KubeRay is a powerful, open-source Kubernetes operator that simplifies the deployment and management of Ray applications on Kubernetes. It offers several key components:
KubeRay core: This is the official, fully-maintained component of KubeRay that provides three custom resource definitions, RayCluster, RayJob, and RayService. These resources are designed to help you run a wide range of workloads with ease.
RayCluster: KubeRay fully manages the lifecycle of RayCluster, including cluster creation/deletion, autoscaling, and ensuring fault tolerance.
RayJob: With RayJob, KubeRay automatically creates a RayCluster and submits a job when the cluster is ready. You can also configure RayJob to automatically delete the RayCluster once the job finishes.
RayService: RayService is made up of two parts: a RayCluster and a Ray Serve deployment graph. RayService offers zero-downtime upgrades for RayCluster and high availability.
Community-managed components (optional): Some components are maintained by the KubeRay community.
KubeRay APIServer: It provides a layer of simplified configuration for KubeRay resources. The KubeRay API server is used internally by some organizations to back user interfaces for KubeRay resource management.
KubeRay Python client: This Python client library provides APIs to handle RayCluster from your Python application.
KubeRay CLI: KubeRay CLI provides the ability to manage KubeRay resources through command-line interface.
Security and isolation must be enforced outside of the Ray Cluster. Restrict network access with Kubernetes or other external controls. Refer to Ray security documentation for more guidance on what controls to implement.
Please report security issues to email@example.com.
The Ray docs¶
You can find even more information on deployments of Ray on Kubernetes at the official Ray docs.