Running Ray at scale on Kubernetes with KubeRay

KubeRay is an open-source Kubernetes operator that enables Ray, a framework for distributed Python and AI workloads, to run as a Kubernetes-native platform without requiring teams to build custom deployment, scaling, recovery, and monitoring logic. It provides custom resources including RayCluster for persistent clusters, RayJob for batch jobs using automatically created and cleaned-up clusters, RayService for highly available Ray Serve applications, and RayCronJob for scheduled jobs. KubeRay bridges Kubernetes, which schedules Pods and manages infrastructure policies, with Ray, which schedules tasks and actors within those Pods, while supporting Ray-aware autoscaling, heterogeneous compute, head-node fault tolerance, zero-downtime service upgrades, quota and gang scheduling integrations, authentication and network security, and Prometheus-based observability. The post outlines installation on Kubernetes platforms such as EKS, GKE, and AKS and demonstrates deploying a PyTorch Fashion MNIST training job through a RayJob resource, where KubeRay provisions the cluster, submits and tracks the workload, reports its status, and optionally removes the cluster after completion.

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