PyTorch Docathon H1 2025!

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PyTorch Docathon H1 2025!

We are excited for you to participate in the PyTorch docathon. This year we have the following repositories participating:

• pytorch/pytorch
• pytorch/tutorials

The docathon starts on June 3 10 AM PST. Please do not work on tasks until then. We will continue accepting new submissions until 5 PM PST on June 15th.

Date and location

WHEN: The docathon starts on June 3 at 10 AM PST. Please do not work on tasks until then. We will continue accepting new submissions until 5 PM PST on June 16th.
WHERE: Virtual
WHAT: Issues with the docathon-h1-2025 label - will be posted on June 3rd.

Watch our intro video to learn more details about the event here.

Can everyone participate?

We encourage everyone to consider participating in the docathon but there are a few things we expect from the participants:

• You must have a GitHub account and know how to use Git and GitHub, how to submit or rebase your PR on the latest main branch, how to fork or clone the repo, how to view errors in the CI and troubleshoot. We reserve the right to reject incorrectly submitted PRs.
• You must be familiar with Python, the basics of Machine Learning, and have at least a basic knowledge of PyTorch. Familiarity with Sphinx, sphinx-gallery, and reStructuredText is a plus.

Before you start contributing make sure to read Linux Foundation Code of Conduct as well as the GitHub Code of Conduct.

What contributions are we looking for?

All issues for this docathon are tagged with the docathon-h1-2024 label. Please note that contributions that address other issues won't be counted. We are primarily looking for the following contributions:

• Documentation bug fixes
• Tutorial fixes and testing

NOTE: Due to the large number of RSVPs, the tasks are provided on a first come first serve basis — please don't hoard the tasks!

Difficulty Levels

The issues have three levels of difficulty: easy, medium, and advanced. If this is your first time contributing to PyTorch, we recommend that you start with an issue that is tagged as easy.

How to contribute to tutorials?

1. Read PyTorch Contributor Document for general guidelines on how the submission process works and overall style and voice.
2. Pick an issue that is labeled as docathon-h1-2025.
3. In the issue, add a comment with the text /assigntome. If the issue is already assigned, please find another issue to work on. We ask that you assign one issue at a time - we want to give everyone a fair chance to participate. When you are done with one issue and get it approved, you can assign another one to yourself and start working on it.
4. If you are submitting a new tutorial, use this template.
5. Fork or clone the PyTorch repository to your computer. For simple fixes, like incorrect URLs, you could use the GitHub UI as well.
6. Create a branch and work on the fix.
7. Test your fix by running the single tutorial locally. Don't run the whole build as it takes hours and requires a GPU. You can run one tutorial as a script python3 <tutorial-name.py> or GALLERY_PATTERN="neural_style_transfer_tutorial.py" make html
8. Make sure to pay attention to the PyTorch Docstring Guidelines as this will affect your PR.
9. After you fix all the issues, you are ready to submit your PR.

Note: In PyTorch, we enforce lint rules on code in order to help us catch common mistakes and enforce a greater degree of uniformity in our codebase than human reviewers can normally enforce. You can find more information regarding PyTorch lintrunner and how to run it here.

Submit Your PR

1. Submit your PR referencing the issue you've picked. For example:
   
2. If you have not yet, sign the Contributor License Agreement (CLA) - prompted as a check in the PR. We can't accept any PRs without a signed CLA.
3. Watch for any CI errors and fix as needed - all checks must pass successfully.
4. When the build is finished, you will see a preview link to preview your changes.
5. The reviewers might provide feedback that we expect you to address.
6. When all feedback is addressed and your PR is approved - one of the reviewers will merge your PR.

Can I partner with someone to work on an issue?

Unless you are working on a completely new tutorial from scratch, most of the issues should be possible to address on your own. If you decide to partner with someone, you can find someone to work with on our Slack channel by posting a free-form request to collaborate. One individual from the group can submit a PR referring others as co-authors by specifying their GitHub usernames in the commit message like this:

Co-authored-by: NAME <NAME@EXAMPLE.COM>
Co-authored-by: ANOTHER-NAME <ANOTHER-NAME@EXAMPLE.COM>

Depending on the complexity of the issue, we reserve the right to decline contributions from multiple co-authors for trivial issues like fixing formatting, broken links, or very small code changes. For all issues that are not new tutorials or examples, please, limit the number of co-authors to two.

Top contributors recognition

For all contributions addressing the docathon-h1-2025 issues merged to the main branch in the participating repos during the period from June 3 to June 15, 5PM PST, you will get a special PyTorch Docathon GitHub badge. The issues will be released on the first day of the docathon. The top contributors will receive additional recognition and will be featured in a PyTorch social media announcement. In addition to the main repo and tutorials, this year, we will explicitly recognize a top contributor in each participating library repository.

Questions?

You can find a lot of useful information in the The Ultimate Guide to PyTorch Contributions.
You can also post your questions in the docathon Discord Server.

cc svekars alanna