KubeRay is a powerful, open-source Kubernetes operator that simplifies the deployment and management of Ray applications on Kubernetes. It offers several key components:

KubeRay core: This is the official, fully-maintained component of KubeRay that provides three custom resource definitions, RayCluster, RayJob, and RayService. These resources are designed to help you run a wide range of workloads with ease.

• RayCluster: KubeRay fully manages the lifecycle of RayCluster, including cluster creation/deletion, autoscaling, and ensuring fault tolerance.

• RayJob: With RayJob, KubeRay automatically creates a RayCluster and submits a job when the cluster is ready. You can also configure RayJob to automatically delete the RayCluster once the job finishes.

• RayService: RayService is made up of two parts: a RayCluster and a Ray Serve deployment graph. RayService offers zero-downtime upgrades for RayCluster and high availability.

Community-managed components (optional): Some components are maintained by the KubeRay community.

• KubeRay APIServer: It provides a layer of simplified configuration for KubeRay resources. The KubeRay API server is used internally by some organizations to back user interfaces for KubeRay resource management.

• KubeRay Python client: This Python client library provides APIs to handle RayCluster from your Python application.

• KubeRay CLI: KubeRay CLI provides the ability to manage KubeRay resources through command-line interface.

• AWS Application Load Balancer
• Nginx
• Prometheus and Grafana
• Volcano
• MCAD
• Kubeflow

• A cloud-native, open-source stack for accelerating foundation model innovation IBM (May 9, 2023).
• AI/ML Models Batch Training at Scale with Open Data Hub Red Hat (May 15, 2023).

You can view detailed documentation and guides at https://ray-project.github.io/kuberay/.

We also recommend checking out the official Ray guides for deploying on Kubernetes at https://docs.ray.io/en/latest/cluster/kubernetes/index.html.

• Try this end-to-end example!
• Please choose the version you would like to install. The examples below use the latest stable version v0.6.0.

Make sure your Kubernetes and Kubectl versions are both within the suggested range. Once you have connected to a Kubernetes cluster, run the following commands to deploy the KubeRay Operator.

# case 1: kubectl >= v1.22.0
export KUBERAY_VERSION=v0.6.0
kubectl create -k "github.com/ray-project/kuberay/ray-operator/config/default?ref=${KUBERAY_VERSION}&timeout=90s"

# case 2: kubectl < v1.22.0
# Clone KubeRay repository and checkout to the desired branch e.g. `release-0.6`.
kubectl create -k ray-operator/config/default

To deploy both the KubeRay Operator and the optional KubeRay API Server run the following commands.

# case 1: kubectl >= v1.22.0
export KUBERAY_VERSION=v0.6.0
kubectl create -k "github.com/ray-project/kuberay/manifests/cluster-scope-resources?ref=${KUBERAY_VERSION}&timeout=90s"
kubectl apply -k "github.com/ray-project/kuberay/manifests/base?ref=${KUBERAY_VERSION}&timeout=90s"

# case 2: kubectl < v1.22.0
# Clone KubeRay repository and checkout to the desired branch e.g. `release-0.4`.
kubectl create -k manifests/cluster-scope-resources
kubectl apply -k manifests/base

Observe that we must use kubectl create to install cluster-scoped resources. The corresponding kubectl apply command will not work. See KubeRay issue #271.

A Helm chart is a collection of files that describe a related set of Kubernetes resources. It can help users to deploy the KubeRay Operator and Ray clusters conveniently. Please read kuberay-operator to deploy the operator and ray-cluster to deploy a configurable Ray cluster. To deploy the optional KubeRay API Server, see kuberay-apiserver.

helm repo add kuberay https://ray-project.github.io/kuberay-helm/

# Install both CRDs and KubeRay operator v0.6.0.
helm install kuberay-operator kuberay/kuberay-operator --version 0.6.0

# Check the KubeRay operator Pod in `default` namespace
kubectl get pods
# NAME                                READY   STATUS    RESTARTS   AGE
# kuberay-operator-6fcbb94f64-mbfnr   1/1     Running   0          17s

Please read our CONTRIBUTING guide before making a pull request. Refer to our DEVELOPMENT to build and run tests locally.

Kuberay has an active community of developers. Here’s how to get involved with the Kuberay community:

Join our community: Join Ray community slack (search for Kuberay channel) or use our discussion board to ask questions and get answers.

If you discover a potential security issue in this project, or think you may have discovered a security issue, we ask that you notify KubeRay Security via our Slack Channel. Please do not create a public GitHub issue.

This project is licensed under the Apache-2.0 License.