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

• [email protected]: For discussions about development or any general questions.
• StackOverflow: For questions about how to use Ray.
• GitHub Issues: For reporting bugs and feature requests.
• Pull Requests: For submitting code contributions.