Distributed AI/ML Computing Framework
42.1k 2026-04-13
ray-project/ray
A unified framework for scaling AI and Python applications from a laptop to a cluster, providing a distributed runtime and AI libraries.
Core Features
Unified framework for scaling Python and AI applications.
Provides a distributed runtime with core abstractions (Tasks, Actors, Objects).
Includes a suite of AI libraries for ML workloads (Data, Train, Tune, RLlib, Serve).
Supports deployment across diverse environments including cloud and Kubernetes.
Offers monitoring and debugging capabilities.
Quick Start
pip install rayDetailed Introduction
Ray addresses the challenge of increasingly compute-intensive ML workloads that single-node environments cannot handle. It offers a unified, general-purpose framework to seamlessly scale Python and AI applications from a laptop to large clusters. By providing a core distributed runtime and a rich set of AI libraries, Ray simplifies the development and deployment of scalable machine learning, deep learning, and reinforcement learning applications, making distributed computing accessible without complex infrastructure setup.