Datasets:

hjerpe

/

github-kubeflow-issues

like 0

https://api.github.com/repos/kubeflow/pipelines/issues/8313

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8313/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8313/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8313/events

https://github.com/kubeflow/pipelines/issues/8313

I_kwDOB-71UM5SupK6

[sdk] 403 error when API key is auto-refreshed with Out of Cluster kube-context

open

CONTRIBUTOR

### Environment * KFP version: <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> We run KFP v1.8.1 via KF v1.5.0. * KFP SDK version: <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> We run a forked version of KFP SDK which is at ~v1.9.0, mostly has a couple of patches for problems we've contributed back but haven't fully reincorporated. * All dependencies version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> From my investigation of the current code on `master` in the open source repo, this bug still exists and should be pretty easy to reproduce with a sufficiently long KFP run such that the API needs to be refreshed. ### Steps to reproduce 1. Instantiate a `Client` using a local Kube config, outside of a Kubernetes cluster. 2. Use a long running method like `wait_for_run_completion` (code [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L1302)). 3. Wait for long enough for the API key to expire. 4. You get a `403` error 😢 It gets a 403 because the URL it is hitting on the Kubernetes API service doesn't exist as we lose the "proxy path" that gets injected, more details below. **PS. This has the potential to impact ANY KFP SDK call to the KFP API service from what I can tell when operating outside of a cluster.** ### Expected result <!-- What should the correct behavior be? --> It simply shouldn't error, it should refresh the `api_key` without needing to reload the `host`. ### Materials and Reference The root cause of the 403 is that in these scenarios instead of making calls to: `<Kube API Server Host>/api/v1/namespaces/<namespace>/services/ml-pipeline:http/proxy//apis/v1beta1/runs/<run id>` Instead it calls (which doesn't exist, thus the 403): `<Kube API Server Host>/apis/v1beta1/runs/<run id>` Effectively it loses the `Client._KUBE_PROXY_PATH` that is injected [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L302). Now why this is happens because: 1. During `Client.__init__` the `load_kube_config` method is (see [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L295)) called in the upstream `kubernetes` library. - Under the hood eventually it sets the `refresh_api_key_hook` (see [here](https://github.com/kubernetes-client/python/blob/ada96faca164f5d5c018fb21b8ef2ecafbdf5e43/kubernetes/base/config/kube_config.py#L576)). - In that `_refresh_api_key` method it calls `_set_config`, which will re-pull the `host` from the `~/.kube/config`. - **Normally this would be fine but [here](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L302) KFP mutates that value to inject `Client._KUBE_PROXY_PATH` 😢** 2. Now for `wait_for_run_completion`, that under the hood calls the `kfp_server_api.api.run_service_api.RunService. get_run_with_http_info`. 3. The `get_run_with_http_info` method calls the `auth_settings` method, which in scenarios when the credentials expire it'll call the `refresh_api_key_hook`. **How to potentially fix this?** I guess my preference would be to work out some way to not mutate the `config.host` to append the `Client._KUBE_PROXY_PATH` as that "hacky" approach clearly has its shortcomings. Unclear though what the alternative is as this is quite a convenient solution most of the time! --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8313/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8312

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8312/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8312/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8312/events

https://github.com/kubeflow/pipelines/issues/8312

I_kwDOB-71UM5SuDez

[sdk] <Bug Name>

closed

NONE

https://api.github.com/repos/kubeflow/pipelines/issues/8312/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8308

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8308/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8308/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8308/events

https://github.com/kubeflow/pipelines/issues/8308

I_kwDOB-71UM5SpE-u

[feature] Container start and end times associated with a Pod

closed

NONE

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> /area backend <!-- /area sdk --> <!-- /area samples --> <!-- /area components --> ### What feature would you like to see? We want to track the start and end times of containers within a pod (Kubeflow Pipeline/Run task). In other words, for better observability, we would like to log the start and end times of the `init`, `wait` and `main` container which are triggered as part of a KFP pod. The `mlpipeline` metabase currently tracks total run and task duration but does not have information on container runtimes. ### What is the use case or pain point? To improve the observability experience, we would like to track the initialization, wait, and execution time for a Kubeflow pipeline task (preferably in the metabase). ### Is there a workaround currently? We are currently polling the Kubernetes API server periodically to retrieve container runtimes for a specific Pod (KFP task), but there is a major issue with short lived tasks (<10 seconds), where the Pod's container details are lost if the node on which the Pod was running is scaled down. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8308/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8307

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8307/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8307/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8307/events

https://github.com/kubeflow/pipelines/issues/8307

I_kwDOB-71UM5SnT-I

[bug] Unable to produce pipeline with basic Component I/O

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> Installed kubeflow manifest **v1.6.0-rc.1** with **minikube v1.26.0** and **kubectl v1.21.14** For detailed output: minikube version: v1.26.0 commit: f4b412861bb746be73053c9f6d2895f12cf78565 kubectl version Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.14", GitCommit:"0f77da5bd4809927e15d1658fb4aa8f13ad890a5", GitTreeState:"clean", BuildDate:"2022-06-15T14:17:29Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"} Server Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.14", GitCommit:"0f77da5bd4809927e15d1658fb4aa8f13ad890a5", GitTreeState:"clean", BuildDate:"2022-06-15T14:11:36Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"} * KFP version: <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> Inside notebook: pip list | grep kfp kfp 1.6.3 kfp-pipeline-spec 0.1.16 kfp-server-api 1.6.0 ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> We tried to produce a basic pipeline that saves pandas dataframes in one component and uses it in the next component using artifacts. We experimented with both Output[Dataset] and OutputPath(str) and tried various samples from v1/v2 SDK without success. To reproduce, create the following options: **Imports** ``` import kfp import kfp.dsl as dsl from kfp.v2.dsl import component, Input, Output, InputPath, OutputPath, Dataset, Metrics, Model, Artifact ``` **Option A: Using csv** ``` @component( packages_to_install = ["pandas", "sklearn"], ) def load(data: Output[Dataset]): import pandas as pd from sklearn import datasets dataset = datasets.load_iris() df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"]) df.to_csv(data.path) @component( packages_to_install = ["pandas"], ) def print_head(data: Input[Dataset]): import pandas as pd df = pd.read_csv(data.path) print(df.head()) @dsl.pipeline( name='Iris', description='iris' ) def pipeline(): load_task = load() print_task = print_head(data=load_task.outputs["data"]) kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( pipeline_func=pipeline, package_path='iris_csv.yaml') ``` **Option B: Using pickle** ``` @component( packages_to_install = ["pandas", "sklearn"], ) def load(data: Output[Dataset]): import pickle import pandas as pd from sklearn import datasets dataset = datasets.load_iris() df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"]) with open(data.path, "wb") as f: pickle.dump(df, f) @component( packages_to_install = ["pandas"], ) def print_head(data: Input[Dataset]): import pickle import pandas as pd with open(data.path, 'rb') as f: df = pickle.load(f) print(df.head()) @dsl.pipeline( name='Iris', description='iris' ) def pipeline(): load_task = load() print_task = print_head(data=load_task.outputs["data"]) kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( pipeline_func=pipeline, package_path='iris_pickle.yaml') ``` **Option C: Using json** ``` @component( packages_to_install = ["pandas", "sklearn"], ) def load(data: Output[Dataset]): import pandas as pd from sklearn import datasets dataset = datasets.load_iris() df = pd.DataFrame(data=dataset.data, columns= ["Petal Length", "Petal Width", "Sepal Length", "Sepal Width"]) json_df = {"data": df} with open(data.path, "wb") as f: f.write(json.dumps(result, default=lambda df: json.loads(df.to_json()))) @component( packages_to_install = ["pandas"], ) def print_head(data: Input[Dataset]): import pandas as pd with open(data.path, 'rb') as f: df = pd.DataFrame(json.loads(f.read())["data"]) print(df.head()) @dsl.pipeline( name='Iris', description='iris' ) def pipeline(): load_task = load() print_task = print_head(data=load_task.outputs["data"]) kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( pipeline_func=pipeline, package_path='iris_json.yaml') ``` All three options result in the following error: ``` time="2022-09-26T09:50:42.264Z" level=info msg="capturing logs" argo=true WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv Traceback (most recent call last): File "/tmp/tmp.CjkMbFoRTh", line 826, in <module> executor_main() File "/tmp/tmp.CjkMbFoRTh", line 820, in executor_main function_to_execute=function_to_execute) File "/tmp/tmp.CjkMbFoRTh", line 549, in __init__ artifacts_list[0]) File "/tmp/tmp.CjkMbFoRTh", line 562, in _make_output_artifact os.makedirs(os.path.dirname(artifact.path), exist_ok=True) File "/usr/local/lib/python3.7/posixpath.py", line 156, in dirname p = os.fspath(p) TypeError: expected str, bytes or os.PathLike object, not NoneType F0926 09:51:03.011047 26 main.go:56] Failed to execute component: exit status 1 time="2022-09-26T09:51:03.028Z" level=error msg="cannot save artifact /tmp/outputs/data/data" argo=true error="stat /tmp/outputs/data/data: no such file or directory" Error: exit status 1 ``` ### Expected result <!-- What should the correct behavior be? --> Clearer examples in https://github.com/kubeflow/pipelines/tree/master/samples. As it is hard to distinguish which sample are actually using the v1 or v2 syntax and are compatible with the latest version. Can you provide a minimal working example that ideally also includes the use of multiple inputs and outputs and the logging of metrics and models. The samples we found in the docs or on github either use the deprecated container_ops instead of components or could not be run by us. ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area testing --> /area sdk /area samples /area components --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8307/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8302

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8302/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8302/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8302/events

https://github.com/kubeflow/pipelines/issues/8302

I_kwDOB-71UM5Sj6Q1

[sdk] KFPv2: Custom Container component set bool default value `False` to `0` which caused runtime failure

closed

COLLABORATOR

### Environment * KFP version: KFP 2.0.0-alpha.4 * KFP SDK version: SDK 2.0.0-beta4 ### Steps to reproduce Create the following custom container component: ``` from kfp.dsl import Input, Output, Artifact, container_component, ContainerSpec @dsl.container_component def create_dataset(experiment_name: str, experiment_namespace: str, experiment_timeout_minutes: int, experiment_spec_json: Input[Artifact], parameter_set: Output[Artifact], delete_finished_experiment: bool = False): return ContainerSpec( image='docker.io/kubeflowkatib/kubeflow-pipelines-launcher', command=['python', 'src/launch_experiment.py'], args=[ '--experiment-name', experiment_name, '--experiment-namespace', experiment_namespace, '--experiment-spec', experiment_spec_json.path, '--experiment-timeout-minutes',experiment_timeout_minutes, '--delete-after-done', delete_finished_experiment, '--output-file', parameter_set.path, ]) ``` Note that `delete_finished_experiment` has a default value `False`. When compiling the pipeline, this component is converted to IR with the following format: ``` tasks: create-dataset: cachingOptions: enableCache: true componentRef: name: comp-create-dataset dependentTasks: - create-katib-experiment-task inputs: artifacts: experiment_spec_json: taskOutputArtifact: outputArtifactKey: experiment_spec_json producerTask: create-katib-experiment-task parameters: delete_finished_experiment: runtimeValue: constant: 0 experiment_name: componentInputParameter: name experiment_namespace: componentInputParameter: namespace experiment_timeout_minutes: runtimeValue: constant: 60 taskInfo: name: create-dataset ``` Note that `delete_finished_experiment` has a runtime value as 0. ### Expected result What should the type of boolean default value be? I am expecting the value to be either of followings: bool `false`, bool `False` or string `False`. I didn't expect the value to be number 0. ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8302/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8300

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8300/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8300/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8300/events

https://github.com/kubeflow/pipelines/issues/8300

I_kwDOB-71UM5ShcYE

[sdk] Unable to import ServiceAccountTokenVolumeCredentials in KFPv2 SDK

open

COLLABORATOR

### Environment * KFP version: KFP 2.0.0-alpha4 * KFP SDK version: 2.0.0-beta.4 ### Steps to reproduce Follow https://www.kubeflow.org/docs/components/pipelines/v1/sdk/connect-api/#full-kubeflow-subfrom-inside-clustersub to set the default SA token path as empty. But `ServiceAccountTokenVolumeCredentials` cannot be imported. From the `__init__.py` file, ServiceAccountTokenVolumeCredentials is not exported: https://github.com/kubeflow/pipelines/blob/e14a784327c83e2d7f3e66dc09f4b3af3323cbc7/sdk/python/kfp/client/__init__.py#L17 If we don't import `ServiceAccountTokenVolumeCredentials`, that means by default it should use default SA token path when creating KFP client: `kfp.Client()`. However, this command has failed due to: ``` File /opt/conda/lib/python3.8/site-packages/kfp/client/client.py:372, in Client._get_config_with_default_credentials(self, config) 360 """Apply default credentials to the configuration object. 361 362 This method accepts a Configuration object and extends it with 363 some default credentials interface. 364 """ 365 # XXX: The default credentials are audience-based service account tokens 366 # projected by the kubelet (ServiceAccountTokenVolumeCredentials). As we 367 # implement more and more credentials, we can have some heuristic and (...) 370 371 # TODO: auth.ServiceAccountCredentials does not exist... dead code path? --> 372 credentials = auth.ServiceAccountTokenVolumeCredentials() 373 config_copy = copy.deepcopy(config) 375 try: AttributeError: module 'kfp.client.auth' has no attribute 'ServiceAccountTokenVolumeCredentials' ``` ### Expected result User can create KFP client using the default SA token path. User can also configure SA token path to be custom value so KFP SDK can read from a different location. User can configure SA token path to be None, in this case, KFP SDK should be able to use environment variable KF_PIPELINES_SA_TOKEN_PATH. cc @chensun @connor-mccarthy --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8300/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8294

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8294/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8294/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8294/events

https://github.com/kubeflow/pipelines/issues/8294

I_kwDOB-71UM5SbpGr

[feature] Stop recurring run when failed

closed

CONTRIBUTOR

### Feature Area /area backend ### What feature would you like to see? Support for stopping recurring runs (or retry N times) if a run fails. ### What is the use case or pain point? At the moment, when a recurring run has a failed run, it will continue scheduling new runs independently of previous state. Ideally, we would like to define a custom behaviour were a scheduled run gets retried N times or it stops scheduling new runs after trying to schedule N runs. This is mostly for cases where running for date X+1 is counterproducing when X is missing. For example for data processing where you want to make sure that no gaps are missing between segments. ### Is there a workaround currently? Currently there is some sort of way which is to use an ExitHandler to stop the run by instrospecting into itself and finding the recurring_run id associated with current run. However that makes it complicated to support. Ideally we make the handling more transparent by doing this on the controller and configuring it from UI. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8294/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8292

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8292/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8292/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8292/events

https://github.com/kubeflow/pipelines/issues/8292

I_kwDOB-71UM5SZrsA

[feature] Add a progress bar in the DAG UI for long-running tasks

closed

NONE

### Feature Area /area frontend /area backend /area sdk ### What feature would you like to see? In the UI displaying the DAG of the pipeline, it would be great to see a progress bar on each task. This would allow to visually see how far the task is to completion. In the code of the component, we could provide information on the progress with a dedicated API in the SDK. ### What is the use case or pain point? It is difficult to estimate how long a task will run. The only option I know, as of today, is to log the progress information. ### Is there a workaround currently? I add some traces/logs in my task, but this implies looking regularly to the logs of each running task. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8292/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8291

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8291/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8291/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8291/events

https://github.com/kubeflow/pipelines/issues/8291

I_kwDOB-71UM5SYma-

[sdk] Package version conflict in kfp and google-cloud-pipeline-components

open

NONE

### Environment * KFP version: 2.0.0b1 ### Steps to reproduce Create a `requirements.in` file: ``` kfp==2.0.0b1 google-cloud-pipeline-components ``` Then run `pip-compile -v requirements.in` ### Result ```Using indexes: https://pypi.org/simple ROUND 1 Current constraints: google-cloud-pipeline-components (from -r requirements.in (line 7)) kfp==2.0.0b1 (from -r requirements.in (line 6)) Finding the best candidates: found candidate google-cloud-pipeline-components==1.0.22 (constraint was <any>) found candidate kfp==2.0.0b1 (constraint was ==2.0.0b1) Finding secondary dependencies: kfp==2.0.0b1 requires absl-py<2,>=0.9, click<9,>=7.1.2, cloudpickle<3,>=2.0.0, Deprecated<2,>=1.2.7, docstring-parser<1,>=0.7.3, fire<1,>=0.3.1, google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5, google-auth<3,>=1.6.1, google-cloud-storage<3,>=2.2.1, jsonschema<4,>=3.0.1, kfp-pipeline-spec<0.2.0,>=0.1.14, kfp-server-api<3.0.0,>=2.0.0a0, kubernetes<19,>=8.0.0, protobuf<4,>=3.13.0, PyYAML<6,>=5.3, requests-toolbelt<1,>=0.8.0, strip-hints<1,>=0.1.8, tabulate<1,>=0.8.6, typer<1.0,>=0.3.2, uritemplate<4,>=3.0.1 google-cloud-pipeline-components==1.0.22 requires google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5, google-cloud-aiplatform<2,>=1.11.0, google-cloud-notebooks>=0.4.0, google-cloud-storage<2,>=1.20.0, kfp<2.0.0,>=1.8.9 New dependencies found in this round: adding ('absl-py', '<2,>=0.9', []) adding ('click', '<9,>=7.1.2', []) adding ('cloudpickle', '<3,>=2.0.0', []) adding ('deprecated', '<2,>=1.2.7', []) adding ('docstring-parser', '<1,>=0.7.3', []) adding ('fire', '<1,>=0.3.1', []) adding ('google-api-core', '!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5', []) adding ('google-auth', '<3,>=1.6.1', []) adding ('google-cloud-aiplatform', '<2,>=1.11.0', []) adding ('google-cloud-notebooks', '>=0.4.0', []) adding ('google-cloud-storage', '<2,<3,>=1.20.0,>=2.2.1', []) adding ('jsonschema', '<4,>=3.0.1', []) adding ('kfp', '<2.0.0,>=1.8.9', []) adding ('kfp-pipeline-spec', '<0.2.0,>=0.1.14', []) adding ('kfp-server-api', '<3.0.0,>=2.0.0a0', []) adding ('kubernetes', '<19,>=8.0.0', []) adding ('protobuf', '<4,>=3.13.0', []) adding ('pyyaml', '<6,>=5.3', []) adding ('requests-toolbelt', '<1,>=0.8.0', []) adding ('strip-hints', '<1,>=0.1.8', []) adding ('tabulate', '<1,>=0.8.6', []) adding ('typer', '<1.0,>=0.3.2', []) adding ('uritemplate', '<4,>=3.0.1', []) Removed dependencies in this round: ------------------------------------------------------------ Result of round 1: not stable ROUND 2 Current constraints: absl-py<2,>=0.9 (from kfp==2.0.0b1->-r requirements.in (line 6)) click<9,>=7.1.2 (from kfp==2.0.0b1->-r requirements.in (line 6)) cloudpickle<3,>=2.0.0 (from kfp==2.0.0b1->-r requirements.in (line 6)) Deprecated<2,>=1.2.7 (from kfp==2.0.0b1->-r requirements.in (line 6)) docstring-parser<1,>=0.7.3 (from kfp==2.0.0b1->-r requirements.in (line 6)) fire<1,>=0.3.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 (from kfp==2.0.0b1->-r requirements.in (line 6)) google-auth<3,>=1.6.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) google-cloud-aiplatform<2,>=1.11.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7)) google-cloud-notebooks>=0.4.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7)) google-cloud-pipeline-components (from -r requirements.in (line 7)) google-cloud-storage<2,<3,>=1.20.0,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) jsonschema<4,>=3.0.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) kfp<2.0.0,==2.0.0b1,>=1.8.9 (from -r requirements.in (line 6)) kfp-pipeline-spec<0.2.0,>=0.1.14 (from kfp==2.0.0b1->-r requirements.in (line 6)) kfp-server-api<3.0.0,>=2.0.0a0 (from kfp==2.0.0b1->-r requirements.in (line 6)) kubernetes<19,>=8.0.0 (from kfp==2.0.0b1->-r requirements.in (line 6)) protobuf<4,>=3.13.0 (from kfp==2.0.0b1->-r requirements.in (line 6)) PyYAML<6,>=5.3 (from kfp==2.0.0b1->-r requirements.in (line 6)) requests-toolbelt<1,>=0.8.0 (from kfp==2.0.0b1->-r requirements.in (line 6)) strip-hints<1,>=0.1.8 (from kfp==2.0.0b1->-r requirements.in (line 6)) tabulate<1,>=0.8.6 (from kfp==2.0.0b1->-r requirements.in (line 6)) typer<1.0,>=0.3.2 (from kfp==2.0.0b1->-r requirements.in (line 6)) uritemplate<4,>=3.0.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) Finding the best candidates: found candidate absl-py==1.2.0 (constraint was >=0.9,<2) found candidate click==8.1.3 (constraint was >=7.1.2,<9) found candidate cloudpickle==2.2.0 (constraint was >=2.0.0,<3) found candidate deprecated==1.2.13 (constraint was >=1.2.7,<2) found candidate docstring-parser==0.15 (constraint was >=0.7.3,<1) found candidate fire==0.4.0 (constraint was >=0.3.1,<1) found candidate google-api-core==2.10.1 (constraint was >=1.31.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev) found candidate google-auth==1.35.0 (constraint was >=1.6.1,<3) found candidate google-cloud-aiplatform==1.17.1 (constraint was >=1.11.0,<2) found candidate google-cloud-notebooks==1.4.2 (constraint was >=0.4.0) found candidate google-cloud-pipeline-components==1.0.22 (constraint was <any>) Could not find a version that matches google-cloud-storage<2,<3,>=1.20.0,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) Tried: 0.20.0, 0.20.0, 0.21.0, 0.21.0, 0.22.0, 0.22.0, 0.23.0, 0.23.0, 0.23.1, 0.23.1, 1.0.0, 1.0.0, 1.1.0, 1.1.0, 1.1.1, 1.1.1, 1.2.0, 1.2.0, 1.3.0, 1.3.0, 1.3.1, 1.3.1, 1.3.2, 1.3.2, 1.4.0, 1.4.0, 1.5.0, 1.5.0, 1.6.0, 1.6.0, 1.7.0, 1.7.0, 1.8.0, 1.8.0, 1.9.0, 1.9.0, 1.10.0, 1.10.0, 1.11.0, 1.11.0, 1.11.1, 1.11.1, 1.12.0, 1.12.0, 1.12.1, 1.12.1, 1.13.0, 1.13.0, 1.13.1, 1.13.1, 1.13.2, 1.13.2, 1.13.3, 1.13.3, 1.14.0, 1.14.0, 1.14.1, 1.14.1, 1.15.0, 1.15.0, 1.15.1, 1.15.1, 1.15.2, 1.15.2, 1.16.0, 1.16.0, 1.16.1, 1.16.1, 1.16.2, 1.16.2, 1.17.0, 1.17.0, 1.17.1, 1.17.1, 1.18.0, 1.18.0, 1.18.1, 1.18.1, 1.19.0, 1.19.0, 1.19.1, 1.19.1, 1.20.0, 1.20.0, 1.21.0, 1.21.0, 1.22.0, 1.22.0, 1.23.0, 1.23.0, 1.24.0, 1.24.0, 1.24.1, 1.24.1, 1.25.0, 1.25.0, 1.26.0, 1.26.0, 1.27.0, 1.27.0, 1.28.0, 1.28.0, 1.28.1, 1.28.1, 1.29.0, 1.29.0, 1.30.0, 1.30.0, 1.31.0, 1.31.0, 1.31.1, 1.31.1, 1.31.2, 1.31.2, 1.32.0, 1.32.0, 1.33.0, 1.33.0, 1.34.0, 1.34.0, 1.35.0, 1.35.0, 1.35.1, 1.35.1, 1.36.0, 1.36.0, 1.36.1, 1.36.1, 1.36.2, 1.36.2, 1.37.0, 1.37.0, 1.37.1, 1.37.1, 1.38.0, 1.38.0, 1.39.0, 1.39.0, 1.40.0, 1.40.0, 1.41.0, 1.41.0, 1.41.1, 1.41.1, 1.42.0, 1.42.0, 1.42.1, 1.42.1, 1.42.2, 1.42.2, 1.42.3, 1.42.3, 1.43.0, 1.43.0, 1.44.0, 1.44.0, 2.0.0, 2.0.0, 2.1.0, 2.1.0, 2.2.0, 2.2.0, 2.2.1, 2.2.1, 2.3.0, 2.3.0, 2.4.0, 2.4.0, 2.5.0, 2.5.0 There are incompatible versions in the resolved dependencies: google-cloud-storage<3,>=2.2.1 (from kfp==2.0.0b1->-r requirements.in (line 6)) google-cloud-storage<2,>=1.20.0 (from google-cloud-pipeline-components==1.0.22->-r requirements.in (line 7)) ``` ### Expected result An output with compatible package versions. ### Possible solution Bump the google-cloud-storage version to include `<3,>=2.2.1` in https://github.com/kubeflow/pipelines/blob/2.0.0b4/components/google-cloud/dependencies.py <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8291/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8283

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8283/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8283/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8283/events

https://github.com/kubeflow/pipelines/issues/8283

I_kwDOB-71UM5SLdwb

[backend] metadata-writer cannot save metadata of S3 artifacts with argo v3.1+

open

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? kubeflow 1.4 manifest (git::https://github.com/kubeflow/manifests.git?ref=v1.4.0 ) and manually change config for S3 * KFP version: 1.7.0 (kubeflow 1.4) * KFP SDK version: 1.7.1 ### Steps to reproduce 1. setting to save artifacts to S3 (change configmap for workflow-controller) 1. Run pipeline: [Tutorial] Data passing in python components metadata-writer output log ``` Kubernetes Pod event: ADDED file-passing-pipelines-txmqj-2254123803 14598940 Traceback (most recent call last): File "/kfp/metadata_writer/metadata_writer.py", line 238, in <module> artifact_uri = argo_artifact_to_uri(argo_artifact) File "/kfp/metadata_writer/metadata_writer.py", line 106, in argo_artifact_to_uri provider=get_object_store_provider(s3_artifact['endpoint']), KeyError: 'endpoint' ``` result: - Artifact in metadb is empty ### Expected result metadata for the artifacts is logged into metadb ### Materials and Reference I think it is caused by changes for argo Key-Only Artifacts. In https://github.com/kubeflow/pipelines/blob/1.7.0/backend/metadata_writer/src/metadata_writer.py#L318, metadata-writer read "workflows.argoproj.io/outputs", but endpoint and bucket are not passed by argo3.1+. So https://github.com/kubeflow/pipelines/blob/1.7.0/backend/metadata_writer/src/metadata_writer.py#L105 cannot set the correct URL. Related frontend issue: https://github.com/kubeflow/pipelines/issues/5930 --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8283/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8269

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8269/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8269/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8269/events

https://github.com/kubeflow/pipelines/issues/8269

I_kwDOB-71UM5R5jjH

Cannot get key for artifact location

open

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? I deploy the Kubeflow Pipelines (standalone) by Kubeflow Manifests (https://github.com/kubeflow/manifests) * KFP version: 1.7.0 * KFP SDK version: 1.8.2 When I submit a pipeline, I will get the error `This step is in Error state with this message: Error (exit code 1): key unsupported: cannot get key for artifact location, because it is invalid`  The code is as follows ``` import kfp from kfp.components import create_component_from_func client = kfp.Client() def add(a: float, b: float) -> float: '''Calculates sum of two arguments''' return a + b add_op = create_component_from_func( add, output_component_file='add_component.yaml') import kfp.dsl as dsl @dsl.pipeline( name='Addition pipeline', description='An example pipeline that performs addition calculations.' ) def add_pipeline( a='1', b='7', ): # Passes a pipeline parameter and a constant value to the `add_op` factory # function. first_add_task = add_op(a, 4) # Passes an output reference from `first_add_task` and a pipeline parameter # to the `add_op` factory function. For operations with a single return # value, the output reference can be accessed as `task.output` or # `task.outputs['output_name']`. second_add_task = add_op(first_add_task.output, b) # Specify argument values for your pipeline run. arguments = {'a': '7', 'b': '8'} # Create a pipeline run, using the client you initialized in a prior step. client.create_run_from_pipeline_func(add_pipeline, arguments=arguments, run_name="add-8") print("submit pipeline") ``` This problem seems to be related to Minio. --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8269/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8267

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8267/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8267/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8267/events

https://github.com/kubeflow/pipelines/issues/8267

I_kwDOB-71UM5R4X5b

[sdk] ssl.SSLError: [SSL: WRONG_VERSION_NUMBER] wrong version number

open

NONE

### Environment * KFP version: 1.8.1 * KFP SDK version: 1.6.2 * All dependencies version: kfp 1.6.2 kfp-pipeline-spec 0.1.16 kfp-server-api 1.8.5 ### Steps to reproduce It happens on GCP, on a Kubeflow 1.5 cluster To reproduce you should execute the following code: ``` from kfp import Client kfp_client = Client( host="", client_id="", other_client_id="", other_client_secret="", namespace="aNamespace" ) kfp_client.set_user_namespace(namespace="aNamespace") pipeline_id = kfp_client.get_pipeline_id(name="aPipelineName") print(pipeline_id) ``` ### Expected result I should get a pipeline id Instead I get Traceback (most recent call last): File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connectionpool.py", line 1040, in _validate_conn conn.connect() File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/connection.py", line 416, in connect self.sock = ssl_wrap_socket( File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/util/ssl_.py", line 449, in ssl_wrap_socket ssl_sock = _ssl_wrap_socket_impl( File "/Users/user/.pyenv/versions/lib/python3.9/site-packages/urllib3/util/ssl_.py", line 493, in _ssl_wrap_socket_impl return ssl_context.wrap_socket(sock, server_hostname=server_hostname) File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 500, in wrap_socket return self.sslsocket_class._create( File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 1040, in _create self.do_handshake() File "/Users/user/.pyenv/versions/3.9.9/lib/python3.9/ssl.py", line 1309, in do_handshake self._sslobj.do_handshake() ssl.SSLError: [SSL: WRONG_VERSION_NUMBER] wrong version number (_ssl.c:1129) --------------------- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8267/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8257

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8257/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8257/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8257/events

https://github.com/kubeflow/pipelines/issues/8257

I_kwDOB-71UM5RslZs

[feature] Allow GCP ModelUploadOp to upload a model version

closed

CONTRIBUTOR

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area samples --> /area components ### What feature would you like to see? The ability to upload a Vertex Model version to the model registry ### What is the use case or pain point? In the REST API for[ model upload](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.models/upload#request-body), the request body has several fields including `parentModel` which can be supplied if uploading a new model version. The current version of [`ModelUploadOp`](https://github.com/kubeflow/pipelines/blob/1739434f4ad2000105e0a8feff41b587e3c9de8c/components/google-cloud/google_cloud_pipeline_components/container/v1/gcp_launcher/upload_model_remote_runner.py#L55) only supplies the `model` field in the request: ```python upload_model_request = { # TODO(IronPan) temporarily remove the empty fields from the spec 'model': json_util.recursive_remove_empty( append_unmanaged_model_artifact_into_payload( executor_input, model_spec)) } ``` ### Is there a workaround currently? I believe this can be done by creating a custom component that uses the `gcloud` sdk and then `gcloud ai model upload ... --parent-model ... --version-aliases ` but I haven't tried it yet. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8257/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8256

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8256/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8256/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8256/events

https://github.com/kubeflow/pipelines/issues/8256

I_kwDOB-71UM5RogQh

[bug] Pipeline metrics Invalid input error: Unknown execution spec

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? Kubeflow/manifest v1.6 AKS v1.22 <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: ml-pipeline/api-server:2.0.0-alpha.3 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: '1.8.13' <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ``` from kfp.components import InputPath, OutputPath, create_component_from_func from kfp import dsl def produce_metrics( # Note when the `create_component_from_func` method converts the function to a component, the function parameter "mlpipeline_metrics_path" becomes an output with name "mlpipeline_metrics" which is the correct name for metrics output. mlpipeline_metrics_path: OutputPath('Metrics'), ): import json accuracy = 0.1 metrics = { 'metrics': [{ 'name': 'accuracy-score', # The name of the metric. Visualized as the column name in the runs table. 'numberValue': accuracy, # The value of the metric. Must be a numeric value. 'format': "PERCENTAGE", # The optional format of the metric. Supported values are "RAW" (displayed in raw format) and "PERCENTAGE" (displayed in percentage format). }] } with open(mlpipeline_metrics_path, 'w') as f: json.dump(metrics, f) produce_metrics_op = create_component_from_func( produce_metrics, base_image='python:3.7', packages_to_install=[], output_component_file='component.yaml', ) from kfp import dsl import os, kfp @dsl.pipeline( name='KF iris PoC', description='Simple TF' ) def test(): task=produce_metrics_op() import os,kfp with open(os.environ['KF_PIPELINES_SA_TOKEN_PATH'], "r") as f: TOKEN = f.read() client = kfp.Client(host='http://ml-pipeline.kubeflow.svc.cluster.local:8888',existing_token=TOKEN) client.create_run_from_pipeline_func( test, mode=kfp.dsl.PipelineExecutionMode.V1_LEGACY, arguments={} ) ``` ### Expected result  <!-- What should the correct behavior be? --> ### Materials and reference https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/ <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> I cannot see the pipeline metrics in the UI but the pipeline artifact is logged i am getting this error in ml-pipeline ``` I0912 09:12:02.846997 8 error.go:259] Invalid input error: Unknown execution spec InternalServerError: failed to unmarshal workflow ```  ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8256/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8255

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8255/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8255/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8255/events

https://github.com/kubeflow/pipelines/issues/8255

I_kwDOB-71UM5Rmh0V

[frontend] Can't see log field in UI (SDK v2)

closed

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? from kubeflow/manifests v1.4 * KFP version: 1.7.0 ### Steps to reproduce QQ When I try to run a simple pipeline on SDK 2, I don't see the main.log fields in the output artifact. Although I can see the log saved in minio.  UI:  Minio:  Log:  ### Expected result Like this:  ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8255/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8252

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8252/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8252/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8252/events

https://github.com/kubeflow/pipelines/issues/8252

I_kwDOB-71UM5RgHXf

[bug] GCP ModelBatchPredictOp is never cached

open

NONE

### Environment * How do you deploy Kubeflow Pipelines (KFP)? Vertex-AI pipelines * KFP version: I don't know (vertex ai) * KFP SDK version: 1.8.12 ### Steps to reproduce Create a pipeline that uses the ModelBatchPredictOp, and run it several times. The task is never cached, but systematically executed. ### Expected result The task should be cached when the input parameters do not change (dataset, model, gcs source uri, gcs destination prefix, and machine type). ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> According to the documentation, the gcs output uri is created with the following pattern: prediction-model-display-name-job-create-time. The usage of the job creation time here prevents any caching of this operator. https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.16/google_cloud_pipeline_components.aiplatform.html?highlight=ModelUploadOp#google_cloud_pipeline_components.aiplatform.ModelUploadOp ### Labels /area sdk /area components --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8252/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8242

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8242/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8242/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8242/events

https://github.com/kubeflow/pipelines/issues/8242

I_kwDOB-71UM5RSUvZ

[feature] Allow Filtering On Pipeline ID for List Runs Api Endpoint

open

CONTRIBUTOR

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> /area backend <!-- /area sdk --> <!-- /area samples --> <!-- /area components --> ### What feature would you like to see? Extend the api endpoint for listing runs to allow selecting or filtering runs by pipeline ID. ### What is the use case or pain point? This feature will be helpful when it is necessary to easily gather all runs associated with a given pipeline ### Is there a workaround currently? No --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8242/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8240

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8240/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8240/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8240/events

https://github.com/kubeflow/pipelines/issues/8240

I_kwDOB-71UM5RLEv8

when using the emissary executor you must either explicitly specify the command, or list the image's command in the index: https://argoproj.github.io/argo-workflows/workflow-executors/#emissary-emissary[bug] <Bug Name>

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: 1.7.1 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version:1.7.1 <!-- Specify the output of the following shell command: $pip list | grep kfp -->DEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support pip 21.0 will remove support for this functionality. ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ### Expected result <!-- What should the correct behavior be? --> ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8240/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8236

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8236/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8236/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8236/events

https://github.com/kubeflow/pipelines/issues/8236

I_kwDOB-71UM5RCTTB

[bug] Disabling caching for a step by set_caching_options lead to disabling caching for subsequent steps

open

CONTRIBUTOR

I'm not sure if it's a bug or an expected behavior, but let me mark as a bug for now. ### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? full Kubeflow deployment <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: 1.8.1 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: 1.8.13 <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ```py from kfp import components, dsl from kfp.v2.dsl import Dataset, Input, component download_op = components.load_component_from_url( "https://raw.githubusercontent.com/kubeflow/pipelines/master/components/contrib/google-cloud/storage/download_blob/component.yaml" ) @component def print_file_content_op(file: Input[Dataset]) -> None: with open(file.path) as f: print(f.read()) @dsl.pipeline(name="caching-test") def pipeline(path: str) -> None: download_task = download_op(path) download_task.set_caching_options(False) print_file_content_task = print_file_content_op(download_task.outputs["data"]) ``` Submit the pipeline using `create_run_from_pipeline_func` with `V2_COMPATIBLE` mode two times. We'll see `print_file_content_task` is not cached and executed on the second run. The behavior is counterintuitive as I don't call `set_caching_options(False)` for `print_file_content_task`. Why I call `download_task.set_caching_options(False)` because the file at the GCS path might be replaced at some point. I know it would be better to upload a file to a different path instead of replacing the current file and make the step deterministic, but to do so we need to put some efforts to update our existing system. ### Expected result <!-- What should the correct behavior be? --> `print_file_content_task` is cached and skipped if the file content is the same as before. ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8236/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8230

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8230/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8230/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8230/events

https://github.com/kubeflow/pipelines/issues/8230

I_kwDOB-71UM5Q_WMV

[bug] Kubeflow Pipeline UI not rendering Pipeline Metrics (again?)

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? Ans: Full Kubeflow Deployment * KFP version: For some reason the sidebar shows : **build version dev_local** but was installed using YAMLs at: https://github.com/kubeflow/manifests Docker Image and Tag info: `ml-pipeline-api-server`: `gcr.io/ml-pipeline/api-server:1.8.1` `ml-pipeline-ui`: `gcr.io/ml-pipeline/frontend:1.8.1` * KFP SDK version: KFP SDK version: 1.8.13 ### Steps to reproduce Built component (part of the pipeline and is the last component as well) using KFP SDK v1 API My Component looks like following: ```py def train_model(customer_id: str, start_time: str, end_time: str, take_target_log: bool, log_addition: float, model: OutputPath('Model'), x_test: OutputPath('CSV'), y_test: OutputPath('CSV'), mlpipeline_metrics_path: OutputPath('Metrics')): metrics = { 'metrics': [ { 'name': 'best_iteration', 'numberValue': 101, 'format': 'RAW' }, { 'name': 'best_score', 'numberValue': 1.2, 'format': 'RAW' } ] } with open(mlpipeline_metrics_path, 'w') as f: json.dump(metrics, f) ``` Above component is added in Pipeline using `kfp.components.create_component_from_func` function. 1. Changed metrics argument in above `train_model` signature to - `mlpipeline_metrics_path: OutputPath('Metrics')` - `mlpipeline_metrics: OutputPath('Metrics')` - `mlpipeline_metrics: OutputPath()` All of them yield YAML with ```yaml outputs: artifacts: - {name: mlpipeline-metrics, path: /tmp/outputs/mlpipeline_metrics/data} ``` `train_model` component in UI shows the Artifacts as :  Weird thing to note here is the `Type` of the artifact. 2. I tried returning `NamedTuple` as : ```py def train_model(customer_id: str, start_time: str, end_time: str, take_target_log: bool, log_addition: float, model: OutputPath('Model'), x_test: OutputPath('CSV'), y_test: OutputPath('CSV')) -> NamedTuple('Outputs', [('mlpipeline_metrics', 'Metrics')]): metrics = { 'metrics': [ { 'name': 'best_iteration', 'numberValue': 101, 'format': 'RAW' }, { 'name': 'best_score', 'numberValue': 1.2, 'format': 'RAW' } ] } return [json.dumps(metrics)] ``` Above generates YAML with ```yaml outputs: artifacts: - {name: mlpipeline-metrics, path: /tmp/outputs/mlpipeline_metrics/data} ``` No diff in component spec, but container args had `----output-paths` extra arguments in the yaml ```yaml '----output-paths', /tmp/outputs/mlpipeline_metrics/data ``` ### Expected result Expected to see ML Pipeline Metrics (like `best_score` and `best_iterations` ) in following screenshot  <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8230/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8224

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8224/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8224/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8224/events

https://github.com/kubeflow/pipelines/issues/8224

I_kwDOB-71UM5Q7bKn

[backend] metadata-grpc-deployment cannot connect to mysql

open

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? follow the standalone deployment doc, but run `kubectl create -k platform-agnostic' instead. * KFP version: 1.5 * KFP SDK version: NA * EKS cluster k8s version: 1.19 ### Steps to reproduce I follow the guideline the deploy the standalone kubeflow pipelines. However, the metadata-grpc-deployment pod always crush, and logs shows ``` F ml_metadata/metadata_store/metadata_store_server_main.cc:220] Non-OK-status: status status: Internal: mysql_real_connect failed: errno: 2005, error: Unknown MySQL server host ‘mysql’ (-3)MetadataStore cannot be created with the given connection config. ``` mysql pod is running fine. ``` mysql: ready for connections. ``` also meta-writer, ml-pipeline,ml-pipeline-persistenceagent also has crashloopbackoff or 0/1 ready status. ### Expected result pods are running ok. ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8224/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8205

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8205/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8205/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8205/events

https://github.com/kubeflow/pipelines/issues/8205

I_kwDOB-71UM5QtckJ

[feature] import python function in component

closed

NONE

for example: I have a file utils.py which defined some functions. How can I import those functions in my components? Any ideas?

https://api.github.com/repos/kubeflow/pipelines/issues/8205/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8203

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8203/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8203/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8203/events

https://github.com/kubeflow/pipelines/issues/8203

I_kwDOB-71UM5QqUmF

[sdk] Can't use pipeline's input object in dsl.Condition

open

CONTRIBUTOR

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? Full Kubeflow deployment * KFP version: 1.8.1 * KFP SDK version: 1.8.12 ### Steps to reproduce ```python @dsl.pipeline(name="Test pipeline", description=__doc__) def test_pipeline( output_config: Optional[dict] = { "s3_bucket": "my-s3-bucket", "s3_prefix": "my-s3-prefix" } ) -> None: with dsl.Condition(output_config is not None): ( # run a container op ) ``` Then there is an error when create the pipeline ```bash if isinstance(group.condition.operand1, dsl.PipelineParam): AttributeError: 'bool' object has no attribute 'operand1' ``` If I change the condition to ``` with dsl.Condition(output_config != None) ``` then when running, there is another error ``` This step is in Error state with this message: Invalid 'when' expression '"{"s3_bucket": "my-s3-bucket", "s3_prefix": "my-s3-prefix}" != "None"': Cannot transition token types from STRING [{] to VARIABLE [s3_bucket] ``` ### Expected result - Able to use dsl.Condition with pipeline's input ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> /area sdk <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8203/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8201

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8201/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8201/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8201/events

https://github.com/kubeflow/pipelines/issues/8201

I_kwDOB-71UM5Qm073

[sdk] sdk documentation of kfp.v2

closed

NONE

I am having difficulties regarding the documentation of the python SDK of pipelines, in particular the kfp.v2 package of the 1.8.x releases. Some functionality from the package kfp.v2.google seems to be referenced on the web/from the vertex ai docu, yet I cannot find any official referenced documentation of it from the project itself, for example on https://kubeflow-pipelines.readthedocs.io . I understand by browsing through the issues that apparently kfp.v2.google contains deprecated code such as the AIPlatformClient. The effect is that for older versions of kfp there is no SDK documentation available of all that was once/is in kfp.v2 release 1.8.x that is later actually removed in kfp release 2.x . I think this is very confusing for people new to kfp, for which the stable release is still 1.8.x, but from tutorials from vertex ai use code from kfp.v2. I would expect this documentation, even now deprecated, is still available in the SDK documentation. PS: Also, what was the original intent of the v2 package? Already making available v2 sdk functionality from 1.8.x ? In the 2.x versions the kfp.v2 seems to point back to the main package kfp itself, which probably makes sense.

https://api.github.com/repos/kubeflow/pipelines/issues/8201/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8200

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8200/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8200/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8200/events

https://github.com/kubeflow/pipelines/issues/8200

I_kwDOB-71UM5QlFFS

[backend] Metadata writer pod always restarting

open

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Manifests in k8s * K8S Version: 1.21 * KFP version: 1.8.1/1.8.2/1.8.3/1.8.4 ### Steps to reproduce Hi. Since release 1.8.1 (can't be sure about older versions) our metadata-writer pod is always restarting infinitely with the following message error: ``` metadata-writer-78fc7d5bb8-ph9kj 2/2 Running 299 78d ``` ``` Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 697, in _update_chunk_length self.chunk_left = int(line, 16) ValueError: invalid literal for int() with base 16: b'' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 438, in _error_catcher yield File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 764, in read_chunked self._update_chunk_length() File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 701, in _update_chunk_length raise InvalidChunkLength(self, line) urllib3.exceptions.InvalidChunkLength: InvalidChunkLength(got length b'', 0 bytes read) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/kfp/metadata_writer/metadata_writer.py", line 157, in <module> for event in pod_stream: File "/usr/local/lib/python3.7/site-packages/kubernetes/watch/watch.py", line 144, in stream for line in iter_resp_lines(resp): File "/usr/local/lib/python3.7/site-packages/kubernetes/watch/watch.py", line 48, in iter_resp_lines for seg in resp.read_chunked(decode_content=False): File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 793, in read_chunked self._original_response.close() File "/usr/local/lib/python3.7/contextlib.py", line 130, in __exit__ self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.7/site-packages/urllib3/response.py", line 455, in _error_catcher raise ProtocolError("Connection broken: %r" % e, e) urllib3.exceptions.ProtocolError: ("Connection broken: InvalidChunkLength(got length b'', 0 bytes read)", InvalidChunkLength(got length b'', 0 bytes read)) ``` We already try the most recent versions of version 1.8 (we did not try version 2.0.0). The pipelines are working very well, and we don't have any problems till now because of this, but this only happens with this pod. This happens in our multiple clusters with multiple installations, so don't look like an issue of a specific cluster. ### Expected result The pod should stop restarting. --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8200/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8189

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8189/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8189/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8189/events

https://github.com/kubeflow/pipelines/issues/8189

I_kwDOB-71UM5QdjD3

[backend] Cannot list artifacts

open

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Using https://www.kubeflow.org/docs/distributions/gke/deploy/upgrade/ * KFP version: ml-pipeline/frontend:1.8.1 ml-pipeline/api-server:1.8.1 ### Steps to reproduce Upgrade from Kubeflow 1.3 to Kubeflow 1.5 allows to replicate the problem ### Expected result I expect to be able to see a list of artifacts when I access myClusterURL/pipeline/artifacts. Instead I get this https://user-images.githubusercontent.com/74205824/186285977-cba538c2-e496-416e-8f27-67fa4950b4cc.png ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8189/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8185

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8185/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8185/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8185/events

https://github.com/kubeflow/pipelines/issues/8185

I_kwDOB-71UM5QUceH

[feature] add delete button for runs to KFP UI

closed

NONE

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> /area frontend <!-- /area backend --> <!-- /area sdk --> <!-- /area samples --> <!-- /area components --> ### What feature would you like to see?  can add delete button for runs ? Currently, there is no delete button. Is there any design concern？ <!-- Provide a description of this feature and the user experience. --> ### What is the use case or pain point? I submitted a batch of tasks and found that they did not meet expectations. I hope to delete them <!-- It helps us understand the benefit of this feature for your use case. --> ### Is there a workaround currently? <!-- Without this feature, how do you accomplish your task today? --> I use kubectl delete workflow xxxx --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8185/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8184

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8184/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8184/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8184/events

https://github.com/kubeflow/pipelines/issues/8184

I_kwDOB-71UM5QUFv3

[backend] disabling task caching doesn't appear to work

closed

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? ``` $ kubectl version --short Client Version: v1.24.4 Kustomize Version: v4.5.4 Server Version: v1.20.2 ``` - minikube v1.18.1 - kubernetes v1.20.2 <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: Deployed using [kubeflow/manifests v1.5.0](https://github.com/kubeflow/manifests/archive/refs/tags/v1.5.0.tar.gz) <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> ``` $ pip list | grep kfp kfp 1.8.13 kfp-pipeline-spec 0.1.16 kfp-server-api 1.8.4 kfp-tekton 1.3.1 ``` ### Steps to reproduce Create a ContainerOp and use [`set_caching_options(False)`](https://kubeflow-pipelines.readthedocs.io/en/1.8.13/source/kfp.dsl.html#kfp.dsl.ContainerOp.set_caching_options) to disable task caching ``` from kfp import dsl from kfp.compiler import Compiler @dsl.pipeline(name='ContainerOp caching test', description='') def pipeline(): co = dsl.ContainerOp( name='sample ContainerOp', image='busybox', command=['echo', 'Hello KFP!'], ) co.set_caching_options(False) if __name__ == '__main__': Compiler().compile(pipeline, __file__ + '.yaml') ``` Compile the pipeline. The compiled pipeline looks as follows: ``` apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: containerop-caching-test- annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.13, pipelines.kubeflow.org/pipeline_compilation_time: '2022-08-23T08:20:01.620863', pipelines.kubeflow.org/pipeline_spec: '{"name": "ContainerOp caching test"}'} labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.13} spec: entrypoint: containerop-caching-test templates: - name: containerop-caching-test dag: tasks: - {name: sample-containerop, template: sample-containerop} - name: sample-containerop container: command: [echo, Hello KFP!] image: busybox metadata: labels: pipelines.kubeflow.org/kfp_sdk_version: 1.8.13 pipelines.kubeflow.org/pipeline-sdk-type: kfp pipelines.kubeflow.org/enable_caching: "false" arguments: parameters: [] serviceAccountName: pipeline-runner ``` Run the pipeline multiple times and review the run info in the Central Dashboard.  fwiw the following labels are defined on the pod: ``` Labels: pipeline/runid=40313ed7-32c0-4d01-9588-7c1f033b1b14 pipelines.kubeflow.org/cache_enabled=true pipelines.kubeflow.org/cache_id=813 pipelines.kubeflow.org/enable_caching=false pipelines.kubeflow.org/kfp_sdk_version=1.8.13 pipelines.kubeflow.org/metadata_execution_id=28 pipelines.kubeflow.org/metadata_written=true pipelines.kubeflow.org/pipeline-sdk-type=kfp pipelines.kubeflow.org/reused_from_cache=true workflows.argoproj.io/completed=true workflows.argoproj.io/workflow=lambda-4mk5p ``` Note `pipelines.kubeflow.org/cache_enabled=true` and `pipelines.kubeflow.org/enable_caching=false` <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ### Expected result <!-- What should the correct behavior be? --> Task results are not re-used across multiple pipeline runs. Not sure what (if anything) I am missing. Disabling caching for entire pipelines or the entire KFP deployment is not a viable option since the caching requirements vary. Please advise. Thank you! ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8184/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8176

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8176/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8176/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8176/events

https://github.com/kubeflow/pipelines/issues/8176

I_kwDOB-71UM5QMUab

[sdk] Missing outputs field in generated component yaml

closed

NONE

### Environment * KFP version: <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP SDK version: 1.8.3 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce I'm writing a component for the KFP v2 Pipeline backend, I wrote it in Python with the following definition: @component def train( dataset: Input[Dataset], config: Input[Artifact], model: Output[Model], bucket_name: str) then, I tried to package it for later usages, using from kfp.v2.components.component_factory import create_component_from_func create_component_from_func( func=train, base_image="[docker.io/](http://docker.io/)...", output_component_file="component_v2.yaml" ) A file showed up without any error, but weirdly it's missing my outputs artifacts, only showing inputs and implementations instead. I dont khow whether this is expected or I did it wrong somehow? ### Expected result <!-- What should the correct behavior be? --> ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8176/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8175

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8175/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8175/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8175/events

https://github.com/kubeflow/pipelines/issues/8175

I_kwDOB-71UM5QMUG0

[backend] <Bug Name>TypeError: compile() got an unexpected keyword argument 'pipeline_parameters'

closed

NONE

### Environment #### How did you deploy Kubeflow Pipelines (KFP)? <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> - minikube latest (kubernets 1.21.14) - kustomize 3.2.0 - kubectl 1.21.14 - kubeflow/manifests v1.6-branch ```shell git clone https://github.com/kubeflow/manifests.git -b v1.6-branch cd manifests bash hack/setup-kubeflow.sh ``` * KFP version: ``` $ print(kfp.__version__) 1.8.13 ``` I tried both: ``` pip install kfp==1.8.13 pip install --pre kfp ``` ### Steps to reproduce https://www.kubeflow.org/docs/components/pipelines/sdk/build-pipeline/#getting-started-building-a-pipeline ### Expected result I cannot get kubeflow pipeline yaml file by following method. And I got `TypeError: compile() got an unexpected keyword argument 'pipeline_parameters'` ``` kfp.compiler.Compiler().compile( pipeline_func=my_pipeline, package_path='pipeline.yaml', pipeline_parameters={'url': 'https://storage.googleapis.com/ml-pipeline-playground/iris-csv-files.tar.gz'}) ``` From this implementation, I should use `pipeline_parameters` argument for `kfp.compiler.Compiler().compile` https://github.com/kubeflow/pipelines/blob/3b1bd08ce0188605cff90e3f14563d55436aac96/sdk/python/kfp/compiler/compiler.py#L62 ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8175/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8171

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8171/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8171/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8171/events

https://github.com/kubeflow/pipelines/issues/8171

I_kwDOB-71UM5QKOA8

[backend] Use the taskInfo name to find artifact

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 * KFP SDK version: 2.0.0b2 <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce 1. Open https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.py 2. Call `.set_display_name('name1')` on `component1` and `.set_display_name('name2')` on `component2` when defining `my_pipeline` 3. Run the file to compile a yaml IR 4. Submit the IR to KFP standalone ### Expected result The pipeline is not able to run as the second component couldn't find the component `component1`, which is renamed. ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.py https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/compiler/test_data/pipelines/two_step_pipeline_containerized.yaml --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8171/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8166

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8166/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8166/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8166/events

https://github.com/kubeflow/pipelines/issues/8166

I_kwDOB-71UM5QDLrh

[sdk] Not able to parse `JsonObject` type input correctly

open

NONE

### Environment * KFP version: 1.8.12 GCP Vertex AI pipelines. * KFP SDK version: GCP Vertex AI pipelines. * All dependencies version: ```shell kfp 1.8.12 kfp-pipeline-spec 0.1.16 kfp-server-api 1.8.4 ``` ```shell google-cloud-pipeline-components 1.0.16 ``` ### Steps to reproduce Using yaml configuration for components, one of the inputs is `JsonObject`type i.e.: ```yaml inputs: - {name: Server resources, type: JsonObject, description: 'A dict describing the amount of resources required to run the server', default: '{"cpu": "1", "memory": "3Gi"}'} ``` Also, it can be `String` type: ```yaml inputs: - {name: Server resources, type: String, description: 'A dict describing the amount of resources required to run the server', default: '{"cpu": "1", "memory": "3Gi"}'} ``` To build a pipeline run we are going threw these stages: 1. Load definition using KFP. 2. Update definition using `create_custom_training_job_from_component` from `google-cloud-pipeline-components` package. 3. Submitting run threw Vertex AI SDK. The component fails due to an error: `Failed to parse the container spec json payload to requested prototype.`. In the Payload the invalid part is this: `"{"cpu": "1", "memory": "3Gi"}"`. PS.: If building pipelines run remove the second step (2. Update definition using `create_custom_training_job_from_component` from `google-cloud-pipeline-components` package.), the component doesn't get incompatible JSON format error and it starts. ### Expected result Component job is provisioned/starts. --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8166/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8165

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8165/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8165/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8165/events

https://github.com/kubeflow/pipelines/issues/8165

I_kwDOB-71UM5QA-KO

[bug] Recurring runs not working after re-installing kubeflow-pipelines modules

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? Using Kubeflow 1.5.1 on EKS and it is updated from version 1.3.1 I did `kustomize build apps/pipeline | kubectl delete -f -` and `kustomize build apps/pipeline | kubectl apply -f -` registering a pipeline and calling a run works fine but registered recurring runs not working <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: gcr.io/ml-pipeline/api-server:1.8.2 gcr.io/ml-pipeline/metadata-writer:1.8.2 gcr.io/ml-pipeline/persistenceagent:1.8.2 gcr.io/ml-pipeline/scheduledworkflow:1.8.2 gcr.io/ml-pipeline/frontend:1.8.2 gcr.io/ml-pipeline/viewer-crd-controller:1.8.2 gcr.io/ml-pipeline/visualization-server:1.8.2 gcr.io/ml-pipeline/workflow-controller:v3.2.3-license-compliance gcr.io/tfx-oss-public/ml_metadata_store_server:1.5.0 gcr.io/ml-pipeline/cache-server:1.8.2 gcr.io/ml-pipeline/metadata-envoy:1.8.2 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce Hi, is there anyone experienced registered recurring runs not working after updating and installing kubeflow-pipeline? I re-deployed kubeflow-pipeline to update the version from 1.3.1 to 1.5.1 Recurring runs registered previously are not being updated but I see new recurring runs are being updated normally Tested query: SELECT uuid, displayname, name, namespace, serviceaccount, description, from_unixtime(CreatedAtInSec) CreatedAtInSec, from_unixtime(UpdatedAtInSec) UpdatedAtInSec, from_unixtime(CronScheduleStartTimeInSec) CronScheduleStartTimeInSec, from_unixtime(CronScheduleEndTimeInSec) CronScheduleEndTimeInSec FROM mlpipeline.jobs; It seems like a certain demon in pipeline module misses records to trace. <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ### Expected result registered recurring runs updated and triggered normally after upgrading kubeflow-pipeline module <!-- What should the correct behavior be? --> ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8165/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8164

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8164/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8164/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8164/events

https://github.com/kubeflow/pipelines/issues/8164

I_kwDOB-71UM5QAw56

[bug] <kubeflow/pipelines mysql && workflow-controller pods failing 'Running' stuck in 'Pending' State with several other pods crashing consistently on Apple M1 architecture Deployed in Kind>

open

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? Deploy in Kind --image kindest/node:v1.21.2 (Apple M1 Silicon Architecture BigSur) <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: 1.7.0 (Also Failed with 1.8.13 i.e. replace with in command sequence -> % export PIPELINE_VERSION=1.8.13) <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: 1.8.13 <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce Activate Python Virtual Environment and run following commands: % kind create cluster --image kindest/node:v1.21.2 % export PIPELINE_VERSION=1.7.0 % kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION&timeout=300" % kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io % kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION&timeout=300" <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. -->  ### Expected result <!-- What should the correct behavior be? --> All pods/deployments should be Successfully Running and Stable. ### Materials and reference https://community.deeplearning.ai/t/c4w3-lab1-kubeflowpipelines-kubectl-f-apply-fail/176728/19 <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> area backend <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> area components --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8164/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8159

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8159/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8159/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8159/events

https://github.com/kubeflow/pipelines/issues/8159

I_kwDOB-71UM5P-CZG

[sdk] Incompatibility between conditionals in component definitions and google-cloud-pipeline-components

open

NONE

### Environment * KFP version: 1.8.12 * All dependencies version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> kfp 1.8.12 kfp-pipeline-spec 0.1.16 kfp-server-api 1.8.4 ### Steps to reproduce Run the following code snippet (not necessarily fully-working, but enough to demonstrate this issue) ``` import kfp from google_cloud_pipeline_components.v1.custom_job import ( create_custom_training_job_from_component, ) component = kfp.components.load_component_from_text( """ name: my-component-name inputs: - { name: Columns, type: String } implementation: container: image: my-custom-image command: [ echo ] args: - if: cond: { isPresent: Columns } then: - --columns - { inputValue: Columns } """ ) job = create_custom_training_job_from_component(component) ``` This is the result I get: ` TypeError: Got unexpected placeholder type for IfPlaceholder(if_structure=IfPlaceholderStructure(condition=IsPresentPlaceholder(input_name='Columns'), then_value=['--columns', InputValuePlaceholder(input_name='Columns')], else_value=None)) ` ### Expected result Expected the conditional argument to be parsed without errors. Seems like the issue comes from here: https://github.com/kubeflow/pipelines/blob/1.8.13/sdk/python/kfp/dsl/dsl_utils.py#L132 - the `IfPlaceholder` type is not handled --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8159/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8155

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8155/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8155/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8155/events

https://github.com/kubeflow/pipelines/issues/8155

I_kwDOB-71UM5P69HZ

Critical CVEs found in gcr.io/ml-pipeline/visualization-server:1.8.4

closed

NONE

### Environment Critical CVEs reported by the `anchore` scanning service: CVE-2020-36242, GHSA-57wx-m983-2f88, GHSA-8vj2-vxx3-667w, GHSA-9j59-75qj-795w , GHSA-cq27-v7xp-c356, GHSA-h6gw-r52c-724r , GHSA-pw3c-h7wp-cvhx * KFP version: `1.8.4` ### Materials and Reference  --- Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8155/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8154

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8154/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8154/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8154/events

https://github.com/kubeflow/pipelines/issues/8154

I_kwDOB-71UM5P68cq

Critical CVEs found in gcr.io/ml-pipeline/frontend:1.8.4

closed

NONE

### Environment Critical CVEs found by the `anchore` scanning service: CVE-2020-3681, CVE-2022-28391, GHSA-896r-f27r-55mw, GHSA-xvch-5gv4-984h, * KFP version: `1.8.4` ### Materials and Reference    --- Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8154/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8153

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8153/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8153/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8153/events

https://github.com/kubeflow/pipelines/issues/8153

I_kwDOB-71UM5P66hm

Critical CVEs found in gcr.io/ml-pipeline/metadata-writer:1.8.4

closed

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Critical Vulnerabilities reported by `anchore` : CVE-2015-20107, CVE-2017-18342, CVE-2021-30473, CVE-2021-30474, CVE-2021-30475, GHSA-8q59-q68h-6hv4 * KFP version: `1.8.4` ### Materials and Reference  --- Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8153/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8141

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8141/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8141/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8141/events

https://github.com/kubeflow/pipelines/issues/8141

I_kwDOB-71UM5PtOWk

[feature] Print deep links outside of Jupyter notebooks

closed

CONTRIBUTOR

### Feature Area /area sdk ### What feature would you like to see? Currently, `client.py` has 5 blocks that look like this: ```python if self._is_ipython(): import IPython html = \ f'<a href="{self._get_url_prefix()}/#/experiments/details/{experiment.id}" target="_blank" >Experiment details</a>.' IPython.display.display(IPython.display.HTML(html)) ``` This is a really helpful feature but it's only available when interacting with the SDK from IPython. It would be nice (and trivial to implement) if links were printed outside of IPython. I'll submit a PR momentarily that implements this. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8141/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8133

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8133/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8133/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8133/events

https://github.com/kubeflow/pipelines/issues/8133

I_kwDOB-71UM5PkxgZ

[frontend] Inaccurate two-level dropdown selection

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 ### Steps to reproduce 1. Create a run which has two Confusion Matrix artifacts of the same type on the same execution - One artifact has the name `Artifact ID #<id>`, and the other artifact has no name but an ID of `<id>` - These confusion matrices should be different - _Note: This may be difficult to accomplish, and is not presented here, but the issue is shown by understanding the underlying code and implementation._ 2. Go to the "Runs" tab on KFP 3. Select that run and one other, and click "Compare Runs" 4. Select the "Confusion Matrix" tab under "Metrics" 5. Select both confusion matrices and view the compare results ### Expected result The Confusion Matrices should be displayed as expected, but instead one of the confusion matrices will be shown twice. ### Materials and Reference On the two-panel layout for Confusion Matrices, HTML, and Markdown, we need to improve the differentiation between display name and ID, likely by adding an underlying value to each dropdown item (rather than requiring each name be unique). This is mentioned in [this GitHub discussion](https://github.com/kubeflow/pipelines/pull/7966#discussion_r913346458); essentially, if an Execution has a displayName of exact value “Execution ID #1” and another Execution has no displayName but has an ID of 1, we run into mis-identified artifacts and dropdown items due to the logic implemented at [getLinkedArtifactFromSelectedItem](https://github.com/kubeflow/pipelines/blob/e47a784035820c0d322c5e50562df676fd81ce4c/frontend/src/components/viewers/MetricsDropdown.tsx#L335-L357). --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8133/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8132

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8132/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8132/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8132/events

https://github.com/kubeflow/pipelines/issues/8132

I_kwDOB-71UM5Pku9w

[frontend] Dropdown and HTML / Markdown artifacts are misaligned

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 ### Steps to reproduce 1. Go to the "Runs" tab on KFP 2. Select multiple runs which have HTML or Markdown artifacts, and click "Compare Runs" 3. Select the "HTML" or "Markdown" tab under "Metrics" 4. Select an artifact which has large width (see "Materials and Reference") ### Expected result The HTML and Markdown artifacts are not left-aligned on the two-panel layout along with the two-level dropdown component. ### Materials and Reference The HTML and Markdown artifacts are not left-aligned alongside the two-level dropdown component.  The below image shows the artifact is currently aligned in the center of the panel of this two-panel layout.  --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8132/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8131

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8131/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8131/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8131/events

https://github.com/kubeflow/pipelines/issues/8131

I_kwDOB-71UM5PkoyK

[frontend] Address KFPv2 Run Comparison page scalability

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 ### Steps to reproduce 1. Go to the "Runs" tab on KFP 2. Select multiple runs which have many artifacts (for instance, some over 100+), and click "Compare Runs" ### Expected result You should see the page load all of the artifacts efficiently and accurately. Instead, the page currently does not load the artifacts past 100 per run, and the loading time will likely be slower in relation to comparing artifacts with few to no runs. ### Materials and Reference The main issue is shown in the `TODO` below: https://github.com/kubeflow/pipelines/blob/a7fddf000d5da254ee872574677a974e4ff4b4db/frontend/src/pages/CompareV2.tsx#L315-L316 However, there is also a broader issue of scalability; though we implemented scalable methods and designed the page to handle a large quantity of artifacts, we have not thoroughly tested this page against runs with 100s of artifacts. Although the design and loading states should be able to provide a nice UX, the scalability of this page is nevertheless an important question to answer. Lastly, this is also related to the issue of fetching very large HTML and Markdown files in an efficient manner, as the KFPv2 Run Comparison page will need to do this repeatedly. The existing implementation and `TODO` for this is available here: https://github.com/kubeflow/pipelines/blob/a7fddf000d5da254ee872574677a974e4ff4b4db/frontend/src/components/viewers/MetricsVisualizations.tsx#L890 --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8131/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8130

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8130/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8130/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8130/events

https://github.com/kubeflow/pipelines/issues/8130

I_kwDOB-71UM5PkmZZ

[frontend] Clarify the two-level dropdown naming schema

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 ### Steps to reproduce 1. Go to the "Runs" tab on KFP 2. Select multiple runs which have Confusion Matrices, and click "Compare Runs" 3. Select the "Confusion Matrix" tab under "Metrics" 4. Click the two-level dropdown ### Expected result You should see the two-level dropdown with some explanation as to the naming schema, but no headers or explanation for this component are provided. ### Materials and Reference The below image shows the existing two-level dropdown used for Confusion Matrices, HTML, and Markdown on the two-panel layout of the KFPv2 Run Comparison page.  This below image shows one proposed solution to clarify the two-level dropdown naming schema. This styling is not finalized, and another solution would be to add a text explanation above the dropdown component itself.  --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8130/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8129

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8129/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8129/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8129/events

https://github.com/kubeflow/pipelines/issues/8129

I_kwDOB-71UM5PkktY

[frontend] Confusion matrix unnecessary spacing

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: 2.0.0-alpha.3 ### Steps to reproduce 1. Go to the "Runs" tab on KFP 2. Select multiple runs which have Confusion Matrices, and click "Compare Runs" 3. Select the "Confusion Matrix" tab under "Metrics" 4. Select any confusion matrix ### Expected result You should see the confusion matrix with correct formatting, but it is instead surrounded by some unused space. ### Materials and Reference The below image shows the space in question which is unused, and could be removed so the scroll bar has a more accurate state.  This should be achievable by conditionally removing padding on the component here: https://github.com/kubeflow/pipelines/blob/bdff332ac69db0589c8b9eb55e7cdf7e3bee710d/frontend/src/components/viewers/MetricsVisualizations.tsx#L755-L767 --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8129/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8126

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8126/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8126/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8126/events

https://github.com/kubeflow/pipelines/issues/8126

I_kwDOB-71UM5PgnBM

[feature] Implement comprehensive unit tests for the KFP SDK Client class

open

CONTRIBUTOR

### Feature Area /area sdk ### What feature would you like to see? We would like to see comprehensive unit tests for the KFP SDK [Client](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L82) class to improve confidence in code changes. The existing coverage looks [quite sparse](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client_test.py). My team and I would be interested in contributing this to the upstream but want to make sure that we're not stepping on anyone's toes / it makes sense to core maintainers. ### What is the use case or pain point? Reduced confidence in code changes / releases. ### Is there a workaround currently? No. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8126/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8125

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8125/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8125/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8125/events

https://github.com/kubeflow/pipelines/issues/8125

I_kwDOB-71UM5Pgl8b

[feature]Add a KubeFlow pipeline for TensorFlowJS/JavaScript

open

NONE

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> /area javascript /area samples /area pipelines ### What feature would you like to see? We would like to see a dedicated pipeline for TensorFlowJS or even a pipeline for JavaScript. Today TensorFlowJS is being used in a lot of production use cases. And it makes sense to have dedicated KubeFlow Pipeline for JavaScript/NodeJS. ### What is the use case or pain point? Today TensorFlowJS is being used in a lot of production use cases. But we don't have any supported KubeFlow pipeline for JavaScript. ### Is there a workaround currently? Today there isn't a KubeFlow pipeline supported for TensorFlowJS / NodeJS. The workaround could be to have a self serve container. But it would be great to have a dedicated KubeFlow pipeline. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8125/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8121

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8121/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8121/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8121/events

https://github.com/kubeflow/pipelines/issues/8121

I_kwDOB-71UM5Pf8bW

[frontend] Table sort mechanism broken on multiple tabs

open

CONTRIBUTOR

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: `HEAD` ### Steps to reproduce 1. Go to the `Runs`, `Artifacts`, or `Executions` tab on KFP 2. Try to sort by any of the desired and sortable columns ### Expected result You should see the table sort itself based on that column, but either nothing changes or the table contents disappear. ### Materials and Reference The below video shows how the sorting mechanism works on the `Pipelines`, `Experiments`, and `Recurring Runs` tabs, but does not work on the `Runs`, `Artifacts`, or `Executions` tabs. https://user-images.githubusercontent.com/7987279/183758095-4b937e5e-b7fb-4610-b763-e600b90beef7.mp4 --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8121/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8119

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8119/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8119/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8119/events

https://github.com/kubeflow/pipelines/issues/8119

I_kwDOB-71UM5Pdx6o

[sdk] kfp==2.0.0b1 is incompatible with google-cloud-pipeline-components

open

NONE

### Environment * KFP version: `2.0.0b1` <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> ### Steps to reproduce `pip3 install kfp==2.0.0b1` followed by `pip3 install google-cloud-pipeline-components` ### Expected result I expect both packages to be compatible. `google-cloud-pipeline-components` is an [SDK](https://pypi.org/project/google-cloud-pipeline-components/) for interacting with Vertex AI, and `kfp>=2.0.0b1` is the version that is suggested/needed to work with Vertex AI - link [here](https://cloud.google.com/vertex-ai/docs/pipelines/create-pipeline-template#before-you-begin) The issue seems to stems from this [line](https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/dependencies.py#L24) --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8119/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8117

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8117/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8117/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8117/events

https://github.com/kubeflow/pipelines/issues/8117

I_kwDOB-71UM5PbzBE

[sdk] SlicedClassificationMetrics Artifact load_confusion_matrix has incorrect call

open

NONE

### Environment * KFP version: N/A * KFP SDK version: `1.8.13` ### Steps to reproduce Use a `SlicedClassificationMetrics` Artifact and try to call `load_confusion_matrix`. Internally this calls `log_confusion_matrix_cell` on the `ClassificationMetrics` object instead of `log_confusion_matrix`, which results in a type error: `TypeError: log_confusion_matrix_cell() missing 1 required positional argument: 'value'` ### Expected result Calling `load_confusion_matrix` on `SlicedClassificationMetrics` doesn't break, and correctly adds the confusion matrix to the right slice in the metadata. ### Materials and Reference Full reproducible sample: ```python from kfp.v2.dsl import component, Output, SlicedClassificationMetrics from kfp.v2 import compiler import kfp @component() def simple_metric_op(sliced_eval_metrics: Output[SlicedClassificationMetrics]): sliced_eval_metrics._sliced_metrics = {} sliced_eval_metrics.load_confusion_matrix('a slice', categories=['cat1', 'cat2'], matrix=[[1,0],[2,4]] ) @kfp.dsl.pipeline(name="test-sliced-metric") def metric_test_pipeline(): m_op = simple_metric_op() compiler.Compiler().compile( pipeline_func=metric_test_pipeline, package_path="evaluation_pipeline.json", ) ``` See the incorrect line of code here: https://github.com/kubeflow/pipelines/blob/061905b6df397c40fbcc4ffafa24d7b3b9daf439/sdk/python/kfp/components/types/artifact_types.py#L452-L464 Where `self._sliced_metrics[slice].log_confusion_matrix_cell( categories, matrix)` Should be `self._sliced_metrics[slice].log_confusion_matrix( categories, matrix)` Since `self._sliced_metrics[slice]` is of type `ClassificationMetrics` after the call to `self._upsert_classification_metrics_for_slice(slice)` As an aside - `_sliced_metrics` is never initialised in the class, leaving it up to the user of the Artifact class, which isn't very intuitive. --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8117/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8111

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8111/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8111/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8111/events

https://github.com/kubeflow/pipelines/issues/8111

I_kwDOB-71UM5PZYcD

[frontend] Artifact API Does not support S3 Regions

open

NONE

<img width="1727" alt="Screenshot 2022-08-08 at 18 16 17" src="https://user-images.githubusercontent.com/832185/183455928-0c6c6c10-da25-4c2b-b19f-13573e43bafe.png"> <img width="1719" alt="Screenshot 2022-08-08 at 18 16 02" src="https://user-images.githubusercontent.com/832185/183455906-ba856df3-d657-4bbb-922f-572c7faf2aae.png"> Artifact fetching API does not seem to support region for s3 resources. If we look here, https://github.com/kubeflow/pipelines/blob/e8abec24fed4c4f8be6f527207b1cec9811ce3e7/frontend/server/minio-helper.ts#L50, the region parameter is not being passed and it is not a part of the artefact API as well. However, MINIO Client API allows you to specify region when creating a client: https://docs.min.io/docs/javascript-client-api-reference.html. I think API should fetch default region using `MINIO_REGION` environment variable which should be passed as a part of `ml-pipeline-ui-artifact` k8s deployment `env` map. ### Environment * How did you deploy Kubeflow Pipelines (KFP)? [kubeflow-manifests](https://github.com/awslabs/kubeflow-manifests) * KFP version: 1.5.1 ### Steps to reproduce 1. Launch any pipeline 2. Go to Run UI 3. Try to download any output 4. Observe the error: Failed to get object in bucket [bucket] at path [path]: S3Error: The authorization header is malformed; the region 'us-east-1' is wrong; expecting '[actual bucket region]' ### Expected result <!-- What should the correct behavior be? --> ### Materials and Reference

https://api.github.com/repos/kubeflow/pipelines/issues/8111/timeline

reopened

https://api.github.com/repos/kubeflow/pipelines/issues/8104

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8104/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8104/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8104/events

https://github.com/kubeflow/pipelines/issues/8104

I_kwDOB-71UM5PNguO

[feature] Make managing the artifact storage and cache available in the UI

open

NONE

I'd like users to be able to manage the artifacts their pipelines have created. This involves disabling or invalidating the cache for certain artifacts so that a pipeline can be re-executed without having to touch code. I've first mentioned this feature [here](https://github.com/kubeflow/pipelines/issues/7939#issuecomment-1204986272)(#7939) and posted the following GIF, exemplifying how such a feature could look like.  Also, [I've created a design document](https://docs.google.com/document/d/1WdPjTgoDj5AdPNYVzqYwVIWKsWjPPphYD3Ck4DFxSLA/edit#heading=h.c1mjwae174cw) that addresses all details. If you like the feature (or don't), please leave comments here on directly in the document. ### Feature Area /area frontend /area backend /area components --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8104/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8103

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8103/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8103/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8103/events

https://github.com/kubeflow/pipelines/issues/8103

I_kwDOB-71UM5PNbF_

[backend] JSONDecodeError: in BigQuery `bigquery_create_model_job` execution

open

NONE

### What steps did you take ### Executing big query create model functionality through pipeline using image: gcr.io/ml-pipeline/google-cloud-pipeline-components:1.0.13 command: [python3, -u, -m, google_cloud_pipeline_components.container.v1.gcp_launcher.launcher] with type: `BigqueryCreateModelJob`, Also followed the component.yaml format as mentioned here: https://github.com/kubeflow/pipelines/blob/google-cloud-pipeline-components-1.0.13/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/create_model/component.yaml I get below error when executing the pipeline: `Traceback (most recent call last): File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/bigquery_job_remote_runner.py", line 332, in bigquery_create_model_job artifact_util.update_output_artifacts(executor_input, [bqml_model_artifact]) File "/opt/python3.7/lib/python3.7/site-packages/google_cloud_pipeline_components/container/v1/gcp_launcher/utils/artifact_util.py", line 51, in update_output_artifacts executor_input_json = json.loads(executor_input) File "/opt/python3.7/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/opt/python3.7/lib/python3.7/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/opt/python3.7/lib/python3.7/json/decoder.py", line 353, in raw_decode obj, end = self.scan_once(s, idx) json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)` This is because of the `--executor_input, "{{$}}",` param in the component.yaml file It is not an input parameter so it should take and proceed but it fails. ### What did you expect to happen: ### That the pipeline would run successfully Please provide any suggestion/solution how to bypass the error and make the pipeline run success

https://api.github.com/repos/kubeflow/pipelines/issues/8103/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8097

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8097/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8097/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8097/events

https://github.com/kubeflow/pipelines/issues/8097

I_kwDOB-71UM5PFKgw

[sdk] Compilation with V2_COMPATIBLE mode fails when inputPath/outputPath name contains an empty space

open

CONTRIBUTOR

### Environment * KFP version: <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP SDK version: 1.8.13 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * All dependencies version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> ``` kfp 1.8.13 kfp-pipeline-spec 0.1.16 kfp-server-api 1.8.3 ``` ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ```py import kfp from kfp.v2 import dsl from kfp.v2.dsl import component from kfp.v2.dsl import Artifact, Output @component def file_generator(path: Output[Artifact]): with open(path, "w") as f: f.write("Hello World!") input_path_op = kfp.components.load_component_from_text(""" name: Input path consumer inputs: - {name: input path} implementation: container: image: alpine command: - sh - -exc - cat $0 - inputPath: input path """) output_path_op = kfp.components.load_component_from_text(""" name: output path generator outputs: - {name: output path} implementation: container: image: alpine command: - sh - -exc - echo Hello World! > $0 - outputPath: output path """) @dsl.pipeline(name='my-pipeline') def my_pipeline(): file_generator_task = file_generator() input_path_op(file_generator_task.outputs["path"]) # Comment this line out when you want to test the behavior of a component with outputs # output_path_op() kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( pipeline_func=my_pipeline, package_path='pipeline.yaml') ``` --- Run the code and we will get the following error. ```console % python3 pipeline.py /Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py:79: UserWarning: V2_COMPATIBLE execution mode is at Beta quality. Some pipeline features may not work as expected. warnings.warn('V2_COMPATIBLE execution mode is at Beta quality.' Traceback (most recent call last): File "/Users/y-nishioka/repos/test_kfp/pipeline.py", line 48, in <module> kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1175, in compile self._create_and_write_workflow( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1227, in _create_and_write_workflow workflow = self._create_workflow(pipeline_func, pipeline_name, File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1058, in _create_workflow workflow = self._create_pipeline_workflow( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 786, in _create_pipeline_workflow templates = self._create_dag_templates(pipeline, op_transformers) File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 751, in _create_dag_templates v2_compat.update_op( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/v2_compat.py", line 158, in update_op "metadataPath": op.input_artifact_paths[artifact_name], KeyError: 'input path' ``` `op.input_artifact_paths` is `{'input-path': '/tmp/inputs/input_path/data'}` and `artifact_name` is `input path` so we're getting this error. --- In the case of `output_path_op`, we will get the following error. ```console % python3 pipeline.py /Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py:79: UserWarning: V2_COMPATIBLE execution mode is at Beta quality. Some pipeline features may not work as exp ected. warnings.warn('V2_COMPATIBLE execution mode is at Beta quality.' Traceback (most recent call last): File "/Users/y-nishioka/repos/test_kfp/pipeline.py", line 48, in <module> kfp.compiler.Compiler(mode=kfp.dsl.PipelineExecutionMode.V2_COMPATIBLE).compile( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1175, in compile self._create_and_write_workflow( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1227, in _create_and_write_workflow workflow = self._create_workflow(pipeline_func, pipeline_name, File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 1058, in _create_workflow workflow = self._create_pipeline_workflow( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 786, in _create_pipeline_workflow templates = self._create_dag_templates(pipeline, op_transformers) File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/compiler.py", line 751, in _create_dag_templates v2_compat.update_op( File "/Users/y-nishioka/repos/test_kfp/venv/lib/python3.9/site-packages/kfp/compiler/v2_compat.py", line 187, in update_op "metadataPath": op.file_outputs[artifact_name], KeyError: 'output path' ``` `op.file_outputs` is `{'output-path': '/tmp/outputs/output_path/data'}` and `artifact_name` is `output path` so we're getting this error. ### Expected result <!-- What should the correct behavior be? --> Compilation succeeds. [v2 SDK doc](https://www.kubeflow.org/docs/components/pipelines/sdk-v2/component-development/#using-your-component-in-a-pipeline) says > Input and output names are converted to Pythonic names (spaces and symbols are replaced with underscores and letters are converted to lowercase). For example, an input named Input 1 is converted to input_1. So using a name with empty spaces should be allowed in v2. ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8097/timeline

reopened

https://api.github.com/repos/kubeflow/pipelines/issues/8096

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8096/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8096/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8096/events

https://github.com/kubeflow/pipelines/issues/8096

I_kwDOB-71UM5PE9oh

container not ready status showing

closed

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: 1.7.1 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ### Expected result <!-- What should the correct behavior be? --> ### Materials and Reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8096/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8095

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8095/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8095/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8095/events

https://github.com/kubeflow/pipelines/issues/8095

I_kwDOB-71UM5PEyvD

[bug] TypeError: Object of type PipelineParam is not JSON serializable

closed

NONE

### Environment <!-- Please fill in those that seem relevant. --> * KFP SDK version: 1.8.13 ### Steps to reproduce ``` import kfp import kfp.v2.dsl as dsl @dsl.component def compute(x: int) -> int: return x @dsl.component def print_op(results: dict): print(results) @dsl.pipeline(name='pipeline') def pipeline(): outputs = {i: compute(i).output for i in range(10)} print_op(outputs) kfp.v2.compiler.Compiler().compile( pipeline_func=pipeline, package_path="/tmp/test_pipeline.json" ) ``` it give error of `TypeError: Object of type PipelineParam is not JSON serializable` ### Expected result it compiles without an error ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> /area backend --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8095/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8094

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8094/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8094/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8094/events

https://github.com/kubeflow/pipelines/issues/8094

I_kwDOB-71UM5PExd-

[feature] Nested component

closed

NONE

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area samples --> /area components ### What feature would you like to see? I would like to be able to do nested call to components, for example ```python @component def foo(): pass @component def bar(): foo() ``` ### What is the use case or pain point? * It helps reuse the code. ### Is there a workaround currently? No. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8094/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8087

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8087/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8087/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8087/events

https://github.com/kubeflow/pipelines/issues/8087

I_kwDOB-71UM5O6xMr

failed to authenticate with kubeflow pipeline [bug] <HTTP 500 error>

open

NONE

* How do you deploy Kubeflow Pipelines (KFP)? Kubeflow is deployed on MiniKF locally downloaded on windows using vagrant and Virtual Box downloading steps : [here](https://v0-7.kubeflow.org/docs/started/workstation/getting-started-windows/) * KFP version: i cant fin it, neither on the dahsboard nor on the minikf window. ### Steps to reproduce The problem can be reproduced just by using Kale to create a pipeline and after dividing any notebook into different steps, clicking on compile and run shows an error that i can find by going to the pipeline window logs, the logs show me this :  the second error message is really long, if someone needs it to maybe understand my issue you will find it as a comment to this post ### Expected result After seeing a lot of videos that implements kale and uses it to create pipelines, the expected result is just a pipeline that runs every cells of the notebook contained in each step using the data of the step before. Thank you in advance for your help. <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8087/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8076

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8076/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8076/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8076/events

https://github.com/kubeflow/pipelines/issues/8076

I_kwDOB-71UM5OrwdD

[bug] Kubeflow Pipelines Standalone Kustomize incompatible with K8s 1.22+

closed

NONE

KFP Kustomize setup does not work with Kubernetes 1.22+. The [kustomization.yaml](https://github.com/kubeflow/pipelines/blob/master/manifests/kustomize/cluster-scoped-resources/kustomization.yaml) references an API that was removed (apiextensions.k8s.io/v1beta1). ### Environment * How do you deploy Kubeflow Pipelines (KFP)? Kubeflow Pipelines Standalone * KFP version: Version: [1.7.1](https://www.github.com/kubeflow/pipelines/commit/1d53de98c731d3cf6ef9017ad19bbfcf19787de3) ### Steps to reproduce ``` > kubectl diff --kustomize "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=1.7.1" error: resource mapping not found for name: "applications.app.k8s.io" namespace: "" from "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=1.7.1": no matches for kind "CustomResourceDefinition" in version "apiextensions.k8s.io/v1beta1" ensure CRDs are installed first ``` ### Expected result It should diff with my current kfp state. ### Materials and reference The beta CustomResourceDefinition API (apiextensions.k8s.io/v1beta1) has been removed. See [Kubernetes 1.22 API changes](https://kubernetes.io/blog/2021/07/14/upcoming-changes-in-kubernetes-1-22/). ### Labels backend --- Impacted by this bug? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8076/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8074

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8074/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8074/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8074/events

https://github.com/kubeflow/pipelines/issues/8074

I_kwDOB-71UM5OoYxB

[feature] Add authorization to all functions in ReportServer

closed

MEMBER

### Feature Area There are two API-server endpoints that still miss authorization - [ReportWorkflow](https://github.com/kubeflow/pipelines/blob/d48776fb2d9752ca48af14ea6256791d2b167a71/backend/src/apiserver/server/report_server.go#L32) and [ReportScheduledWorkflow](https://github.com/kubeflow/pipelines/blob/d48776fb2d9752ca48af14ea6256791d2b167a71/backend/src/apiserver/server/report_server.go#L45). This is a security issue. Each endpoint should validate that the user has permission to call them. **persistent-agent** service calls these endpoints. Once authorization is enabled, the persistent-agent need to authorize itself by providing user information in the request headers. This issue is a related/ follow-up issue to PR https://github.com/kubeflow/pipelines/pull/7819 <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> /area backend <!-- /area sdk --> <!-- /area samples --> <!-- /area components --> ### What feature would you like to see? <!-- Provide a description of this feature and the user experience. --> ### What is the use case or pain point? <!-- It helps us understand the benefit of this feature for your use case. --> ### Is there a workaround currently? no. A security issue exists in the current implementation. <!-- Without this feature, how do you accomplish your task today? --> --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8074/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8067

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8067/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8067/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8067/events

https://github.com/kubeflow/pipelines/issues/8067

I_kwDOB-71UM5Of70l

[question] How does `importer` work under the hood?

closed

CONTRIBUTOR

I'm trying to find/understand the underlying Argo YAML for an `importer`. Is https://github.com/kubeflow/pipelines/blob/3e734ed19146f569e910f75627d12239ec2e86dc/backend/src/v2/compiler/argocompiler/testdata/importer.yaml the right place to look (for an example, of how https://github.com/kubeflow/pipelines/blob/c6125ffc44df0eced27ae83451c6243b8fc8d73f/sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer.py gets converted)? If so, where is the logic to actually download the artifact from Google Cloud Storage? I feel like, looking at definitions for importer nodes and importer specs, I'm not able to trace back to the place where the downloading is actually happening. My current thought is it's happening on some `kfp-launcher` Docker image, but I'm not sure if that's right, and--even if so--I would like to be able to see the underlying logic getting called. edit: I've looked into this more over the past few days, and I've understood that: 1. [The `artifactUri` is extracted from the `ImporterSpec` and returned from `ImportSpecToMLMDArtifact`](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L191-L207). 2. [Find and return a matched artifact (if any)](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L159-L164). Is the implementation of finding the matched artifact dependent on the execution backend? I got a bit lost trying to trace back in there. 3. [Execute](https://github.com/kubeflow/pipelines/blob/master/backend/src/v2/component/importer_launcher.go#L143-L145). I'm still not sure, for example, if you have an `importer` like https://github.com/kubeflow/pipelines/blob/c6125ffc44df0eced27ae83451c6243b8fc8d73f/sdk/python/kfp/compiler/test_data/pipelines/pipeline_with_importer.py#L55-L59 * How does it know how to download an artifact from `gs://` vs `s3://` vs `http://`, etc.? I'm sure there must be some logic to determine protocol, etc., but it's happening somewhere I can't identify. * Is there a reasonable approximation in Argo? Maybe something like: ```yaml arguments: parameters: - name: raw_green_data_url value: https://raw.githubusercontent.com/deepyaman/azureml-examples/main/sdk/jobs/pipelines/2c_nyc_taxi_data_regression/data/greenTaxiData.csv templates: - name: nyc-taxi-data-regression steps: - - name: download-raw-green-data template: importer arguments: artifacts: - name: uri http: # The artifact parameter (this line and the one below) have to be generated programmatically by parsing the URI string. url: "{{workflow.parameters.raw_green_data_url}}" - name: importer inputs: artifacts: - name: uri path: /tmp/data outputs: artifacts: - name: artifact path: /tmp/data container: image: hello-world ```

https://api.github.com/repos/kubeflow/pipelines/issues/8067/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8061

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8061/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8061/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8061/events

https://github.com/kubeflow/pipelines/issues/8061

I_kwDOB-71UM5OYCwk

[bug] open /tmp/outputs/Output/data: permission denied

open

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? with Kubeflow on prem * KFP version: No info, just "Build: dev local | Dashboard v0.0.2- | Isolation-Mode: multi-user" in left corner of Kubeflow UI. I got non-admin access to Kubeflow platform. * KFP SDK version: 1.8.13 ### Steps to reproduce I created some KFP's components: https://gist.github.com/szymonbcoding/faa2b5c3d46a4fd99ba1659410c06ffc And then upload created pipeline's yaml file. Unfortunatelly I got error during run of my pipeline: _This step is in Failed state with this message: Error (exit code 64): failed to open /tmp/outputs/Output/data: open /tmp/outputs/Output/data: permission denied_ Does it mean that I have to root permission to use KFP? ### Expected result Successfully execution of my run

https://api.github.com/repos/kubeflow/pipelines/issues/8061/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8060

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8060/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8060/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8060/events

https://github.com/kubeflow/pipelines/issues/8060

I_kwDOB-71UM5OVx1v

This step is in Error state with this message: Error (exit code 1): failed to put file: You did not provide the number of bytes specified by the Content-Length HTTP header.

open

NONE

My pipeline contains several components, these components have OutputPath type variables, so there will be files stored in minio, but when running, a random component will generate an error: `This step is in Error state with this message: Error (exit code 1): failed to put file: You did not provide the number of bytes specified by the Content-Length HTTP header.` Sometimes Pipeline can run normally again, how is this? What's the matter?  

https://api.github.com/repos/kubeflow/pipelines/issues/8060/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8053

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8053/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8053/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8053/events

https://github.com/kubeflow/pipelines/issues/8053

I_kwDOB-71UM5OLb6Y

[feature] Re-enable extra_code parameter for Python components.

open

CONTRIBUTOR

### Feature Area /area sdk ### What feature would you like to see? In KFP v1, there's an option to extend lightweight python components via `extra_code` which enables to have non-primitive values as defaults supported. However in KFPv2 this seems to be largely unsupported. Even in KFP v1, seems to be discouraged to use as `create_component_from_func` does not support it and `func_to_container_op` seems to be officially discouraged by maintainers (see: #7794 ) ### What is the use case or pain point? Extending python lightweigh decorator functionality to use custom defaults that are not primitives. ### Is there a workaround currently? `func_to_container_op` in KFP v1 does seem to work fine. However this ask is more related to keep this functionality moving forward. --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8053/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8052

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8052/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8052/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8052/events

https://github.com/kubeflow/pipelines/issues/8052

I_kwDOB-71UM5OIw1x

[feature] Ability to add Runtime Class name to Kubeflow Pipeline

open

NONE

### Feature Area /area backend /area sdk ### What feature would you like to see? Would like to have a separate method in kfp py package to add Runtime class (https://kubernetes.io/docs/concepts/containers/runtime-class/) to KF Pipeline Config and/or KFP Components. This is already supported in kubernetes py package (https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1RuntimeClass.md) ### What is the use case or pain point? The latest upgrade to kops 1.23.x is altering the default runtime in containerd to **runc** instead of **nvidia**. This is forcing us to use the Runtime Class name to choose **nvidia** runtime for GPU workloads. ### Is there a workaround currently? We manually override the default runtime set in the Containerd to nvidia and recycle the Containerd service (using some script customisation in the Kops Instance Group config) --- Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8052/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8051

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8051/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8051/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8051/events

https://github.com/kubeflow/pipelines/issues/8051

I_kwDOB-71UM5OG82k

Not able to use Kubeflow PyTorchJob launcher as a Kubeflow Pipeline component - Always waiting

open

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? As part of the Kubeflow Manifest 1.4 * KFP version: * KFP SDK version: 1.8.4 ### Steps to reproduce Trying to use Kubeflow PyTorchJob launcher component in the kubeflow pipeline ,however the pipeline component endlessly waits at the main thread with the following logs and does not proceed further with creation of main and worker pods. ``` Generating job template. Creating launcher client. Submitting CR. Creating kubeflow.org/pytorchjobs pytorch-cnn-dist-file-c3 in namespace kubeflow-user-example-com. Created kubeflow.org/pytorchjobs pytorch-cnn-dist-file-c3 in namespace kubeflow-user-example-com. Monitoring job until status is any of ['Succeeded', 'Failed']. ``` Code: ``` !pip install kfp==1.8.4 pytorch_job_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/pytorch-launcher/component.yaml') @dsl.pipeline(name="PyTorch Training pipeline", description="Sample training job test") def pytorch_cnn_n_b_yaml( namespace=kanwal_namespace ): train_task = pytorch_job_op( name='pytorch-cnn-dist-file-c3', namespace='kubeflow-user-example-com', master_spec='{ \ "replicas": 1, \ "restartPolicy": "OnFailure", \ "template": { \ "metadata": { \ "annotations": { \ "sidecar.istio.io/inject": "false" \ } \ }, \ "spec": { \ "containers": [ \ { \ "name": "pytorch1", \ "image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu116-ubuntu20.04-e3", \ "args": [ \ "python", \ "./sc-claim-dlc/mnist.py", \ "--epochs", "5", \ "--seed", "7", \ "--log-interval", "60" \ ], \ "resources": { \ "limits": { \ "nvidia.com/gpu": 2 \ } \ }, \ "volumeMounts": [ \ { \ "mountPath": "/sc-claim-dlc", \ "name": "sc-claim-dlc" \ } \ ] \ } \ ], \ "volumes": [ \ { \ "name": "sc-claim-dlc", \ "persistentVolumeClaim": { \ "claimName": "sc-claim-dlc" \ } \ } \ ] \ } \ } \ }', worker_spec='{ \ "replicas": 1, \ "restartPolicy": "OnFailure", \ "template": { \ "metadata": { \ "annotations": { \ "sidecar.istio.io/inject": "false" \ } \ }, \ "spec": { \ "containers": [ \ { \ "name": "pytorch2", \ "image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.12.0-gpu-py38-cu116-ubuntu20.04-e3", \ "args": [ \ "python", \ "./sc-claim-dlc/mnist.py", \ "--epochs", "5", \ "--seed", "7", \ "--log-interval", "60" \ ], \ "resources": { \ "limits": { \ "nvidia.com/gpu": 1 \ } \ }, \ "volumeMounts": [ \ { \ "mountPath": "/sc-claim-dlc", \ "name": "sc-claim-dlc" \ } \ ] \ } \ ], \ "volumes": [ \ { \ "name": "sc-claim-dlc", \ "persistentVolumeClaim": { \ "claimName": "sc-claim-dlc" \ } \ } \ ] \ } \ } \ }', delete_after_done=False ) ``` Can you help ? ### Expected result <!-- What should the correct behavior be? --> ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> <!-- /area backend --> <!-- /area sdk --> <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8051/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8038

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8038/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8038/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8038/events

https://github.com/kubeflow/pipelines/issues/8038

I_kwDOB-71UM5N5veH

Unable to set memory for a Kubeflow component using yaml

open

NONE

I'm using yaml for my Kubeflow components which run on Vertex AI. I know that I have alternatives for creating components such as the python SDK, but I would like to use yaml directly if possible (rather than generating it). I see methods like set_memory_limit and set_memory_request for the ContainerOp but when I use methods such as load_component_from_file I have a TaskSpec. Is there a way to set memory and other resources via the Kubeflow component yaml? Thank you.

https://api.github.com/repos/kubeflow/pipelines/issues/8038/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8034

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8034/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8034/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8034/events

https://github.com/kubeflow/pipelines/issues/8034

I_kwDOB-71UM5N0Kks

[frontend] Couldn't get Logs, Pod, and Events of any pipeline components in Runs

closed

NONE

### Environment * How did you deploy Kubeflow Pipelines (KFP)? Following https://github.com/kubeflow/manifests/tree/v1.3.1 * Versions: Kubeflow:1.3.1, Pipeline:1.5.1, "@kubernetes/client-node": "^0.11.2". Original version is "^0.8.2", tried to upgrade to "^0.11.2", but still the same errors. "portable-fetch": "^3.0.0", https://github.com/kubeflow/pipelines/blob/1.5.1/frontend/server/package.json#L16 "node-fetch":1.7.3, portable-fetch require node-fetch:1.7.3 that seems not support IPv6 BTW, using IPv6 in the cluster. I could see Pipelines, Experiment, and Runs list, but couldn't see Logs, Pod, and Events of any pipeline components in Runs. ### Steps to reproduce <img width="1439" alt="Screen Shot 2022-12-27 at 14 51 16" src="https://user-images.githubusercontent.com/37242439/209619418-2e4aebe2-ebb8-4a8a-8c96-aff826ec5ab8.png"> <img width="1418" alt="Screen Shot 2022-12-27 at 14 52 45" src="https://user-images.githubusercontent.com/37242439/209619426-3eab6885-f3d3-4e2c-a12e-58c2347d616f.png"> <img width="1495" alt="Screen Shot 2022-12-27 at 14 53 36" src="https://user-images.githubusercontent.com/37242439/209619432-79ca88b8-7ee5-47f8-b3ad-9fce91f91edc.png"> ### Expected result See Logs, Pod, and Events of pipeline components in Runs ### Materials and Reference npm list in ml-pipeline-ui pod  Error logs of ml-pipeline-ui pod Hostname/IP does not match certificate's altnames: Host: **240b** ``` { argo: { archiveArtifactory: 'minio', archiveBucketName: 'mlpipeline', archiveLogs: false, archivePrefix: 'logs' }, artifacts: 'Artifacts config contains credentials, so it is omitted', metadata: { envoyService: { host: '[ipv6]', port: '9090' } }, pipeline: { host: '[ipv6]', port: '8888' }, server: { apiVersionPrefix: 'apis/v1beta1', basePath: '/pipeline', deployment: 'KUBEFLOW', hideSideNav: true, port: 3000, staticDir: '/client' }, viewer: { tensorboard: { podTemplateSpec: [Object], tfImageName: 'tensorflow/tensorflow' } }, visualizations: { allowCustomVisualizations: true }, gkeMetadata: { disabled: false }, auth: { enabled: true, kubeflowUserIdHeader: 'kubeflow-userid', kubeflowUserIdPrefix: '' } } [HPM] Proxy created: [Function] -> /artifacts [HPM] Proxy created: / -> http://[ipv6]:9090 [HPM] Proxy created: / -> http://127.0.0.1 [HPM] Subscribed to http-proxy events: [ 'error', 'close' ] [HPM] Proxy created: / -> http://127.0.0.1 [HPM] Subscribed to http-proxy events: [ 'error', 'close' ] [HPM] Proxy created: / -> http://[ipv6]:8888 [HPM] Subscribed to http-proxy events: [ 'proxyReq', 'error', 'close' ] [HPM] Proxy created: / -> http://[ipv6]:8888 [HPM] Subscribed to http-proxy events: [ 'proxyReq', 'error', 'close' ] Server listening at http://localhost:3000 GET /apis/v1beta1/healthz ... GET /pipeline/k8s/pod?podname=rflow-demo-07-qfjnq-3523444001&podnamespace=rflow-test Could not get pod demo-07-qfjnq-3523444001 in namespace test: Hostname/IP does not match certificate's altnames: Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:C0E0:202:5404:B4D4:2:0:8001, IP Address:240B:..., IP Address:240B:... Error [ERR_TLS_CERT_ALTNAME_INVALID]: Hostname/IP does not match certificate's altnames: Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:...:8001, IP Address:240B:..., IP Address:240B:... at Object.checkServerIdentity (tls.js:283:12) at TLSSocket.onConnectSecure (_tls_wrap.js:1331:27) at TLSSocket.emit (events.js:223:5) at TLSSocket._finishInit (_tls_wrap.js:794:8) at TLSWrap.ssl.onhandshakedone (_tls_wrap.js:608:12) { reason: "Host: 240b. is not in the cert's altnames: DNS:kubernetes, DNS:kubernetes.default, DNS:kubernetes.default.svc, DNS:kubernetes.default.svc.cluster.local, DNS:uhn7kls2drbms001.test.local, IP Address:240B:...:8001, IP Address:240B:..., IP Address:240B:...", host: '240b', cert: { ... ``` config.ts https://github.com/kubeflow/pipelines/blob/1.5.1/frontend/server/configs.ts ``` const { staticDir, port } = parseArgs(argv); /** All configurable environment variables can be found here. */ const { /** minio client use these to retrieve minio objects/artifacts */ MINIO_ACCESS_KEY = 'minio', MINIO_SECRET_KEY = 'minio123', MINIO_PORT = '9000', MINIO_HOST = 'minio-service', MINIO_NAMESPACE = 'kubeflow', MINIO_SSL = 'false', /** minio client use these to retrieve s3 objects/artifacts */ AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, /** http/https base URL */ HTTP_BASE_URL = '', // tried to use '[::]', but make no difference. /** http/https fetch with this authorization header key (for example: 'Authorization') */ HTTP_AUTHORIZATION_KEY = '', /** http/https fetch with this authorization header value by default when absent in client request at above key */ HTTP_AUTHORIZATION_DEFAULT_VALUE = '', /** API service will listen to this host */ ML_PIPELINE_SERVICE_HOST = '[::]', /** API service will listen to this port */ ML_PIPELINE_SERVICE_PORT = '8888', /** path to viewer:tensorboard pod template spec */ VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH, /** Tensorflow image used for tensorboard viewer */ VIEWER_TENSORBOARD_TF_IMAGE_NAME = 'tensorflow/tensorflow', /** Whether custom visualizations are allowed to be generated by the frontend */ ALLOW_CUSTOM_VISUALIZATIONS = 'false', /** Envoy service will listen to this host */ METADATA_ENVOY_SERVICE_SERVICE_HOST = '[::]', /** Envoy service will listen to this port */ METADATA_ENVOY_SERVICE_SERVICE_PORT = '9090', /** Is Argo log archive enabled? */ ARGO_ARCHIVE_LOGS = 'false', /** Use minio or s3 client to retrieve archives. */ ARGO_ARCHIVE_ARTIFACTORY = 'minio', /** Bucket to retrive logs from */ ARGO_ARCHIVE_BUCKETNAME = 'mlpipeline', /** Prefix to logs. */ ARGO_ARCHIVE_PREFIX = 'logs', /** Should use server API for log streaming? */ STREAM_LOGS_FROM_SERVER_API = 'false', /** Disables GKE metadata endpoint. */ DISABLE_GKE_METADATA = 'true', /** Enable authorization checks for multi user mode. */ ENABLE_AUTHZ = 'false', /** Deployment type. */ DEPLOYMENT: DEPLOYMENT_STR = '', /** * Set to true to hide the SideNav. When DEPLOYMENT is KUBEFLOW, HIDE_SIDENAV * defaults to true if not explicitly set to false. */ HIDE_SIDENAV, /** * A header user requests have when authenticated. It carries user identity information. * The default value works with Google Cloud IAP. */ KUBEFLOW_USERID_HEADER = 'x-goog-authenticated-user-email', /** * KUBEFLOW_USERID_HEADER's value may have a prefix before user identity. * Use this header to specify what the prefix is. * * e.g. a valid header value for default values can be like `accounts.google.com:user@gmail.com`. */ KUBEFLOW_USERID_PREFIX = 'accounts.google.com:', } = env; ``` --- ``` kind: Deployment apiVersion: apps/v1 metadata: name: ml-pipeline-ui namespace: kubeflow labels: app: ml-pipeline-ui app.kubernetes.io/component: ml-pipeline app.kubernetes.io/instance: ml-pipeline app.kubernetes.io/name: kubeflow-pipelines application-crd-id: kubeflow-pipelines annotations: … managedFields: - manager: argocd-application-controller operation: Update apiVersion: apps/v1 time: '2022-04-13T06:18:49Z' fieldsType: FieldsV1 fieldsV1: f:metadata: f:annotations: .: {} f:kubectl.kubernetes.io/last-applied-configuration: {} f:labels: .: {} f:app: {} f:app.kubernetes.io/component: {} f:app.kubernetes.io/instance: {} f:app.kubernetes.io/name: {} f:application-crd-id: {} f:spec: f:progressDeadlineSeconds: {} f:replicas: {} f:revisionHistoryLimit: {} f:selector: {} f:strategy: f:rollingUpdate: .: {} f:maxSurge: {} f:maxUnavailable: {} f:type: {} f:template: f:metadata: f:annotations: .: {} f:cluster-autoscaler.kubernetes.io/safe-to-evict: {} f:labels: .: {} f:app: {} f:app.kubernetes.io/component: {} f:app.kubernetes.io/name: {} f:application-crd-id: {} f:spec: f:containers: k:{"name":"ml-pipeline-ui"}: .: {} f:env: .: {} k:{"name":"ALLOW_CUSTOM_VISUALIZATIONS"}: .: {} f:name: {} f:value: {} k:{"name":"ARTIFACTS_SERVICE_PROXY_ENABLED"}: .: {} f:name: {} f:value: {} k:{"name":"ARTIFACTS_SERVICE_PROXY_NAME"}: .: {} f:name: {} f:value: {} k:{"name":"ARTIFACTS_SERVICE_PROXY_PORT"}: .: {} f:name: {} f:value: {} k:{"name":"DEPLOYMENT"}: .: {} f:name: {} f:value: {} k:{"name":"ENABLE_AUTHZ"}: .: {} f:name: {} f:value: {} k:{"name":"KUBEFLOW_USERID_HEADER"}: .: {} f:name: {} f:value: {} k:{"name":"KUBEFLOW_USERID_PREFIX"}: .: {} f:name: {} k:{"name":"MINIO_ACCESS_KEY"}: .: {} f:name: {} f:valueFrom: .: {} f:secretKeyRef: .: {} f:key: {} f:name: {} k:{"name":"MINIO_NAMESPACE"}: .: {} f:name: {} f:valueFrom: .: {} f:fieldRef: .: {} f:apiVersion: {} f:fieldPath: {} k:{"name":"MINIO_SECRET_KEY"}: .: {} f:name: {} f:valueFrom: .: {} f:secretKeyRef: .: {} f:key: {} f:name: {} k:{"name":"VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH"}: .: {} f:name: {} f:value: {} f:livenessProbe: .: {} f:exec: .: {} f:command: {} f:failureThreshold: {} f:initialDelaySeconds: {} f:periodSeconds: {} f:successThreshold: {} f:timeoutSeconds: {} f:name: {} f:ports: .: {} k:{"containerPort":3000,"protocol":"TCP"}: .: {} f:containerPort: {} f:protocol: {} f:readinessProbe: .: {} f:exec: .: {} f:command: {} f:failureThreshold: {} f:initialDelaySeconds: {} f:periodSeconds: {} f:successThreshold: {} f:timeoutSeconds: {} f:resources: .: {} f:requests: .: {} f:cpu: {} f:memory: {} f:terminationMessagePath: {} f:terminationMessagePolicy: {} f:volumeMounts: .: {} k:{"mountPath":"/etc/config"}: .: {} f:mountPath: {} f:name: {} f:readOnly: {} f:dnsPolicy: {} f:restartPolicy: {} f:schedulerName: {} f:securityContext: {} f:serviceAccount: {} f:serviceAccountName: {} f:terminationGracePeriodSeconds: {} f:volumes: .: {} k:{"name":"config-volume"}: .: {} f:configMap: .: {} f:defaultMode: {} f:name: {} f:name: {} - manager: kubectl-edit operation: Update apiVersion: apps/v1 time: '2022-04-19T04:22:29Z' fieldsType: FieldsV1 fieldsV1: f:spec: f:template: f:spec: f:containers: k:{"name":"ml-pipeline-ui"}: f:env: k:{"name":"METADATA_ENVOY_SERVICE_SERVICE_HOST"}: .: {} f:name: {} f:value: {} k:{"name":"ML_PIPELINE_SERVICE_HOST"}: .: {} f:name: {} f:value: {} f:imagePullPolicy: {} - manager: kubectl-rollout operation: Update apiVersion: apps/v1 time: '2022-05-09T02:59:57Z' fieldsType: FieldsV1 fieldsV1: f:spec: f:template: f:metadata: f:annotations: f:kubectl.kubernetes.io/restartedAt: {} - manager: dashboard operation: Update apiVersion: apps/v1 time: '2022-07-15T01:59:57Z' fieldsType: FieldsV1 fieldsV1: f:spec: f:template: f:spec: f:containers: k:{"name":"ml-pipeline-ui"}: f:env: k:{"name":"HTTP_BASE_URL"}: .: {} f:name: {} f:value: {} f:image: {} - manager: kube-controller-manager operation: Update apiVersion: apps/v1 time: '2022-07-15T02:01:01Z' fieldsType: FieldsV1 fieldsV1: f:metadata: f:annotations: f:deployment.kubernetes.io/revision: {} f:status: f:availableReplicas: {} f:conditions: .: {} k:{"type":"Available"}: .: {} f:lastTransitionTime: {} f:lastUpdateTime: {} f:message: {} f:reason: {} f:status: {} f:type: {} k:{"type":"Progressing"}: .: {} f:lastTransitionTime: {} f:lastUpdateTime: {} f:message: {} f:reason: {} f:status: {} f:type: {} f:observedGeneration: {} f:readyReplicas: {} f:replicas: {} f:updatedReplicas: {} spec: replicas: 1 selector: matchLabels: app: ml-pipeline-ui app.kubernetes.io/component: ml-pipeline app.kubernetes.io/name: kubeflow-pipelines application-crd-id: kubeflow-pipelines template: metadata: creationTimestamp: null labels: app: ml-pipeline-ui app.kubernetes.io/component: ml-pipeline app.kubernetes.io/name: kubeflow-pipelines application-crd-id: kubeflow-pipelines annotations: cluster-autoscaler.kubernetes.io/safe-to-evict: 'true' kubectl.kubernetes.io/restartedAt: '2022-07-13T06:30:53Z' spec: volumes: - name: config-volume configMap: name: ml-pipeline-ui-configmap defaultMode: 420 containers: - name: ml-pipeline-ui image: >- frontend:1.5.1-build10 ports: - containerPort: 3000 protocol: TCP env: - name: VIEWER_TENSORBOARD_POD_TEMPLATE_SPEC_PATH value: /etc/config/viewer-pod-template.json - name: HTTP_BASE_URL value: abcd - name: ML_PIPELINE_SERVICE_HOST value: '[IPv6 address]' - name: METADATA_ENVOY_SERVICE_SERVICE_HOST value: '[IPv6 address]' - name: DEPLOYMENT value: KUBEFLOW - name: ARTIFACTS_SERVICE_PROXY_NAME value: ml-pipeline-ui-artifact - name: ARTIFACTS_SERVICE_PROXY_PORT value: '80' - name: ARTIFACTS_SERVICE_PROXY_ENABLED value: 'true' - name: ENABLE_AUTHZ value: 'true' - name: KUBEFLOW_USERID_HEADER value: kubeflow-userid - name: KUBEFLOW_USERID_PREFIX - name: MINIO_NAMESPACE valueFrom: fieldRef: apiVersion: v1 fieldPath: metadata.namespace - name: MINIO_ACCESS_KEY valueFrom: secretKeyRef: name: mlpipeline-minio-artifact key: accesskey - name: MINIO_SECRET_KEY valueFrom: secretKeyRef: name: mlpipeline-minio-artifact key: secretkey - name: ALLOW_CUSTOM_VISUALIZATIONS value: 'true' ``` <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8034/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8014

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8014/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8014/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8014/events

https://github.com/kubeflow/pipelines/issues/8014

I_kwDOB-71UM5NoTWM

[feature] Add primary key to DB tables

closed

CONTRIBUTOR

### Feature Area <!-- Uncomment the labels below which are relevant to this feature: --> <!-- /area frontend --> /area backend <!-- /area sdk --> <!-- /area samples --> <!-- /area components --> ### What feature would you like to see? That kfp create [its database](https://github.com/kubeflow/pipelines/tree/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model) with a primary key (probably [here](https://github.com/kubeflow/pipelines/blob/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model/default_experiment.go#L18) and [here](https://github.com/kubeflow/pipelines/tree/5a56a9399908033ce97573abf85098fd77f649ae/backend/src/apiserver/model), with something like: ``` DefaultExperimentId string `gorm:"column:DefaultExperimentId; not null; primary key"` ``` ### What is the use case or pain point? Primary keys are required for group replication (high availability) of tables in mysql. Enabling this would enable using more resilient data storage. ### Is there a workaround currently? I don't know if this is safe, but perhaps I can separately create the database following kfp's schema (but with a primary key) and then have kfp connect to that existing DB? I'm not sure what the consequences of this would be. **update:** This appears to work (application starts without errors, etc). Haven't stress tested it yet --- <!-- Don't delete message below to encourage users to support your feature request! --> Love this idea? Give it a 👍. We prioritize fulfilling features with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8014/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/8012

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8012/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8012/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8012/events

https://github.com/kubeflow/pipelines/issues/8012

I_kwDOB-71UM5NlgkN

[bug] executor attempts to kill already terminated kfp-launcher init container

open

NONE

### Environment <!-- Please fill in those that seem relevant. --> * How do you deploy Kubeflow Pipelines (KFP)? [standalone-deployment](https://www.kubeflow.org/docs/components/pipelines/installation/standalone-deployment/#deploying-kubeflow-pipelines) <!-- For more information, see an overview of KFP installation options: https://www.kubeflow.org/docs/pipelines/installation/overview/. --> * KFP version: 1.8.2 <!-- Specify the version of Kubeflow Pipelines that you are using. The version number appears in the left side navigation of user interface. To find the version number, See version number shows on bottom of KFP UI left sidenav. --> * KFP SDK version: 1.8.2 <!-- Specify the output of the following shell command: $pip list | grep kfp --> ### Steps to reproduce The KFPv2 pipelines are completed, but errors occur as shown below, it seems to be a problem caused by attempting to terminate the already terminated the kfp-launcher init container. ``` $ kubectl -n kubeflow-pipeline logs -f pipeline-test-1-gtvhr-2680780413 -c wait time="2022-07-12T06:48:44.957Z" level=info msg="Starting Workflow Executor" executorType=docker version=v3.2.3 time="2022-07-12T06:48:44.961Z" level=info msg="Creating a docker executor" time="2022-07-12T06:48:44.961Z" level=info msg="Executor initialized" deadline="0001-01-01 00:00:00 +0000 UTC" includeScriptOutput=false namespace=kubeflow-pipeline podName=pipeline-test-1-gtvhr-2680780413 template="{\"name\":\"get-data\",\"inputs\":{\"parameters\":[{\"name\":\"pipeline-name\",\"value\":\"pipeline/pipeline-test-1\"},{\"name\":\"pipeline-root\",\"value\":\"\"}]},\"outputs\":{\"artifacts\":[{\"name\":\"get-data-dataset_test\",\"path\":\"/tmp/outputs/dataset_test/data\"},{\"name\":\"get-data-dataset_train\",\"path\":\"/tmp/outputs/dataset_train/data\"}]},\"metadata\":{\"annotations\":{\"pipelines.kubeflow.org/component_ref\":\"{}\",\"pipelines.kubeflow.org/v2_component\":\"true\",\"sidecar.istio.io/inject\":\"false\"},\"labels\":{\"pipelines.kubeflow.org/cache_enabled\":\"true\",\"pipelines.kubeflow.org/enable_caching\":\"false\",\"pipelines.kubeflow.org/kfp_sdk_version\":\"1.8.2\",\"pipelines.kubeflow.org/pipeline-sdk-type\":\"kfp\",\"pipelines.kubeflow.org/v2_component\":\"true\"}},\"container\":{\"name\":\"\",\"image\":\"python:3.7\",\"command\":[\"/kfp-launcher/launch\",\"--mlmd_server_address\",\"$(METADATA_GRPC_SERVICE_HOST)\",\"--mlmd_server_port\",\"$(METADATA_GRPC_SERVICE_PORT)\",\"--runtime_info_json\",\"$(KFP_V2_RUNTIME_INFO)\",\"--container_image\",\"$(KFP_V2_IMAGE)\",\"--task_name\",\"get-data\",\"--pipeline_name\",\"pipeline/pipeline-test-1\",\"--run_id\",\"$(KFP_RUN_ID)\",\"--run_resource\",\"workflows.argoproj.io/$(WORKFLOW_ID)\",\"--namespace\",\"$(KFP_NAMESPACE)\",\"--pod_name\",\"$(KFP_POD_NAME)\",\"--pod_uid\",\"$(KFP_POD_UID)\",\"--pipeline_root\",\"\",\"--enable_caching\",\"$(ENABLE_CACHING)\",\"--\",\"--\"],\"args\":[\"sh\",\"-c\",\"(python3 -m ensurepip || python3 -m ensurepip --user) \\u0026\\u0026 (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'pandas' 'sklearn' 'kfp==1.8.2' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location 'pandas' 'sklearn' 'kfp==1.8.2' --user) \\u0026\\u0026 \\\"$0\\\" \\\"$@\\\"\",\"sh\",\"-ec\",\"program_path=$(mktemp -d)\\nprintf \\\"%s\\\" \\\"$0\\\" \\u003e \\\"$program_path/ephemeral_component.py\\\"\\npython3 -m kfp.v2.components.executor_main --component_module_path \\\"$program_path/ephemeral_component.py\\\" \\\"$@\\\"\\n\",\"\\nfrom kfp.v2.dsl import *\\nfrom typing import *\\n\\ndef get_data(\\n dataset_train: Output[Dataset],\\n dataset_test: Output[Dataset]\\n\\n):\\n\\n from sklearn import datasets\\n from sklearn.model_selection import train_test_split as tts\\n import pandas as pd\\n # import some data to play with\\n\\n data_raw = datasets.load_breast_cancer()\\n data = pd.DataFrame(data_raw.data, columns=data_raw.feature_names)\\n data[\\\"target\\\"] = data_raw.target\\n\\n train, test = tts(data, test_size=0.3)\\n\\n train.to_csv(dataset_train.path)\\n test.to_csv(dataset_test.path)\\n\\n\",\"--executor_input\",\"{{$}}\",\"--function_to_execute\",\"get_data\"],\"envFrom\":[{\"configMapRef\":{\"name\":\"metadata-grpc-configmap\",\"optional\":true}}],\"env\":[{\"name\":\"KFP_POD_NAME\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.name\"}}},{\"name\":\"KFP_POD_UID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.uid\"}}},{\"name\":\"KFP_NAMESPACE\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.namespace\"}}},{\"name\":\"WORKFLOW_ID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['workflows.argoproj.io/workflow']\"}}},{\"name\":\"KFP_RUN_ID\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['pipeline/runid']\"}}},{\"name\":\"ENABLE_CACHING\",\"valueFrom\":{\"fieldRef\":{\"fieldPath\":\"metadata.labels['pipelines.kubeflow.org/enable_caching']\"}}},{\"name\":\"KFP_V2_IMAGE\",\"value\":\"python:3.7\"},{\"name\":\"KFP_V2_RUNTIME_INFO\",\"value\":\"{\\\"inputParameters\\\": {}, \\\"inputArtifacts\\\": {}, \\\"outputParameters\\\": {}, \\\"outputArtifacts\\\": {\\\"dataset_test\\\": {\\\"schemaTitle\\\": \\\"system.Dataset\\\", \\\"instanceSchema\\\": \\\"\\\", \\\"schemaVersion\\\": \\\"0.0.1\\\", \\\"metadataPath\\\": \\\"/tmp/outputs/dataset_test/data\\\"}, \\\"dataset_train\\\": {\\\"schemaTitle\\\": \\\"system.Dataset\\\", \\\"instanceSchema\\\": \\\"\\\", \\\"schemaVersion\\\": \\\"0.0.1\\\", \\\"metadataPath\\\": \\\"/tmp/outputs/dataset_train/data\\\"}}}\"}],\"resources\":{},\"volumeMounts\":[{\"name\":\"kfp-launcher\",\"mountPath\":\"/kfp-launcher\"}]},\"volumes\":[{\"name\":\"kfp-launcher\"}],\"initContainers\":[{\"name\":\"kfp-launcher\",\"image\":\"gcr.io/ml-pipeline/kfp-launcher:1.8.2\",\"command\":[\"launcher\",\"--copy\",\"/kfp-launcher/launch\"],\"resources\":{},\"mirrorVolumeMounts\":true}],\"archiveLocation\":{\"archiveLogs\":true,\"s3\":{\"endpoint\":\"minio-service.kubeflow-pipeline:9000\",\"bucket\":\"mlpipeline\",\"insecure\":true,\"accessKeySecret\":{\"name\":\"clops-minio-artifact\",\"key\":\"accessKey\"},\"secretKeySecret\":{\"name\":\"clops-minio-artifact\",\"key\":\"secretKey\"},\"key\":\"pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413\"}}}" version="&Version{Version:v3.2.3,BuildDate:2021-10-27T01:09:16Z,GitCommit:e5dc961b7846efe0fe36ab3a0964180eaedd2672,GitTag:v3.2.3,GitTreeState:clean,GoVersion:go1.16.9,Compiler:gc,Platform:linux/amd64,}" time="2022-07-12T06:48:44.961Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:44.961Z" level=info msg="Starting deadline monitor" time="2022-07-12T06:48:45.006Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Created {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"wait\" to container ID \"b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c\" (created at 2022-07-12 06:48:44 +0000 UTC, status Up)" time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"kfp-launcher\" to container ID \"3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b\" (created at 2022-07-12 06:48:43 +0000 UTC, status Exited)" time="2022-07-12T06:48:45.006Z" level=info msg="mapped container name \"main\" to container ID \"e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c\" (created at 2022-07-12 06:48:44 +0000 UTC, status Created)" time="2022-07-12T06:48:45.962Z" level=info msg="docker wait e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c" time="2022-07-12T06:48:46.007Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:46.036Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:47.036Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:47.068Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:48.069Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:48.099Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:49.100Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:49.130Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:50.131Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:50.172Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:51.173Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:51.204Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:52.204Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:52.231Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:53.231Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:53.265Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:54.266Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:54.294Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:55.295Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:55.328Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:56.328Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:56.357Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:57.357Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:57.386Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:58.387Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:58.421Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:48:59.422Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:48:59.449Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:00.449Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:00.477Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:01.477Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:01.530Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:02.530Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:02.565Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:03.565Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:03.607Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:04.607Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:04.638Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:05.640Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:05.693Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:06.693Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:06.749Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:07.749Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:07.783Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:08.784Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:08.848Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:09.849Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:09.883Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:10.884Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:10.934Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:11.935Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:11.963Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:12.964Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:12.997Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:13.997Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:14.027Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:15.028Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:15.058Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Up {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:22.093Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:22.195Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:22.195Z" level=info msg="Main container completed" time="2022-07-12T06:49:22.195Z" level=info msg="No Script output reference in workflow. Capturing script output ignored" time="2022-07-12T06:49:22.195Z" level=info msg="Saving logs" time="2022-07-12T06:49:22.195Z" level=info msg="[docker logs e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c]" time="2022-07-12T06:49:22.247Z" level=info msg="S3 Save path: /tmp/argo/outputs/logs/main.log, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/main.log" time="2022-07-12T06:49:22.247Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000" time="2022-07-12T06:49:22.247Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/main.log path=/tmp/argo/outputs/logs/main.log time="2022-07-12T06:49:24.735Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/logs/main.log time="2022-07-12T06:49:24.735Z" level=info msg="Successfully saved file: /tmp/argo/outputs/logs/main.log" time="2022-07-12T06:49:24.735Z" level=info msg="No output parameters" time="2022-07-12T06:49:24.735Z" level=info msg="Saving output artifacts" time="2022-07-12T06:49:24.735Z" level=info msg="Staging artifact: get-data-dataset_test" time="2022-07-12T06:49:24.735Z" level=info msg="Copying /tmp/outputs/dataset_test/data from container base image layer to /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz" time="2022-07-12T06:49:24.735Z" level=info msg="Archiving main:/tmp/outputs/dataset_test/data to /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz" time="2022-07-12T06:49:24.735Z" level=info msg="sh -c docker cp -a e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c:/tmp/outputs/dataset_test/data - | gzip > /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz" time="2022-07-12T06:49:24.915Z" level=info msg="Archiving completed" time="2022-07-12T06:49:24.915Z" level=info msg="S3 Save path: /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_test.tgz" time="2022-07-12T06:49:24.915Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000" time="2022-07-12T06:49:24.915Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_test.tgz path=/tmp/argo/outputs/artifacts/get-data-dataset_test.tgz time="2022-07-12T06:49:25.058Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:25.089Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:26.424Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/artifacts/get-data-dataset_test.tgz time="2022-07-12T06:49:26.424Z" level=info msg="Successfully saved file: /tmp/argo/outputs/artifacts/get-data-dataset_test.tgz" time="2022-07-12T06:49:26.424Z" level=info msg="Staging artifact: get-data-dataset_train" time="2022-07-12T06:49:26.424Z" level=info msg="Copying /tmp/outputs/dataset_train/data from container base image layer to /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz" time="2022-07-12T06:49:26.424Z" level=info msg="Archiving main:/tmp/outputs/dataset_train/data to /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz" time="2022-07-12T06:49:26.424Z" level=info msg="sh -c docker cp -a e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c:/tmp/outputs/dataset_train/data - | gzip > /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz" time="2022-07-12T06:49:26.559Z" level=info msg="Archiving completed" time="2022-07-12T06:49:26.571Z" level=info msg="S3 Save path: /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz, key: pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_train.tgz" time="2022-07-12T06:49:26.571Z" level=info msg="Creating minio client using static credentials" endpoint="minio-service.kubeflow-pipeline:9000" time="2022-07-12T06:49:26.571Z" level=info msg="Saving file to s3" bucket=mlpipeline endpoint="minio-service.kubeflow-pipeline:9000" key=pipeline-test-1-gtvhr/pipeline-test-1-gtvhr-2680780413/get-data-dataset_train.tgz path=/tmp/argo/outputs/artifacts/get-data-dataset_train.tgz time="2022-07-12T06:49:28.158Z" level=info msg="not deleting local artifact" localArtPath=/tmp/argo/outputs/artifacts/get-data-dataset_train.tgz time="2022-07-12T06:49:28.158Z" level=info msg="Successfully saved file: /tmp/argo/outputs/artifacts/get-data-dataset_train.tgz" time="2022-07-12T06:49:28.158Z" level=info msg="Annotating pod with output" time="2022-07-12T06:49:28.211Z" level=info msg="Patch pods 200" time="2022-07-12T06:49:28.218Z" level=info msg="docker ps --all --no-trunc --format={{.Status}}|{{.Label \"io.kubernetes.container.name\"}}|{{.ID}}|{{.CreatedAt}} --filter=label=io.kubernetes.pod.namespace=kubeflow-pipeline --filter=label=io.kubernetes.pod.name=pipeline-test-1-gtvhr-2680780413" time="2022-07-12T06:49:28.250Z" level=info msg="listed containers" containers="map[kfp-launcher:{3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b Exited {0 63793205323 <nil>}} main:{e9f48e33f389b70943f7a04eaed064a646265b4c832216a47bf6c7e20a6aca9c Exited {0 63793205324 <nil>}} wait:{b17b4bd21c798e1cb684b62b80435c92705737169f69e51a09e56e9c980a373c Up {0 63793205324 <nil>}}]" time="2022-07-12T06:49:28.250Z" level=info msg="Killing sidecars [\"kfp-launcher\"]" time="2022-07-12T06:49:28.252Z" level=info msg="Get pods 403" time="2022-07-12T06:49:28.252Z" level=warning msg="Non-transient error: pods \"pipeline-test-1-gtvhr-2680780413\" is forbidden: User \"system:serviceaccount:kubeflow-pipeline:default\" cannot get resource \"pods\" in API group \"\" in the namespace \"kubeflow-pipeline\"" time="2022-07-12T06:49:28.252Z" level=info msg="docker kill --signal TERM 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b" time="2022-07-12T06:49:28.306Z" level=error msg="`docker kill --signal TERM 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b` failed: Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running\n" time="2022-07-12T06:49:28.306Z" level=warning msg="Ignored error from 'docker kill --signal TERM': Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running" time="2022-07-12T06:49:28.306Z" level=info msg="[docker wait 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b]" time="2022-07-12T06:49:28.307Z" level=info msg="Timed out (0s) for containers to terminate gracefully. Killing forcefully" time="2022-07-12T06:49:28.307Z" level=info msg="[docker kill --signal KILL 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b]" time="2022-07-12T06:49:28.405Z" level=warning msg="Ignored error from 'docker kill --signal KILL': exit status 1" time="2022-07-12T06:49:28.405Z" level=error msg="executor error: Error response from daemon: Cannot kill container: 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b: Container 3910ce0b5e1470c0f11ccc5d1e9895feec6e9bec565b7eb0cade8e3f053b843b is not running\ngithub.com/argoproj/argo-workflows/v3/errors.Wrap\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:88\ngithub.com/argoproj/argo-workflows/v3/errors.InternalWrapError\n\t/go/src/github.com/argoproj/argo-workflows/errors/errors.go:71\ngithub.com/argoproj/argo-workflows/v3/workflow/executor/docker.(*DockerExecutor).Kill\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/docker/docker.go:410\ngithub.com/argoproj/argo-workflows/v3/workflow/executor.(*WorkflowExecutor).KillSidecars\n\t/go/src/github.com/argoproj/argo-workflows/workflow/executor/executor.go:989\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:35\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.waitContainer\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:72\ngithub.com/argoproj/argo-workflows/v3/cmd/argoexec/commands.NewWaitCommand.func1\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/commands/wait.go:18\ngithub.com/spf13/cobra.(*Command).execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:860\ngithub.com/spf13/cobra.(*Command).ExecuteC\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:974\ngithub.com/spf13/cobra.(*Command).Execute\n\t/go/pkg/mod/github.com/spf13/cobra@v1.2.1/command.go:902\nmain.main\n\t/go/src/github.com/argoproj/argo-workflows/cmd/argoexec/main.go:15\nruntime.main\n\t/usr/local/go/src/runtime/proc.go:225\nruntime.goexit\n\t/usr/local/go/src/runtime/asm_amd64.s:1371" time="2022-07-12T06:49:28.405Z" level=info msg="Alloc=6364 TotalAlloc=22190 Sys=73297 NumGC=6 Goroutines=15" ``` <!-- Specify how to reproduce the problem. This may include information such as: a description of the process, code snippets, log output, or screenshots. --> ### Expected result <!-- What should the correct behavior be? --> ### Materials and reference <!-- Help us debug this issue by providing resources such as: sample code, background context, or links to references. --> - my workflow-controller-configmap ``` apiVersion: v1 data: artifactRepository: | archiveLogs: true s3: accessKeySecret: key: accesskey name: mlpipeline-minio-artifact bucket: mlpipeline endpoint: minio-service.kubeflow-pipeline:9000 insecure: true secretKeySecret: key: secretkey name: mlpipeline-minio-artifact containerRuntimeExecutor: docker executor: | imagePullPolicy: IfNotPresent resources: limits: cpu: "0.5" memory: 512Mi requests: cpu: "0.01" memory: 32Mi parallelism: "50" kind: ConfigMap metadata: name: workflow-controller-configmap namespace: kubeflow-pipeline ``` - KFPv2 example I tried: https://github.com/kubeflow/pipelines/blob/master/samples/v2/lightweight_python_functions_v2_pipeline/lightweight_python_functions_v2_pipeline.py ### Labels <!-- Please include labels below by uncommenting them to help us better triage issues --> <!-- /area frontend --> /area backend /area sdk <!-- /area testing --> <!-- /area samples --> <!-- /area components --> --- <!-- Don't delete message below to encourage users to support your issue! --> Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

https://api.github.com/repos/kubeflow/pipelines/issues/8012/timeline

https://api.github.com/repos/kubeflow/pipelines/issues/8001

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/8001/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/8001/comments

https://api.github.com/repos/kubeflow/pipelines/issues/8001/events

https://github.com/kubeflow/pipelines/issues/8001

I_kwDOB-71UM5NiePt

Does Kubeflow (1.2) has dependency with any specific kfp python library version?

closed

NONE

i am wondering if there any dependencies in kfp python library version when i want to use it in kubeflow

https://api.github.com/repos/kubeflow/pipelines/issues/8001/timeline

completed

https://api.github.com/repos/kubeflow/pipelines/issues/7995

https://api.github.com/repos/kubeflow/pipelines

https://api.github.com/repos/kubeflow/pipelines/issues/7995/labels{/name}

https://api.github.com/repos/kubeflow/pipelines/issues/7995/comments

https://api.github.com/repos/kubeflow/pipelines/issues/7995/events

https://github.com/kubeflow/pipelines/issues/7995

I_kwDOB-71UM5NZbrV

[frontend] AWS S3 artifacts handler hardcode for s3 endpoint.

closed