A system for parallel and distributed Python that unifies the ML ecosystem.
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Add docs for contributors. (#1191) Nov 10, 2017
.travis [tune] Component notification on node failure + Tests (#3414) Dec 4, 2018
cmake/Modules [sgd] Add file lock to protect compilation of sgd op (#3486) Dec 9, 2018
dev Document the release process. (#2760) Aug 29, 2018
doc [rllib] Learner should not see clipped actions (#3496) Dec 10, 2018
docker [tune] Node Fault Tolerance (#3238) Nov 21, 2018
examples [rllib] Propagate model options correctly in ARS / ES, to action dist… Oct 1, 2018
java Add return value for recontruction RPC. (#3493) Dec 9, 2018
python [rllib] Learner should not see clipped actions (#3496) Dec 10, 2018
site Update Gemfile Jekyll version (#3140) Oct 26, 2018
src/ray Add option to evict keys LRU from the sharded redis tables (#3499) Dec 9, 2018
test [rllib] Learner should not see clipped actions (#3496) Dec 10, 2018
thirdparty/scripts Ship Modin with Ray. (#3109) Nov 29, 2018
.clang-format Remove legacy Ray code. (#3121) Oct 26, 2018
.gitignore Ship Modin with Ray. (#3109) Nov 29, 2018
.style.yapf YAPF, take 3 (#2098) May 19, 2018
.travis.yml Experimental asyncio support (#2015) Dec 7, 2018
CMakeLists.txt UI changes, fix the task timeline and add the object transfer timelin… Nov 25, 2018
CONTRIBUTING.rst Replace special single quote with regular single quote. (#1693) Mar 11, 2018
LICENSE [rllib] add augmented random search (#2714) Aug 25, 2018
README.rst [docs] Switch docs to use rllib train instead of train.py Dec 5, 2018
build-docker.sh adding -x flag for better debugging during builds (#1079) Oct 4, 2017
build.sh enable incremental builds (#3292) Nov 13, 2018
pylintrc adding pylint (#233) Jul 8, 2016
scripts Improve yapf speed and document its usage (#2160) Jun 6, 2018
setup_thirdparty.sh update ray cmake build process (#2853) Sep 12, 2018


https://travis-ci.com/ray-project/ray.svg?branch=master https://readthedocs.org/projects/ray/badge/?version=latest

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():
    return 1

results = [f() for i in range(4)]
# Execute f in parallel.

def f():
    return 1

results = ray.get([f.remote() for i in range(4)])

Ray comes with libraries that accelerate deep learning and reinforcement learning development:


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

Getting Involved