# kubeflow/kubeflow

Machine Learning Toolkit for Kubernetes

Repository: https://github.com/kubeflow/kubeflow
Canonical: https://ross.abutalabs.com/products/kubeflow
Homepage: https://www.kubeflow.org/
License: Apache-2.0
License Family: permissive
Topics: ml, kubernetes, minikube, tensorflow, notebook, google-kubernetes-engine, jupyter, machine-learning, kubeflow
Last push: 2026-08-21T18:25:34+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 57, longevity 100
- inputs: {"age_days": 3198, "days_push": 12, "days_rel": 126, "gap_med": 152, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 15832, forks 2690 (observed 2026-08-28T04:11:14.195795+00:00)

## What it is
Kubeflow is a CNCF-graduated, Kubernetes-native toolkit for building AI/ML platforms, comprising subprojects like Pipelines, Notebooks, Trainer, Katib, Hub, and a Spark Operator. This repository is the gateway to those subprojects and shared metadata, with development happening in individual subproject repos.

## Use cases
- run machine learning pipelines on kubernetes
- fine-tune llms on a kubernetes cluster
- run jupyter notebooks in kubernetes
- hyperparameter tuning and automl on k8s
- orchestrate distributed model training with pytorch
- build an end-to-end mlops platform
- deploy rag and genai workflows at scale
- manage model versions and registry metadata

## When to choose
- you already run Kubernetes and want a modular, composable AI platform on top of it
- you need scalable distributed training, pipelines, notebooks, and hyperparameter search in one ecosystem
- you want a CNCF-graduated, vendor-neutral foundation to build a custom AI platform
- you need GenAI lifecycle tooling from synthetic data to inference and evaluation

## When to avoid
- you have no Kubernetes cluster or want a simple single-node ML setup
- you need a lightweight all-in-one ML tool without significant operational overhead
- you only need one small capability and don't want to deploy a full distribution
- your team lacks Kubernetes administration experience

## Facets
- artifact type: framework
- maturity: stable
- function: machine-learning, llm-training, workflow-automation, scheduling, container-orchestration, developer-tools
- domain: machine-learning, deep-learning, large-language-models, data-science, cloud-computing
- platform: cloud, self-hosted, python, go
- tags: mlops, kubeflow, pipelines, notebooks, hyperparameter-tuning, distributed-training, automl, cncf, genai, model-registry, containers, devops, kubernetes, docker

## Member repositories
- kubeflow/kubeflow (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:14.195795+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:05:31.426540+00:00, confidence not recorded.
  - readme: https://github.com/kubeflow/kubeflow (fetched 2026-08-28T04:11:14.195795+00:00, sha 3e37f216f636)
  - homepage: https://www.kubeflow.org/ (fetched 2026-08-29T08:03:56.428695+00:00, sha ecb33d1c76ff)
  - site_page: https://www.kubeflow.org/docs (fetched 2026-08-29T08:03:56.437829+00:00, sha 2d48c8aa19c2)
  - site_page: https://www.kubeflow.org/docs/about/community (fetched 2026-08-29T08:03:56.439634+00:00, sha 85bbf5395b12)
  - site_page: https://www.kubeflow.org/docs/started (fetched 2026-08-29T08:03:56.441711+00:00, sha 52d4becf41e9)
  - site_page: https://www.kubeflow.org/docs/about/contributing (fetched 2026-08-29T08:03:56.443857+00:00, sha d3383d0a4dbf)
  - site_page: https://www.kubeflow.org/docs/genai/use-cases (fetched 2026-08-29T08:03:56.446260+00:00, sha bfca00da3bad)
  - site_page: https://www.kubeflow.org/docs/started/architecture (fetched 2026-08-29T08:03:56.448569+00:00, sha eeb497b45570)
  - site_page: https://www.kubeflow.org/docs/started/installing-kubeflow (fetched 2026-08-29T08:03:56.462770+00:00, sha a977fcac93bf)
  - site_page: https://www.kubeflow.org/docs/components/notebooks/overview (fetched 2026-08-29T08:03:56.465258+00:00, sha 435150cc5c5d)
- Data as of 2026-08-30T08:39:29.467469+00:00.