/{{pod.name}}\"`",
"@andrijaperovic For now we 're unfortunately having to access the artefacts through the MinIO interface by port-forwarding\r\n\r\n```\r\nkubectl port-forward svc/minio-service 8082:9000 -n kubeflow\r\n```\r\n",
"This is quite an annoying bug for us at the moment. \r\n\r\nI'd be happy to try fix this issue if it still need picking up. If someone can point to where to look that'd be ace",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"It has been one year, did anyone found out how to resolve/workaround this bug that'd be great, Im sure this is one of the major bugs."
] | "2021-08-25T10:13:50" | "2022-09-05T10:27:09" | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? from repo
* KFP version: 1.7.0
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
```
kfp 1.7.0
kfp-pipeline-spec 0.1.9
kfp-server-api 1.7.0
```
### Steps to reproduce
* run a pipeline and store an artifact
* delete the pod
Then we see that:
* The artifact (and its preview) can be seen in the output list of Input/Output tab
* The metrics can be accessed by the visualisations tab
* A wrong link to the metrics is found in the outputs list of the input/Outputs tab
* details read `Failed to get object in bucket mlpipeline at path v2/artifacts/pipeline/example pipeline/e154ea68-d59c-46ba-aea4-ead5b9eb0ea8/square/metrics: S3Error: The specified key does not exist.`
* The logs cannot be retrieved from minio in the logs tab
* details read `Error response: Could not get main container logs: Error: Unable to find pod log archive information from workflow status.`
Looking in MiniO it seems
* the Artifact is stored at
`mlpipeline/v2/artifacts/pipeline/example%20pipeline/e154ea68-d59c-46ba-aea4-ead5b9eb0ea8/square/`
* and the logs and metrics are stored at
`mlpipeline/artifacts/example-pipeline-ljzls/2021/08/25/example-pipeline-ljzls-1034021739/`
minimal code sample:
```python
import kfp
from kfp.v2.dsl import pipeline, component, Output, Metrics, Artifact
@component(base_image='python:3')
def square(x: int, metrics: Output[Metrics], data: Output[Artifact]):
metrics.log_metric('test_metrics', 1)
metrics.log_metric(f'{x} squared', x**2)
print(f'{x} squared is {x**2}')
with open(data.path, 'w') as f:
f.write("some content")
@pipeline(name='example pipeline')
def pipeline():
square(4)
client = kfp.Client(host='http://localhost:8081')
client.create_run_from_pipeline_func(
pipeline, arguments={}, run_name='Example run',
experiment_name='Default', enable_caching=False,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE
)
```
### Expected result
The logs and metrics can be retrieved from MiniO by the frontend even when pods are deleted
### Materials and Reference
Is this a configuration issue? Should I move things to point only to v2 path?
---
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"Yes, m facing the same error for CustomPythonPackageTrainingJobRunOp",
"@Bobgy Were you able to try this one?\r\nI m still stucked at this. \r\n@ckchow : Were you able to fix this one?\r\n",
"/assign @SinaChavoshi @IronPan ",
"I believe this issue was fixed by #6473 . Closing as fixed, this is not the case please reopen this bug. ",
"Hi Sina,\r\n\r\nThis is not the case.\r\nI am already passing python_module_name as str. \r\nBut still getting the error.\r\n\r\nSample for reference:-\r\n\r\nPROJECT_ID = 'xyz'\r\nLOCATION = 'abc'\r\nPIPELINE_ROOT_PATH = 'gs://test'\r\nMODEL_DISPLAY_NAME='custom-test1-template'\r\nPIPELINE_DISPLAY_NAME = 'custom-test-pipeline'\r\nCONTAINER_URI='us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0-23:latest'\r\nSTAGING_BUCKET='gs://test1'\r\nBASE_OUTPUT_DIR='gs://test2/custom/'\r\nPYTHON_PACKAGE_GCS_URI='gs://my-bucket/trainer-0.1.tar.gz'\r\nPYTHON_MODULE_NAME='trainer.task'\r\nENDPOINT_DISPLAY_NAME='custom-pipeline-endpoint'\r\nMODEL_SERVING_CONTAINER_IMAGE_URI='us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest'\r\nTRAINING_JOB_NAME='custom-pipeline-job'\r\nREPLICA_COUNT=1\r\nMACHINE_TYPE='n1-standard-4'\r\n\r\ndef pipeline(\r\n project: str = PROJECT_ID,\r\n location: str = LOCATION,\r\n display_name: str = TRAINING_JOB_NAME,\r\n python_package_gcs_uri: str = PYTHON_PACKAGE_GCS_URI,\r\n python_module_name: str = PYTHON_MODULE_NAME,\r\n container_uri: str = CONTAINER_URI,\r\n staging_bucket: str = STAGING_BUCKET,\r\n base_output_dir: str = BASE_OUTPUT_DIR,\r\n model_serving_container_image_uri: str = MODEL_SERVING_CONTAINER_IMAGE_URI,\r\n replica_count: int = REPLICA_COUNT,\r\n machine_type: str =MACHINE_TYPE ,\r\n model_display_name: str = MODEL_DISPLAY_NAME,\r\n):\r\n \r\n training_job_run_op = gcc_aip.CustomPythonPackageTrainingJobRunOp(\r\n display_name=display_name,\r\n python_package_gcs_uri=python_package_gcs_uri,\r\n python_module=python_module_name,\r\n container_uri=container_uri,\r\n project=project,\r\n location=location,\r\n staging_bucket=staging_bucket,\r\n base_output_dir=base_output_dir,\r\n model_serving_container_image_uri=model_serving_container_image_uri,\r\n replica_count=replica_count,\r\n machine_type=machine_type,\r\n )\r\nNote:- I am not able to reopen this issue.",
"/assign @SinaChavoshi \r\nCan you take a look again?",
"@SinaChavoshi : Any update on this issue?",
"Hi, \r\n\r\nI'm not able to reproduce this error with the sample you provided above. However I noticed in both samples you have parameter named `python_module=python_module_name,`. This parameter does not exist in the API the correct parameter name is `python_module_name`. see [the API documentation](https://google-cloud-pipeline-components.readthedocs.io/en/gcpc11/google_cloud_pipeline_components.aiplatform.html#google_cloud_pipeline_components.aiplatform.CustomPythonPackageTrainingJobRunOp) for details. \r\n\r\ncould you try this and let me know if you are still hitting an issue ? \r\n",
"Hi Sina,\r\nI did tried with the change as \"python_module_name\". Below is my function\r\n\r\n@kfp.dsl.pipeline(name=PIPELINE_DISPLAY_NAME, pipeline_root=PIPELINE_ROOT_PATH)\r\ndef pipeline(\r\n project: str = PROJECT_ID,\r\n location: str = LOCATION,\r\n display_name: str = TRAINING_JOB_NAME,\r\n python_package_gcs_uri: str = PYTHON_PACKAGE_GCS_URI,\r\n python_module_name: str = PYTHON_MODULE_NAME,\r\n container_uri: str = CONTAINER_URI,\r\n staging_bucket: str = STAGING_BUCKET,\r\n base_output_dir: str = BASE_OUTPUT_DIR,\r\n model_serving_container_image_uri: str = MODEL_SERVING_CONTAINER_IMAGE_URI,\r\n replica_count: int = REPLICA_COUNT,\r\n machine_type: str =MACHINE_TYPE ,\r\n model_display_name: str = MODEL_DISPLAY_NAME,\r\n):\r\n \r\n training_job_run_op = gcc_aip.CustomPythonPackageTrainingJobRunOp(\r\n display_name=display_name,\r\n python_package_gcs_uri=python_package_gcs_uri,\r\n python_module_name=python_module_name,\r\n container_uri=container_uri,\r\n project=project,\r\n location=location,\r\n staging_bucket=staging_bucket,\r\n base_output_dir=base_output_dir,\r\n model_serving_container_image_uri=model_serving_container_image_uri,\r\n replica_count=replica_count,\r\n machine_type=machine_type,\r\n )\r\n \r\n # Upload model\r\n model_upload_op = gcc_aip.ModelUploadOp(\r\n project=project,\r\n display_name=model_display_name,\r\n artifact_uri=base_output_dir,\r\n serving_container_image_uri=model_serving_container_image_uri,\r\n )\r\n model_upload_op.after(training_job_run_op)\r\n\r\n model_deploy_op = gcc_aip.ModelDeployOp(\r\n project=project,\r\n model=model_upload_op.outputs[\"model\"],\r\n endpoint=aiplatform.Endpoint(\r\n endpoint_name='personalloan-test2_endpoint').resource_name,\r\n deployed_model_display_name=model_display_name,\r\n machine_type=\"n1-standard-2\",\r\n traffic_percentage=100)\r\n\r\nI am passing PYTHON_MODULE_NAME='trainer.task'\r\n\r\nGetting below error:-\r\n\r\nKeyError Traceback (most recent call last)\r\n<ipython-input-9-57017d2a36e9> in <module>\r\n 2 pip_package_path=PIPELINE_DISPLAY_NAME + \".json\"\r\n 3 compiler.Compiler().compile(\r\n----> 4 pipeline_func=pipeline, package_path=pip_package_path\r\n 5 )\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in compile(self, pipeline_func, package_path, pipeline_name, pipeline_parameters, type_check)\r\n 1137 pipeline_parameters = {\r\n 1138 param.name: param for param in args_list_with_defaults\r\n-> 1139 }\r\n 1140 # Update pipeline parameters override if there were any.\r\n 1141 pipeline_parameters_override = pipeline_parameters_override or {}\r\n\r\n/opt/conda/lib/python3.7/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_v2(self, pipeline_func, pipeline_name, pipeline_parameters_override)\r\n 1070 pipeline_func: Pipeline function with @dsl.pipeline decorator.\r\n 1071 pipeline_name: The name of the pipeline. Optional.\r\n-> 1072 pipeline_parameters_override: The mapping from parameter names to values.\r\n 1073 Optional.\r\n 1074 \r\n\r\n<ipython-input-7-72f66fff9826> in pipeline(project, location, display_name, python_package_gcs_uri, python_module_name, container_uri, staging_bucket, base_output_dir, model_serving_container_image_uri, replica_count, machine_type, model_display_name)\r\n 38 model_serving_container_image_uri=model_serving_container_image_uri,\r\n 39 replica_count=replica_count,\r\n---> 40 machine_type=machine_type,\r\n 41 )\r\n 42 \r\n\r\n/opt/conda/lib/python3.7/site-packages/google_cloud_pipeline_components/aiplatform/utils.py in component_yaml_generator(**kwargs)\r\n 531 additional_args = []\r\n 532 for key, args in args_to_serialize.items():\r\n--> 533 for arg_key, value in args.items():\r\n 534 additional_args.append(f'--{key}.{arg_key}')\r\n 535 additional_args.append(value)\r\n\r\nKeyError: 'python_module_name'\r\n\r\nThanks!",
"We are in the process of deprecating this method - see #6474 given that I'm going to close this issue as wont fix. \r\n\r\nThe method that we have here is a wrapper around [google.cloud.aiplatform.CustomPythonPackageTrainingJob](https://googleapis.dev/python/aiplatform/latest/aiplatform.html?highlight=custom%20package#google.cloud.aiplatform.CustomPythonPackageTrainingJob) . See [source code here](https://github.com/googleapis/python-aiplatform/blob/8fde2ce4441139784bc0fdd62c88d4b833018765/google/cloud/aiplatform/training_jobs.py#L4650).\r\n\r\nGiven the error above I suspect you have an older version of `google-cloud-aiplatfrom` that is triggering this issue. \r\n\r\nWe are working on a `custom_job_wrapper` that would allow you to run any component as a custom component. see [custom_training_job_op](https://github.com/SinaChavoshi/pipelines/blob/9326d1d981cf286d71db57a555d8baee35112c48/components/google-cloud/google_cloud_pipeline_components/experimental/custom_job/CustomTrainingJobOp_sample.ipynb)\r\n\r\n \r\n\r\n",
"Thanks Sina for the update. It helped.\r\nCurrently i upgraded the version of google-cloud-aiplatform and tested using CustomPythonPackageTrainingJobRunOp this wrapper.\r\nCan you please provide us production ETA about this new wrapper function which i understand is currently work in progress.\r\n\r\nThank you!"
] | "2021-08-24T21:28:59" | "2021-09-28T09:32:22" | "2021-09-27T16:27:39" | NONE | null | ### Environment
* KFP version: N/A (vertex AI job)
* KFP SDK version: 1.7.0
* All dependencies version:
```
kfp==1.7.0
kfp-pipeline-spec==0.1.9
kfp-server-api==1.6.0
google-cloud-pipeline-components==0.1.5
```
### Steps to reproduce
```
@dsl.pipeline(
name='name',
pipeline_root='gs://some-place'
)
def train_pipeline(
# dataset
batch_size: int,
train_start_date: str,
train_end_date: str,
val_start_date: str,
val_end_date: str,
test_start_date: str,
test_end_date: str,
bucket_size_s: int,
anomaly_window_size_s: int,
device_column: str,
# model
outer_units: int,
inner_units: int,
learning_rate: float,
# trainer
max_epochs: int,
# dataset [defaults]
table: str = 'project.dataset.table',
shuffle_buffer_size: int = 1_000,
# misc [defaults]
project: str = 'project',
location: str = 'us-central1',
tensorboard_name: str = 'test_tensorboard',
container_uri: str = 'us-docker.pkg.dev/vertex-ai/training/pytorch-xla.1-7:latest',
python_package_gcs_uri: str = 'gs://some-place/package.tar.gz',
python_module: str = 'blah.models.trainer'
):
train_op = gcc.aiplatform.CustomPythonPackageTrainingJobRunOp(
args=[
# dataset
'--batch_size', str(batch_size),
'--train_start_date', str(train_start_date),
'--train_end_date', str(train_end_date),
'--val_start_date', str(val_start_date),
'--val_end_date', str(val_end_date),
'--test_start_date', str(test_start_date),
'--test_end_date', str(test_end_date),
'--bucket_size_s', str(bucket_size_s),
'--anomaly_window_size_s', str(anomaly_window_size_s),
'--device_column', str(device_column),
'--shuffle_buffer_size', str(shuffle_buffer_size),
'--table', str(table),
'--project', str(project),
# model
'--outer_units', str(outer_units),
'--inner_units', str(inner_units),
'--lr', str(learning_rate),
# trainer
'--max_epochs', str(max_epochs),
# misc
'--cloud'
],
replica_count=1,
machine_type='n1-standard-8',
tensorboard=f'projects/{str(project)}/locations/{str(location)}/tensorboard/{str(tensorboard_name)}',
display_name='funky',
python_package_gcs_uri=python_package_gcs_uri,
python_module=python_module,
container_uri=container_uri,
project=project,
location=location
)
compiler.Compiler().compile(train_pipeline, package_path='../pipelines/train_pipeline.json',
type_check=True)
api_client = AIPlatformClient(project_id='funky', region='us-central1')
response = api_client.create_run_from_job_spec(
'../pipelines/train_pipeline.json',
parameter_values={
'batch_size': 32,
'train_start_date': '2021-05-01 UTC',
'train_end_date': '2021-05-14 UTC',
'val_start_date': '2021-05-15 UTC',
'val_end_date': '2021-05-21 UTC',
'test_start_date': '2021-05-22 UTC',
'test_end_date': '2021-05-31 UTC',
'bucket_size_s': 5,
'anomaly_window_size_s': 60,
'device_column': 'device_hardware',
# model
'outer_units': 64,
'inner_units': 32,
'learning_rate': 0.003,
# trainer
'max_epochs': 5,
# dataset [defaults]
'table': 'blah.blah.table',
'shuffle_buffer_size': 1_000,
# misc [defaults]
'project': 'cool-project',
'location': 'us-central1',
'tensorboard_name': 'test_tensorboard',
}
)
```
yields:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-128-54de1a48b582> in <module>
1 # generate json pipeline specification
----> 2 compiler.Compiler().compile(train_pipeline, package_path='../pipelines/train_pipeline.json',
3 type_check=True)
4
5
/opt/conda/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py in compile(self, pipeline_func, package_path, pipeline_name, pipeline_parameters, type_check)
1135 kfp.TYPE_CHECK = type_check
1136 kfp.COMPILING_FOR_V2 = True
-> 1137 pipeline_job = self._create_pipeline_v2(
1138 pipeline_func=pipeline_func,
1139 pipeline_name=pipeline_name,
/opt/conda/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py in _create_pipeline_v2(self, pipeline_func, pipeline_name, pipeline_parameters_override)
1071
1072 with dsl.Pipeline(pipeline_name) as dsl_pipeline:
-> 1073 pipeline_func(*args_list)
1074
1075 self._validate_exit_handler(dsl_pipeline)
<ipython-input-127-62de707bdaca> in train_pipeline(batch_size, train_start_date, train_end_date, val_start_date, val_end_date, test_start_date, test_end_date, bucket_size_s, anomaly_window_size_s, device_column, outer_units, inner_units, learning_rate, max_epochs, table, shuffle_buffer_size, project, location, tensorboard_name, container_uri, python_package_gcs_uri, python_module)
45 python_module: str = 'blah.models.trainer'
46 ):
---> 47 train_op = gcc.aiplatform.CustomPythonPackageTrainingJobRunOp(
/opt/conda/lib/python3.8/site-packages/google_cloud_pipeline_components/aiplatform/utils.py in component_yaml_generator(**kwargs)
548 kfp.dsl._pipeline_param.PipelineParam) or serializer:
549 if is_mb_sdk_resource_noun_type(param_type):
--> 550 metadata_type = map_resource_to_metadata_type(param_type)[1]
551 component_param_type = metadata_type
552 else:
TypeError: 'NoneType' object is not subscriptable
```
I think the aiplatform client / kfp translator function is parsing something wrong. Or perhaps I'm using / not using `str()` enough? It does create a pipeline when I use `str()` on all the op arguments but I think that causes the op to not interpolate the values and I get a different error at ai platform job runtime.
### Expected result
Creation of custom training job in vertex.
### Materials and Reference
---
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6424 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6424/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6424/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6424/events | https://github.com/kubeflow/pipelines/issues/6424 | 978,417,901 | MDU6SXNzdWU5Nzg0MTc5MDE= | 6,424 | [feature] Add helper function to change pipeline/operation ksa | {
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"Hi @jmcarp, thank you for raising the issue.\r\nI can point you to related code in case you want to contribute, I think this can be a good first issue : ).\r\n\r\n* KFP SDK client code: https://github.com/kubeflow/pipelines/blob/27b3511ad5e8283096555efb4f00d5ba5427af11/sdk/python/kfp/_client.py#L828\r\n* How KFP SDK client uses the generated SDK API: https://github.com/kubeflow/pipelines/blob/27b3511ad5e8283096555efb4f00d5ba5427af11/sdk/python/kfp/_client.py#L659-L662\r\n* Generated SDK API reference: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.server_api.html#kfp_server_api.api.run_service_api.RunServiceApi.create_run (Note the service_account field exists in `ApiRun` model.\r\n* Generated SDK API tutorial: https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/",
"@Bobgy I wrote up #6455, but it looks like we're thinking about different parts of the code. Does my PR make sense, or should I try a different approach?",
"> @Bobgy I wrote up #6455, but it looks like we're thinking about different parts of the code. Does my PR make sense, or should I try a different approach?\r\n\r\nI tend to think service account as a submission time configuration. In that sense, adding the option to the client is probably better. That's also what we did for Vertex Pipelines client:\r\nhttps://github.com/kubeflow/pipelines/blob/9afe125a759d8291f8a7732c6f81ca149c8c4ed5/sdk/python/kfp/v2/google/client/client.py#L272\r\n\r\n#6455 would not be compatible with KFP v2 or Vertex Pipelines.",
"Thanks! I rewrote this as suggested--hope this makes more sense."
] | "2021-08-24T19:31:52" | "2021-09-04T01:08:10" | "2021-09-04T01:08:10" | CONTRIBUTOR | null | ### Feature Area
/area sdk
### What feature would you like to see?
I would like to see a helper function in the sdk to run pods using a kubernetes service account other than `pipeline-runner`. Discussed in slack at https://kubeflow.slack.com/archives/CE10KS9M4/p1629610669092400.
### What is the use case or pain point?
I'm using gke workload identity to map gcp service accounts to kubernetes service accounts, and I'm hoping to simplify the process of running different kfp workloads with different gcp iam access.
### Is there a workaround currently?
@Bobgy mentioned that this is possible in the generated sdk, but I'm not sure how that works yet.
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"@zm274310577 can you share a reproducing pipeline sample?",
"@yanniszark @elikatsis do you want to take a look?",
"@Bobgy thinks a lot. The problem has been solved, the pvc is created in another microservice. when the user is cephfs(I created it before), mount failed. change to admin, mount success. here is the code. \r\n\r\n```\r\ncephfs=client.V1CephFSPersistentVolumeSource(monitors=CEPH_MON_URLS.split(\",\"),\r\n path=cephfs_path,\r\n user='admin',\r\n secret_ref=client.V1SecretReference(\r\n namespace=K8S_NAMESPACE,\r\n name=CEPH_FS_SECRET_NAME),\r\n read_only=False)\r\n```\r\n\r\nAs for why cephfs failed, I don't know yet, but it's enough for me"
] | "2021-08-24T01:55:51" | "2021-08-31T10:28:45" | "2021-08-31T10:28:45" | NONE | null | ### What steps did you take
when i create a run, the pipeline require a pvc(the pvc is created in the 'user' namespace and i am using the 'user' namespace )
<!-- A clear and concise description of what the bug is.-->
### What happened:
when i describe the pod, the pvc mount failed.
Events:
Type Reason Age From Message
Normal Scheduled <unknown> default-scheduler Successfully assigned user/laji-fenlei-pipeline-lrmlx-3083496721 to slave2
Warning FailedMount 24m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-737583.scope.
mount error 1 = Operation not permitted
Warning FailedMount 24m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-737593.scope.
mount error 1 = Operation not permitted
Warning FailedMount 24m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-737672.scope.
mount error 1 = Operation not permitted
Warning FailedMount 24m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-737718.scope.
mount error 1 = Operation not permitted
Warning FailedMount 23m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-737877.scope.
mount error 1 = Operation not permitted
Warning FailedMount 23m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
Output: Running scope as unit run-738076.scope.
mount error 1 = Operation not permitted
Warning FailedMount 23m kubelet, slave2 MountVolume.SetUp failed for volume "pvc-c9007abeba5a325a2cce65c6649692d6-pv" : CephFS: mount failed: mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv --scope -- mount -t ceph -o \<masked\>,\<masked\> 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/6d000df9-e380-495e-9738-a10b9082a23f/volumes/kubernetes.io\~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
### What did you expect to happen:
i think the '\<masked\>,\<masked\>' not assigned。when i mount manual. the run can work. for example：systemd-run --scope -- mount -t ceph -o name=admin,secret=\`ceph-authtool -p /etc/ceph/ceph.client.admin.keyring\` 192.168.9.51:6789,192.168.9.52:6789,192.168.9.53:6789:/kubeflow-fs/anli/laji/data /var/lib/kubelet/pods/5d54492e-7d2b-4478-8534-06bbe97eedcd/volumes/kubernetes.io~cephfs/pvc-c9007abeba5a325a2cce65c6649692d6-pv
i think whe 'name=admin,secret=\`ceph-authtool -p /etc/ceph/ceph.client.admin.keyring\`' is missing
### Environment:
the kubernetes version is: v1.18.5
the kubeflow version is: 1.3.0
the pipeline api-version and ui version is: 1.5.0
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
deploy the kubeflow through manifests-1.3.0
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
1.3.0
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
thanks a lot | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-23T14:24:46" | "2022-03-02T21:05:34" | null | NONE | null | 1)Previously same pipeline code was working perfectly but now getting errors in the deployment step. Other steps are working fine.
2)Kubeflow prebuild component for model deployment on GCP Ai platform do not have region option(In which region model should deploy). But through the gcloud command, we can set the region while the model and version create and there are no problems in model deployment.
link for component yml: https://raw.githubusercontent.com/kubeflow/pipelines/1.7.0-rc.3/components/gcp/ml_engine/deploy/component.yaml
<img width="556" alt="Screenshot 2021-08-23 194804" src="https://user-images.githubusercontent.com/49094780/130463719-cc519595-36e2-4c95-8789-146b3b04d065.png">
note: I have storage and kubeflow pipeline in europe-weast region but. GCP component is trying to deploy it inside us region. That part I can not able to change due to no option for the region.
Please Help us fix this issue. thank you
RuntimeError: Failed to complete create version operation projects/churn-internal1ba746a4/operations/create_churn_model_v3-1629726285614: 13 Internal error occurred. Please retry in a few minutes. If you still experience errors, contact Cloud ML. | {
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"cc @chensun ",
"@alexcpn can you try KFP 1.7.2, is the same issue still reproduceable?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-21T14:46:23" | "2022-03-02T21:05:25" | null | NONE | null |
### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
```
kustomize build apps/pipeline/upstream/env/platform-agnostic-multi-user-pns | grep 1.5.1
2021/08/19 11:19:46 nil value at `valueFrom.configMapKeyRef.name` ignored in mutation attempt
2021/08/19 11:19:46 nil value at `valueFrom.secretKeyRef.name` ignored in mutation attempt
2021/08/19 11:19:46 well-defined vars that were never replaced: kfp-app-name,kfp-app-version
appVersion: 1.5.1
image: gcr.io/ml-pipeline/cache-deployer:1.5.1
image: gcr.io/ml-pipeline/cache-server:1.5.1
- image: gcr.io/ml-pipeline/metadata-envoy:1.5.1
image: gcr.io/ml-pipeline/metadata-writer:1.5.1
image: gcr.io/ml-pipeline/api-server:1.5.1
image: gcr.io/ml-pipeline/persistenceagent:1.5.1
image: gcr.io/ml-pipeline/scheduledworkflow:1.5.1
image: gcr.io/ml-pipeline/frontend:1.5.1
image: gcr.io/ml-pipeline/viewer-crd-controller:1.5.1
- image: gcr.io/ml-pipeline/visualization-server:1.5.1
```
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
```
build version dev_local
```
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
```
### Steps to reproduce
I was having error trying to convert Pipeline V1 version to V2 (Output[Dataset]) and Aritifact way. as per this https://github.com/kubeflow/pipelines/issues/6390
Was getting error with Output[DataSet] in my simple component
```
@component(
base_image="tensorflow/tensorflow:2.6.0",
packages_to_install=['pandas==1.1.4','pyarrow'],
output_component_file='component.yaml'
)
def readdata(url: str,out:Output[Dataset]):
import pandas as pd
from collections import namedtuple
print("goinf to read",url)
df = pd.read_csv(url)
print("No of records",df.index)
print("Out.path",out.path)
df.to_parquet(out.path)
```
and then tried to execute the sample code itself https://www.kubeflow.org/docs/components/pipelines/sdk/v2/build-pipeline/
```
component(
packages_to_install=['pandas==1.1.4'],
output_component_file='component.yaml'
)
def merge_csv(tar_data: Input[Artifact], output_csv: Output[Dataset]):
import glob
import pandas as pd
import tarfile
tarfile.open(name=tar_data.path, mode="r|gz").extractall('data')
df = pd.concat(
[pd.read_csv(csv_file, header=None)
for csv_file in glob.glob('data/*.csv')])
df.to_csv(output_csv.path, index=False, header=False)
# Define a pipeline and create a task from a component:
@dsl.pipeline(
name='my-pipeline',
# You can optionally specify your own pipeline_root
# pipeline_root='gs://my-pipeline-root/example-pipeline',
)
def my_pipeline(url: str):
web_downloader_task = web_downloader_op(url=url)
merge_csv_task = merge_csv(tar_data=web_downloader_task.outputs['data'])
# The outputs of the merge_csv_task can be referenced using the
# merge_csv_task.outputs dictionary: merge_csv_task.outputs['output_csv']
web_downloader_op = kfp.components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/web/Download/component-sdk-v2.yaml')
client.create_run_from_pipeline_func(
my_pipeline,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE,
# You can optionally override your pipeline_root when submitting the run too:
# pipeline_root='gs://my-pipeline-root/example-pipeline',
arguments={
'url': 'https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz'
})
```
Logs in error pod - merge_csv_task
```
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead:
https://pip.pypa.io/warnings/venv
Traceback (most recent call last):
File "/tmp/tmp.BvwXtxHYik", line 825, in <module>
executor_main()
File "/tmp/tmp.BvwXtxHYik", line 819, in executor_main
function_to_execute=function_to_execute)
File "/tmp/tmp.BvwXtxHYik", line 549, in __init__
artifacts_list[0])
File "/tmp/tmp.BvwXtxHYik", line 562, in _make_output_artifact
os.makedirs(os.path.dirname(artifact.path), exist_ok=True)
File "/usr/local/lib/python3.7/posixpath.py", line 156, in dirname
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
F0821 14:32:56.171499 53 main.go:56] Failed to execute component: exit status 1
```
### Expected result
Should work without errors
### Materials and Reference
I was evaluating the Kubeflow pipelines and this V1 to V2 change seems to break the existing V1 flow; and make it more complex. At least if OutputPath("SomeCustomType") can be substituted by OutPut[DataSet]) and if things work, then it should be fine; but documentation is not that clear // or the beta version is buggy.
Also is it that V1 version will be obsoleted when V2 comes in from beta to general release ? Or can we conitue to use V1 version
---
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] | closed | false | null | [] | null | [
"cc @chensun for sdk advice.",
"Note: I tried the same thing using the v1 sdk but got the same `unhashable type` error",
"ohhhhhh jeez closing this as it seems I forgot how to define dicts in python 🤦 \r\n\r\n`{\"/mnt/data\", vol}` -> `{\"/mnt/data\": vol}`\r\n\r\n🤦 🤦 🤦 🤦 "
] | "2021-08-19T17:25:51" | "2021-08-23T09:10:41" | "2021-08-23T09:10:40" | CONTRIBUTOR | null | ### Environment
* KFP version: `1.7.0-rc.4`
* KFP SDK version: `1.7.0rc4`
* All dependencies version:
```
kfp 1.7.1
kfp-pipeline-spec 0.1.9
kfp-server-api 1.7.0rc4
```
### Steps to reproduce
I'm trying to mount an existing pvc 'azure-managed-disk' to my components via add_pvolume, but is failing with a type error I can't pin down.
Minimal code snippet:
```python
import kfp
import kfp.v2.dsl as dsl
from kfp.v2.dsl import component
from kubernetes.client.models.v1_volume import V1Volume
@component(base_image='python:3')
def get_data():
import os
print('list:')
print(os.listdir('/mnt/data'))
@dsl.pipeline(name='test')
def pipeline():
vol = kfp.dsl.PipelineVolume(pvc='azure-managed-disk')
# tried with following as well:
# vol = V1Volume(name='vol', persistent_volume_claim='azure-managed-disk')
get_data_task = get_data()
get_data_task.add_pvolumes({"/mnt/data", vol})
client = kfp.Client(host='http://localhost:8081')
client.create_run_from_pipeline_func(
pipeline, arguments={}, run_name='testing',
experiment_name='Default', enable_caching=True,
mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE
)
```
full error trace:
```
Traceback (most recent call last):
File "/Users/jonas/repositories/traffic4cast-2021/pipeline/pipeline_err_snippet.py", line 25, in <module>
client.create_run_from_pipeline_func(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/_client.py", line 881, in create_run_from_pipeline_func
compiler.Compiler(mode=mode, launcher_image=launcher_image).compile(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1061, in compile
self._create_and_write_workflow(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1114, in _create_and_write_workflow
workflow = self._create_workflow(
File "/opt/homebrew/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 900, in _create_workflow
pipeline_func(*args_list, **kwargs_dict)
File "/Users/jonas/repositories/traffic4cast-2021/pipeline/pipeline_err_snippet.py", line 21, in pipeline
get_data_task.add_pvolumes({"/mnt/data", vol})
TypeError: unhashable type: 'PipelineVolume'
```
### Expected result
The pvc would be mount to path `'/mnt/data'`
### Materials and Reference
Or is there something I'm missing that needs to change when using v2 of the sdk?
---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"duplicate https://github.com/kubeflow/pipelines/issues/5711\r\n\r\ncc @chensun "
] | "2021-08-19T06:00:47" | "2021-08-20T00:43:03" | "2021-08-20T00:43:03" | NONE | null | ### Environment
* KFP version:
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
```
kustomize build apps/pipeline/upstream/env/platform-agnostic-multi-user-pns | grep 1.5.1
2021/08/19 11:19:46 nil value at `valueFrom.configMapKeyRef.name` ignored in mutation attempt
2021/08/19 11:19:46 nil value at `valueFrom.secretKeyRef.name` ignored in mutation attempt
2021/08/19 11:19:46 well-defined vars that were never replaced: kfp-app-name,kfp-app-version
appVersion: 1.5.1
image: gcr.io/ml-pipeline/cache-deployer:1.5.1
image: gcr.io/ml-pipeline/cache-server:1.5.1
- image: gcr.io/ml-pipeline/metadata-envoy:1.5.1
image: gcr.io/ml-pipeline/metadata-writer:1.5.1
image: gcr.io/ml-pipeline/api-server:1.5.1
image: gcr.io/ml-pipeline/persistenceagent:1.5.1
image: gcr.io/ml-pipeline/scheduledworkflow:1.5.1
image: gcr.io/ml-pipeline/frontend:1.5.1
image: gcr.io/ml-pipeline/viewer-crd-controller:1.5.1
- image: gcr.io/ml-pipeline/visualization-server:1.5.1
```
* KFP SDK version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
```
build version dev_local
```
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
```
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
```
### Steps to reproduce
For V1 pipeline the following works
```
from typing import NamedTuple
from typing import TypeVar
from kfp.components import InputPath, OutputPath
PandasDataFrame = TypeVar('pandas.core.frame.DataFrame')
#def readdata(url,out: OutputPath(PandasDataFrame)):
def readdata(url:str,out: OutputPath(PandasDataFrame)):
import pandas as pd
from collections import namedtuple
df = pd.read_csv(url)
print("No of records",df.index)
df.to_parquet(out)
-------------------------------------
read_data = create_component_from_func(readdata,base_image='tensorflow/tensorflow:2.6.0', packages_to_install=['pandas==0.24','sklearn','numpy','pyarrow'])
---------------------------------------
import kfp.dsl as dsl
@dsl.pipeline(
name='Get and Process Training Data',
description='Get and Process Training data'
)
def getdata_and_process_pipeline(
a:str="https://raw.githubusercontent.com/alexcpn/neuralnetwork_learn/main/data/heart-attack-prediction/heart.csv"
):
model_path = create_nn_model().output
pd_as_parquet = read_data(url=a).output
process_task =process_data(pandas_parqute)
-----------------
client.create_run_from_pipeline_func(getdata_and_process_pipeline, arguments={})
-----------------------
```
However for V2, the same function is giving an execution error
```
read_data = create_component_from_func_v2(readdata,base_image='tensorflow/tensorflow:2.6.0', packages_to_install=['pandas==0.24','sklearn','numpy','pyarrow'])
-----
import kfp.dsl as dsl
@dsl.pipeline(
name='Get and Process Training Data',
description='Get and Process Training data'
)
def getdata_and_process_pipeline(
a:str="https://raw.githubusercontent.com/alexcpn/neuralnetwork_learn/main/data/heart-attack-prediction/heart.csv"
):
model_path = create_nn_model().output
pd_as_parquet = read_data(url=a).output
----------------
client.create_run_from_pipeline_func(getdata_and_process_pipeline,mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE, arguments={})
----------
Logs for readdata component
NameError: name 'PandasDataFrame' is not defined
F0818 13:29:15.365216 37 main.go:56] Failed to execute component: exit status 1
```
### Expected result
Custom data types should work a in v2 as in v1
### Materials and Reference
Full v1 code -
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
https://colab.research.google.com/drive/1f_p4EVKReT57J4Maz4vRfhccJ_qVv03W?usp=sharing
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"@zijianjoy after you finish `kubectl rollout restart deployment/proxy-agent -n default` step, did you check what's the hostname in the configmap? Note, it will be a different hostname from before.\r\n\r\nBesides, can you also log the exact error code/message you hit when visiting the inverse proxy URL.",
"@Bobgy That is a good point, I noticed that the hostname is still the same even if I reset the inverse-proxy-config and restart proxy-agent. I checked the log from proxy-agent:\r\n\r\n```\r\n \"A proxy request failed: \\\"Get \\\\\\\"https://us-central1.pipelines.cloud.google.com/tun/m/4592f092208ecc84946b8f8f8016274df1b36a14/agent/pending\\\\\\\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)\\\"\"\r\n```",
"> \"A proxy request failed: \\\"Get \\\\\\\"https://us-central1.pipelines.cloud.google.com/tun/m/4592f092208ecc84946b8f8f8016274df1b36a14/agent/pending\\\\\\\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)\\\"\"\r\n\r\nThis error is expected, because inverse proxy's architecture is that:\r\n1. user requests are sent to inverse proxy server and stored\r\n2. proxy agent does long polls to inverse proxy server for new coming requests\r\n\r\nwhen there are actually no new requests coming, the long poll will timeout, and proxy agent will start another long poll again.",
"/assign\r\n@zijianjoy can you give me access to your cluster?",
"The name of cluster is kubeflow-pipelines-emissary-mgd in my project, you are owner of the project. Thank you for helping!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-19T04:14:09" | "2022-03-02T21:05:29" | null | COLLABORATOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
GCP Marketplace hosted KFP
* KFP version:
1.7.0-rc.3
### Steps to reproduce
Deploy 1.7.0-rc.3 using GCP marketplace:
Create a K8s 1.18 cluster using Marketplace, I have deleted and redeploy KFP instance multiple times. When I create KFP again, I select enable emissary executor and managed storage options.
I cannot access the KFP using `OPEN PIPELINES DASHBOARD` button.
I tried to use `kubectl port-foward` and I am able to see the KFP UI with this approach.
I tried to delete inverse-proxy-config configmap, and create an empty configmap placeholder, and restarted proxy-agent. But the issue still exists:
1. `kubectl delete cm inverse-proxy-config -n default`
2.
```
cat <<EOF | kubectl create -f -
> apiVersion: v1
> kind: ConfigMap
> metadata:
> name: inverse-proxy-config
> labels:
> app.kubernetes.io/name: kubeflow-pipelines-emissary-mgd
>
>
> EOF
```
3. `kubectl rollout restart deployment/proxy-agent -n default`
### Expected result
Open Hosted KFP UI using inverse proxy successfully.
cc @Bobgy
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/cc @neuromage @chensun @animeshsingh ",
"/cc @Ark-kun ",
"There are 3 main data passing placeholders supported by `component.yaml`:\r\n\r\n* `{inputValue: Input 1}`\r\n* `{inputPath: Input 1}`\r\n* `{outputPath: Output 1}`\r\n\r\n`InputArgumentPath` in ContainerOp corresponds to `{inputPath: Input 1}`",
"Thanks @Ark-kun for the clarification. One more question, will the `component.yaml` still able to use raw value as input artifacts? e.g.\r\nhttps://github.com/kubeflow/pipelines/blob/fa1cf410f6c0a504d03bb3fc3b0159fcf5fceffc/sdk/python/tests/compiler/testdata/input_artifact_raw_value.py#L53\r\nhttps://github.com/kubeflow/pipelines/blob/fa1cf410f6c0a504d03bb3fc3b0159fcf5fceffc/sdk/python/tests/compiler/testdata/input_artifact_raw_value.yaml#L56-L59",
">will the component.yaml still able to use raw value as input artifacts?\r\n\r\nThere is not change here compared with `ContainerOp`\r\n\r\nPassing constant values to any component inputs is supported by the `component.yaml` design which does not separate inputs by the way the implementation consumes them.\r\n\r\nThis is supported in KFP v1: See the data passing test suite https://github.com/kubeflow/pipelines/blob/9ad37cc2a838fd50a1c2099815402f735ba602cb/samples/test/data_passing.py#L106\r\n\r\nHowever the current version of the Vertex Pipelines compiler (v2 compiler) does not seem to support this yet. Although I've seen PR from Chen that fixes that issue.",
"Thanks @Ark-kun, I converted the example based on the usage in the test suite and it works as expected in V1. We look forward to seeing this feature be supported in V2 as well. "
] | "2021-08-18T23:16:16" | "2021-08-21T00:07:27" | "2021-08-21T00:07:26" | MEMBER | null | ### Environment
* KFP version: 1.7.0-rc3
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP SDK version: 1.7.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
From the testdata [input_artifact_raw_value.py](https://github.com/kubeflow/pipelines/blob/master/sdk/python/tests/compiler/testdata/input_artifact_raw_value.py#L30). We want to know how to convert the `dsl.InputArgumentPath` feature in this example to load from component.yaml.
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/cc @neuromage @chensun @animeshsingh ",
"Changing image name is not supported by component.yaml. The rationale is that `component.yaml` describes a command-line program inside a particular image.\r\n\r\nThere are several workarounds:\r\n\r\n* Use image tags in component.yaml and update the tag in the container registry as needed\r\n* For CI/CD, you might want to patch the component.yaml with freshly-built image\r\n* In general, to support reproducibility, when new image version is released, a new `component.yaml` version should be released alongside, pointing to the new image.\r\n* It might be possible to replace the container image after the component has been instantiated:\r\n\r\n```python\r\nop1 = components.load_component_from_url(...)\r\n\r\ndef my_pipeline():\r\n task1 = op1(...)\r\n task1.container.image = ...\r\n```\r\n",
"Thanks @Ark-kun for the workaround. Replace the container image with `task1.container.image = ...` works for us because we want to keep the backward compatibility for changing image tags using pipeline params. I can close this issue now."
] | "2021-08-18T23:04:42" | "2021-08-20T19:43:12" | "2021-08-20T19:43:12" | MEMBER | null | ### Environment
* KFP version: 1.7.0-rc3
* KFP SDK version: 1.7.0
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
We are trying to convert the [pipelineparams.py](https://github.com/kubeflow/pipelines/blob/master/sdk/python/tests/compiler/testdata/pipelineparams.py#L31) example to load from component.yaml, but we have a hard time to pass the pipeline parameter as the image name. The error we got is about the annotations have variables that are not substituted.
Here is the component we try to change:
```
from kfp import dsl, components
from kubernetes.client.models import V1EnvVar
@dsl.pipeline(name='pipeline-params', description='A pipeline with multiple pipeline params.')
def pipelineparams_pipeline(tag: str = 'latest', sleep_ms: int = 10):
echo = dsl.Sidecar(
name='echo',
image='hashicorp/http-echo:%s' % tag,
args=['-text="hello world"'],
)
op1 = components.load_component_from_text("""
name: download
description: download
outputs:
- {name: data, type: String}
implementation:
container:
image: busy:%s
command:
- sh
- -c
args:
- |
sleep %s; wget localhost:5678 -O $0
- {outputPath: data}
""" % (tag, sleep_ms))()
```
Errors on unresolved PipelineParam in annotations:
```
Traceback (most recent call last):
File "test.py", line 43, in <module>
Compiler().compile(pipelineparams_pipeline, __file__.replace('.py', '.yaml'))
File "/Users/tommyli/Desktop/kfp-new-feature/.venv/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 1061, in compile
self._create_and_write_workflow(
File "/Users/tommyli/Desktop/kfp-new-feature/.venv/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 1121, in _create_and_write_workflow
_validate_workflow(workflow)
File "/Users/tommyli/Desktop/kfp-new-feature/.venv/lib/python3.8/site-packages/kfp/compiler/compiler.py", line 1133, in _validate_workflow
raise RuntimeError(
RuntimeError: Internal compiler error: Found unresolved PipelineParam.
Please create a new issue at https://github.com/kubeflow/pipelines/issues attaching the pipeline code and the pipeline package.
```
### Expected result
The PipelineParam should also be replaced in component annotations
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"/cc @neuromage @chensun @animeshsingh "
] | "2021-08-18T21:48:34" | "2021-09-29T18:27:09" | "2021-09-29T18:27:09" | MEMBER | null | ### Environment
* KFP version: 1.7.0-rc3
* KFP SDK version: 1.7.0
* All dependencies version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
### Steps to reproduce
Below is a simple pipeline to run a parallel loop inside another parallel loop. This works on the V1 compiler but not the V2 IR compiler. The errors we got is about incorrect parameters.
Example pipeline:
```python
import kfp.dsl as dsl
from kfp import components
import kfp
op1_yaml = '''\
name: 'my-in-coop1'
inputs:
- {name: item, type: Integer}
- {name: my_pipe_param, type: Integer}
implementation:
container:
image: library/bash:4.4.23
command: ['sh', '-c']
args:
- |
set -e
echo op1 "$0" "$1"
- {inputValue: item}
- {inputValue: my_pipe_param}
'''
op11_yaml = '''\
name: 'my-inner-inner-coop'
inputs:
- {name: item, type: Integer}
- {name: inner_item, type: Integer}
- {name: my_pipe_param, type: Integer}
implementation:
container:
image: library/bash:4.4.23
command: ['sh', '-c']
args:
- |
set -e
echo op11 "$0" "$1" "$2"
- {inputValue: item}
- {inputValue: inner_item}
- {inputValue: my_pipe_param}
'''
op2_yaml = '''\
name: 'my-in-coop2'
inputs:
- {name: item, type: Integer}
implementation:
container:
image: library/bash:4.4.23
command: ['sh', '-c']
args:
- |
set -e
echo op2 "$0"
- {inputValue: item}
'''
op_out_yaml = '''\
name: 'my-out-cop'
inputs:
- {name: my_pipe_param, type: Integer}
implementation:
container:
image: library/bash:4.4.23
command: ['sh', '-c']
args:
- |
set -e
echo "$0"
- {inputValue: my_pipe_param}
'''
@dsl.pipeline(name='my-pipeline')
def pipeline(my_pipe_param: int = 10):
loop_args = [1, 2]
with dsl.ParallelFor(loop_args) as item:
op1_template = components.load_component_from_text(op1_yaml)
op1 = op1_template(item, my_pipe_param)
with dsl.ParallelFor([100, 200, 300]) as inner_item:
op11_template = components.load_component_from_text(op11_yaml)
op11 = op11_template(item, inner_item, my_pipe_param)
op2_template = components.load_component_from_text(op2_yaml)
op2 = op2_template(item)
op_out_template = components.load_component_from_text(op_out_yaml)
op_out = op_out_template(my_pipe_param)
if __name__ == '__main__':
from kfp.compiler import Compiler
Compiler().compile(pipeline, __file__.replace('.py', '.yaml')) # V1 works
kfp.v2.compiler.Compiler().compile(pipeline, __file__.replace('.py','.json')) # V2 IR has errors
```
Errors we got from the v2 IR compiler:
```
Traceback (most recent call last):
File "test.py", line 92, in <module>
kfp.v2.compiler.Compiler().compile(pipeline, __file__.replace('.py','.json'))
File "/Users/tommyli/opt/anaconda3/envs/py3/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py", line 1137, in compile
pipeline_job = self._create_pipeline_v2(
File "/Users/tommyli/opt/anaconda3/envs/py3/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py", line 1093, in _create_pipeline_v2
pipeline_spec = self._create_pipeline_spec(
File "/Users/tommyli/opt/anaconda3/envs/py3/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py", line 915, in _create_pipeline_spec
self._group_to_dag_spec(
File "/Users/tommyli/opt/anaconda3/envs/py3/lib/python3.8/site-packages/kfp/v2/compiler/compiler.py", line 684, in _group_to_dag_spec
dsl_component_spec.update_task_inputs_spec(
File "/Users/tommyli/opt/anaconda3/envs/py3/lib/python3.8/site-packages/kfp/dsl/component_spec.py", line 336, in update_task_inputs_spec
assert component_input_parameter in parent_component_inputs.parameters, \
AssertionError: component_input_parameter: pipelineparam--pipelineparam--loop-item-param-1 not found. All inputs: parameters {
key: "pipelineparam--loop-item-param-3"
value {
type: STRING
}
}
parameters {
key: "pipelineparam--my_pipe_param"
value {
type: INT
}
}
```
### Expected result
The IR should able to map the input parameters for sub-dag in sub-dag similar to the V1 Argo compiler.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"You can install https://modheader.com/guide/\r\n\r\nThen add the request header: X-Goog-Authenticated-User-Email with value: `accounts.google.com:<user-email-address>`",
"Works as expected. Thank you"
] | "2021-08-18T15:38:55" | "2021-09-28T16:12:41" | "2021-09-28T16:12:40" | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
on-prem Kubernetes cluster - using manifest repo
* KFP version: 1.6.0
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
Kubeflow with multi tenancy is deployed in on-prem Kubernetes cluster. Everything is working as expected.
Now trying to do local development for frontend. Followed all instruction to build and run frontend by proxying node server to k8s. I started getting below error,
`Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace.`
After making below changes, above error got resolved and started getting new error,
Under `index.tsx`
```
ReactDOM.render(
KFP_FLAGS.DEPLOYMENT === Deployments.KUBEFLOW ? (
<NamespaceContextProvider>{app}</NamespaceContextProvider>
) : (
// Uncomment the following for namespace switch during development.
<NamespaceContext.Provider value='kubeflow-sharath'>{app}</NamespaceContext.Provider>
// <NamespaceContext.Provider value={undefined}>{app}</NamespaceContext.Provider>
),
document.getElementById('root'),
);
```
New Error,
```
Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header
```
Can you please help me to start frontend application locally with multi-tenancy enabled?
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
Start frontend application locally for local development
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Hello @pigbrain , if you use `http://...` to connect with KFP API, does it work without verifying ssl? ",
"@zijianjoy Hello, yes, it works fine with http. but, If I add https, I get the above error.",
"@zijianjoy Could you check this issue ? ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"@pigbrain , what is your deployment environment?\r\n\r\n1. Is it full fledged Kubeflow? Or KFP standalone?\r\n2. Which cloud provider do you use? Or is it on-prem environment?\r\n3. How do you reproduce this error?\r\n4. Do you have any failing Deployment in your cluster?",
"We need support for custom CA's, not all certificates are publicly trusted, for example if you are using Kubernetes CSR's (Certificate Signing Requests) the certificate will be issued by the Control Plane CA, and the root CA needs to be added to the trusted certificates, just the same, when working inside an enterprise setup we would need to trust certificates issued internally.\r\n\r\nIn both of these cases we need to use `https` so having the capability to tell the pipeline software to trust certain certificates becomes a necessity "
] | "2021-08-18T13:52:57" | "2022-09-28T14:41:18" | null | NONE | null | I am getting this error in my environment.
```
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='..., port=443): Max retries exceeded with url: /apis/v1beta1/healthz (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1091)')))
```
To avoid this, `Configuration.verify_ssl` should be set to `False`.
However, there doesn't seem to be a way to set this value now.
Do we have any alternatives? | {
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"Work required for migration:\r\n* we need to support running notebooks in v2 sample test (or start to test .py equivalents of those notebooks)\r\n* we need to support tfx samples in v2 sample test\r\n\r\nBonus:\r\n* ~~if we support building component images and replacing component imports, we can also migrate component tests to v2 sample test infra (but they should still be separate tests).~~ Just found out the only component test has been commented, we can just remove it: https://github.com/kubeflow/pipelines/blob/44228c242b6ea778c192de19ad9ade85531d9c48/test/component_test.yaml#L55-L76.",
"TODO:\r\n* [x] update documentation\r\n* [ ] https://github.com/kubeflow/pipelines/issues/7120"
] | "2021-08-18T09:45:35" | "2021-12-30T08:22:54" | "2021-12-30T08:22:54" | CONTRIBUTOR | null | ## Goal
Unify test infra to reduce maintenance cost on v1 sample test, v1 component test and v1 integration test.
## How
Migrate all the tests mentioned above to v2 sample test infra.
## Why
v2 sample test (designed in https://github.com/kubeflow/pipelines/issues/3505) is easy to debug, stabler and faster (1h+ vs 20min) than v1 sample test, because
* (debuggability) it is easy to debug a test pipeline failure using KFP UI
* (faster) it does not build and deploy the entire KFP cluster
* (stabler) it adds resource requests & limits for all tasks
* (stabler) it adds timeouts for pipelines
* (stabler) it adds retry strategy with both onError and onFailure
There is no obvious difference between v1 and v2 sample test coverage on the samples.
The only difference on coverage is verification of KFP api / argo workflow stability. We used to identify many KFP/argo stability issues from flaky e2e/v1 sample tests.
I think the lost coverage is not critical, because KFP api / argo workflow stability is still covered by:
* e2e tests
* during each release we first release and deploy an RC version to v2 sample test cluster, so that we use each RC release to verify backend stability. | {
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"Hi @zijianjoy thank you for the demo today during the pipeline meeting, it's really great to see all the changes happening in the UI V2.\r\n\r\nTwo feedback on possible improvements:\r\n- I personally think that animation of the lines could be distracting especially for a complicated pipeline. Would it be possible to have an option to turn off the animation and have regular arrow lines instead? \r\n- For interactive sub DAG, would it be possible to see how many layers exist at the root? I am not sure if this was the case already or not, but it would be really great to see all layers show up when you first click on DAG so that it will be easy to navigate to n layer without clicking n times\r\n\r\nLet me know if you need any clarification! Thanks!",
"Thank you @annajung for the feedback!\r\n\r\n> I personally think that animation of the lines could be distracting especially for a complicated pipeline. Would it be possible to have an option to turn off the animation and have regular arrow lines instead?\r\n\r\nYes it is possible to disable animation. In fact, we will not show animation for `pipeline template`. In the future, I imagine only the `pipeline run`'s edges with currently `running` state will have such animation, which restricts the number of animated edges. We can also show regular arrow edges in the first iteration, and make a decision towards animated edge in the future.\r\n\r\n> For interactive sub DAG, would it be possible to see how many layers exist at the root? I am not sure if this was the case already or not, but it would be really great to see all layers show up when you first click on DAG so that it will be easy to navigate to n layer without clicking n times\r\n\r\nIt sounds nice to be able to land on nested sub-DAG directly. Can you explain more about the experience on how to see all layers at root? From my understanding, the sub-DAGs structure is like a tree, so each layer will have arbitrary amount of sub-DAG children. Therefore seeing all layers on root is going to be a long list easily. \r\n\r\nMaybe something we can consider is to provide dropdown list in the breadcrumb bar, so you can select the sub-DAG of current layer. It is very similar to how to select a folder and view the folder content by double-clicking in any Operating System.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle frozen"
] | "2021-08-18T03:11:14" | "2022-03-11T05:45:18" | null | COLLABORATOR | null | ### Feature Area
/area frontend
### Description
We are planning to introduce modern KFP UI in the upcoming V2 release. The current focus is around visualization of DAG workflow. Refer to [[External] KFP UI DAG v2 Early Community Sharing](https://docs.google.com/presentation/d/1hb_HaKFEd6g5FE_F5VjbAGSzqOxmjdkYJa5uwGyGv9E/edit#slide=id.ge8140e3d6f_0_527) slidedeck for a sneak peek of new DAG workflow and our main goals in UI V2.
### Request for feedback
We are looking for early feedback about this UI change, and would love to hear from you about any UI feature you want to see in KFP V2. Feel free to comment below or create individual GitHub issues and track them here.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"@BenoitLeguay You can create a json parameter which allows you to specify arbitrary key-value pair. ",
"I will try that ! Thank you @zijianjoy ! Are there any examples or documentation on this method ?",
"@BenoitLeguay You just need to provide a stringify format of JSON to the parameter. For example:\r\n\r\n```\r\nimport kfp\r\nfrom kfp import dsl\r\nfrom kfp.components import create_component_from_func\r\n\r\n\r\n@create_component_from_func\r\ndef test(json_param: str):\r\n print(json_param)\r\n\r\n\r\n@dsl.pipeline(name='bug-json-param')\r\ndef my_pipeline(\r\n json_param: str = '{\"a\":\"b\"}',\r\n value: str = 'c',\r\n):\r\n test(json_param).set_display_name('test1')\r\n test('{\"a\":\"' + f'{value}' + '\"}').set_display_name('test2')\r\n\r\n\r\nif __name__ == '__main__':\r\n kfp.Client().create_run_from_pipeline_func(\r\n my_pipeline,\r\n arguments={}\r\n )\r\n```\r\n\r\nOr if you compile any pipeline with parameter input, then upload them via KFP UI. You can paste `{}` to the `Run parameters` to open JSON editor:\r\n\r\n\r\n",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-17T16:12:14" | "2022-03-02T10:06:40" | null | NONE | null | This feature request comes from [this issue](https://github.com/kubeflow/kubeflow/issues/5639) from @daenuprobst. @DavidSpek asked him to move it from kubeflow/kubeflow to kubeflow/pipelines but as the author did not do it and because I'm really interested in such a feature, I do it by my own. (I only copy paste the issue tell me if you need more info)
/kind feature
Why you need this feature:
When creating a pipeline that exposes a relatively large (5-10+) number of run parameters, the generated list of parameter input fields in the frontend becomes confusing (when creating a new run). Especially when there are parameters with the same name / function for multiple steps in the pipeline, the only way to make them distinguishable is to give them increasingly complex variable names. This can easily lead to mistakes when users create new runs.
Describe the solution you'd like:
For each pipeline parameter there should be a possibility so specify attributes such as a group and a description, which will then be reflected in the frontend. A dict in the dsl.pipeline decorator might be a good place to add this additional information. | {
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* [x] #7043
* [x] #7047
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"WIP",
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Logs
```
time="2021-08-13T18:25:45.311Z" level=info msg="capturing logs" argo=true
I0813 18:25:45.368149 15 cache.go:120] Connecting to cache endpoint 10.119.242.61:8887
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead:
https://pip.pypa.io/warnings/venv
Loading KFP component module ephemeral_component from dir /tmp/tmp.ibcrdjWT5C
W0813 18:26:51.561024 15 launcher.go:548] Local filepath "/gcs/jamxl-kfp-bucket/v2/pipeline/metrics-visualization-pipeline/6872a7f7-70e7-470b-937b-9475b684ba1d/wine-classification/metrics" does not exist
time="2021-08-13T18:26:51.674Z" level=info msg="/tmp/outputs/metrics/data -> /var/run/argo/outputs/artifacts//tmp/outputs/metrics/data.tgz" argo=true
time="2021-08-13T18:26:51.675Z" level=info msg="Taring /tmp/outputs/metrics/data"
```
_Originally posted by @zijianjoy in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-898656999_ | {
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"Hi @alanhdu, \r\n\r\nI think it's a known limitation that v1 components (resulted from `create_component_from_func` doesn't really support `Dict` and `List` typed inputs but expect user code to handle the serialization and deserialization. \r\n\r\nWith v2 components (resulted from `create_component_from_func_v2` or `@kfp.v2.dsl.component` decorator), we do support automatic json serialization and deserialization. Here's a [sample](https://github.com/kubeflow/pipelines/blob/50f7dcd5d0ef87a35eba12000b4c3b44f8fe275e/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py) showing the case. Note that v2 components runs on KFP v2 compatible mode and on Vertex Pipelines but not on KFP v1 (the current default) mode.\r\n\r\n> there are a lot of variations types that should arguably be mapped to the JsonObject (e.g. Dict[str, float], Dict[str, int], Dict[str, str], etc). In particular, even a function with def f(x: Dict) will have the name typing.Dict instead of Dict, so this remapping list doesn't actually do much.\r\n\r\nI believe this was fixed by https://github.com/kubeflow/pipelines/pull/5153. \r\n\r\n> Second, it's weird that \"unrecognized types\" get automatically mapped to strings -- from a user's perspective, I would much rather throw an error when we can't recognize the type name than to have it automagically be mapped to a string and fail at runtime.\r\n\r\nI totally agree with you on this. If we were starting from scratch again, I'd vote for enforcing types and no implicit type guessing/converting. In v2 design, we're putting more strict rules on types. But unfortunately we can't just error out on any \"unrecognized types\", that would make quite a number of existing pipelines stop working. \r\nIn v2, \"unrecognized types\" would be viewed as generic artifact type instead of string parameter type. And we do not allow passing artifacts to parameters or vice versa. In general, we're trying to require more explicitness from the user. You may find more context from a related issue: https://github.com/kubeflow/pipelines/issues/5801.\r\n",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-13T18:12:32" | "2022-03-03T00:05:14" | null | NONE | null | ### Environment
I am using Python 3.8.6 with:
```
kfp==1.6.2
kfp-pipeline-spec==0.1.9
kfp-server-api==1.6.0
```
### Steps to reproduce
If you run:
```python
from typing import Dict
from kfp.components import create_component_from_func
def add(xs: Dict) -> float:
"""Calculates sum of two arguments"""
return xs["a"] + xs["b"]
create_component_from_func(add, output_component_file="add_component.yaml")
```
and look at the output component, you will see that this dictionary is actually being inteprreted as a string:
```python
_parser.add_argument("--xs", dest="xs", type=str, required=True, default=argparse.SUPPRESS)
```
### Expected result
The parse argument should look like:
```python
_parser.add_argument("--xs", dest="xs", type=json.loads, required=True, default=argparse.SUPPRESS)
```
### Materials and Reference
I think there are actually two things wrong here. One of them is that the type name normalization in
https://github.com/kubeflow/pipelines/blob/ee8cbfa31cbf1164ed78b6f1adacc4c31c5c00d9/sdk/python/kfp/components/_data_passing.py#L128-L132
and
https://github.com/kubeflow/pipelines/blob/ee8cbfa31cbf1164ed78b6f1adacc4c31c5c00d9/sdk/python/kfp/components/_data_passing.py#L115
is super naive -- there are a lot of variations types that should arguably be mapped to the JsonObject (e.g. `Dict[str, float]`, `Dict[str, int]`, `Dict[str, str]`, etc). In particular, even a function with `def f(x: Dict)` will have the name `typing.Dict` instead of `Dict`, so this remapping list doesn't actually do much. Only doing `def f(x: dict)` seems to work reliably.
Second, it's weird that "unrecognized types" get automatically mapped to strings -- from a user's perspective, I would *much* rather throw an error when we can't recognize the type name than to have it automagically be mapped to a string and fail at runtime. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6336 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6336/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6336/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6336/events | https://github.com/kubeflow/pipelines/issues/6336 | 970,501,364 | MDU6SXNzdWU5NzA1MDEzNjQ= | 6,336 | [feature] Expose run metrics | {
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"hi @zijianjoy, is there any docs or at least high level idea how this should work ? I'm new to Kubeflow pipelines and looking for a good first issue. ",
"Once you get the argo workflow payload, you should be able to get the corresponding metrics from it. \r\n\r\nRefer to Get run API: https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/#operation--apis-v1beta1-runs--run_id--get, where you can look for `workflow_manifest` in `apiPipelineRuntime`.\r\n\r\nOn UI, you can use this example to get a run: https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/frontend/src/pages/RunDetailsRouter.tsx#L37",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Would love to see this as well, currently it's possible to modify the `argo_workflows` workflow YAML after compiling a pipeline and inject the metric specs into the workflow's container templates, though this is far from ideal. "
] | "2021-08-13T15:25:24" | "2022-05-13T02:24:06" | null | CONTRIBUTOR | null | ### Feature Area
### What feature would you like to see?
I would like to be able to instrument runs with metrics for latency, status, retries, etc. Ideally it would be possible to set an instrumentation policy for all runs. For example, Apache Airflow exposes status and latency by DAG and task by default: https://airflow.apache.org/docs/apache-airflow/stable/logging-monitoring/metrics.html. In terms of implementation, I could imagine adding these metrics to the existing prometheus endpoint in KFP, or exposing the `metrics` option in the argo workflow definition to allow users to configure prometheus metrics for latencies and statuses: https://argoproj.github.io/argo-workflows/metrics/#metric-spec.
### What is the use case or pain point?
I'm interested in building dashboards and alerts without writing custom code to instrument each pipeline.
### Is there a workaround currently?
It's possible to poll the KFP API to generate metrics, but it's inefficient and requires custom code.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Please upvote the issue if it'll be helpful to you!\r\nThanks",
"~~Besides, a great improvement only possible with a tool is that, with `docker inspect`, you must pull the image locally before inspecting its command. However, if we can build a tool, the tool can directly call the container registry API to get the command and args without pulling the image locally.~~\r\n\r\nEDIT: I did not find a container registry agnostic API to get this info.",
"For anyone interested, is it fair to assume you installed docker on your computer and we can use the docker-py library for accessing docker?\r\nhttps://docker-py.readthedocs.io/en/stable/",
"Hi, I am interested in this issue.\r\n\r\n> is it fair to assume you installed docker on your computer\r\n\r\nYes, my team uses docker for each local machine.\r\n\r\n> we can use the docker-py library for accessing docker?\r\n\r\nI think it is acceptable for us. We hired poetry to manage our local & cloud (container) python environment to keep our environment separated from our system python environment. So any tools written in python are acceptable as well as they can be installed from PyPI.",
"Thanks for the confirmation @AseiSugiyama!\r\nI'm still waiting for more interest feedback.\r\n\r\nIn the meantime, if anyone wants to build the tool, welcome doing so!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-13T04:42:25" | "2022-03-02T21:05:35" | null | CONTRIBUTOR | null | For both emissary executor and v2 compatible mode, KFP components must specify `command` explicitly in the component yaml, refer to https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#migrate-pipeline-components-to-run-on-emissary-executor
I think we could easily build a component migration tool which can be called like:
```
kfp component migrate <path-to-component-yaml>
```
The CLI does the following:
1. read the component yaml
2. verify if command is specified
2. if not, call docker pull && docker inspect to get the command, modify component yaml to include the command
it might be more helpful migrating a folder of components via:
```
kfp component migrate --recursive <folder-with-components>
```
However, there is no good way to identify KFP components, we should probably improve on that.
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/6252#issuecomment-895023582_ | {
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"/assign @Bobgy \r\n/assign @capri-xiyue ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-13T01:02:16" | "2022-03-02T21:05:24" | null | COLLABORATOR | null | How should cache work when everything is same except the pipeline_root?
For example,
pipeline #1 uses minio://xxxx as the pipeline_root
pipeline #2 uses gs://xxxx as the pipeline_root
other configs of pipeline#1 and pipeline#2 are same.
Now pipeline #2 will hit the cache of pipeline #1 but it won't use the pipeline root under gs://xxxx, it will use the pipeline root under minio://xxxx as what pipeline#1 does.
Discussed with @Bobgy @IronPan, this is the expected behavior.
EDIT by @Bobgy :
However, before implementing support for downloading artifacts not from the pipeline root, the pipeline still breaks.
TODOs:
* [ ] add this as a known caveat
* [x] when supporting importer, we need to do the same fix
EDIT by @capri-xiyue
For shared pipelines, cross-namespace cache hit is not allowed
TODOs:
* [x] add namespace as part of the cache scope
_Originally posted by @capri-xiyue in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897242291_ | {
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"/assign @chensun \r\n\r\n/cc @Bobgy "
] | "2021-08-13T00:58:04" | "2021-08-16T08:02:06" | "2021-08-16T08:02:06" | COLLABORATOR | null | Compiling the Vertex Pipeline sample produced an error:
...
/opt/conda/lib/python3.7/site-packages/kfp/compiler/v2_compat.py in update_op(op, pipeline_name, pipeline_root, launcher_image)
127 k8s_client.V1EnvFromSource(config_map_ref=config_map_ref))
128
--> 129 op.arguments = list(op.container_spec.command) + list(op.container_spec.args)
130
131 runtime_info = {
AttributeError: 'NoneType' object has no attribute 'command'
_Originally posted by @james-jwu in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897237479_ | {
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] | "2021-08-12T16:26:59" | "2022-03-11T05:42:12" | null | COLLABORATOR | null | The status icon doesn't get updated until refresh the page. (Is this not supported yet or a bug?)
_Originally posted by @rui5i in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897233672_ | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6315 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6315/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6315/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6315/events | https://github.com/kubeflow/pipelines/issues/6315 | 968,381,727 | MDU6SXNzdWU5NjgzODE3Mjc= | 6,315 | [feature] kfp CLI support 'kfp run get <run_id>' in detailed information | {
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"This feature makes sense, welcome contribution!",
"/assign @anencore94 "
] | "2021-08-12T08:51:47" | "2021-08-30T11:21:25" | "2021-08-30T11:21:25" | MEMBER | null | ### Feature Area
/area sdk
### What feature would you like to see?
Currently, `kfp run get <run_id>` only shows ["run id", "name", "status", "created at"].
However, user could want to see other information of the run.
### What is the use case or pain point?
For my case, I want to fetch my **run's output metrics** with kfp CLI
### Is there a workaround currently?
Since `kfp.Client()` already have `get_run()` method in sdk, which have all information including metrics of run,
I think the UX is only what matters: Which information would be useful or useless, and how user could select them.
For example, I guess `pipeline_Spec.workflow_manifest` could always be omitted, since nobody wants such information.
Therefore, I think with `click.option('--detail')` in get() method in `pipelines/sdk/python/kfp/cli/run.py` shows all information except `pipeline_Spec.workflow_manifest` in json format would be fine.
However, if there is other suggestions, please let me know.
Also, If you guys are ok, I'd like to took this issue and implement this feature.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Hello @ChenhanXmu , have you explored using multi-tenancy mode? does it fit your use case?https://www.kubeflow.org/docs/components/pipelines/multi-user/",
"Thanks，I want to deploy KFP alone, but Kubeflow Pipelines multi-user isolation is only supported in the full Kubeflow deployment. \r\nBy the way, is there a minimal deployment plan for KFP multi-user isolation?",
"Since multi-tenancy components live outside the KFP repository, there is no such plan to support multi-user isolation with KFP standalone, to my knowledge. Refer to installation options: https://www.kubeflow.org/docs/components/pipelines/installation/overview/#choosing-an-installation-option",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-12T07:58:40" | "2022-03-02T18:05:34" | null | NONE | null | ### Feature Area
Support runs in different namespaces
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"Hello @ChenhanXmu , Kubernetes 1.12 has been a very old version, which is deprecated for support. Can you try kubernetes 1.18?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-12T07:51:25" | "2022-03-02T21:05:37" | null | NONE | null | ### Feature Area
What version of kubeflow pipeline can be deployed standalone for k8s v1.12?
### What feature would you like to see?
1. What version of kubeflow pipeline can be installed separately for k8s 1.12? I want to see the comparison table of kubeflow pipeline and k8s version.
2. I want to see the functional differences between different versions of kubeflow pipeline. | {
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"before this error shows up, there was a different error that we should also fix:\r\nhttps://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1425263307043901440#1:build-log.txt%3A9072\r\n\r\nThe error seems like a bug in argo, docker executor also do not support optional output artifacts:\r\n> executor error: path /tmp/outputs/MLPipeline_UI_metadata/data does not exist in archive /tmp/argo/outputs/artifacts/launch-python-MLPipeline-UI-metadata.tgz\\ngithub.com/argoproj/argo-workflows/v3/errors.New\\n\\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:49\\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\\n\\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:55\\\r\n\r\nEDIT: I'm not sure now, because I found a passing postsubmit test after 1.7.0-rc.3 release, that proves current argo version can successfully run postsubmit tests.\r\n\r\n\r\nEDIT2: I think I'm getting closer to the root cause, here's argo outputs annotation for the failed step in dataflow pipeline sample:\r\n\r\n> workflows.argoproj.io/outputs: '{\"artifacts\":[{\"name\":\"mlpipeline-ui-metadata\",\"path\":\"/tmp/outputs/MLPipeline_UI_metadata/data\",\"optional\":true},{\"name\":\"launch-python-MLPipeline-UI-metadata\",\"path\":\"/tmp/outputs/MLPipeline_UI_metadata/data\"},{\"name\":\"launch-python-job_id\",\"path\":\"/tmp/outputs/job_id/data\"},{\"name\":\"main-logs\",\"s3\":{\"key\":\"artifacts/dataflow-launch-python-pipeline-8hdph/2021/08/11/dataflow-launch-python-pipeline-8hdph-2670089030/main.log\"}}]}'\r\n\r\nNote that \"launch-python-MLPipeline-UI-metadata\" artifact should have \"optional\": \"true\", but it didn't. That caused the task to fail, so I guess this is a side effect bug caused by one of the recent KFP v1 compiler changes.\r\n\r\nreference: pod info for the failed pod in dataflow sample pipeline: https://storage.googleapis.com/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1425515476460507136/artifacts/pods_info/dataflow-launch-python-pipeline-8hdph-2670089030.txt",
"Thanks @Bobgy. Can you point me to the pipeline sample that fails? ",
"/assign @neuromage ",
"/reopen\r\npostsubmit test is still failing: https://github.com/kubeflow/pipelines/commit/f743dde32f9c468afd6028c282d2440c3e01ecf6",
"@Bobgy: Reopened this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/6311#issuecomment-898202285):\n\n>/reopen\r\n>postsubmit test is still failing: https://github.com/kubeflow/pipelines/commit/f743dde32f9c468afd6028c282d2440c3e01ecf6\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>",
"Build starts to fail again, the error message shows up when building inverse proxy image:\r\n> Digest: sha256:2e3c5ecd1a55b32056f3ce0c4aaac05e31b85c361d501bf3f8a81bec14c4fe87\r\nStatus: Downloaded newer image for gcr.io/inverting-proxy/agent@sha256:2e3c5ecd1a55b32056f3ce0c4aaac05e31b85c361d501bf3f8a81bec14c4fe87\r\n ---> ce436ce6c655\r\nStep 2/14 : RUN apt-get update && apt-get install -y curl jq python-pip\r\n ---> Running in 29b8ef43d3d1\r\nGet:1 http://deb.debian.org/debian buster InRelease [122 kB]\r\nGet:2 http://security.debian.org/debian-security buster/updates InRelease [65.4 kB]\r\nGet:3 http://deb.debian.org/debian buster-updates InRelease [51.9 kB]\r\nReading package lists...\r\nE: Repository 'http://security.debian.org/debian-security buster/updates InRelease' changed its 'Suite' value from 'stable' to 'oldstable'\r\nE: Repository 'http://deb.debian.org/debian buster InRelease' changed its 'Suite' value from 'stable' to 'oldstable'\r\nE: Repository 'http://deb.debian.org/debian buster-updates InRelease' changed its 'Suite' value from 'stable-updates' to 'oldstable-updates'\r\nThe command '/bin/sh -c apt-get update && apt-get install -y curl jq python-pip' returned a non-zero code: 100\r\n\r\nhttps://forums.linuxmint.com/viewtopic.php?t=355148&p=2053997 seems to describe the problem\r\n\r\nI think this basically means inverse proxy debian image is too old, if we used a newer version, it should not hit the problem.\r\n\r\nUPDATE: the latest version of inverse proxy image still has an outdated debian version.\r\ngcr.io/inverting-proxy/agent\r\n\r\nWe can workaround by:\r\n```\r\napt update --allow-releaseinfo-change\r\n```\r\n\r\nUPDATE2: sent https://github.com/kubeflow/pipelines/pull/6351\r\nUPDATE3: after above PR is merged, build recovers and postsubmit test fails like before as mentioned in https://github.com/kubeflow/pipelines/issues/6311#issuecomment-898202285.",
"The latest postsubmit test run still fails, but only container builder and parameterized tfx samples are failing.\r\ncontainer builder failed because kaniko job times out, it's expected flakiness.\r\nTFX sample will be fixed by jiyong.\r\n\r\nhttps://storage.googleapis.com/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1427197975674753024/build-log.txt",
"The TFX sample fails with:\r\n```\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: tensorflow.python.framework.errors_impl.InvalidArgumentError: Error executing an HTTP request: HTTP response code 400 with body '{\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"error\": {\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"code\": 400,\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"message\": \"Invalid bucket name: '{{kfp-default-bucket}}'\",\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"errors\": [\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: {\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"message\": \"Invalid bucket name: '{{kfp-default-bucket}}'\",\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"domain\": \"global\",\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \"reason\": \"invalid\"\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: }\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: ]\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: }\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: }\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: '\r\nsample-test-xcp5g-222029471: parameterized-tfx-oss-7qb9g-305390489: \t when reading metadata of gs://{{kfp-default-bucket}}/tfx_taxi_simple/fe7841ca-0e29-4989-b13a-e32ea09b5d58/model_serving/1629110410\r\nsample-test-xcp5g-222029471: \r\n```\r\nso it seems parameterization isn't fully working in the test infra.\r\n\r\nlog: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1427197975674753024#1:build-log.txt%3A16035",
"I got the the error message even though I filled in {{kfp-default-bucket}} from the pipeline parameter.\r\nIt seems that the placeholder in model pusher is not resolved.\r\n\r\ncc @jiyongjung0 ",
"besides tfx pipeline issues, I see the following error message in test infra very often:\r\n```\r\nfailed to wait for main container to complete: timed out waiting for the condition: container does not exist\r\n```\r\nexample pod https://storage.googleapis.com/oss-prow/pr-logs/pull/kubeflow_pipelines/6363/kubeflow-pipeline-e2e-test/1427862991075807232/artifacts/pods_info/integration-test-c7z5r-4280080516.txt\r\n\r\nidentified the root cause and sent a fix to argo https://github.com/argoproj/argo-workflows/pull/6561",
"There's one last thing, we reached model count quota again, because of https://github.com/kubeflow/pipelines/issues/2356"
] | "2021-08-12T06:07:12" | "2021-08-20T03:35:35" | "2021-08-18T14:12:35" | CONTRIBUTOR | null | https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-standalone-component-test/1425587097510612992#1:build-log.txt%3A5394
For standalone components test, it fails when building sample test image:
> sample-test-rb7fv-2769807467: error: google-api-core 1.26.0 is installed but google-api-core<3.0dev,>=1.29.0 is required by {'google-cloud-storage'}
sample-test-rb7fv-2769807467: The command '/bin/sh -c cd /sdk/python && python3 setup.py install' returned a non-zero code: 1
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"/assign @capri-xiyue \r\n\r\nUpdate kubeflow.org",
"TODO(Bobgy): update the configmap name to `kfp-runtime`\r\nEDIT: decided to not rename, because it's a breaking change, and after we put all the code into kfp launcher, it's fine calling the configmap kfp launcher too.",
"https://github.com/kubeflow/website/pull/2909 submitted"
] | "2021-08-12T04:58:23" | "2021-09-21T18:15:44" | "2021-09-21T18:15:44" | CONTRIBUTOR | null | We need documentation for pipeline root
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897254432_ | {
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"/assign @zijianjoy "
] | "2021-08-12T04:32:53" | "2021-08-13T07:57:54" | "2021-08-13T07:57:54" | CONTRIBUTOR | null | UI bug, for v2 compatible mode, output artifacts have URI "gcs://xxxx", but it should be "gs://xxxx", when using gcs bucket as pipeline root.
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897236847_ | {
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"/assign @chensun ",
"I agree with the consistency on naming it `pipeline-root`. Also TFX is using the term `pipeline-root`: https://www.tensorflow.org/tfx/guide/build_tfx_pipeline#using_the_pipeline_class. "
] | "2021-08-12T04:26:11" | "2021-08-13T07:41:54" | "2021-08-13T07:41:54" | CONTRIBUTOR | null | pipeline root parameter is called `pipeline-output-directory` in KFP UI/SDK
can we rename it to `pipeline-root` to be consistent with SDK API?
_Originally posted by @Bobgy in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897227634_ | {
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"/assign @zijianjoy ",
"Also update http://cloud/ai-platform/pipelines/docs/getting-started",
"https://github.com/kubeflow/pipelines/blob/master/README.md"
] | "2021-08-12T04:24:21" | "2021-08-19T05:04:52" | "2021-08-19T05:04:52" | CONTRIBUTOR | null | FR: add v2 DSL sample to the "Getting Started" page
The sample was added as a built-in Pipeline sample. We should add it to the "Getting Started" page as well.
EDIT:
similar FR by @Bobgy: add v2 DSL documentation to "Getting Started" page
_Originally posted by @james-jwu in https://github.com/kubeflow/pipelines/issues/5779#issuecomment-897225710_ | {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Code seems to have moved to https://github.com/kubeflow/pipelines/blob/f167e15c3ca9b2120f8bb2283b0f3974439fef2f/sdk/python/kfp/components/types/type_utils.py"
] | "2021-08-11T19:09:52" | "2023-05-25T09:36:37" | null | NONE | null | ### Feature Area
</area sdk>
</area components>
### What feature would you like to see?
Right now, the Python components can only input + output a fairly limited set of types (essentially JSON, with some special case support for "file-like" objects / paths). It'd be great if we could plug-in support for a richer set of types where the serialization/deserialization happens *outside* of the Python component.
### What is the use case or pain point?
We are currently trying to use Kubeflow pipelines as part of our research workflow. As part of this, we have scientists (who are not well-versed in Argo or Kubernetes) writing Python function-based components. A lot of the research code, however, requires "complicated objects" -- whether those are NumPy arrays, Pandas Dataframes, or even just sophisticated `dataclass`es for "structured" configuration. These are not natively supported by Kubeflow pipelines, but all of them have straightfoward serialization/deserialization methods we can provide to and from strings (e.g. pandas dataframes can be represented by an S3 URI pointing to CSV that we serialize/deserialize from). Right now, scientists have to provide that boilerplate themselves, which takes a lot of time.
Concrete, it'd be nice if turn something like:
```python
# Old way
import cattr
def run(config: dict):
config = cattr.structure(config, Config)
def aggregate(metrics_path: comp.InputPath(str), output_path: comp.OutputPath(str)):
df = pd.read_csv(metrics_path)
output = process(df)
df.to_csv(output_path)
## New Way
def run(config: Config):
... # automatically serislaized/deserialized from JSON
def aggregate(metrics: pd.DataFrame) -> pd.DataFrame:
... # Path munging happens automatically
```
### Is there a workaround currently?
I haven't implemented these yet, but I could see a couple of implementation options:
1. Hook into the converters at https://github.com/kubeflow/pipelines/blob/a6ab4e4411dcb700e751c458f47d69857a65ee7a/sdk/python/kfp/components/_data_passing.py#L109-L125 to add special serialization + deserialization hooks for the relevant classes.
2. Implement a special `Json` generic type and a `@to_json` decorator that basically does:
```python
@to_json
def run(config: Json[Config]):
pass
```
`Json[T]` would implement the `to_dict` function to register itself as just JSON from kubeflow's POV. `to_json` would handle serializing / deserializing the `Json[T]` to and from `T` based on the type annotation. | {
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"Content:\r\n\r\n1. How to show a sub-DAG node on Canvas? What is the interaction and ID for this node?\r\n2. If click on sub-DAG node, what content to show about this node's detail?\r\n3. If expand on sub-DAG node, how to visualize the internal content of this sub-DAG?\r\n4. How does a user to navigate back to parent DAG?\r\n5. How does external edges connect to sub-DAG (input and output)? What is the difference between parameters and artifacts?\r\n",
"> Content:\r\n> \r\n> 1. How to show a sub-DAG node on Canvas? What is the interaction and ID for this node?\r\n\r\nShow a node which is similar to Execution node, allow user to view detail of it, and allow user to click the expand button to enter the subDAG level. Also have a breadcrumb to allow user to go back to parent layer.\r\n\r\n> 2. If click on sub-DAG node, what content to show about this node's detail?\r\n\r\nInput/Output Parameters and Artifacts.\r\n\r\n> 3. If expand on sub-DAG node, how to visualize the internal content of this sub-DAG?\r\n\r\nWhile being in subDAG layer, have a parent node as the generator of Artifacts from outside the SubDAG. The ancestors layer is past to this element so the execution and artifact in this layer will be shown. The parent node is the same as the SubDAG node in its presentation in parent layer.\r\n\r\n> 4. How does a user to navigate back to parent DAG?\r\n\r\nUse the back button or click the parent layer in breadcrumb.\r\n\r\n> 5. How does external edges connect to sub-DAG (input and output)? What is the difference between parameters and artifacts?\r\n\r\nThe parent node in sub-DAG layer is the bridge, Artifact nodes are upstream of this sub-DAG in parent layer, and are downstream of this sub-DAG in child layer. Parameter is the same but with only edge to/from Execution, but not Artifact node.\r\n"
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"/assign @zijianjoy ",
"another minor problem:\r\n\r\n\r\nthe html visualization does not fill the width in the sidepanel",
"I spent a very long time but couldn't install the TFX successfully in my notebook. Let me fix the minor issue first: https://github.com/kubeflow/pipelines/pull/6319",
"cc @jiyongjung0 Would you like to validate the visualization after TFX upgrade is successful? And would you like to share the reproduce steps here too? Thank you!",
"Note, TFX sample is still failing in postsubmit https://github.com/kubeflow/pipelines/issues/6311#issuecomment-899454628",
"let's keep tracking the issue in https://github.com/kubeflow/pipelines/issues/6138\r\n\r\nno need to have two separate threads"
] | "2021-08-11T08:51:19" | "2021-08-19T05:17:44" | "2021-08-19T05:17:43" | CONTRIBUTOR | null | ### What steps did you take
1. install AI platform pipelines 1.7.0-rc.3
2. run builtin tfx taxi sample
3. check tasks with visualizations
### What happened:
visualization failed:
example validator fails with
> NotFoundError: The specified path gs://gongyuan-dev-kubeflowpipelines-default/tfx_taxi_simple/540e7c51-ee89-4488-bc6a-b40a423b1988/ExampleValidator/anomalies/66/Split-train was not found.
statistics gen fails with
> NotFoundError: Could not find directory gs://gongyuan-dev-kubeflowpipelines-default/tfx_taxi_simple/540e7c51-ee89-4488-bc6a-b40a423b1988/StatisticsGen/statistics/60/Split-eval
### What did you expect to happen:
they should visualize properly
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I think it might not be a problem for our visualization code. I believe we made a change to accommodate for TFX 1.0 -- there was a breaking change. Therefore, to run the sample properly, we need to upgrade the sample to use TFX 1.0+.
### Labels
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---
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Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"@Hmr-ramzi Hello, KFP is using Argo workflow, so you can use the same approach to configure artifact repository: https://argoproj.github.io/argo-workflows/configure-artifact-repository/.\r\n\r\nHow do you connect with a minio gateway, and how do you connect to a minio tenant? Appreciate it for sharing reproduce steps so we can dig in further.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-11T08:36:10" | "2022-03-03T00:05:17" | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
Hello Everyone,
I am currently trying to connect kubeflow pipelines to instead of using an own minio deloyment it would use minio tenant as an underlying file system to kubeflow pipelines. I was able to connect it to a minio gateway (just a simple gateway to a cloud provider) but i am unable to connect it to a minio tenant. Any advise or documentation on the matter ?
### What feature would you like to see?
Connecting Kubeflow pipelines to a minio tenant
<!-- Provide a description of this feature and the user experience. -->
### What is the use case or pain point?
Scaling up the file system
<!-- It helps us understand the benefit of this feature for your use case. -->
### Is there a workaround currently?
Not sure! Thats why i am asking
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"UPDATE: PR merged https://github.com/kubeflow/pipelines/pull/6331\r\n\r\nmkp \"use emissary executor\" option tool tip is malformed, we cannot use markdown there:\r\n",
"UPDATE: fixed\r\n\r\nkfp gcp marketplace -- support section should include github issues, instead of slack channel, because maintainers only check github issues regularly. Also, we should add a link to documentations.\r\n\r\n",
"we also want to add emissary executor feedback issue link to documentation\r\n\r\nEDIT: done",
"we need to add documentation in AI Platform Pipelines installation doc.",
"Done"
] | "2021-08-11T08:13:02" | "2021-09-03T03:13:56" | "2021-09-03T03:13:55" | CONTRIBUTOR | null | https://github.com/kubeflow/pipelines/issues/5779#issuecomment-896761095
needs to be fixed
public doc: https://kubeflow.org/docs/components/pipelines/installation/choose-executor/ | {
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"same issue.",
"This will be released in rc4",
"I get this when trying to run pipelines with tasks that were created from `kfp.components.create_component_from_func_v2`. an example of a compiled task:\r\n\r\n```\r\nkind: Pod\r\napiVersion: v1\r\nmetadata:\r\n name: <name>\r\n namespace: cchow\r\n selfLink: /api/v1/namespaces/cchow/pods/<id>\r\n uid: e295b3e5-c390-4e28-b1b0-0e3f2a6d92ad\r\n resourceVersion: '128321282'\r\n creationTimestamp: '2021-09-15T18:50:34Z'\r\n labels:\r\n RunID: blah <-- I put this in here for testing\r\n pipelines.kubeflow.org/cache_enabled: 'true'\r\n pipelines.kubeflow.org/enable_caching: 'true'\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.7.0\r\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\r\n pipelines.kubeflow.org/v2_component: 'true'\r\n workflows.argoproj.io/completed: 'true'\r\n workflows.argoproj.io/workflow: <workflow>\r\n\r\n<a lot of other stuff>\r\n```",
"@chensun is `create_component_from_func_v2` a supported feature?\r\nI'm not seeing usage of it in our samples.\r\n\r\n@ckchow what is your KFP SDK version?",
"I tried to write a v2 component (i.e. a component that outputs stuff via `model: Output[Model], Output[Metrics]` instead of returning a NamedTuple), and the kfp sdk told me I needed to use the `_v2` creation function.\r\n\r\nI'm using `kfp==1.7.0`",
"Can you try https://www.kubeflow.org/docs/components/pipelines/sdk/v2/python-function-components/?",
"ok. I think I have avoided this issue on some other pipeline by using the `@component` annotation, which I assume calls some other, non-deprecated function.",
"update: I ran into this issue again when using a kubeflow cluster. I had only managed to avoid this problem when I was using kfp on vertex ai.\r\n\r\ncomponent\r\n```\r\nfrom kfp.v2.dsl import component\r\n\r\n<...>\r\n\r\n## preprocessor\r\n@component(\r\n base_image='blah'\r\n)\r\ndef preprocess(\r\n ...\r\n)\r\n```\r\n\r\nkubeflow log\r\n```\r\nF1005 07:56:11.389530 1 main.go:50] Failed to create component launcher: Invalid launcher options: must specify RunID\r\n```\r\n\r\nI can post the pod yaml if necessary. (it's a bit large)",
"@ckchow @Bobgy I'm getting the same error. I've implemented a toy pipeline using the [kfp v2 sdk](https://www.kubeflow.org/docs/components/pipelines/sdk/v2/python-function-components/). I'm on kfp==1.8.4 and compiling my pipeline in v2 compat mode --> I upload my pipeline to an [arrikto minikf aws deployment](https://www.kubeflow.org/docs/distributions/minikf/minikf-aws/) using the generated yaml, run an experiment, and my first component fails with the same error message.\r\n\r\n",
"To be clear, this seems to pop up beyond the original reported context of recurring runs. This just happened to be the only issue I saw in the project that had the same error text that I saw.",
"Hi, I am facing the same issue on version 1.8.9, without recurring runs does anyone know how to work around this?",
"Facing the same issue on kfp version 1.8.9 without recurring runs. I used kfp v2 sdk to write my pipeline, then compiled it using `kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile(\r\n pipeline_func=pipeline_func,\r\n package_path='my_pipeline.yaml')` and then uploaded the yaml file to a local Arrikto MiniKF deployment. Get the following error message `Failed to create component launcher: Invalid launcher options: must specify RunID`",
"I recall that the Arrikto distribution does not have the same behavior for run ID. Can you use an official installation to test this.",
"hi there, I am having the same issue by using SDK V2 to create a simple pipeline. I deployed kubeflow on a blank new EKS by following installation on kubeflow manifest, and using\r\n```python\r\nkfp==1.8.10\r\nkfp-pipeline-spec==0.1.13\r\nkfp-server-api==1.7.1\r\n```\r\n\r\nCode is as followed:\r\n\r\n```python\r\nimport kfp\r\nimport kfp.dsl as dsl\r\nfrom kfp.v2.dsl import component\r\n\r\n@component\r\ndef add(a: float, b: float) -> float:\r\n return a + b\r\n\r\n@dsl.pipeline(\r\n name='addition-pipeline',\r\n description='An example pipeline that performs addition calculations.',\r\n)\r\ndef add_pipeline(a: float, b: float):\r\n add_task = add(a, b)\r\n \r\nclient = kfp.Client()\r\nclient.create_run_from_pipeline_func(\r\n add_pipeline,\r\n arguments={'a': 7, 'b': 8},\r\n mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE,\r\n)\r\n```\r\nFYI: If I modified my code to SDK V1, it totally worked. \r\n\r\nThe complete error message\r\n```\r\nF0119 21:21:04.908316 1 main.go:50] Failed to create component launcher: Invalid launcher options: must specify RunID\r\n```",
"Closing because https://github.com/kubeflow/pipelines/issues/6829"
] | "2021-08-11T07:20:40" | "2022-01-20T12:23:52" | "2022-01-20T12:23:52" | CONTRIBUTOR | null | null | {
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"/assign @zijianjoy ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-11T07:13:14" | "2022-03-05T02:11:40" | null | CONTRIBUTOR | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)? ai platform pipelines
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7.0-rc.3
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
1. go to recurring runs list page
2. notice the button is "Create Run", click it
3. I get to the create run page, but it defaults to create a normal run
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
the default should be chosen to create a recurring run
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
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"Maybe this is related https://github.com/kubeflow/pipelines/issues/5257",
"duplicate #5257"
] | "2021-08-10T01:12:37" | "2021-08-20T01:03:18" | "2021-08-20T01:03:18" | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
From the manifests repository
* KFP version:
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
1.5.1
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
1.6.2
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
Using Kubeflow multi-user:
1.) Create a pipeline which utilizes VolumeOp https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.VolumeOp
2.) Create run of pipeline in a user's namespace. This should pass
3.) Login as a different user and use the same pipeline to create a run. This will fail because the pipeline will try and reuse the volume created in the first users namespace and fail to find it in the current namespace
### Expected result
<!-- What should the correct behavior be? -->
I would expect the pipeline to create a new PVC in the new namespace
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"@InsertCoolNameHere Hello, would you like to report this issue on https://github.com/google/ml-metadata/issues?",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"I met the same issue today..\r\n```\r\n$: kubectl logs -f -n kubeflow metadata-grpc-deployment-6f6f7776c5-wlphl \r\nWARNING: Logging before InitGoogleLogging() is written to STDERR\r\nF0519 02:59:39.447938 1 metadata_store_server_main.cc:236] Check failed: absl::OkStatus() == status (OK vs. INTERNAL: mysql_real_connect failed: errno: , error: [mysql-error-info='']) MetadataStore cannot be created with the given connection config.\r\n*** Check failure stack trace: ***\r\n\r\n```\r\nAfter a while, it would output some errors like \r\n some tables does not exists...\r\n\r\nwhich is the same as @[InsertCoolNameHere](https://github.com/InsertCoolNameHere) said;\r\n"
] | "2021-08-09T22:31:56" | "2022-05-19T03:12:43" | null | NONE | null | ### What steps did you take
We are attempting to deploy the kubeflow pipeline over a local cent-os cluster using kustomize, as mentioned in https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize
### What happened:
However, after the deployment steps, we are seeing the following status of the pods:
```bash
NAME READY STATUS RESTARTS AGE
cache-deployer-deployment-567877df7d-9xm4s 1/1 Running 0 10m
cache-server-79f7ffcff7-znv8r 1/1 Running 0 10m
metadata-envoy-deployment-68dd689b45-nzcfj 1/1 Running 0 10m
metadata-grpc-deployment-748f868f64-6qr8s 0/1 CrashLoopBackOff 7 10m
metadata-writer-7bff7dcc9b-fmrw2 1/1 Running 0 10m
minio-75f599659f-8pz96 1/1 Running 0 10m
ml-pipeline-6f6fcf5ddf-tqs2s 0/1 CrashLoopBackOff 6 10m
ml-pipeline-persistenceagent-5d6894bf96-x4hz5 1/1 Running 4 10m
ml-pipeline-scheduledworkflow-777d64b5ff-q5sl2 1/1 Running 0 10m
ml-pipeline-ui-5f8f4545df-vk9d6 1/1 Running 0 10m
ml-pipeline-viewer-crd-57c8c9f48f-dsbn8 1/1 Running 0 10m
ml-pipeline-visualizationserver-589d77745d-jvwtv 1/1 Running 0 10m
mysql-656d9cd878-dklck 1/1 Running 0 10m
workflow-controller-d95c6bd8b-bvql2 1/1 Running 0 10m
```
It seemed to us that the metadata-grps pod is the main issue, that leads to the ml-pipeline not being able to extract the necessary parameters. The following are a portion of the logs from the metadata-grpc pod:
```bash
➜ kustomize git:(master) ✗ kubectl -n kubeflow logs metadata-grpc-deployment-748f868f64-6qr8s
2021-08-09 20:41:20.603421: W ml_metadata/metadata_store/metadata_store_server_main.cc:221] Connection Aborted with error: Aborted: There are a subset of tables in MLMD instance. This may be due to concurrent connection to the empty database. Please retry the connection. checks: 15 errors: 7, present tables: type_table, parent_type_table, type_property_table, artifact_table, artifact_property_table, execution_table, execution_property_table, event_table, missing tables: event_path_table, mlmd_env_table, context_table, parent_context_table, context_property_table, association_table, attribution_table Errors: mysql_query failed: errno: 1146, error: Table 'metadb.EventPath' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.MLMDEnv' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.Context' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.ParentContext' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.ContextProperty' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.Association' doesn't exist
mysql_query failed: errno: 1146, error: Table 'metadb.Attribution' doesn't exist
2021-08-09 20:41:20.603496: I ml_metadata/metadata_store/metadata_store_server_main.cc:222] Retry attempt 0
```
We have also attached the table in the mysql database under the metadb schema:
Database changed
mysql> show tables;
+-------------------+
| Tables_in_metadb |
+-------------------+
| Artifact |
| ArtifactProperty |
| Event |
| Execution |
| ExecutionProperty |
| ParentType |
| Type |
| TypeProperty |
+-------------------+
### What did you expect to happen:
Successful deployment of all pipeline-related pods and jobs being submitted from the front-end UI
### Environment:
Client Version: version.Info{Major:"1", Minor:"20", GitVersion:"v1.20.4", GitCommit:"e87da0bd6e03ec3fea7933c4b5263d151aafd07c", GitTreeState:"clean", BuildDate:"2021-02-18T16:12:00Z", GoVersion:"go1.15.8", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"20", GitVersion:"v1.20.8", GitCommit:"5575935422cc1cf5169dfc8847cb587aa47bac5a", GitTreeState:"clean", BuildDate:"2021-06-16T12:53:07Z", GoVersion:"go1.15.13", Compiler:"gc", Platform:"linux/amd64"}
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)?
On a custom Kubernetes deployment.
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
1.7.0-rc2
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
* KFP SDK version:
<!-- Specify the output of the following shell command: $pip list | grep kfp -->
kfp (1.6.6)
kfp-pipeline-spec (0.1.8)
kfp-server-api (1.6.0)
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area testing -->
<!-- /area samples -->
/area components
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6269 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6269/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6269/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6269/events | https://github.com/kubeflow/pipelines/issues/6269 | 963,678,457 | MDU6SXNzdWU5NjM2Nzg0NTc= | 6,269 | large tarfile extraction: tarfile.ReadError: file could not be opened successfully | {
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"https://argoproj.github.io/argo-workflows/examples/#artifacts: Artifacts are packaged as Tarballs and gzipped by default. \r\n\r\nYou can try not to unzip the file when reading, because it is automatically unzip by argo.\r\n\r\nOr you can try KFP v2 compatible to see whether it fixes your issue.",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-09T06:11:13" | "2022-03-02T21:05:36" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version:
kfp 1.6.3
kfp-pipeline-spec 0.1.8
kfp-server-api 1.6.0
kfp-tekton 0.9.0
### Steps to reproduce
Just run the below pipeline:
`import kfp
import kfp.components as comp
def extract_img(file_path: comp.InputPath('Tarball'),
):
import glob
import pandas as pd
import tarfile
import logging
logging.basicConfig(level=logging.INFO)
logging.info('opening tarfile: '+file_path)
with tarfile.TarFile.open(name = file_path, mode = 'r:*') as archive:
for member in archive:
try:
file_obj = archive.extractfile(member)
except zipfile.error as e:
logging.info(file_obj)
ccreate_step_extract_img = kfp.components.create_component_from_func(
func=extract_img,
output_component_file='component.yaml', # This is optional. It saves the component spec for future use.
base_image='python:3.7',
packages_to_install=['pandas==1.1.4'])
web_downloader_op = kfp.components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/web/Download/component.yaml')
def my_pipeline(url):
web_downloader_task = web_downloader_op(url=url)
extract_img_task = create_step_extract_img(file=web_downloader_task.outputs['data'])
kfp.compiler.Compiler().compile(
pipeline_func=my_pipeline,
package_path='pipeline.yaml')
`
### Expected result
The tarfile should be extracted
### Materials and Reference
I ran the pipeline on gcp kubeflow for a tarfile size of 150MB, it keep getting the below error. Is there any limitation with the size of file that I can extract in /tmp? If so how I can fix the issue?
`
INFO:root:opening tarfile: /tmp/inputs/file/data
Traceback (most recent call last):
File "/tmp/tmp.HTDYHMwP7m", line 22, in <module>
_outputs = merge_csv(**_parsed_args)
File "/tmp/tmp.HTDYHMwP7m", line 10, in merge_csv
with tarfile.TarFile.open(name = file_path, mode = 'r:*') as archive:
File "/usr/local/lib/python3.7/tarfile.py", line 1580, in open
raise ReadError("file could not be opened successfully")
tarfile.ReadError: file could not be opened successfully`
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Thank you for explaining the use case clearly!\nThis seems helpful to many.\nSo welcome contributions!",
"@Bobgy any pointers to how to add a field? Seems I can should add it https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/backend/api/pipeline.proto#L324 but then not sure how to propagate this across server api and such (or if it needs it somewhere else) or it needs some DB migration. (Im less familiar w the inner workings of KFP sorry)",
"Backend code: https://github.com/kubeflow/pipelines/tree/master/backend\nBackend api pipeline version proto: https://github.com/kubeflow/pipelines/blob/7872bd4fee870d4b912c6a0862106b42a5179c09/backend/api/pipeline.proto#L324\nRegenerate code after updating proto, https://github.com/kubeflow/pipelines/tree/master/backend/api#auto-generation-of-go-client-and-swagger-definitions\nPipeline server: \nhttps://github.com/kubeflow/pipelines/blob/7872bd4fee870d4b912c6a0862106b42a5179c09/backend/src/apiserver/server/pipeline_server.go#L332\nResource manager: \nhttps://github.com/kubeflow/pipelines/blob/7872bd4fee870d4b912c6a0862106b42a5179c09/backend/src/apiserver/resource/resource_manager.go#L1205\nPipeline store (db layer): https://github.com/kubeflow/pipelines/blob/7872bd4fee870d4b912c6a0862106b42a5179c09/backend/src/apiserver/storage/pipeline_store.go#L67\nUI pipeline details page:\nhttps://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/PipelineDetails.tsx\n\nHere are most of related code you need to touch, feel free to ask for help if you have further questions!",
"Started backend changes in #6261 . I regenerated most code and seems backend is working (mostly just ran unit tests to ensure data was being written to DB and could be read back). Missing to run some integration tests (need to spend more time to add integration tests).",
"Thank you for implementing the backend API change! Welcome continuing the UI integration, but you can always leave that to a different contributor too.",
"I'm happy to contribute to UI as well. Using this as a learning opportunity.\r\n\r\nHowever if someone else comes w this issue and wants to implement it, let me know! (I will add to my backlog once backend gets merged, but I dont know when I can focus on this)",
"Start frontend changes, found out that I missed an endpoint for backend (the HTTP upload one, as those dont use grpc)",
"Will also start a separate PR to update kfp sdk",
"There's a report the SDK change caused a regression:\r\n```\r\n pipeline_id=pipeline_id)\r\n File \"/tmpfs/BUILD_ENV/.venv/lib/python3.7/site-packages/kfp/_client.py\", line 1143, in upload_pipeline_version\r\n description=description,\r\n File \"/tmpfs/BUILD_ENV/.venv/lib/python3.7/site-packages/kfp_server_api/api/pipeline_upload_service_api.py\", line 209, in upload_pipeline_version\r\n return self.upload_pipeline_version_with_http_info(uploadfile, **kwargs) # noqa: E501\r\n File \"/tmpfs/BUILD_ENV/.venv/lib/python3.7/site-packages/kfp_server_api/api/pipeline_upload_service_api.py\", line 265, in upload_pipeline_version_with_http_info\r\n \" to method upload_pipeline_version\" % key\r\nkfp_server_api.exceptions.ApiTypeError: Got an unexpected keyword argument 'description' to method upload_pipeline_version\r\n```",
"I'll be taking a look",
"@Bobgy This mismatch is present in the latest released sdk package 1.7.2. Could you build a new patch version with this fixed?\r\n",
"btw, fixed it https://github.com/kubeflow/pipelines/pull/6472 ",
"Merged. I believe now the only missing part is for this to be released on the next KFP release",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-06T18:26:37" | "2022-03-02T15:05:17" | null | CONTRIBUTOR | null | ### Feature Area
/area frontend
/area backend
### What feature would you like to see?
While using KFP, we lack a way to add descriptions to the pipeline version. This would be useful for example to track metadata of the version uploaded (like for example what git sha was this version generated from).
### What is the use case or pain point?
Easier integration into CD systems for continous compilation and deployment of pipeline versions.
### Is there a workaround currently?
Hard to do, you can modify the description of the main pipeline and append new lines for each version, that said it may eventually overload that column and make it hard to read vs just having it on the version itself.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"duplicate #6304"
] | "2021-08-06T17:33:24" | "2021-08-20T01:12:15" | "2021-08-20T01:12:15" | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
<!-- /area samples -->
<!-- /area components -->
### What feature would you like to see?
<!-- Provide a description of this feature and the user experience. -->
When using python functions to create stages using `kfp.components.func_to_container_op` is not possible to return different kinds of data types like `pd.DataFrame` or `tf.data.Dataset`, just to mention a few, would be great if we could return different kinds of data types that are common use for **Dataset**.
### What is the use case or pain point?
<!-- It helps us understand the benefit of this feature for your use case. -->
Return the whole dataset between stages without having to create a persistent volume.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
Building an image for each stage and adding a persistent volume.
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Hi @prasadkyp7 . KFP now supports authentication using ServiceAccountTokens. This was implemented recently. You can find details in the ref issue. \r\nRef issue: #5138 \r\nRef PR: #5676 \r\nYou can look at a sample PodDefault config here https://github.com/kubeflow/pipelines/pull/5676#issuecomment-874755408",
"How about ttl logic.\r\n\r\nclient.create_run_from_pipeline_func(TTL_pipeline,namespace=\"dev\", arguments={},experiment_name=\"TEST\",pipeline_conf=pipeline_conf.set_ttl_seconds_after_finished(seconds=10))\r\n\r\nttl is not working to delete the pods after completion. we upgraded to new kubeflow as it was mentioned that it was a new feature added. But its not working . any update on this?\r\n",
"tracking issue in \r\nhttps://github.com/kubeflow/pipelines/issues/5545"
] | "2021-08-06T16:33:49" | "2021-08-20T01:14:43" | "2021-08-20T01:14:43" | NONE | null | I am trying to use the kfp.client in KUBEFLOW 1.2 and passing cookies for user authorization. I am not finding any other solution to use KFP API or kfp.client.
This is very weird way of creating/using kfp.client.
Is there any other way to pass username and password for authorization of using kfp.client or any other rest api and create a run?? | {
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"I think we could easily build a component migration tool which can be called like:\r\n```\r\nkfp component migrate <path-to-component-yaml>\r\n```\r\n\r\nThe CLI does the following:\r\n1. read the component yaml\r\n2. verify if command is specified\r\n2. if not, call docker pull && docker inspect to get the command, modify component yaml to include the command\r\n\r\nit might be more helpful migrating a folder of components via:\r\n```\r\nkfp component migrate --recursive <folder-with-components>\r\n```\r\nHowever, there is no good way to identify KFP components, we should probably improve on that.\r\n",
"Note that some component.yaml from the search query are only for unit testing, and not meant to be runnable, so they may use a dummy image, for example: https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/serving_component.yaml\r\n\r\nOn the other hand, it's possible that some component yaml aren't showing up if the filename isn't suffixed with \"component.yaml\" (not sure if all component follows this convention).\r\n\r\nI think for the specific actions you described above, maybe a script named something like `back_fill_container_command` or even a script inlined in a README.md somewhere would be just fine. \r\n\r\nThe CLI command `kfp component migrate` sounds too generic, it might give users a wrong idea that it could make any v1 components runnable on v2 (or Vertex Pipeline), which won't be feasible for a number of causes, including: missing type annotation, misuse of placeholders, etc.",
"Actually, out of the 24 results, there are 11 false positives:\r\n\r\nUnit tests with fake image:\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/serving_component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/trainer_component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/fancy_trainer_component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/ingestion_component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/if_placeholder_component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/v2/compiler_cli_tests/test_data/component_yaml/concat_placeholder_component.yaml\r\n\r\nempty file:\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/components/pipeline_component_repository.yaml\r\n\r\ngraph component:\r\n- https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/sdk/python/kfp/components_tests/test_data/retail_product_stockout_prediction_pipeline.component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/561622c9f8d2fe404dc8252ebc0a77e684a55c37/components/XGBoost/Cross_validation_for_regression/from_CSV/component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/561622c9f8d2fe404dc8252ebc0a77e684a55c37/components/XGBoost/Train_and_cross-validate_regression/from_CSV/component.yaml\r\n- https://github.com/kubeflow/pipelines/blob/561622c9f8d2fe404dc8252ebc0a77e684a55c37/components/XGBoost/Train_regression_and_calculate_metrics/from_CSV/component.yaml\r\n\r\n",
"Thanks for figuring them out!\r\nI'll start with a separate CLI tool then, one strong reason I want a tool instead of sth else is that:\r\n```\r\ndocker image inspect -f '{{.Config.Entrypoint}} {{.Config.Cmd}}' hello-world\r\n```\r\nthis depends on pulling the image locally, it will be time consuming to migrate many components. I think migrations should be as easy as possible -- to make users easily stay with the latest versions.\r\n\r\nFor a CLI tool, we can use docker API to only pull the image digests, that'll be much faster.",
"https://medium.com/hackernoon/inspecting-docker-images-without-pulling-them-4de53d34a604",
"Hmm, getting an image's digest does not seem to include their command and args information.\r\n\r\n```\r\nimport docker\r\n\r\nclient = docker.from_env()\r\ndata = client.images.get_registry_data(\r\n 'gcr.io/ml-pipeline/api-server:1.7.0-rc.3'\r\n)\r\nprint(data.attrs)\r\n```\r\n\r\nI got:\r\n```\r\n{'Descriptor': {'mediaType': 'application/vnd.docker.distribution.manifest.v2+json', 'digest': 'sha256:21b2f020425b2a99188c5c80b6eb04722a373b6e2e54e97b723caca1fbd9ddc9', 'size': 2209}, 'Platforms': [{'architecture': 'amd64', 'os': 'linux'}]}\r\n```\r\n\r\ncommand and args are not there",
"However, according to https://github.com/distribution/distribution/issues/1252#issuecomment-274944254, this should be possible, just not documented.",
"Anyway, I cannot find a container registry generic way to do so. I'll stick with `docker pull` for now."
] | "2021-08-06T11:03:08" | "2021-08-17T06:43:42" | "2021-08-17T06:43:42" | CONTRIBUTOR | null | The following 24 components do not have a command in their component yaml:
https://github.com/search?l=YAML&p=3&q=repo%3Akubeflow%2Fpipelines+filename%3Acomponent.yaml+-command&type=Code | {
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"If you have already tried emissary executor:\r\n* thumbs up on this comment if it works well for you\r\n* thumbs down on this comment if you hit some problems (and welcome a separate comment about the problem you hit)",
"Hello @Bogby, I am trying out the emmisary executor with Argo workflows. It appears that simply applying a configmap specifying the emmisary executor is sufficient to break Argo. After application, the workflow will launch, but pods do not initialize. Has anyone else experienced this issue or can you tell me what am I doing wrong?\r\n\r\nmy configmap\r\n```\r\n---\r\n{\r\n \"apiVersion\": \"v1\",\r\n \"data\": {\r\n \"containerRuntimeExecutor\": \"emmisary\",\r\n \"instanceID\": \"hippocampus-controller\",\r\n \"workflowDefaults\": \"{\\\"metadata\\\": {},\\\"spec\\\": {}}\"\r\n },\r\n \"kind\": \"ConfigMap\",\r\n \"metadata\": {\r\n \"name\": \"workflow-controller-configmap\",\r\n }\r\n}\r\n```\r\n\r\nI am testing two different workflows in the default and argo namespaces\r\n\r\n1) hello-world \r\n```\r\nargo submit --watch https://raw.githubusercontent.com/argoproj/argo-workflows/master/examples/hello-world.yaml\r\n```\r\n\r\n2) a test workflow\r\n```\r\n---\r\n{\r\n \"apiVersion\": \"argoproj.io/v1alpha1\",\r\n \"kind\": \"Workflow\",\r\n \"metadata\": {\r\n \"annotations\": {\r\n \"argo\": \"workflows\"\r\n },\r\n \"generateName\": \"hello-test-\",\r\n \"labels\": {\r\n \"workflows.argoproj.io/archive-strategy\": \"false\"\r\n }\r\n },\r\n \"spec\": {\r\n \"entrypoint\": \"entrypoint\",\r\n \"parallelism\": 3,\r\n \"podGC\": {\r\n \"strategy\": \"OnWorkflowSuccess\"\r\n },\r\n \"securityContext\": {\r\n \"runAsNonRoot\": true,\r\n # \"runAsUser\": 1000,\r\n# \"runAsGroup\": 3000,\r\n# \"fsGroup\": 2000,\r\n },\r\n \"templates\": [\r\n {\r\n \"dag\": {\r\n \"tasks\": [\r\n {\r\n \"name\": \"h\",\r\n \"template\": \"hello\"\r\n }\r\n ]\r\n },\r\n \"name\": \"entrypoint\"\r\n },\r\n {\r\n \"name\": \"hello\",\r\n \"script\": {\r\n \"image\": \"alpine:latest\",\r\n \"imagePullPolicy\": \"IfNotPresent\",\r\n \"command\": [\"bin/sh\", \"-c\"],\r\n \"source\": \"echo Hello; exit\",\r\n \"securityContext\": {\r\n \"runAsNonRoot\": true,\r\n },\r\n },\r\n } \r\n ],\r\n \"ttlStrategy\": {\r\n \"secondsAfterSuccess\": 5\r\n }\r\n }\r\n}\r\n...\r\n\r\n```",
"Hi @wdma, thank you for trying out!\n\nYes, when updating the config argo will behave in a completely different mode. If you hit issues, we can help you investigate.\n\nCan you report a separate issue with more details? E.g. can you copy all logs/events for a Pod that do not start?",
"Hi @Bobgy Thank you for your response. I have been working through a range of issues on the Argo Github site. They have been very helpful and I have emissary working for the most part. Can you tell me if this (latest) issue might be related to emissary (https://github.com/argoproj/argo-workflows/issues/6942)? \r\n\r\nIf so, I would greatly appreciate your insights. If not, you can go ahead and close this as I think I am now past my issues with the emissary executor :)\r\n",
"I can report for a team of 10 data scientists that all pipelines in v1 mode work with runasnonroot. I think you should make it the default.",
"@Bogby can you tell me if some setting in emissary might be the cause of my current problem? I can now successfully execute a workflow, but I have no control over the serviceaccount and the permissions for whichever serviceaccount is used are insuffucient. I have described the problem in detail here https://stackoverflow.com/questions/69607918/serviceaccount-name-does-not-stick\r\n\r\nIn short,I think this may be two problems (permissions issue and the service account issue), but I am not certain where to start teasing these two out. I like the fact that I can runasnonroot and just want it to work!",
"> @Bogby can you tell me if some setting in emissary might be the cause of my current problem? I can now successfully execute a workflow, but I have no control over the serviceaccount and the permissions for whichever serviceaccount is used are insuffucient. I have described the problem in detail here https://stackoverflow.com/questions/69607918/serviceaccount-name-does-not-stick\r\n> \r\n> In short,I think this may be two problems (permissions issue and the service account issue), but I am not certain where to start teasing these two out. I like the fact that I can runasnonroot and just want it to work\r\n\r\nJust give default-editor (or whatever service account you are using the necessary permissions) a full Kubeflow install does this by default. Since you are using Argo directly without the kubeflow pipelines SDK you are in the wrong GitHub project.",
"@juliusvonkohout thank you for your response. I have solved this problem. Will note the project in the future!",
"@bobgy Argo itself will make it the default in argo>=3.3. according to https://argoproj.github.io/argo-workflows/workflow-executors/#emissary-emissary",
"Thanks for the FYI, it's good to see that we are making the same choice!",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"https://github.com/kubeflow/pipelines/issues/7986 Trying to convert docx to pdf with libreoffice. it fails there.",
"This may be relevant here: https://github.com/kubeflow/pipelines/issues/9292. Seems to be a bug related to background processes in emissary that is fixed in the latest Argo."
] | "2021-08-06T05:49:29" | "2023-05-16T20:07:52" | null | CONTRIBUTOR | null | Part of https://github.com/kubeflow/pipelines/issues/5718
Emissary Executor Documentation: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/
As proposed, we want to ask for feedback about emissary executor.
Emissary executor is currently in **Alpha** state, but the Kubeflow Pipelines team is actively helping to make it mature. The team has been able to run Kubeflow Pipelines test infra stably with emissary executor. We are now releasing emissary executor as an option when deploying KFP and we are considering making it the default in the future.
Therefore, welcome feedbacks & bug reports for emissary executor!
For **feedbacks**, comment on this issue.
For **bug reports**, create a separate issue logging the details and comment about it here. | {
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"received_events_url": "https://api.github.com/users/zijianjoy/received_events",
"type": "User",
"site_admin": false
} | [
{
"login": "zijianjoy",
"id": 37026441,
"node_id": "MDQ6VXNlcjM3MDI2NDQx",
"avatar_url": "https://avatars.githubusercontent.com/u/37026441?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/zijianjoy",
"html_url": "https://github.com/zijianjoy",
"followers_url": "https://api.github.com/users/zijianjoy/followers",
"following_url": "https://api.github.com/users/zijianjoy/following{/other_user}",
"gists_url": "https://api.github.com/users/zijianjoy/gists{/gist_id}",
"starred_url": "https://api.github.com/users/zijianjoy/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/zijianjoy/subscriptions",
"organizations_url": "https://api.github.com/users/zijianjoy/orgs",
"repos_url": "https://api.github.com/users/zijianjoy/repos",
"events_url": "https://api.github.com/users/zijianjoy/events{/privacy}",
"received_events_url": "https://api.github.com/users/zijianjoy/received_events",
"type": "User",
"site_admin": false
}
] | null | [
"Hello @raphaelTrench , would you like to try KFP backend 1.7.0-rc.2 ? We need to update documentation about it as well.",
"Just to add I'm getting this error too but rerunning usually fixes the problem. \r\n\r\nSeems to happen usually when I haven't run anything for a while before triggering. Possibly when a new node has just spun up but can't say for sure. Running on azure aks.\r\n\r\n```\r\nkfp 1.8.3\r\nkfp-pipeline-spec 0.1.11\r\nkfp-server-api 1.7.0\r\n```",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-05T13:43:49" | "2022-03-02T18:05:08" | null | NONE | null | ### Environment
* How did you deploy Kubeflow Pipelines (KFP)?
[https://github.com/kubeflow/manifests#installation](https://github.com/kubeflow/manifests#installation)
Option 2
* KFP version:
1.5.1
* KFP SDK version:
1.6.4
### Steps to reproduce
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
When running any pipeline compiled with the mode parameter set as `PipelineExecutionMode.V2_COMPATIBLE`, the pipeline instantly fails with the following error in the logs of the first step/component:
F0720 14:17:55.461007 29 main.go:55] Failed to execute component: unable to get pipeline with PipelineName "my-pipeline" PipelineRunID "my-pipeline-2wqhf": Failed GetContextByTypeAndName(type="kfp.Pipeline", name="my-pipeline")
### Expected result
<!-- What should the correct behavior be? -->
### Materials and Reference
This happened when following the sample pipeline presented in https://www.kubeflow.org/docs/components/pipelines/sdk/v2/build-pipeline/
However,I have also tried it with other, different pipelines and this error only happens when compiling to V2. When nothing is passed to. the mode param, the pipelines work just fine.
which generated the following pipeline.yaml:
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: my-pipeline-
annotations:
pipelines.kubeflow.org/kfp_sdk_version: 1.6.4
pipelines.kubeflow.org/pipeline_compilation_time: '2021-07-20T11:08:36.264302'
pipelines.kubeflow.org/pipeline_spec: '{"inputs": [{"name": "url", "type": "String"},
{"default": "", "name": "pipeline-output-directory"}, {"default": "my-pipeline",
"name": "pipeline-name"}], "name": "my-pipeline"}'
pipelines.kubeflow.org/v2_pipeline: "true"
labels:
pipelines.kubeflow.org/v2_pipeline: "true"
pipelines.kubeflow.org/kfp_sdk_version: 1.6.4
spec:
entrypoint: my-pipeline
templates:
- name: download-data-kfp-sdk-v2
container:
args:
- sh
- -exc
- |
url="$0"
output_path="$1"
curl_options="$2"
mkdir -p "$(dirname "$output_path")"
curl --get "$url" --output "$output_path" $curl_options
- '{{$.inputs.parameters[''Url'']}}'
- '{{$.outputs.artifacts[''Data''].path}}'
- '{{$.inputs.parameters[''curl options'']}}'
command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),
--mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),
--container_image, $(KFP_V2_IMAGE), --task_name, download-data-kfp-sdk-v2,
--pipeline_name, '{{inputs.parameters.pipeline-name}}', --pipeline_run_id,
$(WORKFLOW_ID), --pipeline_task_id, $(KFP_POD_NAME), --pipeline_root, '{{inputs.parameters.pipeline-output-directory}}']
env:
- name: KFP_POD_NAME
valueFrom:
fieldRef: {fieldPath: metadata.name}
- name: KFP_NAMESPACE
valueFrom:
fieldRef: {fieldPath: metadata.namespace}
- name: WORKFLOW_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}
- {name: KFP_V2_IMAGE, value: 'byrnedo/alpine-curl@sha256:548379d0a4a0c08b9e55d9d87a592b7d35d9ab3037f4936f5ccd09d0b625a342'}
- {name: KFP_V2_RUNTIME_INFO, value: '{"inputParameters": {"Url": {"type": "STRING",
"value": "BEGIN-KFP-PARAM[{{inputs.parameters.url}}]END-KFP-PARAM"}, "curl
options": {"type": "STRING", "value": "BEGIN-KFP-PARAM[--location]END-KFP-PARAM"}},
"inputArtifacts": {}, "outputParameters": {}, "outputArtifacts": {"Data":
{"schemaTitle": "system.Artifact", "instanceSchema": "", "metadataPath":
"/tmp/outputs/Data/data"}}}'}
envFrom:
- configMapRef: {name: metadata-grpc-configmap, optional: true}
image: byrnedo/alpine-curl@sha256:548379d0a4a0c08b9e55d9d87a592b7d35d9ab3037f4936f5ccd09d0b625a342
volumeMounts:
- {mountPath: /kfp-launcher, name: kfp-launcher}
inputs:
parameters:
- {name: pipeline-name}
- {name: pipeline-output-directory}
- {name: url}
outputs:
artifacts:
- {name: download-data-kfp-sdk-v2-Data, path: /tmp/outputs/Data/data}
metadata:
annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
canonical_location: https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/web/Download/component.yaml
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/component_ref: '{"digest": "09826c017aa91ab1e3528e35e2a43774d4db0fd5e38acb8ba6ebafa09ecb95e1",
"url": "https://raw.githubusercontent.com/kubeflow/pipelines/master/components/web/Download/component-sdk-v2.yaml"}'
pipelines.kubeflow.org/arguments.parameters: '{"Url": "{{inputs.parameters.url}}",
"curl options": "--location"}'
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.6.4
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/v2_component: "true"
initContainers:
- command: [/bin/mount_launcher.sh]
image: gcr.io/ml-pipeline/kfp-launcher:1.6.4
name: kfp-launcher
mirrorVolumeMounts: true
volumes:
- {name: kfp-launcher}
- name: merge-csv
container:
args:
- sh
- -c
- (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
'pandas==1.1.4' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install
--quiet --no-warn-script-location 'pandas==1.1.4' --user) && "$0" "$@"
- sh
- -ec
- |
program_path=$(mktemp)
printf "%s" "$0" > "$program_path"
python3 -u "$program_path" "$@"
- |2
import json
import inspect
from typing import *
# Copyright 2021 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Classes for input/output types in KFP SDK.
These are only compatible with v2 Pipelines.
"""
import os
from typing import Dict, Generic, List, Optional, Type, TypeVar, Union
_GCS_LOCAL_MOUNT_PREFIX = '/gcs/'
_MINIO_LOCAL_MOUNT_PREFIX = '/minio/'
_S3_LOCAL_MOUNT_PREFIX = '/s3/'
class Artifact(object):
"""Generic Artifact class.
This class is meant to represent the metadata around an input or output
machine-learning Artifact. Artifacts have URIs, which can either be a location
on disk (or Cloud storage) or some other resource identifier such as
an API resource name.
Artifacts carry a `metadata` field, which is a dictionary for storing
metadata related to this artifact.
"""
TYPE_NAME = 'system.Artifact'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
"""Initializes the Artifact with the given name, URI and metadata."""
self.uri = uri or ''
self.name = name or ''
self.metadata = metadata or {}
@property
def path(self):
return self._get_path()
@path.setter
def path(self, path):
self._set_path(path)
def _get_path(self) -> Optional[str]:
if self.uri.startswith('gs://'):
return _GCS_LOCAL_MOUNT_PREFIX + self.uri[len('gs://'):]
elif self.uri.startswith('minio://'):
return _MINIO_LOCAL_MOUNT_PREFIX + self.uri[len('minio://'):]
elif self.uri.startswith('s3://'):
return _S3_LOCAL_MOUNT_PREFIX + self.uri[len('s3://'):]
return None
def _set_path(self, path):
if path.startswith(_GCS_LOCAL_MOUNT_PREFIX):
path = 'gs://' + path[len(_GCS_LOCAL_MOUNT_PREFIX):]
elif path.startswith(_MINIO_LOCAL_MOUNT_PREFIX):
path = 'minio://' + path[len(_MINIO_LOCAL_MOUNT_PREFIX):]
elif path.startswith(_S3_LOCAL_MOUNT_PREFIX):
path = 's3://' + path[len(_S3_LOCAL_MOUNT_PREFIX):]
self.uri = path
class Model(Artifact):
"""An artifact representing an ML Model."""
TYPE_NAME = 'system.Model'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
super().__init__(uri=uri, name=name, metadata=metadata)
@property
def framework(self) -> str:
return self._get_framework()
def _get_framework(self) -> str:
return self.metadata.get('framework', '')
@framework.setter
def framework(self, framework: str):
self._set_framework(framework)
def _set_framework(self, framework: str):
self.metadata['framework'] = framework
class Dataset(Artifact):
"""An artifact representing an ML Dataset."""
TYPE_NAME = 'system.Dataset'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
super().__init__(uri=uri, name=name, metadata=metadata)
class Metrics(Artifact):
"""Represent a simple base Artifact type to store key-value scalar metrics."""
TYPE_NAME = 'system.Metrics'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
super().__init__(uri=uri, name=name, metadata=metadata)
def log_metric(self, metric: str, value: float):
"""Sets a custom scalar metric.
Args:
metric: Metric key
value: Value of the metric.
"""
self.metadata[metric] = value
class ClassificationMetrics(Artifact):
"""Represents Artifact class to store Classification Metrics."""
TYPE_NAME = 'system.ClassificationMetrics'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
super().__init__(uri=uri, name=name, metadata=metadata)
def log_roc_data_point(self, fpr: float, tpr: float, threshold: float):
"""Logs a single data point in the ROC Curve.
Args:
fpr: False positive rate value of the data point.
tpr: True positive rate value of the data point.
threshold: Threshold value for the data point.
"""
roc_reading = {
'confidenceThreshold': threshold,
'recall': tpr,
'falsePositiveRate': fpr
}
if 'confidenceMetrics' not in self.metadata.keys():
self.metadata['confidenceMetrics'] = []
self.metadata['confidenceMetrics'].append(roc_reading)
def log_roc_curve(self, fpr: List[float], tpr: List[float],
threshold: List[float]):
"""Logs an ROC curve.
The list length of fpr, tpr and threshold must be the same.
Args:
fpr: List of false positive rate values.
tpr: List of true positive rate values.
threshold: List of threshold values.
"""
if len(fpr) != len(tpr) or len(fpr) != len(threshold) or len(tpr) != len(
threshold):
raise ValueError('Length of fpr, tpr and threshold must be the same. '
'Got lengths {}, {} and {} respectively.'.format(
len(fpr), len(tpr), len(threshold)))
for i in range(len(fpr)):
self.log_roc_data_point(fpr=fpr[i], tpr=tpr[i], threshold=threshold[i])
def set_confusion_matrix_categories(self, categories: List[str]):
"""Stores confusion matrix categories.
Args:
categories: List of strings specifying the categories.
"""
self._categories = []
annotation_specs = []
for category in categories:
annotation_spec = {'displayName': category}
self._categories.append(category)
annotation_specs.append(annotation_spec)
self._matrix = []
for row in range(len(self._categories)):
self._matrix.append({'row': [0] * len(self._categories)})
self._confusion_matrix = {}
self._confusion_matrix['annotationSpecs'] = annotation_specs
self._confusion_matrix['rows'] = self._matrix
self.metadata['confusionMatrix'] = self._confusion_matrix
def log_confusion_matrix_row(self, row_category: str, row: List[float]):
"""Logs a confusion matrix row.
Args:
row_category: Category to which the row belongs.
row: List of integers specifying the values for the row.
Raises:
ValueError: If row_category is not in the list of categories
set in set_categories call.
"""
if row_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
if len(row) != len(self._categories):
raise ValueError('Invalid row. Expected size: {} got: {}'.\
format(len(self._categories), len(row)))
self._matrix[self._categories.index(row_category)] = {'row': row}
self.metadata['confusionMatrix'] = self._confusion_matrix
def log_confusion_matrix_cell(self, row_category: str, col_category: str,
value: int):
"""Logs a cell in the confusion matrix.
Args:
row_category: String representing the name of the row category.
col_category: String representing the name of the column category.
value: Int value of the cell.
Raises:
ValueError: If row_category or col_category is not in the list of
categories set in set_categories.
"""
if row_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
if col_category not in self._categories:
raise ValueError('Invalid category: {} passed. Expected one of: {}'.\
format(row_category, self._categories))
self._matrix[self._categories.index(row_category)]['row'][
self._categories.index(col_category)] = value
self.metadata['confusionMatrix'] = self._confusion_matrix
def log_confusion_matrix(self, categories: List[str],
matrix: List[List[int]]):
"""Logs a confusion matrix.
Args:
categories: List of the category names.
matrix: Complete confusion matrix.
Raises:
ValueError: Length of categories does not match number of rows or columns.
"""
self.set_confusion_matrix_categories(categories)
if len(matrix) != len(categories):
raise ValueError('Invalid matrix: {} passed for categories: {}'.\
format(matrix, categories))
for index in range(len(categories)):
if len(matrix[index]) != len(categories):
raise ValueError('Invalid matrix: {} passed for categories: {}'.\
format(matrix, categories))
self.log_confusion_matrix_row(categories[index], matrix[index])
self.metadata['confusionMatrix'] = self._confusion_matrix
class SlicedClassificationMetrics(Artifact):
"""Metrics class representing Sliced Classification Metrics.
Similar to ClassificationMetrics clients using this class are expected to use
log methods of the class to log metrics with the difference being each log
method takes a slice to associate the ClassificationMetrics.
"""
TYPE_NAME = 'system.SlicedClassificationMetrics'
def __init__(self,
name: Optional[str] = None,
uri: Optional[str] = None,
metadata: Optional[Dict] = None):
super().__init__(uri=uri, name=name, metadata=metadata)
def _upsert_classification_metrics_for_slice(self, slice: str):
"""Upserts the classification metrics instance for a slice."""
if slice not in self._sliced_metrics:
self._sliced_metrics[slice] = ClassificationMetrics()
def _update_metadata(self, slice: str):
"""Updates metadata to adhere to the metrics schema."""
self.metadata = {}
self.metadata['evaluationSlices'] = []
for slice in self._sliced_metrics.keys():
slice_metrics = {
'slice': slice,
'sliceClassificationMetrics': self._sliced_metrics[slice].metadata
}
self.metadata['evaluationSlices'].append(slice_metrics)
def log_roc_reading(self, slice: str, threshold: float, tpr: float,
fpr: float):
"""Logs a single data point in the ROC Curve of a slice.
Args:
slice: String representing slice label.
threshold: Thresold value for the data point.
tpr: True positive rate value of the data point.
fpr: False positive rate value of the data point.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].log_roc_reading(threshold, tpr, fpr)
self._update_metadata(slice)
def load_roc_readings(self, slice: str, readings: List[List[float]]):
"""Supports bulk loading ROC Curve readings for a slice.
Args:
slice: String representing slice label.
readings: A 2-D list providing ROC Curve data points.
The expected order of the data points is: threshold,
true_positive_rate, false_positive_rate.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].load_roc_readings(readings)
self._update_metadata(slice)
def set_confusion_matrix_categories(self, slice: str, categories: List[str]):
"""Stores confusion matrix categories for a slice..
Categories are stored in the internal metrics_utils.ConfusionMatrix
instance of the slice.
Args:
slice: String representing slice label.
categories: List of strings specifying the categories.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].set_confusion_matrix_categories(categories)
self._update_metadata(slice)
def log_confusion_matrix_row(self, slice: str, row_category: str,
row: List[int]):
"""Logs a confusion matrix row for a slice.
Row is updated on the internal metrics_utils.ConfusionMatrix
instance of the slice.
Args:
slice: String representing slice label.
row_category: Category to which the row belongs.
row: List of integers specifying the values for the row.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].log_confusion_matrix_row(row_category, row)
self._update_metadata(slice)
def log_confusion_matrix_cell(self, slice: str, row_category: str,
col_category: str, value: int):
"""Logs a confusion matrix cell for a slice..
Cell is updated on the internal metrics_utils.ConfusionMatrix
instance of the slice.
Args:
slice: String representing slice label.
row_category: String representing the name of the row category.
col_category: String representing the name of the column category.
value: Int value of the cell.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].log_confusion_matrix_cell(
row_category, col_category, value)
self._update_metadata(slice)
def load_confusion_matrix(self, slice: str, categories: List[str],
matrix: List[List[int]]):
"""Supports bulk loading the whole confusion matrix for a slice.
Args:
slice: String representing slice label.
categories: List of the category names.
matrix: Complete confusion matrix.
"""
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].log_confusion_matrix_cell(categories, matrix)
self._update_metadata(slice)
T = TypeVar('T')
class InputAnnotation():
"""Marker type for input artifacts."""
pass
class OutputAnnotation():
"""Marker type for output artifacts."""
pass
# TODO: Use typing.Annotated instead of this hack.
# With typing.Annotated (Python 3.9+ or typing_extensions package), the
# following would look like:
# Input = typing.Annotated[T, InputAnnotation]
# Output = typing.Annotated[T, OutputAnnotation]
# Input represents an Input artifact of type T.
Input = Union[T, InputAnnotation]
# Output represents an Output artifact of type T.
Output = Union[T, OutputAnnotation]
def is_artifact_annotation(typ) -> bool:
if hasattr(typ, '_subs_tree'): # Python 3.6
subs_tree = typ._subs_tree()
return len(subs_tree) == 3 and subs_tree[0] == Union and subs_tree[2] in [InputAnnotation, OutputAnnotation]
if not hasattr(typ, '__origin__'):
return False
if typ.__origin__ != Union and type(typ.__origin__) != type(Union):
return False
if not hasattr(typ, '__args__') or len(typ.__args__) != 2:
return False
if typ.__args__[1] not in [InputAnnotation, OutputAnnotation]:
return False
return True
def is_input_artifact(typ) -> bool:
"""Returns True if typ is of type Input[T]."""
if not is_artifact_annotation(typ):
return False
if hasattr(typ, '_subs_tree'): # Python 3.6
subs_tree = typ._subs_tree()
return len(subs_tree) == 3 and subs_tree[2] == InputAnnotation
return typ.__args__[1] == InputAnnotation
def is_output_artifact(typ) -> bool:
"""Returns True if typ is of type Output[T]."""
if not is_artifact_annotation(typ):
return False
if hasattr(typ, '_subs_tree'): # Python 3.6
subs_tree = typ._subs_tree()
return len(subs_tree) == 3 and subs_tree[2] == OutputAnnotation
return typ.__args__[1] == OutputAnnotation
def get_io_artifact_class(typ):
if not is_artifact_annotation(typ):
return None
if typ == Input or typ == Output:
return None
if hasattr(typ, '_subs_tree'): # Python 3.6
subs_tree = typ._subs_tree()
if len(subs_tree) != 3:
return None
return subs_tree[1]
return typ.__args__[0]
def get_io_artifact_annotation(typ):
if not is_artifact_annotation(typ):
return None
if hasattr(typ, '_subs_tree'): # Python 3.6
subs_tree = typ._subs_tree()
if len(subs_tree) != 3:
return None
return subs_tree[2]
return typ.__args__[1]
_SCHEMA_TITLE_TO_TYPE: Dict[str, Artifact] = {
x.TYPE_NAME: x
for x in [Artifact, Model, Dataset, Metrics, ClassificationMetrics]
}
def create_runtime_artifact(runtime_artifact: Dict) -> Artifact:
"""Creates an Artifact instance from the specified RuntimeArtifact.
Args:
runtime_artifact: Dictionary representing JSON-encoded RuntimeArtifact.
"""
schema_title = runtime_artifact.get('type', {}).get('schemaTitle', '')
artifact_type = _SCHEMA_TITLE_TO_TYPE.get(schema_title)
if not artifact_type:
artifact_type = Artifact
return artifact_type(
uri=runtime_artifact.get('uri', ''),
name=runtime_artifact.get('name', ''),
metadata=runtime_artifact.get('metadata', {}),
)
class InputPath:
'''When creating component from function, :class:`.InputPath` should be used as function parameter annotation to tell the system to pass the *data file path* to the function instead of passing the actual data.'''
def __init__(self, type=None):
self.type = type
class OutputPath:
'''When creating component from function, :class:`.OutputPath` should be used as function parameter annotation to tell the system that the function wants to output data by writing it into a file with the given path instead of returning the data from the function.'''
def __init__(self, type=None):
self.type = type
class Executor():
"""Executor executes v2-based Python function components."""
def __init__(self, executor_input: Dict, function_to_execute: Callable):
self._func = function_to_execute
self._input = executor_input
self._input_artifacts: Dict[str, Artifact] = {}
self._output_artifacts: Dict[str, Artifact] = {}
for name, artifacts in self._input.get('inputs', {}).get('artifacts',
{}).items():
artifacts_list = artifacts.get('artifacts')
if artifacts_list:
self._input_artifacts[name] = self._make_input_artifact(
artifacts_list[0])
for name, artifacts in self._input.get('outputs', {}).get('artifacts',
{}).items():
artifacts_list = artifacts.get('artifacts')
if artifacts_list:
self._output_artifacts[name] = self._make_output_artifact(
artifacts_list[0])
self._return_annotation = inspect.signature(self._func).return_annotation
self._executor_output = {}
@classmethod
def _make_input_artifact(cls, runtime_artifact: Dict):
return create_runtime_artifact(runtime_artifact)
@classmethod
def _make_output_artifact(cls, runtime_artifact: Dict):
import os
artifact = create_runtime_artifact(runtime_artifact)
os.makedirs(os.path.dirname(artifact.path), exist_ok=True)
return artifact
def _get_input_artifact(self, name: str):
return self._input_artifacts.get(name)
def _get_output_artifact(self, name: str):
return self._output_artifacts.get(name)
def _get_input_parameter_value(self, parameter_name: str, parameter_type: Any):
parameter = self._input.get('inputs', {}).get('parameters',
{}).get(parameter_name, None)
if parameter is None:
return None
if parameter.get('stringValue'):
if parameter_type == str:
return parameter['stringValue']
elif parameter_type == bool:
# Use `.lower()` so it can also handle 'True' and 'False' (resulted from
# `str(True)` and `str(False)`, respectively.
return json.loads(parameter['stringValue'].lower())
else:
return json.loads(parameter['stringValue'])
elif parameter.get('intValue'):
return int(parameter['intValue'])
elif parameter.get('doubleValue'):
return float(parameter['doubleValue'])
def _get_output_parameter_path(self, parameter_name: str):
parameter_name = self._maybe_strip_path_suffix(parameter_name)
parameter = self._input.get('outputs',
{}).get('parameters',
{}).get(parameter_name, None)
if parameter is None:
return None
import os
path = parameter.get('outputFile', None)
if path:
os.makedirs(os.path.dirname(path), exist_ok=True)
return path
def _get_output_artifact_path(self, artifact_name: str):
artifact_name = self._maybe_strip_path_suffix(artifact_name)
output_artifact = self._output_artifacts.get(artifact_name)
if not output_artifact:
raise ValueError(
'Failed to get output artifact path for artifact name {}'.format(
artifact_name))
return output_artifact.path
def _get_input_artifact_path(self, artifact_name: str):
artifact_name = self._maybe_strip_path_suffix(artifact_name)
input_artifact = self._input_artifacts.get(artifact_name)
if not input_artifact:
raise ValueError(
'Failed to get input artifact path for artifact name {}'.format(
artifact_name))
return input_artifact.path
def _write_output_parameter_value(self, name: str,
value: Union[str, int, float, bool, dict,
list, Dict, List]):
if type(value) == str:
output = {'stringValue': value}
elif type(value) == int:
output = {'intValue': value}
elif type(value) == float:
output = {'doubleValue': value}
else:
# For bool, list, dict, List, Dict, json serialize the value.
output = {'stringValue': json.dumps(value)}
if not self._executor_output.get('parameters'):
self._executor_output['parameters'] = {}
self._executor_output['parameters'][name] = output
def _write_output_artifact_payload(self, name: str, value: Any):
path = self._get_output_artifact_path(name)
with open(path, 'w') as f:
f.write(str(value))
# TODO: extract to a util
@classmethod
def _get_short_type_name(cls, type_name: str) -> str:
"""Extracts the short form type name.
This method is used for looking up serializer for a given type.
For example:
typing.List -> List
typing.List[int] -> List
typing.Dict[str, str] -> Dict
List -> List
str -> str
Args:
type_name: The original type name.
Returns:
The short form type name or the original name if pattern doesn't match.
"""
import re
match = re.match('(typing\.)?(?P<type>\w+)(?:\[.+\])?', type_name)
if match:
return match.group('type')
else:
return type_name
# TODO: merge with type_utils.is_parameter_type
@classmethod
def _is_parameter(cls, annotation: Any) -> bool:
if type(annotation) == type:
return annotation in [str, int, float, bool, dict, list]
# Annotation could be, for instance `typing.Dict[str, str]`, etc.
return cls._get_short_type_name(str(annotation)) in ['Dict', 'List']
@classmethod
def _is_artifact(cls, annotation: Any) -> bool:
if type(annotation) == type:
return issubclass(annotation, Artifact)
return False
@classmethod
def _is_named_tuple(cls, annotation: Any) -> bool:
if type(annotation) == type:
return issubclass(annotation, tuple) and hasattr(
annotation, '_fields') and hasattr(annotation, '__annotations__')
return False
def _handle_single_return_value(self, output_name: str, annotation_type: Any,
return_value: Any):
if self._is_parameter(annotation_type):
if type(return_value) != annotation_type:
raise ValueError(
'Function `{}` returned value of type {}; want type {}'.format(
self._func.__name__, type(return_value), annotation_type))
self._write_output_parameter_value(output_name, return_value)
elif self._is_artifact(annotation_type):
self._write_output_artifact_payload(output_name, return_value)
else:
raise RuntimeError(
'Unknown return type: {}. Must be one of `str`, `int`, `float`, or a'
' subclass of `Artifact`'.format(annotation_type))
def _write_executor_output(self, func_output: Optional[Any] = None):
if self._output_artifacts:
self._executor_output['artifacts'] = {}
for name, artifact in self._output_artifacts.items():
runtime_artifact = {
'name': artifact.name,
'uri': artifact.uri,
'metadata': artifact.metadata,
}
artifacts_list = {'artifacts': [runtime_artifact]}
self._executor_output['artifacts'][name] = artifacts_list
if func_output is not None:
if self._is_parameter(self._return_annotation) or self._is_artifact(
self._return_annotation):
# Note: single output is named `Output` in component.yaml.
self._handle_single_return_value('Output', self._return_annotation,
func_output)
elif self._is_named_tuple(self._return_annotation):
if len(self._return_annotation._fields) != len(func_output):
raise RuntimeError(
'Expected {} return values from function `{}`, got {}'.format(
len(self._return_annotation._fields), self._func.__name__,
len(func_output)))
for i in range(len(self._return_annotation._fields)):
field = self._return_annotation._fields[i]
field_type = self._return_annotation.__annotations__[field]
if type(func_output) == tuple:
field_value = func_output[i]
else:
field_value = getattr(func_output, field)
self._handle_single_return_value(field, field_type, field_value)
else:
raise RuntimeError(
'Unknown return type: {}. Must be one of `str`, `int`, `float`, a'
' subclass of `Artifact`, or a NamedTuple collection of these types.'
.format(self._return_annotation))
import os
os.makedirs(
os.path.dirname(self._input['outputs']['outputFile']), exist_ok=True)
with open(self._input['outputs']['outputFile'], 'w') as f:
f.write(json.dumps(self._executor_output))
def _maybe_strip_path_suffix(self, name) -> str:
if name.endswith('_path'):
name = name[0:-len('_path')]
if name.endswith('_file'):
name = name[0:-len('_file')]
return name
def execute(self):
annotations = inspect.getfullargspec(self._func).annotations
# Function arguments.
func_kwargs = {}
for k, v in annotations.items():
if k == 'return':
continue
if self._is_parameter(v):
func_kwargs[k] = self._get_input_parameter_value(k, v)
if is_artifact_annotation(v):
if is_input_artifact(v):
func_kwargs[k] = self._get_input_artifact(k)
if is_output_artifact(v):
func_kwargs[k] = self._get_output_artifact(k)
elif isinstance(v, OutputPath):
if self._is_parameter(v.type):
func_kwargs[k] = self._get_output_parameter_path(k)
else:
func_kwargs[k] = self._get_output_artifact_path(k)
elif isinstance(v, InputPath):
func_kwargs[k] = self._get_input_artifact_path(k)
result = self._func(**func_kwargs)
self._write_executor_output(result)
def merge_csv(tar_data: Input[Artifact], output_csv: Output[Dataset]):
import glob
import pandas as pd
import tarfile
tarfile.open(name=tar_data.path, mode="r|gz").extractall("data")
df = pd.concat(
[pd.read_csv(csv_file, header=None) for csv_file in glob.glob("data/*.csv")]
)
df.to_csv(output_csv.path, index=False, header=False)
def executor_main():
import argparse
import json
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--executor_input', type=str)
parser.add_argument('--function_to_execute', type=str)
args, _ = parser.parse_known_args()
executor_input = json.loads(args.executor_input)
function_to_execute = globals()[args.function_to_execute]
executor = Executor(executor_input=executor_input,
function_to_execute=function_to_execute)
executor.execute()
if __name__ == '__main__':
executor_main()
- --executor_input
- '{{$}}'
- --function_to_execute
- merge_csv
- --tar-data-output-path
- '{{$.inputs.artifacts[''tar_data''].path}}'
- --output-csv-output-path
- '{{$.outputs.artifacts[''output_csv''].path}}'
command: [/kfp-launcher/launch, --mlmd_server_address, $(METADATA_GRPC_SERVICE_HOST),
--mlmd_server_port, $(METADATA_GRPC_SERVICE_PORT), --runtime_info_json, $(KFP_V2_RUNTIME_INFO),
--container_image, $(KFP_V2_IMAGE), --task_name, merge-csv, --pipeline_name,
'{{inputs.parameters.pipeline-name}}', --pipeline_run_id, $(WORKFLOW_ID),
--pipeline_task_id, $(KFP_POD_NAME), --pipeline_root, '{{inputs.parameters.pipeline-output-directory}}']
env:
- name: KFP_POD_NAME
valueFrom:
fieldRef: {fieldPath: metadata.name}
- name: KFP_NAMESPACE
valueFrom:
fieldRef: {fieldPath: metadata.namespace}
- name: WORKFLOW_ID
valueFrom:
fieldRef: {fieldPath: 'metadata.labels[''workflows.argoproj.io/workflow'']'}
- {name: KFP_V2_IMAGE, value: 'python:3.7'}
- {name: KFP_V2_RUNTIME_INFO, value: '{"inputParameters": {}, "inputArtifacts":
{"tar_data": {"metadataPath": "/tmp/inputs/tar_data/data", "schemaTitle":
"system.Artifact", "instanceSchema": ""}}, "outputParameters": {}, "outputArtifacts":
{"output_csv": {"schemaTitle": "system.Dataset", "instanceSchema": "", "metadataPath":
"/tmp/outputs/output_csv/data"}}}'}
envFrom:
- configMapRef: {name: metadata-grpc-configmap, optional: true}
image: python:3.7
volumeMounts:
- {mountPath: /kfp-launcher, name: kfp-launcher}
inputs:
parameters:
- {name: pipeline-name}
- {name: pipeline-output-directory}
artifacts:
- {name: download-data-kfp-sdk-v2-Data, path: /tmp/inputs/tar_data/data}
outputs:
artifacts:
- {name: merge-csv-output_csv, path: /tmp/outputs/output_csv/data}
metadata:
annotations:
pipelines.kubeflow.org/v2_component: "true"
pipelines.kubeflow.org/component_ref: '{}'
labels:
pipelines.kubeflow.org/kfp_sdk_version: 1.6.4
pipelines.kubeflow.org/pipeline-sdk-type: kfp
pipelines.kubeflow.org/v2_component: "true"
initContainers:
- command: [/bin/mount_launcher.sh]
image: gcr.io/ml-pipeline/kfp-launcher:1.6.4
name: kfp-launcher
mirrorVolumeMounts: true
volumes:
- {name: kfp-launcher}
- name: my-pipeline
inputs:
parameters:
- {name: pipeline-name}
- {name: pipeline-output-directory}
- {name: url}
dag:
tasks:
- name: download-data-kfp-sdk-v2
template: download-data-kfp-sdk-v2
arguments:
parameters:
- {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}
- {name: pipeline-output-directory, value: '{{inputs.parameters.pipeline-output-directory}}'}
- {name: url, value: '{{inputs.parameters.url}}'}
- name: merge-csv
template: merge-csv
dependencies: [download-data-kfp-sdk-v2]
arguments:
parameters:
- {name: pipeline-name, value: '{{inputs.parameters.pipeline-name}}'}
- {name: pipeline-output-directory, value: '{{inputs.parameters.pipeline-output-directory}}'}
artifacts:
- {name: download-data-kfp-sdk-v2-Data, from: '{{tasks.download-data-kfp-sdk-v2.outputs.artifacts.download-data-kfp-sdk-v2-Data}}'}
arguments:
parameters:
- {name: url}
- {name: pipeline-output-directory, value: ''}
- {name: pipeline-name, value: my-pipeline}
serviceAccountName: pipeline-runner
```
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
| {
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https://api.github.com/repos/kubeflow/pipelines/issues/6238 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6238/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6238/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6238/events | https://github.com/kubeflow/pipelines/issues/6238 | 961,528,392 | MDU6SXNzdWU5NjE1MjgzOTI= | 6,238 | [v2 sample test] kaniko build times out / OOM | {
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"## Proposal\r\n\r\nOur major use-case is building images with go applications, so `ko` seems like a natural fit.\r\n\r\nref: introduction ko vs buildpacks vs docker: https://cloud.google.com/blog/topics/developers-practitioners/ship-your-go-applications-faster-cloud-run-ko\r\nTL;DR: both ko and buildpacks build images from language specific source code, `ko` is only for golang, `buildpacks` supports multiple languages. However, under the hood, `buildpacks` calls into docker, so considering build speed, `ko` is the fastest."
] | "2021-08-05T07:26:20" | "2021-08-05T13:33:19" | "2021-08-05T13:33:19" | CONTRIBUTOR | null | We've observed significantly high build time outs and OOMs with Kaniko recently.
I've tried several combinations:
1. 1.3.0-debug with/without --snapshotMode=redo + 4GB memory
2. 1.6.0-debug with/without --snapshotMode=redo + 8GB memory https://github.com/kubeflow/pipelines/pull/6226
but none of them run stably in reasonable amount of time.
The memory and timeout issues can be found upstream:
* https://github.com/GoogleContainerTools/kaniko/issues/1680
* https://github.com/GoogleContainerTools/kaniko/issues/1333
but they are both long standing, and no one still maintains the repo actively. | {
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https://api.github.com/repos/kubeflow/pipelines/issues/6237 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6237/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6237/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6237/events | https://github.com/kubeflow/pipelines/issues/6237 | 961,471,168 | MDU6SXNzdWU5NjE0NzExNjg= | 6,237 | [feature] support setup.py in gcp/python/launch_dataflow | {
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"/assign @IronPan @SinaChavoshi ",
"fix should be available in the next release. "
] | "2021-08-05T05:53:50" | "2021-12-09T08:37:17" | "2021-12-09T08:37:17" | NONE | null | ### Feature Area
/area components
### What feature would you like to see?
Allow the python dataflow launch component to pass `setup.py` to the dataflow stager program as a requirements-specification option. See [beam docs](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#multiple-file-dependencies) for details.
Only one of `requirements.txt` or `setup.py` should be allowed to be specified.
### What is the use case or pain point?
`requirements.txt` works for most deps, but anything with numpy in the dependency chain takes an incredibly long time to stage / execute. This is due to how the beam stager explicitly downloads and tries to build **all** transitive dependencies for everything in `requirements.txt`.
### Is there a workaround currently?
We would either use a customized dataflow worker image or use a modified dataflow launcher component that has a different commandline.
---
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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https://api.github.com/repos/kubeflow/pipelines/issues/6232 | https://api.github.com/repos/kubeflow/pipelines | https://api.github.com/repos/kubeflow/pipelines/issues/6232/labels{/name} | https://api.github.com/repos/kubeflow/pipelines/issues/6232/comments | https://api.github.com/repos/kubeflow/pipelines/issues/6232/events | https://github.com/kubeflow/pipelines/issues/6232 | 960,867,935 | MDU6SXNzdWU5NjA4Njc5MzU= | 6,232 | Mock first simple V2 pipelineSpec response | {
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"/cc @chensun \r\nI think v2 pipeline spec has more flexible support for exit handler, it's pending DSL change to expose the feature.",
"/assign @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-08-04T07:00:29" | "2022-03-03T00:05:27" | null | NONE | null | ### Feature Area
/area sdk
### What feature would you like to see?
Handling exception during the failed step in workflow. so something similar to ExitHandler but on the step not pipeline level.
### What is the use case or pain point?
Some steps in pipeline might be optional or at least not critical. And if they fail we don't care albeit we would like pipeline to continue and finish with status set to warning e.g.
### Is there a workaround currently?
<!-- Without this feature, how do you accomplish your task today? -->
In the step `try/finally return "Failure"` block ensures that we are always succesfull and then in pipeline:
```
with dsl.Condition(step_x.output == "Failure"):
step_x_fail = catch_fail(message="failure")
step_y.after(step_x)
```
which makes pipeline less readable
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
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"Added in https://github.com/kubeflow/pipelines/blob/master/frontend/src/lib/v2/WorkflowUtils.ts#L35"
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"Duplicates #6199 "
] | "2021-08-02T13:50:47" | "2023-01-18T08:19:12" | "2021-08-02T14:53:43" | CONTRIBUTOR | null | Tasks:
* [x] #6169
* [x] #6170
* [x] #6171 | {
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"I have some personal notes on the topic, will try to document them.",
"Thank you!\nAny chance you had time working on this in the past couple of weeks?",
"@Bobgy in terms of production some guidance on what components can have > 1 replica would be very useful. Initially, I'm planning to try to increase the replica count to 2 for `ml-pipeline-ui`. This should allow users to see something even if other things are down.\r\n\r\nThe other things that I think could have replica count >1 is:\r\n- `ml-pipeline`\r\n- `metadata-grpc-service`\r\n- `ml-pipeline-visualizationserver`\r\n\r\nThings I'm not sure about are:\r\n- `controller-manager-service`",
"Posting my unedited notes first, will try to revisit. Looking forward to any feedback.\r\n\r\nSome of these tips are Google Cloud specific, but most of them are general advice.\r\n\r\n* Deploy in a regional cluster, even if your workload runs on zonal nodepools. Regional clusters have multiple instances of K8s api server, so K8s api is highly available. During scaling, upgrade or many maintenance operations, zonal cluster k8s api servers are not responsive.\r\n\r\n* For KFP on GCP configure a nodepool default Google Service Account (GSA) with minimal permissions. You can grant serviceAccountUser permission to users/GSAs on this GSA to allow access to the proxy.\r\n\r\n* Recommend enabling nodepool autoscaling when there are too many workloads.\r\n\r\n* Set memory/CPU requests/limit on pipeline steps to guarantee they are not evicted when the cluster is under resource constraints. Also, Kubernetes use resource requests as the signal for node pool scaling, so when you enable auto-scaling, you should always set resource requests, so that Kubernetes can properly identify when you want to scale up/down.\r\nResource requests/limits can be set using KFP DSL, example pipeline. Reference: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.Sidecar.set_memory_limit.\r\n\r\n* Set memory/CPU requests/limit on system services, latest KFP release already have sane default values. However, KFP API server (ml-pipeline deployment), KFP persistence agent (ml-pipeline-persistence-agent deployment) and argo workflow controller (workflow-controller deployment) memory/CPU needs are roughly linear to the number of concurrent workflows (even if they are completed), Therefore do:\r\n\r\n* Reduce workflow TTL of completed workflows to match your use-case. Default is 1 day.\r\n\r\n* Monitor these deployments and set requests/limits based on real usage + some buffer.\r\n\r\n* Set up retry strategies for steps in error state. There are two types of failures, error and failure. Error refers to orchestration system problems. While failure refers to user container failures. So it’s recommended to specify retryStrategy at least for errors, and depending on use-case also for failures.\r\nExample: you can set set_retry(policy=\"Always\"). # or “OnError”\r\n\r\n* If you need to customize the deployment, pull KFP manifests as an upstream and follow the off the shelf application workflow of kustomize. This allows infrastructure as code and easy upgrades.\r\n\r\n* A bonus point is to use gitops (there are many tools for similar purposes), put your infrastructure as code in a repo and use a gitops tool to sync it to production. In this way, you can version control, roll back, etc.\r\n\r\n* Use managed storage (Cloud SQL & Cloud Storage) to simplify lifecycle management: https://github.com/kubeflow/pipelines/tree/master/manifests/kustomize/sample .\r\n\r\n* Configure a lifecycle policy (e.g. clean up intermediate artifacts after 7 days) for the object store you are using, e.g. [for minio](https://docs.min.io/docs/minio-bucket-lifecycle-guide.html) and [for gcs](https://cloud.google.com/storage/docs/managing-lifecycles). Note, on the default minio bucket, intermediate artifacts are stored in `minio://mlpipeline/artifacts`, pipeline templates are stored in `minio://mlpipeline/pipelines`, so do not set a lifecycle for pipeline templates, they should be kept.",
"> @Bobgy in terms of production some guidance on what components can have > 1 replica would be very useful. Initially, I'm planning to try to increase the replica count to 2 for `ml-pipeline-ui`. This should allow users to see something even if other things are down.\n> \n> The other things that I think could have replica count >1 is:\n> - `ml-pipeline`\n> - `metadata-grpc-service`\n> - `ml-pipeline-visualizationserver`\n> \n> Things I'm not sure about are:\n> - `controller-manager-service`\n\nThis is something I haven't experimented much, from my understanding:\n* ml-pipeline-ui\n* ml-pipeline*\n* metadata-grpc-service*\n* ml-pipeline-visualizationserver\n\ncan be made multi replica right now.\n\nThere is a caveat that ml-pipeline and metadata-grpc-service upgrade DB schema on start up, so if you are doing an upgrade, recommend changing replica to 1 first.\n\nThe controllers should be able to run in leader election mode: one instance is leader, one instance is standby, whenever the leader dies, the standby instance takes over. However, I believe for KFP controllers some dependency upgrade might be necessary and we need to expose flags.\nArgo workflow controller can be set up this way now. https://argoproj.github.io/argo-workflows/high-availability/",
"@Bobgy I've taken the suggestions above for the things that can have more than replica count one. I've also added in PodDisruptionBudgets and put Pod Topology Spread Constraints to avoid all the replicas going onto a single node.\r\n\r\nI'll have to look into getting the argo workflow controller to have an active / passive mode.\r\n\r\nThanks for suggestions and advice. It's really appreciated. ",
"Cool, interested to see how that plays out.",
"@Bobgy Is there any easy way to integrate kubeflow pipelines directly with gitops? currently we are just converting our pipelines to Argo workflows. We can run and schedule those pipelines, but we are losing all fancy kubeflow capabilities from the UI and complicates things for DataScientists. Any ideas on this?",
"@rubenaranamorera There's a feature request in https://github.com/kubeflow/pipelines/issues/6001.",
"minor update, I added a last point in [my comment above](https://github.com/kubeflow/pipelines/issues/6204#issuecomment-903011330) about configuring a lifecycle policy for the object store.",
"> @Bobgy Is there any easy way to integrate kubeflow pipelines directly with gitops? currently we are just converting our pipelines to Argo workflows. We can run and schedule those pipelines, but we are losing all fancy kubeflow capabilities from the UI and complicates things for DataScientists. Any ideas on this?\r\n\r\nYou(@rubenaranamorera ) can use the SDK if you like to, I did some stuff with this for github actions(it has not been update in quite some time so might need some love to work for you) https://github.com/NikeNano/kubeflow-github-action. ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"/lifecycle freeze",
"So after more than 6 months of running the configuration with replica > 1 there hasn't been any issues. \r\n\r\nAlso, for argocd workflows the controller may not need to be run with more than replica / sharded unless there is huge number of workflows. The pod gracefully restarts on other nodes and is able to pick up work where it left it off.\r\n\r\nWould there be interest in creating an overlay for HA? "
] | "2021-08-01T19:20:14" | "2022-03-03T04:08:32" | null | NONE | null | ### Feature Area
/area documentation
/area samples
/area deployment
### What feature would you like to see?
Documentation for production-grade deployment of kubeflow pipelines.
### What is the use case or pain point?
### Is there a workaround currently?
Unaware
---
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"Hi @zijianjoy, I updated some tasks according to the plan",
"Cool! Thank you @Bobgy for the update!",
"Remaining task: Upload V2 images to gcr.io.",
"> Remaining task: Upload V2 images to gcr.io.\r\n\r\nThis doesn't really belong to API work. Tracking it in https://github.com/kubeflow/pipelines/issues/6184 instead."
] | "2021-07-30T21:24:58" | "2023-01-18T08:27:01" | "2023-01-18T08:27:01" | COLLABORATOR | null | - [x] #6169
- [x] #6170
- [x] #6171
- [x] #7119
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] | null | [] | "2021-07-30T21:21:37" | "2023-05-24T17:23:02" | null | COLLABORATOR | null | * POC and Design
* [x] https://github.com/kubeflow/pipelines/issues/6073
* [x] #6126
* [x] #6301
* proto
* [x] #6205
* [x] #6215
* [x] #6272
* [x] #7611
* [x] #7444
* Mock backend
* [x] #6212
* [x] #6232
* [x] #6302
* [x] Create pipelineSpec sample tests
* Static Pipeline Graph
* [x] #6778
* [x] #6270
* [x] #6280
* [x] #6397
* [x] #6419
* [x] #6398
* [x] #6758
* Pipeline List Page
* [x] Render pipeline name, pipeline ID, source code, description, upload time.
* [x] Render pipeline versions in a paginated dropdown.
* Upload Pipeline or Pipeline Version Page (pending backend+API implementation)
* [x] #6672
* Pipeline Detail Page
* [x] #6179
* [x] #6214
* [x] #6303
* [x] Use pipeline ID to determine specific pipeline template
* [x] Use pipeline ID + version to determine specific pipeline template
* [x] Compile PipelineSpec proto to javascript class.
* [x] Retrieve pipeline spec payload from backend, and convert to jspb object.
* [x] Convert IR to DAG understandable by ReactFlow
* [x] #6633
* [x] #6399
* [x] #6610
* [x] #6611
* [x] #6279
* [x] #6779
* Run List Page
* [x] #6400
* [x] #6780
* New Run Page
* [x] #7598
* [x] #7599
* [ ] #7600
* [x] Support creating new run
* [x] Support creating new recurring run.
* [x] Support Pipeline upload in standalone mode and multi-user mode (namespaced).
* [x] Upload V2 pipeline in project selector of New Run page.
* Runtime Pipeline Graph
* [x] #6686
* [x] Read Context from MLMD store
* [x] Read Execution from MLMD store
* [x] Read Artifact from MLMD store
* [x] #6673
* [x] Define runtime status css styling for Execution, Artifact, sub-DAG. (need UX help)
* [x] #6674
* [x] Convert from MLMD and pipeline template to Stateful DAG when loading runtime DAG.
* Run Detail Page
* [x] #6675
* [x] #6708
* [x] #6710
* [x] #6707
* [x] #6676
* [x] #6739
* [x] #6740
* Side panel: sub-DAG: sub-DAG detail, Input/Output
* [x] Go from Execution detail page to Run Detail page with execution node selected.
* MLMD
* [x] #6436
* [x] #6915
* UX
* [x] Need UX work for all DAG UI components.
* [x] #6283
* Others
* [x] #6264
* [x] #7379
* [x] https://github.com/kubeflow/pipelines/issues/6912
* [x] #7610
### Post-Alpha
* Static Pipeline Graph
* Define static status css styling for Ranking algorithm.
* Run List Page
* https://github.com/kubeflow/pipelines/issues/6912
* https://github.com/kubeflow/pipelines/issues/6132
* Run Detail Page
* Shows Run output: all metrics https://github.com/kubeflow/pipelines/issues/7339
* Shows Run output: all pipeline level output artifacts
* Shows Parameters, pipeline definition link in Detail tab
* Mock backend
* Mock get Metadata response (needs breakdown)
* Side Panel for KFPv2 Run
* Execution
* Volume
* Logs
* Pod
* Events
* Task Details
* #6732
* Run comparison page
* [x] #7756
* [x] #7840
* [x] #7841
* [x] #7842
* Others
* Consider Using [StoryShots and Puppeteer](https://github.com/storybookjs/storybook/tree/master/addons/storyshots/storyshots-puppeteer) for Visual testing.
* Support exitHandler scenario
* Multi-layer lineage graph using MLMD 1.4 https://github.com/google/ml-metadata/releases/tag/v1.4.0
* https://github.com/kubeflow/pipelines/issues/6374
* Add post-submit test for KFPv2
* Compare run: https://github.com/kubeflow/pipelines/issues/7476 | {
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* KFP SDK version: master
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Steps to reproduce
1. Run a pipeline function, but do not annotate the pipeline decorator
```
def abc():
xxxxx
```
2. Verify the pipeline runs properly in v2 compatible mode
3. However, if we look at the pipeline's pipeline name, it is empty -- "pipeline/".
<!--
Specify how to reproduce the problem.
This may include information such as: a description of the process, code snippets, log output, or screenshots.
-->
### Expected result
<!-- What should the correct behavior be? -->
The pipeline should fail without a pipeline name, or we may default to the function's name.
### Materials and Reference
<!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"I think it's best practice to set a timeout for every pipeline, which can resolve this problem.\r\ndocumentation: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.PipelineConf.set_timeout"
] | "2021-07-29T09:00:14" | "2021-09-13T23:49:57" | "2021-09-13T23:49:57" | CONTRIBUTOR | null | ### What steps did you take
Run a workflow which uses an invalid image name.
### What happened:
The workflow is stuck in running state, because the Pod is stuck in Init:InvalidImageName.
However, the workflow is kept in the cluster forever.
### What did you expect to happen:
I'd expect the workflow to time out after a certain time and gets GCed.
### Anything else you would like to add:
<!-- Miscellaneous information that will assist in solving the issue.-->
I'm observing this problem in KFP v2 sample test cluster. It's not necessarily a backend problem, we should probably enforce best practices of always adding a workflow timeout.
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
/area testing
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Found logs:\r\n\r\n2021-07-29 06:30:02.824 HKTtime=\"2021-07-28T22:30:02Z\" level=error msg=\"Transient failure while syncing resource (kubeflow/add-pipeline-28qf9): CustomError (code: 0): Syncing Workflow (add-pipeline-28qf9): transient failure: CustomError (code: 0): Error while reporting workflow resource (code: Internal, message: Report workflow failed.: Failed to update the run.: InternalServerError: Failed to update run 074cc620-ad81-4c2a-bff2-ad6afd7eb077. Row not found: Failed to update run): rpc error: code = Internal desc = Report workflow failed.: Failed to update the run.: InternalServerError: Failed to update run 074cc620-ad81-4c2a-bff2-ad6afd7eb077. Row not found: Failed to update run, \\<deleted-the-entire-workflow-spec>: rpc error: code = Internal desc = Report workflow failed.: Failed to update the run.: InternalServerError: Failed to update run 074cc620-ad81-4c2a-bff2-ad6afd7eb077. Row not found: Failed to update run\"\r\n",
"I cannot find other logs in persistence agent and api server about this workflow.\r\n\r\nI **propose** to mitigate this problem by:\r\n\r\nwhen reporting a workflow, if the workflow is not in KFP DB, we should delete it to avoid leaking resources.",
"Meanwhile, it's important to keep debugging why these workflows were leaked, there might be other bugs hidden somewhere.",
"With the mitigation, remaining workflow count is reduced to:\r\n```\r\nkubectl get wf | wc -l\r\n 377\r\n```\r\n\r\nI further found a different reason for leaked workflow: https://github.com/kubeflow/pipelines/issues/6192",
"FWIW, I ran into this issue twice in the past week. Running KFP 1.7.1. I didn't notice any other interesting log messages around this time. Let me know if I can provide more debug info.\r\n\r\nlog message from `ml-pipelien`:\r\n> \"Cannot find reported workflow name=\"backtesting-96h6b\" namespace=\"kubeflow\" runId=\"c7901d62-971e-4071-b9ce-56d56ddbda37\" in run store. Deleting the workflow to avoid resource leaking. This can be caused by installing two KFP instances that try to manage the same workflows or an unknown bug. If you encounter this, recommend reporting more details in https://github.com/kubeflow/pipelines/issues/6189.\"",
"Thank you! Did this cause any problems for you @jli ?\n\n/cc @chensun ",
"It was just confusing, and caused some delay for me.\n\nI launched a workflow, then came to check on it hours later, but found that it hadn't run.\n\nThe run details page wouldn't load (constant spinner over the dag part of the page). I checked the experiment page, and my run was there but with a grey question mark status icon. When I looked at workflow objects on k8s, I couldn't find anything.",
"This is happening more and more frequently for my team - multiple times a week now. It's disruptive for us, because oncall needs to look up what workflow was lost and notify users that their run is never going to work.\r\n\r\nAre there any workarounds we could try?"
] | "2021-07-29T03:37:51" | "2022-06-27T19:58:30" | null | CONTRIBUTOR | null | ### What steps did you take
1. connect to kfp-standalone-1 cluster in kfp-ci project
2. count current workflows -- 1252
```bash
kubectl get workflow | wc -l
1252
```
3. confirm current workflow ages:
```bash
kubectl get workflow | less
```
### What happened:
I found many workflows with age greater than 1d, our configured workflow GC time.
Because of the issue, there are too many Pods on each node and crashing GKE metrics server.
### What did you expect to happen:
Workflows should be GCed after being persisted to KFP DB.
### Environment:
<!-- Please fill in those that seem relevant. -->
* How do you deploy Kubeflow Pipelines (KFP)? standalone
<!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. -->
* KFP version: 1.7.0-rc.2
<!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface.
To find the version number, See version number shows on bottom of KFP UI left sidenav. -->
### Labels
<!-- Please include labels below by uncommenting them to help us better triage issues -->
<!-- /area frontend -->
/area backend
<!-- /area sdk -->
/area testing
<!-- /area samples -->
<!-- /area components -->
---
<!-- Don't delete message below to encourage users to support your issue! -->
Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.
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"Reassigned it to Joe since it is related to KFP User Metrics",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-07-28T10:46:45" | "2023-01-18T08:38:22" | "2023-01-18T08:38:22" | CONTRIBUTOR | null | null | {
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- [ ] During each release, pick up the images from postsubmit of same commit, and tag as release version.
- [ ] Add license files for V2 image
- [ ] Determine how does API server reference V2 images
- Configuration on rebuild dockerfile in API Server, or use flag in yaml to pinpoint launcher image TAG
Exploration
- [ ] Improve iteration on .release.cloudbuild.yaml, unify replicated steps.
- [ ] Move launcher-v2 and driver to the subcommands of same image
- [ ] Refactor cloud build triggered postsubmit task to KFP pipeline run
- [ ] Wait for image build to finish before starting the release job | {
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"Can you try this example and see if this works for you: https://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/samples/core/caching/caching_sample.py. Uses `create_component_from_func` with `.execution_options.caching_strategy.max_cache_staleness = 'P0D'` ",
"@ferpitol ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-07-27T17:46:41" | "2022-03-03T00:05:35" | null | NONE | null | hi, my function are created with create_component_from_func,
so how can i implement this
```
task = some_op()
task.execution_options.caching_strategy.max_cache_staleness = "P0D"
example of my code
def some_python_func():
#do something
return True
task1 = create_component_from_func(
func=some_python_func(),
base_image="one ddcoker image"
#packages_to_install=['gensim'], # Optional
)
@dsl.pipeline(
name='one name',
description='one description'
)
def hello_world_pipeline():
task1().output
if __name__ == '__main__':
task = hello_world_pipeline()
kfp.compiler.Compiler().compile(task, 'mypipeline.yaml')
when in the main I code
if __name__ == '__main__':
task = hello_world_pipeline()
task.execution_options.caching_strategy.max_cache_staleness = "P0D"
kfp.compiler.Compiler().compile(task, 'mypipeline.yaml')
```
it gives me the error : AttributeError: 'NoneType' object has no attribute 'execution_options'
also I tried
```
def hello_world_pipeline():
task_nc = task1()
task_nc.execution_options.caching_strategy.max_cache_staleness = "P0D"
task_nc().output
```
but it give the error:
AttributeError: 'NoneType' object has no attribute 'caching_strategy'
please could someone help me I really need that any task of my pipeline runs cache and using configurations like` kubectl patch mutatingwebhookconfiguration ... `is not an option | {
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"/cc @chensun ",
"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n"
] | "2021-07-27T16:37:56" | "2022-03-03T00:05:34" | null | NONE | null | ### Feature Area
<!-- Uncomment the labels below which are relevant to this feature: -->
<!-- /area frontend -->
<!-- /area backend -->
<!-- /area sdk -->
<!-- /area samples -->
/area components
### What feature would you like to see?
The ability to override the image command in the function transformers like https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.func_to_container_op and `create_component_from_func`
### What is the use case or pain point?
Looking to use Bitfusion for GPU resources which requires overriding the base command to set the desired GPU resources
### Is there a workaround currently?
Haven't found one yet
---
<!-- Don't delete message below to encourage users to support your feature request! -->
Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.
| {
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"This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n",
"Both recurring run & v2 pipeline are supported.",
"`v2 pipeline` is covered by https://github.com/kubeflow/pipelines/issues/6169\r\nRename the title to cover only recurring run."
] | "2021-07-27T14:15:36" | "2023-01-18T08:25:21" | "2023-01-18T08:19:12" | CONTRIBUTOR | null | null | {
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"Unit tests: https://github.com/kubeflow/pipelines/pull/6751"
] | "2021-07-27T14:15:29" | "2021-10-25T06:13:25" | "2021-10-15T04:01:36" | CONTRIBUTOR | null | support uploading v2 pipeline spec using KFP v1 pipeline/version upload API | {
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"/lifecycle frozen"
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"Fixed by https://github.com/kubeflow/pipelines/pull/6193"
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"fixed by https://github.com/kubeflow/pipelines/pull/6415"
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"Close https://github.com/kubeflow/pipelines/issues/6165 as won't fix.\r\nRemove tracking #6166 (put in backlog)"
] | "2021-07-27T14:10:23" | "2023-01-18T08:36:16" | "2023-01-18T08:36:15" | CONTRIBUTOR | null | Tasks
* [x] #6164
* [x] ~#6165~
* [ ] ~P2 #6166~ | {
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] | "2021-07-27T14:10:15" | "2023-05-02T17:14:06" | "2023-05-02T17:14:06" | CONTRIBUTOR | null | [aggregate outputs as iterator DAG output](https://github.com/kubeflow/pipelines/blob/fef8c03e401a15a9f92c1839fe0f9a5c22f709e1/api/v2alpha1/pipeline_spec.proto#L466-L470) | {
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"P0 items done",
"Non P0 items moved to https://github.com/kubeflow/pipelines/issues/7048"
] | "2021-07-27T14:10:12" | "2022-02-15T07:59:39" | "2021-12-13T04:06:17" | CONTRIBUTOR | null | Tasks:
* [x] P0 https://github.com/kubeflow/pipelines/pull/6985
* [x] P0 #7020 (support [parameter expression selector](https://github.com/kubeflow/pipelines/blob/fef8c03e401a15a9f92c1839fe0f9a5c22f709e1/api/v2alpha1/pipeline_spec.proto#L283-L308))
* [ ] P1 https://github.com/kubeflow/pipelines/issues/6161
* [ ] P2 https://github.com/kubeflow/pipelines/tree/master/samples/core/loop_parallelism
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"Hit https://github.com/argoproj/argo-workflows/issues/7406 when prototyping for this."
] | "2021-07-27T14:10:09" | "2021-12-22T02:14:14" | "2021-12-22T02:13:21" | CONTRIBUTOR | null | * [x] #7059
* [x] #7060
* [x] https://github.com/kubeflow/pipelines/pull/7088 | {
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"/close\r\nfinished",
"@Bobgy: Closing this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/6158#issuecomment-999306641):\n\n>/close\r\n>finished\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>"
] | "2021-07-27T14:10:06" | "2021-12-22T05:54:36" | "2021-12-22T05:54:35" | CONTRIBUTOR | null | * [x] basic sub-DAG support in #6985
sub DAG publisher should be implemented with https://github.com/kubeflow/pipelines/issues/6161 | {
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"received_events_url": "https://api.github.com/users/chensun/received_events",
"type": "User",
"site_admin": false
} | [
{
"login": "chensun",
"id": 2043310,
"node_id": "MDQ6VXNlcjIwNDMzMTA=",
"avatar_url": "https://avatars.githubusercontent.com/u/2043310?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/chensun",
"html_url": "https://github.com/chensun",
"followers_url": "https://api.github.com/users/chensun/followers",
"following_url": "https://api.github.com/users/chensun/following{/other_user}",
"gists_url": "https://api.github.com/users/chensun/gists{/gist_id}",
"starred_url": "https://api.github.com/users/chensun/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/chensun/subscriptions",
"organizations_url": "https://api.github.com/users/chensun/orgs",
"repos_url": "https://api.github.com/users/chensun/repos",
"events_url": "https://api.github.com/users/chensun/events{/privacy}",
"received_events_url": "https://api.github.com/users/chensun/received_events",
"type": "User",
"site_admin": false
},
{
"login": "Linchin",
"id": 12806577,
"node_id": "MDQ6VXNlcjEyODA2NTc3",
"avatar_url": "https://avatars.githubusercontent.com/u/12806577?v=4",
"gravatar_id": "",
"url": "https://api.github.com/users/Linchin",
"html_url": "https://github.com/Linchin",
"followers_url": "https://api.github.com/users/Linchin/followers",
"following_url": "https://api.github.com/users/Linchin/following{/other_user}",
"gists_url": "https://api.github.com/users/Linchin/gists{/gist_id}",
"starred_url": "https://api.github.com/users/Linchin/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/Linchin/subscriptions",
"organizations_url": "https://api.github.com/users/Linchin/orgs",
"repos_url": "https://api.github.com/users/Linchin/repos",
"events_url": "https://api.github.com/users/Linchin/events{/privacy}",
"received_events_url": "https://api.github.com/users/Linchin/received_events",
"type": "User",
"site_admin": false
}
] | null | [
"Remove tracking https://github.com/kubeflow/pipelines/issues/7048, and close."
] | "2021-07-27T14:10:03" | "2023-01-18T08:32:22" | "2023-01-18T08:32:02" | CONTRIBUTOR | null | Design doc: [KFP v2 control flow](https://docs.google.com/document/d/1TZeZtxwPzAImIu8Jk_e-4otSx467Ckf0smNe7JbPReE/edit?usp=sharing&resourcekey=0-lTeZGW_Ys78j1LU60CEARg)
Post Alpha Tasks:
* [x] #6162
* ~[ ] #7048~
KFP v2 Alpha Tasks:
* [x] #6158
* [x] #6159
* [x] #6160 | {
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