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kubeflow/community-distribution resource

Kubeflow Community Distribution observed · 2026-09-03

github.com/kubeflow/community-distribution · homepage · YAML · Apache-2.0 (permissive) observed · 2026-09-03

Health v2 · maintenance only

92/100

  • Activity 100
  • Release rhythm 77
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 90.0
  • age_days: 2745
  • days_rel: 79
  • days_push: 0
  • n_releases_24m: 7

Full methodology

Adoption not part of the score

1036 stars · 1069 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The official Kubeflow Community Distribution providing manifests to install the full Kubeflow Platform (Pipelines, KServe, Central Dashboard, and more) on popular Kubernetes clusters like Kind, Minikube, EKS, AKS, and GKE. It targets enterprises with strict security, legal, and multi-tenancy requirements as well as academic users exploring the end-to-end Kubeflow AI platform.

Use cases

  • install kubeflow on eks
  • deploy a machine learning platform on kubernetes
  • set up kubeflow pipelines and kserve on gke
  • multi-tenant ml platform with authentication
  • run llm fine-tuning pipelines on kubernetes
  • self-hosted ml platform for an enterprise
  • try kubeflow locally with kind or minikube

When to choose

  • you want the full Kubeflow Platform with all components installed via a single command on a supported Kubernetes cluster
  • your organization has security, legal, or multi-tenancy requirements for its ML platform
  • you want a stable, community-supported release cadence with end-to-end integration testing
  • you need composable, Kubernetes-native tooling covering the whole AI lifecycle from data prep to inference

When to avoid

  • you only need a single Kubeflow subproject like Pipelines or Katib and not the full platform
  • you want a commercially supported vendor distribution with SLAs
  • you are not using Kubernetes for your ML workloads
  • you need a lightweight ML tooling setup without cluster-level infrastructure

Facets

infra-config · maturity active

deployment machine-learning container-orchestration infrastructure-as-code llm-training llm-inference workflow-automation security machine-learning cloud-computing infrastructure-as-code large-language-models data-science cloud self-hosted go kubeflow mlops kubernetes-manifests multi-tenancy ml-platform eks gke aks kserve pipelines cncf devops containers kubernetes docker

10 sources

Member repositories

RepositoryRoleHealth v2
kubeflow/community-distributionmain92

For agents

markdown · JSON · MCP: product_card(name="kubeflow/community-distribution")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem