Ray: A System for Distributed AI

Ion Stoica
University of California, Berkeley (UC Berkeley)
EECS Department

Over the past decade, the bulk synchronous processing (BSP) model has been proven highly effective for processing large amounts of data. However, today we are witnessing the emergence of a new class of applications, i.e., AI workloads. These applications exhibit new requirements, such as nested parallelism and highly heterogeneous computations. To support such workloads, we have developed Ray, a distributed systems which provides both task-parallel and actor-like abstractions. Ray is highly scalable employing an in-memory storage system and a distributed scheduler. In this talk, I will discuss some of our design decisions, and the early experience with using Ray to implement a variety of applications.

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