(kuberay-raycluster-quickstart)= # RayCluster Quickstart This guide shows you how to manage and interact with Ray clusters on Kubernetes. ## Preparation * Install [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl) (>= 1.23), [Helm](https://helm.sh/docs/intro/install/) (>= v3.4) if needed, [Kind](https://kind.sigs.k8s.io/docs/user/quick-start/#installation), and [Docker](https://docs.docker.com/engine/install/). * Make sure your Kubernetes cluster has at least 4 CPU and 4 GB RAM. ## Step 1: Create a Kubernetes cluster This step creates a local Kubernetes cluster using [Kind](https://kind.sigs.k8s.io/). If you already have a Kubernetes cluster, you can skip this step. ```sh kind create cluster --image=kindest/node:v1.26.0 ``` ## Step 2: Deploy a KubeRay operator Follow [this document](kuberay-operator-deploy) to install the latest stable KubeRay operator from the Helm repository. (raycluster-deploy)= ## Step 3: Deploy a RayCluster custom resource Once the KubeRay operator is running, you're ready to deploy a RayCluster. Create a RayCluster Custom Resource (CR) in the `default` namespace. ```sh # Deploy a sample RayCluster CR from the KubeRay Helm chart repo: helm install raycluster kuberay/ray-cluster --version 1.6.0 ``` Once the RayCluster CR has been created, you can view it by running: ```sh # Once the RayCluster CR has been created, you can view it by running: kubectl get rayclusters ``` ```sh NAME DESIRED WORKERS AVAILABLE WORKERS CPUS MEMORY GPUS STATUS AGE raycluster-kuberay 1 1 2 3G 0 ready 55s ``` The KubeRay operator detects the RayCluster object and starts your Ray cluster by creating head and worker pods. To view Ray cluster's pods, run the following command: ```sh # View the pods in the RayCluster named "raycluster-kuberay" kubectl get pods --selector=ray.io/cluster=raycluster-kuberay ``` ```sh NAME READY STATUS RESTARTS AGE raycluster-kuberay-head 1/1 Running 0 XXs raycluster-kuberay-worker-workergroup-xvfkr 1/1 Running 0 XXs ``` Wait for the pods to reach `Running` state. This may take a few minutes, downloading the Ray images takes most of this time. If your pods stick in the `Pending` state, you can check for errors using `kubectl describe pod raycluster-kuberay-xxxx-xxxxx` and ensure your Docker resource limits meet the requirements. ## Step 4: Run an application on a RayCluster Now, interact with the RayCluster deployed. ### Method 1: Execute a Ray job in the head Pod The most straightforward way to experiment with your RayCluster is to exec directly into the head pod. First, identify your RayCluster's head pod: ```sh export HEAD_POD=$(kubectl get pods --selector=ray.io/node-type=head -o custom-columns=POD:metadata.name --no-headers) echo $HEAD_POD ``` ```sh raycluster-kuberay-head ``` ```sh # Print the cluster resources. kubectl exec -it $HEAD_POD -- python -c "import ray; ray.init(); print(ray.cluster_resources())" ``` ```sh 2023-04-07 10:57:46,472 INFO worker.py:1243 -- Using address 127.0.0.1:6379 set in the environment variable RAY_ADDRESS 2023-04-07 10:57:46,472 INFO worker.py:1364 -- Connecting to existing Ray cluster at address: 10.244.0.6:6379... 2023-04-07 10:57:46,482 INFO worker.py:1550 -- Connected to Ray cluster. View the dashboard at http://10.244.0.6:8265 {'CPU': 2.0, 'memory': 3000000000.0, 'node:10.244.0.6': 1.0, 'node:10.244.0.7': 1.0, 'node:__internal_head__': 1.0, 'object_store_memory': 749467238.0} ``` ### Method 2: Submit a Ray job to the RayCluster using [ray job submission SDK](jobs-quickstart) Unlike Method 1, this method doesn't require you to execute commands in the Ray head pod. Instead, you can use the [Ray job submission SDK](jobs-quickstart) to submit Ray jobs to the RayCluster through the Ray Dashboard port where Ray listens for Job requests. The KubeRay operator configures a [Kubernetes service](https://kubernetes.io/docs/concepts/services-networking/service/) targeting the Ray head Pod. ```sh kubectl get service raycluster-kuberay-head-svc ``` ```sh NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE raycluster-kuberay-head-svc ClusterIP None 10001/TCP,8265/TCP,6379/TCP,8080/TCP,8000/TCP 57s ``` Now that the service name is available, use port-forwarding to access the Ray Dashboard port which is 8265 by default. ```sh # Execute this in a separate shell. kubectl port-forward service/raycluster-kuberay-head-svc 8265:8265 > /dev/null & ``` Now that the Dashboard port is accessible, submit jobs to the RayCluster: ```sh # The following job's logs will show the Ray cluster's total resource capacity, including 2 CPUs. ray job submit --address http://localhost:8265 -- python -c "import ray; ray.init(); print(ray.cluster_resources())" ``` ```sh Job submission server address: http://localhost:8265 ------------------------------------------------------- Job 'raysubmit_8vJ7dKqYrWKbd17i' submitted successfully ------------------------------------------------------- Next steps Query the logs of the job: ray job logs raysubmit_8vJ7dKqYrWKbd17i Query the status of the job: ray job status raysubmit_8vJ7dKqYrWKbd17i Request the job to be stopped: ray job stop raysubmit_8vJ7dKqYrWKbd17i Tailing logs until the job exits (disable with --no-wait): 2025-03-18 01:27:51,014 INFO job_manager.py:530 -- Runtime env is setting up. 2025-03-18 01:27:51,744 INFO worker.py:1514 -- Using address 10.244.0.6:6379 set in the environment variable RAY_ADDRESS 2025-03-18 01:27:51,744 INFO worker.py:1654 -- Connecting to existing Ray cluster at address: 10.244.0.6:6379... 2025-03-18 01:27:51,750 INFO worker.py:1832 -- Connected to Ray cluster. View the dashboard at 10.244.0.6:8265 {'CPU': 2.0, 'memory': 3000000000.0, 'node:10.244.0.6': 1.0, 'node:10.244.0.7': 1.0, 'node:__internal_head__': 1.0, 'object_store_memory': 749467238.0} ------------------------------------------ Job 'raysubmit_8vJ7dKqYrWKbd17i' succeeded ------------------------------------------ ``` ## Step 5: Access the Ray Dashboard Visit `${YOUR_IP}:8265` in your browser for the Dashboard. For example, `127.0.0.1:8265`. See the job you submitted in Step 4 in the **Recent jobs** pane as shown below. ![Ray Dashboard](../images/ray-dashboard.png) ## Step 6: Cleanup ```sh # Kill the `kubectl port-forward` background job in the earlier step killall kubectl kind delete cluster ```