Ray Clusters Overview — Ray 2.47.1 (original) (raw)

Ray enables seamless scaling of workloads from a laptop to a large cluster. While Ray works out of the box on single machines with just a call to ray.init, to run Ray applications on multiple nodes you must first deploy a Ray cluster.

A Ray cluster is a set of worker nodes connected to a common Ray head node. Ray clusters can be fixed-size, or they may autoscale up and down according to the resources requested by applications running on the cluster.

Where can I deploy Ray clusters?#

Ray provides native cluster deployment support on the following technology stacks:

• On AWS and GCP. Community-supported Azure, Aliyun and vSphere integrations also exist.
• On Kubernetes, via the officially supported KubeRay project.
• On Anyscale, a fully managed Ray platform by the creators of Ray. You can either bring an existing AWS, GCP, Azure and Kubernetes clusters, or use the Anyscale hosted compute layer.

Advanced users may want to deploy Ray manuallyor onto platforms not listed here.

Note

Multi-node Ray clusters are only supported on Linux. At your own risk, you may deploy Windows and OSX clusters by setting the environment variableRAY_ENABLE_WINDOWS_OR_OSX_CLUSTER=1 during deployment.

What’s next?#

Understand the key concepts and main ways of interacting with a Ray cluster.

Deploy a Ray application to a Kubernetes cluster. You can run the tutorial on a Kubernetes cluster or on your laptop via Kind.

Take a sample application designed to run on a laptop and scale it up in the cloud. Access to an AWS or GCP account is required.

Guide to submitting applications as Jobs to existing Ray clusters.