Adam Gleave 89460b8d11 autoscaler: count head node, don't kill below target (fixes #2317) (#2320)
Specifically, subtracts 1 from the target number of workers, taking into
account that the head node has some computational resources.

Do not kill an idle node if it would drop us below the target number of
nodes (in which case we just immediately relaunch).
2018-06-28 15:33:51 -07:00
2018-04-02 00:23:56 -07:00
2016-11-22 17:04:24 -08:00
2018-05-19 16:07:28 -07:00
2018-06-26 23:56:23 -07:00
2016-07-08 12:39:11 -07:00
2016-11-22 17:04:24 -08:00

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.
Readme Multiple Licenses 111 MiB
Languages
Python 56.6%
C++ 28.8%
Java 8.5%
TypeScript 1.7%
Starlark 1.4%
Other 2.8%