wehub-resource-sync
209 lines
14 KiB
Markdown
209 lines
14 KiB
Markdown
(kuberay-rayjob-quickstart)=
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# RayJob Quickstart
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## Prerequisites
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* KubeRay v0.6.0 or higher
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* KubeRay v0.6.0 or v1.0.0: Ray 1.10 or higher.
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* KubeRay v1.1.1 or newer is highly recommended: Ray 2.8.0 or higher.
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## What's a RayJob?
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A RayJob manages two aspects:
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* **RayCluster**: A RayCluster custom resource manages all Pods in a Ray cluster, including a head Pod and multiple worker Pods.
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* **Job**: A Kubernetes Job runs `ray job submit` to submit a Ray job to the RayCluster.
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## What does the RayJob provide?
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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.
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To understand the following content better, you should understand the difference between:
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* RayJob: A Kubernetes custom resource definition provided by KubeRay.
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* Ray job: A Ray job is a packaged Ray application that can run on a remote Ray cluster. See [this document](jobs-overview) for more details.
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* Submitter: The submitter is a Kubernetes Job that runs `ray job submit` to submit a Ray job to the RayCluster.
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## RayJob Configuration
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* RayCluster configuration
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* `rayClusterSpec` - Defines the **RayCluster** custom resource to run the Ray job on.
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* `clusterSelector` - Use existing **RayCluster** custom resources to run the Ray job instead of creating a new one. See [ray-job.use-existing-raycluster.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-job.use-existing-raycluster.yaml) for example configurations.
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* Ray job configuration
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* `entrypoint` - The submitter runs `ray job submit --address ... --submission-id ... -- $entrypoint` to submit a Ray job to the RayCluster.
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* `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:
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```yaml
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spec:
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runtimeEnvYAML: |
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pip:
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- requests==2.26.0
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- pendulum==2.1.2
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env_vars:
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KEY: "VALUE"
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```
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See {ref}`Runtime Environments <runtime-environments>` for more details. _(New in KubeRay version 1.0.0)_
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* `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.
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* `metadata` (Optional): See {ref}`Ray Jobs CLI API Reference <ray-job-submission-cli-ref>` for more details about the `--metadata-json` option.
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* `entrypointNumCpus` / `entrypointNumGpus` / `entrypointResources` (Optional): See {ref}`Ray Jobs CLI API Reference <ray-job-submission-cli-ref>` for more details.
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* `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.
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* Submission configuration
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* `submissionMode` (Optional): Specifies how RayJob submits the Ray job to the RayCluster. There are three possible values, with the default being `K8sJobMode`.
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* `K8sJobMode`: The KubeRay operator creates a submitter Kubernetes Job to submit the Ray job.
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* `HTTPMode`: The KubeRay operator sends a request to the RayCluster to create a Ray job.
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* `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](kubectl-plugin) relies on it.
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* `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`.
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* `submitterPodTemplate` (Optional): Defines the Pod template for the submitter Kubernetes Job. This field is only effective when `submissionMode` is "K8sJobMode".
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* `RAY_DASHBOARD_ADDRESS` - The KubeRay operator injects this environment variable to the submitter Pod. The value is `$HEAD_SERVICE:$DASHBOARD_PORT`.
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* `RAY_JOB_SUBMISSION_ID` - The KubeRay operator injects this environment variable to the submitter Pod. The value is the `RayJob.Status.JobId` of the RayJob.
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* Example: `ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID ...`
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* See [ray-job.sample.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-job.sample.yaml) for more details.
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* `submitterConfig` (Optional): Additional configurations for the submitter Kubernetes Job.
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* `backoffLimit` (Optional, added in version 1.2.0): The number of retries before marking the submitter Job as failed. The default value is 2.
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* Automatic resource cleanup
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* `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).
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* `shutdownAfterJobFinishes` (Optional): Determines whether to recycle the RayCluster after the Ray job finishes. The default value is false.
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* `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.
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* `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.
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* `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.
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* Others
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* `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.
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* `deletionStrategy` (alpha in v1.5.1, beta in v1.6.0): 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:
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* **Rules-based** (Recommended): Define `deletionRules` as a list of deletion actions triggered by specific conditions. Each rule specifies:
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* `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).
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* `condition`: When to trigger the deletion, based on `jobStatus` (`SUCCEEDED` or `FAILED`) and an optional `ttlSeconds` delay.
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* This approach enables flexible, multi-stage cleanup strategies (e.g., delete workers immediately on success, then delete the cluster after 300 seconds).
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* Rules-based mode is incompatible with `shutdownAfterJobFinishes` and the global `ttlSecondsAfterFinished`. Use per-rule `condition.ttlSeconds` instead.
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* See [ray-job.deletion-rules.yaml](https://github.com/ray-project/kuberay/blob/master/ray-operator/config/samples/ray-job.deletion-rules.yaml) for example configurations.
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* **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.
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* Legacy mode can be combined with `shutdownAfterJobFinishes` and the global `ttlSecondsAfterFinished`.
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* For detailed API specifications, see the [KubeRay API Reference](https://ray-project.github.io/kuberay/reference/api/).
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## Example: Run a simple Ray job with RayJob
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## Step 1: Create a Kubernetes cluster with Kind
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```sh
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kind create cluster --image=kindest/node:v1.26.0
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```
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## Step 2: Install the KubeRay operator
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Follow the [KubeRay Operator Installation](kuberay-operator-deploy) to install the latest stable KubeRay operator by Helm repository.
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## Step 3: Install a RayJob
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```sh
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kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.6.0/ray-operator/config/samples/ray-job.sample.yaml
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```
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## Step 4: Verify the Kubernetes cluster status
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```shell
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# Step 4.1: List all RayJob custom resources in the `default` namespace.
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kubectl get rayjob
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# [Example output]
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# NAME JOB STATUS DEPLOYMENT STATUS RAY CLUSTER NAME START TIME END TIME AGE
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# rayjob-sample SUCCEEDED Complete rayjob-sample-qnftt 2025-06-25T16:21:21Z 2025-06-25T16:22:35Z 6m53s
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# Step 4.2: List all RayCluster custom resources in the `default` namespace.
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kubectl get raycluster
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# [Example output]
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# NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE
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# rayjob-sample-qnftt 1 1 400m 0 0 ready 7m48s
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# Step 4.3: List all Pods in the `default` namespace.
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# The Pod created by the Kubernetes Job will be terminated after the Kubernetes Job finishes.
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kubectl get pods
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# [Example output]
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# kuberay-operator-755f666c4b-wbcm4 1/1 Running 0 8m32s
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# rayjob-sample-n2vj5 0/1 Completed 0 7m18ss => Pod created by a Kubernetes Job
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# rayjob-sample-qnftt-head 1/1 Running 0 8m14s
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# rayjob-sample-qnftt-small-group-worker-4f5wz 1/1 Running 0 8m14s
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# Step 4.4: Check the status of the RayJob.
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# The field `jobStatus` in the RayJob custom resource will be updated to `SUCCEEDED` and `jobDeploymentStatus`
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# should be `Complete` once the job finishes.
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kubectl get rayjobs.ray.io rayjob-sample -o jsonpath='{.status.jobStatus}'
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# [Expected output]: "SUCCEEDED"
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kubectl get rayjobs.ray.io rayjob-sample -o jsonpath='{.status.jobDeploymentStatus}'
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# [Expected output]: "Complete"
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```
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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.
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## Step 5: Check the output of the Ray job
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```sh
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kubectl logs -l=job-name=rayjob-sample
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# [Example output]
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# 2025-06-25 09:22:27,963 INFO worker.py:1654 -- Connecting to existing Ray cluster at address: 10.244.0.6:6379...
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# 2025-06-25 09:22:27,977 INFO worker.py:1832 -- Connected to Ray cluster. View the dashboard at 10.244.0.6:8265
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# test_counter got 1
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# test_counter got 2
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# test_counter got 3
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# test_counter got 4
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# test_counter got 5
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# 2025-06-25 09:22:31,719 SUCC cli.py:63 -- -----------------------------------
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# 2025-06-25 09:22:31,719 SUCC cli.py:64 -- Job 'rayjob-sample-zdxm6' succeeded
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# 2025-06-25 09:22:31,719 SUCC cli.py:65 -- -----------------------------------
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```
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The Python script `sample_code.py` used by `entrypoint` is a simple Ray script that executes a counter's increment function 5 times.
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## Step 6: Delete the RayJob
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```sh
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kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.6.0/ray-operator/config/samples/ray-job.sample.yaml
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```
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## Step 7: Create a RayJob with `shutdownAfterJobFinishes` set to true
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```sh
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kubectl apply -f https://raw.githubusercontent.com/ray-project/kuberay/v1.6.0/ray-operator/config/samples/ray-job.shutdown.yaml
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```
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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.
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## Step 8: Check the RayJob status
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```sh
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# Wait until `jobStatus` is `SUCCEEDED` and `jobDeploymentStatus` is `Complete`.
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kubectl get rayjobs.ray.io rayjob-sample-shutdown -o jsonpath='{.status.jobDeploymentStatus}'
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kubectl get rayjobs.ray.io rayjob-sample-shutdown -o jsonpath='{.status.jobStatus}'
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```
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## Step 9: Check if the KubeRay operator deletes the RayCluster
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```sh
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# List the RayCluster custom resources in the `default` namespace. The RayCluster
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# associated with the RayJob `rayjob-sample-shutdown` should be deleted.
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kubectl get raycluster
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```
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## Step 10: Clean up
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```sh
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# Step 10.1: Delete the RayJob
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kubectl delete -f https://raw.githubusercontent.com/ray-project/kuberay/v1.6.0/ray-operator/config/samples/ray-job.shutdown.yaml
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# Step 10.2: Delete the KubeRay operator
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helm uninstall kuberay-operator
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# Step 10.3: Delete the Kubernetes cluster
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kind delete cluster
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```
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## Next steps
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* [RayJob Batch Inference Example](kuberay-batch-inference-example)
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* [Priority Scheduling with RayJob and Kueue](kuberay-kueue-priority-scheduling-example)
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* [Gang Scheduling with RayJob and Kueue](kuberay-kueue-gang-scheduling-example)
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