Ray is a fast and simple framework for building and running distributed applications.

Ray is easy to install: pip install ray

Example Use

Basic Python	Distributed with 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

Installation

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.

More Information

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

Getting Involved

• ray-dev@googlegroups.com: 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.