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