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KubeRay

We have moved all documentation to the ray-project/ray repository. Please refer to the Ray docs for the latest information. The ray-project/kuberay repository hosts the KubeRay source code and community information.

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.

KubeRay ecosystem: Some optional components.

  • Kubectl Plugin (Beta): Starting from KubeRay v1.3.0, you can use the kubectl ray plugin to simplify common workflows when deploying Ray on Kubernetes. If you aren’t familiar with Kubernetes, this plugin simplifies running Ray on Kubernetes. See kubectl-plugin for more details.

  • KubeRay APIServer (Alpha): 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 Dashboard (Experimental): Starting from KubeRay v1.4.0, we have introduced a new dashboard that enables users to view and manage KubeRay resources. While it is not yet production-ready, we welcome your feedback.