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* Initial conversion * Further changes * fixes * some changes * Fixes * Added data pipeline * Added updates to cifar * Currently borken need sep pr * Added test for retriving variables from an optimizer * Removed FlAG ref in environment variables * Added comments to test * Addressed comments * Added updates * Made further changes for tfutils * Fixed finalized bug * Removed ipython * Added accuracy printing * Temp commit * added fixes * changes * Added writing to file * Fixes for gpus * Cleaned up code * Temp commit * Gpu support fully implemented * Updated to use num_gpus for actors * Finished testing gpus implementation * Changed to be more in line with origin implementation * Updated test to use actors * Added support for cpu only systems * Now works with no cpus * Minor changes and some documentation.
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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