Ray on Kubernetes

Overview

In this section we cover how to execute your distributed Ray programs on a Kubernetes cluster.

Using the KubeRay Operator is the recommended way to do so. The operator provides a Kubernetes-native way to manage Ray clusters. Each Ray cluster consists of a head node pod and a collection of worker node pods. Optional autoscaling support allows the KubeRay Operator to size your Ray clusters according to the requirements of your Ray workload, adding and removing Ray pods as needed. KubeRay supports heterogenous compute nodes (including GPUs) as well as running multiple Ray clusters with different Ray versions in the same Kubernetes cluster.

../../_images/ray_on_kubernetes.png

Concretely, you will learn how to:

  • Set up and configure Ray on a Kubernetes cluster

  • Deploy and monitor Ray applications

  • Integrate Ray applications with Kubernetes networking

Learn More

The Ray docs present all the information you need to start running Ray workloads on Kubernetes.

Getting Started

Learn how to start a Ray cluster and deploy Ray applications on Kubernetes.

Examples

Try example Ray workloads on Kubernetes.

User Guides

Learn best practices for configuring Ray clusters on Kubernetes.

API Reference

Find API references on RayCluster configuration.

About KubeRay

Ray’s Kubernetes support is developed at the KubeRay GitHub repository, under the broader Ray project. KubeRay is used by several companies to run production Ray deployments.

  • Visit the KubeRay GitHub repo to track progress, report bugs, propose new features, or contribute to the project.

  • Check out the KubeRay docs for further technical information, developer guides, and discussion of new and upcoming features.

Note

The KubeRay operator replaces the older Ray operator hosted in the Ray repository. Check the linked README for migration notes.

If you have used the legacy Ray operator in the past, make sure to de-register that operator’s CRD before using KubeRay:

kubectl delete crd rayclusters.cluster.ray.io