ray-project / Tutorial
Labels
Projects that are alternatives of or similar to Tutorial
Ray Tutorial
See 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 <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 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
-
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 commandjupyter-lab
orjupyter-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
andimport 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 dopip 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.