EOAS HPC tutorials

This website will eventually be a graduate education companion to the OCESE project for undergraduate teaching with jupyter notebooks.

The basic approach:

1. All material is presented using https://jupyterbook.org/intro.html
2. Each book can be run in a docker container installed on your own computer. These containers will be tested using continuous integration, if they don't work, it's a bug.
3. The tools to change and rebuild the jupyterbook are included in the container.

Examples

Problem solving with Python, Peter D. Kazarinoff, PhD

This is a fork of the git repo for the book: Problem Solving with Python 3.7 Edition by Peter D. Kazarinoff, PhD

If you like this book, please consider purchasing a hard copy version on Amazon: https://www.amazon.com/dp/1693405415

The formatted version: https://phaustin.github.io/Problem-Solving-with-Python-37-Edition/

To actually run the notebooks

1. Install docker

2. checkout the repo on the with_html branch to get the rendered book

git clone https://github.com/phaustin/Problem-Solving-with-Python-37-Edition.git
cd Problem-Solving-with-Python-37-Edition
git checkout with_html
docker pull phaustin/webserver:aug10
docker pull phaustin/user_notebook:aug11
docker-compose up

2. open firefox or chrome and in one tab open:

   localhost:8500
   

3. Now take a look at your local version of section 6.1.5. If you right-click on the rocketship and launch Jupyterhub in a new tab, you will be prompted for a password. Type "friend" (without the quotes) to start a live notebook for that page.

4. To stop and remove all processes, containers and images:

bash bringdown.sh
docker rmi $(docker images -q)

To appear

We'll be posting a series of jupyter books in the next 3 months that cover a range of topics relevant research computing. First up will be a multi-container book that demonstrates some xarray, dask, and joblib workflows. here is a rendered preliminary version