Ray on Cloud VMs
Ray on Cloud VMs#
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
The Ray docs present all the information you need to start running Ray workloads on VMs.
Learn how to start a Ray cluster and deploy Ray applications in the cloud.
Try example Ray workloads in the Cloud
Learn best practices for configuring cloud clusters
Find API references for cloud clusters