{"data":{"slug":"kubeflow-pipelines","name":"pipelines","tagline":"Machine Learning Pipelines for Kubeflow","github_url":"https://github.com/kubeflow/pipelines","owner":"kubeflow","repo":"pipelines","owner_avatar_url":"https://avatars.githubusercontent.com/u/33164907?v=4","primary_language":"Python","stars":4173,"forks":2075,"topics":["data-science","kubeflow","kubeflow-pipelines","kubernetes","machine-learning","mlops","pipeline"],"archived":false,"github_pushed_at":"2026-08-03T15:35:16+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/kubeflow-pipelines","markdown_url":"https://www.graphcanon.com/tools/kubeflow-pipelines.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kubeflow-pipelines","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kubeflow-pipelines","description":"Machine Learning Pipelines for Kubeflow","homepage_url":"https://www.kubeflow.org/docs/components/pipelines/","license":"Apache-2.0","open_issues":512,"watchers":95,"ai_summary":"Provides machine learning pipelines in Kubernetes environments for data science and MLOps workflows.","readme_excerpt":"## 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/).","github_created_at":"2018-05-12T00:31:47+00:00","created_at":"2026-07-11T23:30:21.262466+00:00","updated_at":"2026-08-03T18:01:14.942203+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"data-science","name":"data-science"},{"slug":"kubernetes","name":"kubernetes"},{"slug":"kubflow-pipelines","name":"kubflow-pipelines"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"mlops","name":"mlops"}],"trust":{"provenance":{"is_fork":false,"github_id":133100880,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T18:01:14.082Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":0,"last_release_at":"2026-07-09T15:32:20Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":2,"high_count":0,"last_scan_at":"2026-07-11T23:30:30.809Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T18:01:14.614Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-03T18:01:14.614Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-03T18:01:14.614Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Use Pipelines when you are working in an existing Kubernetes cluster as it integrates seamlessly without any configuration hassles specific to container runtimes like Docker.","Prefer this tool if your project requires seamless model training and deployment within the Kubeflow ecosystem for data science endeavors."],"when_not_to_use":["Avoid Pipelines from Kubeflow if you require support on a non-Kubernetes environment, as it is tightly integrated with Kubernetes services.","Do not use this tool if your operations necessitate legacy Docker container runtime integration without the adaptability provided by Emissary Executor."],"source":"enrich:decision_facts","observed_at":"2026-07-17T05:18:57.464Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default."},{"label":"License detail","value":"Apache-2.0 license offers permissive terms for distribution and modification, allowing proprietary衍生结束于此。许可证的总结应完整并准确。让我们纠正这一点，并继续其他字段的信息提取和总结："}]}}