Benchmarking GPU Scheduling for Massive-Scale Ray Workloads at Minimal Cost

Learn a cost-effective approach for benchmarking GPU scheduling and large-scale Ray workloads on Kubernetes using Kwok (Kubernetes Without Kubelet) to simulate massive Ray clusters without requiring expensive hardware. Discover how Microsoft's Anson Qian demonstrates simulating 10,000 RayCluster and RayJob workloads across 10,000 GPU nodes on a modest 3-node Azure Kubernetes Service setup for approximately $10 per hour. Explore the key challenge of validating scalability and stability of Ray, KubeRay, and Kubernetes GPU scheduling, which traditionally requires extremely large and cost-prohibitive clusters that make realistic production load testing difficult for most teams. Master how Kwok simulates massive Kubernetes clusters without real GPU nodes, enabling stress-testing of KubeRay, the Kubernetes control plane, and Ray's scheduling logic at extreme scale. Understand techniques for validating stability, analyzing performance, and uncovering bottlenecks before real-world deployment, while learning why this simulated approach accelerates development of robust, production-grade ML systems. Gain an actionable blueprint for scaling Ray, testing GPU scheduling logic, and validating ML infrastructure in a highly efficient and affordable way that unlocks testing depth previously accessible only to teams with enormous compute budgets.