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Robert Nishihara 99583f5b08 Clean up rl_pong example. (#365)
* Clean up RL pong example.

* More troubleshooting instructions.

* Typo.

* Fix typo.
2017-03-11 21:16:36 -08:00
.travis Use flatbuffers for some messages from Redis. (#341) 2017-03-10 18:35:25 -08:00
cmake/Modules help cmake find right python interpreter on mac (#251) 2016-07-11 12:16:10 -07:00
doc Clean up rl_pong example. (#365) 2017-03-11 21:16:36 -08:00
docker Use flatbuffers for some messages from Redis. (#341) 2017-03-10 18:35:25 -08:00
examples Clean up rl_pong example. (#365) 2017-03-11 21:16:36 -08:00
python Disallow calling ray.put on an object ID. (#353) 2017-03-11 12:09:28 -08:00
scripts Use flatbuffers for some messages from Redis. (#341) 2017-03-10 18:35:25 -08:00
src Use flatbuffers for some messages from Redis. (#341) 2017-03-10 18:35:25 -08:00
test Disallow calling ray.put on an object ID. (#353) 2017-03-11 12:09:28 -08:00
vsprojects Windows compatibility (#57) 2016-11-22 17:04:24 -08:00
webui Error Messages - UI display (#360) 2017-03-11 18:43:06 -08:00
.clang-format Implement object table notification subscriptions and switch to using Redis modules for object table. (#134) 2016-12-18 18:19:02 -08:00
.editorconfig Update Windows support (#317) 2016-07-28 13:11:13 -07:00
.gitignore Only install ray python packages. (#330) 2017-03-01 23:34:44 -08:00
.travis.yml Properly mock ray submodules when building documentation. (#337) 2017-03-04 23:02:56 -08:00
build-docker.sh Docker Updates (#308) 2017-02-28 18:57:51 -08:00
build.sh Convert task_spec to flatbuffers (#255) 2017-03-05 02:05:02 -08:00
CMakeLists.txt Rename photon -> local scheduler. (#322) 2017-02-27 12:24:07 -08:00
LICENSE Change license to Apache 2 (#20) 2016-11-01 23:19:06 -07:00
pylintrc adding pylint (#233) 2016-07-08 12:39:11 -07:00
Ray.sln Windows compatibility (#57) 2016-11-22 17:04:24 -08:00
README.md Move documentation to ReadTheDocs. (#326) 2017-02-27 21:14:31 -08:00

Ray

Build Status Documentation Status

Ray is an experimental distributed execution engine. It is under development and not ready to be used.

The goal of Ray is to make it easy to write machine learning applications that run on a cluster while providing the development and debugging experience of working on a single machine.

View the documentation.