ray/doc/source/index.rst

Ray
===

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.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png

**Ray is a fast and simple framework for building and running distributed applications.**

.. tip:: Join our `community slack <https://forms.gle/9TSdDYUgxYs8SA9e8>`_ to discuss Ray!

Ray is packaged with the following libraries for accelerating machine learning workloads:

- `Tune`_: Scalable Hyperparameter Tuning
- `RLlib`_: Scalable Reinforcement Learning
- `Distributed Training <distributed_training.html>`__


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>`__.

.. _`on GitHub`: https://github.com/ray-project/ray


Quick Start
-----------

First, install Ray with: ``pip install ray``

.. code-block:: python

    # Execute Python functions in parallel.

    import ray
    ray.init()

    @ray.remote
    def f(x):
        return x * x

    futures = [f.remote(i) for i in range(4)]
    print(ray.get(futures))

To use Ray's actor model:

.. code-block:: python

    import ray
    ray.init()

    @ray.remote
    class Counter(object):
        def __init__(self):
            self.n = 0

        def increment(self):
            self.n += 1

        def read(self):
            return self.n

    counters = [Counter.remote() for i in range(4)]
    [c.increment.remote() for c in counters]
    futures = [c.read.remote() for c in counters]
    print(ray.get(futures))

Visit the `Walkthrough <walkthrough.html>`_ page a more comprehensive overview of Ray features.

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:

``ray submit [CLUSTER.YAML] example.py --start``

Read more about `launching clusters <autoscaling.html>`_.

Tune Quick Start
----------------

`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.

.. note::

    To run this example, you will need to install the following:

    .. code-block:: bash

        $ pip install ray torch torchvision filelock


This example runs a small grid search to train a CNN using PyTorch and Tune.

.. literalinclude:: ../../python/ray/tune/tests/example.py
   :language: python
   :start-after: __quick_start_begin__
   :end-before: __quick_start_end__

If TensorBoard is installed, automatically visualize all trial results:

.. code-block:: bash

    tensorboard --logdir ~/ray_results

.. _`Tune`: tune.html

RLlib Quick Start
-----------------

`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.

.. code-block:: bash

  pip install tensorflow  # or tensorflow-gpu
  pip install ray[rllib]  # also recommended: ray[debug]

.. code-block:: python

    import gym
    from gym.spaces import Discrete, Box
    from ray import tune

    class SimpleCorridor(gym.Env):
        def __init__(self, config):
            self.end_pos = config["corridor_length"]
            self.cur_pos = 0
            self.action_space = Discrete(2)
            self.observation_space = Box(0.0, self.end_pos, shape=(1, ))

        def reset(self):
            self.cur_pos = 0
            return [self.cur_pos]

        def step(self, action):
            if action == 0 and self.cur_pos > 0:
                self.cur_pos -= 1
            elif action == 1:
                self.cur_pos += 1
            done = self.cur_pos >= self.end_pos
            return [self.cur_pos], 1 if done else 0, done, {}

    tune.run(
        "PPO",
        config={
            "env": SimpleCorridor,
            "num_workers": 4,
            "env_config": {"corridor_length": 5}})

.. _`RLlib`: rllib.html


More Information
----------------

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!

Blog and Press
~~~~~~~~~~~~~~

  - `Modern Parallel and Distributed Python: A Quick Tutorial on Ray <https://towardsdatascience.com/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray-99f8d70369b8>`_
  - `Why Every Python Developer Will Love Ray <https://www.datanami.com/2019/11/05/why-every-python-developer-will-love-ray/>`_
  - `Meet Ray, the Real-Time Machine-Learning Replacement for Spark <https://www.datanami.com/2017/03/28/meet-ray-real-time-machine-learning-replacement-spark/>`_
  - `Ray: A Distributed System for AI (BAIR) <http://bair.berkeley.edu/blog/2018/01/09/ray/>`_
  - `10x Faster Parallel Python Without Python Multiprocessing <https://towardsdatascience.com/10x-faster-parallel-python-without-python-multiprocessing-e5017c93cce1>`_
  - `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>`_
  - `Ray Distributed AI Framework Curriculum <https://rise.cs.berkeley.edu/blog/ray-intel-curriculum/>`_
  - `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>`_
  - `First user tips for Ray <https://rise.cs.berkeley.edu/blog/ray-tips-for-first-time-users/>`_
  - [Tune] `Tune: a Python library for fast hyperparameter tuning at any scale <https://towardsdatascience.com/fast-hyperparameter-tuning-at-scale-d428223b081c>`_
  - [Tune] `Cutting edge hyperparameter tuning with Ray Tune <https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf>`_
  - [RLlib] `New Library Targets High Speed Reinforcement Learning <https://www.datanami.com/2018/02/01/rays-new-library-targets-high-speed-reinforcement-learning/>`_
  - [RLlib] `Scaling Multi Agent Reinforcement Learning <http://bair.berkeley.edu/blog/2018/12/12/rllib/>`_
  - [RLlib] `Functional RL with Keras and Tensorflow Eager <https://bair.berkeley.edu/blog/2019/10/14/functional-rl/>`_
  - [Modin] `How to Speed up Pandas by 4x with one line of code <https://www.kdnuggets.com/2019/11/speed-up-pandas-4x.html>`_
  - [Modin] `Quick Tip – Speed up Pandas using Modin <https://pythondata.com/quick-tip-speed-up-pandas-using-modin/>`_
  - `Ray Blog`_

.. _`Ray Blog`: https://ray-project.github.io/

Talks (Videos)
~~~~~~~~~~~~~~

 - `Programming at any Scale with Ray | SF Python Meetup Sept 2019 <https://www.youtube.com/watch?v=LfpHyIXBhlE>`_
 - `Ray for Reinforcement Learning | Data Council 2019 <https://www.youtube.com/watch?v=Ayc0ca150HI>`_
 - `Scaling Interactive Pandas Workflows with Modin <https://www.youtube.com/watch?v=-HjLd_3ahCw>`_
 - `Ray: A Distributed Execution Framework for AI | SciPy 2018 <https://www.youtube.com/watch?v=D_oz7E4v-U0>`_
 - `Ray: A Cluster Computing Engine for Reinforcement Learning Applications | Spark Summit <https://www.youtube.com/watch?v=xadZRRB_TeI>`_
 - `RLlib: Ray Reinforcement Learning Library | RISECamp 2018 <https://www.youtube.com/watch?v=eeRGORQthaQ>`_
 - `Enabling Composition in Distributed Reinforcement Learning | Spark Summit 2018 <https://www.youtube.com/watch?v=jAEPqjkjth4>`_
 - `Tune: Distributed Hyperparameter Search | RISECamp 2018 <https://www.youtube.com/watch?v=38Yd_dXW51Q>`_

Slides
~~~~~~
- `Talk given at UC Berkeley DS100 <https://docs.google.com/presentation/d/1sF5T_ePR9R6fAi2R6uxehHzXuieme63O2n_5i9m7mVE/edit?usp=sharing>`_
- `Talk given in October 2019 <https://docs.google.com/presentation/d/13K0JsogYQX3gUCGhmQ1PQ8HILwEDFysnq0cI2b88XbU/edit?usp=sharing>`_
- [Tune] `Talk given at RISECamp 2019 <https://docs.google.com/presentation/d/1v3IldXWrFNMK-vuONlSdEuM82fuGTrNUDuwtfx4axsQ/edit?usp=sharing>`_

Academic Papers
~~~~~~~~~~~~~~~

- `Ray paper`_
- `Ray HotOS paper`_
- `RLlib paper`_
- `Tune paper`_

.. _`Ray paper`: https://arxiv.org/abs/1712.05889
.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924
.. _`RLlib paper`: https://arxiv.org/abs/1712.09381
.. _`Tune paper`: https://arxiv.org/abs/1807.05118

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



.. toctree::
   :maxdepth: -1
   :caption: Installation

   installation.rst

.. toctree::
   :maxdepth: -1
   :caption: Ray Core

   using-ray.rst
   Tutorials <https://github.com/ray-project/tutorial>
   Examples <auto_examples/overview.rst>
   package-ref.rst

.. toctree::
   :maxdepth: -1
   :caption: Deploying Ray (Cluster Setup)

   autoscaling.rst
   using-ray-on-a-cluster.rst
   deploy-on-yarn.rst
   deploy-on-kubernetes.rst
   deploying-on-slurm.rst


.. toctree::
   :maxdepth: -1
   :caption: Tune

   tune.rst
   tune-tutorial.rst
   tune-usage.rst
   tune-distributed.rst
   tune-schedulers.rst
   tune-searchalg.rst
   tune-package-ref.rst
   tune-design.rst
   tune-examples.rst
   tune-contrib.rst

.. toctree::
   :maxdepth: -1
   :caption: RLlib

   rllib.rst
   rllib-toc.rst
   rllib-training.rst
   rllib-env.rst
   rllib-models.rst
   rllib-algorithms.rst
   rllib-offline.rst
   rllib-concepts.rst
   rllib-examples.rst
   rllib-dev.rst
   rllib-package-ref.rst

.. toctree::
   :maxdepth: -1
   :caption: Experimental

   distributed_training.rst
   tf_distributed_training.rst
   pandas_on_ray.rst
   projects.rst
   signals.rst
   async_api.rst
   serve.rst

.. toctree::
   :maxdepth: -1
   :caption: Development and Internals

   development.rst
   profiling.rst
   fault-tolerance.rst
   getting-involved.rst