[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9611":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":9,"languages":9,"totalLinesOfCode":9,"stars":10,"forks":11,"watchers":12,"openIssues":13,"contributorsCount":13,"subscribersCount":13,"size":13,"stars1d":13,"stars7d":14,"stars30d":15,"stars90d":13,"forks30d":13,"starsTrendScore":16,"compositeScore":17,"rankGlobal":9,"rankLanguage":9,"license":18,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":9,"pushedAt":9,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":13,"starSnapshotCount":13,"syncStatus":34,"lastSyncTime":35,"discoverSource":36},9611,"kubeflow","kubeflow\u002Fkubeflow","Machine Learning Toolkit for Kubernetes","https:\u002F\u002Fwww.kubeflow.org\u002F",null,15714,2671,361,0,9,84,6,45,"Apache License 2.0",false,"master",true,[23,24,5,25,26,27,28,29,30],"google-kubernetes-engine","jupyter","kubernetes","machine-learning","minikube","ml","notebook","tensorflow","2026-06-12 02:02:10","# Kubeflow\n\n[![Join Kubeflow Slack](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fslack-join_chat-white.svg?logo=slack&style=social)](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fabout\u002Fcommunity\u002F#kubeflow-slack-channels)\n[![CLOMonitor](https:\u002F\u002Fimg.shields.io\u002Fendpoint?url=https:\u002F\u002Fclomonitor.io\u002Fapi\u002Fprojects\u002Fcncf\u002Fkubeflow\u002Fbadge)](https:\u002F\u002Fclomonitor.io\u002Fprojects\u002Fcncf\u002Fkubeflow)\n\n\u003Cimg src=\".\u002Flogo\u002Fstacked.svg\" width=\"120\">\n\n## What is Kubeflow\n\n[Kubeflow](https:\u002F\u002Fwww.kubeflow.org\u002F) is the foundation of tools for AI Platforms on Kubernetes.\n\nAI platform teams can build on top of Kubeflow by using each project independently or deploying the\nentire AI reference platform to meet their specific needs. The Kubeflow AI reference platform is\ncomposable, modular, portable, and scalable, backed by an ecosystem of Kubernetes-native\nprojects that cover every stage of the [AI lifecycle](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fstarted\u002Farchitecture\u002F#kubeflow-projects-in-the-ai-lifecycle).\n\nWhether you’re an AI practitioner, a platform administrator, or a team of developers, Kubeflow\noffers modular, scalable, and extensible tools to support your AI use cases.\n\nPlease refer to [the official documentation](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002F) for more information.\n\n## What are Kubeflow Projects\n\nKubeflow is composed of multiple open source projects that address different aspects\nof the AI lifecycle. These projects are designed to be usable both independently and as part of the\nKubeflow AI reference platform. This provides flexibility for users who may not need the full\nend-to-end AI platform capabilities but want to leverage specific functionalities, such as model\ntraining or model serving.\n\n| Kubeflow Project                                                                    | Source Code                                                             |\n| ----------------------------------------------------------------------------------- | ----------------------------------------------------------------------- |\n| [KServe](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fexternal-add-ons\u002Fkserve\u002F)                    | [`kserve\u002Fkserve`](https:\u002F\u002Fgithub.com\u002Fkserve\u002Fkserve)                     |\n| [Kubeflow Katib](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fkatib\u002F)                   | [`kubeflow\u002Fkatib`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fkatib)                   |\n| [Kubeflow Model Registry](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fmodel-registry\u002F) | [`kubeflow\u002Fmodel-registry`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fmodel-registry) |\n| [Kubeflow Notebooks](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fnotebooks\u002F)           | [`kubeflow\u002Fnotebooks`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fnotebooks)           |\n| [Kubeflow Pipelines](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fpipelines\u002F)           | [`kubeflow\u002Fpipelines`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fpipelines)           |\n| [Kubeflow SDK](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fsdk)                                     | [`kubeflow\u002Fsdk`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fsdk)                       |\n| [Kubeflow Spark Operator](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fspark-operator\u002F) | [`kubeflow\u002Fspark-operator`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fspark-operator) |\n| [Kubeflow Trainer](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Ftrainer\u002F)               | [`kubeflow\u002Ftrainer`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Ftrainer)               |\n\n## What is the Kubeflow AI Reference Platform\n\nThe Kubeflow AI reference platform refers to the full suite of Kubeflow projects bundled together\nwith additional integration and management tools. Kubeflow AI reference platform deploys the\ncomprehensive toolkit for the entire AI lifecycle. The Kubeflow AI reference platform can be\ninstalled via [Packaged Distributions](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fstarted\u002Finstalling-kubeflow\u002F#packaged-distributions)\nor [Kubeflow Manifests](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fstarted\u002Finstalling-kubeflow\u002F#kubeflow-manifests).\n\n| Kubeflow AI Reference Platform Tool                                                                 | Source Code                                                   |\n| --------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |\n| [Central Dashboard](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fcentral-dash\u002F)                         | [`kubeflow\u002Fdashboard`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fdashboard) |\n| [Profile Controller](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fcomponents\u002Fcentral-dash\u002Fprofiles\u002F)               | [`kubeflow\u002Fdashboard`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fdashboard) |\n| [Kubeflow Manifests](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fstarted\u002Finstalling-kubeflow\u002F#kubeflow-manifests) | [`kubeflow\u002Fmanifests`](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fmanifests) |\n\n## Kubeflow Community\n\nKubeflow is a community-led project maintained by the\n[Kubeflow Working Groups](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fabout\u002Fgovernance\u002F#4-working-groups)\nunder the guidance of the [Kubeflow Steering Committee](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fabout\u002Fgovernance\u002F#2-kubeflow-steering-committee-ksc).\n\nWe encourage you to learn about the [Kubeflow Community](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fabout\u002Fcommunity\u002F)\nand how to [contribute](https:\u002F\u002Fwww.kubeflow.org\u002Fdocs\u002Fabout\u002Fcontributing\u002F) to the project!\n","Kubeflow 是一个基于 Kubernetes 的机器学习工具包，旨在为AI平台提供一系列强大的工具。其核心功能包括模型训练、模型服务、实验管理等，并且支持Jupyter笔记本、TensorFlow等多种技术栈。Kubeflow具有模块化、可扩展性和便携性特点，能够根据用户需求灵活部署。它适用于需要在Kubernetes上构建和运行机器学习工作流的企业或个人开发者，无论是进行模型开发、测试还是生产环境下的部署与监控，Kubeflow都能提供强有力的支持。",2,"2026-06-11 03:23:46","top_topic"]