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This tests the case in which a worker is blocked in a call to ray.get or ray.wait, and then the worker dies. Then later, the object that the worker was waiting for becomes available. We need to make sure not to try to send a message to the dead worker and then die. Related to #2790.
Implement object table notification subscriptions and switch to using Redis modules for object table. (#134)
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
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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