mirror of
https://github.com/wassname/ray.git
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Kristian Hartikainen
9e0192bc0b
* Change the log syncing behavior * fix up abstractions for syncer * Finished checkpoint syncing * Code * Set of changes to get things running * Fixes for log syncing * Fix parts * Lint and other fixes * fix some test * Remove extra parsing functionality * some test fixes * Fix up cloud syncing * Another thing to do * Fix up tests and local sync Changes LogSync into a mixin, and adds tests for different functionalities. * Fix up tests, start on local migration * fix distributed migrations * comments * formatting * Better checkpoint directory handling * fix tests * fix tests * fix click * comments * formatting comments * formatting and comments * sync function deprecations * syncfunction * Add documentation for Syncing and Uploading * nit * BaseSyncer as base for Mixin in edge case * more docs * clean up assertions * validate * nit * Update test_cluster.py * betterdoc * Update tune-usage.rst * cleanup * nit
.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
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:target: https://travis-ci.com/ray-project/ray
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:target: http://ray.readthedocs.io/en/latest/?badge=latest
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|
**Ray is a fast and simple framework for building and running distributed applications.**
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
----------------
- `ray-dev@googlegroups.com`_: For discussions about development or any general
questions.
- `StackOverflow`_: For questions about how to use Ray.
- `GitHub Issues`_: For reporting bugs and feature requests.
- `Pull Requests`_: For submitting code contributions.
.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
.. _`GitHub Issues`: https://github.com/ray-project/ray/issues
.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
.. _`Pull Requests`: https://github.com/ray-project/ray/pulls