Ray & KubeRay, with Richard Liaw and Kai-Hsun Chen
Kubernetes Podcast from Google
Revolutionizing AI Workflows with Ray and Kubernetes
This chapter explores how Ray and KubeRay enhance authority-centric computation and reduce system overhead for AI and ML implementations. It highlights the evolution of Ray from its origins at UC Berkeley to becoming a comprehensive framework for distributed workloads, alongside discussions on simplifying APIs and improving usability. The chapter also examines the integration of Ray within Kubernetes and its role in streamlining workflows, checkpointing strategies, and multi-hardware resource management.
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