stvreumi / ray

A system for parallel and distributed Python that unifies the ML ecosystem.

Ray

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

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:

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

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

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