KubeRay
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.
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RayCluster: KubeRay fully manages the lifecycle of RayCluster, including cluster creation/deletion, autoscaling, and ensuring fault tolerance.
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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.
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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.
KubeRay ecosystem: Some optional components.
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Kubectl Plugin (Beta): Starting from KubeRay v1.3.0, you can use the kubectl ray plugin to simplify
common workflows when deploying Ray on Kubernetes. If you aren’t familiar with Kubernetes, this
plugin simplifies running Ray on Kubernetes. See kubectl-plugin for more details.
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KubeRay APIServer (Alpha): 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. See KubeRay APIServer V2 for more details.
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KubeRay Dashboard (Experimental): Starting from KubeRay v1.4.0, we have introduced a new dashboard that enables users to view and manage KubeRay resources.
While it is not yet production-ready, we welcome your feedback. See KubeRay Dashboard for more details.
Documentation
From September 2023, all user-facing KubeRay documentation will be hosted on the Ray documentation.
The KubeRay repository only contains documentation related to the development and maintenance of KubeRay.
Quick Start
Examples
KubeRay examples are hosted on the Ray documentation.
Examples span a wide range of use cases, including training, LLM online inference, batch inference, and more.
Kubernetes Ecosystem
KubeRay integrates with the Kubernetes ecosystem, including observability tools (e.g., Prometheus, Grafana, py-spy), queuing systems (e.g., Volcano, Apache YuniKorn, Kueue), ingress controllers (e.g., Nginx), and more.
See KubeRay Ecosystem for more details.
Blog Posts
This is the beginning of the README. Read the rest at the original. Read the full README ↗
