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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:

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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.

Note: This documentation refers to Ant Ray - a fork of Ray maintained by Ant Group. To install this specific version, use:

pip install ant-ray

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.

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Release files for ant-ray-cpp-nightly 3.0.0.dev20250903

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ant_ray_cpp_nightly-3.0.0.dev20250903-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
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Total release size: 79.9 MB

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2.51.2

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