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* convert Ray to C++ * convert task_spec to flatbuffers * fix * it compiles * latest * tests are passing * task2 -> task * fix * fix * fix * fix * fix * linting * fix valgrind * upgrade flatbuffers * use debug mode for valgrind * fix naming and comments * downgrade flatbuffers * fix linting * reintroduce TaskSpec_free * rename TaskSpec -> TaskInfo * refactoring * linting
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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