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

Material for a Jupyter-based Python tutorial, with Introductory, Advanced, Libraries modules
https://github.com/empa-scientific-it/python-tutorial

jupyter-notebooks numpy python python-jupyter-notebooks python-pandas python-tutorials python4everybody scipy

Last synced: 2 days ago
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Material for a Jupyter-based Python tutorial, with Introductory, Advanced, Libraries modules

Awesome Lists containing this project

README 

          
# python-tutorial



[![Renku](https://renkulab.io/renku-badge.svg)](https://renkulab.io/v2/projects/empa-scientific-it/empa-it-python-tutorial/sessions/01JRT57GABCS15JB5NGNTQVTRB/start)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/empa-scientific-it/python-tutorial.git/main?labpath=00_index.ipynb)



[![Build Docker container](https://github.com/empa-scientific-it/python-tutorial/actions/workflows/docker-build.yml/badge.svg)](https://github.com/empa-scientific-it/python-tutorial/actions/workflows/docker-build.yml)



[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=593234387)



## 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](https://docs.conda.io/en/latest/miniconda.html) (or [mamba](https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html)).



It is also suggested that you have a recent version of `git`. Check out [how to install `git`](https://git-scm.com/book/en/v2/Getting-Started-Installing-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:



```console

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](https://github.com/empa-scientific-it/python-tutorial/archive/refs/heads/main.zip)



Extract the archive in a directory of your choice.



#### 2. Create a dedicated environment



Enter the tutorial folder with



```console

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`:



```console

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



```console

conda activate python-tutorial

```



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



```console

conda env update -f binder/environment.yml

```



#### 3. Launch the tutorial via Jupyter



Finally, launch JupyterLab with



```console

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:



```console

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`:

>

> ```console

> 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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) 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:



```console

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](https://github.com/astral-sh/uv) is a fast, Python package installer and resolver written in Rust.



1. Install uv via `pip` or [any other method](https://docs.astral.sh/uv/getting-started/installation/):

   ```console

   pip install uv

   ```



2. Create a virtual environment and install dev dependencies:

   ```console

   # 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:

   ```console

   jupyter lab

   ```



### Using `pip`



1. Create a virtual environment and install dev dependencies:

   ```console

   # 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:

   ```console

   jupyter lab

   ```