22nd of 7 in ML frameworks and libraries
pytorch
Python tensor library with GPU support and tape-based autograd for deep learning
Documentation ↗
(opens in a new tab)Website ↗
(opens in a new tab)Repository on GitHub ↗
(opens in a new tab)
- Stars
- 104.8k
- License
- custom license
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
Checked on GitHub.
Overview
PyTorch is a Python package that provides NumPy-like tensor computation on CPU or GPU, plus a reverse-mode autograd system for building neural networks imperatively. It ships torch.nn, torch.multiprocessing and a DataLoader, and can be extended in Python or C/C++. GPU backends covered in the README are NVIDIA CUDA, AMD ROCm (Linux only) and Intel GPU.
Who it is for: ML researchers and engineers building and training neural networks in Python
Strengths
- Dynamic graphs execute line by line, so debuggers and stack traces point at user code
- Backends for NVIDIA CUDA, AMD ROCm and Intel GPU, plus CPU-only builds
- Layers can be written in Python or via a C++ extension API
- Pre-built pip/conda binaries, Docker images and Jetson wheels are available
Weaknesses
- Building from source needs a C++20 compiler, 10 GB disk and 30-60 minutes
- ROCm support is Linux only; Windows distributed support is described as a prototype
- Source build with CUDA requires matching cuDNN 9.0+, NVTX and compiler versions
- Low-level framework only; no model serving or application layer included
What it needs
- GPU optional
- Docker
- Needs Python 3.10+, C++20 compiler (source builds), CUDA/cuDNN, ROCm or Intel GPU stack (optional), Intel MKL
- Models: torch.nn modules, CUDA, ROCm, Intel XPU, CPU
Head to head
Also in ML frameworks and libraries
See all 7| Rank | Project | Score |
|---|---|---|
| 1 | transformersModel-definition library for text, vision, audio and multimodal models | 92 out of 100 |
| 3 | tensorflowTensor and neural network framework with Python and C++ APIs | 80 out of 100 |
| #4 | ultralyticsPython library and CLI for training and running YOLO vision models | 77 out of 100 |
| #5 | kerasMulti-backend deep learning library running on JAX, TensorFlow and PyTorch | 70 out of 100 |
| #6 | scikit-learnPython library of classical machine learning algorithms built on NumPy and SciPy | 69 out of 100 |