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Welcome to the Anyscale Academy tutorials on Ray, the system for scaling your applications from a laptop to a cluster.
This README tells you how to set up the tutorials, it provides a quick overview of its contents, and it recommends which tutorials to go through depending on your interests.
Tips:
- Anyscale is developing a free, hosted version of these tutorials. Contact us for more information.
- This is an early release of these tutorials. Please report any issues:
- GitHub issues
- The #tutorial channel on the Ray Slack
- If you are attending a live tutorial event, please follow the setup instructions provided well in advance.
- For troubleshooting help, see the Troubleshooting, Tips, and Tricks notebook.
Read one of the following setup sections, as appropriate, then jump to Launching the Tutorials.
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. The README provides instruction for local setup, if desired.
Tip: Make sure you download the notebooks you modified during the session to save those changes.
WARNING: Ray does not currently run on Windows (we're close...). Contact Anyscale for a free hosted option.
If you are using MacOS or Linux, follow these instructions. Note that the setup 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.
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 a provided script to install a graphing library extension in Jupyter Lab and perform other tasks.
conda env create -f environment.yml
conda activate anyscale-academy
tools/fix-jupyter.shNote that Python 3.7 is used. While Ray supports Python 3.8, some dependencies used in RLlib (the Ray reinforcement library) are not yet supported for 3.8.
You can delete the environment later with the following command:
conda env remove --name anyscale-academy
If you don't use Anaconda, you'll have to install these prerequisites first:
- Python 3.6 or 3.7: While Ray supports Python 3.8, some dependencies used in
RLlib(the Ray reinforcement library) and other dependencies are not yet supported for 3.8.- The version of Python that comes with your operating system is probably too old. Try
python --versionto see what you have. - Installation instructions are at python.org.
- The version of Python that comes with your operating system is probably too old. Try
- 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.
Now run the following commands in the root directory of this project to complete the setup. First, run a pip command to install the rest of the libraries required for these tutorials, including Ray. Then, run a provided script to install a graphing library extension in Jupyter Lab and perform other tasks.
pip install -r requirements.txt
tools/fix-jupyter.shThe lessons will start a local Ray "cluster" (one node) on your machine. When you are finished with the tutorials, run the following command to shut down Ray:
ray stopAlso, when you have finished working through the tutorials, run the script tools/cleanup.sh, which prints temporary files, checkpoints, etc. that were created during the lessons. You might want to remove these as they can add up to 100s of MBs.
If you decide to delete all the files and directories listed, the following script will do it:
tools/cleanup.sh | while read x; do rm -rf $x; doneThe 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 labIt 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
jupytercan't be found and you are using the Anaconda setup, make sure you activated theanyscale-academyenvironment, as shown above.
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 (alpha release) |
| A developer or data scientist interested in Reinforcement Learning | Ray RLlib |
| A developer or data scientist interested in Hyperparameter Tuning | Ray Tune (forthcoming) |
| A developer or data scientist interested in accelerated model training with PyTorch | Ray SGD (forthcoming) |
| A developer or data scientist interested in model serving | Ray Serve (forthcoming) |
See the Overview notebook for detailed, up-to-date descriptions for each tutorial and the lessons it contains.
- We use Python 3.7, because a dependency of
RLlib,atari-py, doesn't have a wheel available for Python 3.8 at this time.
See the Troubleshooting, Tips, and Tricks notebook.
For details on the Ray API and the ML libraries, see the Ray Docs.