This blog post, the second in a series on KubeRay, details best practices for running Ray in production on Kubernetes. It focuses on scaling from a single job to hundreds across multiple teams and clusters, addressing challenges like debugging, hardware faults, and default setting tuning. The article highlights features such as GPU sharing, autoscaling workers to zero, and using Kueue for fair resource allocation, providing a YAML example for a production RayJob configuration. AI
IMPACT Provides guidance for efficiently managing and scaling AI workloads on Kubernetes infrastructure.
RANK_REASON Blog post detailing best practices for using a specific software tool (KubeRay) in production environments.
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