How to use the-incredible-pytorch: a guide to its PyTorch resources

What the-incredible-pytorch is and what it is not

The repository is a long list of links related to PyTorch, the Python deep learning library. Each entry points to an external repository or page, and the list covers tutorials, large language models, agentic AI, computer vision, NLP, reinforcement learning and many other subfields.

It does not provide a library, a command-line tool or a runnable package. Everything you do with it is reading, clicking through to other projects and deciding what to try next.

• Good for: finding PyTorch tutorials, example projects and paper implementations.
• Not for: installing a single tool, since the repository only links to other projects.

Get a local copy of the repository

The README gives one install step: clone the repository to your machine. Cloning gives you the README and its table of contents as files, so you can search them offline with your editor or a text search tool.

Run the following command in a terminal where you keep your projects. Git must be installed first.

Find your way around the table of contents

The README opens with a table of contents that links to each section. Sections are grouped by topic, starting with Tutorials, then Large Language Models (LLMs), Agentic AI, Guardrails and AI Safety, and Tabular Data, followed by many computer vision, NLP and research topics.

Later sections cover utilities such as PyTorch Utilities, PyTorch Video Tutorials and Community, plus a section called To be Classified for entries that do not yet have a clear home.

• Start with Tutorials if you are new to PyTorch.
• Use Large Language Models (LLMs) to see which open-source LLM projects are built or run with PyTorch.
• Use the topic sections, such as Object Detection or Transformers, when you already know the problem you want to solve.

Pick a starting point from the Tutorials section

The Tutorials section is the best entry point for beginners. It includes the official PyTorch Tutorials and Examples repositories, along with community courses and small example projects.

Choose one resource and work through it end to end rather than opening many tabs. Entries range from full books such as Dive Into Deep Learning with PyTorch to short projects such as Simple Examples to Introduce PyTorch.

• Official PyTorch Tutorials: github.com/pytorch/tutorials
• Official PyTorch Examples: github.com/pytorch/examples
• Dive Into Deep Learning with PyTorch: github.com/d2l-ai/d2l-en
• Practical Deep Learning with PyTorch: github.com/ritchieng/deep-learning-wizard

Use the LLM and research sections carefully

The LLM section lists projects such as LLaMA-related repositories, TinyLlama, vLLM and Gemma. Each link is to a project maintained by its own authors, so check the linked project's README for its own requirements, licence and status before you rely on it.

The list does not describe any review process beyond accepting pull requests. Treat each entry as a pointer, not a recommendation that has been tested.

• Open the linked repository and read its install instructions there.
• Check the licence on the linked project, since it may differ from this list's MIT licence.
• Look for recent commit activity on the linked project before depending on it.

Contribute a missing resource

The README invites readers to make a pull request to add links to the list. Changes go through the standard GitHub pull request workflow on the repository.

• Fork the repository on GitHub.
• Add your entry to the section that fits it best.
• Open a pull request against the main repository.