torch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

torch Permissive license Apache-2.0 AND Apache-2.0 WITH LLVM-exception AND BSD-2-Clause AND BSD-3-Clause AND BSL-1.0 AND MIT Active ★ 102,360 v2.13.0 released 2026-07-08

Install

torch on PyPI

pip

pip install torch

uv

uv add torch

poetry

poetry add torch

Package facts

License	Apache-2.0 AND Apache-2.0 WITH LLVM-exception AND BSD-2-Clause AND BSD-3-Clause AND BSL-1.0 AND MIT (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	medium — platform-specific wheel
Runtime dependencies	14 — filelock, typing-extensions, setuptools, sympy, networkx, jinja2, fsspec, cuda-toolkit, cuda-bindings, nvidia-cudnn-cu13, nvidia-cusparselt-cu13, nvidia-nccl-cu13, nvidia-nvshmem-cu13, triton
Maintenance	actively maintained — 36 days since the last release
Last repo commit	2026-08-13
First released	2018-12-07
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: torch-2.13.0-cp310-cp310-macosx_14_0_arm64.whl; torch-2.13.0-cp310-cp310-manylinux_2_28_aarch64.whl; torch-2.13.0-cp310-cp310-manylinux_2_28_x86_64.whl; torch-2.13.0-cp310-cp310-win_amd64.whl; torch-2.13.0-cp311-cp311-macosx_14_0_arm64.whl; torch-2.13.0-cp311-cp311-manylinux_2_28_aarch64.whl; torch-2.13.0-cp311-cp311-manylinux_2_28_x86_64.whl; torch-2.13.0-cp311-cp311-win_amd64.whl; torch-2.13.0-cp312-cp312-macosx_14_0_arm64.whl; torch-2.13.0-cp312-cp312-manylinux_2_28_aarch64.whl; torch-2.13.0-cp312-cp312-manylinux_2_28_x86_64.whl; torch-2.13.0-cp312-cp312-win_amd64.whl; torch-2.13.0-cp313-cp313-macosx_14_0_arm64.whl; torch-2.13.0-cp313-cp313-manylinux_2_28_aarch64.whl; torch-2.13.0-cp313-cp313-manylinux_2_28_x86_64.whl; torch-2.13.0-cp313-cp313-win_amd64.whl; torch-2.13.0-cp314-cp314-macosx_14_0_arm64.whl; torch-2.13.0-cp314-cp314-manylinux_2_28_aarch64.whl; torch-2.13.0-cp314-cp314-manylinux_2_28_x86_64.whl; torch-2.13.0-cp314-cp314t-macosx_14_0_arm64.whl

Keywords: pytorch, machine learning

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: C++Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules

About torch

from the package's own PyPI description — quoted content, verbatim

PyTorch Logo (image)

PyTorch is a Python package that provides two high-level features: - Tensor computation (like NumPy) with strong GPU acceleration - Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.

More About PyTorch
A GPU-Ready Tensor Library
Dynamic Neural Networks: Tape-Based Autograd
Python First
Imperative Experiences
Fast and Lean
Extensions Without Pain
Installation
Binaries
- NVIDIA Jetson Platforms
From Source -...

Read as markdown · JSON record · Source repository · Homepage · Docs

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

PyTorch is a tensor computation library with GPU acceleration and a tape-based automatic differentiation system for building and training deep neural networks.

Medium install friction due to 14 runtime dependencies including cuda-toolkit, nvidia-cudnn-cu13, and nvidia-nccl-cu13; prebuilt wheels available for Python 3.10–3.14 across major platforms. Actively maintained with last commit on 2026-08-13.

Permissive multi-license stack (Apache-2.0, Apache-2.0 WITH LLVM-exception, BSD-2-Clause, BSD-3-Clause, BSL-1.0, MIT) allows commercial and proprietary use with minimal restrictions.

Usage

pip install torch==2.13.0
import torch
x = torch.tensor([1.0, 2.0, 3.0])
y = x * 2
print(y)

cuda-toolkit and compatible GPU drivers required for GPU acceleration; CPU-only wheels available but GPU dependencies are listed as runtime dependencies.

Verdict: PyTorch 2.13.0 is production-stable (Development Status 5), actively maintained, and has no known vulnerabilities. Permissive licensing and broad Python 3.10+ support are assets; moderate install friction stems from GPU library dependencies, mitigated by prebuilt wheels across major platforms.

Needs verification

Whether CPU-only installation is possible without the CUDA-related runtime dependencies listed
Performance characteristics and memory efficiency relative to competing frameworks
Specific CUDA version compatibility requirements for nvidia-cudnn-cu13 and related dependencies

gpu tensor computationdeep learning frameworkneural network libraryautomatic differentiationmachine learning pytorchgpu accelerated tensorsdynamic neural networks