mirror of
https://github.com/vale981/ray
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ebf4070d88
* Ray documentation - created new section 'Profiling for Ray Users', opposed to current Profiling section for Ray developers. Completed three sections 'A Basic Profiling Example', 'Timing Performance Using Python's Timestamps', and 'Profiling Using An External Profiler (Line_Profiler).' Left to-do two sections on CProfile and Ray Timeline Visualization.' * Ray documentation - Fixed rst codeblock linebreaks in 'User Profiling' * Ray documentation - For User Profiling, added section on cProfile * Ray documentation - For User Profiling, completed Ray Timeline Visualization section, including graphical images * Ray documentation - made User Profiling timeline image larger, minor wording edits * Ray documentation - minor wording edits to User Profiling * Ray documentation - User Profiling- fixed broken link * Minor wording changes requested by Philipp Moritz addressed. Still need to address (1) compressing the image files, (2) correcting ex 3 to not be remote, and (3) using cProfile on an actor * Ray documentation - For user-profiling.rst, revised example 3 to show a semi-parallelized example. Compressed timeline example image to be under 50 KB, removed view timeline GUI image. Updated timeline example image to reflect revised example 3. cProfile actor example left * Ray documentation - in user-profiling.rst, added a new example including actors in the cProfile section * Ray documentation - For user-profiling.rst, added section header for the Ray actor cProfile example * Update user-profiling.rst * Update user-profiling.rst * 4 space indentation * Update user-profiling.rst * Update user-profiling.rst * Update user-profiling.rst * corrections
138 lines
4.1 KiB
ReStructuredText
138 lines
4.1 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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tune-config.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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rllib-training.rst
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rllib-env.rst
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rllib-algorithms.rst
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rllib-models.rst
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rllib-concepts.rst
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rllib-package-ref.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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redis-memory-management.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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user-profiling.rst
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development.rst
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profiling.rst
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contact.rst
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