Container Service for Kubernetes:Deploy Ray Cluster in ACK

Prerequisites

Before you begin, ensure that you have:

• An ACK Managed Cluster Pro running Kubernetes v1.24 or later. For setup instructions, see Create an ACK managed cluster. To upgrade an existing cluster, see Manually upgrade a cluster.

• At least one node with a minimum of 8 vCPUs and 32 GB of memory (test environment). For production environments, size nodes to match your actual workload. For GPU-accelerated workloads, configure GPU-accelerated nodes. For supported Elastic Compute Service (ECS) instance types, see Instance family.

• kubectl installed on your local machine and connected to your cluster. For connection instructions, see Connect to a cluster using kubectl.

• (Optional) An ApsaraDB for Tair instance for GCS fault tolerance. See Set up GCS fault tolerance below.

Install the KubeRay Operator

1. Log on to the Container Service for Kubernetes (ACK) console.

2. In the left-side navigation pane, click Clusters, then click the name of your cluster.

3. Navigate to Operations > Add-ons > Manage Applications.

4. Under Kuberay-Operator, click Install.

Deploy the Ray Cluster

Run the following command to create a Ray Cluster named myfirst-ray-cluster.

The manifest configures:

Expand to view the complete command code

cat <<EOF | kubectl apply -f -
apiVersion: ray.io/v1
kind: RayCluster
metadata:
  name: myfirst-ray-cluster
  namespace: default
spec:
  suspend: false
  autoscalerOptions:
    env: []
    envFrom: []
    idleTimeoutSeconds: 60
    imagePullPolicy: Always
    resources:
      limits:
        cpu: 2000m
        memory: 2024Mi
      requests:
        cpu: 2000m
        memory: 2024Mi
    securityContext: {}
    upscalingMode: Default
  enableInTreeAutoscaling: false
  headGroupSpec:
    rayStartParams:
      dashboard-host: 0.0.0.0
      num-cpus: "0"
    serviceType: ClusterIP
    template:
      spec:
        containers:
        - image: rayproject/ray:2.36.1
          imagePullPolicy: Always
          name: ray-head
          resources:
            limits:
              cpu: "4"
              memory: 4G
            requests:
              cpu: "1"
              memory: 1G
  workerGroupSpecs:
  - groupName: work1
    maxReplicas: 1000
    minReplicas: 0
    numOfHosts: 1
    rayStartParams: {}
    replicas: 1
    template:
      spec:
        containers:
        - image: rayproject/ray:2.36.1
          imagePullPolicy: Always
          name: ray-worker
          resources:
            limits:
              cpu: "4"
              memory: 4G
            requests:
              cpu: "4"
              memory: 4G
EOF

Verify the deployment

Run the following commands to confirm the cluster is running.

1. Check the Ray Cluster status:

   kubectl get raycluster

   Expected output:

   NAME                  DESIRED WORKERS   AVAILABLE WORKERS   CPUS   MEMORY   GPUS   STATUS   AGE
   myfirst-ray-cluster   1                 1                   5      5G       0      ready    4m19s

2. Check the Pods:

   kubectl get pod

   Expected output:

   NAME                                     READY   STATUS    RESTARTS   AGE
   myfirst-ray-cluster-head-5q2hk           1/1     Running   0          4m37s
   myfirst-ray-cluster-work1-worker-zkjgq   1/1     Running   0          4m31s

3. Check the Services:

   kubectl get svc

   Expected output:

   NAME                           TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)                                         AGE
   kubernetes                     ClusterIP   192.168.0.1   <none>        443/TCP                                         21d
   myfirst-ray-cluster-head-svc   ClusterIP   None          <none>        10001/TCP,8265/TCP,8080/TCP,6379/TCP,8000/TCP   6m57s

When the Ray Cluster status is ready and all Pods show 1/1 Running, the deployment is complete.