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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-12-21T09:26:06"

"2022-04-17T06:27:49"

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[]

[ "cc @chensun ", "I also bumped into the exact same issue while testing the KF 1.5 RC0 manifests https://github.com/kubeflow/manifests/issues/2099\r\n\r\nI think this has definitely something to do with KinD, but I couldn't get to the bottom of it. For me it was:\r\n* KinD cluster with K8s 1.20.7\r\n* `1.8.0-rc.1` commit\r\n\r\nBUT, when testing this with:\r\n* EKS with K8s 1.19\r\n* KFP 1.7.0\r\n\r\nThen the CertificateSigningRequest would get into `Approved` state, but the cache-deployer would still complain that a certificate would not appear.\r\n```\r\nERROR: After approving csr cache-server.kubeflow, the signed certificate did not appear on the resource. Giving up after 10 attempts.\r\n```" ]

"2021-12-21T08:59:01"

"2022-02-08T08:13:42"

"2022-02-08T08:13:42"

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[ "Ohh, my guess was wrong. The project ml-pipeline-test itself is suspended.", "Recovered" ]

"2021-12-20T06:11:50"

"2021-12-20T07:15:14"

"2021-12-20T07:15:14"

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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", "any update on this? i have to include our antifactory images in our organization " ]

"2021-12-16T16:12:56"

"2022-09-14T15:38:34"

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[ "As far as I'm aware, we don't support setting restartPolicy for either main container nor init container via SDK. \r\nBTW, ContainerOp is deprecated, you should received a warning for the code above.", "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" ]

"2021-12-16T08:01:33"

"2022-04-17T06:27:34"

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[ "pr #7065", "Thank you for raising the issue.\r\n\r\nKFP is language-agnostic pipeline system. `None` is a python-specific object that is not present in other languages. Im'm not sure it's a good idea to tie KFP to a specific language.\r\n\r\n>the component recieve 'None'.\r\n\r\nKFP orchestrates containerized command-line programs. Command-line programs, as defined by their POSIX standard, receive command-line arguments as an array of null-terminated strings. Command-line programs cannot receive \"None\" objects.\r\n\r\nThere is a way for a command-line program to distinguish between an empty string argument and a missing argument. Conditional placeholders in the component definitions help with that (`{if: {cond: {isPresent: Input1}, then: [\"--input-1\", {inputValue: Input1}}]}`).\r\n\r\nSDK automatically handles cases where an optional input argument was not specified when instantiating a component.\r\n\r\nHowever, as you've discovered, this might not work as well for pipeline arguments. The reason is that currently the conditional placeholders are evaluated at compile time, but the pipeline arguments can be specified after the compilation.\r\n\r\nThe specification of the compiled pipeline does not allow passing NoneType objects - only strings. So, if you want to be able to pass some value from pipeline parameter to a component and have the component interpret is as `None`, you have to do that on the component side.", "but when you say in the component side what do you mean?\r\na) manually inside the python code, adding `arg1 = None if arg1 == 'None' else arg1` in the pertinent functions\r\nb) automatically inside the component; in the container:command of the json. Which for py func would be something like adding a line inside the body as `locals() = {k:None if v == 'None' else v for v in locals()}` , but for components from yaml i don't know\r\nc) inside the inputs:parameters. Like the parsing that you do from str to bool (which i don't understand how it works). \r\n```\r\n\"input1\": { # string\r\n \"runtimeValue\": {\r\n \"constantValue\": {\r\n \"intValue\": \"1\"\r\n }\r\n }\r\n },```", "Alexey is correct that `None` is not something you can pass via command-line arguments.\r\nThat said, what you get is expected because you are passing string literal `\"None\"` which overrides the default value `None`. The way to receive a Python `None` object is to not pass anything at all, then the default value will be used.\r\n\r\nTry the following:\r\n```python\r\n@component\r\ndef task1(arg1: str = None):\r\n if arg1 is None:\r\n print('arg1 is None')\r\n@pipeline\r\ndef pipeline():\r\n task1()\r\n```", "But that's precisely the problem. The \"not passing anything at all\" is not an option in a function, it's only in the entrypoint.\r\n\r\nWhat I want is setting args with default None (not 'None') in the pipeline (not the component)\r\n\r\nBasically\r\n```py\r\n@component\r\ndef task1(arg1: str = None):\r\n if arg1 is None:\r\n print('arg1 is None')\r\n else:\r\n print(f\"arg1 value = '{arg1}'\")\r\n@pipeline\r\n pipeline(arg1: str = None):\r\n task1(arg1)\r\n```\r\npipeline() prints \"arg1 value = 'None'\" instead of the desired \"arg1 is None\"", "> What I want is setting args with default None (not 'None') in the pipeline (not the component)\r\n\r\nThat's not a supported scenario. As Alexey mentioned above, `None` is a Python-specific object, whereas KFP components are language-agnostic. \r\n\r\nIn your code sample, by passing object` None` to `task1` component, you're assuming that `task1` is a Python program. But that's not necessarily true. Albeit you write `task1` implementation using Python above, that doesn't mean all components are Python-based. In fact, we see quite a lot components written as bash commands.\r\n", "Yep, so agreed that in the pipeline arg1='None'.\r\n\r\nBut again; then, the parsing from 'None' to None, should be done (only) inside the py component? With something like `locals() = {k:None if v == 'None' else v for v in locals()} ` (option b)\r\n\r\nOr not parsing at all, and that py component should recieve 'None' and be treated by the user inside the function? (option a)", "> the parsing from 'None' to None, should be done (only) inside the py component?\r\n\r\nNo, I don't think we should ever convert `'None'` to `None`. It could be a legit user intention that they may want to pass `'None'` and consume it as string. We don't want to be \"oversmart\".\r\n\r\n> Or not parsing at all, and that py component should recieve 'None' and be treated by the user inside the function? (option a)\r\n\r\nYes, users can choose whatever they want, `'None'`, `'null'`, `'NoValue'`, etc. The system doesn't need to be aware of the user-chosen contract.\r\n\r\n", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-12-15T09:13:20"

"2022-04-17T06:27:23"

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[]

[ "Maybe using argo workflows `argo_workflows_count` is a better way to track runs" ]

"2021-12-15T06:05:41"

"2021-12-15T06:55:59"

"2021-12-15T06:39:12"

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[ "hi @Bobgy @zijianjoy, can I take this one? I'm looking for a first issue, to get familiar with the code." ]

"2021-12-14T01:20:58"

"2022-02-15T08:05:04"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-12-13T04:07:19"

"2022-04-17T06:27:46"

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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\r\n\r\n" ]

"2021-12-13T03:02:29"

"2023-04-10T00:51:12"

"2023-04-10T00:51:11"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-12-13T03:01:31"

"2022-04-17T07:27:22"

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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" ]

"2021-12-12T14:34:37"

"2023-03-07T01:22:14"

"2023-03-07T01:22:14"

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[]

[ "@akartsky @surajkota @mbaijal @ryansteakley FYI.", "https://github.com/kubeflow/pipelines/issues/6465", "/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" ]

"2021-12-10T22:14:18"

"2022-04-28T04:59:41"

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[ "/assign @zijianjoy \r\n/cc @haoxins ", "All clients for MLMD have been updated." ]

"2021-12-09T10:36:31"

"2022-01-01T19:26:12"

"2022-01-01T19:26:12"

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[]

[ "cc @chensun ", "Hi @hahamark1, \r\n\r\n`func_to_container_op` function is actually deprecated. To generate the docstrings, we need some amount of work to generate kfp-specific docstrings. We will work on this in the future. Thanks!", "Hi @ji-yaqi , came across this and was wondering whether there is a way users could find out what are the deprecated aspects of v1 SDK from documentation/library itself. Would you be amenable to a PR that adds docstring updates/deprecation warnings to this function/other deprecated functions?" ]

"2021-12-09T09:44:38"

"2022-02-17T20:26:00"

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[]

[ "cc @chensun ", "Hi @juliadeclared I saw you closed the PR. Is the issue still valid? \r\n", "@chensun yes, the issue is still valid. For KF installations, the generated URI should include domain/_/pipeline since this is how its mapped for KF full installation.\r\n\r\nMaybe this can be a small doc update or something similar. But it was quite tricky for us to debug.", "> id. For KF installations, the generated URI should include domain/_/pipeline since this is how its mapped for KF full installation.\r\n\r\nI see. yes, I recall the URL for standalone KFP deployment and full fledge KF deployment are different. And currently we don't have a way to tell from the SDK client side, so we can only return one form of the URL. \r\nYou're probably right that this might need a doc update. Any suggestion where the change could be? Please feel free to create doc update PRs as well. Thanks!", "@chensun here is the doc update PR: https://github.com/kubeflow/website/pull/3105", "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" ]

"2021-12-08T22:49:30"

"2022-04-17T06:27:47"

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[]

[ "Hello @ashissharma97 ,\r\n\r\nWould you like to get the events from failing pods to debug further\r\n\r\n```\r\nkubectl get <pod-id> -n <namespace> -o yaml\r\n```", "Hi @zijianjoy,\r\nThanks for replying\r\nHere is the yaml file of the pod \r\n```\r\napiVersion: v1\r\nkind: Pod\r\nmetadata:\r\n labels:\r\n pipeline/runid: d5d18505-8c11-4caa-8e5a-168e14e35981\r\n pipelines.kubeflow.org/cache_enabled: \"true\"\r\n pipelines.kubeflow.org/cache_id: \"\"\r\n pipelines.kubeflow.org/metadata_context_id: \"3\"\r\n pipelines.kubeflow.org/metadata_execution_id: \"3\"\r\n workflows.argoproj.io/completed: \"false\"\r\n workflows.argoproj.io/workflow: \"[some_name]\"\r\n name: \"[name]\"\r\n namespace: \"[namespace]\"\r\n ownerReferences:\r\n - apiVersion: argoproj.io/v1alpha1\r\n blockOwnerDeletion: true\r\n controller: true\r\n kind: Workflow\r\n name: \"[some_name]\"\r\n uid: b6ff07e0-c798-406c-afc5-275ba61226f2\r\n resourceVersion: \"643080\"\r\n uid: e755c715-c613-401f-b699-a741fc07d35f\r\nspec:\r\n containers:\r\n - command:\r\n - argoexec\r\n - wait\r\n - --loglevel\r\n - info\r\n env:\r\n - name: ARGO_POD_NAME\r\n valueFrom:\r\n fieldRef:\r\n apiVersion: v1\r\n fieldPath: metadata.name\r\n - name: ARGO_CONTAINER_RUNTIME_EXECUTOR\r\n value: docker\r\n - name: GODEBUG\r\n value: x509ignoreCN=0\r\n - name: ARGO_CONTAINER_NAME\r\n value: wait\r\n - name: ARGO_INCLUDE_SCRIPT_OUTPUT\r\n value: \"false\"\r\n image: gcr.io/ml-pipeline/argoexec:v3.1.6-patch-license-compliance\r\n imagePullPolicy: IfNotPresent\r\n name: wait\r\n resources:\r\n limits:\r\n cpu: 500m\r\n memory: 512Mi\r\n requests:\r\n cpu: 10m\r\n memory: 32Mi\r\n terminationMessagePath: /dev/termination-log\r\n terminationMessagePolicy: File\r\n volumeMounts:\r\n - mountPath: /argo/podmetadata\r\n name: podmetadata\r\n - mountPath: /var/run/docker.sock\r\n name: docker-sock\r\n readOnly: true\r\n - mountPath: /argo/secret/mlpipeline-minio-artifact\r\n name: mlpipeline-minio-artifact\r\n readOnly: true\r\n - mountPath: /mainctrfs/obj\r\n name: \"[pvc]\"\r\n - mountPath: /var/run/secrets/kubernetes.io/serviceaccount\r\n name: kube-api-access-zgpz8\r\n readOnly: true\r\n - command:\r\n - python\r\n - print.py\r\n env:\r\n - name: ARGO_CONTAINER_NAME\r\n value: main\r\n - name: ARGO_INCLUDE_SCRIPT_OUTPUT\r\n value: \"false\"\r\n image: \"[imagelink]\"\r\n imagePullPolicy: Always\r\n name: main\r\n resources: {}\r\n terminationMessagePath: /dev/termination-log\r\n terminationMessagePolicy: File\r\n volumeMounts:\r\n - mountPath: /obj\r\n name: \"[volume_path]\"\r\n - mountPath: /var/run/secrets/kubernetes.io/serviceaccount\r\n name: kube-api-access-zgpz8\r\n readOnly: true\r\n dnsPolicy: ClusterFirst\r\n enableServiceLinks: true\r\n nodeName: gke-node\r\n preemptionPolicy: PreemptLowerPriority\r\n priority: 0\r\n restartPolicy: Never\r\n schedulerName: default-scheduler\r\n securityContext: {}\r\n serviceAccount: default-editor\r\n serviceAccountName: default-editor\r\n terminationGracePeriodSeconds: 30\r\n tolerations:\r\n - effect: NoExecute\r\n key: node.kubernetes.io/not-ready\r\n operator: Exists\r\n tolerationSeconds: 300\r\n - effect: NoExecute\r\n key: node.kubernetes.io/unreachable\r\n operator: Exists\r\n tolerationSeconds: 300\r\n volumes:\r\n - downwardAPI:\r\n defaultMode: 420\r\n items:\r\n - fieldRef:\r\n apiVersion: v1\r\n fieldPath: metadata.annotations\r\n path: annotations\r\n name: podmetadata\r\n - hostPath:\r\n path: /var/run/docker.sock\r\n type: Socket\r\n name: docker-sock\r\n - name: pvc_name\r\n persistentVolumeClaim:\r\n claimName: pvc_name\r\n - name: mlpipeline-minio-artifact\r\n secret:\r\n defaultMode: 420\r\n items:\r\n - key: accesskey\r\n path: accesskey\r\n - key: secretkey\r\n path: secretkey\r\n secretName: mlpipeline-minio-artifact\r\n - name: kube-api-access-zgpz8\r\n projected:\r\n defaultMode: 420\r\n sources:\r\n - serviceAccountToken:\r\n expirationSeconds: 3607\r\n path: token\r\n - configMap:\r\n items:\r\n - key: ca.crt\r\n path: ca.crt\r\n name: kube-root-ca.crt\r\n - downwardAPI:\r\n items:\r\n - fieldRef:\r\n apiVersion: v1\r\n fieldPath: metadata.namespace\r\n path: namespace\r\nstatus:\r\n conditions:\r\n - lastProbeTime: null\r\n lastTransitionTime: \"2021-12-10T19:19:28Z\"\r\n status: \"True\"\r\n type: Initialized\r\n - lastProbeTime: null\r\n lastTransitionTime: \"2021-12-10T19:19:28Z\"\r\n message: 'containers with unready status: [main]'\r\n reason: ContainersNotReady\r\n status: \"False\"\r\n type: Ready\r\n - lastProbeTime: null\r\n lastTransitionTime: \"2021-12-10T19:19:28Z\"\r\n message: 'containers with unready status: [main]'\r\n reason: ContainersNotReady\r\n status: \"False\"\r\n type: ContainersReady\r\n - lastProbeTime: null\r\n lastTransitionTime: \"2021-12-10T19:19:28Z\"\r\n status: \"True\"\r\n type: PodScheduled\r\n containerStatuses:\r\n - containerID: containerd://867d5a7e486aa4809f7090221d75c982b698b98cde66a57dd4c6b4cf1ba5eb52\r\n image: \"[container_link]\"\r\n imageID: \"[container_link_id]\"\r\n lastState: {}\r\n name: main\r\n ready: false\r\n restartCount: 0\r\n started: false\r\n state:\r\n terminated:\r\n containerID: containerd://867d5a7e486aa4809f7090221d75c982b698b98cde66a57dd4c6b4cf1ba5eb52\r\n exitCode: 0\r\n finishedAt: \"2021-12-10T19:20:36Z\"\r\n reason: Completed\r\n startedAt: \"2021-12-10T19:20:36Z\"\r\n - containerID: containerd://c78df00a2e6316fdf5ea69c8eddfa3c65adc358c2da852892ae2b7af7461f67f\r\n image: gcr.io/ml-pipeline/argoexec:v3.1.6-patch-license-compliance\r\n imageID: gcr.io/ml-pipeline/argoexec@sha256:44cf8455a51aa5b961d1a86f65e39adf5ffca9bdcd33a745c3b79f430b7439e0\r\n lastState: {}\r\n name: wait\r\n ready: true\r\n restartCount: 0\r\n started: true\r\n state:\r\n running:\r\n startedAt: \"2021-12-10T19:19:51Z\"\r\n hostIP: 10.1.0.31\r\n phase: Running\r\n podIP: 10.244.1.69\r\n podIPs:\r\n - ip: 10.244.1.69\r\n qosClass: Burstable\r\n startTime: \"2021-12-10T19:19:28Z\"\r\n```", "Can you share with me:\r\n\r\n1. Kubernetes version\r\n2. Kubeflow version\r\n3. What executor your are using? https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/", "Sure...\r\nKubernetes Version: 1.21.6-gke.1500\r\nKubeflow Version: v1.4.0\r\nThe executor I am using is the default one which is containerd.\r\n\r\nThanks\r\n\r\n", "Are you using emissary executor?\r\n\r\n`kubectl describe configmap workflow-controller-configmap | grep -A 2 containerRuntimeExecutor`", "Hi @zijianjoy,\r\nNo, I am using docker and this is happening when the task of the pod is completed instead of completed status it is showing NotReady.", "@ashissharma97 , KFP won't work with docker executor if you are using Kubernetes version > 1.19, because docker runtime has been deprecated by Kubernetes. You will have to switch to use emissary executor in order to make your pipeline working.\r\n\r\nIn order to use emissary executor: https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#migrate-to-emissary-executor\r\n\r\nDetailed issue description: https://github.com/kubeflow/pipelines/issues/5714", "Thank You @zijianjoy, This resolved my issue." ]

"2021-12-08T06:30:36"

"2021-12-15T08:14:57"

"2021-12-15T08:14:57"

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[]

[ "cc @chensun ", "@ypitrey The fundamental issue is that `300 * run_duration` is a piece of code not captured in any container. Generally speaking, a pipeline may only use components as building blocks, where each component is a containerized app. There can't be code hanging outside a component. Does that make sense?\r\nI assume you're running on Kubeflow Pipelines rather than Vertex Pipelines, am I right? If so, I would suggest you have an additional pipeline input for memory request:\r\n```python\r\n@dsl.pipeline(name='example_pipeline', description='foo')\r\ndef pipeline(run_duration: float, memory_request: str):\r\n ...\r\n comp = my_component(run_duration)\r\n comp.set_memory_request(memory_request) \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" ]

"2021-12-07T15:31:08"

"2022-04-17T07:27:13"

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[ "/assign @difince ", "This behavior sounds good to us, feel free to contribute to this fix!" ]

"2021-12-07T12:51:16"

"2022-02-09T23:24:33"

"2022-02-09T23:24:33"

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[]

[ "cc @chensun ", "Hi @ysk24ok, in V2, we ask that pipeline name should be specified for better usage. \r\n\r\nYour code should look like:\r\n```\r\n@pipeline(name=\"your_pipeline_name\")\r\ndef sample():\r\n do_nothing_op()\r\n```\r\n\r\nHope this explains!\r\n", "@ji-yaqi Thank you for your response.\r\nThen, how about making `name` arg of `pipeline` function required in v2 SDK?\r\nIt's a bit confusing that the type of`name` arg is `Optional[str]`.", "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", "In kfp v2 (the stable release hasn't been made yet, though), the problem seems to be fixed.\r\n\r\n```console\r\n% cat pipeline.py\r\nfrom kfp.dsl import pipeline, component\r\nfrom kfp.compiler import Compiler\r\n\r\n\r\n@component\r\ndef do_nothing_op():\r\n pass\r\n\r\n\r\n@pipeline()\r\ndef sample_pipeline():\r\n do_nothing_op()\r\n\r\n\r\nCompiler().compile(\r\n pipeline_func=sample_pipeline,\r\n package_path=\"/tmp/pipeline.yaml\",\r\n)\r\n% python pipeline.py\r\n% yq '.pipelineInfo' < /tmp/pipeline.yaml\r\nname: sample-pipeline\r\n```\r\n\r\nThe pipeline name is inferred from the pipeline function name. Closing this issue." ]

"2021-12-07T11:05:12"

"2022-08-29T11:22:11"

"2022-08-29T11:22:10"

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[]

[ "cc @IronPan ", "kfp -> 1.8.10\r\ngoogle-cloud-pipeline-components -> 0.2.1\r\n\r\nI'm experiencing the same issue when running tasks created from `google_cloud_pipeline_components.experimental.hyperparameter_tuning_job` \r\nAnd I located to the same place `google_cloud_pipeline_components\\container\\experimental\\gcp_launcher\\job_remote_runner.py` at line 80, where the inputs of `pattern` and `string` to `re.finall` are flipped.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hi there\r\nUsing `ModelBatchPredictOp` I'm experiencing the same issue:\r\n\r\npython = 3.8\r\ngoogle-cloud = 0.34.0\r\ngoogle-cloud-aiplatform = 1.12.0\r\nkfp = 1.8.12\r\ngoogle-cloud-pipeline-components = 1.0.5\r\n\r\n\r\n```python\r\nprediction_task = ModelBatchPredictOp(\r\n project=project,\r\n location=location,\r\n job_display_name=segment.job_name,\r\n model=loaded_model_task.outputs[\"model\"],\r\n gcs_source_uris=segment.source,\r\n instances_format=\"jsonl\",\r\n gcs_destination_output_uri_prefix=segment.results_prefix,\r\n predictions_format=\"jsonl\",\r\n machine_type=machine_type,\r\n starting_replica_count=segment.starting_replica_count,\r\n max_replica_count=512,\r\n)\r\n```\r\n\r\nWhat happens?\r\n```logs\r\nTraceback (most recent call last):\r\n File \"/opt/python3.7/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\"\r\n \"__main__\", mod_spec)\"\r\n File \"/opt/python3.7/lib/python3.7/runpy.py\", line 85, in _run_code\"\r\n exec(code, run_globals)\"\r\n File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/launcher.py\", line 229, in <module>\"\r\n main(sys.argv[1:])\"\r\n File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/launcher.py\", line 225, in main\r\n _JOB_TYPE_TO_ACTION_MAP[job_type](**parsed_args)\"\r\n File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/batch_prediction_job_remote_runner.py\", line 105, in create_batch_prediction_job\r\n job_name = remote_runner.check_if_job_exists()\r\n File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/job_remote_runner.py\", line 104, in check_if_job_exists\r\n 'Job Name in gcp_resource is not formatted correctly or is empty.'\r\nValueError: Job Name in gcp_resource is not formatted correctly or is empty.\r\n```\r\n\r\nAny ideas or suggestions for dealing with this would be greatly appreciated.", "> Hi there Using `ModelBatchPredictOp` I'm experiencing the same issue:\r\n> \r\n> python = 3.8 google-cloud = 0.34.0 google-cloud-aiplatform = 1.12.0 kfp = 1.8.12 google-cloud-pipeline-components = 1.0.5\r\n> \r\n> ```python\r\n> prediction_task = ModelBatchPredictOp(\r\n> project=project,\r\n> location=location,\r\n> job_display_name=segment.job_name,\r\n> model=loaded_model_task.outputs[\"model\"],\r\n> gcs_source_uris=segment.source,\r\n> instances_format=\"jsonl\",\r\n> gcs_destination_output_uri_prefix=segment.results_prefix,\r\n> predictions_format=\"jsonl\",\r\n> machine_type=machine_type,\r\n> starting_replica_count=segment.starting_replica_count,\r\n> max_replica_count=512,\r\n> )\r\n> ```\r\n> \r\n> What happens?\r\n> \r\n> ```\r\n> Traceback (most recent call last):\r\n> File \"/opt/python3.7/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\"\r\n> \"__main__\", mod_spec)\"\r\n> File \"/opt/python3.7/lib/python3.7/runpy.py\", line 85, in _run_code\"\r\n> exec(code, run_globals)\"\r\n> File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/launcher.py\", line 229, in <module>\"\r\n> main(sys.argv[1:])\"\r\n> File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/launcher.py\", line 225, in main\r\n> _JOB_TYPE_TO_ACTION_MAP[job_type](**parsed_args)\"\r\n> File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/batch_prediction_job_remote_runner.py\", line 105, in create_batch_prediction_job\r\n> job_name = remote_runner.check_if_job_exists()\r\n> File \"/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/job_remote_runner.py\", line 104, in check_if_job_exists\r\n> 'Job Name in gcp_resource is not formatted correctly or is empty.'\r\n> ValueError: Job Name in gcp_resource is not formatted correctly or is empty.\r\n> ```\r\n> \r\n> Any ideas or suggestions for dealing with this would be greatly appreciated.\r\n\r\nI am currently having the same issue when trying to run a HyperparameterTuningJobRunOp. Did you find a solution?", "I have exactly the same issue, when trying to run batch prediction over the uploaded vertex model: `Job Name in gcp_resource is not formatted correctly or is empty.`\r\n\r\nManual batch prediction fails with: \r\n\r\n> Batch prediction job batch_preduct encountered the following errors: Model server terminated: model server container terminated: exit_code: 1 reason: \"Error\" started_at { seconds: 1661869830 } finished_at { seconds: 1661869831 } . \r\n\r\nThe same model works fine when deployed to an endpoint. Any hints or workarounds would be greatly appreciated!\r\n", "Update, according to [that commit](https://github.com/kubeflow/pipelines/commit/582aefa56d1850d3bd9f1cb5bff26bd25baa50dd), the issue was fixed in the code 19 days ago. It doesn't look like it is scheduled for release any time soon." ]

"2021-12-06T20:38:14"

"2022-08-31T13:33:23"

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[]

[ "here is our test pipeline\r\n\r\n\r\n\r\n```\r\nimport os\r\nimport sys\r\n\r\nimport kfp.compiler\r\nimport kfp.components as comp\r\nimport kfp.dsl as dsl\r\nimport json\r\n\r\nstart_comp = comp.load_component_from_file('./start.yaml')\r\nwork_comp = comp.load_component_from_file('./work.yaml')\r\nexit_comp = comp.load_component_from_file('./exit.yaml')\r\n\r\n\r\n@dsl.pipeline(\r\n name=\"[test]exit test pipeline\"\r\n)\r\ndef pipeline(\r\n task_id: int\r\n):\r\n\r\n pipeline_conf = dsl.get_pipeline_conf()\r\n pipeline_conf.set_image_pull_secrets([{\"name\": \"rudder-image-secret\"}])\r\n pipeline_conf.set_image_pull_policy(\"Always\")\r\n pipeline_conf.set_ttl_seconds_after_finished(7200) # one day\r\n\r\n\r\n exit_op = exit_comp(str(task_id)).set_display_name(\"onExit\")\r\n\r\n with dsl.ExitHandler(exit_op):\r\n start_op = start_comp().set_display_name(\"start\")\r\n work_op = work_comp().set_display_name(\"work\")\r\n work_op.after(start_op)\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n kfp.compiler.Compiler().compile(pipeline, __file__.rstrip('.py') + '.yaml')\r\n```\r\n\r\nand the start.yaml, work.yaml, exit.yaml provided here\r\nstart.yaml\r\n\r\n```\r\nname: start\r\ninputs:\r\noutputs:\r\nimplementation:\r\n container:\r\n image: busybox\r\n args: [\r\n '/bin/sh',\r\n '-c',\r\n 'echo pipeline start'\r\n ]\r\n```\r\n\r\nwork.yaml\r\n\r\n```\r\nname: work\r\ninputs:\r\noutputs:\r\nimplementation:\r\n container:\r\n image: busybox\r\n args: [\r\n '/bin/sh',\r\n '-c',\r\n 'echo working... ;sleep 10; echo done'\r\n ]\r\n```\r\n\r\nexit.yaml\r\n\r\n```\r\nname: exit\r\ninputs:\r\n - {name: task_id, type: String, description: 'task id'}\r\noutputs:\r\nimplementation:\r\n container:\r\n image: busybox\r\n args: [\r\n '/bin/sh',\r\n '-c',\r\n 'echo pipeline exit',\r\n {inputValue: task_id}\r\n ]\r\n```\r\n\r\nand use kfp 1.6.0 which can be uploaded in kubeflow 1.0, but can not be uploaded in kubeflow 1.6。and the error is \r\n```\r\n{\r\n \"error_message\": \"Error creating pipeline version: Create pipeline version failed: Failed to get parameters from the workflow: templates.exit inputs.parameters.task_id was not supplied\",\r\n \"error_details\": \"templates.exit inputs.parameters.task_id was not supplied\\ngithub.com/argoproj/argo-workflows/v3/errors.New\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/errors/errors.go:49\\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/errors/errors.go:55\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.(*templateValidationCtx).validateTemplate\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:340\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.(*templateValidationCtx).validateTemplateHolder\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:454\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:210\\ngithub.com/kubeflow/pipelines/backend/src/common/util.ValidateWorkflow\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:60\\ngithub.com/kubeflow/pipelines/backend/src/common/util.GetParameters\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:31\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1153\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to get parameters from the workflow\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/common/util.GetParameters\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:33\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1153\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nCreate pipeline version failed\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1155\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nError creating pipeline version\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:199\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\"\r\n}\r\n```", "hi ,is there any progress ?@ji-yaqi ", "Hi @yiyuanyu17, could you update your SDK version to 1.8.10 (latest)? We have fixed a bunch of bugs since 1.6.0. \r\nYour code can compile in 1.8.10 with no errors. ", "> Hi @yiyuanyu17, could you update your SDK version to 1.8.10 (latest)? We have fixed a bunch of bugs since 1.6.0. Your code can compile in 1.8.10 with no errors.\r\n\r\nif we update sdk version to 1.8.10, the yaml will work with kubeflow 1.0?", "> \r\n\r\n@ji-yaqi we test sdk 1.8.10, and still have the error ", "@yiyuanyu17 Do you have compilation error or upload error? I started on a clean version of 1.8.10 and doesn't have the compilation error. Maybe try force-reinstall the package? ", "> @yiyuanyu17 Do you have compilation error or upload error? I started on a clean version of 1.8.10 and doesn't have the compilation error. Maybe try force-reinstall the package?\r\n\r\n@ji-yaqi \r\n\r\ni reinstall the kfp==1.8.10 , and upload to the kubeflow 1.6.0. and the error still is \r\n\r\n```\r\n{\r\n \"error_message\": \"Error creating pipeline version: Create pipeline version failed: Failed to get parameters from the workflow: templates.exit inputs.parameters.task_id was not supplied\",\r\n \"error_details\": \"templates.exit inputs.parameters.task_id was not supplied\\ngithub.com/argoproj/argo-workflows/v3/errors.New\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/errors/errors.go:49\\ngithub.com/argoproj/argo-workflows/v3/errors.Errorf\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/errors/errors.go:55\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.(*templateValidationCtx).validateTemplate\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:340\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.(*templateValidationCtx).validateTemplateHolder\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:454\\ngithub.com/argoproj/argo-workflows/v3/workflow/validate.ValidateWorkflow\\n\\t/go/pkg/mod/github.com/argoproj/argo-workflows/v3@v3.1.0/workflow/validate/validate.go:210\\ngithub.com/kubeflow/pipelines/backend/src/common/util.ValidateWorkflow\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:60\\ngithub.com/kubeflow/pipelines/backend/src/common/util.GetParameters\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:31\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1153\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nFailed to get parameters from the workflow\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/common/util.GetParameters\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/template_util.go:33\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1153\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nCreate pipeline version failed\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/resource.(*ResourceManager).CreatePipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/resource/resource_manager.go:1155\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:185\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\\nError creating pipeline version\\ngithub.com/kubeflow/pipelines/backend/src/common/util.Wrap\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/common/util/error.go:275\\ngithub.com/kubeflow/pipelines/backend/src/apiserver/server.(*PipelineUploadServer).UploadPipelineVersion\\n\\t/go/src/github.com/kubeflow/pipelines/backend/src/apiserver/server/pipeline_upload_server.go:199\\nnet/http.HandlerFunc.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2007\\ngithub.com/gorilla/mux.(*Router).ServeHTTP\\n\\t/go/pkg/mod/github.com/gorilla/mux@v1.8.0/mux.go:210\\nnet/http.serverHandler.ServeHTTP\\n\\t/usr/local/go/src/net/http/server.go:2802\\nnet/http.(*conn).serve\\n\\t/usr/local/go/src/net/http/server.go:1890\\nruntime.goexit\\n\\t/usr/local/go/src/runtime/asm_amd64.s:1357\"\r\n}\r\n```", "I see. \r\n\r\nWe will need community help on this issue: add `task_id` to exit handler in KFP V1. \r\nWorkaround: manually add param in generated yaml file. \r\n\r\n", "> ssue: add\r\n\r\nit's ok if manually add param in generated yaml file, but it's our product environment, and our actual pipeline is more complex than the demo. so we need help to solve this bug by client. @ji-yaqi @james-jwu ", "> > ssue: add\r\n> \r\n> it's ok if manually add param in generated yaml file, but it's our product environment, and our actual pipeline is more complex than the demo. so we need help to solve this bug by client. @ji-yaqi @james-jwu\r\n\r\nwe want to upgrade our kubeflow to 1.6.0 to use the new functions of kf1.6.0, and we have many pipelines generate by python code, and we use exit hander to do some postprocess in any condition. If we manually add param in generated yaml file, it will be heavy work to modify each pipeline 。 @james-jwu ", "I investigate a bit, there's no behavior change from KFP SDK side.\r\nHere's a simplified yaml I tried, it works on kfp 1.4.1 but not on 1.7.0\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n generateName: test-pipeline-\r\nspec:\r\n entrypoint: test-pipeline\r\n templates:\r\n - name: exit\r\n container:\r\n args: ['echo {{inputs.parameters.task_id}}']\r\n command: [/bin/sh, -c]\r\n image: busybox\r\n inputs:\r\n parameters:\r\n - {name: task_id}\r\n - name: exit-handler-1\r\n dag:\r\n tasks:\r\n - {name: start, template: start}\r\n - name: start\r\n container:\r\n args: [echo pipeline start]\r\n command: [/bin/sh, -c]\r\n image: busybox\r\n - name: test-pipeline\r\n dag:\r\n tasks:\r\n - {name: exit-handler-1, template: exit-handler-1}\r\n arguments:\r\n parameters:\r\n - name: task_id\r\n# if I add `value` for the parameter, then it can pass validation\r\n# value: something\r\n serviceAccountName: pipeline-runner\r\n onExit: exit\r\n```\r\nI believe this is a regression from Argo side, and opened https://github.com/argoproj/argo-workflows/issues/7424\r\n\r\nWhile waiting for Argo's fix, there's another workaround for you: give `task_id` a default value like such:\r\n```python\r\n@dsl.pipeline(\r\n name=\"[test]exit test pipeline\"\r\n)\r\ndef pipeline(\r\n task_id: int = 0 # just give it any dummy value, which will be override by the actual value you provide at submission time.\r\n):\r\n```\r\n\r\nThis should be much easier than modifying the generated yaml file.", "thanks, we will try to add the default value . @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", "I encountered a same issue. Setting default value to python function helps.\r\n\r\nDeployment:\r\n```\r\nkfp pipeline 1.8.1\r\ngcr.io/ml-pipeline/api-server:1.8.1\r\ngcr.io/ml-pipeline/argoexec:v3.2.3-license-compliance\r\n```\r\n\r\nClient:\r\n```\r\nkfp==1.8.12\r\nkfp-api-server==1.8.1\r\n```", "+1 here, do we know if there's any way to fix it from kfp side (by providing some sort of default when param is being used on ExitHandler?) Or by at least failing more gracefully on Compiler if ExitOp contains a param w no default?" ]

"2021-12-06T14:12:56"

"2022-10-07T00:54:16"

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[ "Maybe the following methods could be useful too, especially for testing and debugging purposes:\r\n\r\n```python\r\n@abstractmethod\r\ndef get_all_globals(self) -> Mapping[str, Any]: ...\r\n\r\n@abstractmethod\r\ndef reset_globals(self) -> None: ...\r\n\r\n@abstractmethod\r\ndef set_globals_map(self, values: Mapping[str, Any]) -> None: ...\r\n```\r\nand therefore `set_kfp_ctx` would be:\r\n```python\r\ndef set_kfp_ctx(ctx: KfpCompilationCtx):\r\n global _kfp_ctx\r\n current_globals = _kfp_ctx.get_all_globals()\r\n ctx.reset_globals()\r\n ctx.set_globals_map(current_globals)\r\n _kfp_ctx = ctx\r\n```\r\nThanks to that, any globals that are set by default in KFP will be propagated to the new context when switching to it.", "cc @chensun ", "We are running a python server that generates the python DSL code and compiles them, so it would be great if we can run multi-thread with this approach.", "We have to change the `class Pipeline():` to set the global variables for different threads. If this approach is okay we can open a PR with the implementation.", "> This is very ineffective in case of using KFP DSL compilation in concurrent environments, which could be trivially avoided by moving the global state to some proxy.\r\n\r\n> We are running a python server that generates the python DSL code and compiles them, so it would be great if we can run multi-thread with this approach.\r\n\r\nPipeline compilation are pretty fast, usually instantaneous, so we don't generally think of the need for concurrent compilation. How many pipelines are you compiling? Is sequential compilation posing some performance bottleneck in your flow?", "@chensun\r\n\r\n> Pipeline compilation are pretty fast, usually instantaneous\r\n\r\nAn average compilation takes 0.1-0.2 for small inputs, so it can only really be considered \"instantaneous\" for a single user scenario --- as few as ten pipelines compiling sequentially generate 1s of delay, which is not that small. And that's for minimal cases, not very large pipelines, which may take longer to compile.\r\n\r\n> Is sequential compilation posing some performance bottleneck in your flow?\r\n\r\nTen small compilations per second or even fewer larger ones don't sound great. If one can afford multiple python processes to handle it, it could probably passable, but that's not always a viable solution.\r\n\r\nIt seems to me that the changes I proposed are quite simple for something that could give 3-4 times of a boost (which I think is reasonable to expect of it) in case of concurrent compilation.", "@chensun please let us know if there are any valid arguments why not to do this - else let's move this forward", "> @chensun\r\n> \r\n> > Pipeline compilation are pretty fast, usually instantaneous\r\n> \r\n> An average compilation takes 0.1-0.2 for small inputs, so it can only really be considered \"instantaneous\" for a single user scenario --- as few as ten pipelines compiling sequentially generate 1s of delay, which is not that small. And that's for minimal cases, not very large pipelines, which may take longer to compile.\r\n> \r\n> > Is sequential compilation posing some performance bottleneck in your flow?\r\n> \r\n> Ten small compilations per second or even fewer larger ones don't sound great. If one can afford multiple python processes to handle it, it could probably passable, but that's not always a viable solution.\r\n> \r\n> It seems to me that the changes I proposed are quite simple for something that could give 3-4 times of a boost (which I think is reasonable to expect of it) in case of concurrent compilation.\r\n\r\nYes, that sounds reasonable to me. \r\n@ji-yaqi is also looking into changing the compilation context for the graph component support. So I'll let @ji-yaqi comment on the implementation details.", "Hi @chensun @ji-yaqi, we just want to confirm that you are still okay with these changes? I know there's some work going for the V2 component, should we PR this change to the 1.8 sdk branch or should we PR to the master branch and cherry pick?", "/assign" ]

"2021-12-06T12:29:31"

"2022-04-26T23:21:53"

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[ "/assign @difince ", "/assign @capri-xiyue ", "pipeline name is not unique in KFP. I think we can make the combination of name and namespace unique in KFP. FYI: https://github.com/kubeflow/pipelines/issues/3360#issuecomment-834450654", "Thank you for clarifying this @capri-xiyue. \r\nThis issue is actually inspired by #3360 and I see it as the first issue from a list to cover entirely #3360. \r\nTo your point of uniqueness - in the issue, I have written\r\n\r\n> Because the pipeline name is unique within a namespace - the namespace should be taken into consideration\r\n\r\nSo, now in the [PR,](https://github.com/kubeflow/pipelines/pull/7004#pullrequestreview-827574954) there is a discussion on what is the best way to pass the namespace - using the `resource_reference` to filter on it or to make the namespace part of the URL itself. ", "Note: After the backend change is merged, let's also follow up on the SDK side by using this new endpoint getPipelineByName.", "> Note: After the backend change is merged, let's also follow up on the SDK side by using this new endpoint getPipelineByName.\r\n\r\nWe might want to delay the change, because we know a lot people upgrading KFP SDK to latest all the time, if we switch the SDK method to rely on new API, their usage will break, because their deployment is not upgraded yet. Ideally, when we change existing SDK client implementation, we need to wait until the backend API is released for a while.", "@zijianjoy \r\n> let's also follow up on the SDK side by using this new endpoint getPipelineByName\r\n\r\nCould you clarify what exactly do you mean? \r\nI see that this new endpoint could be incorporated within the CLI. The command could look like this: \r\n`kfp pipeline get --name NAME` \r\nWDYT? \r\n\r\nIs there something else I should consider? \r\n\r\nOnce I know what is supposed to be implemented I will open a dedicated issue about it. \r\n> we need to wait until the backend API is released for a while\r\n\r\nIs this time gone or not yet? \r\n\r\n", "@difince \r\n\r\n> I see that this new endpoint could be incorporated within the CLI. The command could look like this:\r\n> kfp pipeline get --name NAME\r\n\r\nThis makes sense to me, but a bit correction about the order of subcommand: `kfp pipeline get --name NAME`.\r\n\r\n>> we need to wait until the backend API is released for a while\r\n> Is this time gone or not yet?\r\n\r\nI tend to think it is about time SDK can start adding this API with SDK version 2.0.0+. cc @connor-mccarthy who is recently working on KFP SDK CLI." ]

"2021-12-03T11:00:30"

"2022-05-19T07:29:53"

"2022-02-15T17:38:41"

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[ "I tried reproducing this issue with a brand new MLMD grpc server, but I wasn't able to reproduce.", "I'm going to delete the MLMD db manually to unblock the upgrade for development in https://github.com/kubeflow/pipelines/pull/6983", "I connected to in-cluster mysql and deleted MLMD db in kfp-ci cluster.\r\n```\r\nkubectl run -it -n kubeflow --rm --image=mysql:8.0.12 --restart=Never mysql-client -- mysql -h mysql\r\n$ show databases;\r\n$ drop database metadb;\r\n```", "Thank you Yuan for finding this issue! Does it mean KFP 1.8 will have breaking change for KFP upgrade scenario?", "@zijianjoy we need to verify, if we cannot reproduce again, it might not be a breaking change.", "I have deployed a KFP 1.7.1, then upgrade to KFP 1.8.0 alpha 0. I am able to upgrade with no breaking change. \r\n\r\n### KFP 1.7.1 Artifacts (Look at version at bottom left)\r\n\r\n\r\n\r\n### KFP 1.8.0-alpha.0 Artifacts (Look at version at bottom left)\r\n\r\n\r\n\r\n### MLMD image version \r\n\r\n\r\n\r\nSeems that the issue you encountered is not common case? Note that the way I upgraded is to follow instruction: https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#upgrading-kubeflow-pipelines. Then I chose emissary executor again https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/.", "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" ]

"2021-12-01T06:43:11"

"2023-03-21T17:52:11"

"2023-03-21T17:52:11"

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[]

[ "This is known and expected. `run_pipeline_func_locally` was added as an alpha feature by a community contributor. It targets v1 only and has many limitations. \r\nThere's currently no plan to make it work for v2 components and pipelines.", "@chensun @gpoulin-hopper Actually I have implemented the local client for v2 which is only used internally for now. If needed I will be happy to pull a PR.", "@lynnmatrix yes please!", "@frederikfab I created a new project [kfp-local](https://github.com/lynnmatrix/kfp-local) for the local client v2.\r\n@chensun Is it necessary to merge it into kfp sdk?", "@gpoulin-hopper @frederikfab @lynnmatrix \r\nThis topic has been brought up from time to time, so I'd like to add some more contexts and thoughts here:\r\n- We view ML pipelines more than just executing code to completion. One critical area we build around is the ML Metadata. This is even more emphasized in the Kubeflow Pipelines v2 design [[doc](https://docs.google.com/document/d/1fHU29oScMEKPttDA1Th1ibImAKsFVVt2Ynr4ZME05i0/)]. \r\n- Kubeflow Pipelines supports [local deployment](https://www.kubeflow.org/docs/components/pipelines/installation/localcluster-deployment/) which would give you the full set of features. That would probably be a better choice if the goal is to test the end to end experience of running a pipeline in a local environment. \r\n- For quick \"unit tests\"-like testing/debugging, it is on our roadmap to support local execution of a component. But we currently don't plan the same for pipeline due to the aforementioned reasons and the complexity possibly involved in a pipeline: loops, conditions, exit handlers, importer, etc.\r\n\r\n@lynnmatrix Appreciate your effort to make local client work for v2. Going forward, we're proposing to require design docs for changes that affect UX in a nontrivial way. This would give us a more formal process of evaluating and discussing design choices. So if you'd like to merge [kfp-local](https://github.com/lynnmatrix/kfp-local) into KFP repo, could you please start with a design doc? (Consider what I mentioned above would probably be the comments you would receive in your design doc, you may want to address them in your design doc to justify adding the feature). Thanks!", "@chensun LocalClient is mostly for testing/debugging. I'll keep LocalClient in [kfp-local ](https://github.com/lynnmatrix/kfp-local) before figuring out how to write the design doc." ]

"2021-11-30T19:41:45"

"2022-04-24T10:32:45"

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[]

[ "any update on this? I am encountering something similar with using the output of Vertex AI model upload, so I have a `google.VertexModel` that does not match `Input[Model]` in a python component. ", "same problem with the VertexModel\r\nhave a customop defined:\r\n\r\nfrom kfp.v2.dsl import (\r\n component,\r\n Input,\r\n Model,\r\n)\r\ndef custom_deploy_to_endpoint(\r\n model: Input[Model],\r\n endpoint_display_name: str,\r\n project: str,\r\n):\r\n...\r\n\r\ncreating the model using \r\nmodel_upload_op = google_cloud_pipeline_components.aiplatform.ModelUploadOp(...)\r\nand passing to the custom op\r\n\r\ncustom_deploy_to_endpoint(\r\n model=model_upload_op.outputs['model'],\r\n endpoint_display_name=ENDPOINT_NAME,\r\n project=PROJECT_ID\r\n)\r\n\r\n' InconsistentTypeException: Incompatible argument passed to the input \"model\" of component \"Custom deploy to endpoint\": Argument type \"google.VertexModel\" is incompatible with the input type \"Model\" '\r\n\r\n\r\n\r\n\r\n\r\n", "try this:\r\n```\r\nhypertune_job_run_task = run_hptune(\r\n dataset=dataset_create_task.outputs[\"dataset\"].ignore_type(),\r\n hypertune_display_name=hypertune_display_name, \r\n image_uri=container_uri, \r\n staging_bucket=staging_bucket)`\r\n```" ]

"2021-11-30T03:29:19"

"2022-03-01T13:11:44"

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[]

[ "Do you need to customize the annotation for pipeline tasks? For example, KFP provides a interface for user to define the annotations for specific pipeline tasks?", "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" ]

"2021-11-29T11:57:39"

"2022-03-03T02:05:10"

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[ "/assign @zijianjoy @jiyongjung0 ", "@Bobgy: GitHub didn't allow me to assign the following users: jiyongjung0.\n\nNote that only [kubeflow members](https://github.com/orgs/kubeflow/people), repo collaborators and people who have commented on this issue/PR can be assigned. Additionally, issues/PRs can only have 10 assignees at the same time.\nFor more information please see [the contributor guide](https://git.k8s.io/community/contributors/guide/first-contribution.md#issue-assignment-in-github)\n\n<details>\n\nIn response to [this](https://github.com/kubeflow/pipelines/issues/6974#issuecomment-981279275):\n\n>/assign @zijianjoy @jiyongjung0 \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>", "Failure error message coming from upgrading TFX from 1.2 to 1.4:\r\n\r\n```\r\nsample-test-7c4kw-3475281298: =========Argo Workflow Log=========\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: time=\"2021-11-26T14:58:03.147Z\" level=info msg=\"capturing logs\" argo=true\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: 2021-11-26 14:58:05.872653: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: usage: container_entrypoint.py [-h] --pipeline_root PIPELINE_ROOT\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: --kubeflow_metadata_config\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: KUBEFLOW_METADATA_CONFIG --serialized_component\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: SERIALIZED_COMPONENT --tfx_ir TFX_IR --node_id\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: NODE_ID [--runtime_parameter RUNTIME_PARAMETER]\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: container_entrypoint.py: error: the following arguments are required: --serialized_component\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: time=\"2021-11-26T14:58:15.441Z\" level=error msg=\"cannot save artifact /mlpipeline-ui-metadata.json\" argo=true error=\"stat /mlpipeline-ui-metadata.json: no such file or directory\"\r\nsample-test-7c4kw-3475281298: parameterized-tfx-oss-c6n2c-1861378462: Error: exit status 2\r\n```", "Thank you Yuan for finding this issue!\r\n\r\nWill it be related to the tfx version in the https://github.com/kubeflow/pipelines/blob/master/samples/core/parameterized_tfx_oss/parameterized_tfx_oss.py#L167?\r\n\r\nMaybe at least we should upgrade tfx version to 1.4.0 in the parameterized_tfx_oss sample first, then investigate further for the issue?\r\n\r\nTFX release changelog: https://github.com/tensorflow/tfx/blob/master/RELEASE.md", "Thanks! I agree that we need to upgrade the code @zijianjoy pointed.\r\nBy the way, how about using the [default value](https://github.com/tensorflow/tfx/blob/d00b80c92792ec202564e2e90d081b361835e314/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L54) to avoid this kind of failure? It uses the image in the docker hub, then we don't need to update the version every time. (Or we can use tfx.version.__version__ in the parameterized_tfx_oss.py.)", "> Thanks! I agree that we need to upgrade the code @zijianjoy pointed.\n> By the way, how about using the [default value](https://github.com/tensorflow/tfx/blob/d00b80c92792ec202564e2e90d081b361835e314/tfx/orchestration/kubeflow/kubeflow_dag_runner.py#L54) to avoid this kind of failure? It uses the image in the docker hub, then we don't need to update the version every time. (Or we can use tfx.version.__version__ in the parameterized_tfx_oss.py.)\n\nUsing version.__version__ sounds like a good idea, because we don't want new image releases to break the sample.", "Have come across the same problem with upgrading from 1.2 to 1.4. My kubeflow run fails with a final log of:\r\nusage: container_entrypoint.py [-h] --pipeline_root PIPELINE_ROOT\r\n --kubeflow_metadata_config\r\n KUBEFLOW_METADATA_CONFIG --serialized_component\r\n SERIALIZED_COMPONENT --tfx_ir TFX_IR --node_id\r\n NODE_ID [--runtime_parameter RUNTIME_PARAMETER]\r\ncontainer_entrypoint.py: error: the following arguments are required: --serialized_component\r\n\r\n@Bodgy Am I missing somthing but in the PR I'm not seeing how this is being fixed and the issue is getting closed? Is there a work around or will the PR above simply mean 1.5 shouldn't have the same issue?", "Hi, @nroberts1, I think that you have to upgrade the TFX version installed in the container image, too. [The fix](https://github.com/kubeflow/pipelines/commit/3cfff3db1da0906a46980ce4f2b7ceda7458272a) updated the image to the TFX version of the client environment.", "Yes that works I understand the fix now, thanks @jiyongjung0 " ]

"2021-11-29T04:18:29"

"2021-12-23T07:59:05"

"2021-12-02T10:00:06"

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[]

[ "I did some investigation, the root cause is because when creating pipeline, the pipeline api will override the name to pipeline/xxx format, FYI \r\n\r\nhttps://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/backend/src/apiserver/resource/resource_manager.go#L263https://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/backend/src/apiserver/resource/resource_manager.go#L263\r\nand https://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/backend/src/apiserver/template/v2_template.go#L105\r\n\r\nAnd the backend compiler generated name based on the pipeline name https://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/v2/compiler/argo.go#L61\r\n\r\nI found the backend db still store the pipeline name like `v2upload` instead of `namespace/xxx` or `pipeline/xxx`\r\nThe backend compiler currently does not work with such override logic https://github.com/kubeflow/pipelines/blob/cfdfe71a7e4944a3d79f0731ea8d7170738d40e5/backend/src/apiserver/template/v2_template.go#L105\r\n\r\nI think we need to revisit the backend compiler logic to make sure it works with the pipeline v2 api.\r\n\r\ncc @Bobgy \r\n\r\n\r\n" ]

"2021-11-28T05:36:41"

"2021-12-08T05:48:59"

"2021-12-08T05:48:59"

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[]

[ "Thank you for the report! I created the fix https://github.com/kubeflow/website/pull/3081" ]

"2021-11-26T23:16:56"

"2021-11-28T12:58:04"

"2021-11-28T12:58:04"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-11-26T06:03:28"

"2022-03-02T10:06:22"

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[]

[ "An start step would be next code playing with the source of the code to execute it dynamically. But would only work with single output's and you still need to import properly the components as functions (which is verbose)\r\n\r\nstart_step could be confusing and out of the current scope but since this is for debugging, seems a potential useful feature to me\r\n```py\r\ndef run_pipeline_local(start_step=None):\r\n func_steps_output = dict(\r\n step_1=\"step_1_output\",\r\n step_2=\"step_2_output\",\r\n ...)\r\n \r\n func_steps = list(func_steps_output.keys())\r\n\r\n if start_step is not None:\r\n if start_step not in func_steps:\r\n raise ValueError\r\n print(f\"start_step: {start_step}\")\r\n start_index = func_steps.index(start_step)\r\n else:\r\n start_index = -1\r\n\r\n func_imports = {k: f\" def {k}(*args, **kwargs): return {v}\" for k, v in func_steps_output.items()}\r\n func_imports = [v for k, v in func_imports.items() if func_steps.index(k) < start_index]\r\n func_imports = '\\n'.join(func_imports)\r\n from test_module.pipeline import pipeline as func\r\n func_source = inspect.getsource(func)\r\n # delete kfp stuff\r\n func_source = func_source.replace('.output', '')\r\n func_source = func_source.replace('.component_spec', '')\r\n func_source_index_def = func_source.index(\"def churn_pipeline\")\r\n func_source = func_source[func_source_index_def:]\r\n func_source_index_end_def = func_source.index(\"):\")\r\n func_source_def = func_source[:func_source_index_end_def + 3]\r\n func_source_body = func_source[func_source_index_end_def + 3:]\r\n kwargs = {}\r\n # kwargs['start_step'] = start_step\r\n # kwargs['experiment_id'] = experiment_id\r\n exec_source = f\"{func_source_def}\\n{func_imports}\\n{func_source_body}\\n{func.__name__}(**kwargs)\"\r\n print(exec_source)\r\n exec(exec_source)\r\n```", "Do you want to test and debug your python component in local environment?", "Yep, this is for components based on py, not prebuild ones like google_cloud_pipeline_components (but run them also in local would be cool)\r\nThis is in order to\r\na) avoid having 2 files: kfp-pipeline & local-pipeline\r\nb) enter debug mode\r\nc) avoid building the docker image and release for debugging", "cc @chensun ", "Components and pipelines are quite different--one is a containerized app with optional Python function implementation, the other is a set of components organized in a DAG. I'd suggest we clearly define the scope of this request first.\r\n\r\nIt's on our roadmap to support local debugging/testing of a primitive component, but we are not planning for local debugging of pipelines--pipeline requires an orchestration engine, to fully support running a pipeline locally is almost like reimplement KFP backend. ", "yep, the function `run_pipeline_local` defined above only covers the debugging of pipelines with only py-func components (i'm not sure if this is what you call primitive component)\r\n\r\nagree that to locally debug all types of components will be out of this scope, 'cause is far more complex.", "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", "@chensun Do you have any details/updates on `It's on our roadmap to support local debugging/testing of a primitive component`? Thanks much!" ]

"2021-11-24T17:01:06"

"2023-05-02T17:23:30"

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[]

[ "Thank you for creating this issue!\r\n\r\n\r\nFor the discussion of this doc title, I have a couple questions:\r\n\r\n1. Is there any plan for compatibility matrix beyond tfx in the future? (Say PytorchX, MLMD, argo, etc.)\r\n2. How does user find the right combination of ML frameworks+KFP nowadays? (Or how do we expect users to do to find out such combination) For example: A Kubeflow Pipelines user wants to use TFX, therefore they search `What TFX version to use in Kubeflow Pipelines` at Google to find out.", "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" ]

"2021-11-24T00:39:36"

"2022-03-02T10:06:20"

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[ "I encountered a similar issue (but not the same).\r\nWhen I ran:\r\n```\r\n@func_to_container_op\r\ndef op_0(flag: bool) -> bool:\r\n return flag\r\n\r\n@func_to_container_op\r\ndef op_1(flag: bool) -> bool:\r\n return flag\r\n\r\n@func_to_container_op\r\ndef op_2(flag: bool) -> bool:\r\n return flag\r\n\r\n@func_to_container_op\r\ndef op_3(flag: bool):\r\n print(f\"flag={flag}\")\r\n\r\n@dsl.pipeline(name=\"sample\", description=__doc__)\r\ndef sample_pipeline(\r\n flag: bool = True,\r\n) -> None:\r\n a = op_0(flag)\r\n with dsl.Condition(flag == False):\r\n b = op_1(a.output)\r\n with dsl.Condition(flag == True):\r\n b = op_2(a.output)\r\n op_3(b.output)\r\n```\r\n, it caused the following error messages 🤔 :\r\n```\r\nkfp_server_api.exceptions.ApiException: (500)\r\nReason: Internal Server Error\r\nHTTP response headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type': 'application/json', 'date': 'Wed, 08 Dec 2021 01:19:05 GMT', 'content-length': '790', 'connection': 'close'})\r\nHTTP response body: {\"error\":\"Failed to create a new run.: InternalServerError: Failed to validate workflow for (): templates.sample.tasks.condition-2 templates.condition-2.outputs failed to resolve {{tasks.op-2.outputs.parameters.op-2-Output}}\",\"code\":13,\"message\":\"Failed to create a new run.: InternalServerError: Failed to validate workflow for (): templates.sample.tasks.condition-2 templates.condition-2.outputs failed to resolve {{tasks.op-2.outputs.parameters.op-2-Output}}\",\"details\":[{\"@type\":\"type.googleapis.com/api.Error\",\"error_message\":\"Internal Server Error\",\"error_details\":\"Failed to create a new run.: InternalServerError: Failed to validate workflow for (): templates.sample.tasks.condition-2 templates.condition-2.outputs failed to resolve {{tasks.op-2.outputs.parameters.op-2-Output}}\"}]}\r\n```\r\n\r\nWhen I commented out the last line `op_3(b.output)`, it worked correctly.", "I have been facing this issue too, basically anytime a variable could have two possible values due to being the result of a condition, on one of the conditions the pipeline will fail with:\r\n`This step is in Error state with this message: Unable to resolve: {{tasks.condition-Full-Experiment-1.outputs.artifacts.split-dataset-output_train}}`\r\n\r\nIm not sure if this is supposed to be the expected behaviour, but the only workarounds I was able to find is to put the rest of the pipeline that would depend upon the output ,separated in the conditional branch or to encapsulate the \"steps\" and returning multiple values. It doesn't seem intuitive to me but I was not able to find any other workarounds..\r\nAn example:\r\n\r\n### Separating steps in the conditionals\r\n```\r\n@dsl.pipeline(\r\n name='addition-pipeline',\r\n description='An example pipeline that performs addition calculations.',\r\n)\r\ndef add_pipeline(a: float, b: float,\r\n c: float, d: float,\r\n is_true: bool = True\r\n):\r\n\r\n add_task = add1(a, b)\r\n\r\n with dsl.Condition(is_true == True, name='is-True'):\r\n add_task_2_cd = add2(c, d)\r\n add_task_3 = add3(a, add_task_2_cd.output)\r\n with dsl.Condition(is_true == False, name='is-False'):\r\n add_task_2_ac = add2(a, c)\r\n add_task_3 = add3(a, add_task_2_ac.output) \r\n```", "I have encountered the same stuff. Much like raphael, I'm not sure if it's a bug or not.", "+1 to this. In my case I was trying to do two branches, one for hyperparameter tuning and one for not, then deployment steps following. I get this same error when trying to bring both conditional paths back to a common op.", "+1, I'm facing the same issue. This is what I'm trying to do\r\n1. Parse choices\r\n2. Execute a container based on a condition and produce an output\r\n3. Execute another container based on another condition and consume the output from step 2 above. \r\n\r\nTriggering a run ends up with \r\nkfp_server_api.exceptions.ApiException: (500)\r\nReason: Internal Server Error\r\nHTTP response headers: HTTPHeaderDict({'X-Powered-By': 'Express', 'content-type': 'application/json',\r\nHTTP response body: {\"error\":\"Failed to create a new run.: InternalServerError: Failed to validate workflow for (): templates.sample.tasks.condition-2 templates.condition-2.outputs failed to resolve {{tasks.op-2.outputs.parameters.op-2-" ]

"2021-11-23T15:35:36"

"2023-01-31T04:07:36"

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[]

[ "Well. A colleague found the undocumented solution. In case someone else wonders how you can rename the task:\r\n\r\n```\r\n task_get_model = op_get_minio_file(\r\n minio_uri=minio_model_uri\r\n )\r\n task_get_model.apply(use_aws_secret(\r\n 'mlpipeline-minio-artifact',\r\n 'accesskey',\r\n 'secretkey'\r\n ))\r\n task_get_model.set_display_name('load modelfile')\r\n```" ]

"2021-11-23T13:22:18"

"2021-11-23T13:31:43"

"2021-11-23T13:31:42"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-11-22T18:09:08"

"2022-03-02T10:06:24"

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[]

[ "PR #7049", "IIUC, what you want to achieve after the PR is something like this:\r\n\r\n```python\r\nfrom kfp_test.test import test\r\n \r\ntest_op = create_component_from_func(func=test, base_image=base_image)\r\n```\r\n\r\nThere're a couple of issues with this approach:\r\n1. It requires installing the `kfp_test` package locally in order to compile, but that's really not necessary.\r\n2. It makes an assumption that the locally installed package is the same version as that in the base image. But that's not something we can verify here.\r\n3. If we want to try different versions of the base image--assuming there's interface changes to the test function--it would be a big pain as we have to remember to update the local package to match the image or switch among different virtual environments.\r\n", "yep, you're right.\r\n\r\nThen another approach would be to call the `base_image` inside py with an entrypoint like\r\n\r\n```sh\r\nFUNC_SOURCE=${python3 -m kfp.v2.component_factory.get_func_source(func)}\r\n```\r\n\r\nwith `kfp.v2.component_factory.get_func_source` as in the PR:\r\n\r\n```py\r\ndef get_func_source(func):\r\n name = func.__name__\r\n signature = inspect.signature(func)\r\n func_source = f' def {name}{signature}:\\n' \\\r\n f' kwargs = locals()\\n ' \\\r\n f'from {func.__module__} import {name}\\n ' \\\r\n f'return {name}(**kwargs)\\n'\r\n return func_source\r\n```\r\n\r\nWith this we solve your problems cause now the usage would be\r\n```py\r\ntest_op = create_component_from_func(func='main_module.test', base_image=base_image)\r\n````\r\n\r\nBut main problem that I see now is that you need the image up and running before compiling the json.\r\nSomething like\r\n\r\n* start container (in whatever your server is)\r\n* run entrypoint\r\n* compile json\r\n* run pipeline (in whatever your server is)\r\n\r\nIf you have a server that's able to run pipelines, you should be able to start containers too. But I admit, that this back and forth is not very clean.\r\n\r\nWhat are your thoughts?", "I'm not sure I understand. \r\nStarting a container involves running its entrypoint. The compiled json tells the backend system how to run a container.", "entrypoint would be\r\n```sh\r\npython3 -m kfp.v2.component_factory.get_func_source(func)\r\n```\r\n\r\nthe idea would be to start the container alone before the pipeline to get the func definition. And then with the info, define the components and run the pipeline.\r\n\r\nYou can also think it as running first a one-step pipeline, with the `get_func_source` . \r\nAnd then setting up the components args for the desired pipeline\r\n\r\nAs said, is a little bit back and forth...\r\n\r\n", "> entrypoint would be\r\n> \r\n> ```shell\r\n> python3 -m kfp.v2.component_factory.get_func_source(func)\r\n> ```\r\n> \r\n> the idea would be to start the container alone before the pipeline to get the func definition. And then with the info, define the components and run the pipeline.\r\n> \r\n> You can also think it as running first a one-step pipeline, with the `get_func_source` . And then setting up the components args for the desired pipeline\r\n> \r\n> As said, is a little bit back and forth...\r\n\r\nI see. Probably not a good idea- that's even heavier than the current state...", "yep...\r\nAnway, if someone out there is in a similar situation:\r\nWants to convert py-funcs in a custom py-package into kfp-py-func components. But without changing the code (i.e. without putting the imports inside, etc.) \r\nHere you have an util `create-wrapper-components` that will create a components.py file automatically [link](https://github.com/iuiu34/create-wrapper-components-kfp)\r\n\r\nRequirements:\r\n- your functions should have type hint for input and output args (and only kfp-py-func available types)\r\n- your base_image for the components and the image/pc where you run the `create-wrapper-components` should have the same `your_custom_package` version", "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" ]

"2021-11-22T02:04:07"

"2022-04-17T06:27:26"

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[ "This sounds like a useful feature to add. \r\n\r\nHello Xiyue, would you like to take a look and see if we are able to support filtering by job status from backend? Reference: https://github.com/kubeflow/pipelines/blob/949cfdca3794416210805ca1579adbd44ebec592/backend/src/apiserver/storage/job_store.go#L59\r\n\r\n/assign @capri-xiyue ", "@jli , would you like to contribute to this feature? We are happy to provide resources and areas to change if you want to implement it.", "@zijianjoy I don't think I have the time at the moment to contribute unfortunately, sorry!", "@jli , that is okay, we will keep this feature open for anyone interested.", "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" ]

"2021-11-19T21:16:13"

"2022-04-21T00:22:01"

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[]

[]

[ "How do you solve this problem?", "For version 1.4 from what I see driver image is hardcoded in driver.go " ]

"2021-11-19T15:04:47"

"2022-01-22T20:01:28"

"2021-11-30T13:55:30"

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[]

[ "Do you see any errors when the pipeline run? Can you share your pipeline yaml?", "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" ]

"2021-11-19T12:52:34"

"2022-03-03T02:05:09"

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[]

"2021-11-19T12:07:54"

"2021-11-20T18:18:36"

"2021-11-20T18:18:36"

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[ "This specific one is because of the mix use of v1 and v2 imports in the test sample file\r\nhttps://github.com/kubeflow/pipelines/blob/feebc6b66a719dc950c84500e00d23e36b37ae34/samples/v2/producer_consumer_param.py#L16-L19\r\n\r\nwhich I'm fixing in https://github.com/kubeflow/pipelines/pull/6932", "I wonder whether we can return a meaningful error message. This one is impossible to understand why.", "> I wonder whether we can return a meaningful error message. This one is impossible to understand why.\r\n\r\nAgree, will try to improve the message.", "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" ]

"2021-11-19T11:14:39"

"2022-03-02T10:06:27"

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[]

[ "@pretidav Can you share your pipeline component yaml?", "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" ]

"2021-11-19T08:16:37"

"2022-03-03T02:05:11"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-11-18T21:25:18"

"2022-03-02T10:06:29"

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[]

[ "There is no KFP profile controller image, it is by design that we allow user to customize profile controller behavior as followed.\r\n\r\nYou can change https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/base/installs/multi-user/pipelines-profile-controller/sync.py#L161 in the way that you want to append labels, then apply the manifests to achieve your goal. This file will be packaged into configmap, and be executed as is during runtime." ]

"2021-11-18T21:13:49"

"2021-11-19T01:14:22"

"2021-11-19T01:14:22"

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[]

[]

[ "Can you look at the the yaml definition, events and logs in `ml-pipeline`? \r\n\r\n```\r\nkubectl logs my-pod --previous \r\n```\r\n\r\nhttps://kubernetes.io/docs/reference/kubectl/cheatsheet/#interacting-with-running-pods", "> kubectl logs my-pod --previous\r\n\r\nThanks for the reply. \r\n\r\n**ml-pipelines: logs**\r\n\r\n```bash\r\nI1119 07:39:09.049099 7 client_manager.go:160] Initializing client manager\r\nI1119 07:39:09.049182 7 config.go:57] Config DBConfig.ExtraParams not specified, skipping\r\n```\r\n\r\n**ml-pipelines: events**\r\n\r\n```bash\r\n25m Warning Unhealthy pod/ml-pipeline-f55c9d8cf-xbd2m Readiness probe failed:\r\n9m53s Warning BackOff pod/ml-pipeline-f55c9d8cf-xbd2m Back-off restarting failed container\r\n5m1s Warning Unhealthy pod/ml-pipeline-f55c9d8cf-xbd2m Liveness probe failed:\r\n```\r\n\r\nml-pipelines-apiserver-deployment.yaml\r\n\r\n```yaml\r\napiVersion: apps/v1\r\nkind: Deployment\r\nmetadata:\r\n labels:\r\n app: ml-pipeline\r\n name: ml-pipeline\r\nspec:\r\n selector:\r\n matchLabels:\r\n app: ml-pipeline\r\n template:\r\n metadata:\r\n labels:\r\n app: ml-pipeline\r\n annotations:\r\n cluster-autoscaler.kubernetes.io/safe-to-evict: \"true\"\r\n spec:\r\n securityContext:\r\n runAsUser: 1000\r\n containers:\r\n - env:\r\n - name: AUTO_UPDATE_PIPELINE_DEFAULT_VERSION\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: autoUpdatePipelineDefaultVersion\r\n - name: POD_NAMESPACE\r\n valueFrom:\r\n fieldRef:\r\n fieldPath: metadata.namespace\r\n - name: OBJECTSTORECONFIG_SECURE\r\n value: \"false\"\r\n - name: OBJECTSTORECONFIG_BUCKETNAME\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: bucketName\r\n - name: DBCONFIG_USER\r\n valueFrom:\r\n secretKeyRef:\r\n name: mysql-secret\r\n key: username\r\n - name: DBCONFIG_PASSWORD\r\n valueFrom:\r\n secretKeyRef:\r\n name: mysql-secret\r\n key: password\r\n - name: DBCONFIG_DBNAME\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: pipelineDb\r\n - name: DBCONFIG_HOST\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: dbHost\r\n - name: DBCONFIG_PORT\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: dbPort\r\n - name: DBCONFIG_CONMAXLIFETIMESEC\r\n valueFrom:\r\n configMapKeyRef:\r\n name: pipeline-install-config\r\n key: ConMaxLifeTimeSec\r\n - name: OBJECTSTORECONFIG_ACCESSKEY\r\n valueFrom:\r\n secretKeyRef:\r\n name: mlpipeline-minio-artifact\r\n key: accesskey\r\n - name: OBJECTSTORECONFIG_SECRETACCESSKEY\r\n valueFrom:\r\n secretKeyRef:\r\n name: mlpipeline-minio-artifact\r\n key: secretkey\r\n image: registry.app.corpintra.net/dna/ml-pipeline/api-server:dummy\r\n imagePullPolicy: IfNotPresent\r\n name: ml-pipeline-api-server\r\n ports:\r\n - name: http\r\n containerPort: 8888\r\n - name: grpc\r\n containerPort: 8887\r\n readinessProbe:\r\n exec:\r\n command:\r\n - wget\r\n - -q # quiet\r\n - -S # show server response\r\n - -O\r\n - \"-\" # Redirect output to stdout\r\n - http://localhost:8888/apis/v1beta1/healthz\r\n initialDelaySeconds: 3\r\n periodSeconds: 5\r\n timeoutSeconds: 2\r\n livenessProbe:\r\n exec:\r\n command:\r\n - wget\r\n - -q # quiet\r\n - -S # show server response\r\n - -O\r\n - \"-\" # Redirect output to stdout\r\n - http://localhost:8888/apis/v1beta1/healthz\r\n initialDelaySeconds: 3\r\n periodSeconds: 5\r\n timeoutSeconds: 2\r\n resources:\r\n requests:\r\n cpu: 250m\r\n memory: 500Mi\r\n serviceAccountName: ml-pipeline\r\n```\r\n\r\nAlso, I want to add here (maybe this can help) that the DBs are not created inside the MySQL Pod\r\n\r\n\r\n\r\n\r\n\r\nLet me know if you need anything more. Thanks for helping me out.", "Closing this issue because it was a problem of our Network Policy. Thanks for the help.", "I'm having the exact same error, can you mind elaborating what your issue was? ", "I am also facing the same error. Would u elaborate on how you fixed the issue?", "Hello any idea, how to fix this error?\r\n", "Can you explain what was the fix?", "If you are looking for a working solution, we open-sourced our code. \r\n\r\nYou can find it in this repo: 👉 https://github.com/mercedes-benz/DnA" ]

"2021-11-18T11:56:17"

"2023-04-19T10:22:06"

"2021-11-19T15:22:14"

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[ "/assign @zijianjoy ", "@zijianjoy I gave this a try in #6920. Let me know if I'm on the right track--or if it's easier for you to do this instead.", "@jmcarp If you add me to edit your branch https://github.com/jmcarp/pipelines/tree/jmcarp/argo-workflows-v3.2, I can take care of the image upload part in https://github.com/kubeflow/pipelines/tree/master/third_party/argo#upgrade-argo-image", "@zijianjoy you might already have access because I checked `Allow edits by maintainers` on the PR, but just in case I also added you to my fork." ]

"2021-11-17T02:27:30"

"2021-12-01T06:19:14"

"2021-12-01T06:19:14"

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[ "Switch to ts-proto: https://github.com/kubeflow/pipelines/commit/d60bc99bb61a9f23fce8eabd78634236e45a5dc1\r\n\r\nWe are pending for parameter for create run to be implemented.\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", "/lifecycle frozen" ]

"2021-11-16T17:53:48"

"2022-08-24T05:40:16"

"2022-08-24T05:40:16"

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[]

[ "Hey how did you solve this?", "@kciy \r\n\r\nHey, the problem was the Kind cluster.\r\nFor some reason no pod was able to detect the GPU on the host, once I shifted to an AWS EKS the same exact configuration worked...\r\nI didn't find a way to use the \"--gpus all\" flag in Kind." ]

"2021-11-16T13:37:48"

"2021-12-20T15:28:57"

"2021-12-09T15:53:06"

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[ "@tomalbrecht This feature is only supported in v1, but not v2 compatible. ", "@zijianjoy Could we get an update on this issue? \r\n\r\nIt looks like it received quite a few thumbs up but was not addressed in v2 compatible mode issue tacker https://github.com/kubeflow/pipelines/issues/6132\r\n\r\nI think you guys use thumbs up to help prioritizing issue, I just want to express that the issue I linked seems linked to this one and had a good amount of thumbs up that might not be reflected by this page.", "@AlexandreBrown Thank you for bringing this up!\r\n\r\nWe have put the V2 compatible work on hold and currently work fully to V2. We are currently tracking it in our KFP V2 project. That said, we are aware of this requirement and working towards it. The thumbs-up information helps us prioritize the work in the V2 timeline. ", "@zijianjoy : Please can you provide an update on this issue kubeflow/pipelines#7339", "I think we need to make some design decision in order to support this feature on KFPv2. It will be very helpful if you can provide information like this:\r\n\r\n1. What kind of metrics do you want to show on RunList? (For example: accuracy, roc score)\r\n2. How do you want to export these metrics in your pipeline? (For example: create a component with a fixed name that generates parameters with name accuracy/roc-score.)\r\n3. How do you want to use this feature on UI? (Show on RunList? Show on a different page? Do you need Filtering/Sorting?) Do you need any customization?", "We no longer support v2 compatible mode, and the general metrics visualization is support in v2. \r\nOne of the fixing PRs: https://github.com/kubeflow/pipelines/pull/7905" ]

"2021-11-16T10:24:55"

"2023-01-18T08:53:20"

"2023-01-18T08:53:19"

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[]

[]

[ "The failure is caused by https://github.com/tensorflow/tfx/commit/f796bcb059afa612a3ea9734b7c5e58cb2f2c782 which depends on https://github.com/google/ml-metadata/commit/aa492c474f8b3a54dbb6461ddb7b35c7e69b4a47,\r\nyet none of the changes are in a released package.\r\n\r\nIdeally, the ml-metadata change should be released first, then tfx can make the change with the ml-metadata dependency update. \r\nAssuming the tfx change won't be reverted, which is likely the case, we'll need to wait for the next ml-metadata release and the following dependency updates in tfx.\r\n\r\n/cc @jiyongjung0 " ]

"2021-11-13T07:53:39"

"2021-11-13T09:19:40"

"2021-11-13T09:19:40"

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[]

[ "Hello @andrijaperovic , are you able to run a sample code using ProcessPoolExecutor in an independent Kubernetes pod successfully? It doesn't seem like an issue related to Kubeflow.", "Hi @zijianjoy , looks like the issue was caused primarily by the node pool where the pipeline task was being scheduled which has a limited number of cores (vCPU's). Once we allocated a node pool in our k8s cluster with a suitable number of vCPU's, the pipeline ran successfully. Will go ahead and close this issue. Thanks for your help." ]

"2021-11-12T20:26:39"

"2021-11-19T01:59:40"

"2021-11-19T01:59:40"

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[ "/assign @zijianjoy ", "Please guide on how to provide value pipeline/workspace and name in artifact via KFP library\r\nfacing same issue", "This is fixed by https://github.com/kubeflow/pipelines/pull/6989. We are planning to make new release for it." ]

"2021-11-12T13:08:41"

"2022-02-04T20:51:47"

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[]

[ "cc @chensun ", "Hi @haoxins, in order not to break our existing users, we will not update the image to 3.8 until we reach a point of deprecating 3.7. Thanks for the advice!" ]

"2021-11-12T03:54:06"

"2021-12-13T19:45:26"

"2021-12-13T19:45:26"

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[]

[ "Hi @kabilan6 . There aren't any specific code samples for config maps but It should work the same way as other Kubernetes objects. For creating a PVC you can leverage [VolumeOp](https://github.com/kubeflow/pipelines/tree/master/samples/core/volume_ops) which utilises ResourceOps behind the scene. You can also leverage pre-built components by the community (https://github.com/kubeflow/pipelines/tree/master/components/contrib/kubernetes). ", "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" ]

"2021-11-11T21:46:06"

"2022-03-02T10:06:36"

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[]

[ "Can you try to use emissary executor https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#emissary-executor", "> Can you try to use emissary executor https://www.kubeflow.org/docs/components/pipelines/installation/choose-executor/#emissary-executor\r\nThanks for your suggestions, I will try. After I use emissary executor, can I still use docker image? My pipeline has problems only after it has all four components, and it can work normally when it has only four or less components. Why is this?\r\n", "@SeibertronSS \r\n\r\nHow does the pipeline run work after applying emissary executor? \r\n\r\nFor the error message of your issue, would you like to look at this SO post to see if this applies to your deployment resource yaml? https://stackoverflow.com/questions/64990279/docker-error-process-linux-go319-getting-the-final-childs-pid-from-pipe-cause\r\n\r\nIf your pipeline run still fails, would you like to share your pipeline template which has 4 components?", "> @SeibertronSS\r\n> \r\n> How does the pipeline run work after applying emissary executor?\r\n> \r\n> For the error message of your issue, would you like to look at this SO post to see if this applies to your deployment resource yaml? https://stackoverflow.com/questions/64990279/docker-error-process-linux-go319-getting-the-final-childs-pid-from-pipe-cause\r\n> \r\n> If your pipeline run still fails, would you like to share your pipeline template which has 4 components?\r\n\r\nAfter our investigation, we found that there was a problem with the routing table of our cluster, which caused duplicate ClusterIP to appear. I will close this issue. Thank you very much for your time." ]

"2021-11-11T14:16:41"

"2021-11-19T06:36:17"

"2021-11-19T06:36:17"

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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", "I'm having this problem too, any update?", "If you are able to structure your code so that you pass a directory instead (containing the files you're interested in), I think that should work. Alternatively you can use mounted volumes to pass arbitrary files.\r\n\r\nBut AFAIK there's no fix for this specific issue.", "Hello, I'm very new to Kubeflow, just stumbled on this issue when I was looking for a solution for a similar issue. \r\nIn my case, I've a Modeling function that consumes a dataframe created by another function and also it requires to consume output path from a previous function that handles the download, so that it can read the files. I was wondering if there is an option to pass multiple InputPaths to a container. As I mentioned, I'm very new to ML and MLOps, so if anything that I've mentioned is clear, I can try to explain again. Please let me know", "Please ignore my question, I figured I can use InputPath twice in the same function and point them to different artifcats from previous containers. ", "Yup, if you just have two paths that works. It doesn't work well for a large number of paths / dynamic number of paths though." ]

"2021-11-11T10:07:09"

"2022-12-23T07:43:35"

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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" ]

"2021-11-11T08:23:02"

"2022-03-02T10:06:41"

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[ "/cc @capri-xiyue @Bobgy ", "/cc @james-jwu ", "My 2 cents, we should also try to keep the interface clean enough so that not just argo or tekton but airflow or any work flow engine should be able to integrate well. ", "> My 2 cents, we should also try to keep the interface clean enough so that not just argo or tekton but airflow or any work flow engine should be able to integrate well.\r\n\r\nYes, we will try to keep Argo and Tekton names out of the pictures for this new interface. ", "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" ]

"2021-11-10T02:01:40"

"2022-03-02T10:06:18"

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[]

[ "In kubeflow you can use exit handler https://github.com/kubeflow/pipelines/blob/927d2a9f2dfdb90ae156979b9e0d72afa14adcd6/samples/core/exit_handler/exit_handler.py#L47. Ref: https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ExitHandler", "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" ]

"2021-11-09T15:30:55"

"2022-03-02T11:00:52"

"2022-03-02T11:00:52"

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"2021-11-08T14:39:30"

"2021-11-23T19:35:09"

"2021-11-23T19:35:09"

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[]

[ "Not sure if there's anything special with `/dev/shm`. But KFP DSL v1 supports `add_volume` and `add_volume_mount`. Would that work for you?", "Hi @arunnalpet, I encountered this problem too, and I resolved as follows.\r\n\r\n```python\r\nimport kfp.dsl as dsl\r\nimport kubernetes as k8s # should pip install kubernetes first\r\n\r\n@dsl.pipeline(name=\"test_training\")\r\ndef training_pipeline():\r\n volume = dsl.PipelineVolume(volume=k8s.client.V1Volume(\r\n name=\"shm\",\r\n empty_dir=k8s.client.V1EmptyDirVolumeSource(medium='Memory')))\r\n\r\n training_op.add_pvolumes({'/dev/shm': volume})\r\n\r\n```\r\n\r\nHope this helps.\r\n\r\n*Rerference: https://stackoverflow.com/a/63806311*\r\n*As for why medium is set to **Memory** is described [here](https://kubernetes.io/docs/concepts/storage/volumes/#emptydir)*", "Thanks @b02202050 .. let me try this out, and will close the issue subsequently.. ", "Adding @b02202050 code results in `This step is in Error state with this message: OOMKilled (exit code 137)` after training step in `wait` container.\r\n" ]

"2021-11-08T06:41:38"

"2022-02-24T15:42:25"

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[]

[ "Thanks for the report, this is fixed in https://github.com/kubeflow/pipelines/pull/6440, I expect the next release to fix it." ]

"2021-11-05T17:33:52"

"2021-11-12T00:46:17"

"2021-11-12T00:46:17"

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[]

[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-11-05T09:02:12"

"2022-03-02T10:06:47"

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[ "Hello @jli , we should probably change the fetching of all visualization artifacts when user switch to `Run output` tab on top of Run Details page. https://github.com/kubeflow/pipelines/blob/master/frontend/src/pages/RunDetails.tsx#L581-L615", "@zijianjoy, I also noticed that the run details page refreshes artifacts periodically until the run is finished, meaning that we load mostly the same artifacts many times. Can artifacts change for a given step after they're written? I was wondering if we needed to keep refetching artifacts for completed nodes.", "@jmcarp After Artifact is written, its state can still be changed (For example: MARK_FOR_DELETE). But we don't need to fetch Artifact if user is not on `Run Output` tab, so by default you are not fetching all artifacts on `Graph` tab. We can separate the fetching mechanism among tabs in `RunDetails.tsx` file. ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

"2021-11-04T20:23:35"

"2022-03-23T06:11:04"

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[ "cc @chensun ", "Hi @haoxins, thanks for opening this issue. \r\nWe're removing google-api-python-client for v2. \r\nPlease use https://github.com/googleapis/python-aiplatform for submitting pipeline jobs. " ]

"2021-11-04T09:53:14"

"2021-11-10T22:19:26"

"2021-11-10T22:19:26"

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[ "Looks like this is related to keras 2.7.\r\nTest was passing previously when it installed keras 2.6\r\n https://oss-prow.knative.dev/view/gs/oss-prow/pr-logs/pull/kubeflow_pipelines/6854/kubeflow-pipelines-tfx-python37/1455697869322326016", "Root cause: https://github.com/keras-team/keras/issues/15579" ]

"2021-11-03T20:41:49"

"2021-11-03T22:51:22"

"2021-11-03T22:51:22"

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[]

[ "@Bobgy @zijianjoy Please can you take a look at this issue.", "I think this is resolved in.the later version of the SDK.\r\n@jdalbosc-cisco Can you please upgrade your sdk and try again?", "@chensun Thank you for your reply.\r\n\r\nI've upgraded kfp to 1.8.9 and indeed the problem is solved. The content of the yaml argo workflow is now:\r\n```\r\n resources:\r\n limits: {nvidia: '{{inputs.parameters.num_gpus}}', memory: 4G}\r\n```\r\nWhich is what I wan't I guess (not sure about nvidia not being nvidia/gpu but apart from that it's ok).\r\n\r\nNow the problem is that when I try to load this yaml in Kubeflow 1.3 UI I have an error message complaining about the format of the quantity not being valid:\r\n\r\n**Pipeline version creation failed**\r\n_{\"error_message\":\"Error creating pipeline version: Create pipeline version failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'\",\"error_details\":\"Error creating pipeline version: Create pipeline version failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'\"}_\r\n\r\nI guess I might have to update to a newer version of Kubeflow like 1.4 (which I am not in control of)? Is there a compatibility matrix between Kfp and Kubeflow?", "Hello @jdalbosc-cisco , can you upload a simplified version of your pipeline version file? So we can try it out on the latest version of KFP backend v1.7.1 to see whether this issue is fixed.", "Hello @zijianjoy,\r\n\r\nThank you for your reply.\r\n\r\nHere are two files that enable to reproduce the problem (renamed to .txt to workaround Github extensions filtering). The first one is the code that compiles the pipeline, and the second one is the pipeline yaml file created with the latest version of Kfp 1.8.9.\r\n\r\nWhen I try to upload the gpu_sum.yaml file in the _Upload Pipeline or Pipeline version_ of the Kubeflow 1.3 UI and click on _Create_ to create the pipeline, I have the following error message that shows up in the UI that prevents me from uploading the pipeline:\r\n\r\n**Pipeline version creation failed**\r\n\r\n_{\"error_message\":\"Error creating pipeline: Create pipeline failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'\",\"error_details\":\"Error creating pipeline: Create pipeline failed: Failed to get parameters from the workflow: InvalidInputError: Failed to parse the parameter.: error unmarshaling JSON: quantities must match the regular expression '^([+-]?[0-9.]+)([eEinumkKMGTP]*[-+]?[0-9]*)$'\"}_\r\n\r\nHope this helps.\r\nRegards,\r\n\r\n[compile.py.txt](https://github.com/kubeflow/pipelines/files/7481670/compile.py.txt)\r\n[gpu_sum.yaml.txt](https://github.com/kubeflow/pipelines/files/7481671/gpu_sum.yaml.txt)\r\n\r\n\r\n", "This seems like an argo issue, backend doesn't accept this format because of this line in gpu_sum.yaml:\r\n```\r\nlimits: {memory: 100M, nvidia: '{{inputs.parameters.num_gpus}}'} \r\n```\r\n\r\nIt doesn't fit in the format validation:\r\n\r\n```\r\n'^([+-]?[0-9.]+)([eEinumkKMGTP][-+]?[0-9])$'\"\r\n```\r\n\r\nI think either KFP backend or Argo failed to recognize this as parameter, cc @Bobgy to confirm.", "\r\n\r\n> This seems like an argo issue, backend doesn't accept this format because of this line in gpu_sum.yaml:\r\n> \r\n> ```\r\n> limits: {memory: 100M, nvidia: '{{inputs.parameters.num_gpus}}'} \r\n> ```\r\n> \r\n> It doesn't fit in the format validation:\r\n> \r\n> ```\r\n> '^([+-]?[0-9.]+)([eEinumkKMGTP][-+]?[0-9])$'\"\r\n> ```\r\n> \r\n> I think either KFP backend or Argo failed to recognize this as parameter, cc @Bobgy to confirm.\r\n\r\nI have the same issue on Kubeflow 1.4 (deployed by manifest), KFP backend v1.7.0. KFP SDK 1.8.9\r\n", "@zijianjoy \r\n\r\n> This seems like an argo issue, backend doesn't accept this format ...\r\n\r\nPlease try submitting this argo workflow which has pretty much the same GPU limit syntax and worked fine for me (argo version that comes with kubeflow 1.4):\r\n```yaml\r\napiVersion: argoproj.io/v1alpha1\r\nkind: Workflow\r\nmetadata:\r\n name: cuda-vector-add\r\nspec:\r\n entrypoint: main\r\n arguments:\r\n parameters:\r\n - name: gpus\r\n value: 0\r\n templates:\r\n - name: main\r\n container:\r\n image: \"k8s.gcr.io/cuda-vector-add:v0.1\"\r\n resources:\r\n limits: {memory: 100M, nvidia: '{{workflow.parameters.gpus}}'} \r\n```\r\n\r\n\r\nIf it's any help, please consider using this very simple pipeline as a minimum reproducible example:\r\n```python\r\nimport kfp\r\nfrom kfp import dsl\r\n\r\n@kfp.components.func_to_container_op\r\ndef test():\r\n print(\"test\")\r\n\r\n@dsl.pipeline(name=\"Test\")\r\ndef test_pipeline(gpus: int = 0):\r\n test_task = test()\r\n test_task.set_cpu_request(\"4\").set_memory_request(\"4Gi\").set_gpu_limit(gpus)\r\n test_task.container.image = \"k8s.gcr.io/cuda-vector-add:v0.1\"\r\n\r\nif __name__ == \"__main__\":\r\n kfp.compiler.Compiler().compile(\r\n pipeline_func=test_pipeline,\r\n package_path=__file__.replace(\".py\", \".yaml\"),\r\n )\r\n```\r\n\r\nThis bug is a major issue for our team, given that we upload pipelines in a GitOps sort of way. I hope you could look into it soon.\r\n", "Hello @ashrafgt , after some investigation, we now think that we should create PodSpecPatch for GPU resources. Refer to issues:\r\n\r\nhttps://github.com/kubeflow/pipelines/issues/1956\r\nhttps://github.com/kubeflow/pipelines/issues/4877\r\n\r\nPodSpecPatch example is in https://github.com/argoproj/argo-workflows/blob/master/examples/pod-spec-patch.yaml. Please note that both examples you provided (argo workflow yaml and KFP pipeline in python) have failed for the same error message on my side. This is why I think we should change to use podSpecPatch (which I suspect such functionality has already been provided by https://github.com/kubeflow/pipelines/pull/5972). cc @NikeNano ", "@zijianjoy Thank you for checking this! I think the argo example probably didn't work because you're using an older version. I remember that I had tl use pod spec patches before, but not anymore. Regardless of how it's implemented, it'll be great to be able to dynamically control GPU resources :)", "Thanks @zijianjoy, using kfp 1.8.9, **add_resource_request** and **add_resource_limit** methods instead of set_gpu_limit, I was able to specify a gpu limit using a pipeline input parameter. The updated code below that worked for me.\r\nThanks again! :slightly_smiling_face:\r\n\r\n```\r\nimport functools\r\nimport kfp\r\nimport kfp.components\r\n\r\n\r\ndef set_resources(cpu: str = '200m', memory: str = '100M', gpu: int = None):\r\n \"\"\"\r\n This is a decorator that applies CPU and memory settings to the specified component (pod).\r\n\r\n For security reasons, the following rules must be adhered to pass pod validations on the cluster:\r\n - CPU limits must not be set\r\n - CPU request must be set\r\n - Memory limits and requests must be set and equal\r\n \"\"\"\r\n def decorator(component_op):\r\n @functools.wraps(component_op)\r\n def wrapper(*args, **kwargs):\r\n component = component_op(*args, **kwargs)\r\n component.set_cpu_request(cpu)\r\n component.set_memory_request(memory)\r\n component.set_memory_limit(memory)\r\n if gpu is not None:\r\n component.set_gpu_limit(gpu)\r\n return component\r\n return wrapper\r\n return decorator\r\n\r\ndef gpu_sum_pipeline(num_gpus: int, a: float = 1., b: float = 2.):\r\n @set_resources(gpu=None)\r\n def gpu_sum_op(a: float, b: float) -> None:\r\n def gpu_sum(a: float, b: float) -> None:\r\n print(a+b)\r\n gpu_sum_task_factory=kfp.components.create_component_from_func(gpu_sum,\r\n base_image='python:3.9')\r\n return gpu_sum_task_factory(a, b)\r\n\r\n gpu_sum_task = gpu_sum_op(a, b)\r\n gpu_sum_task.add_resource_request('nvidia.com/gpu', num_gpus)\r\n gpu_sum_task.add_resource_limit('nvidia.com/gpu', num_gpus)\r\n\r\nkfp.compiler.Compiler().compile(\r\n pipeline_func=gpu_sum_pipeline,\r\n package_path='gpu_sum.yaml')\r\n```", "It is awesome to see your problem resolved!", "Can I use like this code snippet?\r\n```\r\n@dsl.pipeline()\r\ndef pipeline(\r\n timestamp: str, tag: str, language: str,\r\n):\r\n\r\nop = dsl.ContainerOp( # this session is not involved \r\n name=f'clustering-{timestamp}',\r\n image=f'{REGISTRY_IMAGE}/clustering_stage:{tag}',\r\n command=['python3'],\r\n arguments=[\r\n 'main.py', timestamp, 1, language,\r\n params_str,\r\n output_path\r\n ],\r\n )\r\n\r\nwith dsl.Condition(tag == 'prod'):\r\n if gpu:\r\n op.set_cpu_limit(\"2500m\")\r\n op.set_memory_limit(\"8000Mi\")\r\n else:\r\n op.set_cpu_limit(\"800m\")\r\n op.set_memory_limit(\"4000Mi\")\r\n\r\n if gpu:\r\n op.add_resource_limit('gpu.ailabs.tw/geforce-rtx-2080-ti', '1')\r\n op.set_cpu_request(\"20m\")\r\n op.set_memory_request(\"50Mi\")\r\n\r\n```\r\nI want to ONLY set resource limit when tag value is \"prod\". ( tag value is from params when `run_pipeline`\r\nI already do like above code.\r\nHowever, it does not work.\r\nCan anyone give me help? Thanks" ]

"2021-11-03T16:55:13"

"2022-04-27T17:20:54"

"2022-02-04T20:48:33"

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[ "@zijianjoy I've discussed this with Vedant Padwal and he thinks the sample may need to be updated.", "@jnatale11 Can you try to run the sample to validate if this is working? I don't think there should be limitation on the types of GPUs by Kubeflow. I think the comment in this `add_node_selector_constrain` is not listing all GPUs. You can run this command to find all GPUs `gcloud compute accelerator-types list`?\r\n\r\nhttp://cloud/kubernetes-engine/docs/how-to/gpus", "Thank you for your help James! It seems there's just some contradiction in the docs." ]

"2021-11-03T16:20:55"

"2021-11-03T21:46:30"

"2021-11-03T21:46:30"

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