python-tutorial

Run the tutorial on your computer

You have two ways in which you can run the tutorial locally.

1. With a conda environment

0. Prerequisites

To run the tutorial locally, you should first install conda (or mamba).

It is also suggested that you have a recent version of git. Check out how to install git on your operating system.

1. Download the material

Go to the directory on your machine where you want to download the material and clone the repository:

git clone https://github.com/empa-scientific-it/python-tutorial

Alternatively, you can manually download a ZIP archive with the latest version of the material:

Download ZIP archive

Extract the archive in a directory of your choice.

2. Create a dedicated environment

Enter the tutorial folder with

cd /path/to/python-tutorial

If you have installed Anaconda, then you can use Anaconda Prompt to run the following commands.

You should now create a new environment with conda:

conda env create -f binder/environment.yml

Warning

If you are on Windows and using Command Prompt or the PowerShell, please make sure to adjust the paths in the commands above accordingly.

Then activate the environment with

conda activate python-tutorial

You can update the existing environment (that is, downloading the latest version of the packages) with:

conda env update -f binder/environment.yml

3. Launch the tutorial via Jupyter

Finally, launch JupyterLab with

jupyter lab

2. With Docker

Note

The following instructions are for Windows. With minor changes, the steps work on macOS or Linux as well.

1. Install Docker Desktop: First, you need to install Docker Desktop on your Windows machine. You can download it from the official Docker website: https://www.docker.com/products/docker-desktop.

2. Create a folder: Open File Explorer and create a new folder where you want to save the tutorial's materials. For example, you could create a folder called "python-tutorial" on your Desktop.

3. Open PowerShell: Once Docker Desktop is installed, open PowerShell on your Windows machine. You can do this by pressing the "Windows" key and typing "PowerShell" in the search bar.

4. Pull the Docker image: In PowerShell, run the following command to pull the Docker image:

docker pull ghcr.io/empa-scientific-it/python-tutorial:latest

Note

The latest tag points to the CPU-only variant of the image, which is optimized for size and compatibility. If you have a CUDA-compatible GPU and want to use GPU acceleration for PyTorch operations, you can use the CUDA-enabled variant by replacing latest with cuda:

docker pull ghcr.io/empa-scientific-it/python-tutorial:cuda

Important

Using the CUDA variant requires a NVIDIA GPU with compatible drivers properly installed and configured for Docker. See NVIDIA Container Toolkit for setup instructions.

5. Run the Docker container: Once the image is downloaded, run the following command to start a Docker container from the image:

docker run -p 8888:8888 --name python_tutorial -v /path/to/python-tutorial:/home/jovyan/python-tutorial ghcr.io/empa-scientific-it/python-tutorial:latest jupyter lab --ip 0.0.0.0 --no-browser

Note

If you pulled the CUDA variant, replace :latest with :cuda in the command above.

Replace /path/to/python-tutorial with the path to the folder you created in step 2, for example C:/Users/yourusername/Desktop/python-tutorial.

Note

The above command will mirror the content of your local folder (e.g., C:/Users/yourusername/Desktop/python-tutorial) to the ~/python-tutorial folder inside the container. In this way, every file or folder you copy or create into ~/python-tutorial will be saved on your machine, and will remain there even if you stop Docker.

6. Access the Jupyter Notebook: Open a web browser and navigate to http://localhost:8888/lab. You should see the Jupyter Notebook interface. Enter the token provided in the PowerShell console to access the notebook. Alternatively, you can directly click on the link that appears in the PowerShell after the container has started.

You can now use the Jupyter in the Docker container to run the python-tutorial. When you're done, you can stop the container by pressing Ctrl+C in the PowerShell console.

Note

If you want to restart the container, you can simply run the command docker container start python_tutorial.

Setup a local dev environment

If you prefer not to use Conda and/or Docker, you can set up a lightweight development environment using either "uv" (a faster alternative to pip) or traditional "pip". Both methods will install the development dependencies specified in the pyproject.toml file.

Both setups assume that you have already cloned the repository with git clone https://github.com/empa-scientific-it/python-tutorial. The commands below should be run from inside the tutorial directory.

Using uv (Recommended for speed)

uv is a fast, Python package installer and resolver written in Rust.

1. Install uv via pip or any other method:

   pip install uv

2. Create a virtual environment and install dev dependencies:

   # Create and activate a virtual environment
   uv venv
   # On Windows
   .venv\Scripts\activate
   # On macOS/Linux
   source .venv/bin/activate
   
   # Install dev dependencies
   uv pip install -e ".[dev]"

3. Launch JupyterLab:

   jupyter lab

Using pip

1. Create a virtual environment and install dev dependencies:

   # Create and activate a virtual environment
   python -m venv .venv
   # On Windows
   .venv\Scripts\activate
   # On macOS/Linux
   source .venv/bin/activate
   
   # Install dev dependencies
   pip install -e ".[dev]"

2. Launch JupyterLab:

   jupyter lab