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Scaling AI Computing with Ray: Large-Scale Implementation and Optimization
- Existing infrastructure faces resource, deployment, application orchestration, and platform issues when applied to AI computing.
- Ray is a general-purpose distributed computing engine adopted by many large companies, providing simple and intuitive distributed programming, extensive AI framework integration, and efficient scaling.
- A platform based on Ray called AstraRay was built to address the challenges of low-cost, high-throughput, high-reliability, and easy-to-use AI computing.
- The architecture of AstraRay addresses challenges with managing million-scale pod clusters, ensuring stability with unstable resources, and simplifying application deployment.
- To support scaling to millions of nodes, AstraRay adopts a shared scheduling architecture, resolving resource allocation conflicts using optimistic concurrent scheduling locks.
- AstraRay enables quick handling of unstable nodes and efficient scheduling through fast disaster recovery scheduling and dynamic weighted SWRR routing algorithm.
- AstraRay simplifies AI application deployment through approaches such as multi-model extension, fast model distribution, multi-module extension, and multi-hardware extension.
- AstraRay addresses challenges related to multi-hardware extension and diverse inference business types through building on the TFCC framework.
- AstraRay has already established a solid foundation for AI applications in production environments and continues to undergo optimization and improvements.
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