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609b5c1a4cfc01e01ce3c3965d9e88c1b072e32d
* Add manylinux setup * Switch to cp27mu * python/MANIFEST.in * Fix MANIFEST.in * Add build-wheel-manylinux1.sh * Update readme * Install correct version of numpy * Fix typo in README-manylinux1.md * Don't install cmake * Remove commented line from setup.py * Delete unused manylinux1.sh * Run setup.py bdist_wheel twice * Don't use package_data and MANIFEST.in. * Small aesthetic change. * Trigger build_ext in setup.py. * Remove nonexistent file from MANIFEST.in. * Manually copy files in MANIFEST.in to where Python expects them in order to prevent setup.py from having to be run twice. * Only run setup.py once when building wheels. * Aesthetic change to readme. * Copy generated flatbuffer Python files in build_ext. * Fix permission denied error by making sure to preserve executableness when copying files. * Remove unnecessary argument to setup.py. * Remove MANIFEST.in and move files to include into list in setup.py. * Fix numpy version when building wheels and replace rm with git clean.
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
Ray
===
.. 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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