Deploying a Static Ray Cluster on Kubernetes

This document gives an example of how to manually deploy a non-autoscaling Ray cluster on Kubernetes.

  • Learn about deploying an autoscaling Ray cluster using the Ray Helm chart.

Creating a Ray Namespace

First, create a Kubernetes Namespace for Ray resources on your cluster. The following commands will create resources under this Namespace, so if you want to use a different one than ray, please be sure to also change the namespace fields in the provided yaml files and anytime you see a -n flag passed to kubectl.

$ kubectl create namespace ray

Starting a Ray Cluster

A Ray cluster consists of a single head node and a set of worker nodes (the provided ray-cluster.yaml file will start 3 worker nodes). In the example Kubernetes configuration, this is implemented as:

  • A ray-head Kubernetes Service that enables the worker nodes to discover the location of the head node on start up. This Service also enables access to the Ray Client and Ray Dashboard.

  • A ray-head Kubernetes Deployment that backs the ray-head Service with a single head node pod (replica).

  • A ray-worker Kubernetes Deployment with multiple worker node pods (replicas) that connect to the ray-head pod using the ray-head Service.

Note that because the head and worker nodes are Deployments, Kubernetes will automatically restart pods that crash to maintain the correct number of replicas.

  • If a worker node goes down, a replacement pod will be started and joined to the cluster.

  • If the head node goes down, it will be restarted. This will start a new Ray cluster. Worker nodes that were connected to the old head node will crash and be restarted, connecting to the new head node when they come back up.

Try deploying a cluster with the provided Kubernetes config by running the following command:

$ kubectl apply -f ray/doc/kubernetes/ray-cluster.yaml

Verify that the pods are running by running kubectl get pods -n ray. You may have to wait up to a few minutes for the pods to enter the ‘Running’ state on the first run.

$ kubectl -n ray get pods
NAME                          READY   STATUS    RESTARTS   AGE
ray-head-5455bb66c9-6bxvz     1/1     Running   0          10s
ray-worker-5c49b7cc57-c6xs8   1/1     Running   0          5s
ray-worker-5c49b7cc57-d9m86   1/1     Running   0          5s
ray-worker-5c49b7cc57-kzk4s   1/1     Running   0          5s


You might see a nonzero number of RESTARTS for the worker pods. That can happen when the worker pods start up before the head pod and the workers aren’t able to connect. This shouldn’t affect the behavior of the cluster.

To change the number of worker nodes in the cluster, change the replicas field in the worker deployment configuration in that file and then re-apply the config as follows:

# Edit 'ray/doc/kubernetes/ray-cluster.yaml' and change the 'replicas'
# field under the ray-worker deployment to, e.g., 4.

# Re-apply the new configuration to the running deployment.
$ kubectl apply -f ray/doc/kubernetes/ray-cluster.yaml
service/ray-head unchanged
deployment.apps/ray-head unchanged
deployment.apps/ray-worker configured

# Verify that there are now the correct number of worker pods running.
$ kubectl -n ray get pods
NAME                          READY   STATUS    RESTARTS   AGE
ray-head-5455bb66c9-6bxvz     1/1     Running   0          30s
ray-worker-5c49b7cc57-c6xs8   1/1     Running   0          25s
ray-worker-5c49b7cc57-d9m86   1/1     Running   0          25s
ray-worker-5c49b7cc57-kzk4s   1/1     Running   0          25s
ray-worker-5c49b7cc57-zzfg2   1/1     Running   0          0s

To validate that the restart behavior is working properly, try killing pods and checking that they are restarted by Kubernetes:

# Delete a worker pod.
$ kubectl -n ray delete pod ray-worker-5c49b7cc57-c6xs8
pod "ray-worker-5c49b7cc57-c6xs8" deleted

# Check that a new worker pod was started (this may take a few seconds).
$ kubectl -n ray get pods
NAME                          READY   STATUS    RESTARTS   AGE
ray-head-5455bb66c9-6bxvz     1/1     Running   0          45s
ray-worker-5c49b7cc57-d9m86   1/1     Running   0          40s
ray-worker-5c49b7cc57-kzk4s   1/1     Running   0          40s
ray-worker-5c49b7cc57-ypq8x   1/1     Running   0          0s

# Delete the head pod.
$ kubectl -n ray delete pod ray-head-5455bb66c9-6bxvz
pod "ray-head-5455bb66c9-6bxvz" deleted

# Check that a new head pod was started and the worker pods were restarted.
$ kubectl -n ray get pods
NAME                          READY   STATUS    RESTARTS   AGE
ray-head-5455bb66c9-gqzql     1/1     Running   0          0s
ray-worker-5c49b7cc57-d9m86   1/1     Running   1          50s
ray-worker-5c49b7cc57-kzk4s   1/1     Running   1          50s
ray-worker-5c49b7cc57-ypq8x   1/1     Running   1          10s

# You can even try deleting all of the pods in the Ray namespace and checking
# that Kubernetes brings the right number back up.
$ kubectl -n ray delete pods --all
$ kubectl -n ray get pods
NAME                          READY   STATUS    RESTARTS   AGE
ray-head-5455bb66c9-7l6xj     1/1     Running   0          10s
ray-worker-5c49b7cc57-57tpv   1/1     Running   0          10s
ray-worker-5c49b7cc57-6m4kp   1/1     Running   0          10s
ray-worker-5c49b7cc57-jx2w2   1/1     Running   0          10s

Now that we have a running cluster, we can execute Ray programs.

Cleaning Up

To delete a running Ray cluster, you can run the following command:

kubectl delete -f ray/doc/kubernetes/ray-cluster.yaml

Questions or Issues?

You can post questions or issues or feedback through the following channels:

  1. Discussion Board: For questions about Ray usage or feature requests.

  2. GitHub Issues: For bug reports.

  3. Ray Slack: For getting in touch with Ray maintainers.

  4. StackOverflow: Use the [ray] tag questions about Ray.