Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads:

Learn more about Ray AIR and its libraries:

• Datasets: Distributed Data Preprocessing
• Train: Distributed Training
• Tune: Scalable Hyperparameter Tuning
• RLlib: Scalable Reinforcement Learning
• Serve: Scalable and Programmable Serving

Or more about Ray Core and its key abstractions:

• Tasks: Stateless functions executed in the cluster.
• Actors: Stateful worker processes created in the cluster.
• Objects: Immutable values accessible across the cluster.

Ray runs on any machine, cluster, cloud provider, and Kubernetes, and also features a growing ecosystem of community integrations.

Install Ray with: pip install ray. For nightly wheels, see the Installation page.

• Documentation
• Ray Architecture whitepaper
• Exoshuffle: large-scale data shuffle in Ray
• Ownership: a distributed futures system for fine-grained tasks
• RLlib paper
• Tune paper

Older documents:

• Ray paper
• Ray HotOS paper