Introduction to the course

This is the website for the Inria Academy course on the skrub package: it contains all the material used for the course, including the datasets and exercises used during the session.

The material is available in two formats:

• a book, in book/, with more detailed explanations;
• a set of slides, in slides/, for instructor-led sessions.

Structure of the course

The course covers the main features of skrub, from data exploration to pipeline construction. While skrub DataOps are a major feature of the package, they are also expansive enough to deserve their own course, and as such only a short introduction will be given here.

Each chapter includes a section that describes how a specific feature may assist in building a machine learning pipeline, along with practical code examples, and a quiz at the end.

The course is split in sections, which group relevant material together. Each section is wrapped up by an exercise that covers what has been explained in the section. These exercises are stored in content/exercises as py files; running pixi run materials converts them to Jupyter notebooks in content/notebooks and puts the datasets in content/data, ready to be used.

Preparation and setup

Using Jupyterlite

The easiest way to work on the exercises is simply by using Jupyterlite: this will create a notebook interface directly from the browser that allows to run the exercises without needing to create a local environment.

Setting up a local environment

If you still want to work locally (for example, if you want to use your own IDE), you can clone the GitHub repo to have access to the exercises.

Finding the material

All the datasets are made available to the notebooks by cloning the repo; the exercises are stored in content/exercises.

Run

pixi run materials

to generate the notebooks in content/notebooks (using jupytext) and copy the datasets to content/data.

Using pixi

The easiest way to set up the environment is by installing and using pixi. Follow the platform-specific instructions in the link to install pixi, then open a terminal window in the root of the repository and run

pixi install
pixi run materials
pixi run lab

to create the environment, generate the notebooks and datasets, and start a Jupyter lab instance.

Using pip

Create and activate the environment:

python -m venv skrub-tutorial
source skrub-tutorial/bin/activate

Install the required dependencies using the requirements.txt file:

pip install -r requirements.txt

Start the Jupyter lab instance from the root of the repository:

jupyter lab

Building the website locally

The website (landing page, book, slides and the JupyterLite instance) is built with quarto and published on GitHub Pages by a GitHub Actions workflow on every push to main.

To rebuild the full website locally, run

pixi run render
pixi run serve

then open http://localhost:8000 in a browser. The rendered site is placed in the _build directory.