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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.

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.

Why Ray?

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.

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

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