This is the package we use to build the documentation of our Hugging Face repos.
You can install from PyPi with
pip install hf-doc-builder
To install from source, clone this repository then
cd doc-builder
pip install -e .
To preview the docs, use the following command:
doc-builder preview {package_name} {path_to_docs}
For example:
doc-builder preview datasets ~/Desktop/datasets/docs/source/
**preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).
**preview command does not work with Windows.
To build the documentation of a given package, use the following command:
#Add --not_python_module if not building doc for a python lib
doc-builder build {package_name} {path_to_docs} --build_dir {build_dir}
For instance, here is how you can build the Datasets documentation (requires pip install datasets[dev]) if you have cloned the repo in ~/git/datasets:
doc-builder build datasets ~/git/datasets/docs/source --build_dir ~/tmp/test-build
This will generate MDX files that you can preview like any Markdown file in your favorite editor. To have a look at the documentation in HTML, you need to install node version 14 or higher. Then you can run (still with the example on Datasets)
doc-builder build datasets ~/git/datasets/docs/source --build_dir ~/tmp/test-build --html
which will build HTML files in ~/tmp/test-build. You can then inspect those files in your browser.
doc-builder can also automatically convert some of the documentation guides or tutorials into notebooks. This requires two steps:
[[open-in-colab]] in the tutorial for which you want to build a notebook--notebook_dir {path_to_notebook_folder} to the build command.You can write your docs in jupyter notebooks & use doc-builder to: turn jupyter notebooks into mdx files.
In some situations, such as course & tutorials, it makes more sense to write in jupyter notebooks (& use doc-builder converter) rather than writing in mdx files directly.
The process is:
build_main_documentation.yml & build_pr_documentation.yml enable the flag convert_notebooks: true.Moreover, you can locally convert .ipynb files into mdx files.
doc-builder notebook-to-mdx {path to notebook file or folder containing notebook files}
doc-builder provides templates for GitHub Actions, so you can build your documentation with every pull request, push to some branch etc. To use them in your project, simply create the following three files in the .github/workflows/ directory:
build_main_documentation.yml: responsible for building the docs for the main branch, releases etc.build_pr_documentation.yml: responsible for building the docs on each PR.upload_pr_documentation.yml: responsible for uploading the PR artifacts to the Hugging Face Hub.delete_doc_comment_trigger.yml: responsible for removing the comments from the HuggingFaceDocBuilder bot that provides a URL to the PR docs.Within each workflow, the main thing to include is a pointer from the uses field to the corresponding workflow in doc-builder. For example, this is what the PR workflow looks like in the datasets library:
name: Build PR Documentation
on:
pull_request:
concurrency:
group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
build:
uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main # Runs this doc-builder workflow
with:
commit_sha: ${{ github.event.pull_request.head.sha }}
pr_number: ${{ github.event.number }}
package: datasets # Replace this with your package name
Note the use of special arguments like pr_number and package under the with field. You can find the various options by inspecting each of the doc-builder workflow files.
doc-builder can also convert documentation that's been translated from the English source into one or more languages. To enable the conversion, the documentation directories should be structured as follows:
doc_folder
├── en
│ ├── _toctree.yml
│ ├── _redirects.yml
│ ...
└── es
├── _toctree.yml
├── _redirects.yml
...
Note that each language directory has its own table of contents file _toctree.yml and that all languages are arranged under a single doc_folder directory - see the course repo for an example. You can then build the individual language subsets as follows:
doc-builder build {package_name} {path_to_docs} --build_dir {build_dir} --language {lang_id}
To automatically build the documentation for all languages via the GitHub Actions templates, simply provide the languages argument to your workflow, with a space-separated list of the languages you wish to build, e.g. languages: en es.
You can optionally provide _redirects.yml for "old links". The yml file should look like:
how_to: getting_started
package_reference/classes: package_reference/main_classes
# old_local: new_local
If you are working on a fix or an update of the doc-builder tool itself, you will eventually want to test it in the CI of another repository (transformers, diffusers, courses, etc.). To do so you should set the doc_builder_revision argument in your workflow file to point to your branch. Here is an example of what it would look like in the transformers.js project:
jobs:
build:
uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@my-test-branch
with:
repo_owner: xenova
commit_sha: ${{ github.sha }}
pr_number: ${{ github.event.number }}
package: transformers.js
path_to_docs: transformers.js/docs/source
pre_command: cd transformers.js && npm install && npm run docs-api
additional_args: --not_python_module
doc_builder_revision: my-test-branch # <- add this line
Once the docs build is complete in your project, you can drop that change.
doc-builder expects Markdown so you should write any new documentation in ".mdx" files for tutorials, guides, API documentations. For docstrings, we follow the Google format with the main difference that you should use Markdown instead of restructured text (hopefully, that will be easier!)
Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names
and objects like True, None or any strings should usually be put in code.
Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
```
# first line of code
# second line
# etc
```
We follow the doctest syntax for the examples to automatically test the results stay consistent with the library.
Syntax:
[`XXXClass`] or [~`XXXClass`] // for class
[`XXXClass.method`] or [~`XXXClass.method`] // for method
Example: here & here (as used inside docstring).
When mentioning a class, function or method, it is recommended to use the following syntax for internal links so that our tool automatically adds a link to its documentation: [`XXXClass`] or [`function`]. This requires the class or function to be in the main package.
If you want to create a link to some internal class or function, you need to
provide its path. For instance, in the Transformers documentation [`file_utils.ModelOutput`] will create a link to the documentation of ModelOutput. This link will have file_utils.ModelOutput in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: [`~file_utils.ModelOutput`] will generate a link with ModelOutput in the description.
The same works for methods, so you can either use [`XXXClass.method`] or [~`XXXClass.method`].
Syntax:
[`XXXLibrary.XXXClass`] or [~`XXXLibrary.XXXClass`] // for class
[`XXXLibrary.XXXClass.method`] or [~`XXXLibrary.XXXClass.method`] // for method
Example: here linking object from accelerate inside transformers.
To write a block that you'd like to see highlighted as a note or warning, place your content between the following markers.
Syntax:
> [!TIP]
> Here is a tip. Go to this url [website](www.tip.com)
>
> Second line
or
<Tip>
Write your note here
</Tip>
Example: here
For warnings, change the introduction to:
Syntax:
> [!WARNING]
or
`<Tip warning={true}>`
Example: here
If your documentation has a block that is framework-dependent (PyTorch vs TensorFlow vs Flax), you can use the following syntax:
Syntax:
<frameworkcontent>
<pt>
PyTorch content goes here
</pt>
<tf>
TensorFlow content goes here
</tf>
<flax>
Flax content goes here
</flax>
</frameworkcontent>
Example: here
Note: all frameworks are optional (you can write a PyTorch-only block for instance) and the order does not matter.
Show alternatives (let's say code blocks for different version of a library etc.) in a way where a user can select an option and see the selected option content:
Syntax:
<hfoptions id="some id">
<hfoption id="id for option 1">
{YOUR MARKDOWN}
</hfoption>
<hfoption id="id for option 2">
{YOUR MARKDOWN}
</hfoption>
... however many <hfoption> tags
</hfoptions>
Example: here
Note: for multiple <hfoptions id="some id"> in a same page, you may consider using same id so that when a user selects one option it affects all other hfoptions blocks. If you don't want this behaviour, use different ids.
Anchor links for markdown headings are generated automatically (with the following rule: 1. lowercase, 2. replace space with dash -, 3. strip [^a-z0-9-]):
Syntax:
## My awesome section
// the anchor link is: `my-awesome-section`
Example: here
Moreover, there is a way to customize the anchor link.
Syntax:
## My awesome section[[some-section]]
// the anchor link is: `some-section`
Example: here
Latex display mode. $$...$$
Syntax:
$$Y = X * \textbf{dequantize}(W); \text{quantize}(W)$$
Example: here
Latex inline mode. \\( ... )\\
Syntax:
\\( Y = X * \textbf{dequantize}(W); \text{quantize}(W) )\\
Example: here
Code blocks are written using a regular markdown syntax ```. However, there is a special flag you can put in your mdx files to change the wrapping style of the resulting html from overflow/scrollbar to wrap.
Syntax:
<!-- WRAP CODE BLOCKS -->
Example: here
To show the full documentation of any object of the python library you are documenting, use the [[autodoc]] marker.
Syntax:
[[autodoc]] SomeObject
Example: here
If the object is a class, this will include every public method of it that is documented. If for some reason you wish for a method not to be displayed in the documentation, you can do so by specifying which methods should be in the docs, here is an example:
Syntax:
[[autodoc]] XXXTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
Example: here
If you just want to add a method that is not documented (for instance magic method like __call__ are not documented
by default) you can put the list of methods to add in a list that contains all:
Syntax:
## XXXTokenizer
[[autodoc]] XXXTokenizer
- all
- __call__
Example: here
You can create a code-block by referencing a file excerpt with <literalinclude> (sphinx-inspired) syntax.
There should be json between <literalinclude> open & close tags.
Syntax:
<literalinclude>
{"path": "./data/convert_literalinclude_dummy.txt", # relative path
"language": "python", # defaults to " (empty str)
"start-after": "START python_import", # defaults to start of file
"end-before": "END python_import", # defaults to end of file
"dedent": 7 # defaults to 0
}
</literalinclude>
For a class or function description string, use markdown with all the custom syntax of doc-builder.
Example: here
Arguments of a function/class/method should be defined with the Args: (or Arguments: or Parameters:) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:
Syntax:
Args:
n_layers (`int`): The number of layers of the model.
Example: here
If the description is too long to fit in one line, another indentation is necessary before writing the description after the argument.
Syntax:
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
[`~PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
Example: here
You can check the full example it comes from here
If a class is similar to that of a dataclass but the parameters do not align to the available attributes of the class, such as in the below example, Attributes instance should be rewritten as **Attributes** in order to have the documentation properly render these. Otherwise it will assume that Attributes is synonymous to Parameters.
Syntax:
class SomeClass:
"""
Docstring
- Attributes:
+ **Attributes**:
- **attr_a** (`type_a`) -- Doc a
- **attr_b** (`type_b`) -- Doc b
"""
def __init__(self, param_a, param_b):
...
For optional arguments or arguments with defaults we follow the following syntax. Imagine we have a function with the following signature:
def my_function(x: str = None, a: float = 1):
then its documentation should look like this:
Syntax:
Args:
x (`str`, *optional*):
This argument controls ...
a (`float`, *optional*, defaults to 1):
This argument is used to ...
Example: here
Note that we always omit the "defaults to `None`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
however write as many lines as you want in the indented description (see the example above with input_ids).
If your argument has for type a class defined in the package, you can use the syntax we saw earlier to link to its documentation:
Args:
config ([`BertConfig`]):
Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
The return block should be introduced with the Returns: prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.
Here's an example for a single value return:
Syntax:
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
Example: here
Here's an example for tuple return, comprising several objects:
Syntax:
Returns:
`tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
- ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
- **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
Example: here
Similarly, Yields is also supported.
Syntax:
Yields:
`tuple[str, io.BufferedReader]`:
2-tuple (path_within_archive, file_object).
File object is opened in binary mode.
Example: here
You can also document Raises.
Syntax:
Args:
config ([`BertConfig`]):
Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Raises:
`pa.ArrowInvalidError`: if the arrow data casting fails
TypeError: if the target type is not supported according, e.g.
- point1
- point2
[`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError) if credentials are invalid
[`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError) if connection got lost
Returns:
`List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
Example: here
There are directives for Added, Changed, & Deprecated.
Syntax:
Args:
cache_dir (`str`, *optional*): Directory to cache data.
config_name (`str`, *optional*): Name of the dataset configuration.
It affects the data generated on disk: different configurations will have their own subdirectories and
versions.
If not provided, the default configuration is used (if it exists).
<Added version="2.3.0">
`name` was renamed to `config_name`.
</Added>
name (`str`): Configuration name for the dataset.
<Deprecated version="2.3.0">
Use `config_name` instead.
</Deprecated>
Example: here
We use svelte components for doc UI (Tip component, Docstring component, etc.).
Follow these steps to develop svelte locally:
doc-builder/kit/src/routes/_toctree.yml. Contents should be:- sections:
- local: index
title: Index page
title: Index page
doc-builder/kit/src/routes/index.mdx. Contents should be whatever you'd like to test. For example:<script lang="ts">
import Tip from "$lib/Tip.svelte";
import Youtube from "$lib/Youtube.svelte";
import Docstring from "$lib/Docstring.svelte";
import CodeBlock from "$lib/CodeBlock.svelte";
import CodeBlockFw from "$lib/CodeBlockFw.svelte";
</script>
<Tip>
[Here](https://myurl.com)
</Tip>
## Some heading
And some text [Here](https://myurl.com)
Physics is the natural science that studies matter,[a] its fundamental constituents, its motion and behavior through space and time, and the related entities of energy and force.[2] Physics is one of the most fundamental scientific disciplines, with its main goal being to understand how the universe behaves.[b][3][4][5] A scientist who specializes in the field of physics is called a physicist.
cd doc-builder/kit
npm ci
npm run dev -- --open
doc-builder/kit/src/lib for reference. The flow should be:
doc-builder/kit/src/libdoc-builder/kit/src/routes/index.mdxFAQs
Doc building utility
We found that hf-doc-builder demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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