This release is a pre-release and may not be stable for production use.
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.
Note: This documentation refers to Ant Ray - a fork of Ray maintained by Ant Group. To install this specific version, use:
pip install ant-rayWhy 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
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Purpose |
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For discussions about development and questions about usage. |
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< 2 days |
Ray OSS Team |
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For collaborating with other Ray users. |
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3-5 days |
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Release files for ant-ray-cpp-nightly 3.0.0.dev20251225
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Built distributions (wheels)
Total release size: 84.3 MB
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