mirror of
https://github.com/vale981/ray
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277 lines
6.9 KiB
ReStructuredText
277 lines
6.9 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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.. 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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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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Install Ray with: ``pip install ray``. For nightly wheels, see the `Installation page <installation.html>`__.
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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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Quick Start
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-----------
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Execute Python functions in parallel.
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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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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():
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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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- `Tutorial`_
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- `Blog`_
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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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.. _`Tutorial`: https://github.com/ray-project/tutorial
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.. _`Blog`: https://ray-project.github.io/
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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: Using Ray
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walkthrough.rst
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actors.rst
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using-ray-with-gpus.rst
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user-profiling.rst
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inspect.rst
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object-store.rst
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configure.rst
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memory-management.rst
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advanced.rst
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troubleshooting.rst
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package-ref.rst
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.. toctree::
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:maxdepth: -1
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:caption: Cluster Setup
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autoscaling.rst
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using-ray-on-a-cluster.rst
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deploy-on-kubernetes.rst
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deploying-on-slurm.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-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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.. 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-parameter-server.rst
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example-newsreader.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-streaming.rst
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using-ray-with-tensorflow.rst
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using-ray-with-pytorch.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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internals-overview.rst
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fault-tolerance.rst
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contrib.rst
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