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:

• Ray Tune: Hyperparameter Optimization Framework
• Ray RLlib: A Scalable Reinforcement Learning Library

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 ray-dev@googlegroups.com.
• Please report bugs by submitting a GitHub issue.
• Submit contributions using pull requests.