Key Concepts

This page introduces key concepts for Ray clusters:

Ray Cluster

A Ray cluster consists of a single head node and any number of connected worker nodes:


A Ray cluster with two worker nodes. Each node runs Ray helper processes to facilitate distributed scheduling and memory management. The head node runs additional control processes (highlighted in blue).

The number of worker nodes may be autoscaled with application demand as specified by your Ray cluster configuration. The head node runs the autoscaler.


Ray nodes are implemented as pods when running on Kubernetes.

Users can submit jobs for execution on the Ray cluster, or can interactively use the cluster by connecting to the head node and running ray.init. See Ray Jobs for more information.

Head Node

Every Ray cluster has one node which is designated as the head node of the cluster. The head node is identical to other worker nodes, except that it also runs singleton processes responsible for cluster management such as the autoscaler and the Ray driver processes which run Ray jobs. Ray may schedule tasks and actors on the head node just like any other worker node, unless configured otherwise.

Worker Node

Worker nodes do not run any head node management processes, and serve only to run user code in Ray tasks and actors. They participate in distributed scheduling, as well as the storage and distribution of Ray objects in cluster memory.


The Ray autoscaler is a process that runs on the head node (or as a sidecar container in the head pod if using Kubernetes). When the resource demands of the Ray workload exceed the current capacity of the cluster, the autoscaler will try to increase the number of worker nodes. When worker nodes sit idle, the autoscaler will remove worker nodes from the cluster.

It is important to understand that the autoscaler only reacts to task and actor resource requests, and not application metrics or physical resource utilization. To learn more about autoscaling, refer to the user guides for Ray clusters on VMs and Kubernetes.

Ray Jobs

The main method for running a workload on a Ray cluster is to use Ray Jobs. Ray Jobs enable users to submit locally developed-and-tested applications to a remote Ray cluster. Ray Job Submission simplifies the experience of packaging, deploying, and managing a Ray application.

For interactive development, the following additional methods are available:

  • Directly running a script or notebook on any head or worker node.

  • Using the Ray Client to connect remotely to the cluster.

To learn how to run workloads on a Ray cluster, refer to the Ray Jobs guide.