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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:

https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg

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:

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

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

12 maintainers

PyPI data
Data sourced directly from PyPI's database.
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Credits

Author: Ray Team

License

Apache 2.0

Requires

Python >=3.10

Provides Extra

cgraph client data default observability serve tune adag serve-grpc serve-async-inference cpp rllib train air all all-cpp llm

Tags

ray distributed parallel machine-learning hyperparameter-tuningreinforcement-learning deep-learning serving python

Classifiers

Report project as malware

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This release

2.58.0 This release

20 files

2.57.0

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2.56.1

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2.56.0

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2.55.1

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2.32.0rc0

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2.7.2

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2.7.0rc0

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2.6.3

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2.3.0rc0

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1.7.1

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1.7.0

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12 files

1.4.0

9 files

1.3.0

9 files

1.2.0

9 files

1.1.0

9 files

1.0.1

9 files

1.0.0

9 files

0.8.7

9 files

0.8.6

11 files

0.8.5

8 files

0.8.4

8 files

0.8.3

6 files

0.8.2

6 files

0.8.1

6 files

0.8.0

8 files

0.7.7

8 files

0.7.6

8 files

0.7.5

8 files

0.7.4

8 files

0.7.3

8 files

0.7.2

8 files

0.7.1

8 files

0.7.0

8 files

0.6.6

8 files

0.6.5

8 files

0.6.4

10 files

0.6.3

10 files

0.6.2

10 files

0.6.1

10 files

0.6.0

10 files

0.5.3

8 files

0.5.2

8 files

0.5.0

8 files

0.4.0

9 files

0.3.1

9 files

0.3.0

9 files

0.2.2

9 files

0.2.1

9 files

0.2.0

9 files

0.1.2

9 files

0.1.1

9 files

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