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* Implement sharding in the Ray core * Single node Python modifications to do sharding * Do the sharding in redis.cc * Pipe num_redis_shards through start_ray.py and worker.py. * Use multiple redis shards in multinode tests. * first steps for sharding ray.global_state * Fix problem in multinode docker test. * fix runtest.py * fix some tests * fix redis shard startup * fix redis sharding * fix * fix bug introduced by the map-iterator being consumed * fix sharding bug * shard event table * update number of Redis clients to be 64K * Fix object table tests by flushing shards in between unit tests * Fix local scheduler tests * Documentation * Register shard locations in the primary shard * Add plasma unit tests back to build * lint * lint and fix build * Fix * Address Robert's comments * Refactor start_ray_processes to start Redis shard * lint * Fix global scheduler python tests * Fix redis module test * Fix plasma test * Fix component failure test * Fix local scheduler test * Fix runtest.py * Fix global scheduler test for python3 * Fix task_table_test_and_update bug, from actor task table submission race * Fix jenkins tests. * Retry Redis shard connections * Fix test cases * Convert database clients to DBClient struct * Fix race condition when subscribing to db client table * Remove unused lines, add APITest for sharded Ray * Fix * Fix memory leak * Suppress ReconstructionTests output * Suppress output for APITestSharded * Reissue task table add/update commands if initial command does not publish to any subscribers. * fix * Fix linting. * fix tests * fix linting * fix python test * 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
|
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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