Alok Singh 42a9233e1d Improve yapf speed and document its usage (#2160)
* Allow yapf to lint individual files

* Add tip for using yapf

* Update doc

* Update script to autoformat changed py files

The new default is for the script to only updated changed files to encourage
using it as a pre-push hook. Travis still checks all since it's not that big an
increase to runtime.

* Exclude formatting thirdparty/autogen py files

* Symlink .travis -> scripts

Hidden directories may get glossed over otherwise.

* .travis -> scripts in docs

They are symlinks to the same thing, but `scripts` is more dev-friendly, while
`.travis` is really only for Travis CI.

* Document different yapf format functions

Most devs will only need `format_changed`, and this is run by default.
`format_changed` should be fast enough in most cases to work as a pre-commit
hook.

* Speed up yapf by only formatting changed files

* Update docs

1. Mention how yapf can be used a pre-commit hook
2. rm `bash`, script is executable

* Update yapf.sh

* Update development.rst

* Update yapf.sh

* Use bash arrays for correct argument splitting

Playing fast and loose with whitespace in bash is a terrible idea.

* Only format non-excluded by default

* Check changes against master

Normally, the remote is called `origin`, but naming it explicit

* Adding missing directory to `format_all`

* Cleanup YAPF code

Remove unused function and move around code to make clearer and adding lines
give cleaner diffs.

* Ensure correct files are autoformatted

* Fix cmd line arg splitting

Each arg has to be in its own set of quotes.

* Diff against mergebase

TIL there's a clean syntax for doing that, but it's too clever to belong in a
shell script.

We use `mapfile -t` to ensure no problems down the line with weird filenames.
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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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