Dataset card

PyTorch Tutorials

All the tutorials are now presented as sphinx style documentation at:

https://pytorch.org/tutorials

Asking a question

If you have a question about a tutorial, post in https://dev-discuss.pytorch.org/ rather than creating an issue in this repo. Your question will be answered much faster on the dev-discuss forum.

Submitting an issue

You can submit the following types of issues:

Feature request - request a new tutorial to be added. Please explain why this tutorial is needed and how it demonstrates PyTorch value.
Bug report - report a failure or outdated information in an existing tutorial. When submitting a bug report, please run: python3 -m torch.utils.collect_env to get information about your environment and add the output to the bug report.

Contributing

We use sphinx-gallery's notebook styled examples to create the tutorials. Syntax is very simple. In essence, you write a slightly well formatted Python file and it shows up as an HTML page. In addition, a Jupyter notebook is autogenerated and available to run in Google Colab.

Here is how you can create a new tutorial (for a detailed description, see CONTRIBUTING.md):

Create a Python file. If you want it executed while inserted into documentation, save the file with the suffix tutorial so that the file name is your_tutorial.py.
Put it in one of the beginner_source, intermediate_source, advanced_source directory based on the level of difficulty. If it is a recipe, add it to recipes_source. For tutorials demonstrating unstable prototype features, add to the prototype_source.
For Tutorials (except if it is a prototype feature), include it in the toctree directive and create a customcarditem in index.rst.
For Tutorials (except if it is a prototype feature), create a thumbnail in the index.rst file using a command like .. customcarditem:: beginner/your_tutorial.html. For Recipes, create a thumbnail in the recipes_index.rst

If you are starting off with a Jupyter notebook, you can use this script to convert the notebook to Python file. After conversion and addition to the project, please make sure that section headings and other things are in logical order.

Building locally

The tutorial build is very large and requires a GPU. If your machine does not have a GPU device, you can preview your HTML build without actually downloading the data and running the tutorial code:

Install required dependencies by running: pip install -r requirements.txt.

Typically, you would run either in conda or virtualenv. If you want to use virtualenv, in the root of the repo, run: virtualenv venv, then source venv/bin/activate.

If you have a GPU-powered laptop, you can build using make docs. This will download the data, execute the tutorials and build the documentation to docs/ directory. This might take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
You can skip the computationally intensive graph generation by running make html-noplot to build basic html documentation to _build/html. This way, you can quickly preview your tutorial.

If you get ModuleNotFoundError: No module named 'pytorch_sphinx_theme' make: * [html-noplot] Error 2 from /tutorials/src/pytorch-sphinx-theme or /venv/src/pytorch-sphinx-theme (while using virtualenv), run python setup.py install.

Building a single tutorial

You can build a single tutorial by using the GALLERY_PATTERN environment variable. For example to run only neural_style_transfer_tutorial.py, run:

GALLERY_PATTERN="neural_style_transfer_tutorial.py" make html

GALLERY_PATTERN="neural_style_transfer_tutorial.py" sphinx-build . _build

The GALLERY_PATTERN variable respects regular expressions.

About contributing to PyTorch Documentation and Tutorials

You can find information about contributing to PyTorch documentation in the
PyTorch Repo README.md file.
Additional information can be found in PyTorch CONTRIBUTING.md.

License

PyTorch Tutorials is BSD licensed, as found in the LICENSE file.

Contents of the License File:

BSD 3-Clause License

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.