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Merge branch 'master' of https://github.com/kubeflow/pipelines into task-setters
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.github/workflows/backend-visualization.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.8'
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python-version: '3.9'
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- name: Run tests
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run: ./test/presubmit-backend-visualization.sh

.github/workflows/kfp-kubernetes-library-test.yml

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strategy:
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matrix:
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python: [
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{ 'version': '3.8' },
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{ 'version': '3.9' },
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{ 'version': '3.10' },
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{ 'version': '3.11' },

.github/workflows/kfp-sdk-runtime-tests.yml

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runs-on: ubuntu-24.04
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strategy:
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matrix:
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python: ['3.8', '3.9', '3.10', '3.11', '3.12']
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python: ['3.9', '3.10', '3.11', '3.12']
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steps:
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- name: Checkout code
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uses: actions/checkout@v4

.github/workflows/kfp-sdk-tests.yml

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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.8, 3.9, 3.10, 3.11, 3.12]
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python-version: [3.9, 3.10, 3.11, 3.12]
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steps:
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- name: Checkout code

.github/workflows/kubeflow-pipelines-integration-v2.yml

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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: [3.8]
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python-version: [3.9]
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- name: Checkout code

.github/workflows/sdk-component-yaml.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: 3.8
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python-version: 3.9
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- name: apt-get update
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run: sudo apt-get update

.github/workflows/sdk-docformatter.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: 3.8
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python-version: 3.9
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- name: Run docformatter tests
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run: ./test/presubmit-docformatter-sdk.sh

.github/workflows/sdk-isort.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: 3.8
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python-version: 3.9
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- name: Run isort tests
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run: ./test/presubmit-isort-sdk.sh

.github/workflows/sdk-upgrade.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: 3.8
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python-version: 3.9
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- name: Run SDK upgrade tests
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run: ./test/presubmit-test-sdk-upgrade.sh

.github/workflows/sdk-yapf.yml

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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.8'
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python-version: '3.9'
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- name: Install dependencies
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run: pip install yapf

.readthedocs.yml

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build:
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os: ubuntu-22.04
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tools:
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python: "3.8"
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python: "3.9"

RELEASE.md

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@@ -169,7 +169,7 @@ Do the following things before a release:
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1. Verify cloudbuild and postsubmit tests are passing: visit <https://github.com/kubeflow/pipelines/commits/master> for master branch.
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![How to very cloudbuild and postsubmit status](release-status-check.png)
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![How to very cloudbuild and postsubmit status](images/release-status-check.png)
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If not, contact the KFP team to determine if the failure(s) would block the release. You can also retry the failed job by opening the detail page of prow job, and click the refresh button next ot the job title.
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on Google Cloud Build" and then "Retry".
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NOTE: you can find your latest release commit in <https://github.com/kubeflow/pipelines/commits/master> and select your release branch.
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![How to very cloudbuild and postsubmit status](release-status-check.png)
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![How to very cloudbuild and postsubmit status](images/release-status-check.png)
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1. Select the `release-on-tag` cloudbuild job that copies built images and artifacts to
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public image registry and gcs bucket. This job should have already failed because
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It will create a build with "Branch: $BRANCH" instead of "TAG: $VERSION".
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Open "View more details on Google Cloud Build", and rerun from there.
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![Retry release-on-tag from the Google Cloud Build details page](retry-release-on-tag.png)
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![Retry release-on-tag from the Google Cloud Build details page](images/retry-release-on-tag.png)
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![Verify that you're retrying the right build](verify-retry-the-right-build.png)
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![Verify that you're retrying the right build](images/verify-retry-the-right-build.png)
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TODO: we should have an automation KFP cluster, and the waiting and submiting
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`release-on-tag` cloudbuild task should happen automatically.
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<pre>
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To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in [here](https://www.kubeflow.org/docs/pipelines/standalone-deployment-gcp/) or via UI [here](https://console.cloud.google.com/ai-platform/pipelines)
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Install python SDK (python 3.7 above) by running:
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Install python SDK (python 3.9 above) by running:
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```bash
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python3 -m pip install kfp kfp-server-api --upgrade
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<pre>
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To deploy Kubeflow Pipelines in an existing cluster, follow the instruction in [here](https://www.kubeflow.org/docs/pipelines/standalone-deployment-gcp/).
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Install kfp-server-api package (python 3.7 above) by running:
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Install kfp-server-api package (python 3.9 above) by running:
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```bash
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python3 -m pip install kfp-server-api==$VERSION --upgrade

api/v2alpha1/python/setup.py

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author_email='kubeflow-pipelines@google.com',
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url='https://github.com/kubeflow/pipelines',
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packages=setuptools.find_namespace_packages(include=['kfp.*']),
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python_requires='>=3.7.0,<3.13.0',
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python_requires='>=3.9.0,<3.13.0',
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install_requires=['protobuf>=4.21.1,<5'],
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include_package_data=True,
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license='Apache 2.0',

backend/Dockerfile

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# 2. Compile preloaded pipeline samples
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FROM python:3.8 as compiler
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FROM python:3.9 as compiler
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RUN apt-get update -y && apt-get install --no-install-recommends -y -q default-jdk python3-setuptools python3-dev jq
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RUN wget https://bootstrap.pypa.io/get-pip.py && python3 get-pip.py
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COPY backend/requirements.txt .

backend/Dockerfile.conformance

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FROM alpine:3.8
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FROM alpine:3.9
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backend/api/v1beta1/python_http_client/.gitlab-ci.yml

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backend/api/v1beta1/python_http_client/.travis.yml

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backend/api/v2beta1/python_http_client/.gitlab-ci.yml

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backend/api/v2beta1/python_http_client/.travis.yml

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backend/metadata_writer/Dockerfile

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# ml-metadata package depends on tensorflow package
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FROM python:3.8
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FROM python:3.9
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COPY backend/metadata_writer/requirements.txt /kfp/metadata_writer/
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RUN python3 -m pip install -r /kfp/metadata_writer/requirements.txt
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backend/metadata_writer/requirements.txt

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#
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# This file is autogenerated by pip-compile with Python 3.8
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# This file is autogenerated by pip-compile with Python 3.9
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# by the following command:
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#
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# pip-compile --output-file=- -
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#!/bin/bash
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# This image should be in sync with Dockerfile.
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IMAGE="python:3.8"
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IMAGE="python:3.9"
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../../hack/update-requirements.sh $IMAGE <requirements.in >requirements.txt

backend/src/apiserver/config/config.json

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"DEFAULTPIPELINERUNNERSERVICEACCOUNT": "pipeline-runner",
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"CacheEnabled": "true",
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"CRON_SCHEDULE_TIMEZONE": "UTC",
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"CACHE_IMAGE": "gcr.io/google-containers/busybox",
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"CACHE_IMAGE": "registry.k8s.io/busybox",
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"CACHE_NODE_RESTRICTIONS": "false"
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}

backend/src/apiserver/resource/resource_manager_test.go

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- name: ENABLE_CACHING
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valueFrom:
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fieldRef: {fieldPath: 'metadata.labels[''pipelines.kubeflow.org/enable_caching'']'}
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- {name: KFP_V2_IMAGE, value: 'python:3.7'}
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- {name: KFP_V2_IMAGE, value: 'python:3.9'}
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- {name: KFP_V2_RUNTIME_INFO, value: '{"inputParameters": {"num_steps": {"type":
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"INT"}}, "inputArtifacts": {"dataset": {"metadataPath": "/tmp/inputs/dataset/data",
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"schemaTitle": "system.Dataset", "instanceSchema": ""}}, "outputParameters":
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{}, "outputArtifacts": {"model": {"schemaTitle": "system.Model", "instanceSchema":
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"", "metadataPath": "/tmp/outputs/model/data"}}}'}
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envFrom:
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image: python:3.9
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volumeMounts:
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inputs:
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_parsed_args = vars(_parser.parse_args())
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_outputs = hello_world(**_parsed_args)
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pipelineInfo:
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root:
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executors:
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exec-hello-world:
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container:
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pipelineInfo:
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backend/src/apiserver/resource/resource_manager_util_test.go

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resources: {}
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