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.
Install Jupyter with
pip install jupyter
. Verify that you can start a Jupyter notebook with the commandjupyter-notebook
.Install Ray by running
pip install ray
. Verify that you can runimport ray ray.init()
in a Python interpreter.
Clone the tutorial repository with
git clone https://github.com/ray-project/tutorial.git
Install the following additional dependencies.
pip install tensorflow pip install gym pip install scipy pip install smart_open pip install opencv-python pip install bokeh pip install ipywidgets==6.0.0
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.
Each file exercises/exercise*.ipynb
is a separate exercise. They can be
opened in a Jupyter notebook by running the following commands.
cd tutorial/exercises
jupyter-notebook
Instructions are written in each file. To do each exercise, first run all of the cells in the Jupyter notebook. 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: Pass object IDs into tasks to construct dependencies between tasks.
Exercise 4: Call remote functions from within remote functions.
Exercise 5: Use ray.wait
to ignore stragglers. See the
documentation for wait.
Exercise 6: Use ray.wait
to process tasks in the order that they finish.
See the documentation for wait.
Exercise 7: Use actors to share state between tasks. See the documentation on using actors.
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: 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.
Each file in rl_exercises/rl_exercise*.ipynb
is a separate Jupyter notebook.
These exercises should be done in order. They can be opened in a Jupyter
notebook by running the following commands.
cd tutorial/rl_exercises
jupyter-notebook