mirror of
https://github.com/vale981/ray
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294 lines
10 KiB
ReStructuredText
294 lines
10 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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.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
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**Ray is a fast and simple framework for building and running distributed applications.**
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.. tip:: Join our `community slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`_ to discuss Ray!
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Ray is packaged with the following libraries for accelerating machine learning workloads:
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- `Tune`_: Scalable Hyperparameter Tuning
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- `RLlib`_: Scalable Reinforcement Learning
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- `Distributed Training <distributed_training.html>`__
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Star us on `on GitHub`_. You can also get started by visiting our `Tutorials <https://github.com/ray-project/tutorial>`_. For the latest wheels (nightlies), see the `installation page <installation.html>`__.
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.. _`on GitHub`: https://github.com/ray-project/ray
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Quick Start
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-----------
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First, install Ray with: ``pip install ray``
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.. code-block:: python
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# Execute Python functions in parallel.
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import ray
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ray.init()
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@ray.remote
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def f(x):
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return x * x
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futures = [f.remote(i) for i in range(4)]
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print(ray.get(futures))
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To use Ray's actor model:
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.. code-block:: python
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import ray
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ray.init()
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@ray.remote
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class Counter(object):
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def __init__(self):
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self.n = 0
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def increment(self):
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self.n += 1
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def read(self):
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return self.n
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counters = [Counter.remote() for i in range(4)]
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[c.increment.remote() for c in counters]
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futures = [c.read.remote() for c in counters]
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print(ray.get(futures))
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Visit the `Walkthrough <walkthrough.html>`_ page a more comprehensive overview of Ray features.
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Ray programs can run on a single machine, and can also seamlessly scale to large clusters. To execute the above Ray script in the cloud, just download `this configuration file <https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-full.yaml>`__, and run:
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``ray submit [CLUSTER.YAML] example.py --start``
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Read more about `launching clusters <autoscaling.html>`_.
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Tune Quick Start
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----------------
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`Tune`_ is a library for hyperparameter tuning at any scale. With Tune, you can launch a multi-node distributed hyperparameter sweep in less than 10 lines of code. Tune supports any deep learning framework, including PyTorch, TensorFlow, and Keras.
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.. note::
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To run this example, you will need to install the following:
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.. code-block:: bash
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$ pip install ray torch torchvision filelock
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This example runs a small grid search to train a CNN using PyTorch and Tune.
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.. literalinclude:: ../../python/ray/tune/tests/example.py
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:language: python
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:start-after: __quick_start_begin__
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:end-before: __quick_start_end__
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If TensorBoard is installed, automatically visualize all trial results:
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.. code-block:: bash
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tensorboard --logdir ~/ray_results
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.. _`Tune`: tune.html
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RLlib Quick Start
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-----------------
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`RLlib`_ is an open-source library for reinforcement learning built on top of Ray that offers both high scalability and a unified API for a variety of applications.
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.. code-block:: bash
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pip install tensorflow # or tensorflow-gpu
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pip install ray[rllib] # also recommended: ray[debug]
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.. code-block:: python
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import gym
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from gym.spaces import Discrete, Box
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from ray import tune
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class SimpleCorridor(gym.Env):
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def __init__(self, config):
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self.end_pos = config["corridor_length"]
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self.cur_pos = 0
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self.action_space = Discrete(2)
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self.observation_space = Box(0.0, self.end_pos, shape=(1, ))
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def reset(self):
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self.cur_pos = 0
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return [self.cur_pos]
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def step(self, action):
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if action == 0 and self.cur_pos > 0:
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self.cur_pos -= 1
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elif action == 1:
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self.cur_pos += 1
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done = self.cur_pos >= self.end_pos
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return [self.cur_pos], 1 if done else 0, done, {}
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tune.run(
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"PPO",
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config={
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"env": SimpleCorridor,
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"num_workers": 4,
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"env_config": {"corridor_length": 5}})
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.. _`RLlib`: rllib.html
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More Information
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----------------
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Here are some talks, papers, and press coverage involving Ray and its libraries. Please raise an issue if any of the below links are broken!
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Blog and Press
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~~~~~~~~~~~~~~
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- `Modern Parallel and Distributed Python: A Quick Tutorial on Ray <https://towardsdatascience.com/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray-99f8d70369b8>`_
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- `Why Every Python Developer Will Love Ray <https://www.datanami.com/2019/11/05/why-every-python-developer-will-love-ray/>`_
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- `Ray: A Distributed System for AI (BAIR) <http://bair.berkeley.edu/blog/2018/01/09/ray/>`_
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- `10x Faster Parallel Python Without Python Multiprocessing <https://towardsdatascience.com/10x-faster-parallel-python-without-python-multiprocessing-e5017c93cce1>`_
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- `Implementing A Parameter Server in 15 Lines of Python with Ray <https://ray-project.github.io/2018/07/15/parameter-server-in-fifteen-lines.html>`_
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- `Ray Distributed AI Framework Curriculum <https://rise.cs.berkeley.edu/blog/ray-intel-curriculum/>`_
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- `RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo <https://medium.com/riselab/rayonspark-running-emerging-ai-applications-on-big-data-clusters-with-ray-and-analytics-zoo-923e0136ed6a>`_
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- `First user tips for Ray <https://rise.cs.berkeley.edu/blog/ray-tips-for-first-time-users/>`_
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- [Tune] `Tune: a Python library for fast hyperparameter tuning at any scale <https://towardsdatascience.com/fast-hyperparameter-tuning-at-scale-d428223b081c>`_
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- [Tune] `Cutting edge hyperparameter tuning with Ray Tune <https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf>`_
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- [RLlib] `New Library Targets High Speed Reinforcement Learning <https://www.datanami.com/2018/02/01/rays-new-library-targets-high-speed-reinforcement-learning/>`_
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- [RLlib] `Scaling Multi Agent Reinforcement Learning <http://bair.berkeley.edu/blog/2018/12/12/rllib/>`_
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- [RLlib] `Functional RL with Keras and Tensorflow Eager <https://bair.berkeley.edu/blog/2019/10/14/functional-rl/>`_
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- [Modin] `How to Speed up Pandas by 4x with one line of code <https://www.kdnuggets.com/2019/11/speed-up-pandas-4x.html>`_
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- [Modin] `Quick Tip – Speed up Pandas using Modin <https://pythondata.com/quick-tip-speed-up-pandas-using-modin/>`_
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- `Ray Blog`_
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.. _`Ray Blog`: https://ray-project.github.io/
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Talks (Videos)
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~~~~~~~~~~~~~~
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- `Programming at any Scale with Ray | SF Python Meetup Sept 2019 <https://www.youtube.com/watch?v=LfpHyIXBhlE>`_
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- `Ray for Reinforcement Learning | Data Council 2019 <https://www.youtube.com/watch?v=Ayc0ca150HI>`_
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- `Scaling Interactive Pandas Workflows with Modin <https://www.youtube.com/watch?v=-HjLd_3ahCw>`_
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- `Ray: A Distributed Execution Framework for AI | SciPy 2018 <https://www.youtube.com/watch?v=D_oz7E4v-U0>`_
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- `Ray: A Cluster Computing Engine for Reinforcement Learning Applications | Spark Summit <https://www.youtube.com/watch?v=xadZRRB_TeI>`_
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- `RLlib: Ray Reinforcement Learning Library | RISECamp 2018 <https://www.youtube.com/watch?v=eeRGORQthaQ>`_
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- `Enabling Composition in Distributed Reinforcement Learning | Spark Summit 2018 <https://www.youtube.com/watch?v=jAEPqjkjth4>`_
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- `Tune: Distributed Hyperparameter Search | RISECamp 2018 <https://www.youtube.com/watch?v=38Yd_dXW51Q>`_
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Slides
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~~~~~~
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- `Talk given at UC Berkeley DS100 <https://docs.google.com/presentation/d/1sF5T_ePR9R6fAi2R6uxehHzXuieme63O2n_5i9m7mVE/edit?usp=sharing>`_
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- `Talk given in October 2019 <https://docs.google.com/presentation/d/13K0JsogYQX3gUCGhmQ1PQ8HILwEDFysnq0cI2b88XbU/edit?usp=sharing>`_
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- [Tune] `Talk given at RISECamp 2019 <https://docs.google.com/presentation/d/1v3IldXWrFNMK-vuONlSdEuM82fuGTrNUDuwtfx4axsQ/edit?usp=sharing>`_
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Academic Papers
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~~~~~~~~~~~~~~~
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- `Ray paper`_
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- `Ray HotOS paper`_
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- `RLlib paper`_
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- `Tune paper`_
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.. _`Ray paper`: https://arxiv.org/abs/1712.05889
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.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924
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.. _`RLlib paper`: https://arxiv.org/abs/1712.09381
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.. _`Tune paper`: https://arxiv.org/abs/1807.05118
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Getting Involved
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----------------
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- `ray-dev@googlegroups.com`_: For discussions about development or any general
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questions.
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- `StackOverflow`_: For questions about how to use Ray.
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- `GitHub Issues`_: For reporting bugs and feature requests.
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- `Pull Requests`_: For submitting code contributions.
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.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
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.. _`GitHub Issues`: https://github.com/ray-project/ray/issues
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.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
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.. _`Pull Requests`: https://github.com/ray-project/ray/pulls
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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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.. toctree::
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:maxdepth: -1
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:caption: Ray Core
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using-ray.rst
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configure.rst
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cluster-index.rst
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Tutorials <https://github.com/ray-project/tutorial>
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Examples <auto_examples/overview.rst>
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package-ref.rst
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.. toctree::
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:maxdepth: -1
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:caption: Tune
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tune.rst
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tune-tutorial.rst
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tune-advanced-tutorial.rst
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tune-usage.rst
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tune-distributed.rst
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tune-schedulers.rst
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tune-searchalg.rst
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tune-package-ref.rst
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tune-design.rst
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tune-examples.rst
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tune-contrib.rst
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.. toctree::
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:maxdepth: -1
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:caption: RLlib
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rllib.rst
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rllib-toc.rst
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rllib-training.rst
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rllib-env.rst
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rllib-models.rst
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rllib-algorithms.rst
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rllib-offline.rst
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rllib-concepts.rst
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rllib-examples.rst
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rllib-dev.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: Experimental
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distributed_training.rst
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tf_distributed_training.rst
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pandas_on_ray.rst
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projects.rst
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signals.rst
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async_api.rst
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serve.rst
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.. toctree::
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:maxdepth: -1
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:caption: Development and Internals
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development.rst
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profiling.rst
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fault-tolerance.rst
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getting-involved.rst
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