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* Refactor ActorID, TaskID on the Java side. Left a TODO comment WIP for ObjectID ADD test Fix Add java part Fix Java test Fix Refine test. Enable test in CI * Extra a helper function. * Resolve TODOs * Fix Python CI * Fix Java lint * Update .travis.yml Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Address some comments. Address some comments. Add id_specification.rst Reanme id_specification.rst to id_specification.md typo Address zhijun's comments. Fix test Address comments. Fix lint Address comments * Fix test * Address comments. * Fix build error * Update src/ray/design_docs/id_specification.md Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Address comments * Update src/ray/common/id.h Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/common/id.h Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/common/id.h Co-Authored-By: Stephanie Wang <swang@cs.berkeley.edu> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Update src/ray/design_docs/id_specification.md Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Address comments. * Address comments. * Address comments. * Update C++ part to make sure task id is generated determantic * WIP * Fix core worker * Fix Java part * Fix comments. * Add Python side * Fix python * Address comments * Fix linting * Fix * Fix C++ linting * Add JobId() method to TaskID * Fix linting * Update src/ray/common/id.h Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Update java/api/src/main/java/org/ray/api/id/TaskId.java Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Update java/api/src/main/java/org/ray/api/id/TaskId.java Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Update java/api/src/main/java/org/ray/api/id/ActorId.java Co-Authored-By: Hao Chen <chenh1024@gmail.com> * Address comments * Add DriverTaskId embeding job id * Fix tests * Add python dor_fake_driver_id * Address comments and fix linting * Fix CI
[Bazel] Modifying WORKSPACE file, so that you can make the project used as a thirdparty project (#4711)
.. 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
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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_training.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
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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