TensorFlow welcomes documentation contributions—if you improve the documentation, you improve the TensorFlow library itself. Documentation on tensorflow.org falls into the following categories:
- API reference —The API reference docs are generated from docstrings in the TensorFlow source code.
- Narrative documentation —These are tutorials, guides, and other writing that's not part of the TensorFlow code. This documentation is in the tensorflow/docs GitHub repository.
- Community translations —These are guides and tutorials translated by the community. All community translations live in the tensorflow/docs repo.
Some TensorFlow projects keep documentation
source files near the code in a separate repository, usually in a
directory. See the project's
CONTRIBUTING.md file or contact the maintainer to
To participate in the TensorFlow docs community:
For details, use the TensorFlow API docs contributor guide. This shows you how to find the source file and edit the symbol's docstring. Many API reference pages on tensorflow.org include a link to the source file where the symbol is defined. Docstrings support Markdown and can be (approximately) previewed using any Markdown previewer.
Versions and branches
The site's API reference
version defaults to the latest stable binary—this matches the package installed
pip install tensorflow.
The default TensorFlow package is built from the stable branch
rX.x in the
repo. The reference documentation is generated from code comments
and docstrings in the source code for
Previous versions of the TensorFlow documentation are available as rX.x branches in the TensorFlow Docs repository. These branches are added when a new version is released.
Build API docs
tensorflow_docs package includes the generator for the
Python API reference docs. To
pip install git+https://github.com/tensorflow/docs
To generate the TensorFlow 2 reference docs, use the
git clone https://github.com/tensorflow/tensorflow tensorflow
pip install tensorflow
python generate2.py --output_dir=/tmp/out
TensorFlow guides and
tutorials are written as
files and interactive
Jupyter notebooks. Notebooks
can be run in your browser using
The narrative docs on tensorflow.org are built
master branch. Older versions are available in GitHub on the
The easiest way to make straightforward documentation updates to Markdown files is to use GitHub's web-based file editor. Browse the tensorflow/docs repository to find the Markdown that roughly corresponds to the tensorflow.org URL structure. In the upper right corner of the file view, click the pencil icon to open the file editor. Edit the file and then submit a new pull request.
Set up a local Git repo
For multi-file edits or more complex updates, it's better to use a local Git workflow to create a pull request.
The following Git steps are only required the first time you set up a local project.
Fork the tensorflow/docs repo
On the tensorflow/docs GitHub page, click the Fork button to create your own repo copy under your GitHub account. Once forked, you're responsible for keeping your repo copy up-to-date with the upstream TensorFlow repo.
Clone your repo
Download a copy of your remote username/docs repo to your local machine. This is the working directory where you will make changes:
git clone email@example.com:username/docs
Add an upstream repo to keep up-to-date (optional)
To keep your local repository in sync with
tensorflow/docs, add an upstream
remote to download the latest changes.
Add a remote:
git remote add upstream firstname.lastname@example.org:tensorflow/docs.git# View remote repos
git remote -vorigin email@example.com:username/docs.git (fetch) origin firstname.lastname@example.org:username/docs.git (push) upstream email@example.com:tensorflow/docs.git (fetch) upstream firstname.lastname@example.org:tensorflow/docs.git (push)
git checkout master
git pull upstream master
git push# Push changes to your GitHub account (defaults to origin)
1. Create a new branch
After you update your repo from
tensorflow/docs, create a new branch from the
local master branch:
git checkout -b feature-name
git branch# List local branches master * feature-name
2. Make changes
Edit files in your favorite editor and please follow the TensorFlow documentation style guide.
Commit your file change:
# View changes
git status# See which files have changed
git diff# See changes within files
git add path/to/file.md
git commit -m "Your meaningful commit message for the change."
Add more commits, as necessary.
3. Create a pull request
Upload your local branch to your remote GitHub repo (github.com/username/docs):
After the push completes, a message may display a URL to automatically submit a pull request to the upstream repo. If not, go to the tensorflow/docs repo—or your own repo—and GitHub will prompt you to create a pull request.
Maintainers and other contributors will review your pull request. Please participate in the discussion and make the requested changes. When your pull request is approved, it will be merged into the upstream TensorFlow docs repo.
There is a separate publishing step to update tensorflow.org from the GitHub repo. Typically, changes are batched together and the site is updated on a regular cadence.
While it's possible to edit the notebook JSON file with GitHub's web-based file editor, it's not recommended since malformed JSON can corrupt the file. Make sure to test the notebook before submitting a pull request.
is a hosted notebook environment that makes it easy to edit—and run—notebook
documentation. Notebooks in GitHub are loaded in Google Colab by passing the
path to the Colab URL, for example,
the notebook located in GitHub here:
can be loaded into Google Colab at this URL: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/classification.ipynb
There is an
Open in Colab
Chrome extension that performs this URL substitution when browsing a notebook on
GitHub. This is useful when opening a notebook in your repo fork, because the
top buttons always link to the TensorFlow Docs
A notebook formatting tool makes Jupyter notebook source diffs consistent and
easier to review. Since notebook authoring environments differ with regards to
file output, indentation, metadata and other non-specified fields;
opinionated defaults with a preference for the TensorFlow docs Colab workflow.
To format a notebook, install the
docs notebook tools and run the
# Install the tensorflow-docs package: $ python3 -m pip install -U [--user] git+https://github.com/tensorflow/docs $ python3 -m tensorflow_docs.tools.nbfmt [options] notebook.ipynb [...]
For TensorFlow docs projects, notebooks without output cells are executed and
tested; notebooks with saved output cells are published as-is.
respects the notebook state and uses the
--remove_outputs option to explicitly
remove output cells.
To create a new notebook, copy and edit the TensorFlow docs notebook template.
Edit in Colab
Within the Google Colab environment, double-click cells to edit text and code blocks. Text cells use Markdown and should follow the TensorFlow docs style guide.
Download notebook files from Colab with File > Download .pynb. Commit this file to your local Git repo and send a pull request.
To create a new notebook, copy and edit the TensorFlow notebook template.
Instead of downloading a notebook file and using a local Git workflow, you can edit and update your forked GitHub repo directly from Google Colab:
- In your forked username/docs repo, use the GitHub web UI to create a new branch.
- Navigate to the notebook file to edit.
- Open the notebook in Google Colab: use the URL swap or the Open in Colab Chrome extension.
- Edit the notebook in Colab.
- Commit the changes to your repo from Colab with File > Save a copy in GitHub.... The save dialog should link to the appropriate repo and branch. Add a meaningful commit message.
- After saving, browse to your repo or the tensorflow/docs repo, GitHub should prompt you to create a pull request.
- The pull request is reviewed by maintainers.
The TensorFlow team works with the community and vendors to provide translations for tensorflow.org. Translations of notebooks and other technical content are located in the tensorflow/docs-l10n GitHub repo. Please submit pull requests through the TensorFlow GitLocalize project.
The English docs are the source-of-truth and translations should follow these guides as close as possible. That said, translations are written for the communities they serve. If the English terminology, phrasing, style, or tone does not translate to another language, please use a translation appropriate for the reader.
Language support is determined by a number of factors including—but not limited to—site metrics and demand, community support, English proficiency, audience preference, and other indicators. Since each supported language incurs a cost, unmaintained languages are removed. Support for new languages will be announced on the TensorFlow blog or Twitter.
If your preferred language is not supported, you are welcome to maintain a community fork for open source contributors. These are not published to tensorflow.org.