Daniel Ho d9eeaaf00a [tune] Fix bug in example where config hyperparameters were ignored (#2860)
A fix to an example for tune (`python/ray/tune/examples/pbt_tune_cifar10_with_keras.py`) where the hyperparameters for the optimizer, learning rate and decay, were not being passed into the optimizer. 

This means that the current optimizer uses default values for the hyperparameters no matter the config.
2018-09-12 09:17:56 -07:00
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Ray
===

.. image:: https://travis-ci.com/ray-project/ray.svg?branch=master
    :target: https://travis-ci.com/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 is easy to install: ``pip install ray``

Example Use
-----------

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


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

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

.. _`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 or to install the nightly versions, see the `installation documentation`_.

.. _`installation documentation`: http://ray.readthedocs.io/en/latest/installation.html

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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