# pytorch/tutorials

PyTorch tutorials.

Repository: https://github.com/pytorch/tutorials
Canonical: https://ross.abutalabs.com/products/pytorch-tutorials
Homepage: https://pytorch.org/tutorials/
Language: Python
License: BSD-3-Clause
License Family: permissive
Last push: 2026-08-26T14:40:56+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 3624, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9290, forks 4428 (observed 2026-08-28T04:10:30.335142+00:00)

## What it is
The official PyTorch tutorials repository, containing Sphinx-style documentation with runnable Python examples and Jupyter notebooks covering everything from beginner basics to advanced deep learning topics. Tutorials are published at pytorch.org/tutorials and can be run in Google Colab or locally.

## Use cases
- learn pytorch from scratch
- pytorch tutorial for beginners
- how to train a neural network in pytorch
- optimize dataloader performance in pytorch
- visualize model training with tensorboard
- understand torch.nn and autograd
- pytorch gpu data transfer best practices
- learn to build and save ML models

## When to choose
- you are learning PyTorch or deep learning workflows for the first time
- you want official, maintained, runnable examples with Colab support
- you need guidance on specific PyTorch features like DataLoader tuning, TensorBoard, or pin_memory

## When to avoid
- you need API reference documentation rather than guided tutorials
- you use a different framework like TensorFlow or JAX
- you want a production-ready library to import rather than educational material

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, gpu-computing, data-science
- domain: deep-learning, machine-learning, tutorials, developer-tools
- platform: python, cross-platform
- tags: pytorch, tutorials, documentation, sphinx-gallery, jupyter-notebooks, neural-networks, beginner-friendly, gpu

## Member repositories
- pytorch/tutorials (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:30.335142+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:22:33.050170+00:00, confidence not recorded.
  - readme: https://github.com/pytorch/tutorials (fetched 2026-08-28T04:10:30.335142+00:00, sha 450d9ea80321)
  - homepage: https://pytorch.org/tutorials/ (fetched 2026-08-29T08:22:46.782553+00:00, sha bba01495b478)
  - site_page: https://docs.pytorch.org/tutorials/recipes/recipes/reasoning_about_shapes.html (fetched 2026-08-29T08:22:46.792131+00:00, sha eb294b08ba08)
  - site_page: https://docs.pytorch.org/tutorials/beginner/basics/intro.html (fetched 2026-08-29T08:22:46.793936+00:00, sha 75ef37033b0a)
  - site_page: https://docs.pytorch.org/tutorials/beginner/introyt/introyt_index.html (fetched 2026-08-29T08:22:46.795574+00:00, sha 06340119421e)
  - site_page: https://docs.pytorch.org/tutorials/beginner/pytorch_with_examples.html (fetched 2026-08-29T08:22:46.797180+00:00, sha a6e8edbe8634)
  - site_page: https://docs.pytorch.org/tutorials/beginner/nn_tutorial.html (fetched 2026-08-29T08:22:46.799501+00:00, sha 88e592a9ac0e)
  - site_page: https://docs.pytorch.org/tutorials/intermediate/tensorboard_tutorial.html (fetched 2026-08-29T08:22:46.802921+00:00, sha 77598be92009)
  - site_page: https://docs.pytorch.org/tutorials/intermediate/pinmem_nonblock.html (fetched 2026-08-29T08:22:46.805145+00:00, sha 51f4df34ca14)
  - site_page: https://docs.pytorch.org/tutorials/intermediate/intermediate_data_loading_tutorial.html (fetched 2026-08-29T08:22:46.807877+00:00, sha a8127771f01b)
- Data as of 2026-08-30T08:39:29.467469+00:00.