GraphCanon updated 3w · GitHub synced 3w · 30 views this month
Decision brief
Dynamic computation graphs with GPU acceleration.
Good fit when
- Required dynamic computation graph functionality for flexible model architectures
- GPU-accelerated tensor operations critical due to high-dimensional data
Avoid when
- Static graph frameworks like TensorFlow are preferred for simpler, less variable models
- Environments with limited GPU support or requiring multi-language compatibility
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Adoption
Package downloads where a registry match exists. GitHub stars (102,144) are secondary evidence.
- Docker Hub pulls (30d)
- 19,445,815·Docker Hub API·3w
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install pytorchHow it fits your stack(12)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Depends on
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A machine learning library for Python that includes support for dynamic computation graphs and automatic differentiation. It is designed to be easy to use and has extensive support for using GPUs.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 3, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 3, 2026
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 3, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
You can pass `PYTHON_VERSION=x.y` make variable to specify which Python version is to be used by Miniconda, or leave itSource link
Tags
README
magma installation: run with active conda environment. specify CUDA version to install
.ci/docker/common/install_magma_conda.sh 12.4
(optional) If using torch.compile with inductor/triton, install the matching version of triton
Docker Image
Using pre-built images
You can also pull a pre-built docker image from Docker Hub and run with docker v23.0+
docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest
Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.
for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you
should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.
Building the image yourself
NOTE: Must be built with a Docker version >= 23.0
The Dockerfile is supplied to build images with CUDA 12.1 support and cuDNN v9.
You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it
unset to use the default, as the Dockerfile uses system Python.
make -f docker.Makefile
---
# images are tagged as docker.io/${your_docker_username}/pytorch
You can also pass the CMAKE_VARS="..." environment variable to specify additional CMake variables to be passed to CMake during the build.
See cmake/EnvVarForwarding.cmake for the list of available variables.
make -f docker.Makefile
Getting Started
Pointers to get you started:
- Tutorials: get you started with understanding and using PyTorch
- Examples: easy to understand PyTorch code across all domains
- The API Reference
- Glossary
License
PyTorch has a BSD-style license, as found in the LICENSE file.
For agents
This page has a .md twin and JSON over the API.