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Ray is a flexible, high-performance distributed execution framework.

Ray is easy to install: pip install ray

# Execute f serially.


def f():
    time.sleep(1)
    return 1



results = [f() for i in range(4)]

# Execute f in parallel.

@ray.remote
def f():
    time.sleep(1)
    return 1


ray.init()
results = ray.get([f.remote() for i in range(4)])

Ray comes with libraries that accelerate deep learning and reinforcement learning development:

• Tune: Hyperparameter Optimization Framework
• RLlib: Scalable Reinforcement Learning
• Distributed Training

Ray can be installed on Linux and Mac with pip install ray.

To build Ray from source or to install the nightly versions, see the installation documentation.

• Documentation
• Tutorial
• Blog
• Ray paper
• Ray HotOS paper

• Ask questions on our mailing list [email protected].
• Please report bugs by submitting a GitHub issue.
• Submit contributions using pull requests.