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Contributing

The source files, which should be modified, are in the python_scripts directory. The notebooks are generated from these Python files with Jupytext.

If you are planning to contribute to this repo you should set-up your environment using the environment-dev.yml or requirements-dev.txt file.

Recommended workflow : Visual Studio Code

  • Download Visual Studio Code
  • Install the Jupyter Extension. This will allow you to run Python files in a cell by cell fashion
  • notebooks are generated through the Jupytext command-line interface (see below)

It may well be that alternative editors (e.g. PyCharm) allow you to have a similar workflow, the only feature you need is to be able to run a .py file in a cell-by-cell fashion with # %% cell markers.

Updating the notebooks

To update all the notebooks:

$ make

If you want to generate a single notebook, you can do something similar to this:

$ make notebooks/02_numerical_pipeline_scaling.ipynb

Alternative workflow within Jupyter

  • you can configure Jupyter to open .py files as notebooks (see below for instructions) though Jupytext
  • when saving the notebook inside Jupyter it will actually write to the .py file

In our experience, this workflow is less convenient (Visual Studio Code is a nicer developping environment) and also it tends to add some not very important (and different on everyone's machine) metadata changes in the .py file, for example about jupytext version, Jupyter kernel, Python version, etc ...

Setting up jupytext

When jupytext is properly connected to jupyter, the python files can be opened in jupyter and are directly displayed as notebooks

With jupyter notebook

Once jupytext is installed, run the following command:

jupyter serverextension enable jupytext

With jupyter lab

To make it work with "jupyter lab" (instead of "jupyter notebook"), you have to install nodejs (conda install nodejs works if you use conda). Then in jupyter lab you have to right click "Open with -> notebook" to open the python scripts with the notebook interface.

Useful make commands to generate derived files

Generating exercises from the solutions

make exercises

The convention in solution files:

  • using # solution comment inside a cell will remove all the content afterwards (including the # solution line) and replace it with # Write your code here.
  • using tags=["solution"] on the same line as the cell marker removes the whole cell, for example:
    • for Python cells: # %% tags=["solution"]
    • for Markdown cells: # %% [markdown] tags=["solution"]

Generating quizzes questions from the gitlab quizzes

To update the github quizzes (quizzes without solutions) from the gitlab quizzes (quizzes with solutions).

make quizzes

Generating full-index.md

full-index.md is useful when giving the course live. It makes it easier to guide people through the notebooks.

make full-index

JupyterBook

JupyterBook is the tool we use to generate our .github.io website from our .py and .md files (note than .ipynb files are not used in our JupyterBook setup).

make jupyter-book

Generated files are in jupyter-book/_build/html. To open the generated JupyterBook with Firefox:

firefox jupyter-book/_build/html/index.html

Note: for slides (and maybe a few other things), you need to start a local webserver otherwise there are issues with the way we do iframes etc ...

cd jupyter-book/_build/html/index.html && python -m http.server

and then open a browser at localhost:8000.

Troubleshooting sphinx failures

I could not find a built-in way to pass sphinx -P in JupyterBook to have pdb on failure. The simplest thing I found was to edit your jupyter_book package (in jupyter_book/sphinx.py) and use:

debug_args.pdb = True  # False by default

See https://github.com/executablebooks/jupyter-book/blob/18d52700b8636773f96631b3e187e38075557041/jupyter_book/sphinx.py#L87-L91 for the JupyterBook code.

API diagrams

We are using app.diagrams.net to create and update some diagrams in figures, notably the API ones.

To edit the diagrams: https://app.diagrams.net/#HINRIA%2Fscikit-learn-mooc%2Fmain%2Ffigures%2Fapi_diagram.drawio

All the diagrams are in the same project, you can use the tabs at the bottom (Google-sheet style).

Then each SVG needs to be exported individually (there could well be a better way, update this doc if you find it): File -> Export As -> SVG

New modal (SVG): Click Export

New modal (Save as):

  • make sure the export SVG name is correct
  • click Github and select (each time sorry ...) the github project, branch and folder
  • At the end it will ask you this file already exists do you want to override it: say yes
  • I think it asks you to tweak the commit message if you want

Once you have done this, the SVG should have been updated in the github repo. Make sure the github svg looks the way you want: https://github.com/INRIA/scikit-learn-mooc/tree/main/figures

Jupytext useful use cases

Get wrap-up quiz solutions code

You can convert the quiz .md file to .py with valid Python code. This is particularly useful to check quiz solutions or to try to debug user questions on the forum.

jupytext --to py jupyter-book/linear_models/linear_models_wrap_up_quiz.md

This creates jupyter-book/linear_models/linear_models_wrap_up_quiz.py with executable code.

Note: for this to work nicely with Jupytext you need to use python and not py in the block codes i.e.:

```python
```

and not:

```py
```

Workflow notes

See this.

Direct binder links to OVH, GESIS and GKE to trigger and cache builds