Ray on Cloud VMs

Overview

In this section we cover how to launch Ray clusters on Cloud VMs. Ray ships with built-in support for launching AWS and GCP clusters, and also has community-maintained integrations for Azure and Aliyun. Each Ray cluster consists of a head node and a collection of worker nodes. Optional autoscaling support allows the Ray cluster to be sized according to the requirements of your Ray workload, adding and removing worker nodes as needed. Ray supports clusters composed of multiple heterogenous compute nodes (including GPU nodes).

Concretely, you will learn how to:

  • Set up and configure Ray in public clouds

  • Deploy applications and monitor your cluster

Learn More

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

Getting Started

Learn how to start a Ray cluster and deploy Ray applications in the cloud.

Examples

Try example Ray workloads in the Cloud

User Guides

Learn best practices for configuring cloud clusters

API Reference

Find API references for cloud clusters