Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:
Learn more about Ray AI 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.
Learn more about Monitoring and Debugging:
- Monitor Ray apps and clusters with the Ray Dashboard.
- Debug Ray apps with the Ray Distributed Debugger.
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
- Exoshuffle: large-scale data shuffle in Ray
- Ownership: a distributed futures system for fine-grained tasks
- RLlib paper
- Tune paper
Older documents: