Kuberay on Debian 13

KubeRay is an open-source Kubernetes-native solution designed to deploy, manage, and scale Ray clusters for distributed computing workloads such as machine learning, data processing, and AI applications. It simplifies running Ray on Kubernetes by automating cluster lifecycle management without requiring manual configuration.

The solution supports common distributed computing workflows including parallel data processing, model training, hyperparameter tuning, and scalable inference. KubeRay enables dynamic scaling of Ray head and worker nodes, making it ideal for cloud-native, containerized environments.

Features of KubeRay:

• Kubernetes operator for managing Ray clusters.
• Automated deployment and lifecycle management of Ray head and worker nodes.
• Elastic autoscaling based on workload demand.
• Native integration with Kubernetes scheduling, networking, and storage.
• Support for distributed machine learning, data analytics, and AI workloads.
• Built-in Ray Dashboard for cluster monitoring and observability.

To check if KubeRay is installed and accessible, use the following steps:

Check KubeRay operator installation:
$ helm list

Verify KubeRay operator pod:
$ kubectl get pods -A | grep kuberay

Check Ray cluster status:
$ kubectl get rayclusters

Check Ray version from head pod:
$ kubectl exec -it <ray-head-pod> -- ray --version

To access the Ray Dashboard:

List Ray services:
$ kubectl get svc

Port-forward the Ray head service:
$ kubectl port-forward svc/<ray-head-service> 8265:8265

Open the dashboard in a browser:
http://localhost:8265

Note: If the Kubernetes cluster is running on a remote server, access the dashboard using SSH port forwarding or bind the port-forward to all interfaces as required.

Disclaimer: KubeRay is provided “as is” under applicable open-source licenses. Users are responsible for proper Kubernetes configuration, resource allocation, and security controls. This solution is best suited for scalable distributed computing, machine learning pipelines, and cloud-native AI workloads in Kubernetes environments.