{"id":13416223,"url":"https://github.com/kubeflow/pipelines","last_synced_at":"2025-05-12T03:43:00.302Z","repository":{"id":37489736,"uuid":"133100880","full_name":"kubeflow/pipelines","owner":"kubeflow","description":"Machine Learning Pipelines for Kubeflow","archived":false,"fork":false,"pushed_at":"2025-05-08T16:57:40.000Z","size":342554,"stargazers_count":3808,"open_issues_count":278,"forks_count":1725,"subscribers_count":101,"default_branch":"master","last_synced_at":"2025-05-08T17:48:11.658Z","etag":null,"topics":["data-science","kubeflow","kubeflow-pipelines","kubernetes","machine-learning","mlops","pipeline"],"latest_commit_sha":null,"homepage":"https://www.kubeflow.org/docs/components/pipelines/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kubeflow.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":"ROADMAP.md","authors":"AUTHORS","dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-05-12T00:31:47.000Z","updated_at":"2025-05-08T16:57:45.000Z","dependencies_parsed_at":"2023-09-22T13:19:02.714Z","dependency_job_id":"ce403b76-70df-4bba-ac24-74b0e5439e52","html_url":"https://github.com/kubeflow/pipelines","commit_stats":{"total_commits":5962,"total_committers":468,"mean_commits":"12.739316239316238","dds":0.9245219724924522,"last_synced_commit":"581b7e5b7e888a12652a48f81d9fcd3f5e195e37"},"previous_names":[],"tags_count":176,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kubeflow%2Fpipelines","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kubeflow%2Fpipelines/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kubeflow%2Fpipelines/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kubeflow%2Fpipelines/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kubeflow","download_url":"https://codeload.github.com/kubeflow/pipelines/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253670844,"owners_count":21945341,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","kubeflow","kubeflow-pipelines","kubernetes","machine-learning","mlops","pipeline"],"created_at":"2024-07-30T21:00:55.680Z","updated_at":"2025-05-12T03:43:00.285Z","avatar_url":"https://github.com/kubeflow.png","language":"Python","funding_links":[],"categories":["Python","Uncategorized","🎯 Tool Categories","Large Scale Deployment","Ecosystem Projects","Workflow","其他_机器学习与深度学习","Full fledged product","Workflow Orchestration","data-science","📚 Project Purpose","8. MLOps / LLMOps \u0026 Production"],"sub_categories":["Uncategorized","⚡ Modern Orchestration Tools","Workflow","LangManus","Machine Learning (Intermediate-Level"],"readme":"# Kubeflow Pipelines\n\n[![Coverage Status](https://coveralls.io/repos/github/kubeflow/pipelines/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/pipelines?branch=master)\n[![SDK Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)\n[![SDK Package version](https://img.shields.io/pypi/v/kfp?color=%2334D058\u0026label=pypi%20package)](https://pypi.org/project/kfp)\n[![SDK Supported Python versions](https://img.shields.io/pypi/pyversions/kfp.svg?color=%2334D058)](https://pypi.org/project/kfp)\n[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9938/badge)](https://www.bestpractices.dev/projects/9938)\n\n## Overview of the Kubeflow pipelines service\n\n[Kubeflow](https://www.kubeflow.org/) is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.\n\n**Kubeflow pipelines** are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.\n\nThe Kubeflow pipelines service has the following goals:\n\n* End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines\n* Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.\n* Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.\n\n## Installation\n\n* Kubeflow Pipelines can be installed as part of the [Kubeflow Platform](https://www.kubeflow.org/docs/started/installing-kubeflow/#kubeflow-platform). Alternatively you can deploy [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/) as a standalone service.\n\n* The Docker container runtime has been deprecated on Kubernetes 1.20+. Kubeflow Pipelines has switched to use [Emissary Executor](https://www.kubeflow.org/docs/components/pipelines/legacy-v1/installation/choose-executor/#emissary-executor) by default from Kubeflow Pipelines 1.8. Emissary executor is Container runtime agnostic, meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any [Container runtimes](https://kubernetes.io/docs/setup/production-environment/container-runtimes/).\n\n## Documentation\n\nGet started with your first pipeline and read further information in the [Kubeflow Pipelines overview](https://www.kubeflow.org/docs/components/pipelines/overview/).\n\nSee the various ways you can [use the Kubeflow Pipelines SDK](https://kubeflow-pipelines.readthedocs.io/en/stable/).\n\nSee the Kubeflow [Pipelines API doc](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/) for API specification.\n\nConsult the [Python SDK reference docs](https://kubeflow-pipelines.readthedocs.io/en/stable/) when writing pipelines using the Python SDK.\n\n## Contributing to Kubeflow Pipelines\n\nBefore you start contributing to Kubeflow Pipelines, read the guidelines in [How to Contribute](./CONTRIBUTING.md). To learn how to build and deploy Kubeflow Pipelines from source code, read the [developer guide](./developer_guide.md).\n\n## Kubeflow Pipelines Community\n\n### Community Meeting\n\nThe Kubeflow Pipelines Community Meeting occurs every other Wed 10-11AM (PST).\n\n[Calendar Invite](https://calendar.google.com/event?action=TEMPLATE\u0026tmeid=NTdoNG5uMDBtcnJlYmdlOWt1c2lkY25jdmlfMjAxOTExMTNUMTgwMDAwWiBqZXNzaWV6aHVAZ29vZ2xlLmNvbQ\u0026tmsrc=jessiezhu%40google.com\u0026scp=ALL)\n\n[Direct Meeting Link](https://zoom.us/j/92607298595?pwd%3DVlKLUbiguGkbT9oKbaoDmCxrhbRop7.1\u0026sa=D\u0026source=calendar\u0026ust=1736264977415448\u0026usg=AOvVaw1EIkjFsKy0d4yQPptIJS3x)\n\n[Meeting notes](http://bit.ly/kfp-meeting-notes)\n\n### Slack\n\nWe also have a slack channel (#kubeflow-pipelines) on the Cloud Native Computing Foundation Slack workspace. You can find more details at [https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)\n\n## Architecture\n\nDetails about the KFP Architecture can be found at [Architecture.md](docs/Architecture.md)\n\n## Blog posts\n\n* [Getting started with Kubeflow Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines) (By Amy Unruh)\n* How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)\n  * [Part 1: How to create and deploy a Kubeflow Machine Learning Pipeline](https://medium.com/data-science/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f)\n  * [Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline](https://medium.com/data-science/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3)\n  * [Part 3: How to carry out CI/CD in Machine Learning (“MLOps”) using Kubeflow ML pipelines](https://medium.com/google-cloud/how-to-carry-out-ci-cd-in-machine-learning-mlops-using-kubeflow-ml-pipelines-part-3-bdaf68082112)\n\n## Acknowledgments\n\nKubeflow pipelines uses [Argo Workflows](https://github.com/argoproj/argo-workflows) by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkubeflow%2Fpipelines","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkubeflow%2Fpipelines","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkubeflow%2Fpipelines/lists"}