~ PyTorch Docathon H2 2023 ~

[sekyondaMeta]

~ PyTorch Docathon H2 2023 ~

We have a large backlog of issues that we want to address and it's a great opportunity for you to start contributing to PyTorch. We have limited this docathon to the pytorch/tutorials and pytorch/pytorch repositories, so please work on the issues from these two repositories.

NOTE: This issue outlines the work in the pytorch/tutorials repo. If you would prefer to work on the PyTorch docstrings issues, please go to the pytorch/pytorch Docathon issue.

Date and location

WHEN: The docathon starts on November 1st 10 AM PST. Please do not work on tasks until then. We will continue accepting new submissions until 5 PM PST on November 12th.
WHERE: Virtual
WHAT: Issues with the docathon-h2-2023 label - will be posted on November 1st.

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

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. 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.

What contributions are we looking for?

All issues for this docathon are tagged with the docathon-h2-2023 label. Please note that contributions that address other issues won't be counted. We are primarily looking for the following contributions:

NOTE: Please avoid working on issues with intel, amd, and nvidia labels which are reserved for our partners.

• Bug fixes in the pytorch/tutorials repo tagged with the docathon-h2-2023 label - see the list repo.
• Docstring fixes in the pytorch/pytorch repo tagged with the docathon-h2-2023 label - see this list repo.

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 or medium.

How to contribute to tutorials?

1. Read pytorch/tutorials/CONTRIBUTING.md for general guidelines on how the submission process works and overall style and voice.

2. Pick an issue that is labeled as docathon-h2-2023.

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. After you fix all the issues, you are ready to submit your PR.

Submit Your PR

1. Submit your PR referencing the issue you've picked. For example:

2. Pick an issue that is labeled as docathon-h2-2023.
3. 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.
4. Watch for any errors and fix as needed - all checks must pass successfully.
5. Check the resulting HTML. The link to preview will be posted to your PR by pytorch-bot.

7. The reviewers might provide feedback that we expect you to address.
8. 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 DIscord server 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 <[email protected]>
Co-authored-by: ANOTHER-NAME <[email protected]>

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 issues with the docathon-h2-2023 label merged to the main branch in pytorch/tutorials or pytorch/pytorch repos during the period from November 1 to November 15, you will get a special PyTorch 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.

Questions?

You can post your questions here.

cc @svekars @carljparker @NicolasHug @kit1980 @subramen

[svekars]

/assigntome

[derrickmo]

@svekars

Thanks for the note on another issue. I didn't look into it carefully. Just like to bring it to your attention that these two links provided in the description are linked to pull requests page, not issues page.

Bug fixes in the pytorch/tutorials repo tagged with the docathon-h2-2023 label - see the list repo.
Docstring fixes in the pytorch/pytorch repo tagged with the docathon-h2-2023 label - see this list repo.

[sekyondaMeta]

@svekars

Thanks for the note on another issue. I didn't look into it carefully. Just like to bring it to your attention that these two links provided in the description are linked to pull requests page, not issues page.

Bug fixes in the pytorch/tutorials repo tagged with the docathon-h2-2023 label - see the list repo.
Docstring fixes in the pytorch/pytorch repo tagged with the docathon-h2-2023 label - see this list repo.

@derrickmo Thanks for pointing this out. We are fixing it now.