PyTorch
PyTorch is a framework maintained by PyTorch Foundation1011, released 2016. Its source code is public1 under the BSD-style license (3-clause)32.
PyTorch is an open-source machine learning framework for tensors and dynamic neural networks in Python, with GPU acceleration and a C++ distribution (LibTorch). Meta originally developed it and released it in 2016. Since September 2022 it has been hosted by the PyTorch Foundation under the Linux Foundation.1812
- Website: Website (external site: pytorch.org)
- Repository: GitHub repository (external site: github.com)
- Documentation: Documentation (external site: docs.pytorch.org)
- License: License (external site: github.com)
- Release notes: Releases (external site: github.com)
Availability and license
Overall availability
Documented as available to the general public. Access conditions and license terms may still apply.13
Availability is separate from permission: read the license before using or redistributing.
BSD-style license (3-clause) (external site: github.com)32
Plain-language guide to the BSD 3-Clause License
The README calls the license "BSD-style". The LICENSE file contains the three BSD clauses, preceded by copyright notices for PyTorch and Caffe2 contributors such as Facebook, Inc., Idiap Research Institute, DeepMind Technologies, NEC Laboratories America, and NYU. GitHub's license detection assigns no SPDX identifier (it reports "Other"); this catalog records BSD-3-Clause because the text fits the SPDX BSD-3-Clause matching template, whose third clause allows named organizations in place of "the copyright holder".3245
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | Developed in the public pytorch/pytorch GitHub repository.1 |
| DocumentationIs user documentation published? | Public | Versioned documentation is published at docs.pytorch.org (version 2.14 at review time).7 |
| InstallationAre installation instructions or packages publicly available? | Public | The project documents pip installation for Python, LibTorch for C++, and building from source. The latest stable release requires Python 3.10 or later.8 |
| Supported platformsAre supported operating systems or hardware documented? | Public | The install page covers Linux (distributions with glibc 2.28 or later), macOS 10.15 or later, and Windows, with CUDA, ROCm, and CPU builds.8 |
| Release statusAre versioned releases published? | Public | Versioned releases are published on GitHub, including v2.14.0 on September 2, 2026.6 |
What it is useful for
Organization context
Provenance and derivatives
In the news
Amazon announces an Apache 2.0 release of TorchNeuron, a native PyTorch backend for AWS Trainium
Amazon said TorchNeuron, which it describes as running existing PyTorch code on its Trainium chips without a rewrite by plugging into PyTorch's standard mechanism for registering new hardware, would be released under the Apache 2.0 license at PyTorch Conference North America on October 20, 2026. Amazon says TorchNeuron supports Trainium2 and Trainium3 and that it is contributing changes upstream to PyTorch. The aws-neuron/torch-neuronx repository on GitHub is public, lists the Apache License 2.0, and describes itself as a native PyTorch backend for AWS Trainium. A Hugging Face blog post dated October 19 describes running Hugging Face models with it. USASI has not tested TorchNeuron.
PyTorch 2.14.0 released with torch.switch, NVGEMM in Inductor, and a preview NCCL backend rewrite
PyTorch 2.14.0 was published on GitHub on September 2, 2026. The release notes highlight NVGEMM, which brings CuTeDSL-generated CUTLASS kernels to Inductor, and
torch.switch, which generalizestorch.condto multi-way branching. Other highlights include declarative dynamic shapes via@dynamic_spec, experimentaltorch.compilesupport for complex-valued tensors, a preview of a rewritten NCCL backend ported from torchcomms, and native linear algebra on Apple Silicon. The notes also say Inductor now targets Rubin (sm_107). USASI has not tested this release.PyTorch 2.13.0 released with FlexAttention on Apple Silicon and the torchcomms backend
PyTorch 2.13.0 was published on GitHub on July 8, 2026. The release notes highlight FlexAttention support on Apple Silicon (MPS) and a deterministic FlexAttention backward path on CUDA, a prototype CuTeDSL "Native DSL" backend for Inductor alongside Triton, and
nn.LinearCrossEntropyLoss, which combines the final prediction and loss computation to reduce peak GPU memory. They also introduce torchcomms, a new communications backend for PyTorch Distributed, and Python 3.15 wheels on Linux, while dropping free-threaded CPython 3.13t binaries and Bazel build support. USASI has not tested this release.
U.S. eligibility
Eligible · basis: U.S.-governed project
The PyTorch code base is established as PyTorch a Series of LF Projects, LLC, and supported by the PyTorch Foundation, a directed fund of The Linux Foundation. The Linux Foundation is a U.S. 501(c)(6) nonprofit whose privacy policy gives a contact mailing address in San Francisco, California. Technical decisions are made by individual maintainers under the project's published governance, separate from the foundation's business governance.1113149
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
Share or cite this page
- X (opens X)
- Bluesky (opens Bluesky)
- LinkedIn (opens LinkedIn)
- Reddit (opens Reddit)
- Hacker News (opens Hacker News)
- Email (opens your email app)
- RSS updates
Cite this page
APA style
USASI. (2026, September 29). PyTorch. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/pytorch/
BibTeX
@misc{usasi_pytorch,
author = {{USASI}},
title = {PyTorch},
year = {2026},
month = sep,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/pytorch/}},
note = {United States of America Superintelligence. Last updated 2026-09-29}
}The date is when this page was last updated. Add the date you read it if your style needs one.
For maintainers: a “Listed in USASI” badge
Links to this page. It says the entry is listed, not endorsed or rated. About the badge
Markdown (for a README)
[](https://unitedstatesofamericasuperintelligence.com/open/pytorch/)
HTML
<a href="https://unitedstatesofamericasuperintelligence.com/open/pytorch/"><img src="https://unitedstatesofamericasuperintelligence.com/badges/listed.svg" alt="Listed in the USASI catalog" height="20"></a>