Distributed AI Compute Engine
42.3k 2026-04-26
ray-project/ray
Ray is a unified framework for scaling AI and Python applications from a laptop to a cluster, simplifying complex ML workloads with a distributed runtime and specialized libraries.
Core Features
Unified framework for scaling AI and Python applications.
Core distributed runtime with key abstractions (Tasks, Actors, Objects).
Comprehensive AI Libraries for ML workloads (Data, Train, Tune, RLlib, Serve).
Seamless scalability from local development to large clusters.
Built-in monitoring and debugging tools (Dashboard, Distributed Debugger).
Quick Start
pip install rayDetailed Introduction
Ray addresses the growing compute demands of modern ML workloads by providing a unified, general-purpose framework for scaling Python and AI applications. It enables developers to seamlessly transition their code from a single-node environment to a distributed cluster without requiring significant infrastructure changes. With its core distributed runtime and a rich set of AI libraries, Ray simplifies the development, training, tuning, and serving of complex machine learning models, making distributed computing accessible and efficient for AI practitioners.