KubeRay in production: best practices from the field

Running Ray workloads with KubeRay in production requires additional operational practices as deployments grow across teams, clusters, and shared GPU infrastructure. Key challenges include preserving logs and events after ephemeral RayJobs terminate, detecting hardware failures that can appear as application errors, tuning autoscaling and scheduling for contention, protecting against head-node disruption, integrating with existing Kubernetes controls such as service meshes and quotas, and automating responses to critical alerts. Recommended approaches include using the KubeRay History Server and external log shipping, monitoring GPU health through NVIDIA DCGM and node taints, applying gang scheduling and autoscaler headroom, enabling GCS fault tolerance with isolated Redis instances, keeping Ray workloads in dedicated namespaces, and tracking ownership for cost attribution. The post argues that these measures can become a significant maintenance burden at fleet scale and presents Anyscale KubeRay Connect, a private-preview platform layer designed to add managed observability, telemetry collection, orchestration, security, governance, and support while retaining existing KubeRay, Kubernetes, Kueue, and GitOps workflows.

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