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, Aliyun and vSphere. 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 heterogeneous 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