PyTorch

PyTorch is the open-source machine learning framework with 104,000+ stars on GitHub that powers a large share of the world’s AI research and production systems.

Overview

PyTorch is a tensor library and deep learning framework built around three ideas: NumPy-like tensors with strong GPU acceleration, automatic differentiation that records operations as you execute them, and a Python-first, imperative style that makes models feel like ordinary code you can debug with ordinary tools. That combination made it the default choice for research, and its ecosystem grew around it — TorchVision, TorchAudio, distributed training, quantization and deployment tooling included. Models trained in PyTorch can be exported and served at scale, and the framework runs on NVIDIA, AMD and Apple silicon as well as CPUs and specialized accelerators. Governed by the PyTorch Foundation under the Linux Foundation and maintained by a huge contributor community, it is under constant development, with repository activity in October 2026.

Key Features

• Dynamic computation graphs – define-by-run autograd that behaves like normal Python.
• GPU-accelerated tensors – NumPy-style arrays on CUDA, ROCm and Metal with a huge operator library.
• Complete training stack – optimizers, data loading, distributed training and mixed precision built in.
• Massive ecosystem – Hugging Face, Lightning and thousands of libraries build on PyTorch.
• Production paths – export to ONNX or TorchScript and deploy with TorchServe or custom runtimes.
• Cross-platform – train in the cloud, then deploy to servers, desktops and mobile devices.

💳 Pricing

PyTorch is completely free and open source, developed by the PyTorch Foundation and its community. There are no licenses, tiers or usage fees.

Who Is It For

Machine learning researchers, engineers and students — anyone who trains, fine-tunes or deploys neural networks, from classroom experiments to frontier model development.