Anyscale Academy - Tutorials on Ray and Ray-based Libraries

© 2018-2022, Anyscale. All Rights Reserved

Welcome to the Anyscale Academy tutorials on Ray, the system for scaling your Python and ML applications from a laptop to a cluster.

This README tells you how to set up the tutorials and decide which content is best for your interests.

IMPORTANT NOTE: Modules and materials in this Academy have been updated and tested with Ray release 1.11 and supported Python 3.8. Older modules and tutorials have been retired into their respective retired directories. Hence, current material content will not match the Ray Summit 2020 tutorial.

Also, the setup process has been streamlined and Python dependencies and packages reduced.

Tips:

1. How to Contact us for more information.
2. Please report any issues or suggestions:
   • GitHub issues
   • The #tutorial channel on the Ray Slack Click here to join.
   • Email
3. If you are attending a live tutorial event, please follow the setup instructions provided in advance. It will take too long to do these instructions during the event.
4. For troubleshooting help, see the Troubleshooting, Tips, and Tricks notebook.

Tutorial Setup

Read the appropriate setup section that follows, then jump to Launching the Tutorials.

Setup for Anyscale Academy Hosted Sessions

There is nothing you need to setup, as the hosted environment will provide everything.

However, consider cloning or downloading a release of the tutorial notebooks and supporting software from the Academy repo, so you have a local copy of everything.

Tip: If you modify any notebooks during the hosted session, make sure you download them to save those changes.

Setup for a Local Machine

Note: Ray support for Windows is new. See these release notes for details.

Follow these instructions to use the tutorials. Note that some commands can take a while to finish.

Clone the Academy GitHub repo or download the latest release.

Now install the dependencies using either Anaconda or pip in your Python environment. We recommend using Anaconda.

Which Python Version?

Python 3.7+ is recommended. While Ray supports Python 3.8, some dependencies used in RLlib (the Ray reinforcement library) are not yet supported for 3.8, at the time of this writing.

Using Anaconda

If you need to install Anaconda, follow the instructions here. If you already have Anaconda installed, consider running conda upgrade --all.

Run the following commands in the root directory of this project. First, use conda to install the other dependencies, including Ray. Then activate the newly-created environment, named anyscale-academy. Finally, run the provided tools/fix-jupyter.sh script to install a graphing library extension in Jupyter Lab and perform other tasks.

conda create -n anyscale-academy python=3.8
conda activate anyscale-academy
python3 -m pip install -r requirements.txt
python3 -m pip install typing-extensions --upgrade
tools/fix-jupyter.sh

If you are using Windows, see the Fixing Jupyter Lab on Windows below for an alternative to using tools/fix-jupyter.sh.

Note that Python 3.8.is used. Ignore the similar-looking environment-docker.yml file. It is used to build Docker images.

You can delete the environment later with the following command:

conda env remove --name anyscale-academy

Using Pip

If you don't use Anaconda, you'll have to install these prerequisites first:

• Python 3.7:
  • See notes above about problems with 3.6 and 3.8. Don't use 3.8, but 3.6 may work for you.
  • The version of Python that comes with your operating system is probably too old. Try python --version to see what you have.
  • Installation instructions are at python.org.
• Pip: A recent version - consider upgrading if it's not the latest version.
  • Installation instructions are at pip.pypa.io.
• Node.js: Required for some of the Jupyter Lab graphics extensions we use.
  • Installation instructions are here.
• SWIG: Required for building dependencies.
  • Use the package manager of your system (e.g. apt on Ubuntu, brew on MacOS) to install, or download here.

Next, run the following commands in the root directory of this project to complete the setup. First, run the pip command to install the rest of the libraries required for these tutorials, including Ray. Then, run the provided script to install a graphing library extension in Jupyter Lab and perform other tasks.

python3 -m pip install -r requirements.txt
python3 -m pip install typing-extensions --upgrade
tools/fix-jupyter.sh

If you are using Windows, see the Fixing Jupyter Lab on Windows below for an alternative to using tools/fix-jupyter.sh.

Fixing Jupyter Lab on Windows

The tools/fix-jupyter.sh shell script runs the following commands. If you are using Windows, run them yourself as shown here.

First, see if the following pyviz extension is installed:

jupyter labextension check --installed "@pyviz/jupyterlab_pyviz"

If not, run this command:

jupyter labextension install "@pyviz/jupyterlab_pyviz"

Finally, run these commands:

jupyter labextension update --all
jupyter lab build
jupyter labextension list

Note: A Windows and Mac M1 version are experimental in Ray 1.8.

Launching the Tutorials

The previous steps installed Jupyter Lab, the notebook-based environment we'll use for all the lessons. To start run the following command in the project root directory:

jupyter lab

It should automatically open a browser window with the lab environment, but if not, the console output will show the URL you should use.

Tip: If you get an error that jupyter can't be found and you are using the Anaconda setup, make sure you activated the anyscale-academy environment, as shown above.

Which Tutorials Are Right for Me?

Here is a recommended reading list, based on your interests:

You Are...	Best Tutorials
A developer who is new to Ray	First, Ray Crash Course, then Advanced Ray
A developer who is experienced with Ray	Advanced Ray
A developer or data scientist interested in Reinforcement Learning	Ray RLlib
A developer or data scientist interested in Hyperparameter Tuning	Ray Tune
A developer or data scientist interested in distributed training models in PyTorch or TensorFlow	Ray Train(renamed from Ray SGD)
A developer or data scientist interested in model serving	Ray Serve
A DevOps engineer interested in managing Ray clusters	Ray Cluster Launcher (forthcoming)

See the Overview notebook for detailed, up-to-date descriptions for each tutorial and the lessons it contains.

Troubleshooting and Further Information

See the Troubleshooting, Tips, and Tricks notebook.

For details on the Ray API and the ML libraries, see the Ray Docs. For other information, see ray.io, including the Ray blog.

Ray started at U.C. Berkeley RISELab. It is now developed in artisanal, small batches at Anyscale.