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Using MkDocs for technical reporting

In this post I will explain why MkDocs is well suited for writing technical reports on machine learning models and introduce relevant parts of the MkDocs ecosystem.

Introduction

Machine learning might be a sexy field, writing documentation for models is not. towardsdatascience.com hardly has any blogposts on writing documentation. Spending time on writing good documentation with your models is often overlooked.

In my work writing good technical documentation is an essential part of a machine learning project. The docs contain business context, list model performance metrics, document procedures around model updating and monitoring, explain important decisions, capture many crucially important considerations around model fairness, ethics, explainability, model limitations and much more. Our model documentation template contains guidelines that help ensure quality and consistency between projects, and promote best practices.

Writing documentation cannot be made more fun, but it can be made easier. There a lot of different tools and packages out there, and it can take a long time to find a good setup to create a smooth documentation workflow. In this post I'll discuss some alternatives and introduce you to MkDocs and some of the most useful tools in the MkDocs ecosystem for writing technical documentation.

MkDocs vs alternatives

A good solution for writing documentation should be:

  • Version controllable (flat files part of your git repo)
  • Easy to convert to HTML and PDF
  • Easy to learn (also for less-technical project members)
  • Easy to edit (also for less-technical project members)
  • Able to insert content dynamically
  • Python-based (easy to install)
  • Really good looking! 💃

MkDocs fits all these requirements, for but reference, there are some alternatives:

  • There are several solutions that use nbconvert to convert jupyter notebooks to HTML or PDF. fastpages creates a blog site from a directory of notebooks, and the newly rewritten jupyter-book is a great solution for writing an entire book from notebook(s). The notebook file format however is JSON-based and contains not only markdown, but also the code and the output. That makes it hard to collaborate on a notebook in a team through version control (f.e. reviewing changes).
  • Then there is Sphinx, a mature package initially for documenting python packages. It has a large eco-system of packages to extend sphinx, and can also support markdown via recommonmark. However I find it is harder to setup and learn if you're just looking to write simple documentation instead of documenting python objects.

MkDocs

MkDocs is a simple site generator that can create a website from a directory of markdown files. It's easy to extend through 1) themes, 2) plugins and 3) extensions to the markdown language. Because it uses simple markdown files, you can write docs in your IDE and use your normal git workflow. That keeps your code in sync with your docs, which definitely beats sending around lots of report_final22.docx files via email!

Because MkDocs offers such a large eco-system, it's easy to miss some of the best functionalities. The rest of the blog will give you a quick tour:

The bare basics

In your project directory, run:

pip install mkdocs
mkdocs new .

Which will create these files:

./
├── docs
   └── index.md
└── mkdocs.yml

Which you view using mkdocs build (creates a new site/ directory with your website), or using mkdocs serve (starts a local webserver).

The mkdocs.yml file is where you can customize and extend MkDocs. One setting I recommend changing is setting use_directory_urls to false. This ensures you can specify relative paths to images and your website navigation works using local files as well.

# mkdocs.yml
use_directory_urls : False

Adding a theme

There are several themes for mkdocs, but there is only one really killer theme for MkDocs: mkdocs-material. To enable it, pip install mkdocs-material and add these lines to your mkdocs.yml:

# mkdocs.yml
theme:
  name: material

The mkdocs-material theme documentation is very structured and offers many options for customizing the theme (including a dark mode!).

Adding markdown extensions

The basic markdown syntax can be expanded to include other use cases. The mkdocs-material theme already has styling support for the extensions in the pymdown-extensions package and has examples and guides for many of them. Make sure to consider enabling these extensions when writing technical reports:

For reference, there are many more packages that extend the markdown syntax and can be used with MkDocs.

Adding plugins

MkDocs is python based, and allows you to write plugins that execute scripts during many different points in the build process (where markdown is converted to HTML). There are a lot of useful plugins. I'd like to highlight five which I wrote specifically to make writing reproducible technical documents easier:

Some other very useful plugins to explore:

Conclusion & further reading

MkDocs enables writing elegant docs that live next to your source code, and is a great fit for writing technical reports in data science teams. With the theme, plugins and markdown extensions introduced, you should have a great place to get started. For more info, see: