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 simplifying ML compute:

Learn more about Ray AIR and its libraries:

• Data: Scalable Datasets for ML
• 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.

Monitor and debug Ray applications and clusters using the Ray dashboard.

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

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

Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.

Ray is a unified way to scale Python and AI applications from a laptop to a cluster.

With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.

• Documentation
• Ray Architecture whitepaper
• Ray AIR Technical 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
• Ray Architecture v1 whitepaper