Eric Liang b948405532 [tune] clean up population based training prototype (#1478)
* patch up pbt

* Sat Jan 27 01:00:03 PST 2018

* Sat Jan 27 01:04:14 PST 2018

* Sat Jan 27 01:04:21 PST 2018

* Sat Jan 27 01:15:15 PST 2018

* Sat Jan 27 01:15:42 PST 2018

* Sat Jan 27 01:16:14 PST 2018

* Sat Jan 27 01:38:42 PST 2018

* Sat Jan 27 01:39:21 PST 2018

* add pbt

* Sat Jan 27 01:41:19 PST 2018

* Sat Jan 27 01:44:21 PST 2018

* Sat Jan 27 01:45:46 PST 2018

* Sat Jan 27 16:54:42 PST 2018

* Sat Jan 27 16:57:53 PST 2018

* clean up test

* Sat Jan 27 18:01:15 PST 2018

* Sat Jan 27 18:02:54 PST 2018

* Sat Jan 27 18:11:18 PST 2018

* Sat Jan 27 18:11:55 PST 2018

* Sat Jan 27 18:14:09 PST 2018

* review

* try out a ppo example

* some tweaks to ppo example

* add postprocess hook

* Sun Jan 28 15:00:40 PST 2018

* clean up custom explore fn

* Sun Jan 28 15:10:21 PST 2018

* Sun Jan 28 15:14:53 PST 2018

* Sun Jan 28 15:17:04 PST 2018

* Sun Jan 28 15:33:13 PST 2018

* Sun Jan 28 15:56:40 PST 2018

* Sun Jan 28 15:57:36 PST 2018

* Sun Jan 28 16:00:35 PST 2018

* Sun Jan 28 16:02:58 PST 2018

* Sun Jan 28 16:29:50 PST 2018

* Sun Jan 28 16:30:36 PST 2018

* Sun Jan 28 16:31:44 PST 2018

* improve tune doc

* concepts

* update humanoid

* Fri Feb  2 18:03:33 PST 2018

* fix example

* show error file
2018-02-02 23:03:12 -08:00
2017-11-30 16:24:34 -08:00
2016-11-22 17:04:24 -08:00
2016-07-28 13:11:13 -07:00
2016-07-08 12:39:11 -07:00
2016-11-22 17:04:24 -08:00

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.

Ray comes with libraries that accelerate deep learning and reinforcement learning development:

- `Ray Tune`_: Hyperparameter Optimization Framework
- `Ray RLlib`_: A Scalable Reinforcement Learning Library

.. _`Ray Tune`: http://ray.readthedocs.io/en/latest/tune.html
.. _`Ray RLlib`: http://ray.readthedocs.io/en/latest/rllib.html


Installation
------------

- Ray can be installed on Linux and Mac with ``pip install ray``.
- To build Ray from source, see the instructions for `Ubuntu`_ and `Mac`_.

.. _`Ubuntu`: http://ray.readthedocs.io/en/latest/install-on-ubuntu.html
.. _`Mac`: http://ray.readthedocs.io/en/latest/install-on-macosx.html


Example Program
---------------

+------------------------------------------------+----------------------------------------------+
| **Basic Python**                               | **Distributed with Ray**                     |
+------------------------------------------------+----------------------------------------------+
|.. code:: python                                |.. code-block:: python                        |
|                                                |                                              |
|  import time                                   |  import time                                 |
|                                                |  import ray                                  |
|                                                |                                              |
|                                                |  ray.init()                                  |
|                                                |                                              |
|                                                |  @ray.remote                                 |
|  def f():                                      |  def f():                                    |
|      time.sleep(1)                             |      time.sleep(1)                           |
|      return 1                                  |      return 1                                |
|                                                |                                              |
|  # Execute f serially.                         |  # Execute f in parallel.                    |
|  results = [f() for i in range(4)]             |  object_ids = [f.remote() for i in range(4)] |
|                                                |  results = ray.get(object_ids)               |
+------------------------------------------------+----------------------------------------------+


More Information
----------------

- `Documentation`_
- `Tutorial`_
- `Blog`_
- `Ray paper`_
- `Ray HotOS paper`_

.. _`Documentation`: http://ray.readthedocs.io/en/latest/index.html
.. _`Tutorial`: https://github.com/ray-project/tutorial
.. _`Blog`: https://ray-project.github.io/
.. _`Ray paper`: https://arxiv.org/abs/1712.05889
.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924

Getting Involved
----------------

- Ask questions on our mailing list `ray-dev@googlegroups.com`_.
- Please report bugs by submitting a `GitHub issue`_.
- Submit contributions using `pull requests`_.

.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
.. _`GitHub issue`: https://github.com/ray-project/ray/issues
.. _`pull requests`: https://github.com/ray-project/ray/pulls
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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