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.
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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Getting Involved
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Purpose |
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Support Level |
|---|---|---|---|
For discussions about development and questions about usage. |
< 1 day |
Community |
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For reporting bugs and filing feature requests. |
< 2 days |
Ray OSS Team |
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For collaborating with other Ray users. |
< 2 days |
Community |
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For asking questions about how to use Ray. |
3-5 days |
Community |
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For learning about Ray projects and best practices. |
Monthly |
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For staying up-to-date on new features. |
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Metadata
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Release files for ray-cpp 2.58.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| ray_cpp-2.58.1-py3-none-win_amd64.whl | Python 3 | none | Windows x86-64 | Details |
| ray_cpp-2.58.1-py3-none-manylinux2014_x86_64.whl | Python 3 | none | Linux glibc 2.17+ x86-64 | Details |
| ray_cpp-2.58.1-py3-none-manylinux2014_aarch64.whl | Python 3 | none | Linux glibc 2.17+ ARM64 | Details |
| ray_cpp-2.58.1-py3-none-macosx_12_0_arm64.whl | Python 3 | none | macOS 12.0+ ARM64 | Details |
Total release size: 60.1 MB
Release files / ray_cpp-2.58.1-py3-none-win_amd64.whl
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Release files / ray_cpp-2.58.1-py3-none-manylinux2014_x86_64.whl
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Release files / ray_cpp-2.58.1-py3-none-macosx_12_0_arm64.whl
| Download URL | ray_cpp-2.58.1-py3-none-macosx_12_0_arm64.whl |
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| Tags | Python 3 macOS 12.0+ ARM64 |
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Security for ray-cpp 2.58.1
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