Datasets:

hjerpe

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github-kubeflow-issues

like 0

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[ "/assign @capri-xiyue ", "PR merged https://github.com/kubeflow/pipelines/pull/6569\r\nTO DO \r\n- [ ] Update v2 caching doc ", "reassigned it to @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", "Untrack https://github.com/kubeflow/pipelines/issues/8696 and close." ]

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[ "Is this an issue for tracking the implementation of replacements for things such as `{{workflow.status}}`? \r\n\r\nIf so:\r\n\r\n`{{$.pipeline_task_name}}` and `{{$.pipeline_task_uuid}}` don't seem to work on Vertex AI at the moment (not sure about a self hosted KFP). Is this expected? I've also tried various placeholders to try to access the Pipeline job's status/error (eg. `{{$.error}}`, `{{$.status}}`, `{{$.error.code}}`, etc.) and nothing seems to work. How is this templating working and how does it differ from the old Argo templating?", "Hi, this is for tracking implementation of {{$.pipeline_task_name}} and {{$.pipeline_task_uuid}} like placeholders, but not status.", "Can you submit a separate issue for your feature request? And make it clearer it's for KFP v2 or Vertex.", "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" ]

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[ "P0 work items done" ]

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[ "Done" ]

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[]

[]

[ "I'll directly track this in #6110" ]

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[ "The AIPlatformClient in KFP SDK will be deprecated and we would recommend starting to use `google-cloud-aiplatform`. \r\n\r\nCurrently, the `create_run_from_job_spec` is completed moved over and `create_schedule_from_job_spec` is in-progress. We are working on a code sample of usage in `google-cloud-aiplatform` (Vertex SDK) and will add to the deprecation warning when it's ready. \r\n\r\nIf it's blocking you, you can continue submit PR to KFP client to unblock, until the Vertex SDK client is ready. Otherwise, please file a feature request and assign to me. I will be reviewing them and consider adding them to Vertex SDK. \r\n\r\nThank you for your contributions!", "@ji-yaqi Thank you for your reply! Then we will keep using the KFP SDK for a while and prepare migration to the Vertex SDK. ", "@ji-yaqi what is the Google Cloud SDK equivalent of `create_schedule_from_job_spec`?" ]

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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" ]

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[ "I've also just tested the issues with 1.7.0-rc2.\r\n\r\nI've tried kfp 1.4.0 and 1.6.6.", "Downgrading the metadata images seems to have solved this for me:\r\n\r\n```\r\nimages:\r\n - name: gcr.io/ml-pipeline/metadata-envoy\r\n newTag: 1.4.0\r\n - name: gcr.io/tfx-oss-public/ml_metadata_store_server\r\n newTag: 0.25.1\r\n - name: gcr.io/ml-pipeline/metadata-writer\r\n newTag: 1.4.0\r\n```\r\n\r\nI believe the `metadata-writer` container version is the most important one here, though I have no context.", "Actually reverting doesn't solve the problem completely, I still find the Statistics and Evaluator artifacts don't display.\r\n\r\nIn the markdown they have zeroed out id's:\r\n\r\n```\r\nid: 0\r\nspan: None\r\ntype_id: 0\r\n```", "There seems to be a problem with ml-metadata 1.0.0 which is required by tfx 1.0.0 and matches the grpc server of 1.7.1.\r\n\r\nDowngrading the the metadata-grpc-server doesn't work solve anything.\r\n\r\nI've gone back down to tfx 0.30 which uses metadata 0.30.0\r\n\r\nVisualisations work when I downgrade gcr.io/tfx-oss-public/ml_metadata_store_server to `0.25.1` as well.", "Hello @vaskozl , what is the TFX version you are using when you are using KFP 1.4.0?", "/cc @jiyongjung0 \r\ndo you know any context?", "Possible related: https://github.com/tensorflow/tensorflow/issues/50045", "When on 1.4.0 everything from TFX 0.27 to 1.0.0 wrote metadata. (visualizations didn't work but they do in the 1.7.0 release). I do recall having to bump the grpcio version in requirements up and down while hopping up to TFX 1.0.0 .\r\n\r\nTFX 1.0.0 -> Kubeflow 1.7.0 (with metadata) No metadata written\r\nTFX 1.0.0 -> Kubeflow 1.4.0 Partial metadata written it seemed\r\nTFX 0.30.0 -> Kubeflow 1.4.0 All as expected\r\n\r\nAs I said I'm now using 1.7.0 with just the ml_metadata_store_server downgraded below 1.0.0.", "I found some possible cause. It is related to the changes in the way TFX stores their contexts since 1.0.0 (which is related to the changes in the execution stack using TFX IR).\r\n\r\nIn TFX 0.X, the context were\r\n- type:pipeline, value: \"my-pipeline\"\r\n- type:run, value: \"my-pipeline.my-pipeline-xaew1\" (some hash is appended in the second part.)\r\n- type:component_run, value: \"my-pipeline.my-pipeline-xaew1.CsvExampleGen\"\r\n[Related code](https://github.com/tensorflow/tfx/blob/859b668f3423cd89895443d838e0b2a3e65ce0b7/tfx/orchestration/metadata.py#L60-L62)\r\n\r\nHowever in TFX 1.0, the context became\r\n- type:pipeline, value: \"my-pipeline\"\r\n- type:pipeline_run, value: \"my-pipeline-xaew1\"\r\n- type:node, value: \"my-pipeline.CsvExampleGen\"\r\n\r\n[Related code](https://github.com/tensorflow/tfx/blob/859b668f3423cd89895443d838e0b2a3e65ce0b7/tfx/dsl/compiler/compiler.py#L134-L154)\r\n\r\nSo it seems like Kubeflow Pipelines cannot find context (and artifacts) properly.\r\nI think that we should change mlmd access code [like here](https://github.com/kubeflow/pipelines/blob/a94e92e5309b371967917dded31090c63321f93c/frontend/src/mlmd/MlmdUtils.ts#L83).\r\n\r\nCC. @zhitaoli , @1025KB , @Bobgy \r\n\r\n", "Thank you @jiyongjung0 for finding the difference between TFX 0.X and 1.0+! \r\n\r\nFor backward compatibility, what should KFP frontend do to detect if a TFX pipeline is TFX 0.X or TFX 1.0+? ", "Unfortunately, it seems that there is no direct clue when finding executions. (Artifacts has `tfx_version` property, but there is no such information in Context / Execution.)\r\n\r\nI think that we can try to find 1.0 context first, and fallback to 0.X context if not found.", "Makes sense to me to find 1.0 context first and then fall back to 0.X", "Sounds good, agree with the fallback strategy here.", "Hello @jiyongjung0 ,\r\n\r\nCurrently we use this [code logic](https://github.com/kubeflow/pipelines/blob/a94e92e5309b371967917dded31090c63321f93c/frontend/src/mlmd/MlmdUtils.ts#L163-L169) to identify the execution for a specific node using node ID (which is the corresponding Pod ID). However, with the latest integration with TFX, we are unable to find this connection from Execution, see one of the following example for a statisticsGen execution:\r\n\r\n\r\n\r\nHow do I fix the TFX integration that I get the properties correctly like the following? (From an old deployment)\r\n\r\n\r\n", "Hi, this is a bug from TFX side introduced in https://github.com/tensorflow/tfx/commit/24fc5d1db9198a75db11af25cf05c4d3ae05491f. It seems like we don't record pod names in TFX 1.0.0. I'll try to fix this ASAP, and will update the release plan.", "I'm trying to include the above fix in the TFX 1.2.0 which is expected to be released tomorrow. I'll update you when the release is finalized.", "TFX 1.2.0 was released today and this should be fixed. Thank you again for reporting this!", "Appreciate it a lot @jiyongjung0 for the quick fix and release of TFX 1.2.0!\r\n\r\nThe following is what I found:\r\n1. I need to make changes to notebook example for TFX v1.2.0 for pip installation and image. I send a PR for this: https://github.com/kubeflow/pipelines/pull/6381, appreciate for your review in advance!\r\n2. I am able to see HTML visualization for staticsgen, schemagen, etc. (yay!), but I am not able to see the visualization of `transform` step, for the artifact like `pre_transform_stats`. Because KFP is trying to visit `Split-eval` and `Split-train` in the code `files = tf.io.gfile.listdir('${specificUri}')`: https://github.com/kubeflow/pipelines/blob/master/frontend/src/lib/OutputArtifactLoader.ts#L304, where I don't have those files, see the screenshot below:\r\n\r\n\r\n3. The step `evaluator` fails with the following logs, how does TFX utilize the KFP visualization feature?\r\n```\r\n File \"apache_beam/runners/common.py\", line 572, in apache_beam.runners.common.SimpleInvoker.invoke_process\r\n File \"/opt/conda/lib/python3.7/site-packages/tensorflow_model_analysis/model_util.py\", line 779, in process\r\n result.extend(self._batch_reducible_process(unbatched_element))\r\n File \"/opt/conda/lib/python3.7/site-packages/tensorflow_model_analysis/model_util.py\", line 928, in _batch_reducible_process\r\n input_specs = get_input_specs(model, signature_name, required) or {}\r\n File \"/opt/conda/lib/python3.7/site-packages/tensorflow_model_analysis/model_util.py\", line 472, in get_input_specs\r\n signature_name, model.signatures))\r\nValueError: tft_layer not found in model signatures: _SignatureMap({'serving_default': <ConcreteFunction signature_wrapper(*, examples) at 0x7FCBCA4E62D0>, 'transform_features': <ConcreteFunction signature_wrapper(*, examples) at 0x7FCBCA4E3410>}) [while running 'ExtractEvaluateAndWriteResults/ExtractAndEvaluate/ExtractTransformedFeatures/Predict']\r\ntime=\"2021-08-18T16:09:09.989Z\" level=error msg=\"cannot save artifact /mlpipeline-ui-metadata.json\" argo=true error=\"stat /mlpipeline-ui-metadata.json: no such file or directory\"\r\nError: exit status 1\r\n```", "@zijianjoy\r\n\r\nThank you so much!!\r\n\r\nFor 2.(Transform output) It seems like an inconsistency in TFX implementation. The artifact from Transform was added recently and I never tried before. I'll talk with other TFX folks and get back to you.\r\n\r\nFor 3.(Evaluator), We need to change the `preprocessing_function_names` in the evaluator config in 1.2.0, because the example was changed in 1.2.0. Please see https://github.com/tensorflow/tfx/blob/34bdbc8c0f7c2d0da36559c9cb7afd603e44a5e3/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_native_keras.py#L126\r\n\r\n", "Document the conversation:\r\n\r\nNow TFX moves the information in Execution to Context with type `node`. see code: https://github.com/tensorflow/tfx/blob/master/tfx/orchestration/metadata.py#L428-L435.\r\n\r\nKFP will consider the possibility to pulling context for TFX.", "@zijianjoy Was the TFX > 1.0.0 fix included in the KFP 1.7.0 release?\r\n", "@ConverJens I can confirm it has.\r\n\r\nTFX 1.2.0 and Pipelines 1.7.0 work perfectly with no patches.", "@vaskozl Great news, thank you for the information!", "Yes @ConverJens , it is as confirmed by @vaskozl .\r\n\r\nBTW, @Bobgy is working on a compatibility matrix for KFP and TFX (and more) which shows the working version combinations in the future.", "@zijianjoy Great! A compatibility matrix would be awsome!", "Hello @ConverJens , you can check out the compatibility matrix in https://www.kubeflow.org/docs/components/pipelines/installation/compatibility-matrix/ now.", "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", "Hi, there.\r\nI am using kfp v.1.5.1 with tfx v0.28.0. \r\nI faced the issue of loading ML metadata in the Runs page, although in the compatibility matrix it is \"Fully Compatible\".\r\nThat was because of the contextName inconsistency between what frontend wants to find and that stored in the MySQL. \r\nfrontend wanted to find something like \"my_pipeline.my-pipeline-xaew1\" (some hash is appended in the second part.), but what is stored in the MySQL is \"my-pipeline.my-pipeline-xaew1\", so error happened.\r\nSee: https://github.com/kubeflow/pipelines/blob/1.5.1/frontend/src/lib/MlmdUtils.ts#L66\r\n\r\n\r\nAfter changing it from .join('_') to .join('-'), it works.\r\n```\r\n const pipelineName = argoWorkflowName\r\n .split('-')\r\n .slice(0, -1)\r\n .join('-');\r\n```\r\n\r\nAnd it also had an issue with getKfpPod using KFP_POD_NAME, because kfpRun wasn't written into the DB.\r\nSee: \r\nhttps://github.com/kubeflow/pipelines/blob/1.5.1/frontend/src/lib/MlmdUtils.ts#L146\r\nTook a workaround, using POD_NAME, made it works. \r\n```\r\nexport enum KfpExecutionProperties {\r\n KFP_POD_NAME = 'kfp_pod_name',\r\n POD_NAME = 'pod_name',\r\n}\r\n...\r\n getKfpPod(execution: Execution): string | number | undefined {\r\n return (\r\n getResourceProperty(execution, KfpExecutionProperties.POD_NAME, true) ||\r\n getResourceProperty(execution, KfpExecutionProperties.KFP_POD_NAME) ||\r\n getResourceProperty(execution, KfpExecutionProperties.KFP_POD_NAME, true) ||\r\n undefined\r\n );\r\n },\r\n```\r\n\r\n" ]

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[ "/assign @chensun @neuromage ", "This is a known issues, and it was a regression we introduced when workaround the limitation that IR doesn't support optional inputs.\r\nI left a TODO in the code:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/42e13ebc41c6d318eebe797047ccd0d83641c0d8/sdk/python/kfp/dsl/_component_bridge.py#L593\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" ]

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[ "Closing because v2 compatible is no longer supported, we are working on the complete KFP v2.\n\n/cc @chensun \nWe used to have this issue for v2 compatible, a lot of them are still unsolved for v2 engine mode as well." ]

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[]

[ "Hello, on our side this is one of the most important feature for v2. \r\nBeing able to run multiple pipelines and then easily compare the metrics of each pipeline in a single view is key in our workflow. \r\nWe loved that v1 offered this and we cannot wait for it to be available on v2. \r\nCheers!", "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", "Closing as v2 compatible mode is no longer supported." ]

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[ "/assign @IronPan ", "/assign @SinaChavoshi ", "P0, because this breaks all postsubmit builds, thus blocking us to release", "Pasting error messages:\r\n\r\n```\r\nStep 4/9 : RUN python3 -m pip install -r requirements.txt --quiet --no-cache-dir && rm -f requirements.txt\r\n ---> Running in ac5c4843faba\r\n ERROR: Command errored out with exit status 1:\r\n command: /usr/local/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' bdist_wheel -d /tmp/pip-wheel-6ld2iia3\r\n cwd: /tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/\r\n```\r\n\r\nComplete logs\r\n```\r\ncreating build/temp.linux-x86_64-3.7\r\n creating build/temp.linux-x86_64-3.7/googlecloudprofiler\r\n creating build/temp.linux-x86_64-3.7/googlecloudprofiler/src\r\n gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -Igooglecloudprofiler/src -I/usr/local/include/python3.7m -c googlecloudprofiler/src/log.cc -o build/temp.linux-x86_64-3.7/googlecloudprofiler/src/log.o -std=c++11\r\n unable to execute 'gcc': No such file or directory\r\n error: command 'gcc' failed with exit status 1\r\n ----------------------------------------\r\n ERROR: Failed building wheel for google-cloud-profiler\r\n ERROR: Command errored out with exit status 1:\r\n command: /usr/local/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-4ynx33up/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.7m/google-cloud-profiler\r\n cwd: /tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/\r\n Complete output (22 lines):\r\n running install\r\n running build\r\n running build_py\r\n creating build\r\n creating build/lib.linux-x86_64-3.7\r\n creating build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/cpu_profiler.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/profile_pb2.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/__version__.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/backoff.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/__init__.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/client.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/builder.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n copying googlecloudprofiler/pythonprofiler.py -> build/lib.linux-x86_64-3.7/googlecloudprofiler\r\n running build_ext\r\n building 'googlecloudprofiler._profiler' extension\r\n creating build/temp.linux-x86_64-3.7\r\n creating build/temp.linux-x86_64-3.7/googlecloudprofiler\r\n creating build/temp.linux-x86_64-3.7/googlecloudprofiler/src\r\n gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -Igooglecloudprofiler/src -I/usr/local/include/python3.7m -c googlecloudprofiler/src/log.cc -o build/temp.linux-x86_64-3.7/googlecloudprofiler/src/log.o -std=c++11\r\n unable to execute 'gcc': No such file or directory\r\n error: command 'gcc' failed with exit status 1\r\n ----------------------------------------\r\nERROR: Command errored out with exit status 1: /usr/local/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-3gpukc09/google-cloud-profiler_717d01c8a0174005b25a65673c363257/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-4ynx33up/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.7m/google-cloud-profiler Check the logs for full command output.\r\nThe command '/bin/sh -c python3 -m pip install -r requirements.txt --quiet --no-cache-dir && rm -f requirements.txt' returned a non-zero code: 1\r\n```", "Fixed by https://github.com/kubeflow/pipelines/pull/6141" ]

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[ "POC is available at this branch\r\n\r\nhttps://github.com/zijianjoy/pipelines/tree/dag-poc" ]

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[ "Hi @ReggieCarey!\r\nCan you give output of `kubectl logs ml-pipeline-9b68d49cb-x67mp ml-pipeline-api-server --previous`? that should include logs from last failed container.", "For metadata-writer, what is status of `metadata-grpc-server`? metadata-writer fails to connect to the store", "Thanks Yuan Gong,\r\n\r\nAs per request:\r\n```bash\r\nThu Aug 05 15:43:51 $ kubectl logs ml-pipeline-9b68d49cb-6z2tq ml-pipeline-api-server --previous\r\nI0806 00:55:18.818889 7 client_manager.go:154] Initializing client manager\r\nI0806 00:55:18.818963 7 config.go:57] Config DBConfig.ExtraParams not specified, skipping\r\n```\r\nSadly, that's it.\r\n\r\nReggie", "For metadata-writer issue: The status of metadata-grpc-deployment is stable(ish). It has restarted 533 times.\r\n\r\nHere is output from \"describe\", the logs for \"container\" are empty\r\n\r\n```\r\nThu Aug 05 15:42:03 $ kubectl describe pod metadata-grpc-deployment-c8f784fdf-hdvgr \r\nmetacontroller-0\r\nmetadata-envoy-deployment-6756c995c9-fl7gb\r\nmetadata-grpc-deployment-c8f784fdf-hdvgr\r\nmetadata-writer-6bf5cfd7d8-v7fzb\r\nThu Aug 05 15:42:03 $ kubectl describe pod metadata-grpc-deployment-c8f784fdf-hdvgr \r\nName: metadata-grpc-deployment-c8f784fdf-hdvgr\r\nNamespace: kubeflow\r\nPriority: 0\r\nNode: cpu-compute-05/10.164.208.183\r\nStart Time: Tue, 20 Jul 2021 21:27:57 -0400\r\nLabels: application-crd-id=kubeflow-pipelines\r\n component=metadata-grpc-server\r\n istio.io/rev=default\r\n pod-template-hash=c8f784fdf\r\n security.istio.io/tlsMode=istio\r\n service.istio.io/canonical-name=metadata-grpc-deployment\r\n service.istio.io/canonical-revision=latest\r\nAnnotations: kubectl.kubernetes.io/default-logs-container: container\r\n prometheus.io/path: /stats/prometheus\r\n prometheus.io/port: 15020\r\n prometheus.io/scrape: true\r\n sidecar.istio.io/status:\r\n {\"initContainers\":[\"istio-init\"],\"containers\":[\"istio-proxy\"],\"volumes\":[\"istio-envoy\",\"istio-data\",\"istio-podinfo\",\"istio-token\",\"istiod-...\r\nStatus: Running\r\nIP: 10.0.0.135\r\nIPs:\r\n IP: 10.0.0.135\r\nControlled By: ReplicaSet/metadata-grpc-deployment-c8f784fdf\r\nInit Containers:\r\n istio-init:\r\n Container ID: docker://b6c7ccd562a209578a835ec5f5a0b3799e150f2c3be4e428be03788591c707e7\r\n Image: docker.io/istio/proxyv2:1.9.0\r\n Image ID: docker-pullable://istio/proxyv2@sha256:286b821197d7a9233d1d889119f090cd9a9394468d3a312f66ea24f6e16b2294\r\n Port: <none>\r\n Host Port: <none>\r\n Args:\r\n istio-iptables\r\n -p\r\n 15001\r\n -z\r\n 15006\r\n -u\r\n 1337\r\n -m\r\n REDIRECT\r\n -i\r\n *\r\n -x\r\n \r\n -b\r\n *\r\n -d\r\n 15090,15021,15020\r\n State: Terminated\r\n Reason: Completed\r\n Exit Code: 0\r\n Started: Thu, 29 Jul 2021 18:15:54 -0400\r\n Finished: Thu, 29 Jul 2021 18:15:54 -0400\r\n Ready: True\r\n Restart Count: 0\r\n Limits:\r\n cpu: 2\r\n memory: 1Gi\r\n Requests:\r\n cpu: 10m\r\n memory: 40Mi\r\n Environment: <none>\r\n Mounts:\r\n /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-4qdbv (ro)\r\nContainers:\r\n container:\r\n Container ID: docker://b5c4b93b8034fdfed2181814fd9b37ff9fe2b154cf3163a988bc4ab4f29e202c\r\n Image: gcr.io/tfx-oss-public/ml_metadata_store_server:0.25.1\r\n Image ID: docker-pullable://gcr.io/tfx-oss-public/ml_metadata_store_server@sha256:01691247116fe048e0761ae8033efaad3ddd82438d0198f2235afb37c1757d48\r\n Port: 8080/TCP\r\n Host Port: 0/TCP\r\n Command:\r\n /bin/metadata_store_server\r\n Args:\r\n --grpc_port=8080\r\n --mysql_config_database=$(MYSQL_DATABASE)\r\n --mysql_config_host=$(MYSQL_HOST)\r\n --mysql_config_port=$(MYSQL_PORT)\r\n --mysql_config_user=$(DBCONFIG_USER)\r\n --mysql_config_password=$(DBCONFIG_PASSWORD)\r\n --enable_database_upgrade=true\r\n State: Running\r\n Started: Thu, 05 Aug 2021 20:36:26 -0400\r\n Last State: Terminated\r\n Reason: Error\r\n Exit Code: 139\r\n Started: Thu, 05 Aug 2021 19:36:25 -0400\r\n Finished: Thu, 05 Aug 2021 20:36:25 -0400\r\n Ready: True\r\n Restart Count: 532\r\n Liveness: tcp-socket :grpc-api delay=3s timeout=2s period=5s #success=1 #failure=3\r\n Readiness: tcp-socket :grpc-api delay=3s timeout=2s period=5s #success=1 #failure=3\r\n Environment:\r\n DBCONFIG_USER: <set to the key 'username' in secret 'mysql-secret'> Optional: false\r\n DBCONFIG_PASSWORD: <set to the key 'password' in secret 'mysql-secret'> Optional: false\r\n MYSQL_DATABASE: <set to the key 'mlmdDb' of config map 'pipeline-install-config'> Optional: false\r\n MYSQL_HOST: <set to the key 'dbHost' of config map 'pipeline-install-config'> Optional: false\r\n MYSQL_PORT: <set to the key 'dbPort' of config map 'pipeline-install-config'> Optional: false\r\n Mounts:\r\n /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-4qdbv (ro)\r\n istio-proxy:\r\n Container ID: docker://82b1c17880c0ec145d97cd26ca16526552122e7362e3fe4a0956555ebd63635b\r\n Image: docker.io/istio/proxyv2:1.9.0\r\n Image ID: docker-pullable://istio/proxyv2@sha256:286b821197d7a9233d1d889119f090cd9a9394468d3a312f66ea24f6e16b2294\r\n Port: 15090/TCP\r\n Host Port: 0/TCP\r\n Args:\r\n proxy\r\n sidecar\r\n --domain\r\n $(POD_NAMESPACE).svc.cluster.local\r\n --serviceCluster\r\n metadata-grpc-deployment.kubeflow\r\n --proxyLogLevel=warning\r\n --proxyComponentLogLevel=misc:error\r\n --log_output_level=default:info\r\n --concurrency\r\n 2\r\n State: Running\r\n Started: Thu, 29 Jul 2021 18:16:00 -0400\r\n Last State: Terminated\r\n Reason: Error\r\n Exit Code: 255\r\n Started: Tue, 20 Jul 2021 21:28:03 -0400\r\n Finished: Thu, 29 Jul 2021 17:51:17 -0400\r\n Ready: True\r\n Restart Count: 1\r\n Limits:\r\n cpu: 2\r\n memory: 1Gi\r\n Requests:\r\n cpu: 10m\r\n memory: 40Mi\r\n Readiness: http-get http://:15021/healthz/ready delay=1s timeout=3s period=2s #success=1 #failure=30\r\n Environment:\r\n JWT_POLICY: third-party-jwt\r\n PILOT_CERT_PROVIDER: istiod\r\n CA_ADDR: istiod.istio-system.svc:15012\r\n POD_NAME: metadata-grpc-deployment-c8f784fdf-hdvgr (v1:metadata.name)\r\n POD_NAMESPACE: kubeflow (v1:metadata.namespace)\r\n INSTANCE_IP: (v1:status.podIP)\r\n SERVICE_ACCOUNT: (v1:spec.serviceAccountName)\r\n HOST_IP: (v1:status.hostIP)\r\n CANONICAL_SERVICE: (v1:metadata.labels['service.istio.io/canonical-name'])\r\n CANONICAL_REVISION: (v1:metadata.labels['service.istio.io/canonical-revision'])\r\n PROXY_CONFIG: {}\r\n \r\n ISTIO_META_POD_PORTS: [\r\n {\"name\":\"grpc-api\",\"containerPort\":8080,\"protocol\":\"TCP\"}\r\n ]\r\n ISTIO_META_APP_CONTAINERS: container\r\n ISTIO_META_CLUSTER_ID: Kubernetes\r\n ISTIO_META_INTERCEPTION_MODE: REDIRECT\r\n ISTIO_META_WORKLOAD_NAME: metadata-grpc-deployment\r\n ISTIO_META_OWNER: kubernetes://apis/apps/v1/namespaces/kubeflow/deployments/metadata-grpc-deployment\r\n ISTIO_META_MESH_ID: cluster.local\r\n TRUST_DOMAIN: cluster.local\r\n Mounts:\r\n /etc/istio/pod from istio-podinfo (rw)\r\n /etc/istio/proxy from istio-envoy (rw)\r\n /var/lib/istio/data from istio-data (rw)\r\n /var/run/secrets/istio from istiod-ca-cert (rw)\r\n /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-4qdbv (ro)\r\n /var/run/secrets/tokens from istio-token (rw)\r\nConditions:\r\n Type Status\r\n Initialized True \r\n Ready True \r\n ContainersReady True \r\n PodScheduled True \r\nVolumes:\r\n istio-envoy:\r\n Type: EmptyDir (a temporary directory that shares a pod's lifetime)\r\n Medium: Memory\r\n SizeLimit: <unset>\r\n istio-data:\r\n Type: EmptyDir (a temporary directory that shares a pod's lifetime)\r\n Medium: \r\n SizeLimit: <unset>\r\n istio-podinfo:\r\n Type: DownwardAPI (a volume populated by information about the pod)\r\n Items:\r\n metadata.labels -> labels\r\n metadata.annotations -> annotations\r\n limits.cpu -> cpu-limit\r\n requests.cpu -> cpu-request\r\n istio-token:\r\n Type: Projected (a volume that contains injected data from multiple sources)\r\n TokenExpirationSeconds: 43200\r\n istiod-ca-cert:\r\n Type: ConfigMap (a volume populated by a ConfigMap)\r\n Name: istio-ca-root-cert\r\n Optional: false\r\n kube-api-access-4qdbv:\r\n Type: Projected (a volume that contains injected data from multiple sources)\r\n TokenExpirationSeconds: 3607\r\n ConfigMapName: kube-root-ca.crt\r\n ConfigMapOptional: <nil>\r\n DownwardAPI: true\r\nQoS Class: Burstable\r\nNode-Selectors: <none>\r\nTolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s\r\n node.kubernetes.io/unreachable:NoExecute op=Exists for 300s\r\nEvents:\r\n Type Reason Age From Message\r\n ---- ------ ---- ---- -------\r\n Normal Pulled 31m (x350 over 7d2h) kubelet Container image \"gcr.io/tfx-oss-public/ml_metadata_store_server:0.25.1\" already present on machine\r\n Normal Created 31m (x350 over 7d2h) kubelet Created container container\r\n Normal Started 31m (x350 over 7d2h) kubelet Started container container\r\n```\r\n", "Both servers are part of the istio service mesh. In the past, the mysql process was implicated. That process remains up and stable with 0 restarts.\r\n\r\n<img width=\"1052\" alt=\"image\" src=\"https://user-images.githubusercontent.com/10270182/128441376-53624383-92a0-45d4-bf59-83927864bebd.png\">\r\n\r\nBoth pods go to a state of Running then fail but they are not synchronized in this failure.\r\n\r\nI should also add additional info that cache-server-* has restarted some 132 times in 15 days.\r\n```\r\n[mysql] 2021/08/05 17:34:16 packets.go:36: unexpected EOF\r\n[mysql] 2021/08/05 18:34:16 packets.go:36: unexpected EOF\r\n[mysql] 2021/08/05 19:34:16 packets.go:36: unexpected EOF\r\n[mysql] 2021/08/05 20:34:16 packets.go:36: unexpected EOF\r\nF0805 20:34:16.890285 1 error.go:325] driver: bad connection\r\ngoroutine 1 [running]:\r\ngithub.com/golang/glog.stacks(0xc00043e300, 0xc000168000, 0x43, 0x96)\r\n\t/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:769 +0xb8\r\ngithub.com/golang/glog.(*loggingT).output(0x28daaa0, 0xc000000003, 0xc000412000, 0x280d034, 0x8, 0x145, 0x0)\r\n\t/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:720 +0x372\r\ngithub.com/golang/glog.(*loggingT).printf(0x28daaa0, 0x3, 0x199ee22, 0x2, 0xc0002f38d8, 0x1, 0x1)\r\n\t/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:655 +0x14b\r\ngithub.com/golang/glog.Fatalf(...)\r\n\t/go/pkg/mod/github.com/golang/glog@v0.0.0-20160126235308-23def4e6c14b/glog.go:1148\r\ngithub.com/kubeflow/pipelines/backend/src/common/util.TerminateIfError(...)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:325\r\nmain.initMysql(0x7fffe3a0c170, 0x5, 0x7fffe3a0c180, 0x5, 0x7fffe3a0c190, 0x4, 0x7fffe3a0c19f, 0x7, 0x7fffe3a0c1b1, 0x4, ...)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:157 +0x4f1\r\nmain.initDBClient(0x7fffe3a0c170, 0x5, 0x7fffe3a0c180, 0x5, 0x7fffe3a0c190, 0x4, 0x7fffe3a0c19f, 0x7, 0x7fffe3a0c1b1, 0x4, ...)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:71 +0x681\r\nmain.(*ClientManager).init(0xc0002f3e98, 0x7fffe3a0c170, 0x5, 0x7fffe3a0c180, 0x5, 0x7fffe3a0c190, 0x4, 0x7fffe3a0c19f, 0x7, 0x7fffe3a0c1b1, ...)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:57 +0x80\r\nmain.NewClientManager(...)\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/cache/client_manager.go:169\r\nmain.main()\r\n\t/go/src/github.com/kubeflow/pipelines/backend/src/cache/main.go:77 +0x66e\r\n```\r\n\r\nMaybe this is an issue with istio and service mesh configuration on bare metal.\r\n\r\nIn past incarnations of this bug, there was a repair offered up to establish PeerAuthentication - this resource does not exist in my cluster. The old suggestion was to apply:\r\n```yaml\r\napiVersion: security.istio.io/v1beta1\r\nkind: PeerAuthentication\r\nmetadata:\r\n name: \"default\"\r\nspec:\r\n mtls:\r\n mode: STRICT\r\n```\r\n\r\nI do not know if this is still applicable in the the Istio 1.9.0 world.\r\n\r\n", "Any progress or ideas or things to try? KFP probably represents one of the most beneficial parts of Kubeflow 1.3.0 to us. As of now all I have is Jupiter Notebooks with a Kubeflow Dashboard wrapper. I can't use KFP at all.", "It's been 17 days and I have not heard any movement on this bug. I really want to get this resolved. I have switched my ISTIO service mesh to require mTLS (STRICT) everywhere. I have verified that the processes listed here as well as the mysql process are all within the service mesh - as evidenced by istio-proxy and istio-init being injected into the three pods.\r\n\r\nThis really does appear to be a problem with access to the mysql store.\r\n\r\nMy next experiments will be to stand up an ubuntu container with mysql client in the kubeflow namespace. From there I hope to be able to validate connectivity.\r\n\r\nI can see two outcomes:\r\n\r\n1) Connectivity works\r\n2) Connectivity fails\r\n\r\nIn both cases the next step is still : What do I do given this additional information? \r\n\r\nAs an FYI, the mysql process' istio-proxy shows the following in the logs:\r\n```\r\n2021-08-26T15:15:46.314059Z\tinfo\txdsproxy\tconnected to upstream XDS server: istiod.istio-system.svc:15012\r\n2021-08-26T15:44:53.280933Z\twarning\tenvoy config\tStreamAggregatedResources gRPC config stream closed: 0, \r\n2021-08-26T15:44:53.515221Z\tinfo\txdsproxy\tconnected to upstream XDS server: istiod.istio-system.svc:15012\r\n2021-08-26T16:12:54.444630Z\twarning\tenvoy config\tStreamAggregatedResources gRPC config stream closed: 0, \r\n2021-08-26T16:12:54.806062Z\tinfo\txdsproxy\tconnected to upstream XDS server: istiod.istio-system.svc:15012\r\n2021-08-26T16:45:50.518921Z\twarning\tenvoy config\tStreamAggregatedResources gRPC config stream closed: 0, \r\n2021-08-26T16:45:50.786400Z\tinfo\txdsproxy\tconnected to upstream XDS server: istiod.istio-system.svc:15012\r\n```\r\n", "UPDATE:\r\n\r\nWas able to get to partial success:\r\n\r\nWhat I did was to edit KubeFlow/v1.3.0/manifests/apps/pipeline/upstream/third-party/mysql/base/mysql-deployment.yaml\r\nAdd in:\r\n```\r\nspec:\r\n template:\r\n metadata:\r\n annotations:\r\n sidecar.istio.io/inject: \"false\"\r\n```\r\n\r\n(NOTE: I also changed to use image: mysql:8 - but I don't think this is the issue)\r\n\r\nAnd then, I underplayed and redeployed the KFP - I know I could have just applied the changes.\r\n```\r\n$ kustomize build apps/pipeline/upstream/env/platform-agnostic-multi-user | kubectl delete -f -\r\n$ kustomize build apps/pipeline/upstream/env/platform-agnostic-multi-user | kubectl apply -f -\r\n```\r\n\r\nThe downside is that metadata-grpc-deployment and metadata-writer now fail.\r\n\r\nfor metadata-grpc-deployment, the log reads:\r\n```\r\n2021-08-26 21:43:31.894833: F ml_metadata/metadata_store/metadata_store_server_main.cc:220] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 2059, error: Plugin caching_sha2_password could not be loaded: lib/mariadb/plugin/caching_sha2_password.so: cannot open shared object file: No such file or directoryMetadataStore cannot be created with the given connection config.\r\n```\r\n\r\nfor metadata-writer, the logs read:\r\n```\r\nFailed to access the Metadata store. Exception: \"no healthy upstream\"\r\n```\r\n\r\nNext I tried to use \"platform-agnostic-multi-user-legacy\"...\r\n\r\n```\r\n$ kustomize build apps/pipeline/upstream/env/platform-agnostic-multi-user-legacy | kubectl apply -f -\r\n```\r\n\r\nAnd all processes are now running - except this now shows up:\r\n\r\n\r\n\r\n\r\nAgain: Any suggestions and assistance is highly appreciated.", "I can confirm that this is issue is seen with KF 1.3.1 too. The bug is very annoying as KFP remains inaccessible. ", "Ohh, sorry this fell through the cracks. Let me take a look tmr.", "/assign @zijianjoy \nThis looks like an Istio compatibility issue.", "When you disable sidecar injection, also find all the destination rules and delete the destination rule for mysql. Otherwise, all other clients will fail to access MySQL assuming mTLS is turned on.\n\nEdit: this is a workaround by pulling MySQL out of the mesh.", "If you want MySQL in the mesh, you need to check Istio documentation for troubleshooting instructions. I agree Istio is very hard to troubleshoot, got the same frustration when configuring these up.", "I had the similar problem on metadatawriter, after the pod of deployment `metadata-grpc-deployment` has envoy sidecar, the metadatawriter stopped the crashloopbackoff.\r\n\r\nso please check any component in `kubeflow` ns has required envoy sidecar, especially when you enforced the strict mTLS via peerAuthentication.", "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 also encountered this issue. \r\nMy env:\r\nk8s: 1.19.16\r\nkubeflow manifests: v1.5.1 tag\r\nkustomize: 3.2.0\r\n\r\ninstall command:\r\n```\r\nwhile ! kustomize build example | kubectl apply -f -; do echo \"Retrying to apply resources\"; sleep 10; done\r\n```\r\n\r\nmetadata-grpc-deployment-6f6f7776c5-pq6hq log:\r\n```\r\nWARNING: Logging before InitGoogleLogging() is written to STDERR\r\nF0321 09:45:37.213575 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\nmysql-f7b9b7dd4-5m4tb logs:\r\n```\r\n2023-03-21 09:47:24+00:00 [Note] [Entrypoint]: Entrypoint script for MySQL Server 5.7.33-1debian10 started.\r\n2023-03-21 09:47:27+00:00 [ERROR] [Entrypoint]: mysqld failed while attempting to check config\r\n command was: mysqld --ignore-db-dir=lost+found --datadir /var/lib/mysql --verbose --help --log-bin-index=/tmp/tmp.fbqLrtywDf\r\n\r\n```\r\n\r\nmetadata-writer-d7ff8d4bc-8thjn logs:\r\n```\r\nFailed to access the Metadata store. Exception: \"no healthy upstream\"\r\nFailed to access the Metadata store. Exception: \"no healthy upstream\"\r\nFailed to access the Metadata store. Exception: \"no healthy upstream\"\r\nTraceback (most recent call last):\r\n File \"/kfp/metadata_writer/metadata_writer.py\", line 63, in <module>\r\n mlmd_store = connect_to_mlmd()\r\n File \"/kfp/metadata_writer/metadata_helpers.py\", line 62, in connect_to_mlmd\r\n raise RuntimeError('Could not connect to the Metadata store.')\r\nRuntimeError: Could not connect to the Metadata store.\r\n```\r\n\r\n\r\nAnd I tried k8s 1.21.2, still has the same problem.", "NOTE: Everything I wrote below is totally not useful to this thread about cloud deployments. I just picked this issue out of the five I was looking at. I seriously thought it was contextually appropriate but it is not. The below information may be useful to you, but it was put here on accident essentially. I also think it's useful info and I don't want to just delete it. I will move what I've said below into it's own issue tomorrow. \r\n\r\n-------\r\n\r\nI have this problem on some systems and believe it is because of the max_user_{instance,watches} settings. Which can be fixed by setting this. \r\n\r\n```\r\nsudo sysctl fs.inotify.max_user_instances=1280 \r\nsudo sysctl fs.inotify.max_user_watches=655360\r\n```\r\n\r\nI have found that it should be set before I deploy the cluster and kubeflow but if a cluster is running you can kill the pods that get hung but in my experience that has been a waste of time compared to just starting over from a clean slate. \r\n\r\nI also had this issue on a machine that was running off of an SSD booted over USB-C and setting the max_user_* didn't fix it. I still don't really \"know\" why I couldn't get that system to work but basically I think this problem amounts to any situation where the disk throughput gets maxed out. \r\n\r\nKubeflow is a massive beast that does a LOT of stuff and on many machines you will hit limits where it can't function and it usually looks like what you've posted here. I know that's not the most technical solution to an open issue, but the problem is kind of squishy and I think can occur for multiple reasons. ", "I believe this problem is consistently produced by following the Charmed Kubeflow documentation on a fresh Ubuntu 22.04 or 20.04 system. I would be surprised if anyone can follow the current installation instructions with any success as the range of systems I'm testing on right now is some pretty high end server stuff and a couple consumer sort of desktop machines. \r\n\r\nNot that this likely matters but I am doing GPU stuff so I've enabled GPU with microk8s. I can easily pass validation and push up a container to do `nvidia-smi` so i don't think that would be causing the issue. I mention this because one of my systems had an infoROM issue on one GPU for a while and if I didn't disable it containers would fail to launch and the failures would look a lot like this issue sometimes (depending on a lot of factors). If anyone has an infoROM issue on a GPU, when you search for that Nvidia's official folks tell you it's unserviceable which is untrue. You just have to use `nvflash` a windows tool and you can't download it directly from nvidia for some reason it's only available from some websites that IMO look pretty sketchy. Use at your own risk.\r\n\r\nSince I do have a running microk8s 1.7 kubeflow setup, I think I should be able to solve this and commit back but I'm pretty certain the current Charmed Kubeflow setup instructions are very broken. \r\n\r\n", "The numbers I used above were not sufficient to solve the `too many open files` problem. I just ripped them from this issue and at one point a couple months ago, they did work for me. I would be very interested to know what changed in the packages that made the instances/watches requirements increase but I'm very much too busy to formally care. Here's the issue I got these numbers from. \r\n\r\nhttps://github.com/kubeflow/manifests/issues/2087\r\n\r\nI have no idea how to calculate what to raise the numbers to or the consequences of raising the limit too high. I just upped the largest digit by one. \r\n\r\n```\r\nsudo sysctl fs.inotify.max_user_instances=2280 \r\nsudo sysctl fs.inotify.max_user_watches=755360\r\n```\r\n\r\nAnd I totally forgot about this other issue I've had. GVFS backends! There is a client that runs on a default Ubuntu Desktop install for each user that's logged in. It is monitoring the disk and when you fire up Kubeflow it flips out and takes up 120-200% of the cpu for each user. I do not need this tool for my deployments. I'm unsure if this is a problem with a barebones server install but this is critical to solving the problem of launching kubeflow on a fresh installed Ubuntu Desktop 20.04/22.04 system.\r\n\r\n`sudo apt-get remove gvfs-backends -y`\r\n\r\nHooray. I think this solves a reproducibility problem I've had for over a month. I haven't quite figured out if there are licensing issues with distributing this, but I've built an ubuntu LiveCD with CUDA, docker, and kubeflow (microk8s). I'll ping folks on slack and see if there's any interest in it. I've got a solid pipeline for doing the CI/CD for releases and man, the little things to get going are really a big barrier. \r\n\r\nIt is very possible that the max_user_* doesn't need to be raised so high if the `gvfs-backends` are removed from the start but I will not be trying to figure that out explicitly in the near term.", "Also `tensorboard-controller` was failing because the ingress wasn't coming up correctly. That has been covered in a ton of issues. \r\n\r\nhttps://discourse.charmhub.io/t/charmed-kubeflow-upgrade-error/7007/7\r\n\r\n`juju run --unit istio-pilot/0 -- \"export JUJU_DISPATCH_PATH=hooks/config-changed; ./dispatch\"`\r\n\r\nSolves that. I've tested this on multiple machines now and raising the max_user_* limits, uninstalling gvfs-backends, and fixing the ingress with the above command solves all of the problems consistently. I'm working on a Zero to Kubeflow tutorial, but I'll submit a PR for the Charmed Kubeflow instructions that covers these things if someone can point me at where to submit it.\r\n\r\nI am realizing after some review that in this situation and the other issues I've read relating to a similar failure, most people are running in the cloud and not on bare metal. I do think the gist of what I've pointed out is still valid though but on this thread what I've posted is just not directly useful. It's squishy. These problems usually are related to disk throughput but that gets weird to sort in the cloud. Anyway.... All of what I said above has nothing to do with this issue I am realizing. Sorry for posting all this in the wrong place. I don't know where else to put all this info, so I'm going to leave it here for now. \r\n\r\nI'll collect it and put it into it's own issue tomorrow and remove it from this thread. Sorry if this confused anyone. It's been a long day.", "If you are using an external MySQL database (especially if its MySQL 8.0), you are likely experiencing this issue around support for `caching_sha2_password` authentication, see here for more:\r\n- https://dev.mysql.com/blog-archive/upgrading-to-mysql-8-0-default-authentication-plugin-considerations/\r\n\r\nFYI, Kubeflow Pipelines itself[ fixed MySQL 8.0 and `caching_sha2_password` support](https://github.com/kubeflow/pipelines/pull/8351) in `2.0.0-alpha.7` (ships with Kubeflow 1.7), but there is still an issue with the upstream `ml-metadata` project:\r\n- https://github.com/google/ml-metadata/issues/178", "I had the same problem. For me it was a cilium networking provider compatibility issue. I had to move to kubenet and it worked.\r\n", "> \r\n\r\nCan you elaborate the compatibility issue please? As I'm using cilium as well." ]

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[ "IIRC, Kubeflow v1.3 enables multi-user isolation by default. @Bobgy, is that right?\r\n\r\nIn that case, `list_experiments()` requires passing a namespace: https://github.com/kubeflow/pipelines/blob/24c551d3fc0b955f48af1206db5c80ec011db3dd/sdk/python/kfp/_client.py#L452-L461", "@chensun I think that is correct yes. I did try passing a namespace, but still get the error. \r\n`client.list_experiments(namespace='kubeflow')`\r\n\r\nI did also try passing the namespace 'kubeflow-example-user-com' which is what is visible from the UI. Neither works. I'm not sure whether I'm passing it incorrectly?\r\n\r\n```\r\nApiException: (500)\r\nReason: Internal Server Error\r\nHTTP response headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type': 'application/json', 'trailer': 'Grpc-Trailer-Content-Type', 'date': 'Fri, 23 Jul 2021 12:42:14 GMT', 'x-envoy-upstream-service-time': '11', 'server': 'envoy', 'connection': 'close', 'transfer-encoding': 'chunked'})\r\nHTTP response body: {\"error\":\"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\",\"code\":13,\"message\":\"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\",\"details\":[{\"@type\":\"type.googleapis.com/api.Error\",\"error_message\":\"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\",\"error_details\":\"Internal error: Unauthenticated: Request header error: there is no user identity header.: Request header error: there is no user identity header.\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).canAccessExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:249\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:148\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to authorize with API resource references\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*ExperimentServer).ListExperiment\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/experiment_server.go:150\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler.func1\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:748\\nmain.apiServerInterceptor\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/interceptor.go:30\\ngithub.com/kubeflow/pipelines/backend/api/go_client._ExperimentService_ListExperiment_Handler\\n\\t/go/src/github.com/kubeflow/pipelines/backend/api/go_client/experiment.pb.go:750\\ngoogle.golang.org/grpc.(*Server).processUnaryRPC\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1210\\ngoogle.golang.org/grpc.(*Server).handleStream\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:1533\\ngoogle.golang.org/grpc.(*Server).serveStreams.func1.2\\n\\t/go/pkg/mod/google.golang.org/grpc@v1.34.0/server.go:871\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\"}]}\r\n```", "Hi @TanjaDuPlessis, I wonder if you may missed any setup required for multi tenancy.\r\nCan you please check if you went through all the steps: https://www.kubeflow.org/docs/components/multi-tenancy/getting-started/\r\n\r\n/cc @Bobgy ", "@TanjaDuPlessis refer to https://www.kubeflow.org/docs/components/pipelines/multi-user/#in-cluster-api-request-authentication, it's one of the current caveats of multi-user mode.\r\n\r\nWe are now supporting this usage through https://github.com/kubeflow/pipelines/issues/5138.", "@chensun this is expected behavior, because KFP API server is authorizing requests.", "hello @Bobgy , if it's ecpected does that mean we can't use it ? \r\nI have the same issue but from notebook, should i add username/password somewhere .\r\nthanks for your time !\r\nI saw there is some kind of fix was it included in 1.3 ?", "@rexad yes, and it's now documented in https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/#connect-to-kubeflow-pipelines-from-the-same-cluster" ]

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[]

[ "Is this a duplicate of https://github.com/kubeflow/pipelines/issues/3763?\r\n@liguodongIOT ", "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", "你好,我是李国冬，我已收到你的邮件，我会尽快给您回复。" ]

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[ "Taking HTML visualization as an example, the usage on V2 will look like:\r\n\r\n```\r\n@component \r\ndef write_html(html_artifact: Output[HTML]):\r\n html_content = '<!DOCTYPE html><html><body><h1>Hello world</h1></body></html>'\r\n with open(html_artifact.path, 'w') as f:\r\n f.write(html_content)\r\n```\r\n\r\nHere you specify `Output[]` with artifact type `HTML`. Similarly, you can do it for Model, Metrics, etc.\r\nFor example: https://github.com/kubeflow/pipelines/blob/master/samples/test/metrics_visualization_v2.py#L28\r\n\r\nDoes it work to your usecase?\r\n\r\ncc @chensun ", "> Taking HTML visualization as an example, the usage on V2 will look like:\n> \n> \n> \n> ```\n> \n> @component \n> \n> def write_html(html_artifact: Output[HTML]):\n> \n> html_content = '<!DOCTYPE html><html><body><h1>Hello world</h1></body></html>'\n> \n> with open(html_artifact.path, 'w') as f:\n> \n> f.write(html_content)\n> \n> ```\n> \n> \n> \n> Here you specify `Output[]` with artifact type `HTML`. Similarly, you can do it for Model, Metrics, etc.\n> \n> For example: https://github.com/kubeflow/pipelines/blob/master/samples/test/metrics_visualization_v2.py#L28\n> \n> \n> \n> Does it work to your usecase?\n> \n> \n> \n> cc @chensun \n\nI'm aware that you can do this when creating a component with the v2 decorator, I'm asking if it's possible to do so in a component that was not generated with the v2 decorator. Like a completely separate component that is loaded in by its YAML definition (perhaps even a different language)", "Hi @parthmishra, \r\n\r\nAt this moment, it would be quite challenging for a user to replicate the supports for `Input[Model]`, `Output[Metrics]`, etc. in their custom container.\r\n\r\nHere's a sample code of what the container interface would look like using the v2 `@component` decorator: https://github.com/kubeflow/pipelines/blob/d69b6ae82a8c4afcf4bd3e7d444089302ba23e28/sdk/python/kfp/v2/compiler_cli_tests/test_data/lightweight_python_functions_v2_pipeline.json#L143-L149 (The Python code is inlined in the container commands, but they could be moved to inside the container).\r\n\r\nIf you were able to inspect that code sample, you would find that, other than the user code, the code also contains the entire code from following files:\r\nhttps://github.com/kubeflow/pipelines/blob/d69b6ae82a8c4afcf4bd3e7d444089302ba23e28/sdk/python/kfp/components/executor.py\r\nhttps://github.com/kubeflow/pipelines/blob/d69b6ae82a8c4afcf4bd3e7d444089302ba23e28/sdk/python/kfp/components/executor_main.py\r\nhttps://github.com/kubeflow/pipelines/blob/d69b6ae82a8c4afcf4bd3e7d444089302ba23e28/sdk/python/kfp/dsl/io_types.py\r\n\r\nSo technically, you could implement your own version following this code. But we are not expecting users to do so. \r\n\r\nIt is on our roadmap that we would help users include such code in their own components by packaging and installing the code into their custom container -- assuming the container is Python based. For non-Python based container, we would document the expected interface so that users can follow that to implement their own. ", "@chensun following from this, is it possible to pass something like a pandas DataFrame (as a csv file probably) throughout **custom** components? For example, let's assume there are four components that all do different types of preprocessing for our data; how can we pass the data through without specifying an outside filepath (i.e. string that is a GCS path)?", "@chensun \r\n\r\nThanks for the explanation, I think the v2 SDK docs for \"regular\" component building should state that these Artifact types are not useable and that users wishing to implement these inputs/outputs should instead write them using Python-function based components. The [current docs](https://www.kubeflow.org/docs/components/pipelines/sdk/v2/component-development/#designing-a-pipeline-component) are misleading in this regard and make it seem like there is equal feature parity between the two methods of implementing Components. ", "> @chensun following from this, is it possible to pass something like a pandas DataFrame (as a csv file probably) throughout **custom** components? For example, let's assume there are four components that all do different types of preprocessing for our data; how can we pass the data through without specifying an outside filepath (i.e. string that is a GCS path)?\r\n\r\n@nicokuzak \r\nYes, you can pass a DataFrame as a file throughout custom components. And you don't need to provide a GCS path yourself, the system generates such a path.\r\nIf you write component.yaml, you need `{outputPath: output_name}` placeholder, or if you write Python-function based component, you need to type annotate the output like such: `output_name: OutputPath('CSV')`. At runtime, your code should expect a local file path, and you need to dump the DataFrame object into the file. The downstream component can take such output as an input using `{inputPath: input_name}` or `input_name: InputPath('CSV')`. Then you can read from the file, and load it into a DataFrame object.\r\n\r\nOn Vertex Pipelines, the local path is backed by GCS Fuse, meaning it maps to a GCS location like `gs://my-bucket/some-blob`, whatever content you write to the local file will be \"synced\" to the GCS location. ", "> @chensun\r\n> \r\n> Thanks for the explanation, I think the v2 SDK docs for \"regular\" component building should state that these Artifact types are not useable and that users wishing to implement these inputs/outputs should instead write them using Python-function based components. The [current docs](https://www.kubeflow.org/docs/components/pipelines/sdk/v2/component-development/#designing-a-pipeline-component) are misleading in this regard and make it seem like there is equal feature parity between the two methods of implementing Components.\r\n\r\n@parthmishra Thank you for your feedback. Agree that our current doc isn't in a good shape. We will continuously improve our documentation. Meanwhile, we're designing the next generation of component authoring experience that could make these Artifacts types available for custom container components as well.", "I would like to also add that it would be nice to be able to use the v2 Metrics capabilities with the manually written `component.yaml` approach. Since this is not possible, I have to create op from python functions directly if I want to use the Metrics capability (for display in Vertex AI). As mentioned here in v1, this was possible by dumping to a specified JSON file and the Metrics were read from there. If there are any workarounds please let us know, thank you. \r\n\r\nPreferred File structure example as noted:\r\n-build.sh\r\n-component.yaml\r\n-Dockerfile\r\n-src/train.py\r\n\r\nThis structure is optimal for more complex code and portability, however, without Metrics capability, cannot use this approach if Metrics needed. ", "> I would like to also add that it would be nice to be able to use the v2 Metrics capabilities with the manually written `component.yaml` approach. Since this is not possible, I have to create op from python functions directly if I want to use the Metrics capability (for display in Vertex AI). As mentioned here in v1, this was possible by dumping to a specified JSON file and the Metrics were read from there. If there are any workarounds please let us know, thank you.\r\n> \r\n> Preferred File structure example as noted: -build.sh -component.yaml -Dockerfile -src/train.py\r\n> \r\n> This structure is optimal for more complex code and portability, however, without Metrics capability, cannot use this approach if Metrics needed.\r\n\r\n@vemqar I could be wrong, but If you're using Python for your component, you can use the component decorator to output a `component.yaml` definition file (and specify custom base image as output of `build.sh`). The function you decorate can essentially just be used for serializing inputs, passing them to the rest of your code (e.g. `src/train.py`) and then serializing the outputs. Not ideal as you clutter up the `component.yaml` file with in-lined code, but I don't see why it wouldn't work.\r\n\r\nPerhaps you could also just directly use the KFP SDK to serialize Artifacts, essentially what the in-lined code is doing for Python-function based components anyways.", "Thanks @parthmishra for the advice. I did try to essentially import the `Metrics` class definition into my src/ code , but it doesn't work because it needs to be initialized. I realized that the kfp `Artifact` needs to be initialized with GCP paths which will specify where the Metrics Artifacts will be stored. So what I understand, essentially I would have to myself initialize `Artifact` with a `uri` path then call `Metrics` . If you have a working example though, would appreciate it. I did inspect the `component.yaml` of a function with Metrics Output and didn't see an obvious way to integrate that into a custom written component file. ", "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 found a somewhat hacky solution to this problem. I'm using Kubeflow's `Executor` class (which is the one used by function-based components) to easily instantiate the Artifact objects. I could iterate through `executor_input` and create all the objects myself, but I think it's a lot more convenient to use `Executor`, even if I'm not using it for what is was designed.\r\n\r\nYou need to include `{executorInput: null}` in your component.yaml file and your python script would look something like this:\r\n\r\n```python\r\nfrom kfp.v2.components.executor import Executor\r\nfrom kfp.v2.dsl import Metrics, Model\r\nimport argparse\r\nimport json\r\n\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"--executor-input\", type=str, required=True)\r\n\r\nargs = parser.parse_args()\r\n\r\n# carrega argumentos do executor\r\nexecutor_input = json.loads(args.executor_input)\r\n\r\n# carrega configuracoes\r\nexecutor = Executor(executor_input, lambda x: x)\r\n\r\n# obtem objetos Kubeflow\r\nmetrics:Metrics = executor._output_artifacts['metrics']\r\nmodel:Model = executor._output_artifacts['model']\r\n\r\n# log de metricas\r\nmetrics.log_metric(\"accuracy\", 0.9)\r\n\r\n# salva modelo\r\nwith open(model.path, \"w\") as f:\r\n f.write(\"data\")\r\n\r\n# salva saidas\r\nexecutor._write_executor_output()\r\n```\r\n\r\nI'm also attaching all the files necessary to run this example, as well as some screenshots to show you that it works (at least on Vertex AI pipelines). Just so we don't have to build and publish a docker image, I included the python script in the component.yaml file.\r\n\r\n## Code:\r\n[code.zip](https://github.com/kubeflow/pipelines/files/8186268/code.zip)\r\n\r\n## Screenshots:\r\n\r\n<img width=\"360\" alt=\"Captura de Tela 2022-03-04 às 10 38 54\" src=\"https://user-images.githubusercontent.com/5862030/156774012-459291ae-1bd9-4989-b620-021716356269.png\">\r\n<img width=\"479\" alt=\"Captura de Tela 2022-03-04 às 10 39 07\" src=\"https://user-images.githubusercontent.com/5862030/156774016-3f55ab91-49f5-4cc7-8b48-ebe0d3ab79db.png\">\r\n\r\nEdit: after commenting I realized what I did was kind of what was suggested in https://github.com/kubeflow/pipelines/issues/6116#issuecomment-885506281 . So I just wanted to give them credits.", "@jordyantunes \r\nMen, you're a genius!! Thank you so much!\r\n\r\nWith you permission, I did a few changes in your code to also accept input artifacts and input parameters (Little rusty that part, but I did it in a rush haha), and put it in a repo so anyone can used it as a example (I'm planning to put it in a medium article explaining how to implement CI/CD in Vertex Pipelines and of course I will mention you :D) \r\n\r\nhttps://github.com/juansebashr/VertexPipelinesCICD" ]

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[ "Hello. Thank you for finding this issue.\r\n\r\nDue to bandwidth, we do not currently prioritize issue where the cache is NOT reused. (The issues where the cache is reused incorrectly have high priority though.)\r\n\r\nThis issue is difficult to solve given the architecture of the KFP compiler. Even the file passing features `inputPath`/`outputPath` were extremely hard to hack-in.\r\n\r\nIt would be pretty hard for cache server to detect the case and download the artifact value to return as the output value.\r\n\r\nP.S. There are much more serious issues with cache reuse. For example, the cache cannot be reused in the same pipeline due to the unique names of all outputs (another issue of the KFP compiler).", "@Ark-kun For the issue you referenced:\r\n___\r\nP.S. There are much more serious issues with cache reuse. For example, the cache cannot be reused in the same pipeline due to the unique names of all outputs (another issue of the KFP compiler).\r\n___\r\nIs there an existing issue for that I could use for more context? I don't understand what you mean by \"unique names of all outputs\". ", "We solved this issue in v2 compatible mode(fyi: bit.ly/kfp-v2-compatible\r\n). No matter what you use for downstream components, the cache result is the same. We have not fully released kfp v2 compatible mode. You need to wait for Kubeflow Pipeline 1.7.0" ]

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[]

[ "That's strange. Do you have any other code between `client.set_user_namespace('my-namespace')` and `client.get_user_namespace()`?\r\n\r\nIf you look at the code, they just set/read a dictionary entry using the same key. (The dictionary can be dump to or load from a local file).\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/27051ab9d1c9e8ca1464c17b10d12ed73c8227f1/sdk/python/kfp/_client.py#L335-L347\r\n\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/27051ab9d1c9e8ca1464c17b10d12ed73c8227f1/sdk/python/kfp/_client.py#L372-L378", "Hey @chensun,\r\n\r\nThanks for the reply. I discovered the source for this, it's in my custom scripts .. \r\n\r\nThere was a faulty line overriding the namespace as the username.\r\n```\r\nnamespace = user or default_data.get(\"namespace\")\r\n```\r\n\r\nClosing this as it's not relevant! Thanks again." ]

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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" ]

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[ "Sure, I can take this.", "I'm scheduling to 1.8 release, do you want it earlier?", "Closing this feature request, as we no longer want to move forward with https://github.com/kubeflow/pipelines/pull/6080." ]

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[]

[ "Kubernetes containers running on the usually remote compute nodes are not expected to get access to any local files.\r\n\r\nPlease read the Data-passing tutorial for [command-line programs](https://github.com/Ark-kun/kfp_samples/blob/ae1a5b6/2019-10%20Kubeflow%20summit/106%20-%20Creating%20components%20from%20command-line%20programs/106%20-%20Creating%20components%20from%20command-line%20programs.ipynb) and [Python-based components]( https://github.com/kubeflow/pipelines/blob/d902cae/samples/tutorials/Data%20passing%20in%20python%20components.ipynb).\r\n\r\nIn general you always need to put your data files to some place where they have a URI, then use some \"downloader\" component to import that data into the pipeline.\r\n\r\nP.S. This naming here is not great. You seem to be getting a path of a path:\r\n```\r\n --input-path, {inputPath: input_path}, \r\n --output-path, {outputPath: output_path},\r\n```\r\nThis naming is better:\r\n```\r\n --input-path, {inputPath: input1}, \r\n --output-path, {outputPath: output1},\r\n```", "I see, will take a look at the tutorials.\r\n\r\nThanks!" ]

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[ "Hi @Nagarajj, thank you for drafting this detailed proposal!\r\n\r\n> Specified as a container which runs after publisher actions (Python Container rather than Go Binary).\r\n\r\nAre you OK with the perf implication? Because a container will have to be a separate Pod, and the overhead of scheduling & starting a Pod is at least 3~5 seconds from what I observe on GKE.\r\n\r\nMaybe we can improve using https://argoproj.github.io/argo-workflows/container-set-template/, but it's an Alpha feature and it's only supported by argo emissary executor.\r\n\r\nI was talking about go binary as an option, because it would have minimal perf overhead. A binary can be mounted in a volume to the same main container and executed after your main command (and typically it should finish really fast).", "> Synchronous execution for reliability reasons (Synchronous over asynchronous).\r\n\r\n+1, I've heard similar requirements before", "> output values/artifacts, input values/artifacts\r\n\r\nDo you need access to the artifact files? This will also be not very efficient if postpublish hook runs on a separate Pod.", "On the highest level, we have two objectives:\r\n\r\n1. Run container before and after each user component as part of our custom caching layer. This can be further optimized by the built-in caching logic because starting new containers lead to performance overhead. Is it possible to override the built-in caching logic with customized code? \r\n\r\n2. For each component, we want to capture where its arguments come from to build data lineage. This is particularly critical to reproducibility and debugging. Unfortunately, right now there's no way to achieve such things.\r\n", "> Hi @Nagarajj, thank you for drafting this detailed proposal!\r\n> \r\n> > Specified as a container which runs after publisher actions (Python Container rather than Go Binary).\r\n> \r\n> Are you OK with the perf implication? Because a container will have to be a separate Pod, and the overhead of scheduling & starting a Pod is at least 3~5 seconds from what I observe on GKE.\r\n> \r\n> Maybe we can improve using https://argoproj.github.io/argo-workflows/container-set-template/, but it's an Alpha feature and it's only supported by argo emissary executor.\r\n> \r\n> I was talking about go binary as an option, because it would have minimal perf overhead. A binary can be mounted in a volume to the same main container and executed after your main command (and typically it should finish really fast).\r\n\r\nThanks @Bobgy for looking into this. \r\n\r\nPerf should not be an implication in our case, as these are batch pipelines. The only concern with go binary is, Python has low barrier of entry/maintenance.\r\nYes, ContainerSet Template would do good for these scenarios. ", "> > output values/artifacts, input values/artifacts\r\n> \r\n> Do you need access to the artifact files? This will also be not very efficient if postpublish hook runs on a separate Pod.\r\n\r\n@Bobgy Would ContainerSet Template help here as well ?", "Yes, I believe ContainerSet template seems like a good fit.\r\nIt's just released as Alpha in argo, so encourage anyone interested to try it out and mature it.\r\n\r\n> Run container before and after each user component as part of our custom caching layer. This can be further optimized by the built-in caching logic because starting new containers lead to performance overhead. Is it possible to override the built-in caching logic with customized code?\r\n\r\nWill ContainerSet be enough for your case @zzxvictor? As far as I can tell, multiple containers in one Pod has less overhead than multiple Pods, because they can be scheduled only once and they share local volumes.\r\n\r\nAnother option I mentioned before is using go binaries, if you mount a go binary that KFP cacher can call into to get the caching decision -- that will be faster than container set I believe. However, as mentioned it has higher barrier for entry, because now language is limited to those that can be compiled to binaries.\r\n\r\nAs a last resort, you can always fork, we plan to build built-in caching logic as HTTP template in argo (a long running service receiving cache requests from argo workflows), so if you fork our handler code and replace it with your own caching code you can achieve the goal you want. However, I'm not sure there are enough people who wants to customize so deeply.", "> For each component, we want to capture where its arguments come from to build data lineage. This is particularly critical to reproducibility and debugging. Unfortunately, right now there's no way to achieve such things.\r\n\r\nIn [v2 compatible mode](https://bit.ly/kfp-v2-compatible) and KFP v2 we are building, these info are already captured in the ml-metadata store deployed with KFP. What's missing there?", "Just to clarify our process to move this forward.\r\nEither\r\n* a contributor drives the discussions and proposes a design\r\n* there's enough interest, so that KFP team wants to take over this\r\n\r\nWe are currently collecting feedback and different use-cases with this. Here are some questions that I am still unclear with:\r\n1. Who will be the person that configures post publish hooks? Pipeline authors or platform admins or there are use-cases for both?\r\n2. What will be the most appropriate interface for the post publish hook? golang binary or container?\r\n3. Following 2, what will be minimum perf requirement for the hook?", "If we can have post_publish be a Lifecycle Hook, similar to [PreCache check](https://github.com/kubeflow/pipelines/blob/c89ed71cf6339cdcdd957d4dca4b1f32c10db9c9/api/v2alpha1/pipeline_spec.proto#L546-L566) that should be an ideal experience. \r\n\r\n1. These LifeCycle hooks, would let platform admins extend the capabilities of Kubeflow Pipelines Runtime in interesting ways, without having to hack things like launcher (integration with existing metadata systems etc). \r\n2. If Platform admins are building this, it should be ok it being a golang binary.\r\n3. If these are built as optional/opt-in hooks (and in golang), platform teams should be able to decide if added performance overhead provides enough value. ", "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" ]

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[ "Thank you for the report! Let me try to reproduce", "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" ]

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[ "Facing same issue. Any update on 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", "We still face the issue. Can I help in someway to fix the issue?" ]

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[ "/assign @zijianjoy ", "manifests presubmit test is already failing with kpt v1: https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/5926/kubeflow-pipelines-manifests/1417793964035018752", "```\r\n+ kpt cfg tree /home/prow/go/src/github.com/kubeflow/pipelines/manifests/kustomize/hack/..\r\nerror: unknown command \"cfg\" for \"kpt\"\r\nDid you mean this?\r\n\tfn\r\n\tpkg\r\n```", "FYI, I temporarily disabled the test that uses kpt in https://github.com/kubeflow/pipelines/commit/efe22ca0a4c8b051c4284d7fd83be2aac58162d0\r\n\r\nWe can re-enable after upgrade", "Candidate for Kubeflow 1.4 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", "Validated the manifest test is now adopted to kpt v1.0.0-beta.13" ]

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[ "1. I think you can list the endpoint via kubectl `kubectl describe configmap inverse-proxy-config -n kubeflow | grep googleusercontent.com`, you probably need some script to query for all k8s context you have. Reference: https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines\r\n2. I don't know approaches other than https://cloud.google.com/iap/docs/authentication-howto#authenticating_from_a_desktop_app, I would also be interested in any potential solution to it.", "Strange, I think you can just get the token by `gcloud auth print-access-token` and add it to header `Authorization: Bearer <Token>`.\r\n\r\nDid you make a typo there? It should be Authorization Bearer: https://swagger.io/docs/specification/authentication/bearer-authentication/", "> Can I programmatically list the Hosted KFP instances (as I see in the Google Cloud AI Platform UX) and get their inverse proxy URLs?\n\nIt's not a public API from my understanding", "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" ]

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[ "/assign @IronPan @SinaChavoshi ", "Good find, seems like we are splitting on \"=\" instead of splitting on the first instance of '=' and taking the remaining argument as the input query string. Marking as P0 as directly query is broken with this issue. " ]

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[ "/assign", "You are always more than welcome to make a PR @sfujiwara if you find things like this. ", "@NikeNano Thanks. I made PR #6098. Could you please review it?" ]

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[ "@Hmr-ramzi What is the distribution you use to deploy your Kubeflow cluster? https://www.kubeflow.org/docs/started/installing-kubeflow/#install-a-packaged-kubeflow-distribution", "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" ]

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[]

[ "/cc @Bobgy ", "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" ]

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[]

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[ "cc @sasha-gitg @SinaChavoshi @ji-yaqi ", "Close this since the client is moved to Vertex SDK. " ]

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[ "Hi Axel, do you mean to skip checking for permission for APIs, and check those permissions at the time of API usage? ", "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" ]

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[ "cc @sasha-gitg @SinaChavoshi ", "Assigning to @chensun for comments/ triage. ", "This AIPlatformClient would be deprecated soon. Thanks for the FR, we have it in our options to add into Vertex SDK. ", "@ji-yaqi any updates on this?", "Hi @axelmagn, we will deprecate AIplatformClient in v2.0.0. Meanwhile, you can use scheduling here: https://cloud.google.com/vertex-ai/docs/pipelines/schedule-cloud-scheduler", "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" ]

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[ "On Firefox it is showing another error message:\r\n\r\n<img width=\"1440\" alt=\"Screen Shot 2021-07-17 at 09 09 51\" src=\"https://user-images.githubusercontent.com/1009397/126036414-8e763826-6a7b-4901-8ed2-e712bef5a44d.png\">\r\n" ]

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[ "Can it be resolved by adding istio injection label to true as https://github.com/kubeflow/manifests/pull/1153?\r\n\r\ncc @Bobgy to confirm the use case.", "@karlschriek which argo executor supports Istio sudecars?\n\nFrom what I learned from https://argoproj.github.io/argo-workflows/sidecar-injection/. It seems that Istio sidecar injection is still not supported.", "The Argo Workflows team seem to be of the opinion that it should work with v3 onward, though I also cannot find a PR or release note that specifically mentions it, but the issue https://github.com/argoproj/argo-workflows/issues/1282 was closed with a note that this should now be supported. @Bobgy I just saw that you also asked for clarification there!", "I've updated https://github.com/argoproj/argo-workflows/blob/master/docs/sidecar-injection.md with more detail for you." ]

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[ "I think the problem happens in the line\r\n```\r\n for (let tab in SidePanelTab) {\r\n```\r\n\r\nIn typescript, an enum object also contains look up map for the string -> enum conversion, so 'MANIFEST', 'INPUT_OUTPUT', each enum key is also iterated here.", "That is a good catch, thank you @Bobgy !\r\n\r\nI created a PR to fix this: https://github.com/kubeflow/pipelines/pull/6077" ]

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[ "The package from `pip install google_cloud_pipeline_components` does not include YAML files. But there should be YAML file in the SDK. E.g., https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/experimental/gcp_launcher/component.yaml\r\n", "/assign @SinaChavoshi ", "I found a way, need to include lines like\r\n package_data={'google_cloud_pipeline_components': ['google/automl/tabular/*.yaml']},\r\nin setup.py, reference: https://screenshot.googleplex.com/BFyR2qutgQs9GdH\r\n\r\nI think we need to add the intended yaml.", "Thank you for raising this issue. fixed by https://github.com/kubeflow/pipelines/pull/6066" ]

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[ "/assign @chensun ", "For this problem, I think I know the reason. But it comes to the other question, how do I retrieve the pipelineParam value?", "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" ]

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[ "@zijianjoy Why did you downgrade to TFX 0.26?", "@vaskozl Sorry for missing to include the context in PR description, I tried to upgrade TFX to the latest. However, the dependency graph doesn't resolve because of the requirement conflict on `grpcio`. We will have to wait for https://github.com/tensorflow/tensorflow/issues/50045 so I can upgrade tensorflow image as well as TFX related libraries. " ]

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[]

[ "This is a current limitation. Welcome contributions!", "Related code:\r\n* persistence agent calls read artifact API in backend to read metrics artifacts: https://github.com/kubeflow/pipelines/blob/fff62e1521e89dd31c87eec03073b8f8627d73a3/backend/src/agent/persistence/worker/metrics_reporter.go#L149\r\n* read artifact API gets object using the object store: https://github.com/kubeflow/pipelines/blob/fff62e1521e89dd31c87eec03073b8f8627d73a3/backend/src/apiserver/resource/resource_manager.go#L1163\r\n* we only support the minio object store: https://github.com/kubeflow/pipelines/blob/fff62e1521e89dd31c87eec03073b8f8627d73a3/backend/src/apiserver/storage/object_store.go#L81", "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" ]

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[ "Hi @dutingda, you can follow this example for filtering (same feature exists for runs too): https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/#example-2-listing-pipelines-with-a-filter.\r\n\r\nPlease let us know what's missing from documentation and feature", "> Hi @dutingda, you can follow this example for filtering (same feature exists for runs too): https://www.kubeflow.org/docs/components/pipelines/tutorials/sdk-examples/#example-2-listing-pipelines-with-a-filter.\r\n> \r\n> Please let us know what's missing from documentation and feature\r\n\r\n@Bobgy So what should be the key if I want to filter by start time and what should be the return value of this filter. And what is the version for kfp client sdk, I am using kfp 1.0.0 version now.", "@Bobgy How do I read the response object type and what is the type?", "/assign @zijianjoy \nCan you help answer this question?\nThanks", "Hello @dutingda , \r\n\r\n- API definition for `list_runs`: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.server_api.html#kfp_server_api.api.run_service_api.RunServiceApi.list_runs\r\n- Fields for time related filter: `created_at`, `finished_at`, `scheduled_at`. Format of time value is `2021-08-26T23:00:34Z`. Example: `{\"predicates\":[{\"key\":\"created_at\",\"op\":\"GREATER_THAN\",\"string_value\":\"2021-08-26T23:00:34Z\"}]}`\r\n- kfp 1.0.0 seems to be too old. You can find the new versions in https://pypi.org/project/kfp/#history. But makes sure you use the corresponded version of KFP backend too.", "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" ]

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[]

[ "Hello @jalola , this is related to Argo workflow issue, would you like to create an issue there to resolve this problem? https://github.com/argoproj/argo-workflows", "Thanks @zijianjoy \r\nThere is an issue related: https://github.com/argoproj/argo-workflows/issues/6201\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" ]

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[]

[ "Nevermind, found it:\r\nhttps://github.com/kubeflow/pipelines/blob/ec9021f5b04521d394fb3a95903b86b478bf6550/v2/test/README.md" ]

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[ "/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" ]

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[ "/cc @chensun \r\nI think we welcome contributions if you would like to.", "@Bobgy If this feature is still needed and no one has worked on it yet, can I pick it up as a first time 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" ]

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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" ]

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[]

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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", "We have completed the last item: switch to Emissary executor by default: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#docker-executor" ]

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[ "I think we need to plan this for the next release.", "Hi KFP team, my team uses GKE and we got an email from GCP today: \"[Action Required] Prepare for GKE 1.22 Open source upstream beta API Removal\". The email includes:\r\n\r\n> As of Kubernetes 1.22, API clients will no longer be able to use previously deprecated Beta API versions. Manifests using those versions will no longer be able to be applied.\r\n\r\nBased on the [GKE release schedule](https://cloud.google.com/kubernetes-engine/docs/release-schedule), seems like k8s 1.22 is already available for the \"rapid\" release channel, and will affect the \"regular\" release channel in November 2021 (my team is on \"regular\", which I believe is the default and what most GKE users are likely using).\r\n\r\nIf I understand correctly, this means when our GKE cluster is updated to 1.22, then we won't be able to deploy *any* current version of KFP to it. And, even if the next KFP release (1.8) is updated, we won't be able to roll back to pre-1.8 KFP versions if we encounter any issues with the new release. Does that match your understanding?\r\n\r\nIf so, I think it's important to have a new KFP release soon so users can test it before GKE starts updating users' clusters to 1.22.", "Research: Application CRD currently only has v1beta1 version: https://github.com/kubernetes-sigs/application/blob/master/config/crd/bases/app.k8s.io_applications.yaml#L46", "All manifests are upgraded to v1. I have personally deployed to a k8s 1.22 cluster and have validated the deployment." ]

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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" ]

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[ "Does your cache work other than cases of recurring run ?\r\n\r\nI have seen this issue occurring in the past. Where in cache did not work at all. Try restarting the deployment called `cache-server`.\r\n\r\n```\r\nkubectl rollout restart deploy/cache-server\r\n```", "> Does your cache work other than cases of recurring run ?\r\n> \r\n> I have seen this issue occurring in the past. Where in cache did not work at all. Try restarting the deployment called `cache-server`.\r\n> \r\n> ```\r\n> kubectl rollout restart deploy/cache-server\r\n> ```\r\n\r\nThanks for your answer !\r\nUnfortunately, this did not change anything and the cache is still not working in recurring runs.", "Hello @Itega ,\r\n\r\nCache will be enabled only when workflow template is the same between first pipeline **run** and second pipeline **run**. Would you like to share the first and second pipeline run pods information?", "Hello @zijianjoy ,\r\nI will happily provide you any needed information, here are the first and second runs pods information :\r\n\r\nRecurring run :\r\n- First run, First step : https://pastebin.com/PE0EfiLK\r\n- First run, Second step : https://pastebin.com/HgSgSUyr\r\n- Second run, First step : https://pastebin.com/X2zKM26y\r\n- Second run, Second step : https://pastebin.com/gQuMzKXM\r\n\r\nManual run (working as expected) :\r\n\r\n- First run, First step : https://pastebin.com/SpNXKuDT\r\n- First run, Second step : https://pastebin.com/qP3VGZGq\r\n- Second run, First step : https://pastebin.com/Ut09aNYw\r\n- Second run, Second step : https://pastebin.com/BjGELhHF", "Hello @Itega ,\r\n\r\nIn your manual run, you have caching enabled for your pipeline, see annotation `pipelines.kubeflow.org/cache_enabled: 'true'`. However, this annotation doesn't exist in Recurring run, so KFP didn't generate the cache key for you `pipelines.kubeflow.org/execution_cache_key`.\r\n\r\nWould you like to enable caching for recurring run and see if this fixes your issue? \r\n\r\nReference: https://github.com/kubeflow/pipelines/blob/master/backend/src/cache/server/mutation.go#L87-L100", "Hello @zijianjoy ,\r\n\r\nThanks for the explanations !\r\nI found that I can enable caching only in V2 compatible mode so I updated KFP to 1.7.0-rc.1 (master branch), KFP SDK to 1.6.5 and KFP server api to 1.7.0a2.\r\n\r\nI did some tests using the sample from https://www.kubeflow.org/docs/components/pipelines/sdk/v2/python-function-components/#getting-started-with-python-function-based-components\r\n\r\nThe first run is fine but the second run tries to get result from cache and throw an error : `Failed to execute component: failed to store output parameter value from cache: failed to parse parameter name=\"Output\" value =11 to double: %!w(<nil>)`\r\n\r\nWhen replacing `float` with `str`, this is working well when using manual runs but in recurring runs I get : `Failed to execute component: failed to create cache entry: failed to create task: rpc error: code = InvalidArgument desc = Validate create task request failed.: Invalid input error: Invalid task: must specify RunId`.\r\n\r\nShould I create some new issues and close this one ?", "I am not sure what is the root cause for experiencing this `invalid input error` failure, it sounds good for creating a new issue to debug this 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" ]

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[ "@munagekar Welcome contributing to create a RecurringRun CRD for this feature!", "/assign\r\nI will take a look at this. ", "To get some more background, what is the difference that you expect from this `CRD` compare to the [scheduledworkflow](https://github.com/kubeflow/pipelines/tree/master/backend/src/crd#running-scheduledworkflow-controller-from-the-command-line) `CRD` that Kubeflow pipelines uses to today for recurring runs? @munagekar ", "@NikeNano Thank you for looking into this.\r\n\r\nI was not aware of the `scheduled workflow` crd, when I created the issue. This CRD largely solves the issue.\r\n\r\nHowever, kubeflow pipelines stores scheduled workflows in a db instead of relying on etcd. This can lead to inconsistency when creating & deleting recurring runs from the kubectl. The scheduled workflows shown in the UI do not match those on the cluster.\r\n\r\nREF: https://github.com/kubeflow/pipelines/issues/4862", " ~~Hmm do you mean that `etcd` should replace the `Mysql` database? I think in that case it will be completely seperated from kubeflow pipelines and all the tooling around it~~\r\n\r\n ~~As I understand the `CRD`:s will be stored in `etcd` since they are custom resources? But I think I might miss your point, could you elaborate @munagekar~~\r\n\r\n\r\nUPDATE: missed to check your ref @munagekar, will do it. \r\n", "After reading up and checking the code I definitely see your point @munagekar. I will continue to dig in to what is required to actually get ride of using `Mysql` for recurring runs. \r\n\r\nI think we can continue the discussion from https://github.com/kubeflow/pipelines/issues/4862#issuecomment-906323221 here. What was the original reason for using `Mysql`, it seems to have the same information as on the CRD when I check now @Bobgy ?", "I will write a design doc during the week. ", "To explain a bit of history, KFP decided to use mysql as source of truth instead of Kubernetes API, because many KFP features rely heavily on\r\n* filter by field, order by field, pagination functionalities that is not very well supported by Kubernetes API\r\n* number of pipeline runs can be large enough that the Kubernetes API cannot handle efficiently\r\n\r\nHowever, it seems reasonable to assume the number of enabled recurring run configs should be at a size that can be handled by Kubernetes API. The tooling for gitops + kubernetes API seems like a natural fit for managing recurring runs. That's why I think https://github.com/kubeflow/pipelines/issues/4862#issuecomment-906090718 is a brilliant idea.\r\n\r\nIt's still worth investigating whether Kubernetes API's capability for filter, order and pagination is enough for recurring run API or not. That's what I'd like to see in the design doc. Even if there's sth still missing, we may now consider a solution that takes Kubernetes API as source of truth, so that new recurring run custom resources can be auto-synced to KFP DB.", "cc @NikeNano, what I mentioned in https://github.com/kubeflow/pipelines/pull/6207#issuecomment-906807494 is exactly meant for this use-case. If users can manually author recurring run spec and apply them to cluster using gitops, then it's more user-friendly to let them specify pipeline names + default pipeline version, or pipeline name + version name, compared to IDs.", "I have started to work on the design doc, will update it during the next days but leave it here if you like to add something already now: https://docs.google.com/document/d/1En7UCME3PabqPwaJZSk0GSx61B8BPdkhW_kW9t7Xdkg/edit?usp=sharing", "Will update it during the weekend, have a better understanding of the persistance agent now. \r\n", "Would be great to get some feeback from you guys @Bobgy and @munagekar ", "@NikeNano I went through the design doc. The proposed changes sound good.\r\n\r\nLike you mentioned in your design doc, if advanced filtering for scheduled jobs is a requirement, it might make sense to replicate changes from kubernetes to mysql, otherwise relying on kubernetes api makes sense and should be faster to implement.", "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", "Hello, you are always very helpful. Thank you for your efforts.\r\nHas the ability to control scheduledworkflows with kubectl been developed? It's hard to keep track of the exact progress.\r\nAs shown below, when scheduledworkflows are deleted with kubectl, they are not deleted in kubeflow web, but the resources are deleted and the pipeline does not run according to the schedule.\r\nI want to check the synced deletion status on the web, what should I do?\r\n`kubectl delete scheduledworkflows --all`\r\n<img width=\"676\" alt=\"image\" src=\"https://user-images.githubusercontent.com/56992106/210510699-def87822-e958-43dd-97b6-783bd42f9291.png\">\r\n<img width=\"1127\" alt=\"image\" src=\"https://user-images.githubusercontent.com/56992106/210510743-1472b520-4eb6-4bb5-9355-e40946e06a2d.png\">\r\n", "Hi guys, @Bobgy @NikeNano \r\n\r\nThank you for the thoughtful discussion. \r\nIs there any progress on this issue? ", "@chensun @connor-mccarthy I think this is a good idea for KFP: symmetrically syncing between the `ScheduledRun` CRD and the MySQL backend, because right now, if you PATCH the CRD (or CREATE one, not using the KFP API), you will end up in an out-of-sync state.\r\n\r\nThis feature will also allow people to use git-ops tools like ArgoCD to manage which pipelines are scheduled in each namespace.", "Following up on this feature request: I think we should consider the scope of Kubernetes Resource Management (KRM) support in KFP in general. I suspect that if we implement RecurringRun CRD today, we will receive similar requests for Run/Pipeline and other objects in the future.\r\n\r\nAgain, welcome contributions on this CRD for KFP topic!\r\n\r\nReference: We are currently using a mock API group and CRD to represent KFP objects: https://github.com/kubeflow/pipelines/blob/f626629f79c833b159336fe9963d44b77071c14f/backend/src/apiserver/common/const.go#L18-L20. ", "Thank you guys, @thesuperzapper and @zijianjoy \r\nRegarding contribution, I see that the design proposal already was provided - https://docs.google.com/document/d/1En7UCME3PabqPwaJZSk0GSx61B8BPdkhW_kW9t7Xdkg/edit?pli=1#heading=h.rebcfzcla50x\r\n\r\nDoes it still require an agreement? ", "> Hi guys, @Bobgy @NikeNano\r\n> \r\n> Thank you for the thoughtful discussion. Is there any progress on this issue?\r\n\r\nHey, I sadly don't have time to work on this so feel free to pick this up. Would be great so see this be used. ", "@zijianjoy @NikeNano this issue is related to supporting updates on Scheduled Runs (which is not currently supported), see my comment in that issue for reference:\r\n\r\n- https://github.com/kubeflow/pipelines/issues/3789#issuecomment-1581396796", "Hey everyone, I have released a reference repository that demonstrates using GitOps to manage the schedules and definitions of Kubeflow Pipelines.\r\n\r\n### You can find it at [`deployKF/kubeflow-pipelines-gitops`](https://github.com/deployKF/kubeflow-pipelines-gitops)\r\n\r\nIt has limitations caused by Kubeflow Pipeline not allowing updates to \"recurring runs\" (only deleting and recreating), but I think the solution it uses will work for most production use-cases." ]

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[ "Hello @harshsaini , for now you can create two components, one for launching the Katib jobs, another for waiting for this external job to finish. \r\n\r\nFor the performance issue, we need to implement a control plane concept to introduce a watcher for external job completion. This is currently blocked for the introduction of control plane. We will prioritize it accordingly.", "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" ]

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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", "Closing as v2 compatible mode is no longer supported." ]

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[ "the issue is too small to track" ]

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[ "There are several possible solutions to this.\r\n\r\nFirst thing to understand is how the `create_component_from_func` function works. This function realizes the \"Lightweight python components\" feature.\r\nAll KFP components are `ComponentSpec` files (`component.yaml`). Many people write them manually and that's fine. But for python functions I've added a way to *generate* the `component.yaml` from the function signature and code. The resulting components are \"Lightweight\" is sense that there is no need to build and push a new container. The code is included in the command-line. This makes development much easier, but also limits the amount of code that can be included.\r\n\r\nSo, what can be done:\r\n\r\n1) You can put the `utils` in the container and use it as base image. The `kfp.container.build_image_from_working_dir` can help you with that. See the [sample](https://github.com/kubeflow/pipelines/blob/master/samples/core/container_build/container_build.ipynb)\r\n2) Put `utils` into its own package that can be installed via `packages_to_install`. BTW, python can install from GIT.\r\n3) Use code pickling to include extra dependencies in the lightweight component. This option (`use_code_pickling`) is not exposed in `create_component_from_func`, but the older `func_to_container_op` still has it. You need to be very careful to use the same python version in your environment and in the container.\r\n\r\n4) There is a passive idea to add a new feature - `additional_files` to the `create_component_from_func` function, but I think it stretches the idea of \" code inlining\" too far.\r\n\r\n>same_step_0.generated_main\r\n\r\nIf you're generating the main function yourself, maybe you want to generate the whole `kfp.components.structures.ComponentSpec`/`component.yaml`?\r\n", "This is really interesting! I think what I'm going to try first is just to pickle all files in the sub directories, and inline them into the generated root file. I think this will work though is obviously pretty fragile (assumes all sub files are of a certain layout/etc). \r\n\r\nThe challenge here, of course, is we do WANT data scientists breaking out their functions into submodules without having to build packages or containers - would this be the idea about the \"additional_files\"?", "/cc @chensun ", "> 4. There is a passive idea to add a new feature - `additional_files` to the `create_component_from_func` function, but I think it stretches the idea of \" code inlining\" too far.\r\n\r\n@Ark-kun I landed on this issue because I was looking for a way to add other functions to the component file -- do you think that's a viable option at all?\r\n\r\nMy team and I are finding it useful to use functions defined for components in other contexts, but it would be incredibly useful if it were possible to inline both a kfp \"wrapper\" function and a main function that did the work. For example, something like:\r\n\r\n def process_data(\r\n params: typing.Dict[str,str], input: pd.DataFrame\r\n ) -> pd.DataFrame:\r\n ...\r\n return processed_data\r\n\r\n def process_csv_data(\r\n params: typing.Dict[str,str], input_path: InputPath('CSV'),\r\n output_path: OutputPath('CSV')\r\n ):\r\n input = pd.read_csv(input_path)\r\n processed_data = process_data(params, input)\r\n processed_data.to_csv(output_path)\r\n\r\nIf we could pass `process_data` as an extra function to inline to `create_component_from_func(process_csv_data, ...)` then this would allow us to have a kfp component but also be able to export `process_data` as a function that can be packaged and offered in a module for use by other applications. An `extra_functions` parameter would be very useful, but the `additional_files` parameter would probably also solve the same need.", "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" ]

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[]

[ "The typing hints using `typing` did not work, which is fixed in the PR here.\r\nhttps://github.com/kubeflow/pipelines/pull/5979", "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" ]

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[ "Discussed with @zijianjoy offline,\r\nCurrently with v2 compatible mode, \r\n\r\n- cached status icon\r\n- a link to cached execution\r\nThe above tasks can be achieved now.\r\n\r\nFor the following tasks, currently we can't fetch original execution with current logic in launcher.\r\nWe may need to store the original mlmd execution id in the cache mlmd execution first(Maybe via a custom property called `cached_execution_id`). I'm not sure whether we need to support these in v2 compatible mode. I think these features will be useful to users once we support cache staleness. \r\nFor my perspective, we can defer these two tasks until we support cache staless. @Bobgy What do you think of it?\r\n\r\n- (p1) a link to original execution this cache is taken from\r\n- (p2) the original execution should have a link to the original run (deleting the original run will invalidate cache)", "I think it will be great to describe the user scenario for referencing original execution from cached execution. Based on the discussion, looks like there is no cache management exposed to user in v2 compatible mode, so the linking from cached execution to original execution is informational at this point, and help user to understand more about the caching mechanism (debugging).\r\n\r\nThe solution with custom property `cached_execution_id` should work, but I suggest to get confirmation from the team about defining this field, so we can avoid changing/removing this property in the future, where FE needs to support property field compatibility across versions." ]

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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", "This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n" ]

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