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 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
Tuning hyperparameters is often the most expensive part of the machine learning workflow. Ray Tune <http://tune.io>_ is built to address this, demonstrating an efficient and scalable solution for this pain point.
Exercise 1 <https://github.com/ray-project/tutorial/tree/master/tune_exercises/exercise_1_basics.ipynb>_ covers basics of using Tune - creating your first training function and using Tune. This tutorial uses Keras.
.. raw:: html
<a href="https://colab.research.google.com/github/ray-project/tutorial/blob/master/tune_exercises/exercise_1_basics.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Tune Tutorial"/>
</a>Exercise 2 <https://github.com/ray-project/tutorial/tree/master/tune_exercises/exercise_2_optimize.ipynb>_ covers Search algorithms and Trial Schedulers. This tutorial uses PyTorch.
.. raw:: html
<a href="https://colab.research.google.com/github/ray-project/tutorial/blob/master/tune_exercises/exercise_2_optimize.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Tune Tutorial"/>
</a>Exercise 3 <https://github.com/ray-project/tutorial/tree/master/tune_exercises/exercise_3_pbt.ipynb>_ covers using Population-Based Training (PBT) and uses the advanced Trainable API with save and restore functions and checkpointing.
.. raw:: html
<a href="https://colab.research.google.com/github/ray-project/tutorial/blob/master/tune_exercises/exercise_3_pbt.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Tune Tutorial"/>
</a>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
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
Install Jupyter with pip install jupyter. Verify that you can start
Jupyter lab with the command jupyter-lab or jupyter-notebook.
Install Ray by running pip install -U ray. Verify that you can run
.. code-block:: bash
import ray ray.init()
in a Python interpreter.
Clone the tutorial repository with
.. code-block:: bash
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.
If you want to run the pong exercise (in rl_exercises/rl_exercise05.ipynb),
you will need to do pip install utilities/pong_py.
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
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/
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.