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* Only install ray python packages. * Add some __init__.py files. * Install Ray before building documentation. * Fix install-ray.sh. * Fix.
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
Ray
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.
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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