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Ray Cluster

Make sure ray-operator has been deployed.

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for simplifying ML compute.

Helm

$ helm version
version.BuildInfo{Version:"v3.6.2", GitCommit:"ee407bdf364942bcb8e8c665f82e15aa28009b71", GitTreeState:"dirty", GoVersion:"go1.16.5"}

TL;DR;

# Because the ray-cluster chart in release 0.3.0 has some bugs, we need to clone the KubeRay repo and install the latest ray-cluster chart until release 0.4.0.
cd helm-chart/ray-cluster
helm install ray-cluster --namespace ray-system --create-namespace .

Installing the Chart

To install the chart with the release name my-release:

# Because the ray-cluster chart in release 0.3.0 has some bugs, we need to clone the KubeRay repo and install the latest ray-cluster chart until release 0.4.0.
cd helm-chart/ray-cluster
helm install my-release --namespace ray-system --create-namespace .

note: The chart will submit a RayCluster.

Uninstalling the Chart

To uninstall/delete the my-release deployment:

helm delete my-release -n ray-system

The command removes nearly all the Kubernetes components associated with the chart and deletes the release.

Check Cluster status

Get Service

$ kubectl get svc -l ray.io/cluster=ray-cluster
NAME                       TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)                       AGE
ray-cluster-head-svc   ClusterIP   10.103.36.68   <none>        10001/TCP,6379/TCP,8265/TCP   9m24s

Forward to dashboard

$ kubectl get pod -o wide
NAME                                       READY   STATUS    RESTARTS   AGE    IP            NODE             NOMINATED NODE   READINESS GATES
ray-cluster-head-sd77l                 1/1     Running   0          8h     10.1.61.208   docker-desktop   <none>           <none>
ray-cluster-worker-workergroup-czxd6   1/1     Running   0          8h     10.1.61.207   docker-desktop   <none>           <none>
kuberay-operator-687785b964-jgfhv          1/1     Running   6          3d4h   10.1.61.196   docker-desktop   <none>           <none>

$ kubectl port-forward ray-cluster-head-sd77l 8265
Forwarding from 127.0.0.1:8265 -> 8265
Forwarding from [::1]:8265 -> 8265