mirror of
https://github.com/wassname/ray.git
synced 2026-06-27 19:32:11 +08:00
ae17ebd03289a35ae42c0eb893c6e301920f46f5
* Add map, reduce, merge_dtypes bug fixes Unify dtypes on DataFrame creation Formatting and comments Cache dtypes Fix bug in _merge_dtypes Fix bug Changed caching logic Fix dtypes issue in read_csv Invalidate dtypes cache when inserting column Simplify unifying dtypes and improve caching Fix typo Better caching of dtypes Fix merge conflicts Implemented some to_csv functions Support read_csv from buffers Expose date_range, NaT, Timedelta from pandas Add testing utils Redirect imports to Pandas Fix imports Fix read_csv when index_col is specified Update imports from Pandas Fix bugs Use util API Fix nasty bug Add missing import Don't distribute reading of compressed files Add test utilities for Pandas tests Add test for to_csv Add warnings Fix rebase artifacts * Fix rebase artifacts * Fix bugs in read_csv indexing * Fix bugs in read_csv * Fix bug for IndexMetadata with _length 1 Remove testing imports * Rebase artifacts and formatting * Start to_csv without CSV formatter * Fix bug in _map_partitions * Initial implementation for improved to_csv * Fix bug with insert * Bugfixes * Remove CSV Formatter * Formatting * Fix python2 bug * Fix additional python2 issue
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
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 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
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%