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* Initial testing of checkpointing functions. * Save checkpoints in Redis. * Pipe checkpoint_interval through remote decorator. * Add a test. * Small cleanups. * Submit dummy tasks when reconstructing tasks before the most recent tasks so that we don't end up reconstructing the arguments for those tasks. * Remove old checkpoints to save space. * Fix linting.
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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