A system for parallel and distributed Python that unifies the ML ecosystem.
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Add docs for contributors. (#1191) Nov 10, 2017
.travis Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
cmake/Modules Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
dev Document the release process. (#2760) Aug 29, 2018
doc [sgd] Modify: add interface for model (#3458) Dec 13, 2018
docker Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
examples [rllib] Propagate model options correctly in ARS / ES, to action dist… Oct 1, 2018
java Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
python Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
site Update Gemfile Jekyll version (#3140) Oct 26, 2018
src/ray Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
test Update arrow to reduce plasma IPCs. (#3497) Dec 15, 2018
thirdparty/scripts Add a script to collect built thirdparty libs to avoid download and b… Dec 14, 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 [rllib] Run simple regressions tests for all algs in jenkins (#3498) Dec 12, 2018
CMakeLists.txt Convert the raylet client (the code in local_scheduler_client.cc) to … Dec 13, 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

README.rst

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():
    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:

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

Getting Involved