Ray: A Distributed Framework for Emerging AI Applications

TL;DR

Ray is a distributed framework designed for emerging AI applications that require high performance and flexibility, supporting task and actor computations with scalable execution and fault tolerance.

Contribution

Ray introduces a unified distributed system with a dynamic execution engine supporting both task and actor models, achieving high scalability and performance.

Findings

Scaling beyond 1.8 million tasks per second

Outperforms existing systems in reinforcement learning tasks

Supports flexible and fault-tolerant AI application development

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.