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Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for simplifying ML compute:

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Learn more about Ray AIR and its libraries:

  • Datasets: Distributed Data Preprocessing

  • 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.

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

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Discourse Forum

For discussions about development and questions about usage.

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< 2 days

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For collaborating with other Ray users.

< 2 days

Community

StackOverflow

For asking questions about how to use Ray.

3-5 days

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Monthly

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Release files for secretflow-ray 2.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for secretflow-ray 2.2.0
File
secretflow_ray-2.2.0-cp310-cp310-manylinux2014_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details
secretflow_ray-2.2.0-cp39-cp39-manylinux2014_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64 Details
secretflow_ray-2.2.0-cp38-cp38-manylinux2014_x86_64.whl CPython 3.8 CPython 3.8 Linux glibc 2.17+ x86-64 Details
secretflow_ray-2.2.0-cp38-cp38-macosx_11_0_arm64.whl CPython 3.8 CPython 3.8 macOS 11.0+ ARM64 Details
secretflow_ray-2.2.0-cp38-cp38-macosx_10_16_x86_64.whl CPython 3.8 CPython 3.8 macOS 10.16+ x86-64 Details
secretflow_ray-2.2.0-cp37-cp37m-manylinux2014_x86_64.whl CPython 3.7 CPython 3.7 pymalloc Linux glibc 2.17+ x86-64 Details
secretflow_ray-2.2.0-cp36-cp36m-manylinux2014_x86_64.whl CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.17+ x86-64 Details

Total release size: 197.0 MB

Release files / secretflow_ray-2.2.0-cp310-cp310-manylinux2014_x86_64.whl

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Release files / secretflow_ray-2.2.0-cp39-cp39-manylinux2014_x86_64.whl

Download URL secretflow_ray-2.2.0-cp39-cp39-manylinux2014_x86_64.whl
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Release files / secretflow_ray-2.2.0-cp38-cp38-manylinux2014_x86_64.whl

Download URL secretflow_ray-2.2.0-cp38-cp38-manylinux2014_x86_64.whl
Size 27.8 MB
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Release files / secretflow_ray-2.2.0-cp38-cp38-macosx_11_0_arm64.whl

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Release files / secretflow_ray-2.2.0-cp38-cp38-macosx_10_16_x86_64.whl

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Release files / secretflow_ray-2.2.0-cp37-cp37m-manylinux2014_x86_64.whl

Download URL secretflow_ray-2.2.0-cp37-cp37m-manylinux2014_x86_64.whl
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Release files / secretflow_ray-2.2.0-cp36-cp36m-manylinux2014_x86_64.whl

Download URL secretflow_ray-2.2.0-cp36-cp36m-manylinux2014_x86_64.whl
Size 28.2 MB
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