Run A RayJob

Run a Kueue scheduled RayJob.

This page shows how to leverage Kueue’s scheduling and resource management capabilities when running KubeRay’s RayJob.

This guide is for batch users that have a basic understanding of Kueue. For more information, see Kueue’s overview.

Before you begin

  1. Make sure you are using Kueue v0.6.0 version or newer and KubeRay v1.1.0 or newer.

  2. Check Administer cluster quotas for details on the initial Kueue setup.

  3. See KubeRay Installation for installation and configuration details of KubeRay.

RayJob definition

When running RayJobs on Kueue, take into consideration the following aspects:

a. Queue selection

The target local queue should be specified in the metadata.labels section of the RayJob configuration.

metadata:
  labels:
    kueue.x-k8s.io/queue-name: user-queue

b. Configure the resource needs

The resource needs of the workload can be configured in the spec.rayClusterSpec.

spec:
  rayClusterSpec:
    headGroupSpec:
      template:
        spec:
          containers:
            - resources:
                requests:
                  cpu: "1"
    workerGroupSpecs:
      - template:
          spec:
            containers:
              - resources:
                  requests:
                    cpu: "1"

c. Limitations

  • A Kueue managed RayJob cannot use an existing RayCluster.
  • The RayCluster should be deleted at the end of the job execution, spec.ShutdownAfterJobFinishes should be true.
  • Because Kueue will reserve resources for the RayCluster, spec.rayClusterSpec.enableInTreeAutoscaling should be false.
  • Because a Kueue workload can have a maximum of 8 PodSets, the maximum number of spec.rayClusterSpec.workerGroupSpecs is 7.

Example RayJob

In this example, the code is provided to the Ray framework via a ConfigMap.

apiVersion: v1
kind: ConfigMap
metadata:
  name: ray-job-code-sample
data:
  sample_code.py: |
    import ray
    import os
    import requests

    ray.init()

    @ray.remote
    class Counter:
        def __init__(self):
            # Used to verify runtimeEnv
            self.name = os.getenv("counter_name")
            assert self.name == "test_counter"
            self.counter = 0

        def inc(self):
            self.counter += 1

        def get_counter(self):
            return "{} got {}".format(self.name, self.counter)

    counter = Counter.remote()

    for _ in range(5):
        ray.get(counter.inc.remote())
        print(ray.get(counter.get_counter.remote()))

    # Verify that the correct runtime env was used for the job.
    assert requests.__version__ == "2.26.0"    

The RayJob looks like the following:

apiVersion: ray.io/v1
kind: RayJob
metadata:
  name: rayjob-sample
  labels:
    kueue.x-k8s.io/queue-name: user-queue
spec:
  suspend: true
  shutdownAfterJobFinishes: true
  # submissionMode specifies how RayJob submits the Ray job to the RayCluster.
  # The default value is "K8sJobMode", meaning RayJob will submit the Ray job via a submitter Kubernetes Job.
  # The alternative value is "HTTPMode", indicating that KubeRay will submit the Ray job by sending an HTTP request to the RayCluster.
  # submissionMode: "K8sJobMode"
  entrypoint: python /home/ray/samples/sample_code.py
  # shutdownAfterJobFinishes specifies whether the RayCluster should be deleted after the RayJob finishes. Default is false.
  # shutdownAfterJobFinishes: false

  # ttlSecondsAfterFinished specifies the number of seconds after which the RayCluster will be deleted after the RayJob finishes.
  # ttlSecondsAfterFinished: 10

  # activeDeadlineSeconds is the duration in seconds that the RayJob may be active before
  # KubeRay actively tries to terminate the RayJob; value must be positive integer.
  # activeDeadlineSeconds: 120

  # RuntimeEnvYAML represents the runtime environment configuration provided as a multi-line YAML string.
  # See https://docs.ray.io/en/latest/ray-core/handling-dependencies.html for details.
  # (New in KubeRay version 1.0.)
  runtimeEnvYAML: |
    pip:
      - requests==2.26.0
      - pendulum==2.1.2
    env_vars:
      counter_name: "test_counter"    

  # Suspend specifies whether the RayJob controller should create a RayCluster instance.
  # If a job is applied with the suspend field set to true, the RayCluster will not be created and we will wait for the transition to false.
  # If the RayCluster is already created, it will be deleted. In the case of transition to false, a new RayCluste rwill be created.
  # suspend: false

  # rayClusterSpec specifies the RayCluster instance to be created by the RayJob controller.
  rayClusterSpec:
    rayVersion: '2.9.0' # should match the Ray version in the image of the containers
    # Ray head pod template
    headGroupSpec:
      # 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:
        spec:
          containers:
            - name: ray-head
              image: rayproject/ray:2.9.0
              ports:
                - containerPort: 6379
                  name: gcs-server
                - containerPort: 8265 # Ray dashboard
                  name: dashboard
                - containerPort: 10001
                  name: client
              resources:
                limits:
                  cpu: "1"
                requests:
                  cpu: "1"
              volumeMounts:
                - mountPath: /home/ray/samples
                  name: code-sample
          volumes:
            # You set volumes at the Pod level, then mount them into containers inside that Pod
            - name: code-sample
              configMap:
                # Provide the name of the ConfigMap you want to mount.
                name: ray-job-code-sample
                # An array of keys from the ConfigMap to create as files
                items:
                  - key: sample_code.py
                    path: sample_code.py
    workerGroupSpecs:
      # the pod replicas in this group typed worker
      - replicas: 1
        minReplicas: 1
        maxReplicas: 5
        # logical group name, for this called small-group, also can be functional
        groupName: small-group
        # 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 # must consist of lower case alphanumeric characters or '-', and must start and end with an alphanumeric character (e.g. 'my-name',  or '123-abc'
                image: rayproject/ray:2.9.0
                lifecycle:
                  preStop:
                    exec:
                      command: [ "/bin/sh","-c","ray stop" ]
                resources:
                  limits:
                    cpu: "1"
                  requests:
                    cpu: "1"
  # SubmitterPodTemplate is the template for the pod that will run the `ray job submit` command against the RayCluster.
  # If SubmitterPodTemplate is specified, the first container is assumed to be the submitter container.
  # submitterPodTemplate:
  #   spec:
  #     restartPolicy: Never
  #     containers:
  #       - name: my-custom-rayjob-submitter-pod
  #         image: rayproject/ray:2.9.0
  #         # If Command is not specified, the correct command will be supplied at runtime using the RayJob spec `entrypoint` field.
  #         # Specifying Command is not recommended.
  #         # command: ["sh", "-c", "ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID -- echo hello world"]

You can run this RayJob with the following commands:

# Create the code ConfigMap (once)
kubectl apply -f ray-job-code-sample.yaml
# Create a RayJob. You can run this command multiple times
# to observe the queueing and admission of the jobs.
kubectl create -f ray-job-sample.yaml