ray-project/kuberay resource

Health v2 · maintenance only

Adoption not part of the score

2650 stars · 830 forks observed · 2026-08-28

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

KubeRay is a Kubernetes operator and toolkit for deploying and managing Ray applications on Kubernetes. It provides RayCluster, RayJob, and RayService custom resources that handle cluster lifecycle, autoscaling, job submission, and zero-downtime serving upgrades.

Use cases

• run ray clusters on kubernetes
• autoscale distributed machine learning training jobs
• submit ray jobs to kubernetes and clean up after completion
• serve ray serve models with zero-downtime upgrades
• manage fault-tolerant ray workloads on k8s
• deploy llm inference with ray on kubernetes

When to choose

• you already run Ray workloads and want them managed on Kubernetes
• you need autoscaling, fault tolerance, or zero-downtime upgrades for Ray clusters
• you want declarative CRDs for Ray cluster, job, and serving lifecycle

When to avoid

• you don't use Kubernetes or Ray
• you need a simple single-node Ray setup without orchestration
• you want a fully managed Ray service rather than self-managed operator

Facets

infra-config · maturity active

container-orchestration deployment machine-learning deep-learning llm-inference scheduling workflow-automation machine-learning deep-learning cloud-computing infrastructure-as-code microservices cloud go self-hosted ray kubernetes-operator crd raycluster rayjob rayservice autoscaling distributed-computing kubectl-plugin containers devops kubernetes docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ray-project/kuberay")