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* write config to s3 * add train file * write performance to S3 * writing needs to be fixed, replacing result.json at the moment * update * add experiment_id * more logging and example queries * update * add info * fill in other algorithms * fix linting * convert readme to rst * fixes * simplejson -> json * make files executable * edit README.rst * unify storing logs in S3 and on local filesystem * use 'info' entry in TrainingResult for algorithm specific info * don't install smart_open with ray * fixes * linting fixes
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
Ray
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.. image:: https://travis-ci.org/ray-project/ray.svg?branch=master
:target: https://travis-ci.org/ray-project/ray
.. image:: https://readthedocs.org/projects/ray/badge/?version=latest
:target: http://ray.readthedocs.io/en/latest/?badge=latest
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Ray is a flexible, high-performance distributed execution framework.
View the `documentation`_.
.. _`documentation`: http://ray.readthedocs.io/en/latest/index.html
Description
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
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