Merge branch 'master' into fix_display_bug

Author: Svetlana Karslioglu

CONTRIBUTING.md

@@ -1,31 +1,372 @@
 # Contributing to tutorials
+
 We want to make contributing to this project as easy and transparent as
-possible.
+possible. This file covers information on flagging issues, contributing
+updates to existing tutorials--and also submitting new tutorials.
+
+NOTE: This guide assumes that you have your GitHub account properly
+configured, such as having an SSH key. If this is your first time
+contributing on GitHub, see the [GitHub
+Documentation](https://docs.github.com/en/get-started/quickstart/contributing-to-projects)
+on contributing to projects.
+
+
+# Issues
+
+We use [GitHub Issues](https://github.com/pytorch/tutorials/issues) to
+track public bugs. Please ensure your description is clear and has
+sufficient instructions to be able to reproduce the issue.
+
+
+# Security Bugs
 
-## Pull Requests
-We actively welcome your pull requests.
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for
+the safe disclosure of security bugs. For these types of issues, please
+go through the process outlined on that page and do not file a public
+issue.
 
-1. Fork the repo and create your branch from `master`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
+# Contributor License Agreement ("CLA")
 
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Facebook's open source projects.
+In order to accept a pull request, you need to submit a CLA. You only
+need to do this once and you will be able to work on all of Facebook's
+open source projects, not just PyTorch.
 
 Complete your CLA here: <https://code.facebook.com/cla>
 
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
 
-Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
+# License
+
+By contributing to the tutorials, you agree that your contributions will
+be licensed as described in the `LICENSE` file in the root directory of
+this source tree.
+
+
+# Updates to existing tutorials
+
+We welcome your pull requests (PR) for updates and fixes.
+
+1. If you haven't already, complete the Contributor License Agreement
+   ("CLA").
+1. Fork the repo and create a branch from
+   [`master`](https://github.com/pytorch/tutorials).
+1. Test your code.
+1. Lint your code with a tool such as
+   [Pylint](https://pylint.pycqa.org/en/latest/).
+1. Submit your PR for review.
+
+
+# New Tutorials
+
+There are three types of tutorial content that we host on
+[`pytorch.org/tutorials`](https://github.com/pytorch/tutorials):
+
+* **Interactive tutorials** are authored and submitted as Python files.
+  The build system  converts these into Jupyter notebooks and HTML. The
+  code in these tutorials is run every time they are built. To keep
+  these tutorials up and running all their package dependencies need to
+  be resolved--which makes it more challenging to maintain this type of
+  tutorial. 
+
+* **Non-interactive tutorials** are authored and submitted as
+  reStructuredText files. The build system only converts them into HTML;
+  the code in them does not run on build. These tutorials are easier to
+  create and maintain but they do not provide an interactive experience.
+  An example is the [Dynamic Quantization
+  tutorial](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html).
+
+* **Recipes** are tutorials that provide bite-sized, actionable
+  examples of how to use specific features, which differentiates them
+  from full-length tutorials. Recipes can be interactive or
+  non-interactive.
+
+
+# Managing data that is used by your tutorial 
+
+Your tutorial might depend on external data, such as pre-trained models,
+training data, or test data. We recommend storing this data in a
+commonly-used storage service, such as Amazon S3, and instructing your
+users to download the data at the beginning of your tutorial. 
+
+The
+[Makefile](https://github.com/pytorch/tutorials/blob/master/Makefile)
+that we use to build the tutorials contains automation that downloads
+required data files.
+
+
+# Python packages used by your tutorial
+
+If your tutorial has dependencies that are not already defined in
+`requirements.txt`, you should add them to that file. We recommend that
+you use only mature, well-supported packages in your tutorial. Packages
+that are obscure or not well-maintained may break as a result of, for
+example, updates to Python or PyTorch or other packages. If your
+tutorial fails to build in our Continuous Integration (CI) system, we
+might contact you in order to resolve the issue.
+
+
+# Deprecation of tutorials 
+
+Under some circumstances, we might deprecate--and subsequently
+archive--a tutorial removing it from the site. For example, if the
+tutorial breaks in our CI and we are not able to resolve the issue and
+are also not able to reach you, we might archive the tutorial. In these
+situations, resolving the breaking issue would normally be sufficient to
+make the tutorial available again.
+
+Another situation in which a tutorial might be deprecated is if it
+consistently receives low ratings--or low usage--by the community. Again,
+if this occurs, we will attempt to contact you.
+
+If we identify, or suspect, that your tutorial--or a package that your
+tutorial uses--has a **security or privacy** issue, we will immediately
+take the tutorial off the site.
+
+
+# Guidance for authoring tutorials and recipes
+
+In this section, we describe the process for creating tutorials and
+recipes for Pytorch.
+
+The first step is to decide which type of tutorial you want to create,
+taking into account how much support you can provide to keep the
+tutorial up-to-date. Ideally, your tutorial should demonstrate PyTorch
+functionality that is not duplicated in other tutorials.
+
+As described earlier, tutorials are resources that provide a holistic
+end-to-end understanding of how to use PyTorch. Recipes are scoped
+examples of how to use specific features; the goal of a recipe is to
+teach readers how to easily leverage features of PyTorch for their
+needs. Tutorials and recipes are always _actionable_. If the material is
+purely informative, consider adding it to the API docs instead. 
+
+View our current [full-length tutorials](https://pytorch.org/tutorials/).
+
+To create actionable tutorials, start by identifying _learning
+objectives_, which are the end goals. Working backwards from these
+objectives will help to eliminate extraneous information.
+
+
+## Learning objectives ##
+
+To create the learning objectives, focus on what the user will
+implement. Set expectations by explicitly stating what the recipe will
+cover and what users will implement by the end. Here are some examples:
+
+- Create a custom dataset
+- Integrate a dataset using a library
+- Iterate over samples in the dataset
+- Apply a transform to the dataset
+
+
+## Voice and writing style ##
+
+Write for a global audience with an instructive and directive voice.
+
+- PyTorch has a global audience; use clear, easy to understand
+  language. Avoid idioms or other figures of speech.
+- To keep your instructions concise, use 
+  [active voice](https://writing.wisc.edu/handbook/style/ccs_activevoice/) as much as possible. 
+- For a short guide on the essentials of writing style, 
+  [The Elements of Style](https://www.gutenberg.org/files/37134/37134-h/37134-h.htm)
+  is invaluable.
+- For extensive guidance on technical-writing style, the Google developer documentation 
+  [google style](https://developers.google.com/style)
+  is a great resource.
+- Think of the process as similar to creating a (really practical)
+  Medium post.
+
+
+## Structure ##
+
+We recommend that tutorials use the following structure which guides users through the learning experience and provides appropriate context:
+
+1. Introduction
+1. Motivation: Why is this topic important?
+1. Link to relevant research papers or other background material.
+1. Learning objectives: Clearly state what the tutorial covers and what
+   users will implement by the end. For example: Provide a summary of
+   how the Integrated Gradients feature works and how to implement it
+   using Captum. The
+   [TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html)
+   tutorial provides a good example of how to specify learning
+   objectives.
+1. Setup and requirements. Call out any required setup or data
+   downloads.
+1. Step-by-step instructions. Ideally, the steps in the tutorial should
+   map back to the learning objectives. Consider adding comments in the
+   code that correspond to these steps and that help to clarify what
+   each section of the code is doing. 
+1. Link to relevant [PyTorch
+   documentation](https://pytorch.org/docs/stable/index.html). This
+   helps readers have context for the tutorial source code and better
+   understand how and why it implements the technique you’re
+   demonstrating.
+1. Recap/Conclusion: Summarize the steps and concepts covered. Highlight
+   key takeaways.
+1. (Optional) Additional practice exercises for users to test their
+   knowledge. An example is [NLP From Scratch: Generating Names with a
+   Character-Level RNN tutorial](https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html#exercises).
+1. Additional resources for more learning, such as documentation, other
+   tutorials, or relevant research.
+
+
+## Example Tutorials ##
+
+The following tutorials do a good job of demonstrating the ideas
+described in the preceding sections:
+
+- [Chatbot Tutorial](https://pytorch.org/tutorials/beginner/chatbot_tutorial.html)
+- [Tensorboard Tutorial](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html)
+- [NLP From Scratch: Generating Names with a Character-Level RNN
+Tutorial](https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html)
+
+If you are creating a recipe, we recommend that you use [this
+template](https://github.com/pytorch/tutorials/blob/tutorials_refresh/recipes_source/recipes/example_recipe.py)
+as a guide.
+
+
+# Submission Process #
+
+Submit your tutorial as either a Python (`.py`) file or a
+reStructuredText (`.rst`) file. For Python files, the filename for your
+tutorial should end in "`_tutorial.py`"; for example,
+"`cool_pytorch_feature_tutorial.py`". 
+
+Do not submit a Jupyter notebook. If you develop your tutorial in
+Jupyter, you'll need to convert it to Python. This
+[script](https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe)
+is one option for performing this conversion.
+
+For Python files, our CI system runs your code during each build.
+
+
+## Add Your Tutorial Code ##
+
+1. [Fork and
+   clone](https://docs.github.com/en/get-started/quickstart/contributing-to-projects)
+   the repo:
+   [https://github.com/pytorch/tutorials](https://github.com/pytorch/tutorials)
+
+1. Put the tutorial in one of the
+   [`beginner_source`](https://github.com/pytorch/tutorials/tree/master/beginner_source),
+   [`intermediate_source`](https://github.com/pytorch/tutorials/tree/master/intermediate_source),
+   [`advanced_source`](https://github.com/pytorch/tutorials/tree/master/advanced_source)
+   based on the technical level of the content. For recipes, put the
+   recipe in
+   [`recipes_source`](https://github.com/pytorch/tutorials/tree/master/recipes_source).
+   In addition, for recipes, add the recipe in the recipes
+   [README.txt](https://github.com/pytorch/tutorials/blob/master/recipes_source/recipes/README.txt)
+   file.
+
+
+## Include Your Tutorial in `index.rst`#
+
+In order for your tutorial to appear on the website, and through tag
+search, you need to include it in `index.rst`, or for recipes, in
+`recipes_index.rst`. 
+
+1. Open the relevant file
+   [`index.rst`](https://github.com/pytorch/tutorials/blob/master/index.rst)
+   or
+   [`recipes_index.rst`](https://github.com/pytorch/tutorials/blob/master/recipes_source/recipes_index.rst)
+1. Add a _card_ in reStructuredText format similar to the following:
+
+```
+.. customcarditem::
+   :header: Learn the Basics # Tutorial title
+   :card_description: A step-by-step guide to building a complete ML workflow with PyTorch.  # Short description
+   :image: _static/img/thumbnails/cropped/60-min-blitz.png  # Image that appears with the card
+   :link: beginner/basics/intro.html 
+   :tags: Getting-Started 
+```
+
+
+### Link ###
+
+The `link` should be the path to your tutorial in the source tree. For
+example, if the tutorial is in `beginner_source`, the link will be
+`beginner_source/rest/of/the/path.html`
+
+
+### Tags ###
+
+Choose tags from the existing tags in the file. Reach out to a project
+maintainer to create a new tag. The list of tags should not have any
+white space between the words. Multi-word tags, such as “Getting
+Started”, should be hyphenated: Getting-Started. Otherwise, the tutorial
+might fail to build, and the cards will not display properly.
+
+
+### Image ###
+
+Add a thumbnail to the
+[`_static/img/thumbnails/cropped`](https://github.com/pytorch/tutorials/tree/master/_static/img/thumbnails/cropped)
+directory. Images that render the best are square--that is, they have
+equal `x` and `y` dimensions--and also have high resolution. [Here is an
+example](https://github.com/pytorch/tutorials/blob/master/_static/img/thumbnails/cropped/loading-data.PNG).
+
+## `toctree` ##
+
+1. Add your tutorial under the corresponding toctree (also in
+   `index.rst`). For example, if you are adding a tutorial that
+   demonstrates the PyTorch ability to process images or video, add it
+   under `Image and Video`:
+
+```
+.. toctree::
+   :maxdepth: 2
+   :includehidden:
+   :hidden:
+   :caption: Image and Video
+
+   intermediate/torchvision_tutorial
+   beginner/my-new-tutorial
+```
+
+
+## Test Your Tutorial Locally ##
+
+The following command builds an HTML version of the tutorial website.
+
+    ```
+    make html-noplot
+    ```
+
+This command does not run your tutorial code. To build the tutorial in a
+way that executes the code, use `make docs`. However, unless you have a
+GPU-powered machine and a proper PyTorch CUDA setup, running this `make`
+command locally won't work. The continuous integration (CI) system will
+test your tutorial when you submit your PR.
+
+
+## Submit the PR ##
+ 
+NOTE: Please do not use [ghstack](https://github.com/ezyang/ghstack). We
+do not support ghstack in the [`pytorch/tutorials`](https://github.com/pytorch/tutorials) repo.
+
+Submit the changes as a PR to the master branch of
+[`pytorch/tutorials`](https://github.com/pytorch/tutorials).
+
+1. Add your changes, commit, and push:
+
+    ```
+    git add -A
+    git commit -m "Add <mytutorial>"
+    git push --set-upstream mybranch
+    ```
+
+1. Submit the PR and tag individuals on the PyTorch project who can review
+   your PR.
+1. Address all feedback comments from your reviewers.
+1. Make sure all CI checks are passing.
+
+Once you submit your PR, you can see a generated Netlify preview of your
+build. You can see an example Netlify preview at the following URL:
+
+>  <https://deploy-preview-954--pytorch-tutorials-preview.netlify.app/>
+
+
+## Do not merge the PR yourself ##
+
+Please **DO NOT MERGE** your own PR; the tutorial won't be published. In order to avoid potential build breaks with the tutorials site, only certain maintainers can authorize publishing. 
 
-## License
-By contributing to tutorials, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.