$ show pytorch/tutorials
This is the official PyTorch tutorials repository, a Sphinx-Gallery documentation site that publishes to pytorch.org/tutorials. It contains hundreds of executable tutorial scripts covering beginner to advanced PyTorch topics (neural networks, distributed training, torch.compile, performance profiling, custom operators, etc.). The repository is not a standalone application but a documentation generator with sophisticated CI/CD pipelines for building, sharding, and testing tutorials across multiple GPU configurations. It includes extensive build tooling (Docker, Jenkins, GitHub Actions), linters, spell checking, link validation, and redirect management. The code within tutorial files is pedagogical and not production-ready; however, several utility scripts and patterns are reusable for documentation projects and PyTorch extension development.
The LLM identified 5 files worth highlighting for reuse:
Implements a worker/manager pattern for parallel CI sharding: a manager selects files to run on each worker based on current worker count, uploads a list to S3, and workers claim and execute files. This pattern is reusable for any CI system needing distributed test execution with S3 coordination, demonstrating fault-tolerant pipeline orchestration.
A standalone HTML post-processing script that strips invisible code blocks (e.g., those between sphinx_gallery_start_ignore/sphinx_gallery_end_ignore markers) from generated tutorial HTML. It uses BeautifulSoup to parse and modify the DOM. Reusable for any Sphinx-Gallery build that needs to remove auxiliary code from the final documentation output.
A comprehensive guide to PyTorch's torch.utils.benchmark module, demonstrating Timer.blocked_autorange, collect_callgrind, FunctionCounts transforms, and Compare for microbenchmarking. The patterns shown (callgrind diff, kernel-level profiling, timer setup) are directly reusable for performance analysis in any PyTorch project.
Demonstrates how to create a custom autograd Function that fuses Conv2D and BatchNorm2D into a single kernel. Implements manual backward pass, uses save_for_backward, and validates with gradcheck. The pattern is reusable for implementing any fusion of PyTorch operations with custom gradient logic, particularly for memory optimization.
Shows registration of custom Python operators via torch.library, including defining schemas, implementing forward/backward, and using them in training loops. Reusable as a template for creating new PyTorch operators that integrate with the dispatcher and autograd system, avoiding the need for C++ extensions.