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* wip * more work * fix apex * docs * apex doc * pool comment * clean up * make wrap stack pluggable * Mon Mar 12 21:45:50 PDT 2018 * clean up comment * table * Mon Mar 12 22:51:57 PDT 2018 * Mon Mar 12 22:53:05 PDT 2018 * Mon Mar 12 22:55:03 PDT 2018 * Mon Mar 12 22:56:18 PDT 2018 * Mon Mar 12 22:59:54 PDT 2018 * Update apex_optimizer.py * Update index.rst * Update README.rst * Update README.rst * comments * Wed Mar 14 19:01:02 PDT 2018
131 lines
3.9 KiB
ReStructuredText
131 lines
3.9 KiB
ReStructuredText
Ray
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===
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.. raw:: html
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<embed>
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<a href="https://github.com/ray-project/ray"><img style="position: absolute; top: 0; right: 0; border: 0;" src="https://camo.githubusercontent.com/365986a132ccd6a44c23a9169022c0b5c890c387/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f7265645f6161303030302e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_red_aa0000.png"></a>
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</embed>
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*Ray is a flexible, high-performance distributed execution framework.*
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Ray is easy to install: ``pip install ray``
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Example Use
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-----------
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+------------------------------------------------+----------------------------------------------------+
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| **Basic Python** | **Distributed with Ray** |
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+------------------------------------------------+----------------------------------------------------+
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|.. code-block:: python |.. code-block:: python |
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| # Execute f serially. | # Execute f in parallel. |
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| | @ray.remote |
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| def f(): | def f(): |
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| time.sleep(1) | time.sleep(1) |
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| return 1 | return 1 |
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| | ray.init() |
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| results = [f() for i in range(4)] | results = ray.get([f.remote() for i in range(4)]) |
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+------------------------------------------------+----------------------------------------------------+
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View the `codebase on GitHub`_.
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.. _`codebase on GitHub`: https://github.com/ray-project/ray
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Ray comes with libraries that accelerate deep learning and reinforcement learning development:
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- `Ray Tune`_: Hyperparameter Optimization Framework
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- `Ray RLlib`_: Scalable Reinforcement Learning
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.. _`Ray Tune`: tune.html
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.. _`Ray RLlib`: rllib.html
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.. toctree::
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:maxdepth: 1
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:caption: Installation
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installation.rst
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install-on-docker.rst
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installation-troubleshooting.rst
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.. toctree::
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:maxdepth: 1
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:caption: Getting Started
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tutorial.rst
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api.rst
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actors.rst
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using-ray-with-gpus.rst
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webui.rst
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.. toctree::
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:maxdepth: 1
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:caption: Ray Tune
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tune.rst
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hyperband.rst
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pbt.rst
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.. toctree::
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:maxdepth: 1
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:caption: Ray RLlib
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rllib.rst
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policy-optimizers.rst
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rllib-dev.rst
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.. toctree::
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:maxdepth: 1
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:caption: Pandas on Ray
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pandas_on_ray.rst
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.. toctree::
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:maxdepth: 1
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:caption: Examples
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example-rl-pong.rst
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example-policy-gradient.rst
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example-parameter-server.rst
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example-resnet.rst
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example-a3c.rst
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example-lbfgs.rst
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example-evolution-strategies.rst
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example-cython.rst
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example-streaming.rst
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using-ray-with-tensorflow.rst
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.. toctree::
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:maxdepth: 1
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:caption: Design
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internals-overview.rst
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serialization.rst
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fault-tolerance.rst
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plasma-object-store.rst
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resources.rst
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.. toctree::
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:maxdepth: 1
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:caption: Cluster Usage
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autoscaling.rst
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using-ray-on-a-cluster.rst
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using-ray-on-a-large-cluster.rst
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using-ray-and-docker-on-a-cluster.md
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.. toctree::
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:maxdepth: 1
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:caption: Help
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troubleshooting.rst
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development.rst
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profiling.rst
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contact.rst
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