(kuberay-rayjob-quickstart)=

RayJob Quickstart

Prerequisites

KubeRay v0.6.0 or higher
- KubeRay v0.6.0 or v1.0.0: Ray 1.10 or higher.
- KubeRay v1.1.1 or newer is highly recommended: Ray 2.8.0 or higher.

What's a RayJob?

A RayJob manages two aspects:

RayCluster: A RayCluster custom resource manages all Pods in a Ray cluster, including a head Pod and multiple worker Pods.
Job: A Kubernetes Job runs ray job submit to submit a Ray job to the RayCluster.

What does the RayJob provide?

With RayJob, KubeRay automatically creates a RayCluster and submits a job when the cluster is ready. You can also configure RayJob to automatically delete the RayCluster once the Ray job finishes.

To understand the following content better, you should understand the difference between:

RayJob: A Kubernetes custom resource definition provided by KubeRay.
Ray job: A Ray job is a packaged Ray application that can run on a remote Ray cluster. See this document for more details.
Submitter: The submitter is a Kubernetes Job that runs ray job submit to submit a Ray job to the RayCluster.

RayJob Configuration

RayCluster configuration
- rayClusterSpec - Defines the RayCluster custom resource to run the Ray job on.
- clusterSelector - Use existing RayCluster custom resources to run the Ray job instead of creating a new one. See ray-job.use-existing-raycluster.yaml for example configurations.
Ray job configuration
- entrypoint - The submitter runs ray job submit --address ... --submission-id ... -- $entrypoint to submit a Ray job to the RayCluster.
- runtimeEnvYAML (Optional): A runtime environment that describes the dependencies the Ray job needs to run, including files, packages, environment variables, and more. Provide the configuration as a multi-line YAML string. Example:
  yaml
```
spec:
  runtimeEnvYAML: |
    pip:
      - requests==2.26.0
      - pendulum==2.1.2
    env_vars:
      KEY: "VALUE"
```
See {ref}Runtime Environments <runtime-environments> for more details. (New in KubeRay version 1.0.0)
- jobId (Optional): Defines the submission ID for the Ray job. If not provided, KubeRay generates one automatically. See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details about the submission ID.
- metadata (Optional): See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details about the --metadata-json option.
- entrypointNumCpus / entrypointNumGpus / entrypointResources (Optional): See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details.
- backoffLimit (Optional, added in version 1.2.0): Specifies the number of retries before marking this RayJob failed. Each retry creates a new RayCluster. The default value is 0.
Submission configuration
- submissionMode (Optional): Specifies how RayJob submits the Ray job to the RayCluster. There are three possible values, with the default being K8sJobMode.
  - K8sJobMode: The KubeRay operator creates a submitter Kubernetes Job to submit the Ray job.
  - HTTPMode: The KubeRay operator sends a request to the RayCluster to create a Ray job.
  - InteractiveMode: The KubeRay operator waits for the user to submit a job to the RayCluster. This mode is currently in alpha and the KubeRay kubectl plugin relies on it.
  - SidecarMode: The KubeRay operator injects a container into the Ray head Pod to submit the Ray job. This mode does not support clusterSelector, submitterPodTemplate, and submitterConfig, and requires the head Pod's restart policy to be Never.
- submitterPodTemplate (Optional): Defines the Pod template for the submitter Kubernetes Job. This field is only effective when submissionMode is "K8sJobMode".
  - RAY_DASHBOARD_ADDRESS - The KubeRay operator injects this environment variable to the submitter Pod. The value is $HEAD_SERVICE:$DASHBOARD_PORT.
  - RAY_JOB_SUBMISSION_ID - The KubeRay operator injects this environment variable to the submitter Pod. The value is the RayJob.Status.JobId of the RayJob.
  - Example: ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID ...
  - See ray-job.sample.yaml for more details.
- submitterConfig (Optional): Additional configurations for the submitter Kubernetes Job.
  - backoffLimit (Optional, added in version 1.2.0): The number of retries before marking the submitter Job as failed. The default value is 2.
Automatic resource cleanup
- preRunningDeadlineSeconds (Optional): If the RayJob doesn't transition the JobDeploymentStatus to Running within preRunningDeadlineSeconds seconds, the KubeRay operator transitions the JobDeploymentStatus to Failed with reason PreRunningDeadlineExceeded. The default value is 0 (no pre-running deadline is enforced).
- shutdownAfterJobFinishes (Optional): Determines whether to recycle the RayCluster after the Ray job finishes. The default value is false.
- ttlSecondsAfterFinished (Optional): Only works if shutdownAfterJobFinishes is true. The KubeRay operator deletes the RayCluster and the submitter ttlSecondsAfterFinished seconds after the Ray job finishes. The default value is 0.
- activeDeadlineSeconds (Optional): If the RayJob doesn't transition the JobDeploymentStatus to Complete or Failed within activeDeadlineSeconds, the KubeRay operator transitions the JobDeploymentStatus to Failed, citing DeadlineExceeded as the reason.
- DELETE_RAYJOB_CR_AFTER_JOB_FINISHES (Optional, added in version 1.2.0): Set this environment variable for the KubeRay operator, not the RayJob resource. If you set this environment variable to true, the RayJob custom resource itself is deleted if you also set shutdownAfterJobFinishes to true. Note that KubeRay deletes all resources created by the RayJob, including the Kubernetes Job.
Others
- suspend (Optional): If suspend is true, KubeRay deletes both the RayCluster and the submitter. Note that Kueue also implements scheduling strategies by mutating this field. Avoid manually updating this field if you use Kueue to schedule RayJob.
- deletionStrategy (Optional, alpha in v1.5.1): Configures automated cleanup after the RayJob reaches a terminal state. This field requires the RayJobDeletionPolicy feature gate to be enabled. Two mutually exclusive styles are supported:
  - Rules-based (Recommended): Define deletionRules as a list of deletion actions triggered by specific conditions. Each rule specifies:
    - policy: The deletion action to perform — DeleteCluster (delete the entire RayCluster and its Pods), DeleteWorkers (delete only worker Pods), DeleteSelf (delete the RayJob and all associated resources), or DeleteNone (no deletion).
    - condition: When to trigger the deletion, based on jobStatus (SUCCEEDED or FAILED) and an optional ttlSeconds delay.
    - This approach enables flexible, multi-stage cleanup strategies (e.g., delete workers immediately on success, then delete the cluster after 300 seconds).
    - Rules-based mode is incompatible with shutdownAfterJobFinishes and the global ttlSecondsAfterFinished. Use per-rule condition.ttlSeconds instead.
    - See ray-job.deletion-rules.yaml for example configurations.
  - Legacy (Deprecated): Define both onSuccess and onFailure policies. This approach is deprecated and will be removed in v1.6.0. Migration to deletionRules is strongly encouraged.
    - Legacy mode can be combined with shutdownAfterJobFinishes and the global ttlSecondsAfterFinished.
  - For detailed API specifications, see the KubeRay API Reference.

Example: Run a simple Ray job with RayJob

Step 1: Create a Kubernetes cluster with Kind

kind create cluster --image=kindest/node:v1.26.0

Step 2: Install the KubeRay operator

Follow the KubeRay Operator Installation to install the latest stable KubeRay operator by Helm repository.

Step 3: Install a RayJob

kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.1/ray-operator/config/samples/ray-job.sample.yaml

Step 4: Verify the Kubernetes cluster status

shell

# Step 4.1: List all RayJob custom resources in the `default` namespace.
kubectl get rayjob

# [Example output]
# NAME            JOB STATUS   DEPLOYMENT STATUS   RAY CLUSTER NAME      START TIME             END TIME               AGE
# rayjob-sample   SUCCEEDED    Complete            rayjob-sample-qnftt   2025-06-25T16:21:21Z   2025-06-25T16:22:35Z   6m53s

# Step 4.2: List all RayCluster custom resources in the `default` namespace.
kubectl get raycluster

# [Example output]
# NAME                  DESIRED WORKERS   AVAILABLE WORKERS   CPUS   MEMORY   GPUS   STATUS   AGE
# rayjob-sample-qnftt   1                 1                   400m   0        0      ready    7m48s

# Step 4.3: List all Pods in the `default` namespace.
# The Pod created by the Kubernetes Job will be terminated after the Kubernetes Job finishes.
kubectl get pods

# [Example output]
# kuberay-operator-755f666c4b-wbcm4              1/1     Running     0          8m32s
# rayjob-sample-n2vj5                            0/1     Completed   0          7m18ss => Pod created by a Kubernetes Job
# rayjob-sample-qnftt-head                       1/1     Running     0          8m14s
# rayjob-sample-qnftt-small-group-worker-4f5wz   1/1     Running     0          8m14s

# Step 4.4: Check the status of the RayJob.
# The field `jobStatus` in the RayJob custom resource will be updated to `SUCCEEDED` and `jobDeploymentStatus`
# should be `Complete` once the job finishes.
kubectl get rayjobs.ray.io rayjob-sample -o jsonpath='{.status.jobStatus}'
# [Expected output]: "SUCCEEDED"

kubectl get rayjobs.ray.io rayjob-sample -o jsonpath='{.status.jobDeploymentStatus}'
# [Expected output]: "Complete"

The KubeRay operator creates a RayCluster custom resource based on the rayClusterSpec and a submitter Kubernetes Job to submit a Ray job to the RayCluster. In this example, the entrypoint is python /home/ray/samples/sample_code.py, and sample_code.py is a Python script stored in a Kubernetes ConfigMap mounted to the head Pod of the RayCluster. Because the default value of shutdownAfterJobFinishes is false, the KubeRay operator doesn't delete the RayCluster or the submitter when the Ray job finishes.

Step 5: Check the output of the Ray job

kubectl logs -l=job-name=rayjob-sample

# [Example output]
# 2025-06-25 09:22:27,963 INFO worker.py:1654 -- Connecting to existing Ray cluster at address: 10.244.0.6:6379...
# 2025-06-25 09:22:27,977 INFO worker.py:1832 -- Connected to Ray cluster. View the dashboard at 10.244.0.6:8265 
# test_counter got 1
# test_counter got 2
# test_counter got 3
# test_counter got 4
# test_counter got 5
# 2025-06-25 09:22:31,719 SUCC cli.py:63 -- -----------------------------------
# 2025-06-25 09:22:31,719 SUCC cli.py:64 -- Job 'rayjob-sample-zdxm6' succeeded
# 2025-06-25 09:22:31,719 SUCC cli.py:65 -- -----------------------------------

The Python script sample_code.py used by entrypoint is a simple Ray script that executes a counter's increment function 5 times.

Step 6: Delete the RayJob

kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.1/ray-operator/config/samples/ray-job.sample.yaml

Step 7: Create a RayJob with `shutdownAfterJobFinishes` set to true

kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.1/ray-operator/config/samples/ray-job.shutdown.yaml

The ray-job.shutdown.yaml defines a RayJob custom resource with shutdownAfterJobFinishes: true and ttlSecondsAfterFinished: 10. Hence, the KubeRay operator deletes the RayCluster 10 seconds after the Ray job finishes. Note that the submitter job isn't deleted because it contains the ray job logs and doesn't use any cluster resources once completed. In addition, the RayJob cleans up the submitter job when the RayJob is eventually deleted due to its owner reference back to the RayJob.

Step 8: Check the RayJob status

# Wait until `jobStatus` is `SUCCEEDED` and `jobDeploymentStatus` is `Complete`.
kubectl get rayjobs.ray.io rayjob-sample-shutdown -o jsonpath='{.status.jobDeploymentStatus}'
kubectl get rayjobs.ray.io rayjob-sample-shutdown -o jsonpath='{.status.jobStatus}'

Step 9: Check if the KubeRay operator deletes the RayCluster

# List the RayCluster custom resources in the `default` namespace. The RayCluster
# associated with the RayJob `rayjob-sample-shutdown` should be deleted.
kubectl get raycluster

Step 10: Clean up

# Step 10.1: Delete the RayJob
kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.5.1/ray-operator/config/samples/ray-job.shutdown.yaml

# Step 10.2: Delete the KubeRay operator
helm uninstall kuberay-operator

# Step 10.3: Delete the Kubernetes cluster
kind delete cluster

RayJob Quickstart

RayJob Quickstart

Prerequisites

What's a RayJob?

What does the RayJob provide?

RayJob Configuration

Example: Run a simple Ray job with RayJob

Step 1: Create a Kubernetes cluster with Kind

Step 2: Install the KubeRay operator

Step 3: Install a RayJob

Step 4: Verify the Kubernetes cluster status

Step 5: Check the output of the Ray job

Step 6: Delete the RayJob

Step 7: Create a RayJob with shutdownAfterJobFinishes set to true

Step 8: Check the RayJob status

Step 9: Check if the KubeRay operator deletes the RayCluster

Step 10: Clean up

Next steps

Step 7: Create a RayJob with `shutdownAfterJobFinishes` set to true