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‎tutorial_submission_policy.md

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# PyTorch Tutorial Submission Policy
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This policy outlines the criteria and process for submitting new tutorials to the PyTorch community.
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Our goal is to ensure that all tutorials are of high quality, relevant, and up-to-date, supporting
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both the growth of our users and the evolution of the PyTorch framework itself. By following these
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guidelines, contributors can help us maintain a robust and informative educational environment.
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This policy outlines the criteria and process for submitting new
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tutorials to the PyTorch community.
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Our goal is to ensure that all tutorials are of high quality,
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relevant, and up-to-date, supporting both the growth of the PyTorch
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users and the evolution of the PyTorch framework itself. By following
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these guidelines, contributors can help us maintain a robust and
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informative educational environment.
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## Acceptance Criteria For New Tutorials
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We are committed to enhancing the learning experience for PyTorch users by providing high-quality
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tutorials. We accept new tutorials that adhere to one of the following use cases:
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* **Support for New PyTorch Features:** Tutorials that support new features for upcoming PyTorch
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releases are typically authored by the engineers who are developing these features. These tutorials
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are crucial for showcasing the latest advancements in PyTorch.
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* **Tutorials from PyTorch Partners:** We welcome tutorials from our partners who are actively
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collaborating with us to grow the PyTorch ecosystem.
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* **Direct Requests from PyTorch Maintainers:** If a PyTorch maintainer has directly reached out
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to you to request a tutorial or otherwise, endorsed a tutorial, this indicates a specific need
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within the community or the project.
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* **Tutorials from the open-source community members:** We accept tutorials from the community
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members. However, as we can't accept all of them, we have identified the process below.
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Please read below before sending a PR.
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The first three use cases do not require following the submission process described below.
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If your tutorial falls under the fourth category, please read and follow the instructions in
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the **Submission Process For Community-Contributed Tutorials** section.
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We accept new tutorials that adhere to one of the following use cases:
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* **Support for New PyTorch Features:** Tutorials that support new features
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for upcoming PyTorch releases are typically authored by the engineers who
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are developing these features. These tutorials are crucial for showcasing
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the latest advancements in PyTorch. We typically don't require more than
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one tutorial per feature.
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* **Tutorials showcasing PyTorch usage with other tools and libraries:** We
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accept community-contributed tutorials that illustrate innovative uses of
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PyTorch alongside other open-source projects, models, and tools. Please
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ensure that your tutorial remains neutral and does not promote or endorse
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proprietary technologies over others.
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The first use case does not require going through the submission
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process outlined below. If your tutorial falls under the second category,
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please read and follow the instructions in the
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**Submission Process For Community-Contributed Tutorials** section.
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## Submission Process For Community-Contributed Tutorials
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To maintain the quality and relevance of tutorials, community-contributed tutorials must undergo
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a review process. If you are interested in contributing a tutorial, please follow these steps:
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To maintain the quality and relevance of tutorials, we request that
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community-contributed tutorials undergo a review process. If you are
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interested in contributing a tutorial, please follow these steps:
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1. **Create an issue:**
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* Open an issue in the pytorch/tutorials repository proposing the new tutorial. Clearly
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explain the importance of the tutorial and confirm that there is no existing tutorial
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covering the same or similar topic. A tutorial can't endorse one technology over another.
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* Open an issue in the pytorch/tutorials repository proposing the
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new tutorial. Clearly explain the importance of the tutorial and
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confirm that there is no existing tutorial covering the same or
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similar topic. A tutorial should not disproportionately endorse
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one technology over another. Please consult with Core Maintainers
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to ensure your content adheres to these guidelines.
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Use the provided `ISSUE_TEMPLATE` for the new tutorial request.
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* If there is a tutorial on the existing topic that you would like
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to significantly refactor, you can just submit a PR. In the
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description of the PR explain why the changes are needed and
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how they improve the tutorial.
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* These issues will be triaged by PyTorch maintainers on a case-by-case basis.
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* Link any supporting materials including discussions in other repositories".
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* Link any supporting materials including discussions in other repositories.
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1. **Await Approval:**
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* Wait for a response from the PyTorch maintainers. They will review your proposal and
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determine whether a tutorial on the proposed topic is desirable. **Do not submit a PR with
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your tutorial** before you receive an approval.
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* Wait for a response from the PyTorch maintainers. A PyTorch
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tutorial maintainer will review your proposal and
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determine whether a tutorial on the proposed topic is desirable.
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A comment and an **approved** label will be added to your tutorial
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by a maintainer. Review process for new tutorial PRs submitted
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without the corresponding issue will take longer.
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1. **Adhere to writing and styling guidelines:**
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* Once approved, follow the guidelines outlined in [CONTRIBUTING.md](https://github.com/pytorch/tutorials/blob/main/CONTRIBUTING.md)
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and use the provided [template](https://github.com/pytorch/tutorials/blob/main/beginner_source/template_tutorial.py) for creating your tutorial.
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* Link the issue where you received an approval for your tutorial in the PR.
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* We accept tutorials in both ``.rst`` (ReStructuredText) and ``.py`` (Python)
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formats. However, unless your tutorial involves using multiple GPU, parallel/distributed
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training, or requires extended execution time (25 minutes or more), we prefer submissions
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* Link the issue in which you received an approval for your tutorial
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in the PR.
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* We accept tutorials in both ``.rst`` (ReStructuredText) and ``.py``
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(Python) formats. However, unless your tutorial involves using
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multiple GPU, parallel/distributed training, or requires extended
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execution time (25 minutes or more), we prefer submissions
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in Python file format versus ``.rst``.
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1. **Maintenance:**
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* By submitting a new tutorial, you agree to maintain it up-to-date with the latest PyTorch
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updates and new features, review issues and pull requests and bugs submitted against the
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tutorials and overall take proactive actions to ensure quality of the tutorial.
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* Each tutorial must be updated by its owners at least once a year. Tutorials that are
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abandoned by the owners will be eventually deprecated.
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We reserve the right to decline any new tutorial submissions that do not follow this policy.
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## Maintening Tutorials
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When you submit a new tutorial, we encourage you to keep it updated
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with the latest PyTorch updates and features. Additionally, we may
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contact you to review any PRs, issues, and other related matters to
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ensure the tutorial remains a valuable resource.
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Please note the following:
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* If a tutorial is broken against the main branch, the tutorial will
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be excluded from the build and an issue will be filed against it
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with the author/maintainer notified about the issue. If the issue
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is not resolved within 90 days, the tutorial might be deleted from
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the repository.
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* We recommend that each tutorial is reviewed at least once a year to
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ensure its relevance.
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## Deleting Stale Tutorials
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A tutorial might be considered stale when it has not been updated for
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a significant period (more than a year) and no longer aligns with the
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latest PyTorch updates, features, or best practices. Other indicators
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of a stale tutorial include:
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* The tutorial is no longer functional due to changes in PyTorch or
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its dependencies
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* The tutorial has been superseded by a newer, more comprehensive, or
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more accurate tutorial
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* The tutorial does not run successfully in the (CI), indicating
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potential compatibility or dependency issues.
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If a tutorial is deemed stale, we will attempt to contact the code owner
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or someone from the tutorial mainatainers might attempt to update it.
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However, if despite those attempts, we fail to fix it, the tutorial
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might be removed from the repository.

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