Run A RayCluster

apiVersion: ray.io/v1
kind: RayCluster
metadata:
  labels:
    kueue.x-k8s.io/queue-name: user-queue
    controller-tools.k8s.io: "1.0"
    # A unique identifier for the head node and workers of this cluster.
  name: raycluster-complete
spec:
  rayVersion: '2.9.0'
  # Ray head pod configuration
  headGroupSpec:
    # Kubernetes Service Type. This is an optional field, and the default value is ClusterIP.
    # Refer to https://kubernetes.io/docs/concepts/services-networking/service/#publishing-services-service-types.
    serviceType: ClusterIP
    # The `rayStartParams` are used to configure the `ray start` command.
    # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
    # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
    rayStartParams:
      dashboard-host: '0.0.0.0'
    # pod template
    template:
      metadata:
        # Custom labels. NOTE: To avoid conflicts with KubeRay operator, do not define custom labels start with `raycluster`.
        # Refer to https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/
        labels: {}
      spec:
        containers:
          - name: ray-head
            image: rayproject/ray:2.9.0
            ports:
              - containerPort: 6379
                name: gcs
              - containerPort: 8265
                name: dashboard
              - containerPort: 10001
                name: client
            lifecycle:
              preStop:
                exec:
                  command: ["/bin/sh","-c","ray stop"]
            volumeMounts:
              - mountPath: /tmp/ray
                name: ray-logs
            # The resource requests and limits in this config are too small for production!
            # For an example with more realistic resource configuration, see
            # ray-cluster.autoscaler.large.yaml.
            # It is better to use a few large Ray pod than many small ones.
            # For production, it is ideal to size each Ray pod to take up the
            # entire Kubernetes node on which it is scheduled.
            resources:
              limits:
                cpu: "1"
                memory: "2G"
              requests:
                # For production use-cases, we recommend specifying integer CPU reqests and limits.
                # We also recommend setting requests equal to limits for both CPU and memory.
                # For this example, we use a 500m CPU request to accomodate resource-constrained local
                # Kubernetes testing environments such as KinD and minikube.
                cpu: "1"
                memory: "2G"
        volumes:
          - name: ray-logs
            emptyDir: {}
  workerGroupSpecs:
    # the pod replicas in this group typed worker
    - replicas: 1
      minReplicas: 1
      maxReplicas: 10
      # logical group name, for this called small-group, also can be functional
      groupName: small-group
      # If worker pods need to be added, we can increment the replicas.
      # If worker pods need to be removed, we decrement the replicas, and populate the workersToDelete list.
      # The operator will remove pods from the list until the desired number of replicas is satisfied.
      # If the difference between the current replica count and the desired replicas is greater than the
      # number of entries in workersToDelete, random worker pods will be deleted.
      #scaleStrategy:
      #  workersToDelete:
      #  - raycluster-complete-worker-small-group-bdtwh
      #  - raycluster-complete-worker-small-group-hv457
      #  - raycluster-complete-worker-small-group-k8tj7
      # The `rayStartParams` are used to configure the `ray start` command.
      # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
      # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
      rayStartParams: {}
      #pod template
      template:
        spec:
          containers:
            - name: ray-worker
              image: rayproject/ray:2.9.0
              lifecycle:
                preStop:
                  exec:
                    command: ["/bin/sh","-c","ray stop"]
              # use volumeMounts.Optional.
              # Refer to https://kubernetes.io/docs/concepts/storage/volumes/
              volumeMounts:
                - mountPath: /tmp/ray
                  name: ray-logs
              # The resource requests and limits in this config are too small for production!
              # For an example with more realistic resource configuration, see
              # ray-cluster.autoscaler.large.yaml.
              # It is better to use a few large Ray pod than many small ones.
              # For production, it is ideal to size each Ray pod to take up the
              # entire Kubernetes node on which it is scheduled.
              resources:
                limits:
                  cpu: "1"
                  memory: "1G"
                # For production use-cases, we recommend specifying integer CPU reqests and limits.
                # We also recommend setting requests equal to limits for both CPU and memory.
                # For this example, we use a 500m CPU request to accomodate resource-constrained local
                # Kubernetes testing environments such as KinD and minikube.
                requests:
                  # For production use-cases, we recommend specifying integer CPU reqests and limits.
                  # We also recommend setting requests equal to limits for both CPU and memory.
                  # For this example, we use a 500m CPU request to accomodate resource-constrained local
                  # Kubernetes testing environments such as KinD and minikube.
                  cpu: "1"
                  # For production use-cases, we recommend allocating at least 8Gb memory for each Ray container.
                  memory: "1G"
          # use volumes
          # Refer to https://kubernetes.io/docs/concepts/storage/volumes/
          volumes:
            - name: ray-logs
              emptyDir: {}