/rl_ray

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.

Primary LanguagePythonApache License 2.0Apache-2.0

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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.

More Information

Older documents:

Getting Involved

Platform Purpose Estimated Response Time Support Level
Discourse Forum For discussions about development and questions about usage. < 1 day Community
GitHub Issues For reporting bugs and filing feature requests. < 2 days Ray OSS Team
Slack For collaborating with other Ray users. < 2 days Community
StackOverflow For asking questions about how to use Ray. 3-5 days Community
Meetup Group For learning about Ray projects and best practices. Monthly Ray DevRel
Twitter For staying up-to-date on new features. Daily Ray DevRel