Contribute to the documentation¶
The fastai doc project is just getting underway! So now is a great time to get involved. Here are some thoughts and guidelines to help you get oriented…
Project goals and approach¶
The idea of this project is to create documentation that makes readers say “wow that’s the most fantastic documentation I’ve ever read”. So… no pressure. :) How do we do this? By taking the philosophies demonstrated in fast.ai’s courses and bringing them to the world of documentation. Here are a few guidelines to consider:
- Assume the reader is intelligent and interested
- Don’t assume the reader has any specific knowledge about the field you’re documenting
- If you need the reader to have some knowledge to understand your documentation, and there is some effective external resource they can learn from, point them there rather than trying to do it all yourself
- Use code to describe what’s going on where possible, not math
- Create a notebook demonstrating the ideas you’re documenting (include the notebook in this repo) and show examples from the notebook directly in your docs
- Use a top-down approach; that is, first explain what problem the code is meant to solve, and at a high level how it solves it, and then go deeper into the details once those concepts are well understood
- For common tasks, show full end-to-end examples of how to complete the task.
Use pictures, tables, analogies, and other explanatory devices (even embedded video!) wherever they can help the reader understand. Use hyperlinks liberally, both within these docs and to external resources.
We don’t want this detailed documentation to create clutter in the code, and we also don’t want to overwhelm the user when they just want a quick summary of what a method does. Therefore, docstrings should generally be limited to a single line. The python standard library is documented this way–for instance, the docstring for
re.compile() is the single line “Compile a regular expression pattern, returning a pattern object.” But the full documentation of the
re library on the python web site goes into detail about this method, how it’s used, and its relation to other parts of the library.
How to contribute¶
This documentation is build with sphinx, but that does not really have to bother you. Most of the documentation can be done in markwown, which is easy to read and write. If you want to incorporate docstrings you have to do it with a reStructuredText code block. Please go to the documentation, have a look at the
fastai.example_module in the navigation bar and compare it to the
example_module.md file in the docs source folder and you will see how easy it is to write the source for this documentation.
How to build locally¶
You can build the documentation locally if you want to see your changes. You can either do it in your fast.ai environment by additionally pip installing
sphinx, sphinx_rtd_theme, recommonmark, sphinx_markdown_tables or you can use a provided docker container with all dependencies. Since most users are using cloud services it is recommended to use the docker build method on your local machine. Make sure that you have installed docker and execute the command (adjust
~/PathTo/local/fastai to your local fast.ai clone):
docker run -it --rm -v ~/PathTo/local/fastai:/fastai kailicht/fastai:docs
It will take a bit longer the first time, because the image has to be pulled from dockerhub. The command mounts your local fast.ai copy and executes the build command inside the container. You’ll get the full status messages from sphinx. If the build was successful the output with the
index.html will be in the