Ray: A Distributed Framework for Emerging AI Applications

TLDR

The next generation of AI applications will continuously interact with the environment and learn from these interactions, demanding high performance and flexibility from their underlying systems. This paper introduces Ray, a distributed system designed to meet those demanding performance and flexibility requirements. Ray provides a unified interface for both task‑parallel and actor‑based computations, powered by a single dynamic execution engine, a distributed scheduler, and a fault‑tolerant store for control state. Experiments show Ray scales to over 1.8 million tasks per second and outperforms existing specialized systems on several challenging reinforcement‑learning workloads.

Abstract

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system's control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.