mirror of
https://github.com/wassname/ray.git
synced 2026-07-06 01:59:56 +08:00
93d54110f8d3812a0dc009b92994c25ea3cd1f6d
This change ensures that Ray set up fault handlers only if it has not been enabled by other applications. Otherwise some applications could face strange issues when using Ray, and some unittests using xml runners will fail.
.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
.. 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
.. image:: https://img.shields.io/badge/pypi-0.6.1-blue.svg
:target: https://pypi.org/project/ray/
|
**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:
- `Tune`_: Hyperparameter Optimization Framework
- `RLlib`_: Scalable Reinforcement Learning
- `Distributed Training <http://ray.readthedocs.io/en/latest/distributed_sgd.html>`__
.. _`Tune`: http://ray.readthedocs.io/en/latest/tune.html
.. _`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
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.
Languages
Python
56.6%
C++
28.8%
Java
8.5%
TypeScript
1.7%
Starlark
1.4%
Other
2.8%