Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/ray-project/tutorial


https://github.com/ray-project/tutorial

Last synced: 8 months ago
JSON representation

Awesome Lists containing this project

README 

          
Ray Tutorial

============



**NOTE**: These sets of tutorials have been **deprecated**. A portion of their modules have

been incorporated into the new Anyscale Academy tutorials at https://github.com/anyscale/academy.



Try Ray on Google Colab

-----------------------



Try the Ray tutorials online using Google Colab:



- `Remote Functions`_

- `Remote Actors`_

- `In-Order Task Processing`_

- `Reinforcement Learning with RLlib`_



.. _`Remote Functions`: https://colab.research.google.com/github/ray-project/tutorial/blob/master/exercises/colab01-03.ipynb

.. _`Remote Actors`: https://colab.research.google.com/github/ray-project/tutorial/blob/master/exercises/colab04-05.ipynb

.. _`In-Order Task Processing`: https://colab.research.google.com/github/ray-project/tutorial/blob/master/exercises/colab06-07.ipynb

.. _`Reinforcement Learning with RLlib`: https://colab.research.google.com/github/ray-project/tutorial/blob/master/rllib_exercises/rllib_colab.ipynb



Try Tune on Google Colab

------------------------



Tuning hyperparameters is often the most expensive part of the machine learning workflow. `Ray Tune `_ is built to address this, demonstrating an efficient and scalable solution for this pain point.



`Exercise 1 `_ covers basics of using Tune - creating your first training function and using Tune. This tutorial uses Keras.



.. raw:: html



    

    Tune Tutorial

    



`Exercise 2 `_ covers Search algorithms and Trial Schedulers. This tutorial uses PyTorch.



.. raw:: html



    

    Tune Tutorial

    



`Exercise 3 `_  covers using Population-Based Training (PBT) and uses the advanced Trainable API with save and restore functions and checkpointing.



.. raw:: html



    

    Tune Tutorial

    



Try Ray on Binder

-----------------



Try the Ray tutorials online on `Binder`_. Note that Binder will use very small

machines, so the degree of parallelism will be limited.



.. _`Binder`: https://mybinder.org/v2/gh/ray-project/tutorial/master?urlpath=lab



Local Setup

-----------



1. Make sure you have Python installed (we recommend using the `Anaconda Python

   distribution`_). Ray works with both Python 2 and Python 3. If you are unsure

   which to use, then use Python 3.



   **If not using conda**, continue to step 2.



   **If using conda**, you can then run the following commands and skip the next 4 steps:



   .. code-block:: bash



       git clone https://github.com/ray-project/tutorial

       cd tutorial

       conda env create -f environment.yml

       conda activate ray-tutorial



2. **Install Jupyter** with ``pip install jupyter``. Verify that you can start

   Jupyter lab with the command ``jupyter-lab`` or ``jupyter-notebook``.



3. **Install Ray** by running ``pip install -U ray``. Verify that you can run



    .. code-block:: bash



      import ray

      ray.init()



   in a Python interpreter.



4. Clone the tutorial repository with



    .. code-block:: bash



      git clone https://github.com/ray-project/tutorial.git



5. Install the additional dependencies.



   Either install them from the given requirements.txt



    .. code-block:: bash

      pip install -r requirements.txt



   Or install them manually



    .. code-block:: bash



      pip install modin

      pip install tensorflow

      pip install gym

      pip install scipy

      pip install opencv-python

      pip install bokeh

      pip install ipywidgets==6.0.0

      pip install keras



   Verify that you can run ``import tensorflow`` and ``import gym`` in a Python

   interpreter.



   **Note:** If you have trouble installing these Python modules, note that

   almost all of the exercises can be done without them.



6. If you want to run the pong exercise (in `rl_exercises/rl_exercise05.ipynb`),

   you will need to do `pip install utilities/pong_py`.



Exercises

---------



Each file ``exercises/exercise*.ipynb`` is a separate exercise. They can be

opened in Jupyter lab by running the following commands.



.. code-block:: bash



  cd tutorial/exercises

  jupyter-lab



If you don't have `jupyter-lab`, try `jupyter-notebook`. If it asks for a password, just hit enter.



Instructions are written in each file. To do each exercise, first run all of

the cells in Jupyter lab. Then modify the ones that need to be modified

in order to prevent any exceptions from being raised. Throughout these

exercises, you may find the `Ray documentation`_ helpful.



**Exercise 1:** Define a remote function, and execute multiple remote functions

in parallel.



**Exercise 2:** Execute remote functions in parallel with some dependencies.



**Exercise 3:** Call remote functions from within remote functions.



**Exercise 4:** Use actors to share state between tasks. See the documentation

on `using actors`_.



**Exercise 5:** Pass actor handles to tasks so that multiple tasks can invoke

methods on the same actor.



**Exercise 6:** Use ``ray.wait`` to ignore stragglers. See the

`documentation for wait`_.



**Exercise 7:** Use ``ray.wait`` to process tasks in the order that they finish.

See the `documentation for wait`_.



**Exercise 8:** Use ``ray.put`` to avoid serializing and copying the same

object into shared memory multiple times.



**Exercise 9:** Specify that an actor requires some GPUs. For a complete

example that does something similar, you may want to see the `ResNet example`_.



**Exercise 10:** Specify that a remote function requires certain custom

resources. See the documentation on `custom resources`_.



**Exercise 11:** Extract neural network weights from an actor on one process,

and set them in another actor. You may want to read the documentation on

`using Ray with TensorFlow`_.



**Exercise 12:** Pass object IDs into tasks to construct dependencies between

tasks and perform a tree reduce.



.. _`Anaconda Python distribution`: https://www.continuum.io/downloads

.. _`Ray documentation`: https://ray.readthedocs.io/en/latest/?badge=latest

.. _`documentation for wait`: https://ray.readthedocs.io/en/latest/api.html#ray.wait

.. _`using actors`: https://ray.readthedocs.io/en/latest/actors.html

.. _`using Ray with TensorFlow`: https://ray.readthedocs.io/en/latest/using-ray-with-tensorflow.html

.. _`ResNet example`: https://ray.readthedocs.io/en/latest/example-resnet.html

.. _`custom resources`: https://ray.readthedocs.io/en/latest/resources.html#custom-resources



More In-Depth Examples

----------------------



**Sharded Parameter Server:** This exercise involves implementing a parameter

server as a Ray actor, implementing a simple asynchronous distributed training

algorithm, and sharding the parameter server to improve throughput.



**Speed Up Pandas:** This exercise involves using `Modin`_ to speed up your

pandas workloads.



**MapReduce:** This exercise shows how to implement a toy version of the

MapReduce system on top of Ray.



.. _`Modin`: https://modin.readthedocs.io/en/latest/



RL Exercises

------------



The exercises in ``rl_exercises/rl_exercise*.ipynb`` should be done in order.

They can be opened in Jupyter lab by running the following commands.



.. code-block:: bash



  cd tutorial/rl_exercises

  jupyter-lab



**Exercise 1:** Introduction to Markov Decision Processes.



**Exercise 2:** Derivative free optimization.



**Exercise 3:** Introduction to proximal policy optimization (PPO).



**Exercise 4:** Introduction to asynchronous advantage actor-critic (A3C).



**Exercise 5:** Train a policy to play pong using RLlib. Deploy it using actors,

and play against the trained policy.