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[ "A GKE nodepool has a VM service account. This is used to pull images. You need to grant storage permission to this SA. ", "Thank you for quick response!\r\n\r\nSo, workload identity cannot affect service account to pull images? I also bound the google service account that has enough permissions to some Kubeflow Pipelines service account such as `pipeline-runner`, but it does not work.\r\n\r\nIs it only the node pool SA that can pull images?\r\n\r\nIn addition, I found problem of `set_image_pull_secrets` of `Config` object. It does not change the secret form `pipeline-runner-token-*****`.\r\nMaybe related to https://github.com/kubeflow/pipelines/issues/3847. I will add another issue with more confirmation.", "> Is it only the node pool SA that can pull images?\r\n\r\nIn my organization we grant permission to nodepoolSA. I suppose docker pull secrets might work as well. \r\nHowever default-editor/pipeline-runner does not pull images and hence granting permission to it will not work.\r\n\r\n", "> In addition, I found problem of set_image_pull_secrets of Config object. It does not change the secret form pipeline-runner-token-*****.\r\n\r\nThis was my mistake. Maybe specifying `imagePullSecret` is the best way. ", "Please close this issue if this is out of scope.", "Closed because it sounds like GKE expected behavior" ]

"2021-04-05T11:04:11"

"2021-04-09T00:55:54"

"2021-04-09T00:55:54"

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[ "Hi @shrinath-suresh, can you explain the difference between --db and --logdir, are there any references?\n\nKFP doesn't currently support changing the argument, but we can make it configurable.\n\nWhat kind of configuration experience do you prefer? Do all s3 uris need to change the argument?\n\n\nOr we can add an input textbox on KFP UI where users may come and configure their args?\n\nOr do you have any other suggestions", "@Bobgy Thanks for the reply. \r\n\r\nWe were able to log the tensorboard events to S3. Only the Dashboard was having issues when opened with S3 Path. I was looking into couple of blog posts related to the tensorboard S3 integration. One of them suggested to have the paramter as `db` instead of `logdir` (i will try to see if i can get the link). However, i couldnt do it in KFP, as there is no option to set the `db` parameter.\r\n\r\nI have quick question before the suggestion. **Does KFP support Tensorboard with S3 urls as backend?**. \r\n\r\nIt would be helpful to have an extra parameter in `mlpipeline-ui-metadata.json` file. For Example:\r\n\r\n`{\"outputs\": [{\"type\": \"tensorboard\", \"source\": \"s3://<S3_path>\", \"type\" : \"db\"}]}`\r\n", "It doesn't work out of the box, you may refer to this issue how to configure credentials:\nhttps://github.com/kubeflow/pipelines/issues/4364", "The suggestion sounds reasonable, but I'd still want to confirm why db arg is used and what it means.", "@Bobgy \r\n\r\n`tensorboard --help` output shows the db argument help as below\r\n\r\n` --db URI [experimental] sets SQL database URI and enables DB backend mode, which is read-only unless --db_import is also passed.`\r\n\r\nLooks like it is used specifically for SQLITE databases. I couldnt find the link where db argument is suggested to be used for S3 URLs ", "> It doesn't work out of the box, you may refer to this issue how to configure credentials:\r\n> #4364\r\n\r\nWe have installed kubeflow-pipelines using this branch - https://github.com/e2fyi/kubeflow-aws/tree/master (1.4.1). And looks like, the configuration which is mentioned in the #4364 is already part of installation. ", "@shrinath-suresh thanks for these additional information\r\n\r\njust to be sure, I want to confirm this --\r\n\r\nwhen you see \"empty dashboard is shown\", can you try to get logs of the tensorboard instance? It may have more detailed error messages.", "It doesn't wanna work for me either on tensorflow 2.0.0 with following Error message: Unable to connect to endpoint. While it works on tensorflow 2.3.2. Is there a way to bump up tensorflow version for this viewer?", "@lechwolowski it's not currently, but I just filed https://github.com/kubeflow/pipelines/issues/5471 which tries to support customizing tensorboard image", "@Bobgy We have identified the solution. Tensorboard with S3 buckets works based on region. The bucket which we used was in different region. Once we updated the AWS_REGION variable in cluster, we are able to see the tensorboard UI", "Cool @shrinath-suresh it'll be helpful commenting what exactly is required (like an example patch of tensorboard), so other people finding this issue can get their problems solved", "Solution: Tensorboard with S3 URLs works **based on the region where bucket is placed**. Ensure to set `AWS_REGION` value as same as the bucket region. ", "@Bobgy It will be more helpful if the documentation is updated about the supported systems - https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/#tensorboard . What do you think ?", "That's a very good point, do you want to contribute that?", "Besides that, IIUC `AWS_REGION` can be different per pipeline?\r\nSo I think it'll be ideal to support https://github.com/kubeflow/pipelines/issues/4714, e.g. you can configure the AWS_REGION in your mlpipeline-ui-metadata as a field, and let it auto picked up by KFP started tensorboard.", "WDYT @shrinath-suresh ", "@Bobgy @shrinath-suresh Generally, to use anything Tensorflow-based (e.g. TF serving, TFX) the following environment variables needs to be set:\r\nENDPOINT_URL\r\nS3_USE_HTTPS\r\nS3_VERIFY_SSL\r\nAWS_ACCESS_KEY_ID\r\nAWS_SECRET_ACCESS_KEY\r\n\r\nAdditionally, one might need to add own certificates if you're self-hosting your S3 solution (e.g. Minio, Ceph) on-prem.", "> That's a very good point, do you want to contribute that?\r\n\r\nAlready occupied with deliverables. I can pick it up after 2 weeks. I will make a note of it.", "> WDYT @shrinath-suresh\r\n\r\nIt makes more sense to have it as a parameter in the mlpipeline-ui-metatadata and it would be easy to configure as well. ", "Thanks for the feedback @ConverJens @shrinath-suresh!\r\nLet's move the discussion to https://github.com/kubeflow/pipelines/issues/4714" ]

"2021-04-04T10:26:33"

"2021-04-16T10:10:01"

"2021-04-16T06:59:38"

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[ "From PR https://github.com/kubeflow/pipelines/pull/5424", "Do you know what this error comes from? These images should be on google container registry as I understand, are there any rate limiting? ", "Let me elaborate, the above error occurs when we are building KFP docker images, we need to pull some common images from dockerhub as the base.", "Ok, thanks! Do you expect these errors to be due to the rate limit? Could we move the base images to gcr as well?", "I agree. Looks like it recovered, if it keeps happening, we should do that", "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" ]

"2021-04-03T14:52:23"

"2022-04-18T17:27:41"

"2022-04-18T17:27:41"

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[ "/assign", "The failing line was recently changed when upgrading argo in https://github.com/kubeflow/pipelines/pull/5041, it seems we need to check nil pointer there.\r\nhttps://github.com/kubeflow/pipelines/blob/14b447a07c975a21b7b525d49f6676058f020cd8/backend/src/common/util/workflow.go#L54\r\n/cc @NikeNano ", "I built a reproduction pipeline, when submitting it with any missing parameters.\r\nKFP API server crashes like above\r\n\r\n```python\r\nfrom kfp import dsl, components\r\n\r\necho = components.load_component_from_text(\r\n \"\"\"\r\nname: Echo\r\ninputs:\r\n- {name: text, type: String}\r\nimplementation:\r\n container:\r\n image: alpine\r\n command:\r\n - echo\r\n - {inputValue: text}\r\n\"\"\"\r\n)\r\n\r\n\r\n@dsl.pipeline(name='missing_parameter')\r\ndef pipeline(\r\n parameter:\r\n str # parameter should be specified when submitting, but we are missing it in the test\r\n):\r\n echo_op = echo(text=parameter)\r\n```" ]

"2021-04-03T13:20:03"

"2021-04-04T01:27:22"

"2021-04-04T01:27:22"

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[ "/kind question", "cc @Udiknedormin ", "@Tomcli Per my understanding:\r\n\r\n* running an op after condition group should run whenever the predicate was passed or not, as I want run it after the whole DAG\r\n* running an op after some op in the group should only run if the predicate was passed, as I only want to run it if the actual conditional task was executed\r\n\r\nOf course it should work with data-passing too, in the op-after-op case (the op-after-DAG cannot use it, for obvious reasons). That is --- unless condition's status is exposed as a pipelineparam, which would introduce additional interesting possibilities. A component which runs after the condition could check if the condition DAG was run or not.", "/assign @chensun ", "/assign @neuromage ", ">We are wondering is it the expected DSL behavior where the task dependencies should point to the Argo condition task in both cases?\r\n\r\nThis is unexpected behavior.\r\n\r\nI did my own experiment and got and found several compiler bugs:\r\n\r\n```python\r\nimport kfp\r\nfrom kfp import components\r\n\r\n@components.create_component_from_func\r\ndef produce_str() -> str:\r\n return \"Hello world\"\r\n\r\n@components.create_component_from_func\r\ndef consume_str(message: str):\r\n print(message)\r\n\r\ndef my_pipeline():\r\n hello_str = produce_str().output\r\n with kfp.dsl.Condition(hello_str == \"Hello world\"):\r\n produce_task = produce_str()\r\n consume_str(produce_task.output)\r\n\r\nif __name__ == '__main__':\r\n kfp.compiler.Compiler().compile(my_pipeline, '2021-04-09 Check contitionals.yaml')\r\n```\r\n\r\nError: `invalid spec: templates.my-pipeline.tasks.condition-1 templates.condition-1.outputs failed to resolve {{tasks.produce-str-2.outputs.parameters.produce-str-2-Output}}`\r\nThis is a compiler bug - the data passing rewriter did not detect that `produce-str-2-Output` was used as parameter downstream, so only artifact remained.\r\n\r\nNow trying the same with artifacts:\r\n\r\n```python\r\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\r\n@create_component_from_func\r\ndef produce_file(output_path: OutputPath()):\r\n from pathlib import Path\r\n Path(output_path).write_text(output_path)\r\n\r\n@create_component_from_func\r\ndef consume_file(message_path: InputPath()):\r\n from pathlib import Path\r\n message = Path(message_path).read_text()\r\n print(message)\r\n\r\ndef my_file_pipeline():\r\n hello_str = produce_str().output\r\n with kfp.dsl.Condition(hello_str == \"Hello world\"):\r\n produce_file_task = produce_file()\r\n consume_file(produce_file_task.output)\r\n\r\n with kfp.dsl.Condition(hello_str == \"Tails\"):\r\n produce_file2_task = produce_file()\r\n consume_file(produce_file2_task.output)\r\n\r\n \r\nif __name__ == '__main__':\r\n import kfp\r\n kfp.compiler.Compiler().compile(my_file_pipeline, '2021-04-09 Check conditionals.arifacts.yaml')\r\n```\r\n\r\nThe first part works fine, but the second consume task fails with `This step is in Error state with this message: Unable to resolve: {{tasks.condition-2.outputs.artifacts.produce-file-2-output}}`\r\n\r\n\r\nTo sum it up, I've found 3 issues:\r\n\r\n1) Argo seems to treat Skipped tasks same as Succeeded when evaluating `dependencies`. I consider this to be surprising and against the spirit of the dependency concept. We can try contacting Argo and changing this on their side. Meanwhile, as a workaround, we should fix that at compiler level. Dependency should mean \"dependency on success\". (Issue 3 complicates the fix though.)\r\n2) KFP compiler (data passing rewriter) seems to miss output value usage in `when`. This is a bug.\r\n3) KFP compiler seems to compile dependency on conditional task as dependency on condition DAG. This does not seem to be correct.\r\n\r\nP.S. I'm not sure using `.after` on `OpsGroup`/`Condition` was intended to be supported.\r\n", "Thanks @Ark-kun. Good to know this is a bug in the KFP Argo compiler. We are able to work around on the KFP Tekton compiler because we decided not to put conditional tasks into a sub-dag to optimize it for Tekton. \r\n\r\nWe want to make sure KFP Argo also compiles dependency on conditional task, so we are not diverging the runtime behavior with the same KFP DSL.", "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" ]

"2021-04-03T01:24:39"

"2022-04-18T17:27:33"

"2022-04-18T17:27:33"

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[ "/assign @yanniszark " ]

"2021-04-01T20:43:04"

"2021-04-02T09:43:20"

"2021-04-02T09:43:20"

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[ "Thank you!\nIt's a gateway to allow using grpcweb from KFP UI.", "Do we also need to apply the same change to `metadata-grpc-deployment` as well?\r\n\r\nMy deployment looks like:\r\n\r\n" ]

"2021-04-01T19:12:17"

"2021-04-02T09:43:20"

"2021-04-02T09:43:20"

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[ "I will be opening a PR with the aforementioned fix asap\r\n/assign @yanniszark " ]

"2021-04-01T19:07:31"

"2021-04-02T08:56:20"

"2021-04-02T08:56:20"

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[]

[ "@DavidSpek @Bobgy it seems like someone wrote a new image that does not runasnonroot\r\nhttps://github.com/kubeflow/manifests/pull/1756\r\n\r\nCan you update to a newer version of gcr.io/ml-pipeline/application-crd-controller:1.0-beta-non-cluster-role ? It seems to be from here https://github.com/kubernetes-sigs/application according to https://github.com/kubeflow/pipelines/blob/1cd189567d4ba7e71aa1f31ddaf9a46c17ae8ae3/manifests/kustomize/third-party/application/application-controller-deployment.yaml#L27\r\nMaybe that will fix the issue. This image is very old (2019)", "The permissions in the base image seem to be wrong\r\n\r\n```\r\ndrwx------. 1 root root 14 Nov 11 2019 root\r\n```\r\nis the problem. Someone forgot to allow group 0 read on that directory\r\n\r\nFull output\r\n\r\n```\r\ndr-xr-xr-x. 1 root root 6 Apr 1 11:45 .\r\ndr-xr-xr-x. 1 root root 6 Apr 1 11:45 ..\r\ndrwxr-xr-x. 1 root root 902 Oct 29 2019 bin\r\ndrwxr-xr-x. 1 root root 0 Apr 24 2018 boot\r\ndrwxr-xr-x. 5 root root 360 Apr 1 11:45 dev\r\ndrwxr-xr-x. 1 root root 14 Oct 31 2019 etc\r\ndrwxr-xr-x. 1 root root 0 Apr 24 2018 home\r\ndrwxr-xr-x. 1 root root 84 May 23 2017 lib\r\ndrwxr-xr-x. 1 root root 40 Oct 29 2019 lib64\r\ndrwxr-xr-x. 1 root root 0 Oct 29 2019 media\r\ndrwxr-xr-x. 1 root root 0 Oct 29 2019 mnt\r\ndrwxr-xr-x. 1 root root 0 Oct 29 2019 opt\r\ndr-xr-xr-x. 514 root root 0 Apr 1 11:45 proc\r\ndrwx------. 1 root root 14 Nov 11 2019 root\r\ndrwxr-xr-x. 1 root root 14 Apr 1 11:45 run\r\ndrwxr-xr-x. 1 root root 14 Oct 31 2019 sbin\r\ndrwxr-xr-x. 1 root root 0 Oct 29 2019 srv\r\ndr-xr-xr-x. 13 root root 0 Mar 28 21:41 sys\r\ndrwxrwxrwt. 1 root root 0 Oct 29 2019 tmp\r\ndrwxr-xr-x. 1 root root 8 Oct 29 2019 usr\r\ndrwxr-xr-x. 1 root root 6 Oct 29 2019 var\r\n```", "@juliusvonkohout the image is only used to provide k8s application monitoring. It's an optional component. May be remove it from your deployment?", "kubectl apply -k github.com/kubeflow/pipelines/manifests/kustomize/env/playform-agnostic?ref=$PIPELINE_VERSION\n\nAnd it will no longer get installed", "@Bobgy Yes, the pipelines still work without it. But there are still two questions:\n\n1. Will it be included in the full kubeflow 1.3 by default? If yes it must be fixed.\n\n2. Should the documentation be updated then? Because more and more clusters will enforce security policies. In the end I strongly prefer a proper solution. Either removing it or fixing it.\n\nI would like to work on a solution, if you propose one.", "> @Bobgy Yes, the pipelines still work without it. But there are still two questions:\r\n> \r\n> 1. Will it be included in the full kubeflow 1.3 by default? If yes it must be fixed.\r\n\r\nNo, it won't for pipelines, but you may find another copy in kubeflow/manifests repo, it might be deployed by default, so some applications rely on it. I'd suggest confirm with @yanniszark the release manager about it.\r\n\r\n> 2. Should the documentation be updated then? Because more and more clusters will enforce security policies. In the end I strongly prefer a proper solution. Either removing it or fixing it.\r\n\r\nDo you mean KFP standalone documentation? Yes, I'd love to see an update. Do you want to contribute to this?\r\n\r\n> I would like to work on a solution, if you propose one.\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", "Hi @juliusvonkohout, metacontroller is not application controller (this issue's topic). Did you confuse them together?", "So far it works in 1.4" ]

"2021-04-01T11:33:00"

"2021-10-15T13:30:19"

"2021-10-15T13:30:19"

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[ "/area backend", "related issue: https://github.com/kubeflow/pipelines/issues/482", "/assign @neuromage @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", "This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n", "/reopen", "@maganaluis: You can't reopen an issue/PR unless you authored it or you are a collaborator.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5404#issuecomment-1205812558):\n\n>/reopen\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>", "workaround\r\n\r\n```py\r\n@component\r\ndef or_op(x: bool, y: bool) -> bool:\r\n return x or y\r\n\r\n\r\n@component\r\ndef and_op(x: bool, y: bool) -> bool:\r\n return x and y\r\n\r\n\r\n@component\r\ndef not_op(x: bool) -> bool:\r\n return not x\r\n\r\ndef pipeline_fn():\r\n ...\r\n with dsl.Condition(or_op(param1 == value1, not_op(param2 == value2).output).output == \"true\"):\r\n```", "Hi @hrsma2i \r\nI tried your approach. Since in my case param1 and param2 are output of task above this conditional statement, I get the following error:\r\n\r\n`ValueError: Constant argument inputs must be one of type ['String', 'Integer', 'Float', 'Boolean', 'List', 'Dict'] Got: ConditionOperator(operator='>', left_operand={{channel:task=fraud-model-evaluator;name=auPrc;type=Float;}}, right_operand=0.188) of type <class 'kfp.components.pipeline_channel.ConditionOperator'>.`\r\n\r\n\r\nany idea?" ]

"2021-03-31T23:08:11"

"2023-09-13T15:03:35"

"2022-04-18T17:27:39"

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[ "/assign @yanniszark " ]

"2021-03-31T18:37:18"

"2021-04-01T16:16:53"

"2021-04-01T01:43:19"

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[]

[ "This is one of the issues we plan to solve together in bit.ly/kfp-v2. Putting it in IR-based KFP project", "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", "still waiting on a bug fix/feature release on this. im not able to view what's at bit.ly/kfp-v2 but would like to be notified when this is prioritized. thanks!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-31T17:47:02"

"2022-03-02T15:05:19"

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[ "/assign @yanniszark ", "metacontroller is also used in GCP distribution, but since KFP needs it, I agree we can put it here." ]

"2021-03-31T17:26:46"

"2021-04-01T01:43:19"

"2021-04-01T01:43:19"

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[ "/assign @yanniszark " ]

"2021-03-31T17:00:55"

"2021-04-01T01:43:19"

"2021-04-01T01:43:19"

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[ "/assign @yanniszark " ]

"2021-03-31T16:50:06"

"2021-04-01T01:43:19"

"2021-04-01T01:43:19"

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[]

[ "/assign @kubeflow/aws ", "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", "/area components/aws/sagemaker", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "/lifecycle frozen" ]

"2021-03-30T23:15:08"

"2022-08-08T19:28:23"

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[]

[ "This would be nice. We based our KFP repo on [this example](https://github.com/ksalama/kubeflow-examples/tree/master/kfp-cloudbuild), which is referred to in this [GCP article](https://cloud.google.com/solutions/machine-learning/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture) about using KFP with Cloud Build.\r\n\r\nThe [Cloud Build pipeline](https://github.com/ksalama/kubeflow-examples/tree/master/kfp-cloudbuild#cloud-build-steps) from the example includes the following steps:\r\n\r\n> 6. Compile Pipeline: This executes dsl-compile to compile the pipeline defined in [workflow.py](https://github.com/ksalama/kubeflow-examples/blob/master/kfp-cloudbuild/pipeline/workflow.py), to generate pipeline.tar.gz package.\r\n> 7. Upload to GCS: This uploads the pipeline.tar.gz package to GCS.\r\n> 8. Deploy & Run Pipeline: This executes the deploy_pipeline method in the [helper.py](https://github.com/ksalama/kubeflow-examples/blob/master/kfp-cloudbuild/pipeline/helper.py) module, given the pipeline.tar.gz package and pipeline version. The method deploys the pipeline package to KFP. If an optional --run argument is passed, the method also creates and experiment in KFP, and run pipeline given the created experiment, compiled pipeline package, and loaded parameter values from the [settings.yaml](https://github.com/ksalama/kubeflow-examples/blob/master/kfp-cloudbuild/pipeline/settings.yaml) file.\r\n\r\nWe've modified the helper.py script a bit, but the core functionality of uploading and running a pipeline remains the same.\r\n\r\nA few comments:\r\n1. We are basically doing the same thing as the \"use dsl-compile and then kfp upload and then kfp run\" option mentioned in the description. I think this proposed feature would allow us to replace the helper.py script with a call to `kfp run`.\r\n2. helper.py loads a settings.yaml file, converts the key-value pairs in settings.yaml into a dict, then passes that dict into `kfp.Client.run_pipeline`'s `params` parameter. I would still want to store our pipeline parameters into a separate config file, so I would probably write a script that parses settings.yaml and pass them into the proposed `--params=key1=value1,key2=value2,...` command line option.\r\n3. By using helper.py, we don't need a `__main__` in our pipelines and don't need to call `kfp.Client`, similar to the proposed `kfp run`.\r\n4. Our CI pipeline retains step 7 from the example repo, \"Upload to GCS.\" We have a push trigger that will run CI when PRs are merged to our main branch, which will compile our KFP pipelines and upload them to a GCS bucket. We have a separate CI pipeline that runs each night and, instead of compiling the KFP pipelines, will download the latest compiled KFP pipeline from GCS and run that. So, even though the proposed `kfp run` command removes the need to call `dsl-compile`, it might still be nice to optionally produce the compiled pipeline, for archival purposes.\r\n", "Let me know if you need help. I have built exactly this in Go (but it calls `dsl-compile` for the compilation step for the time being). Doing it in Python should be even easier.", "This is super interesting to us! We're working on some higher level abstractions here too and would love to collaborate. Has this already been committed to? We'd love to sync offline and see what we could do together.", "@aronchick Is it possible to present a demo on KFP community meetings? Or you need to wait until it's open?", "I think i'd like to wait until it's open, but happy to give as many folks as would like private demos.\n________________________________\nFrom: Yuan (Bob) Gong ***@***.***>\nSent: Tuesday, March 30, 2021 8:14 PM\nTo: kubeflow/pipelines ***@***.***>\nCc: David Aronchick ***@***.***>; Mention ***@***.***>\nSubject: Re: [kubeflow/pipelines] [feature] CLI command to run a pipeline from python files (#5381)\n\n\n@aronchick<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Faronchick&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7C09477d4bb02546f98b2108d8f3f3121b%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637527572602641398%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=2ZxOZP5kPw%2F6148e1mEG1Yx1DrKKyKO5q9wohM2Cpxw%3D&reserved=0> Is it possible to present a demo on KFP community meetings? Or you need to wait until it's open?\n\n—\nYou are receiving this because you were mentioned.\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fkubeflow%2Fpipelines%2Fissues%2F5381%23issuecomment-810726304&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7C09477d4bb02546f98b2108d8f3f3121b%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637527572602651393%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=NK2eX5XZ%2BFHFmqQhoni%2FEV6XwS%2BeUCpQdDyy1MW845E%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAAAMQ5KQ37K2TNB5WBTHUWDTGKHQTANCNFSM4Z4MSSLQ&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7C09477d4bb02546f98b2108d8f3f3121b%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637527572602661393%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=WYaV4j3hQPzr6Sw9gBl0DSPbZ7hB0JpmfTT4y%2FasO6I%3D&reserved=0>.\n", "Thanks for the heads up! I'd love to wait for a presentation in KFP community meeting when it's open.", "With the number of things on my hand for v2 and v2 compatible. I don't currently have bandwidth on this. Welcome anyone who's interested to pick up", "Cross posting for context, I think @aronchick is talking about [The SAME Project: A Cloud Native Approach to Reproducible Machine Learning - David Aronchick](https://www.youtube.com/watch?v=aQvvGKVX25I)", "This is correct 🙂 I'll do a demo in the coming weeks - as soon as i can find the time!\r\n________________________________\r\nFrom: Yuan (Bob) Gong ***@***.***>\r\nSent: Thursday, July 1, 2021 3:06 AM\r\nTo: kubeflow/pipelines ***@***.***>\r\nCc: David Aronchick ***@***.***>; Mention ***@***.***>\r\nSubject: [EXTERNAL] Re: [kubeflow/pipelines] [feature] CLI command to run a pipeline from python files (#5381)\r\n\r\n\r\nCross posting for context, I think @aronchick<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Faronchick&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7Ca53824335d68462b152508d93c77e32d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637607307879377677%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=g5GtbOdP0C%2B4lzEbllVRUK4gpTalpybv14x%2BtUxv380%3D&reserved=0> is talking about The SAME Project: A Cloud Native Approach to Reproducible Machine Learning - David Aronchick<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DaQvvGKVX25I&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7Ca53824335d68462b152508d93c77e32d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637607307879377677%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=AdfdEUTYvIwsQZdlCTmiZaMdyOHg42UIY2o%2B%2FUok7AI%3D&reserved=0>\r\n\r\n—\r\nYou are receiving this because you were mentioned.\r\nReply to this email directly, view it on GitHub<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fkubeflow%2Fpipelines%2Fissues%2F5381%23issuecomment-872108450&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7Ca53824335d68462b152508d93c77e32d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637607307879387671%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=zfhFXOgTZgd3QGR5C1NVmGOXvg%2FOxUKBWoDhmxrExXM%3D&reserved=0>, or unsubscribe<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fnotifications%2Funsubscribe-auth%2FAAAMQ5PZVXWXWK5VGHM4UI3TVQ42DANCNFSM4Z4MSSLQ&data=04%7C01%7Cdavid.aronchick%40microsoft.com%7Ca53824335d68462b152508d93c77e32d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637607307879387671%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=p9Vs7JyNkMCEqlH4V%2BiTWwThR9aIgGbAicEwNO%2BvgOs%3D&reserved=0>.\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n" ]

"2021-03-27T02:13:46"

"2022-03-03T03:05:38"

"2022-03-03T03:05:38"

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[ "We currently do not provide a way to re-join conditional branches.\r\n\r\nThis leads to some code duplication:\r\nhttps://github.com/kubeflow/pipelines/blob/a80421191db917322ff312626409526b0a76aa68/samples/core/continue_training_from_prod/continue_training_from_prod.py\r\n\r\nOne workaround might be to have the training process encapsulated in a `@dsl.graph_component` function. Then you can call it with or without passing the pre-trained model.\r\n(I'm not 100% sure it will work though.)\r\n\r\nYou could also do something similar with graph `component.yaml` components.", "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" ]

"2021-03-26T11:33:15"

"2022-04-18T17:27:35"

"2022-04-18T17:27:35"

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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" ]

"2021-03-25T09:58:37"

"2022-04-18T17:27:36"

"2022-04-18T17:27:36"

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[ "![Uploading Screen Shot 2021-03-24 at 2.21.49 PM.png…]()\r\n", "Pending reproduction", "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" ]

"2021-03-24T21:33:49"

"2022-04-18T17:27:37"

"2022-04-18T17:27:37"

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[ "@Bobgy: The label(s) `area/deployment` cannot be applied, because the repository doesn't have them.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5368):\n\n>### What steps did you take\r\n>\r\n>```bash\r\n>kpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomize@1.5.0-rc.0\r\n>```\r\n>\r\n>\r\n>### What happened:\r\n>> $ kpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomize@1.5.0-rc.0 kfp-standalone-1/kustomize/upstream\r\n>fetching package /manifests/kustomize from https://github.com/kubeflow/pipelines to kfp-standalone-1/kustomize/upstream\r\n>error: /home/ext_gongyuan_google_com/github/kf/testing/test-infra/kfp/kfp-standalone-1/kustomize/upstream/third-party/argo/upstream/manifests/namespace-install/overlays/argo-server-deployment.yaml: wrong Node Kind for expected: MappingNode was SequenceNode: value: {- op: add\r\n> path: /spec/template/spec/containers/0/args/-\r\n> value: --namespaced}\r\n>\r\n>### What did you expect to happen:\r\n>There should be no error message when fetching using kpt.\r\n>\r\n>### Environment:\r\n>kpt version\r\n>0.37.1\r\n>\r\n>### Anything else you would like to add:\r\n>\r\n>\r\n>\r\n>### Labels\r\n>\r\n>\r\n>\r\n>\r\n>\r\n>\r\n>\r\n>\r\n>/area deployment\r\n>\r\n>---\r\n>\r\n>\r\n>Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.\r\n>\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>", "upstream issue: https://github.com/GoogleContainerTools/kpt/issues/1218\r\n\r\nthe fix we should apply: https://github.com/GoogleContainerTools/kpt/issues/1218#issuecomment-756472616\r\n\r\n> kpt can only parse and process valid Kubernetes Resource files. Since the file has .yaml extension and is not a valid kubernetes resource, you must intimate kpt to not parse it by creating a file with name .krmignore in the package directory and add the file name(list_only.yaml) to it. This way, kpt ignores the file and leave it untouched.", "Because we plan to use the package in KF 1.3, we should fix it before the final release.", "I'm seeing some problems with `.krmignore`.\r\nhttps://github.com/GoogleContainerTools/kpt/issues/1218#issuecomment-806305825", "Before there is a kpt improvement, I think we should avoid using YAML to represent arrays for JSON patch.", "Workaround merged, so this is no longer P0", "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 comment is directed towards branch upgradekpt of the fork by zijianjoy. \r\n\r\nI assumed it is appropriate to address that here because there is no option to raise an issue in that repo, and that repo is the referenced by the official kubeflow getting started guide: https://www.kubeflow.org/docs/distributions/gke/deploy/deploy-cli/\r\nSpecifically, from the file https://github.com/kubeflow/gcp-blueprints/blob/master/kubeflow/apps/pipelines/pull-upstream.sh\r\n\r\nRunning the command in that file:\r\n```\r\nkpt pkg get https://github.com/zijianjoy/pipelines.git/manifests/kustomize/@upgradekpt upstream\r\n```\r\nfrom Google cloud command line shell results in this error:\r\n```\r\nError: /home/test_user/upstream/third-party/argo/upstream/manifests/namespace-install/overlays/argo-server-deployment.yaml: wrong Node Kind for expected: MappingNode was SequenceNode: value: {- op: add\r\n path: /spec/template/spec/containers/0/args/-\r\n value: --namespaced}\r\n```\r\nThis command is part of the scripts that deploy kubeflow, and as a result I cannot complete the process.\r\n\r\nTrying to run on the root of the repo\r\n```\r\nkpt pkg get https://github.com/zijianjoy/pipelines.git/@upgradekpt upstream\r\n```\r\nGives this error:\r\n```\r\nError: /home/test_user/upstream/backend/src/crd/samples/scheduledworkflow/invalid.yaml: yaml: line 22: did not find expected whitespace or line break\r\n```\r\n\r\nRunning\r\n```\r\nkpt pkg get https://github.com/kubeflow/pipelines.git/manifests/kustomize/@google-cloud-pipeline-components-0.1.4 upstream\r\n```\r\nwhich seems to be related to the version used by the getting started guide gives this error:\r\n```\r\nError: Kptfile at \"/tmp/kpt-get-448525949/manifests/kustomize/third-party/argo/upstream/manifests\" has an old version (\"v1alpha1\") of the Kptfile schema.\r\nPlease update the package to the latest format by following https://kpt.dev/installation/migration.\r\n```\r\n\r\nI am not sure how to proceed to install kubeflow." ]

"2021-03-24T13:49:39"

"2021-10-25T06:15:07"

"2021-10-25T06:15:07"

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[ "Thank you for the report!\r\nContributions welcomed, the code path related is https://github.com/kubeflow/pipelines/blob/eb558423ecdca969f2dea55e0d1b0197fafbcb13/frontend/server/handlers/artifacts.ts#L200\r\nand development guide: https://github.com/kubeflow/pipelines/tree/master/frontend", "/cc @zijianjoy ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "/lifecycle frozen" ]

"2021-03-24T10:34:28"

"2021-07-09T17:59:27"

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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", "/lifecycle frozen", "Some challenges for implementing wrapping ---\r\n\r\nBecause we used react-virtualized to make log viewer work with large scale of logs, which seems like a reasonable request.\r\nIt's a very challenging problem if you want both virtualization and wrapping -- you cannot easily know how many lines some text will wrap into without actually rendering it, therefore it's hard to calculate height of each item. Most virtualization UI libraries only support fixed height items.\r\n\r\nHowever, KFP UI currently loads [the entire logs file as a string](https://github.com/kubeflow/pipelines/blob/cc83e1089b573256e781ed2e4ac90f604129e769/frontend/src/components/LogViewer.tsx#L71) into browser memory. I don't think it actually scales well. If people are not getting problems with the existing LogViewer, maybe we don't need so much scalability.", "## Proposal\r\n\r\n* Change LogViewer to not virtualize (virtualize means when there are 1000 lines of logs, only render the visible 20 lines in the browser).\r\n* If total log size is over a limit, hide the middle part of the log by default (because usually what's at the beginning and ending are most useful), and allow users to open a separate raw text window to check the full log\r\n* wrap log lines by default\r\n\r\nWith my proposal, this should be relatively easy to implement and shouldn't break when log volume is huge.", "It sounds good to me for skipping middle part of logs and opening raw text window for full log.\r\n\r\nI have a question about not to virtualize + wrap log lines: How do we know how to break a long line to multiple small lines? The side panel size is adjustable, and the chrome tab can be zoom in and out. Therefore number of characters each line is dynamic.", "@zijianjoy that's the default behavior of browser, if you put some text in a div, they wrap when the div width is not enough.\n\nThere is a css property that disables this behavior, you may want to search for it." ]

"2021-03-24T10:18:55"

"2021-07-29T00:41:19"

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"2021-03-22T20:52:00"

"2021-03-29T15:23:50"

"2021-03-29T15:23:50"

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"2021-03-22T20:34:26"

"2021-03-22T23:43:43"

"2021-03-22T23:43:43"

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[ "Can you provide more info such as sample pipeline code that prompts the error and the version of KFServing you have installed?", "```\r\nimport kfp.dsl as dsl\r\nimport kfp.compiler as compiler\r\nfrom kfp import components\r\n\r\nkfserving_op = components.load_component_from_url(\r\n \"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml\"\r\n)\r\n\r\n\r\ndef deploy_inference_image_op(action=\"create\",\r\n model_name=\"kfserving-inference-model-v1\",\r\n namespace=\"kfserving-test\",\r\n framework=\"custom\",\r\n raw_yaml=\"\",\r\n watch_timeout=\"300\"):\r\n return kfserving_op(\r\n action=action,\r\n model_name=model_name,\r\n namespace=namespace,\r\n framework=framework,\r\n service_account=\"ass\",\r\n inferenceservice_yaml=raw_yaml,\r\n watch_timeout=watch_timeout\r\n )\r\n\r\n\r\n@dsl.pipeline(\r\n name=\"kfp-kfserving-model-deploy\", description=\"deploy into kubeflow with kfserving\",\r\n)\r\ndef deploy_kf_pipeline(model_name: str, kf_inference_yaml: str, watch_timeout: str = '300'):\r\n deploy_inference_image_op(model_name=model_name, raw_yaml=kf_inference_yaml, watch_timeout=watch_timeout)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n file_name = __file__ + \".tar.gz\"\r\n compiler.Compiler().compile(deploy_kf_pipeline, file_name)\r\n\r\n```\r\n\r\nkfserving 0.5.1", "Hmm, what is being passed in as the inferenceservice_yaml in your case? There could potentially be something there causing an issue. Might be worth checking if the yaml can be deployed normally using kubectl. \r\n\r\nAlso, if you need to use a specific service account for the predictor pods while using the 'inferenceservice_yaml` argument, currently you need to pass it in with the yaml itself with:\r\n\r\n```\r\nspec:\r\n predictor:\r\n serviceAccountName: \"some-service-account\"\r\n ...\r\n```\r\nPassing it in as an arg currently doesn't overwrite or update the YAML passed in (though maybe it should).", "@pvaneck i was able deploy inference yaml with kubectl CLI,. \r\n\r\nYes, though i have mentioned service account in above code, same service account is mentioned in yaml also, same configuration is working for below commit in pipelines \r\n\r\n``` \r\ncommit 65bed9b6d1d676ef2d541a970d3edc0aee12400d\r\nAuthor: juliusvonkohout <45896133+juliusvonkohout@users.noreply.github.com>\r\nDate: Wed Feb 24 21:54:14 2021 +0100\r\n\r\n Changes for kfserving 0.4.1 (#4479)\r\n \r\n * Changes for kfserving 0.4.0\r\n \r\n * update to kfserving==0.4.0\r\n\r\npip freeze | grep kfserving\r\nkfserving==0.4.1\r\n\r\n```\r\n\r\nSo with latest code, i,e kfserving==0.5.1\r\n\r\n````\r\nfrom kfserving import V1alpha2EndpointSpec\r\nfrom kfserving import V1alpha2PredictorSpec\r\nfrom kfserving import V1alpha2TensorflowSpec\r\nfrom kfserving import V1alpha2PyTorchSpec\r\nfrom kfserving import V1alpha2SKLearnSpec\r\nfrom kfserving import V1alpha2XGBoostSpec\r\nfrom kfserving.models.v1alpha2_onnx_spec import V1alpha2ONNXSpec\r\nfrom kfserving import V1alpha2TritonSpec\r\nfrom kfserving import V1alpha2CustomSpec\r\nfrom kfserving import V1alpha2InferenceServiceSpec\r\nfrom kfserving import V1alpha2InferenceService\r\n\r\n````\r\nabove imports are moved to \r\n\r\n```\r\nfrom kfserving import V1beta1InferenceService\r\nfrom kfserving import V1beta1InferenceServiceSpec\r\nfrom kfserving import V1beta1LightGBMSpec\r\nfrom kfserving import V1beta1ONNXRuntimeSpec\r\nfrom kfserving import V1beta1PMMLSpec\r\nfrom kfserving import V1beta1PredictorSpec\r\nfrom kfserving import V1beta1SKLearnSpec\r\nfrom kfserving import V1beta1TFServingSpec\r\nfrom kfserving import V1beta1TorchServeSpec\r\nfrom kfserving import V1beta1TritonSpec\r\nfrom kfserving import V1beta1XGBoostSpec\r\n```\r\ni,e for v1alpha2 to v1beta1 so if run kfserving deploy pipeline with latest code its was looking for v1beta1 API,which is not found so its giving 404 error \r\n\r\n{'api_version': 'serving.kubeflow.org/v1beta1',", "Ah, seems like your cluster doesn't have KFServing >= 0.5.0 installed which is required for using the v1beta1 API. You had the right idea with using the old commit. Just continue to use your code, but load the old components yaml file. i.e:\r\n\r\n```\r\nhttps://github.com/kubeflow/pipelines/blob/65bed9b6d1d676ef2d541a970d3edc0aee12400d/components/kubeflow/kfserving/component.yaml\r\n```\r\n\r\nSorry for the hiccup. I will update the README to make this a bit clearer." ]

"2021-03-22T15:37:11"

"2021-04-08T01:10:02"

"2021-04-08T01:10:02"

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[ "Thank you for reporting! I can validate that this issue happens on Safari browser, which doesn't enforce the time format as it does on Chrome: \r\n\r\n\r\nTrying to find for a fix for this usecase.\r\n", "Validated Safari support for native `date` and `time` picker in TextField: https://caniuse.com/input-datetime", "Explored the option to use @material-ui/pickers but that is going to be a huge change: https://material-ui.com/components/pickers/. Most of them are not related to the issue itself, but related to many breaking changes from `@material-ui/core` because of @material-ui/pickers has a requirement on `@material-ui/core@^4.0.0`. \r\n\r\nI am leaning towards changing the behavior of current TextField to unblock Safari usage.", "Closing this issue with the PR which prevented throwing error when datetime format is invalid." ]

"2021-03-22T07:39:05"

"2021-04-02T16:26:41"

"2021-04-02T16:26:41"

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[]

[ "@hilcj hello. I think this issue should be labeld to `area/backend`", "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" ]

"2021-03-22T05:56:41"

"2022-04-18T17:27:46"

"2022-04-18T17:27:46"

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[ "I think the main reason for the error is that you're not using full paths in `file_outputs`. The system cannot find those files.\r\nCan you try specifying the full paths everywhere?\r\n\r\nAdditionally I see couple of other issues (although they should not be leading to errors).\r\n\r\n* Directly constructing `ContainerOp` objects is deprecated. Please create components in `component.yaml` format. Its very easy and is supported. See https://github.com/kubeflow/pipelines/blob/master/samples/tutorials/Data%20passing%20in%20python%20components.ipynb and https://www.kubeflow.org/docs/pipelines/sdk/component-development/\r\n* We encourage users to specify the program command in the component definition (and not just relying on the container ENTRYPOINT)", "Found another root cause: `np.save` adds extension to the file path:\r\nHere is the documentation for `np.save`\r\n```\r\nIf file is a string or Path, a ``.npy``\r\n extension will be appended to the filename if it does not already\r\n have one.\r\n```", "I've tried to recreate your case using the best practices:\r\n\r\n```python\r\n# Component:\r\nfrom kfp.components import create_component_from_func, InputPath, OutputPath\r\n\r\ndef sklearn_datasets_load_boston(\r\n x_train_path: OutputPath('NumPy'),\r\n y_train_path: OutputPath('NumPy'),\r\n x_test_path: OutputPath('NumPy'),\r\n y_test_path: OutputPath('NumPy'),\r\n test_size: float = 0.33,\r\n): \r\n import os\r\n import numpy as np\r\n from sklearn import datasets\r\n from sklearn.model_selection import train_test_split \r\n\r\n x, y = datasets.load_boston(return_X_y=True)\r\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size)\r\n\r\n with open(x_train_path, 'wb') as x_train_file:\r\n np.save(x_train_file, x_train)\r\n with open(y_train_path, 'wb') as y_train_file:\r\n np.save(y_train_file, y_train)\r\n with open(x_test_path, 'wb') as x_test_file:\r\n np.save(x_test_file, x_test)\r\n with open(y_test_path, 'wb') as y_test_file:\r\n np.save(y_test_file, y_test)\r\n \r\n\r\n\r\nif __name__ == '__main__':\r\n sklearn_datasets_load_boston_op = create_component_from_func(\r\n sklearn_datasets_load_boston,\r\n packages_to_install=['scikit-learn==0.24.1'],\r\n output_component_file='component.yaml',\r\n )\r\n\r\n\r\n# Pipeline:\r\ndef load_data_pipeline():\r\n load_data_task = sklearn_datasets_load_boston_op(test_size=0.33)\r\n\r\n\r\nif __name__ == '__main__':\r\n import kfp\r\n kfp_endpoint = None\r\n kfp.Client(host=kfp_endpoint).create_run_from_pipeline_func(\r\n load_data_pipeline,\r\n arguments={}\r\n )\r\n```", "> I've tried to recreate your case using the best practices:\r\n> \r\n> ```python\r\n> # Component:\r\n> from kfp.components import create_component_from_func, InputPath, OutputPath\r\n> \r\n> def sklearn_datasets_load_boston(\r\n> x_train_path: OutputPath('NumPy'),\r\n> y_train_path: OutputPath('NumPy'),\r\n> x_test_path: OutputPath('NumPy'),\r\n> y_test_path: OutputPath('NumPy'),\r\n> test_size: float = 0.33,\r\n> ): \r\n> import os\r\n> import numpy as np\r\n> from sklearn import datasets\r\n> from sklearn.model_selection import train_test_split \r\n> \r\n> x, y = datasets.load_boston(return_X_y=True)\r\n> x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size)\r\n> \r\n> with open(x_train_path, 'wb') as x_train_file:\r\n> np.save(x_train_file, x_train)\r\n> with open(y_train_path, 'wb') as y_train_file:\r\n> np.save(y_train_file, y_train)\r\n> with open(x_test_path, 'wb') as x_test_file:\r\n> np.save(x_test_file, x_test)\r\n> with open(y_test_path, 'wb') as y_test_file:\r\n> np.save(y_test_file, y_test)\r\n> \r\n> \r\n> \r\n> if __name__ == '__main__':\r\n> sklearn_datasets_load_boston_op = create_component_from_func(\r\n> sklearn_datasets_load_boston,\r\n> packages_to_install=['scikit-learn==0.24.1'],\r\n> output_component_file='component.yaml',\r\n> )\r\n> \r\n> \r\n> # Pipeline:\r\n> def load_data_pipeline():\r\n> load_data_task = sklearn_datasets_load_boston_op(test_size=0.33)\r\n> \r\n> \r\n> if __name__ == '__main__':\r\n> import kfp\r\n> kfp_endpoint = None\r\n> kfp.Client(host=kfp_endpoint).create_run_from_pipeline_func(\r\n> load_data_pipeline,\r\n> arguments={}\r\n> )\r\n> ```\r\n\r\nThank you so much for your reply,I‘m trying to figure out the difference between our code.\r\n**And I have another question:how to point kfp_endpoint?**\r\nMy KF-cluster is deployed on two local virtual machines,I wrote code in the local python environment and upload it to the cluster through the UI interface;How should I point the kfp_endpoint?\r\nThe master node'ip is 192.168.52.10,the svc state like these\r\n\r\n", ">I‘m trying to figure out the difference between our code.\r\n\r\nThe root issue in your code was probably using non-absolute paths in `file_outputs`.\r\n\r\nWhile fixing the issue I've also discovered that `np.save(data_path, data)` writes to `data_path + \".npy\"`, which was leading to a similar error message in my code. To fix that I've used `np.save` with pre-opened files.", ">how to point kfp_endpoint?\r\n>My KF-cluster is deployed on two local virtual machines,I wrote code in the local python environment and upload it to the cluster through the UI interface;How should I point the kfp_endpoint?\r\n\r\nI'm not sure the direct connection to the API serve with `kfp_endpoint = \"http://10.100.203.15:8888\"` will work.\r\nYou'll probably need to port forward the api server `kubectl port-forward svc/ml-pipeline 8888` and use `kfp_endpoint \r\n = http://127.0.0.1:8888`\r\n\r\n/cc @Bobgy ", "> > I‘m trying to figure out the difference between our code.\r\n> \r\n> The root issue in your code was probably using non-absolute paths in `file_outputs`.\r\n> \r\n> While fixing the issue I've also discovered that `np.save(data_path, data)` writes to `data_path + \".npy\"`, which was leading to a similar error message in my code. To fix that I've used `np.save` with pre-opened files.\r\n\r\nthank you so much for your reply ,After I improved my code according to your guidance,I found maybe my problem is not same as this problem [https://github.com/kubeflow/pipelines/issues/750].Because the load_data image can save data to the path in containter,like this\r\n\r\nThen I check the pod logs I get the following error.\r\n\r\nLook forward your help!", "> > I‘m trying to figure out the difference between our code.\r\n> \r\n> The root issue in your code was probably using non-absolute paths in `file_outputs`.\r\n> \r\n> While fixing the issue I've also discovered that `np.save(data_path, data)` writes to `data_path + \".npy\"`, which was leading to a similar error message in my code. To fix that I've used `np.save` with pre-opened files.\r\n\r\nThe screenshot is not clear,here is the information of the error\r\n`time=\"2021-03-24T19:04:42Z\" level=info msg=\"Creating a docker executor\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Executor (version: v2.3.0, build_date: 2019-05-20T22:10:54Z) initialized (pod: kubeflow/load-data-2-mctsc-3737213461) with template:\\n{\\\"name\\\":\\\"load-data\\\",\\\"inputs\\\":{},\\\"outputs\\\":{\\\"artifacts\\\":[{\\\"name\\\":\\\"load-data-X_train\\\",\\\"path\\\":\\\"/X_train.npy\\\"}]},\\\"metadata\\\":{},\\\"container\\\":{\\\"name\\\":\\\"\\\",\\\"image\\\":\\\"load_data2:v0.0.2\\\",\\\"command\\\":[\\\"python\\\",\\\"load_data.py\\\"],\\\"resources\\\":{}},\\\"archiveLocation\\\":{\\\"s3\\\":{\\\"endpoint\\\":\\\"minio-service.kubeflow:9000\\\",\\\"bucket\\\":\\\"mlpipeline\\\",\\\"insecure\\\":true,\\\"accessKeySecret\\\":{\\\"name\\\":\\\"mlpipeline-minio-artifact\\\",\\\"key\\\":\\\"accesskey\\\"},\\\"secretKeySecret\\\":{\\\"name\\\":\\\"mlpipeline-minio-artifact\\\",\\\"key\\\":\\\"secretkey\\\"},\\\"key\\\":\\\"artifacts/load-data-2-mctsc/load-data-2-mctsc-3737213461\\\"}}}\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Waiting on main container\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"main container started with container ID: 9f20576bc2d9b749429bc259cf10c932a4636fbdd1041043c8cc5550d2d74158\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Starting annotations monitor\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"docker wait 9f20576bc2d9b749429bc259cf10c932a4636fbdd1041043c8cc5550d2d74158\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Starting deadline monitor\"\r\ntime=\"2021-03-24T19:04:42Z\" level=error msg=\"executor error: fork/exec /usr/local/bin/docker: exec format error\\ngithub.com/argoproj/argo/errors.Wrap\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:88\\ngithub.com/argoproj/argo/errors.InternalWrapError\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:71\\ngithub.com/argoproj/argo/workflow/common.RunCommand\\n\\t/go/src/github.com/argoproj/argo/workflow/common/util.go:397\\ngithub.com/argoproj/argo/workflow/executor/docker.(*DockerExecutor).Wait\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/docker/docker.go:95\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).Wait\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:867\\ngithub.com/argoproj/argo/cmd/argoexec/commands.waitContainer\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:32\\ngithub.com/argoproj/argo/cmd/argoexec/commands.NewWaitCommand.func1\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:16\\ngithub.com/spf13/cobra.(*Command).execute\\n\\t/go/src/github.com/spf13/cobra/command.go:766\\ngithub.com/spf13/cobra.(*Command).ExecuteC\\n\\t/go/src/github.com/spf13/cobra/command.go:852\\ngithub.com/spf13/cobra.(*Command).Execute\\n\\t/go/src/github.com/spf13/cobra/command.go:800\\nmain.main\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/main.go:17\\nruntime.main\\n\\t/usr/local/go/src/runtime/proc.go:201\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1333\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"No sidecars\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"No output parameters\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Saving output artifacts\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Staging artifact: load-data-X_train\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Copying /X_train.npy from container base image layer to /argo/outputs/artifacts/load-data-X_train.tgz\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Archiving 9f20576bc2d9b749429bc259cf10c932a4636fbdd1041043c8cc5550d2d74158:/X_train.npy to /argo/outputs/artifacts/load-data-X_train.tgz\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"sh -c docker cp -a 9f20576bc2d9b749429bc259cf10c932a4636fbdd1041043c8cc5550d2d74158:/X_train.npy - | gzip > /argo/outputs/artifacts/load-data-X_train.tgz\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Annotations monitor stopped\"\r\ntime=\"2021-03-24T19:04:42Z\" level=warning msg=\"path /X_train.npy does not exist (or /X_train.npy is empty) in archive /argo/outputs/artifacts/load-data-X_train.tgz\"\r\ntime=\"2021-03-24T19:04:42Z\" level=error msg=\"executor error: path /X_train.npy does not exist (or /X_train.npy is empty) in archive /argo/outputs/artifacts/load-data-X_train.tgz\\ngithub.com/argoproj/argo/errors.New\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:49\\ngithub.com/argoproj/argo/errors.Errorf\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:55\\ngithub.com/argoproj/argo/workflow/executor/docker.(*DockerExecutor).CopyFile\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/docker/docker.go:66\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).stageArchiveFile\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:344\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).saveArtifact\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:245\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).SaveArtifacts\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:231\\ngithub.com/argoproj/argo/cmd/argoexec/commands.waitContainer\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:54\\ngithub.com/argoproj/argo/cmd/argoexec/commands.NewWaitCommand.func1\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:16\\ngithub.com/spf13/cobra.(*Command).execute\\n\\t/go/src/github.com/spf13/cobra/command.go:766\\ngithub.com/spf13/cobra.(*Command).ExecuteC\\n\\t/go/src/github.com/spf13/cobra/command.go:852\\ngithub.com/spf13/cobra.(*Command).Execute\\n\\t/go/src/github.com/spf13/cobra/command.go:800\\nmain.main\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/main.go:17\\nruntime.main\\n\\t/usr/local/go/src/runtime/proc.go:201\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1333\"\r\ntime=\"2021-03-24T19:04:42Z\" level=info msg=\"Alloc=4108 TotalAlloc=11155 Sys=70590 NumGC=4 Goroutines=8\"\r\ntime=\"2021-03-24T19:04:42Z\" level=fatal msg=\"path /X_train.npy does not exist (or /X_train.npy is empty) in archive /argo/outputs/artifacts/load-data-X_train.tgz\\ngithub.com/argoproj/argo/errors.New\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:49\\ngithub.com/argoproj/argo/errors.Errorf\\n\\t/go/src/github.com/argoproj/argo/errors/errors.go:55\\ngithub.com/argoproj/argo/workflow/executor/docker.(*DockerExecutor).CopyFile\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/docker/docker.go:66\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).stageArchiveFile\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:344\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).saveArtifact\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:245\\ngithub.com/argoproj/argo/workflow/executor.(*WorkflowExecutor).SaveArtifacts\\n\\t/go/src/github.com/argoproj/argo/workflow/executor/executor.go:231\\ngithub.com/argoproj/argo/cmd/argoexec/commands.waitContainer\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:54\\ngithub.com/argoproj/argo/cmd/argoexec/commands.NewWaitCommand.func1\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/commands/wait.go:16\\ngithub.com/spf13/cobra.(*Command).execute\\n\\t/go/src/github.com/spf13/cobra/command.go:766\\ngithub.com/spf13/cobra.(*Command).ExecuteC\\n\\t/go/src/github.com/spf13/cobra/command.go:852\\ngithub.com/spf13/cobra.(*Command).Execute\\n\\t/go/src/github.com/spf13/cobra/command.go:800\\nmain.main\\n\\t/go/src/github.com/argoproj/argo/cmd/argoexec/main.go:17\\nruntime.main\\n\\t/usr/local/go/src/runtime/proc.go:201\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1333\"`", "Do you still get the error when you run the pipeline code I've posted? It worked for me.", "> Do you still get the error when you run the pipeline code I've posted? It worked for me.\r\n\r\nYES，I still got the error (T_T)", "Duplicate of https://github.com/kubeflow/pipelines/issues/1654\n\nI think this is the problem -- kubernetes 1.19 has dropped built-in docker support. So you should switch to use argo pns executor.", "> Duplicate of #1654\r\n> \r\n> I think this is the problem -- kubernetes 1.19 has dropped built-in docker support. So you should switch to use argo pns executor.\r\nYES,You are right,this problem didn't reappear after Ichange rhe k8s version。", "Looks like this particular issue is resolved." ]

"2021-03-22T03:38:33"

"2021-04-10T04:09:16"

"2021-04-10T04:09:16"

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[]

[ "Bump @pvaneck ", "Just a note that any of the PodSpec fields can be used with the component if you pass in yaml directly:\r\n```python\r\n isvc_yaml = '''\r\n apiVersion: \"serving.kubeflow.org/v1beta1\"\r\n kind: \"InferenceService\"\r\n metadata:\r\n name: \"sklearn-iris2\"\r\n spec:\r\n predictor:\r\n timeout: 123\r\n sklearn:\r\n storageUri: \"gs://kfserving-samples/models/sklearn/iris\"\r\n '''\r\n kfserving_op(\r\n action='apply',\r\n inferenceservice_yaml=isvc_yaml\r\n```\r\n\r\nHowever, I'd be open to having timeout be an actual arg of the component as it seems to be a field that may be commonly used.", "/assign @kubeflow/wg-serving-leads ", "Hi @Bobgy. I will be opening a PR soon. I have added it and I'm currently testing.", "@pvaneck Shall I wait for PR #5386 to be merged? Also, shall I push the image with tag `v0.5.1` or `v0.5.1.1`. Please guide.", "@midhun1998 You can go ahead and submit a PR as it can always be rebased, and I don't foresee any conflicts. I will build and push the image with your changes once it passes the review process. " ]

"2021-03-20T16:41:13"

"2021-04-08T03:25:57"

"2021-04-08T03:25:57"

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[]

[ "HI @rexshang,\r\n\r\nI can think of three ways to improve:\r\n* set up a retry strategy, so that the pipeline can auto retry a few times when the instability happens (you should always expect other services may fail)\r\n* use managed storage, gcs is probably stabler than minio\r\n* upgrade minio server, the default one is very old", "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 @Bobgy,\r\n\r\nI have the exact same problem and it's happening really often. I already have a retry strategy but it's not retrying when this fail.\r\nI'm on KFP 1.7.1 deployed on GKE via the marketplace, using managed storage and managed sql.\r\n\r\nCan I upgrade minio without breaking KFP ? Is there anything else I could do ?\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-19T06:40:14"

"2022-04-18T15:26:52"

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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", "/lifecycle frozen", "superceded by https://github.com/kubeflow/pipelines/issues/6204" ]

"2021-03-19T01:01:48"

"2021-08-25T06:35:13"

"2021-08-25T06:35:13"

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[]

[ "Thank you for the suggestion @maganaluis ! It makes sense to change the lifetime for DB connection, and make it configurable. Welcome to contribute this feature!", "@zijianjoy I created the PR. After changing this configuration we did not experience any of these errors anymore, so seems to be the correct approach here. ", "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", "For anyone coming across this issue, any time you see an error like `[mysql] XXXX/XX/XX XX:XX:XX packets.go:36: unexpected EOF` in the `cache-server` pods, you are almost certainly dealing with a network-level issue between your pods and the MySQL database.\r\n\r\nThe most likely case is that there is some __asymmetric routing__ going on. That is, your Pods might be able to create a connection to MySQL, but MySQL might not have a route back to your Pod. Note, [MySQL connections are complex](https://dev.mysql.com/doc/dev/mysql-server/latest/page_protocol_connection_phase.html) and will sometimes initiate new TCP connections back to the client, which will fail in the previous case.\r\n\r\nI have a more detailed write up on this issue: https://github.com/kubeflow/pipelines/issues/3763#issuecomment-1547009188" ]

"2021-03-18T14:11:06"

"2023-05-14T21:59:38"

"2021-08-04T21:28:23"

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[]

[ "Thank you @AntPeixe for this suggestion! Currently we are able to filter by pipeline name, but we might need to take an action to give some examples about how to use it, and improve reference documentation.", "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" ]

"2021-03-18T13:13:06"

"2022-04-18T17:27:49"

"2022-04-18T17:27:49"

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[ "Duplicate of https://github.com/kubeflow/pipelines/issues/3763", "Can you confirm?", "Confirmed this fixes the issue\r\n`kubectl delete pod ml-pipeline-persistenceagent-xxxxxxx-xxxx -n kubeflow`\r\n\r\nAlso observed that the persistence-agent was looping over an old copy of pipelines possibly from a previous deployment of Kubeflow on Kubernetes that has already been reset by kubeadm on the same machine.\r\n\r\nAfter the restart, the correct pipelines are being read correctly and the status of new runs can be displayed.", "Thanks for the confirmation, if you have additional information how this situation could happen, please comment on #3763, that will be very helpful. I could not reproduce it any more.\r\n\r\nI am closing this issue to allow all discussion happen in the same thread" ]

"2021-03-18T09:12:12"

"2021-03-19T02:58:36"

"2021-03-19T02:58:36"

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[ "/cc @zijianjoy ", "Example pipeline\r\n\r\n```\r\nHOST = '<redacted>'\r\n\r\nimport kfp\r\nimport kfp.components as comp\r\n\r\n\r\ndef fail():\r\n import sys\r\n println(\"failed\")\r\n sys.exit(1)\r\n\r\n\r\ndef echo():\r\n println(\"hello world\")\r\n\r\n\r\ndef main():\r\n fail_op = comp.func_to_container_op(fail)\r\n echo_op = comp.func_to_container_op(echo)\r\n\r\n @kfp.dsl.pipeline(name='fail sample')\r\n def fail_sample():\r\n fail_task = fail_op()\r\n echo_task = echo_op()\r\n echo_task.after(fail_task)\r\n\r\n client = kfp.Client(host=HOST)\r\n client.create_run_from_pipeline_func(fail_sample, {})\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```", "However, here is the pipeline visualization of the failed pipeline\r\n\r\n\r\n\r\nThe pipeline has two tasks, the first fails, so the second one is omitted.\r\nWhat happened?\r\nHowever, the already completed and failed pipeline visualization shows the last task as pending further execution.\r\nand the echo op status is unknown.\r\n\r\nExpected: there should be no unfinished sign after the last step. and echo op should be shown as omitted", "Fixed by #5339 \r\n@zijianjoy FYI, if you use `Fixes #5322` in your PR's title. This issue will be auto closed when your PR is merged. That's best practice for keeping the issue lifecycle up-to-date." ]

"2021-03-18T05:29:16"

"2021-03-24T08:28:23"

"2021-03-24T08:28:23"

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[ "Duplicate of https://github.com/kubeflow/pipelines/issues/5223", "No one maintains Kubeflow 1.2 istio-dex distribution" ]

"2021-03-18T02:23:18"

"2021-03-19T01:06:49"

"2021-03-19T01:06:49"

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[]

[ "> i think the kubeflow is triggering the pipelines to be in a single node. \r\n\r\nAre you sure this is the case? KFP compiler creates and Argo Workflow object and Argo controller just creates Kubernetes Pods. Do you see anything strange with the pods that Argo creates? Perhaps the pods are not allowed to be scheduled on the auto-scaling node pool.", "Yes, I think Argo Controller is creating all the pods for pipelines under a single nod. i checked it from kubernetes side.\r\nso stressing a single nod is killing the pods. why Argo Controller is putting all the pods under a single node. if so , how can we change that while creating the pipeline using pipeline compiler", "This issue happens when Kubernetes always thinks that current node's resource is enough for the pipeline job, so it keeps assigning to the same node. \r\n\r\nSolution: Set memory/CPU requests/limit on pipeline steps to guarantee they are not evicted when the cluster is under resource constraints. They can be set using KFP DSL. After you get container ops, they have a method for setting memory/CPU limit/request: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.Sidecar.set_memory_limit . Once Kubernetes knows that your job's resource requirement cannot be met with current node, it will assign pipeline to another node and that is when autoscaling plays a role.\r\n\r\nWould you like to give it a try and reply if this resolves your issue?", "Hello, \r\nI faced the same issue and then set memory_request and cpu_request. I also randomly increased the number of nodes in my cluster and enabled auto-scaling. Now, the pipelines do not crash, but they are taking too long to execute( the pipeline has been running for two days) . Is there anyway of knowing how many nodes my pipeline will need? Also, each node has 2 CPUs, can we increase the CPUs? ", "@sharmahemlata \r\n\r\nYou can add some logging to your pipeline, so you can dig in further which step in your component is taking a long time to finish. Argo workflow handles the execution so it might be better to follow up on Argo workflow side. You can set https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.Sidecar.set_cpu_request (set_cpu_request) to increase the minimum CPUs you use.", "@zijianjoy Thanks for responding. There is logging throughout the program. As soon as the container starts, 'Inside the container' is logged. I have increased the number of CPUs to a point where the CPUs are underutilised. \r\n\r\nI recently got the kubernetes application developer certificate to try and understand yaml files in case the issue was with the argo workflow controller. I have used the updated version of the argo workflow controller. and changed some configuration according to the issues on the argo workflow issues page, nothing there has been useful either. \r\n\r\nI noticed something interested lately, when my container has errors (eg: syntax errors), then the logging happens up until the error occurs. If there are no errors in my code, there is no logging whatsoever. ", "> @zijianjoy Thanks for responding. There is logging throughout the program. As soon as the container starts, 'Inside the container' is logged. I have increased the number of CPUs to a point where the CPUs are underutilised.\r\n> \r\n> I recently got the kubernetes application developer certificate to try and understand yaml files in case the issue was with the argo workflow controller. I have used the updated version of the argo workflow controller. and changed some configuration according to the issues on the argo workflow issues page, nothing there has been useful either.\r\n> \r\n> I noticed something interested lately, when my container has errors (eg: syntax errors), then the logging happens up until the error occurs. If there are no errors in my code, there is no logging whatsoever.\r\n\r\nAs it turns out, my python code was not logging anything because I need to flush the output stream (sys.stdout.flush()). \r\nMy issue seems to inside the container source code. Sorry for the bother. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-17T20:53:31"

"2022-03-03T00:05:15"

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[]

[ "Hello!\r\n\r\nThere are two old issues about this, so let's link to them:\r\n* https://github.com/kubeflow/pipelines/issues/3226\r\n* https://github.com/kubeflow/pipelines/issues/3731\r\n\r\nMoreover, to fix this I suggest we rely on existing pagination support of the backend, thus making the the UI backwards compatible.\r\n\r\n1. MLMetadata supports pagination in `GetArtifacts`, `GetExecutions`, and `GetContexts` APIs since 0.23.0 ([release notes](https://github.com/google/ml-metadata/releases/tag/v0.23.0))\r\n2. MLMetadata support pagination in `GetExecutionsByContext` and\r\n`GetArtifactsByContext` APIs since 0.25.1 ([release notes](https://github.com/google/ml-metadata/releases/tag/v0.25.1))\r\n3. KFP manifests deploy MLMetadata version 0.25.1 ([source](https://github.com/kubeflow/pipelines/blob/5990285894d655b0b52ca4d9a2f08756a99f1ec8/manifests/kustomize/base/metadata/metadata-grpc-deployment.yaml#L25))\r\n\r\nTherefore, it's just a matter of\r\n1. updating the client used by the UI, and\r\n2. update the UI code to show pages similar to runs list, experiments list, ...\r\n\r\nIn the end, there will be no breaking changes, no backend API changes, or different metadata store communication. What do you think?", "Yes, the original plan was to support pagination when backend supports that. And the backend support has already been released as @elikatsis pointed out.\n\nSuggest close either this issue or the existing issue, so that we do not have two threads for the same topic", "Thank you @elikatsis and @Bobgy for the reference! It makes sense to change client since metadata has already supported pagination. I will close this issue here. Since we have linked this issue to the previous ones, we can find the reference for our decision on making client changes for supporting pagination." ]

"2021-03-17T16:45:07"

"2021-03-18T17:38:03"

"2021-03-18T17:38:03"

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[ "Moving this to v2 compatible project, because this especially affects MLMD artifacts etc, they are important in v2 compatible mode" ]

"2021-03-17T16:40:04"

"2021-07-15T20:36:38"

"2021-07-15T20:36:38"

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[ "I'm not sure this is related to Kubeflow Pipelines. Have you checked how the created pod looks like? You can also replace the command with `sleep 9999` and then use `kubectl exec` to probe and debug the pod.\r\n\r\nWe advice our users to avoid using volumes directly (and creating ContainerOp directly as well).\r\n\r\n```python\r\npreprocess_op = kfp.components.load_component_from_text('''\r\nname: process_data\r\ninputs:\r\n- {name: input1}\r\noutputs:\r\n- {name: output1}\r\nimplementation:\r\n container:\r\n image: image:latest\r\n command:\r\n - sh\r\n - -exc\r\n - cd /home && python test.py\r\n - --input-path\r\n - - {inputPath: input1}\r\n - --output-path\r\n - - {outputPath: output1}\r\n''')\r\n```\r\n\r\nIs there a reason this won't work for you?\r\n\r\nIf the data is too large for the standard artifact passing, then there is a compilation option that seamlessly replaces the intermediate artifact storage with a volume without changing the pipeline code.", "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" ]

"2021-03-16T00:45:04"

"2022-04-18T17:27:50"

"2022-04-18T17:27:50"

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[ "This example is no longer maintained in this repo. I tried to fix it for kubeflow 1.0 at https://github.com/kubeflow/pipelines/pull/4126 a while ago, but seems like the original author is no longer active", "cc @andreyvelich : What is the current status of this component? Should we perform a fix or remove this example?", "Thank you for mentioning me @zijianjoy.\r\nYes, I have plans to refactor e2e mnist example with the new launchers for Katib, KFServing and TF-Operator.\r\nI want to make it after the Kubeflow 1.3 release.\r\n\r\n/assign @andreyvelich ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-15T21:58:34"

"2021-07-27T18:22:44"

"2021-07-27T18:22:44"

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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" ]

"2021-03-15T17:58:13"

"2022-04-18T17:27:48"

"2022-04-18T17:27:48"

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[ "/assign", "@zijianjoy should we aim for making [all arguments](https://cloud.google.com/sdk/gcloud/reference/ml-engine/versions/create) available for the user or keep a selected few(I see that we currently only support some options)? ", "After looking in to this I believe this is actually already possible @Amrit-Mann, if you look at the options: for `model` and `version` the take a json following:\r\n- model, https://cloud.google.com/ml-engine/reference/rest/v1/projects.models\r\n- version , https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions \r\n\r\nwhere in you have the options to specify the region that you like to deploy to. \r\n\r\n> @zijianjoy should we aim for making [all arguments](https://cloud.google.com/sdk/gcloud/reference/ml-engine/versions/create) available for the user or keep a selected few(I see that we currently only support some options)?\r\n\r\nAll available options should already be possible to set. Let me know if you still has some issues @Amrit-Mann ", "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", "Thanks @NikeNano for the research! Since you can use ML-engine REST API, looks like you should be able to call this endpoint to specify regions.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-15T14:37:51"

"2022-03-03T00:05:29"

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[ "Hi @SaschaHeyer, thank you for the report!\n\nWhat is your TFX version? KFP cache shouldn't run on TFX pipelines. This is a bug.", "Hi @Bobgy \r\nissue can be reproduced with 0.27.0 and 0.28.0", "@SaschaHeyer can you provide a minimum reproducible TFX pipeline with a requirements.txt for all dependencies versions?\r\nI tried TFX 0.28.0 with KFP 1.5.0-rc.0 (which should be very similar to 1.4.1 you tested on), and cannot reproduce this problem.", "Hi @Bobgy \r\nhere is the example you can use https://github.com/SaschaHeyer/Sentiment-Analysis-TFX", "Thank you! I will try it out today", "Hi @SaschaHeyer, sorry I do not know how to compile and run your pipeline.\r\nCan you document that?", "An important thing to verify is whether the following annotation exists on your pods:\r\n\r\n\r\n`pipelines.kubeflow.org/pipeline-sdk-type: tfx`", "@Bobgy I have the same issue, and I have the annotation `pipelines.kubeflow.org/pipeline-sdk-type: tfx` on my pods.\r\nCould you explain how this annotation affect if it exists?", "@judyliou sorry for the late reply, the annotation is used to distinguish tfx pipelines from KFP pipelines.\r\nFor TFX pipelines, they should not be cached by KFP cache controller, because TFX come with their first party caching feature. This bug is caused by KFP caching TFX pipelines.\r\n\r\nCan you confirm your KFP backend is upgraded to 1.5.0+? The fix was released in 1.5.0, see https://github.com/kubeflow/pipelines/commit/76cad52d3b405f44b987e9e3a9d27a40d2b0c0d5.", "Ohh, so that explains this original issue, KFP backend is 1.4.1 and the fix was only released in 1.5.0", "Google Marketplace does not offer 1.5.0 yet, only 1.4.1. Is there a workaround to bring back visualisations (as described in [this ticket](https://github.com/kubeflow/pipelines/issues/5302), which leads here)?", "@axeltidemann one workaround is to upgrade cache server after install \n\n```\n# 1. connect to cluster kubectl\n\nkubectl edit deployment cache-server -n namespace\n\n# edit the image to gcr.io/ml-pipeline/cache-server:1.5.0\n```", "@Bobgy That would not work. Google Marketplace does not have any container images with a version 1.5.0 tag, you can check that [here](https://console.cloud.google.com/gcr/images/cloud-marketplace/GLOBAL/google-cloud-ai-platform/kubeflow-pipelines/cacheserver?gcrImageListsize=30). ", "@aneeshc-c you are right, I fixed the instruction to use oss image repo instead", "That did not bring back visualisations, @Bobgy . Any other suggestions?", "@axeltidemann can you report your ML Metadata tab screenshot after the fix? (And it should be a new run too)", "Sure, @Bobgy. You can tell that the Outputs are wrong, it should be Type **ExampleStatistics**.\r\n\r\n", "@axeltidemann thanks for the extra info, that reminded me https://github.com/kubeflow/pipelines/pull/5364 fix is also required. (the metadata you showed are logged from KFP metadata-writer, but we should show metadata of native TFX pipelines).\r\n\r\nRefer to https://www.kubeflow.org/docs/components/pipelines/installation/overview/#choosing-an-installation-option, I'd suggest deploying Kubeflow Pipelines standalone 1.6.0 to get all the fixes, before GCP AI Platform Pipelines has an update. Feature diff between KFP Standalone and GCP AI Platform Pipelines is very minimal, you only get extra features from KFP standalone, because of the flexibility.", "Thank you for that suggestion, @Bobgy. We are a bit hesitant to not use the Google Marketplace one. But thanks for letting us know we should wait for 1.6.0.", "I'm curious why, but I'm working on blockers to release the latest version. Rough ETA would be 2 weeks", "@Bobgy roughly 2 weeks to get 1.6.0 on Google Marketplace? That is great!", "@Bobgy We also have the standalone pipeline installation through Google Cloud Marketplace, now I'm wondering what's the proper upgrade strategy? We have had 3 clusters so far, `0.4`, `1.0` and `1.4`, and everytime `upgrade` for us has been:\r\n* Go through the market place installation\r\n* Create a new cluster and install the newer version \r\n* Try to deprecate the older one and migrate pipelines into the newer one\r\n\r\n1. Is there anyway to `upgrade` instead of `fresh re-install` from the Marketplace package?\r\n2. Considering there's no way to `upgrade` from Market place, do you recommend us follow the manual upgrade docs [here](https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#upgrading-kubeflow-pipelines) for gcp market place installations or the same `install and migrate` strategy?\r\n", "@AlirezaSadeghi did you read the docs: https://www.kubeflow.org/docs/distributions/gke/pipelines/upgrade/?", "Regarding this issue, I also just learned that tfx templates may override to use `tfx-template` as label value: https://github.com/tensorflow/tfx/blob/985105d58d04292163fd1e25d51a319d32448b9a/tfx/experimental/templates/penguin/kubeflow_runner.py#L66.\r\n\r\nSo KFP should match loosely, any label value with the word tfx should be ignored by KFP cacher and metadata-writer." ]

"2021-03-15T13:10:14"

"2021-07-14T05:24:59"

"2021-07-14T05:24:59"

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[ "Duplicate of #5303", "This has the same root cause, so I am closing it" ]

"2021-03-15T13:06:45"

"2021-03-27T00:58:41"

"2021-03-27T00:58:41"

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[ "/assign @Ark-kun @chensun " ]

"2021-03-13T12:41:59"

"2021-03-19T04:10:17"

"2021-03-19T04:10:17"

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[]

[ "We currently have the following support for time formatting: https://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/backend/src/crd/samples/scheduledworkflow/parameterized.yaml#L29-L33.\r\n\r\nDoes it fit your use case?", "Hi @zijianjoy tried exploring the documentation. \r\n\r\nLet me provide a deeper detail:\r\n- We are looking to parametrize our TFX pipeline to pick up ScheduledTime so that it runs on the correct partition for that run. \r\n- For that, we need some way to extract days since epoch (which is what TFX defines as `span` as seen [here](https://github.com/tensorflow/tfx/blob/v0.28.0/tfx/components/example_gen/utils.py#L240-L242) )\r\n- In addition, we need to parse it as an integer which is problematic, since when specified it's a string on TFX RuntimParameter. This is due to TFX needing an integer [here](https://github.com/tensorflow/tfx/blob/v0.28.0/tfx/proto/range_config.proto#L21)\r\n\r\n\r\n\r\n\r\n\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n" ]

"2021-03-13T01:59:16"

"2022-04-18T17:27:53"

"2022-04-18T17:27:53"

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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" ]

"2021-03-12T16:41:48"

"2022-04-18T17:28:01"

"2022-04-18T17:28:01"

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[ "cc @Ark-kun ", "Thank you @lynnmatrix ! This bug makes sense and would you like to contribute a PR for the fix?", "@zijianjoy, My colleague @michaelliqx has fixed it in PR #5290.", "Thank you for the discovery and the fix.\r\nI wonder how this worked before....?\r\n\r\nUpdate: I see that this code was somewhat unused - it was only there to check the cache value being overwritten. I guess with the bug it was truncating the file, getting 0 for the `old_data` and then overwriting the file with the new value.", "This bug raises exception: `TypeError: write_bytes() missing 1 required positional argument: 'data'`.\r\n\r\nYou are right, this code was unused.\r\nKeyValueStore is only used in ComponentStore._refresh_component_cache, and it will check the existence of the key before writing, so there is no chance to overwrite." ]

"2021-03-12T03:37:16"

"2021-03-24T06:08:06"

"2021-03-24T00:03:44"

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[ "/assign @chensun @neuromage \r\n/cc @Ark-kun @capri-xiyue \r\n\r\nI think we need to discuss this problem, from KFP side, we could automatically mount emptyDir for users.", "/cc @jessesuen @alexec\r\nDo you have any suggestions? Is my above understanding of the workarounds accurate?", "Hmm, strange, I'm not seeing flakiness anymore\r\n\r\nI'll keep observing this problem", "I see postsubmit flakyness. https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1371547567694811136", "I'll revert back to docker as default for current release, but we should evaluate possibility of mounting emptyDir volumes for artifact paths for users.", "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 now recommend emissary executor instead: https://github.com/kubeflow/pipelines/issues/1654#issuecomment-903224028" ]

"2021-03-11T12:22:54"

"2021-08-22T07:03:03"

"2021-08-22T07:03:03"

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[]

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[ "> Examples:\r\n> \\# Print the logs of a workflow:\r\n> \r\n> argo logs my-wf" ]

"2021-03-11T11:53:09"

"2021-03-12T03:39:24"

"2021-03-12T03:39:24"

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[ "@Bobgy is it possible to confirm what the expected behavior is with optional params?", "/assign @chensun @Ark-kun ", "Thank you for the detailed issue report.\r\n\r\nThe behavior is by design. There is no special/different behavior for multiple optional arguments. What you see is mostly the way you program parses the command-line.\r\n\r\nThe question is a bit unclear regarding what behavior you are expecting any why.\r\n\r\n`optional: true` means that the pipeline author may skip passing any argument to the optional input.\r\nIn current implementation, the input placeholder can be replaced with *nothing* if no argument was passed, but TBH, an attempt to resolve a placeholder for a missing input is more like an error. We should probably add a warning for that case.\r\n\r\nCommand-line programs can be made to handle such CLI arguments.\r\nIf your program cannot handle them, you can either change the program or change the specification.\r\n\r\n>set a default value of null \r\n\r\nThe container component specification describes command-line programs. Command-lines have no concept of \"null\". Command line arguments are strings. This is an OS limitation. Additionally, when parsing the YAML we treat `default: null` same as unspecified.\r\n\r\nYou could set default value to an empty string BTW, if that works for you.\r\n\r\n>optional params get passed as flags instead of as omitted arguments as expected\r\n>assigns 40 to A\r\n>passes in --A as a flag;\r\n>passed both arguments in as flags instead of arguments\r\n\r\nLet's try to understand what KFP does. KFP does not \"assign\" anything to the variables of your python program. Nor does it quite \"pass\" things to programs. It does not look into your program code. What KFP does is execute command-line programs after replacing the placeholders. (You can easily check the command-line arguments of your program in the \"Pod\" tab. You can also debug programs by using the `command: [\"bash\", \"-c\", 'echo \"$0\" \"$@\"']`).\r\nCommand-lines do not have concepts like \"assigning\", \"passing\" or \"flags\". Command line consists of executable name and an array of arguments (which are null-terminated C strings).\r\nWhen you omit arguments for optional inputs, you get a pretty expected command-line: `python3 /src/myprint.py --param1 --param2`. Then you program interprets that the way you've observed.\r\n\r\nAn important thing to understand is that the interface is the command-line. An array of strings. Your program must be able to interpret its command-line. If you want your program to receive special values like `None`, `-Infinity` , `<built-in function>` or `dict`, you need to think about how you're going to represent them on the command-line.\r\n\r\n### Solution\r\n\r\nIf you want to add/remove parts of command-line based on the presence of argument for an optional input, just use the `if` placeholder:\r\n```yaml\r\nname: myprint\r\ninputs:\r\n- {name: A, optional: true, type: String}\r\n- {name: B, optional: true, type: String}\r\nimplementation:\r\n container:\r\n image: gcr.io/.../mycomp\r\n command: [python3, /src/myprint.py]\r\n args:\r\n - if:\r\n cond: {isPresent: A}\r\n then: [--param1, {inputValue: A}]\r\n - if:\r\n cond: {isPresent: B}\r\n then: [--param2, {inputValue: B}]\r\n```\r\n\r\nPlease check how components generated by `kfp.components.create_component_from_func` look like: https://github.com/kubeflow/pipelines/blob/a80421191db917322ff312626409526b0a76aa68/components/json/Build_list/component.yaml#L78", "@Ark-kun Thank you for your detailed response! I just want to clarify,\r\n\r\n## 1. \r\nI think the main issue that I've seen is with regards to what you mention about:\r\n> Additionally, when parsing the YAML we treat default: null same as unspecified.\r\n\r\nIf we have a program `myprint.py` that can optionally accept an option `--param1`, a user running the program in the normal course of action can choose to omit specifying a value for the option by omitting it from the program execution altogther: running `python a.py` without `--param1=some_arg`.\r\n\r\nBut in the YAML spec, there doesn't appear to be a way to have KFP _not_ to pass in `--param1`, if the user doesn't supply the argument for the component in the pipeline. e.g. Given the YAML:\r\n```\r\nname: myprint\r\ninputs:\r\n- {name: A, optional: true, type: String}\r\nimplementation:\r\n container:\r\n image: gcr.io/.../mycomp\r\n command: [python3, /src/myprint.py]\r\n args:\r\n - --param1\r\n - {inputValue: A}\r\n```\r\nOmitting `param1` for the component in the pipeline:\r\n```\r\n@kfp.dsl.pipeline()\r\ndef pipeline():\r\n first_add_task = myprint_op()\r\n```\r\nThis seems to produce the equivalent of calling (command line receives 3 tokens):\r\n`python myprint.py --param1`\r\n\r\nInstead of what is expected (command line receives 2 tokens):\r\n`python myprint.py`\r\n\r\nThis behaviour seems a little incongruent to me. Especially in the case of when we specify param1 to take on a **non-null** default value:\r\n`{name: A, optional: true, default: \"hello\", type: String}`\r\n\r\nIn this case, when we omit param1 from the pipeline, the default gets passed as expected:\r\n> python myprint.py --param1=hello\r\n\r\nBut if we specify a null default value in the YAML spec:\r\n`{name: A, optional: true, default: ~, type: String}`\r\n\r\nTo stay consistent with the non-null case, it _should_ yield:\r\n`python myprint.py`\r\n\r\nBut instead, we get:\r\n`python myprint.py --param1`\r\n\r\nFrom the perspective of the command line parser, the params take on completely different meanings. The former being that since no `--param1` was found, the parser will assign it some value. In other words, param1 interpreted as a **key** in a key-value pair. But in the latter case, param1 is interpreted as a boolean flag.\r\n\r\n**I just want to confirm if this is indeed the expected behaviour?** If so, that in order to omit an option from being passed in altogether if it's optional, that I should be employing the conditionals in the YAML component spec as described?\r\n\r\n## 2.\r\n\r\nLastly, I want to double check the version of KFP that supports those conditional statements in the YAML.\r\n\r\nWhenever I have specified them, I'm unable to load the component:\r\n```Error: Structure \"OrderedDict(['container', OrderedDict([...]), ('args', OrderedDict([('if', None), ('cond', OrderedDict([('isPresent', 'A')])), ('then', ['--param1', OrderedDict([('inputValue', 'A')])])])]) is incompatible with type \"typing.Union[kfp.components._structures.ContainerImplementation, kfp.components._structures.GraphImplementation, NoneType]\" - none of the types in Union are compatible.```", "Hey @amyxst, did you check https://github.com/kubeflow/pipelines/blob/master/samples/test/placeholder_if.py?\n\nCurrently, KFP doesn't really support runtime if placeholder, so suggest wrapping your command call in bash or python and add the conditional there", "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", "@amyxst \r\n\r\n>I don't think TC interface is deterministic, because it looks like mouse and focus based multi modal.\r\n\r\nI've listed an example that does just that using the `if` placeholder.\r\n\r\n```\r\nname: myprint\r\ninputs:\r\n- {name: A, optional: true, type: String}\r\n- {name: B, optional: true, type: String}\r\nimplementation:\r\n container:\r\n image: gcr.io/.../mycomp\r\n command: [python3, /src/myprint.py]\r\n args:\r\n - if:\r\n cond: {isPresent: A}\r\n then: [--param1, {inputValue: A}]\r\n - if:\r\n cond: {isPresent: B}\r\n then: [--param2, {inputValue: B}]\r\n```\r\n\r\n>I just want to confirm if this is indeed the expected behaviour?\r\n\r\nYes. In your case, the command line is `[\"python3\", \"/src/myprint.py\", \"--param1\", A]. There are 4 elements in the list. Each one is independent. `\"--param1\"` is completely independent from `A`.\r\n\r\nIf you want more complex behavior, use the `if` placeholder created e\\specifically for this case.\r\n\r\n>Lastly, I want to double check the version of KFP that supports those conditional statements in the YAML.\r\n\r\nYes.\r\n\r\n>Lastly, I want to double check the version of KFP that supports those conditional statements in the YAML.\r\n\r\nYour YAML data likely has a formatting issue. Please post the full component YAML and the full error message.\r\n\r\n" ]

"2021-03-11T07:36:42"

"2022-03-13T01:10:31"

"2022-03-03T06:05:32"

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[ "Interesting. The hydra command-line format is different from the normal way of specifying command-line arguments that 99% of other command-line tools use (separating flag and value, instead of joining them with `=`).\r\n\r\nHowever, the `ComponentSpec`/`component.yaml` format supports such command lines via the `concat`: placeholders:\r\n\r\n```yaml\r\nname: TransformOp\r\ninputs:\r\n- {name: template_prj, type: String}\r\n- {name: env, type: String}\r\n- {name: project_api_key, type: String}\r\n- {name: data_api_key, type: String}\r\n- {name: model_id, type: String}\r\nimplementation:\r\n container:\r\n image: gcr.io/my-org/kubeflow-projects/causal_discovery\r\n args:\r\n - concat: [\"component=\", {inputValue: template_prj}, \"/transformer\"]\r\n - concat: [\"common=\", {inputValue: env}]\r\n - concat: [\"common.template_prj=\", {inputValue: template_prj}]\r\n - concat: [\"common.project_api_key=\", {inputValue: project_api_key}]\r\n - concat: [\"common.data_api_key=\", {inputValue: data_api_key}]\r\n - concat: [\"common.model_id=\", {inputValue: model_id}]\r\n```\r\n\r\n>This is because when we load and parse the YAML, common.project_api_key={inputValue is taken as a key of a dictionary, while project_api_key} is taken as the the value for the key, which is apparently wrong.\r\nSo we have a limitation here that a placeholder like {inputValue: project_api_key} must be on its own line (the list item).\r\n\r\nI'm not sure that's the case. `- common.project_api_key={inputValue: project_api_key}` is just wrong YAML syntax. An item should wither be a string or dict, but not string-o-dict. Using quoting prevents parts of the value from being parsed as dict by the YAML parser. `- \"common.project_api_key={inputValue: project_api_key}\"`. However this does not solve the user issue - a string is just a string. The placeholder won't be substituted. We decided not to invent a new placeholder language that needs to be parsed. We use the YAML language instead. Placeholders are clearly distinguished from the verbatim strings by their type: verbatim strings are strings while placeholders are maps. For a proper solution, see the above code.\r\n\r\nP.S. YAML looks nice and compact, but the syntax may be harder to learn. Fortunately, YAML is superset of JSON, so you can just use JSON to define the component:\r\n\r\n```json\r\n{\r\n \"name\": \"TransformOp\",\r\n \"inputs\": [\r\n {\"name\": \"template_prj\", \"type\": \"String\"},\r\n {\"name\": \"env\", \"type\": \"String\"},\r\n {\"name\": \"project_api_key\", \"type\": \"String\"},\r\n {\"name\": \"data_api_key\", \"type\": \"String\"},\r\n {\"name\": \"model_id\", \"type\": \"String\"}\r\n ],\r\n \"implementation\": {\r\n \"container\": {\r\n \"image\": \"gcr.io/my-org/kubeflow-projects/causal_discovery\",\r\n \"args\": [\r\n {\r\n \"concat\": [\r\n \"component=\",\r\n {\"inputValue\": \"template_prj\"},\r\n \"/transformer\"\r\n ]\r\n },\r\n {\r\n \"concat\": [\r\n \"common=\",\r\n {\"inputValue\": \"env\"}\r\n ]\r\n },\r\n {\r\n \"concat\": [\r\n \"common.template_prj=\",\r\n {\"inputValue\": \"template_prj\"}\r\n ]\r\n },\r\n {\r\n \"concat\": [\r\n \"common.project_api_key=\",\r\n {\"inputValue\": \"project_api_key\"}\r\n ]\r\n },\r\n {\r\n \"concat\": [\r\n \"common.data_api_key=\",\r\n {\"inputValue\": \"data_api_key\"}\r\n ]\r\n },\r\n {\r\n \"concat\": [\r\n \"common.model_id=\",\r\n {\"inputValue\": \"model_id\"}\r\n ]\r\n }\r\n ]\r\n }\r\n }\r\n}\r\n```", "The customer has verified that the `concat` solution works. I've created and issue to improve the `concat` placeholder documentation: https://github.com/kubeflow/pipelines/issues/5305" ]

"2021-03-11T06:13:45"

"2021-03-15T17:59:03"

"2021-03-15T17:57:21"

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[ "/assign @chensun \r\n/cc @Ark-kun ", "Hmm.\r\nInteresting. We had this issue before and @munagekar fixed it by changing the warning type from `DeprecationWarning` to `FutureWarning`. https://github.com/kubeflow/pipelines/pull/4658\r\n\r\nIt looks like that warning is still not show to the users even when we see it in tests.", "This is really weird. Unlike `DeprecationWarning`, `FutureWarning` should always be shown: https://docs.python.org/3/library/warnings.html#default-warning-filter\r\n\r\nI can reproduce the behavior. I also see that when the warning is shown it's only shown once (in a Jupyter session).", "> I also see that when the warning is shown it's only shown once (in a Jupyter session).\r\n\r\nThis is the usual behavior with python warnings. Warning in a loop get called once only.\r\n\r\n> This is really weird. Unlike DeprecationWarning, FutureWarning should always be shown\r\n\r\nhttps://docs.python.org/3/library/warnings.html#default-warning-filter\r\nThis is correct, however this is not a python issue.\r\n\r\nI have investigated this further, the reason the compiler doesn't produce the warning is because the code in the first case does not reach the warning. The attached screenshot explains it further.\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/a12e88d1da57b897a3a25b5c44540fc7d3c9a40e/sdk/python/kfp/dsl/_container_op.py#L1104-L1105\r\n\r\nThe core issue is that `__eq__` in `kfp.dsl._pipeline_param.PipelineParam` is incorrectly returning NamedTuple. `__eq__ `should return either True or False. A named tuple is assumed to be True.\r\nhttps://github.com/kubeflow/pipelines/blob/67afca4938c35e6b8be29e61df3883af11554220/sdk/python/kfp/dsl/_pipeline_param.py#L227-L228\r\n\r\n\r\n\r\n\r\n", "Thank you for clear explanation. We are facing this issue and want to solve it.\r\n\r\nLet me try to fix this issue, I'll create PR.", "I created PR https://github.com/kubeflow/pipelines/pull/5299", "> The core issue is that `__eq__` in `kfp.dsl._pipeline_param.PipelineParam` is incorrectly returning NamedTuple. `__eq__ `should return either True or False. A named tuple is assumed to be True.\r\n\r\nOh my! Thank you for the great investigation." ]

"2021-03-11T05:38:46"

"2021-03-30T09:56:18"

"2021-03-30T09:56:18"

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[ "/assign @Ark-kun @neuromage @chensun ", "There seems to be an error in your function definition. I'm not fully sure what is the behavior you've intended, but you probably should not be using the `InputPath` annotation given your component function code:\r\nJust use `def hydrate_schema(synced_local_path: str`. Or better maybe even call it `synced_uri`, since it seems to be a URI not local path.\r\n\r\nCan you try this solution and tell us whether it has fixed your problem?\r\n\r\nP.S. If you tried to call your component, you'd have noticed that `synced_local_path` does not really receive what you expected. It would have been a real local path (`/tmp/inputs/synced_local/data`) containing whatever data the component received from the upstream (probably a URI). This is what `InputPath` does.\r\n\r\n### Long explanation:\r\n\r\nThe behavior is: When using create_component_from_func/func_to_container_op: When a function parameter uses `InputPath` or `OutputPath` annotation and the parameter name ends with `_path` or `_file`, that part is stripped when generating the input/output name.\r\n\r\nLet me try to explain why this design was chosen.\r\n\r\nWhen you use `create_component_from_func`, there are two separate architecture layers: component layer and python function layer.\r\nOn the pipeline level, the author passes artifacts between components. The pipeline author does not manually pass URIs or local paths. Instead they just connect outputs to inputs.\r\nHowever you function has slightly different interface and gets some data from local files, a concept not existing for the pipeline authors.\r\n`create_component_from_func` generates glue command-line program code to bridge between those layers. Annotations like `InputPath` and `OutputPath` influence the way that bridge is constructed.\r\n\r\n`InputPath` means \"write the passed artifact contents to a local file and give me path to that file instead of the content itself\". When the component receives a \"Dataset\" (big text file in CSV format), you function receives a \"Dataset path\" (a small string with local path). These are very different kinds of objects, so it's natural that the names are different.\r\n\r\nThis difference becomes especially apparent if you consider numbers:\r\nNotice how the function expects a string path, but the component input has type `Integer`\r\nFunction: \r\n```python\r\ndef consume_num(number_path: InputPath(int)):\r\n open(number_path) as f:\r\n number = f.read()\r\n```\r\nComponent:\r\n```yaml\r\ninputs:\r\n- {name: Number, type: Integer}\r\n....\r\nimplementation:\r\n container:\r\n args:\r\n - --number-path, {inputPath: Number}\r\n```\r\nPipeline:\r\n```python\r\ndef my_pipeline():\r\n consume_num_op(number=42)\r\n```\r\n\r\nObserve the flow:\r\n1. The pipeline author passes value `42` to the input `Number` using `number=42`\r\n2. The component specification says that the value for the Number input needs to be written to a local file (`/tmp/outputs/Number/data`)\r\n3. The program receives the path as an argument: `--number-path` `/tmp/outputs/Number/data`\r\n4. The path is parsed from the command-line and passed to the function: `number_path=\"/tmp/outputs/Number/data\"`\r\n5. The function code uses the path to read the number from the file\r\n\r\nIf the `create_component_from_func` did not strip `_path` when naming the inputs, this would look wrong and weird for the pipeline author:\r\n```python\r\ndef my_pipeline():\r\n consume_num_op(number_path=42)\r\n```\r\n`number_path=42` looks wrong since 42 is not a valid path - it's an integer.", "@Ark-kun Thanks for the very comprehensive explanation. \r\n\r\n> The behavior is: When using create_component_from_func/func_to_container_op: When a function parameter uses InputPath or OutputPath annotation and the parameter name ends with _path or _file, that part is stripped when generating the input/output name.\r\n\r\nThis is something I was not aware of. I used `InputPath` since `synced_local_path` is expecting some output data from a previous component. But I was not aware of the auto-stripping mechanism so that the argument name needs to be changed when calling the function from the pipeline.\r\n\r\nThat answers my question. Is there a way this is documented somewhere in the user guide?\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", "This issue has been automatically closed because it has not had recent activity. Please comment \"/reopen\" to reopen it.\n" ]

"2021-03-10T20:32:53"

"2022-04-18T17:27:32"

"2022-04-18T17:27:32"

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[]

[ "For long term 3 sound like the way to go. ", "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" ]

"2021-03-10T16:51:51"

"2022-04-18T17:27:53"

"2022-04-18T17:27:53"

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[ "For aiplatform, here is a sample of test failure log:\r\n\r\n```\r\n\r\nsample-test-92wvs-4170907803: Using endpoint [https://ml.googleapis.com/]\r\nsample-test-92wvs-4170907803: ERROR: (gcloud.ai-platform.predict) HTTP request failed. Response: {\r\nsample-test-92wvs-4170907803: \"error\": {\r\nsample-test-92wvs-4170907803: \"code\": 404,\r\nsample-test-92wvs-4170907803: \"message\": \"Field: name Error: Online prediction is unavailable for this version. Please verify that CreateVersion has completed successfully.\",\r\nsample-test-92wvs-4170907803: \"status\": \"NOT_FOUND\",\r\nsample-test-92wvs-4170907803: \"details\": [\r\nsample-test-92wvs-4170907803: {\r\nsample-test-92wvs-4170907803: \"@type\": \"type.googleapis.com/google.rpc.BadRequest\",\r\nsample-test-92wvs-4170907803: \"fieldViolations\": [\r\nsample-test-92wvs-4170907803: {\r\nsample-test-92wvs-4170907803: \"field\": \"name\",\r\nsample-test-92wvs-4170907803: \"description\": \"Online prediction is unavailable for this version. Please verify that CreateVersion has completed successfully.\"\r\nsample-test-92wvs-4170907803: }\r\nsample-test-92wvs-4170907803: ]\r\nsample-test-92wvs-4170907803: }\r\nsample-test-92wvs-4170907803: ]\r\nsample-test-92wvs-4170907803: }\r\nsample-test-92wvs-4170907803: }\r\nsample-test-92wvs-4170907803: \r\n```\r\n\r\n", "Possible related: https://github.com/kubeflow/pipelines/blob/master/samples/core/ai_platform/ai_platform.ipynb", "it was because of argo pns executor, after reverting back to docker executor the first postsubmit succeeded", "ohhh, you are right. There's another flakiness in aiplatform sample. keep this open", "Regarding `container_build`:\r\n\r\nError message is as followed:\r\n\r\n```\r\n\r\nsample-test-6gw5p-2308294888: KFP API host is 302db4d8b0ea18f8-dot-us-east1.pipelines.googleusercontent.com\r\nsample-test-6gw5p-2308294888: Run the sample tests...\r\nsample-test-6gw5p-2308294888: Traceback (most recent call last):\r\nsample-test-6gw5p-2308294888: File \"/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py\", line 260, in <module>\r\nsample-test-6gw5p-2308294888: main()\r\nsample-test-6gw5p-2308294888: File \"/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py\", line 256, in main\r\nsample-test-6gw5p-2308294888: 'component_test': ComponentTest\r\nsample-test-6gw5p-2308294888: File \"/usr/local/lib/python3.7/dist-packages/fire/core.py\", line 141, in Fire\r\nsample-test-6gw5p-2308294888: component_trace = _Fire(component, args, parsed_flag_args, context, name)\r\nsample-test-6gw5p-2308294888: File \"/usr/local/lib/python3.7/dist-packages/fire/core.py\", line 471, in _Fire\r\nsample-test-6gw5p-2308294888: target=component.__name__)\r\nsample-test-6gw5p-2308294888: File \"/usr/local/lib/python3.7/dist-packages/fire/core.py\", line 681, in _CallAndUpdateTrace\r\nsample-test-6gw5p-2308294888: component = fn(*varargs, **kwargs)\r\nsample-test-6gw5p-2308294888: File \"/python/src/github.com/kubeflow/pipelines/test/sample-test/sample_test_launcher.py\", line 183, in run_test\r\nsample-test-6gw5p-2308294888: nbchecker.check()\r\nsample-test-6gw5p-2308294888: File \"/python/src/github.com/kubeflow/pipelines/test/sample-test/check_notebook_results.py\", line 74, in check\r\nsample-test-6gw5p-2308294888: experiment_id = client.get_experiment(experiment_name=experiment).id\r\nsample-test-6gw5p-2308294888: File \"/usr/local/lib/python3.7/dist-packages/kfp-1.4.1rc1-py3.7.egg/kfp/_client.py\", line 462, in get_experiment\r\nsample-test-6gw5p-2308294888: raise ValueError('No experiment is found with name {}.'.format(experiment_name))\r\nsample-test-6gw5p-2308294888: ValueError: No experiment is found with name container_build-test.\r\n```\r\n\r\nSuspecting it has something to do with backend not able to return the experiment in request, or this experiment doesn't exist.", "Example logs: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1374652399771193344", "[Prow](https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-integration-test/1374652399771193344)\r\n\r\n[Pod](\r\nhttps://00f74ba44b420418175dd31cb3c371cfc9ef2b71ab-apidata.googleusercontent.com/download/storage/v1/b/oss-prow/o/logs%2Fkubeflow-pipeline-postsubmit-integration-test%2F1374652399771193344%2Fartifacts%2Fpods_info%2Fkaniko-7v2gs.txt?jk=AFshE3WgSYrbaJ_tnqAP2uHklKC4QfRLQr2DCqSwJD9nn-3B25X_SpBMabvOAGh5QTEVwpFX7qqxyf1xZdrtLcciykBwxsSebvvHSvYAk8BtaSfFfN6ShsF6nb5CjV6DpyOUDen76mPvGGHR4ov0a1S-OHrB3UySfVJ9ur9XqFJJ6iowcb1yjFtol14JsxgtUnaHcxWIiZ7IKhVQGcx30Lp-YU--1H1efLZXu1eANHH8uwKp1nSQ6XYG8JnnzD-zlbuIuA0r93YmYvZpJXL6xcVeagEPniE4FHGjIC38jOz2zreM2ZZjKyRoGNnwAvWgk93EdrmFXyfw1Qd50ZC9iWjr6_AWl8Oake2pY2m4CYJ_3cVDAwKOeO6f8HC4fFA7Qff_WqBqqNz-F9EipRvcBLZ2yqve-zvb2u5WBNDmzKo-CdPdGFMDBtXuo36CQJnTP8BqqbLCciw6qwZqWRdMokhD705dvTUuTPJ3TM46WB5Cjwt5_5784BVBUyN96nRPgMwydMgOdOmoRwTGyScbjHDi9eFMNPPOXbwTUyAuOgKHuc8pkac9n-D3ya-J6sSjWrWZXoUeBkygOytxn9QVh_VxNSjx-cu0cnyrzlTeroWwr_nPqhjVNk5zE3FTavD1cDxknd_fB3qTgKQP9nRVEH6MrCnf0M13Uqtl46nma3Ql45LfJuZZISFA00N8u904Jr4MnU6ST2QXT8Q2c0dpGrECI5eN7G-M6I0bvT6gH5HZ6KyQkl_1Ys8wBW36e-BIyTHct7o-3X3CqAIy3uWFVi_fdUi2ip8R1Bg-xoPeR8q-QAJR1LN-RzcnJk55jMfyFQLup7ejzncOz0pqJgJ9_RC93UVYx3YrdlqBkTzImjY0axORxMcL7C7uLWdLixS9UfFXaZ94S1SglUdf8PGflzDcWgO22MTpqXcEosmeBCW-rI9PfviEkdvRWKI6pZpbjXHIDTzknCo6Uh1b8NiSnFC5pc7uua3I0zjqCAJq-lf0QN6xrtRJRXvZ0ESyo2dMN5XL0J4SQy1phSKAL4gTqJV8bB4tuMbkHvoSNCivFb33hrd4Mcxw0QgV3_YsSLVN5Bj2X3p5S0U&isca=1)\r\n\r\n[Kaniko logs](\r\nhttps://pantheon.corp.google.com/logs/query;query=resource.type%3D%22k8s_container%22%0Aresource.labels.project_id%3D%22ml-pipeline-test%22%0Aresource.labels.location%3D%22us-east1-b%22%0Aresource.labels.cluster_name%3D%22sample-d9c0196-6848%22%0Aresource.labels.namespace_name%3D%22kubeflow%22%0Aresource.labels.pod_name%3D%22kaniko-7v2gs%22;timeRange=P7D;cursorTimestamp=2021-03-24T10:28:06.449898604Z?project=ml-pipeline-test)\r\n\r\nMaybe it has failed to download the image.\r\n```\r\n\u001b[36mINFO\u001b[0m[0001] Resolved base name gcr.io/deeplearning-platform-release/tf-cpu.1-14 to gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0001] Resolved base name gcr.io/deeplearning-platform-release/tf-cpu.1-14 to gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0001] Downloading base image gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0001] Error while retrieving image from cache: getting file info: stat /cache/sha256:bd464d284317fdd4fedea19b72b6424710ed2a7695ce391f718d74278a3ab97a: no such file or directory\r\n\u001b[36mINFO\u001b[0m[0001] Downloading base image gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0002] Built cross stage deps: map[]\r\n\u001b[36mINFO\u001b[0m[0002] Downloading base image gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0002] Error while retrieving image from cache: getting file info: stat /cache/sha256:bd464d284317fdd4fedea19b72b6424710ed2a7695ce391f718d74278a3ab97a: no such file or directory\r\n\u001b[36mINFO\u001b[0m[0002] Downloading base image gcr.io/deeplearning-platform-release/tf-cpu.1-14\r\n\u001b[36mINFO\u001b[0m[0002] Using files from context: [/kaniko/buildcontext/requirements.txt]\r\n\u001b[36mINFO\u001b[0m[0002] Checking for cached layer gcr.io/ml-pipeline-test/7841878234294039750/kfp_container/cache:ba7a7556fbb481e0dac58fa95852cd1657d89697c8e490d6d4a4299955fb85a6...\r\n\u001b[36mINFO\u001b[0m[0002] No cached layer found for cmd RUN python3 -m pip install -r requirements.txt\r\n\u001b[36mINFO\u001b[0m[0002] Unpacking rootfs as cmd RUN python3 -m pip install -r requirements.txt requires it.\r\n\u001b[36mINFO\u001b[0m[0275] Taking snapshot of full filesystem...\r\n```\r\n\r\nPerhaps we should use a smaller image for that sample/test.", "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", "Obsolete" ]

"2021-03-10T00:58:54"

"2021-08-22T07:00:13"

"2021-08-22T07:00:13"

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[ "Good catch.\r\nSome time ago the return value of the component function was not passed completely correctly. So the components had to whap output in a tuple when outputting a single list. The problem was fixed in the SDK, but the test was not updated." ]

"2021-03-09T21:47:37"

"2021-03-11T22:06:25"

"2021-03-11T22:06:25"

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[]

[ "I'm also getting this error in the main landing page:\r\n```json\r\n{error: \"Invalid input error: ListRuns must filter by resource reference in multi-user mode.\",…}\r\ncode: 3\r\ndetails: [{@type: \"type.googleapis.com/api.Error\",…}]\r\n0: {@type: \"type.googleapis.com/api.Error\",…}\r\n@type: \"type.googleapis.com/api.Error\"\r\nerror_details: \"Invalid input error: ListRuns must filter by resource reference in multi-user mode.\"\r\nerror_message: \"ListRuns must filter by resource reference in multi-user mode.\"\r\nerror: \"Invalid input error: ListRuns must filter by resource reference in multi-user mode.\"\r\nmessage: \"Invalid input error: ListRuns must filter by resource reference in multi-user mode.\"\r\n```\r\n\r\n\r\nI think it's related to the same underlying root cause. ", "we can actually see that the userid header is propagated to the `ml-pipeline` service:\r\n\r\n\r\nany advice on what might be failing?", "looking more at the issue and the code it looks like the code where the issue arise is here:\r\nhttps://github.com/kubeflow/pipelines/blob/5445ce82c7cd79bd517565750f72cf6a893c8cee/backend/src/apiserver/server/run_server.go#L179\r\n\r\nwhere `ReferenceKey` must be null. \r\nwhy might it be happening?", "@raffaelespazzoli I'm not familiar with k8s_istio config, but can you confirm whether the following are configured properly for your env?\r\n\r\nEnv var config on ml-pipeline-ui deployment:\r\nhttps://github.com/kubeflow/pipelines/blob/4c51b576f577e337049599cb85b098006664265f/frontend/server/configs.ts#L106-L113\r\n\r\nEnv var config on ml-pipeline deployment:\r\nhttps://github.com/kubeflow/pipelines/blob/d9c019641ef9ebd78db60cdb78ea29b0d9933008/backend/src/apiserver/common/config.go#L31-L32", "@Bobgy both variable are set in both services via a shared configmap. I can prove that the header is propagated looking at the traces with Jaeger.", "@raffaelespazzoli sorry I didn't see the notification, maybe you can try Kubeflow 1.3 soon.\r\n\r\nNow they have verified KFP multi-user mode working properly for installation on K8s cluster only.\r\nhttps://github.com/kubeflow/manifests", "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" ]

"2021-03-09T18:29:51"

"2022-03-03T06:05:33"

"2022-03-03T06:05:33"

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[]

"2021-03-09T00:50:22"

"2021-03-09T19:56:23"

"2021-03-09T19:56:23"

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[ "/assign @elikatsis \r\nCan you help with this issue?", "Kubeflow cache's steps, so given the same inputs, it skips the step and instead fetches outputs from minio. \r\nVolume op has no outputs in Minio so ideally VolumeOp should not be cached. \r\n\r\nIn this case you would need to change the name of the PVC. \r\n\r\nYou can also refer to https://github.com/kubeflow/pipelines/issues/5055#issue-796655777. ", "Does the following work ?\r\n```python\r\n vop = kfp.dsl.VolumeOp(\r\n name=\"volume_creation\",\r\n resource_name=f\"{{{{workflow.name}}}}-sharedpvc\",\r\n size=\"5Gi\",\r\n modes=[\"RWO\"]\r\n )\r\n```\r\n\r\nThis will hopefully change the input to the ResourceOp and hence prevent caching.\r\n \r\n Update: This workound doesn't seem to work with Kubeflow 1.3/latest kfp version. I think this workaround worked with Kubeflow 1.2. ", "> \r\n> \r\n> Does the following work ?\r\n> \r\n> ```python\r\n> vop = kfp.dsl.VolumeOp(\r\n> name=\"volume_creation\",\r\n> resource_name=f\"{{{{workflow.name}}}}-sharedpvc\",\r\n> size=\"5Gi\",\r\n> modes=[\"RWO\"]\r\n> )\r\n> ```\r\n> \r\n> This will hopefully change the input to the ResourceOp and hence prevent caching.\r\n\r\nYes, the problem is fixed after I disabled the cache. Thanks for the help.", "@Bobgy sorry I had totally missed this.\r\n\r\nI think the problem here is that\r\n1. the mechanism is caching steps that it shouldn't do so\r\n2. there is no \"user\" selection on whether to cache some specific step or not, only global API server configuration. The API server overrides any configuration: https://github.com/kubeflow/pipelines/blob/601b104c0beba8049dfee493d0b07c0aea390b78/backend/src/apiserver/resource/resource_manager.go#L377", "I'll reopen this issue as it needs some fix apart from globally disabling the cache\r\n\r\n/reopen", "@elikatsis: Reopened this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5257#issuecomment-852327111):\n\n>I'll reopen this issue as it needs some fix apart from globally disabling the cache\r\n>\r\n>/reopen\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>", "Can we rename this issue? The typo makes it hard to find.", "> Does the following work ?\r\n> \r\n> ```python\r\n> vop = kfp.dsl.VolumeOp(\r\n> name=\"volume_creation\",\r\n> resource_name=f\"{{{{workflow.name}}}}-sharedpvc\",\r\n> size=\"5Gi\",\r\n> modes=[\"RWO\"]\r\n> )\r\n> ```\r\n> \r\n> This will hopefully change the input to the ResourceOp and hence prevent caching.\r\n\r\nHi, for some reason, this workaround does not work for me. KF version: 1.6.0, Kubeflow SDK 1.6.3. I used the same pipeline from the issue description. My pcv was created only once.\r\n\r\nThank you", "Facing the same issue. `VolumeOp` fails to create PVC with log message `This step output is taken from cache.`\r\n\r\nTried to change `resource_name`, but that workaround didn't work (if it worked before).", "I think KFP v1 caching should not cache volume op / resource op, because the side effect is intended.\r\n/cc @Ark-kun \r\n\r\nWelcome contributions to fix this.\r\nCaching webhook: https://github.com/kubeflow/pipelines/tree/master/backend/src/cache.\r\n\r\nBesides that, caching for KFP v2 compatible mode should no longer cache volume ops. You can also consider trying it out too when it's released and documented for your env. (currently documented for KFP standalone, but not full Kubeflow.)", "Updated workaround for getting around VolumeOp Caching. This seems to work with kfp version 1.7.2. \r\n\r\nREF: https://github.com/kubeflow/pipelines/issues/4857#issuecomment-740279537\r\n\r\n```python\r\n test_vop = kfp.dsl.VolumeOp(\r\n name=\"volume\",\r\n resource_name=\"pvc-name\",\r\n modes=['RWO'],\r\n storage_class=\"standard\",\r\n size=\"10Gi\"\r\n ).add_pod_annotation(name=\"pipelines.kubeflow.org/max_cache_staleness\", value=\"P0D\")\r\n```", "Not sure this is the correct place to bring this up. And I am not familiar with v2 component configuration yet but it looks like the mutation webhook in cache-server backend is looking for key pipelines.kubeflow.org/enable_caching while the python sdk is creating key pipelines.kubeflow.org/cache_enabled when using set_cache_enabled.\r\n\r\nSee following files:\r\nhttps://github.com/kubeflow/pipelines/blob/master/backend/src/cache/server/mutation.go#L36\r\nhttps://github.com/kubeflow/pipelines/blob/dec03067ca1f89f1ca23c7397830d60201448fa6/sdk/python/kfp/compiler/_op_to_template.py#L186\r\n\r\nAlthough it was mentioned to use .add_pod_annotation(name=\"pipelines.kubeflow.org/max_cache_staleness\", value=\"P0D\") which looks like it should work from the code in mutation.go. If that's the case will the base_op function set_caching_enabled be deprecated in the future. \r\n\r\nThe change on cache-server to allow the function of set_caching_enabled to work will be easy. I made the changes and tested them on my forked version of this repo. I can make a PR if the set_caching_enabled is not going to be deprecated. ", "> \r\n> \r\n> Updated workaround for getting around VolumeOp Caching. This seems to work with kfp version 1.7.2.\r\n> \r\n> REF: [#4857 (comment)](https://github.com/kubeflow/pipelines/issues/4857#issuecomment-740279537)\r\n> \r\n> ```python\r\n> test_vop = kfp.dsl.VolumeOp(\r\n> name=\"volume\",\r\n> resource_name=\"pvc-name\",\r\n> modes=['RWO'],\r\n> storage_class=\"standard\",\r\n> size=\"10Gi\"\r\n> ).add_pod_annotation(name=\"pipelines.kubeflow.org/max_cache_staleness\", value=\"P0D\")\r\n> ```\r\n\r\nThank you very much, i was experiencing the same issue", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-08T08:24:43"

"2022-03-02T18:05:18"

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[ "/cc @chensun \n\nRecently, KFP sdk has been released in separate schedule than backend. There hasn't been enough communication about it.\n\nKfp-server-api 1.4.0 missing was a mistake, but I no longer see the need to release it when 1.4.1 is out.", "Thanks for confirming this. This is something I wanted to clear up for the new Notebook images. Are you expecting to release new versions before the release still? ", "@chensun do you have a suggestion for David?", "I'm now running into a dependency issue when installing the latest `kfserving` package (0.5.1) and `kfp` 1.4.0: `kfp 1.4.0 requires kubernetes<12.0.0,>=8.0.0, but you have kubernetes 12.0.1 which is incompatible`\r\n\r\nIt would be nice to have this solved so the latest packages can be installed in the notebook images. ", "/assign @chensun ", "The kubernetes version issue is fixed by https://github.com/kubeflow/pipelines/pull/5349.\r\n\r\nAlso I think the question is answered.", "@Bobgy Will this require a push to PyPi?", "@DavidSpek yes, it will be in the next release", "Thanks for confirming!", "@Bobgy It seems like https://github.com/kubeflow/pipelines/pull/5349 is not included in kfp 1.4.1rc1. Is there a timeline for an updated release of the kfp package?", "@neuromage to comment on this", "@neuromage I could you chime in on this? It would be nice to have an updated version of the kfp SDK on PyPI so the notebook images can be updated. ", "We're planning an SDK release this week. Probably have an RC version out mid-week at least.\r\n\r\n/cc @chensun " ]

"2021-03-06T15:15:10"

"2021-05-10T00:50:48"

"2021-03-24T08:29:54"

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"2021-03-05T17:28:43"

"2021-03-08T15:17:59"

"2021-03-08T15:17:59"

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[ "/assign @chensun @Ark-kun ", "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", "Any update on this?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-05T06:24:52"

"2022-03-02T10:06:30"

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[ "@dtzar @eedorenko @sudivate", "In azure, the problem is that istio pods are not being injected to the pods in kubeflow. \r\n\r\nAzure enforces that the `MutatingWebhookConfiguration - istio-sidecar-injector` in kubeflow is being automatically edited by AKS to add the following match expression in the namespaceSelector:\r\n\r\n```\r\nmatchExpressions:\r\n- key: control-plane\r\n operator: DoesNotExist\r\n```\r\n\r\nSo the MutatingWebhookConfiguration looks like this:\r\n\r\n```\r\nnamespaceSelector:\r\n\tmatchExpressions:\r\n - key: control-plane\r\n operator: DoesNotExist\r\n\tmatchLabels:\r\n \t\tistio-injection: enabled\r\n```\r\n\r\nThis will exclude the kubeflow namespace, since the namespace have the label:\r\n\r\n```\r\nlabels:\r\n control-plane: kubeflow\r\n```\r\n\r\nTo solve this issue, you need to deactivate the admission enforcer from aks, using the following annotation in the MutatingWebhookConfiguration:\r\n\r\n```\r\napiVersion: admissionregistration.k8s.io/v1\r\nkind: MutatingWebhookConfiguration\r\nmetadata:\r\n annotations:\r\n admissions.enforcer/disabled: \"true\"\r\n name: istio-sidecar-injector\r\n```\r\n\r\nI believe, that the following issues are all related to this, so you don't need to disable istio (changing the DestinationRules from ISTIO_MUTUAL to DISABLE):\r\n\r\nhttps://github.com/kubeflow/pipelines/issues/4469\r\nhttps://github.com/kubeflow/kubeflow/issues/5271\r\nhttps://github.com/kubeflow/kubeflow/issues/5277\r\nhttps://github.com/Azure/AKS/issues/1771\r\n\r\n+info:\r\nhttps://docs.microsoft.com/en-us/azure/aks/faq#can-i-use-admission-controller-webhooks-on-aks\r\n", "Yes, the point of my issue was suggesting we can update docs by explaining the need for adding `admissions.enforcer/disabled: \"true\"`, or possibly add it in the azure overlays.", "@andre-lx I updated the istio-sidecar-injector MutatingWebhookConfiguration by adding the annotation as you described but istio sidecars were not injected with the pipeline components. I tried deleting the pods, restarting the deployments and even deleted and applied the pipeline component. Adding a label instead of an annotation also seemed to have no effect.\r\n\r\nThen I edited the istio-sidecar-injector and removed the matchExpression added by AKS in the namespaceSelector and that solved the issue.\r\n\r\nDid you recommend to add the annotation / label in the istio-sidecar-injector MutatingWebhookConfiguration only or is there more to it?\r\n\r\nI am installing Kubeflow 1.3 by installing individual components as described [here](https://github.com/kubeflow/manifests) \r\n", "Hi @danishsamad. Not sure, actually. \r\n\r\nIn my case, deleting the match expression didn't solve the issue, since AKS automatically add that match expression. \r\n\r\nI'm not sure if I did the manual delete or if the annotation deleted it.\r\n\r\nMaybe I added the annotation and eliminated the match expression manually.\r\n\r\nAnyway, in new clusters, the annotation worked as expected -> the match expression is not added and the istio pods are injected to the pipelines pods. ", "@andre-lx Thanks for the fix. I'll be looking at adding this to the Istio installation used by [ArgoFlow-Azure](https://github.com/argoflow/argoflow-azure), which is still in the very early stages but will hopefully have more integrations with Azure soon. Any help to add Azure integrations to this deployment are greatly appreciated.", "I just ran into the same problem installing manifests 1.3.1 using kustomize.\r\nAdding the label `admissions.enforcer/disabled: \"true\"` at runtime (even after restrting all cluster pods) did not work for me.\r\nAdding the annotation before the installation, however, **did work** (i.e. modfying or overlaying the MutatingWebhookConfiguration in common/istio-1-9/istio-install/base/install.yaml). No other modifications needed.\r\nThanks!", "Hi, k8s newbie here, do you mind providing more details on how to solve this issue @TobiasGoerke ?", "Hello @Roman-Ka, there's a file in your manifests folder at `common/istio-1-9/istio-install/base/install.yaml` that you will somehow need to modify. The easiest (but least maintainable way) is to just edit the file directly. \r\nThere a resource hidden in there that looks like this:\r\n\r\n```\r\napiVersion: admissionregistration.k8s.io/v1beta1\r\nkind: MutatingWebhookConfiguration\r\nmetadata:\r\n name: istio-sidecar-injector\r\n labels:\r\n istio.io/rev: default\r\n install.operator.istio.io/owning-resource: unknown\r\n operator.istio.io/component: \"Pilot\"\r\n app: sidecar-injector\r\n release: istio\r\n ....\r\n```\r\n\r\nHere you will need to add the annotation so that it looks like this:\r\n```\r\napiVersion: admissionregistration.k8s.io/v1beta1\r\nkind: MutatingWebhookConfiguration\r\nmetadata:\r\n name: istio-sidecar-injector\r\n annotations: \r\n admissions.enforcer/disabled: \"true\"\r\n labels:\r\n istio.io/rev: default\r\n install.operator.istio.io/owning-resource: unknown\r\n operator.istio.io/component: \"Pilot\"\r\n app: sidecar-injector\r\n release: istio\r\n ....\r\n```\r\n\r\nThen install Kubeflow using that folder and you should be good.\r\n\r\nHope that helps!\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-03-05T00:30:56"

"2022-03-11T06:04:49"

"2022-03-11T06:04:49"

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"2021-03-04T19:00:01"

"2021-03-04T20:43:48"

"2021-03-04T20:43:48"

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[ "@vishalsmb I think https://github.com/kubeflow/pipelines/issues/4327#issuecomment-687255001 is probably a good example" ]

"2021-03-04T07:01:24"

"2021-03-30T02:42:53"

"2021-03-30T02:42:53"

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[ "The failure: https://oss-prow.knative.dev/view/gs/oss-prow/logs/kubeflow-pipeline-postsubmit-mkp-e2e-test/1365452157549023232#1:build-log.txt%3A7816\r\n\r\nStep #1 - \"verify\": 37s Warning FailedScheduling pod/ml-pipeline-55bbc45946-m7s66 0/3 nodes are available: 2 Insufficient memory, 3 Insufficient cpu.", "It seems that the added resource request make it impossible to schedule ml-pipeline pod in the cluster.", "The default cluster for marketplace has 3 nodes each with 2 CPUs and 3GB memory allocatable.\r\nSo the new requirements set in #5158 seems too large, it marked 4GB memory for ml-pipeline server. I assume it was prepared more for large scale production env.\r\n\r\nWe need to reduce the request to fit into the default cluster.", "/cc @NikeNano ", "/assign\r\n\r\nI'll fix this", "Another issue to address is that, mkp test should be triggered on presubmit if a PR touches MKP manifest. Let me add the auto trigger.", "Some reading on requests & limits: https://cloud.google.com/blog/products/containers-kubernetes/kubernetes-best-practices-resource-requests-and-limits", "Some investigation into other OSS projects, argo doesn't provide default for requests/limits for the most part: https://github.com/argoproj/argo-workflows/tree/master/manifests.\r\nThere's a doc page to introduce how to improve cost optimization: https://argoproj.github.io/argo-workflows/cost-optimisation/.", "I think we'll need an operator manual documentation to tell people how to adjust their resource requests, but maybe as a next step.\r\nAdding some requests as default only make it better than before -- without setting it, it's assumed to be 0. Also, when requested number is reached, the Pod is not killed like a limit.", "> I think we'll need an operator manual documentation to tell people how to adjust their resource requests, but maybe as a next step.\r\n> \r\n\r\nI think this sounds good, also if we could provide some ball park figures. I guess however if we set them to high we will request more resources than actually used for most people. ", "Caused by https://github.com/kubeflow/pipelines/issues/5148" ]

"2021-03-04T02:50:29"

"2021-04-01T02:07:40"

"2021-03-04T06:03:41"

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[ "Hi @andre-lx, is https://github.com/kubeflow/pipelines/issues/3938 the corresponding config?", "I think: \r\n```\r\n.set_ttl_seconds_after_finished(seconds: int)\r\n```\r\nis what you are looking for if you like to remove the underlying pipeline after if is finished for a specific pipeline, reference posted by Bobgy above will be for the all pipeline in the cluster. Docs can be found [here](https://kubeflow-pipelines.readthedocs.io/en/latest/_modules/kfp/dsl/_pipeline.html#PipelineConf.set_ttl_seconds_after_finished)", "Hi. \r\n\r\nI had already tested this, but I tried again with both gpu and non gpu pods, with this config:\r\n\r\n```python\r\n@dsl.pipeline(\r\n name=\"test\",\r\n description=\"test\",\r\n)\r\n\r\ndef some_func():\r\n some_task = kfp_generator().set_gpu_limit(1, 'nvidia')\r\n\r\n dsl.get_pipeline_conf().set_ttl_seconds_after_finished(20)\r\n```\r\n\r\nBut nothing happen after the 20 seconds. \r\n\r\nI am missing something? \r\n\r\nThanks @Bobgy and @NikeNano ", "I tested the following pipeline: \r\n\r\n```python\r\nimport kfp\r\nfrom kfp import dsl\r\n\r\n\r\ndef random_num_op(low, high):\r\n \"\"\"Generate a random number between low and high.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Generate random number',\r\n image='python:alpine3.6',\r\n command=['sh', '-c'],\r\n arguments=['python -c \"import random; print(random.randint($0, $1))\" | tee $2', str(low), str(high), '/tmp/output'],\r\n file_outputs={'output': '/tmp/output'}\r\n )\r\n\r\n\r\ndef flip_coin_op():\r\n \"\"\"Flip a coin and output heads or tails randomly.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Flip coin',\r\n image='python:alpine3.6',\r\n command=['sh', '-c'],\r\n arguments=['python -c \"import random; result = \\'heads\\' if random.randint(0,1) == 0 '\r\n 'else \\'tails\\'; print(result)\" | tee /tmp/output'],\r\n file_outputs={'output': '/tmp/output'}\r\n )\r\n\r\n\r\ndef print_op(msg):\r\n \"\"\"Print a message.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Print',\r\n image='alpine:3.6',\r\n command=['echo', msg],\r\n )\r\n \r\n\r\n@dsl.pipeline(\r\n name='Conditional execution pipeline',\r\n description='Shows how to use dsl.Condition().'\r\n)\r\ndef flipcoin_pipeline():\r\n flip = flip_coin_op()\r\n with dsl.Condition(flip.output == 'heads'):\r\n random_num_head = random_num_op(0, 9)\r\n with dsl.Condition(random_num_head.output > 5):\r\n print_op('heads and %s > 5!' % random_num_head.output)\r\n with dsl.Condition(random_num_head.output <= 5):\r\n print_op('heads and %s <= 5!' % random_num_head.output)\r\n\r\n with dsl.Condition(flip.output == 'tails'):\r\n random_num_tail = random_num_op(10, 19)\r\n with dsl.Condition(random_num_tail.output > 15):\r\n print_op('tails and %s > 15!' % random_num_tail.output)\r\n with dsl.Condition(random_num_tail.output <= 15):\r\n print_op('tails and %s <= 15!' % random_num_tail.output)\r\n dsl.get_pipeline_conf().set_ttl_seconds_after_finished(20)\r\n\r\n```\r\n\r\nWatching the kubeflow namespace for the pods: \r\n\r\n```\r\nwatch kubectl get pods -n kubeflow\r\n```\r\n\r\nI could confirm that the pods where deleted ~20 seconds from completion. Also checked removing the line: \r\n\r\n```\r\n\r\ndsl.get_pipeline_conf().set_ttl_seconds_after_finished(20)\r\n```\r\nwhich resulted in that the pods where not deleted. \r\n\r\nCould you @andre-lx share your version of `kubeflow pipelines` and a full example where it is not working for you so I can try to reproduce it? ", "Hi @NikeNano. Thanks for the full example. I also tried with that example, and it didn't work. The only thing I added is the part of compiling and running the pipeline. \r\n\r\n```python\r\npipeline_func = flipcoin_pipeline\r\npipeline_filename = pipeline_func.__name__ + \".pipeline.tar.gz\"\r\n\r\nimport kfp.compiler as comp\r\n\r\ncomp.Compiler().compile(pipeline_func, pipeline_filename)\r\n\r\nclient = kfp.Client()\r\nmy_experiment = client.create_experiment(name='demo')\r\nmy_run = client.run_pipeline(my_experiment.id, pipeline_func.__name__, \r\n pipeline_filename)\r\n```\r\n\r\nAfter that, the pipeline run successful:\r\n\r\n\r\n\r\nBut the pods didn't get deleted:\r\n\r\n```cli\r\nkubectl get pods -n xx\r\nNAME READY STATUS RESTARTS AGE\r\nconditional-execution-pipeline-bh8ln-1858453776 0/2 Completed 0 2m48s\r\nconditional-execution-pipeline-bh8ln-2630484688 0/2 Completed 0 2m44s\r\nconditional-execution-pipeline-bh8ln-3054820269 0/2 Completed 0 2m51s\r\n```\r\n\r\nI can also see the \"flag\" in the workflow yaml:\r\n\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n ...\r\nspec:\r\n ...\r\n ttlSecondsAfterFinished: 20\r\n\r\n```\r\n\r\nI'm using the following versions:\r\n\r\n```\r\npip freeze | grep kfp\r\nkfp==1.4.0\r\nkfp-pipeline-spec==0.1.6\r\n```\r\n\r\nAnd:\r\n\r\n```\r\nimages:\r\n- name: gcr.io/ml-pipeline/frontend\r\n newTag: 1.3.0\r\n- name: gcr.io/ml-pipeline/api-server\r\n newTag: 1.3.0\r\n- name: gcr.io/ml-pipeline/persistenceagent\r\n newTag: 1.3.0\r\n- name: gcr.io/ml-pipeline/scheduledworkflow\r\n newTag: 1.3.0\r\n- name: gcr.io/ml-pipeline/viewer-crd-controller\r\n newTag: 1.3.0\r\n- name: gcr.io/ml-pipeline/visualization-server\r\n newTag: 1.3.0\r\n```", "I can not recreate this issues @andre-lx, I am running Kubeflow pipelines 1.3 as well but get the pods get killed. In between different attempts it take some time(minutes extra when I try it out). \r\n\r\nComplete example:\r\n``` \r\nimport kfp\r\nfrom kfp import dsl\r\n\r\n\r\ndef random_num_op(low, high):\r\n \"\"\"Generate a random number between low and high.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Generate random number',\r\n image='python:alpine3.6',\r\n command=['sh', '-c'],\r\n arguments=['python -c \"import random; print(random.randint($0, $1))\" | tee $2', str(low), str(high), '/tmp/output'],\r\n file_outputs={'output': '/tmp/output'}\r\n )\r\n\r\n\r\ndef flip_coin_op():\r\n \"\"\"Flip a coin and output heads or tails randomly.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Flip coin',\r\n image='python:alpine3.6',\r\n command=['sh', '-c'],\r\n arguments=['python -c \"import random; result = \\'heads\\' if random.randint(0,1) == 0 '\r\n 'else \\'tails\\'; print(result)\" | tee /tmp/output'],\r\n file_outputs={'output': '/tmp/output'}\r\n )\r\n\r\n\r\ndef print_op(msg):\r\n \"\"\"Print a message.\"\"\"\r\n return dsl.ContainerOp(\r\n name='Print',\r\n image='alpine:3.6',\r\n command=['echo', msg],\r\n )\r\n \r\n\r\n@dsl.pipeline(\r\n name='Conditional execution pipeline',\r\n description='Shows how to use dsl.Condition().'\r\n)\r\ndef flipcoin_pipeline():\r\n flip = flip_coin_op()\r\n with dsl.Condition(flip.output == 'heads'):\r\n random_num_head = random_num_op(0, 9)\r\n with dsl.Condition(random_num_head.output > 5):\r\n print_op('heads and %s > 5!' % random_num_head.output)\r\n with dsl.Condition(random_num_head.output <= 5):\r\n print_op('heads and %s <= 5!' % random_num_head.output)\r\n\r\n with dsl.Condition(flip.output == 'tails'):\r\n random_num_tail = random_num_op(10, 19)\r\n with dsl.Condition(random_num_tail.output > 15):\r\n print_op('tails and %s > 15!' % random_num_tail.output)\r\n with dsl.Condition(random_num_tail.output <= 15):\r\n print_op('tails and %s <= 15!' % random_num_tail.output)\r\n dsl.get_pipeline_conf().set_ttl_seconds_after_finished(20)\r\n```\r\n\r\n```\r\npipeline_func = flipcoin_pipeline\r\npipeline_filename = pipeline_func.__name__ + \".pipeline.tar.gz\"\r\n\r\nimport kfp.compiler as comp\r\n\r\ncomp.Compiler().compile(flipcoin_pipeline, pipeline_filename)\r\n\r\nclient = kfp.Client(host=\"http://localhost:8080/\")\r\nmy_experiment = client.create_experiment(name='demo')\r\nmy_run = client.run_pipeline(my_experiment.id, pipeline_func.__name__, \r\n pipeline_filename)\r\n```\r\n\r\nI run this on a minkube cluster, using the following set up with commit c484cfa46cfae1e9d11b5d00e0799b8e52c15e33, instructions can be found here; https://github.com/kubeflow/pipelines/tree/1.3.0/manifests/kustomize#option-1-install-it-to-any-k8s-cluster", "Thanks for the quick response. \r\n\r\nI'm doing a few debug around the workflow and the pipelines code. And I notice a few things, that can maybe help solving my issue. \r\n\r\nI also downgrade the kfp sdk to version 1.3.0 to match all the resources:\r\n\r\n```\r\npip freeze | grep kfp\r\nkfp==1.3.0\r\nkfp-pipeline-spec==0.1.7\r\n```\r\n\r\nChanging the default value in the persistenceagent deployment, it works well, the pods are deleted after the 20 seconds for example:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/19830416e77e2e9327e5865dc54da088cc73f55b/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml#L24-L25\r\n\r\nThen, I set again the env variable to the default of 86400.\r\n\r\n\r\nI checked the logs, and I'm getting the following logs for the example you provided:\r\n\r\n```\r\nlevel=info msg=\"Skip syncing Workflow (conditional-execution-pipeline-mkr59): workflow marked as persisted.\"\r\nlevel=info msg=\"Success while syncing resource (xxxx/conditional-execution-pipeline-mkr59)\"\r\n```\r\n\r\nAfter checking the code, I saw that the logs are printed by the function:\r\n\r\nhttps://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/backend/src/agent/persistence/worker/workflow_saver.go#L63-L68\r\n\r\nAnalysing the workflow yaml, I can see that the label, the ttl and the finish at exists:\r\n\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n annotations:\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.3.0\r\n pipelines.kubeflow.org/pipeline_compilation_time: ...\r\n pipelines.kubeflow.org/pipeline_spec: '...'\r\n labels:\r\n pipeline/persistedFinalState: \"true\"\r\n pipeline/runid: e8d0c54a-57fe-44d2-a242-8880e170ac92\r\n pipelines.kubeflow.org/kfp_sdk_version: 1.3.0\r\n workflows.argoproj.io/completed: \"true\"\r\n workflows.argoproj.io/phase: Succeeded\r\n name: conditional-execution-pipeline-mkr59\r\n namespace: xxxx\r\nspec:\r\n arguments: {}\r\n entrypoint: conditional-execution-pipeline\r\n serviceAccountName: default-editor\r\n templates:\r\n - dag:\r\n ...\r\n - dag:\r\n .\r\n .\r\n .\r\n - container:\r\n ...\r\n - container:\r\n .\r\n .\r\n .\r\n ttlSecondsAfterWorkflowFinished: 20\r\nstatus:\r\n finishedAt: \"2021-03-20T16:45:04Z\"\r\n nodes:\r\n ...\r\n phase: Succeeded\r\n startedAt: \"2021-03-20T16:44:54Z\"\r\n\r\n```\r\n\r\nSo, looking to the function, assuming for example that the run finished at 00h:10m and the time now, is 00h:12m, 12-10=2 minutes = 120 seconds, and 120 seconds are > than the 20 seconds defined for the ttl. So, why are this log being printed? \r\n\r\nI looks like that the ttl is not being passed, or is overrided by the default value. \r\n\r\n**Additional notes:**\r\n\r\nI also made the same tests without the env variable set in the deployment.\r\n\r\nI also try it out with resources version 1.4.1 and kfp 1.4.0:\r\n\r\n```\r\npip freeze | grep kfp\r\nkfp==1.4.0\r\nkfp-pipeline-spec==0.1.7\r\n```\r\n\r\n```yaml\r\nName: conditional-execution-pipeline-xsbfw\r\nNamespace: xxxx\r\nLabels: pipeline/persistedFinalState=true\r\n pipeline/runid=1bfdfa01-31c2-4fde-8892-0c78193b7b3e\r\n pipelines.kubeflow.org/kfp_sdk_version=1.4.0\r\n workflows.argoproj.io/completed=true\r\n workflows.argoproj.io/phase=Succeeded\r\nAnnotations: pipelines.kubeflow.org/kfp_sdk_version: 1.4.0\r\n pipelines.kubeflow.org/pipeline_compilation_time: ...\r\n pipelines.kubeflow.org/pipeline_spec: ...\r\n pipelines.kubeflow.org/run_name: flipcoin_pipeline\r\nAPI Version: argoproj.io/v1alpha1\r\nKind: Workflow\r\nMetadata:\r\n Creation Timestamp: 2021-03-20T18:50:26Z\r\n Generate Name: conditional-execution-pipeline-\r\nSpec:\r\n Arguments:\r\n Entrypoint: conditional-execution-pipeline\r\n Service Account Name: default-editor\r\n Templates:\r\n Dag:\r\n .\r\n .\r\n .\r\n Container:\r\n .\r\n .\r\n .\r\n Ttl Seconds After Finished: 20\r\nStatus:\r\n Finished At: 2021-03-20T18:50:34Z\r\n Nodes:\r\n ...\r\n Phase: Succeeded\r\n Started At: 2021-03-20T18:50:26Z\r\n````\r\n\r\nNo changes at all. \r\n\r\nThanks ", "I will give it a look and come back. Is this something you know more about @Bobgy? ", "Quick first notes, there are a two types of garbage collection at play here, the one `argo` does and the one tha persistence agent does. I am not sure though why it seems for you like the one related to the persistence agent `TTL_SECONDS_AFTER_WORKFLOW_FINISH ` affects the one set for argo: `ttlSecondsAfterWorkflowFinished` in this case I think the shorter one should decide when the workflow gets deleted but might be missing something. ", "I'd suggest to use `TTL_SECONDS_AFTER_WORKFLOW_FINISH`, and avoid using the argo one, because `TTL_SECONDS_AFTER_WORKFLOW_FINISH` will only GC the workflow after persistence agent has backed it up in KFP mysql DB. If you use the argo parameter and the workflow was not backed up before it was GCed, the record would be lost forever.\r\n\r\n> level=info msg=\"Skip syncing Workflow (conditional-execution-pipeline-mkr59): workflow marked as persisted.\"\r\nlevel=info msg=\"Success while syncing resource (xxxx/conditional-execution-pipeline-mkr59)\"\r\n\r\nThe logs are expected, because persistence agent loops over these workflows periodically, it won't take any actions until the TTL has reached.\r\n", "The conversation has been pretty long, are there any remaining questions not addressed?", "Found it!\r\n\r\nAfter checking the `workflow-controller` logs, I found the error:\r\n\r\n```\r\nE0324 14:29:49.195776 1 ttlcontroller.go:124] error deleting 'namespace_name/workflow_name': workflows.argoproj.io \"workflow_name\" is forbidden: User \"system:serviceaccount:kubeflow:argo\" cannot delete resource \"workflows\" in API group \"argoproj.io\" in the namespace \"namespace_name\"\r\n```\r\n\r\nhttps://github.com/kubeflow/manifests/blob/3e08dc102059def5a0b0d04560c7d119959bf506/contrib/argo/base/cluster-role.yaml#L36-L46\r\n\r\nSo, after add the missing verb, everything worked out with the `set_ttl_seconds_after_finished` config:\r\n\r\n\r\n```yaml \r\napiVersion: rbac.authorization.k8s.io/v1beta1\r\nkind: ClusterRole\r\nmetadata:\r\n labels:\r\n app: argo\r\n name: argo\r\nrules:\r\n- apiGroups:\r\n...\r\n- apiGroups:\r\n - argoproj.io\r\n resources:\r\n - workflows\r\n - workflows/finalizers\r\n verbs:\r\n - get\r\n - list\r\n - watch\r\n - update\r\n - patch\r\n - delete\r\n- apiGroups:\r\n...\r\n```\r\n\r\nThanks to both! \r\n" ]

"2021-03-03T12:02:46"

"2021-08-11T19:26:12"

"2021-03-24T14:41:52"

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[ "/assign", "I found that there's now a maintained tool to extract licenses from go libraries: https://github.com/google/go-licenses.\r\n\r\nWe can use it instead of our home-made tool.\r\n\r\nUPDATE: I took a look and it seems this new tool doesn't support go modules, so it could only get licenses using a library's latest version IIUC. See https://github.com/google/go-licenses/issues/33.", "I'll have to keep using https://github.com/kubeflow/testing/tree/master/py/kubeflow/testing/go-license-tools", "Noticed a problem with kubeflow go-license-tools, although\r\n\r\n> The command `go list -m all` lists the current module and all its dependencies:\r\n\r\nI found that when using this command in argo repo, it lists a dependency: `github.com/fasthttp-contrib/websocket`. However, this repo doesn't have a license file.\r\n\r\nSo I kept investigating, and found that argo is not using this module. It's mentioned in `go.sum`, but not used anywhere in the repo.\r\n\r\nTherefore, I can conclude that `go list -m all` lists too many dependencies, including those not used.\r\n\r\nUPDATE: I take it back.\r\n\r\nI learned that I can use `go mod graph` to search for dependency graph. https://stackoverflow.com/a/63779350/8745218\r\n\r\nAnd I figured out that `fasthttp-contrib/websocket` is introduced by `github.com/gavv/httpexpect/v2 v2.0.3`\r\n\r\n```\r\n$ go mod graph | grep fasthttp-contrib/websocket\r\ngithub.com/gavv/httpexpect/v2@v2.0.3 github.com/fasthttp-contrib/websocket@v0.0.0-20160511215533-1f3b11f56072\r\n$ go mod graph | grep httpexpect\r\ngithub.com/argoproj/argo/v2 github.com/gavv/httpexpect/v2@v2.0.3\r\n```\r\n\r\nhttpexpect is only used in tests, but not built in the binary, so it's not a license issue for us.", "In summary, two dependencies are problematic:\r\n\r\n* https://github.com/yudai/pp mentions it is licensed under MIT, but it doesn't keep a copy of MIT license in the repo.\r\n* https://github.com/fasthttp-contrib/websocket doesn't have a license\r\n\r\nBoth of them were imported by httpexpect v2.0.3 (a dev dependency), ~~so I guess httpexpect isn't very well maintained either~~.\r\nWell, httpexpect fixed this problem for fasthttp-contrib/websocket in https://github.com/gavv/httpexpect/issues/74, and released 2.1.0 and 2.2.0 after that.\r\n\r\nBecause they are dev dependencies not built into images, we don't need to worry about them.", "There are several images I'll need to generate license info, so I built a better tool to automate this: https://github.com/Bobgy/go-mod-licenses.", "@Bobgy Just came across this, thought it would be relevant. https://blog.argoproj.io/argo-workflows-v3-0-4d0b69f15a6e", "@DavidSpek thanks, rationale for choosing 2.12 was recorded in https://github.com/kubeflow/pipelines/issues/4553#issuecomment-768018127", "Hmm, I just discovered https://github.com/github/licensed, it's written in Ruby, but it seems to support a wide range of languages and a robust workflow for working with licenses & cache.", "I'm starting to notice some limitations of my approach:\r\n* dev dependencies are pulled in as well -- this greatly increases number of licenses and their problems to deal with (but in fact they may not be in the binary).\r\n* only supports repos using one single license (not yet seeing an exception)", "/reopen\r\nI also need to update gcp marketplace manifests", "@Bobgy: Reopened this issue.\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/5232#issuecomment-795302385):\n\n>/reopen\r\n>I also need to update gcp marketplace manifests\n\n\nInstructions for interacting with me using PR comments are available [here](https://git.k8s.io/community/contributors/guide/pull-requests.md). If you have questions or suggestions related to my behavior, please file an issue against the [kubernetes/test-infra](https://github.com/kubernetes/test-infra/issues/new?title=Prow%20issue:) repository.\n</details>" ]

"2021-03-03T08:56:23"

"2021-03-11T06:03:11"

"2021-03-11T06:03:10"

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