Philipp MoritzandRobert Nishihara c24c07613c [rllib] unify writing performance metrics and make it queryable (#708)
* write config to s3

* add train file

* write performance to S3

* writing needs to be fixed, replacing result.json at the moment

* update

* add experiment_id

* more logging and example queries

* update

* add info

* fill in other algorithms

* fix linting

* convert readme to rst

* fixes

* simplejson -> json

* make files executable

* edit README.rst

* unify storing logs in S3 and on local filesystem

* use 'info' entry in TrainingResult for algorithm specific info

* don't install smart_open with ray

* fixes

* linting fixes
2017-07-11 01:36:14 +02:00
2016-11-22 17:04:24 -08:00
2016-07-28 13:11:13 -07:00
2017-05-06 18:57:08 -07:00
2017-03-27 20:55:50 -07:00
2016-07-08 12:39:11 -07:00
2016-11-22 17:04:24 -08:00
2017-03-17 16:48:25 -07:00

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
S
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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