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Infrastructure / Core ML frameworks & libraries

PyTorch

PyTorch Foundation (Linux Foundation)

PyTorch is a Python library for tensor computation with GPU acceleration and deep neural networks built on a tape-based autograd system. Originally developed at Meta, it is now governed by the PyTorch Foundation under the Linux Foundation, and it is the framework most of the model records on this map are trained and served with.

Verified 2026-08-13 via GitHub and the LICENSE body.

Openness

5 high confidence
5.0
license
BSD-3-Clause(OSI)
source
public
core-gated
ungated

BSD 3-Clause license with multiple contributor copyright lines; full source public. The repository is public and unarchived, and the README describes the whole library with no paid, enterprise or hosted tier beside it, so source is public and the core ungated. The GitHub API reports no SPDX identifier for the license, because the LICENSE file prepends per-contributor copyright lines to the BSD text; the license is therefore read from the file body rather than from the API field.

  • https://github.com/pytorch/pytorch/blob/main/LICENSE recorded 2026-08-13

    LICENSE body carries the three BSD 3-Clause conditions and the 'Neither the names ... may be used to endorse' clause, under per-contributor copyright lines (Facebook, Idiap, Deepmind, NEC, NYU and others)

  • https://api.github.com/repos/pytorch/pytorch recorded 2026-08-13

    Repo metadata - license spdx_id NOASSERTION, private false, archived false, default branch main - for pytorch/pytorch.

  • https://raw.githubusercontent.com/pytorch/pytorch/main/README.md recorded 2026-08-13

    README describes the whole package - tensor computation with GPU acceleration and tape-based autograd - plus build-from-source instructions. No paid, enterprise or hosted PyTorch tier appears; the one 'Enterprise' string is Visual Studio Enterprise in the Windows build notes.

Adoption

5 high confidence
5.0

98,643,378 PyPI downloads of `torch` in the trailing 30 days, which falls in the top band of the software usage scale, over 10M a month, level 5. PyPI is one of several channels here - conda and PyTorch's own wheel index are not counted - so the figure is a floor rather than a full count. It is already the top band, so the undercount cannot move the level.

Capability

5 high confidence
5.0

Defines the model-training layer alongside TensorFlow.

  • https://github.com/pytorch/pytorch recorded 2026-08-13

    README still describes tensor computation with strong GPU acceleration and deep neural networks on a tape-based autograd system

Verified 2026-08-13