Experimental Features#

We provide an overview of new and experimental features available for deployments of Ray on Kubernetes.


The RayService controller enables fault-tolerant deployments of Ray Serve applications on Kubernetes.

If your Ray Serve application enters an unhealthy state, the RayService controller will create a new Ray Cluster. Once the new cluster is ready, Ray Serve traffic will be re-routed to the new Ray cluster.

For details, see the guide on Kubernetes-based RayServe deployments.

GCS Fault Tolerance#

In addition to the application-level fault-tolerance provided by the RayService controller, Ray now supports infrastructure-level fault tolerance for the Ray head pod.

You can set up an external Redis instance as a data store for the Ray head. If the Ray head crashes, a new head will be created without restarting the Ray cluster. The Ray head’s GCS will recover its state from the external Redis instance.

See the Ray Serve documentation for more information and the KubeRay docs on GCS Fault Tolerance for a detailed guide.


The RayJob custom resource consists of two elements:

  1. Configuration for a Ray cluster.

  2. A job, i.e. a Ray program to be executed on the Ray cluster.

To run a Ray job, you create a RayJob CR:

kubectl apply -f rayjob.yaml

The RayJob controller then creates the Ray cluster and runs the job. If you wish, you may configure the Ray cluster to be deleted when the job finishes.

See the KubeRay docs on RayJobs for details.