pytorch/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

-------------------------------------------------------------------------------- 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](https://hud.pytorch.org/ci/pytorch/pytorch/main). - [More About PyTorch](#more-about-pytorch) - [A GPU-Ready Tensor Library](#a-gpu-ready-tensor-library) - [Dynamic Neural Networks: Tape-Based Autograd](#dynamic-neural-networks-tape-based-autograd) - [Python First](#python-first) - [Imperative Experiences](#imperative-experiences) - [Fast and Lean](#fast-and-lean) - [Extensions Without Pain](#extensions-without-pain) - [Installation](#installation) - [Binaries](#binaries) - [NVIDIA Jetson Platforms](#nvidia-jetson-platforms) - [From Source](#from-source) - [Prerequisites](#prerequisites) - [NVIDIA CUDA Support](#nvidia-cuda-support) - [AMD ROCm Support](#amd-rocm-support) - [Intel GPU Support](#intel-gpu-support) - [Get the PyTorch Source](#get-the-pytorch-source) - [Install Dependencies](#install-dependencies) - [Install PyTorch](#install-pytorch) - [Adjust Build Options (Optional)](#adjust-build-options-optional) - [Docker Image](#docker-image) - [Using pre-built images](#using-pre-built-images) - [Building the image yourself](#building-the-image-yourself) - [Building the Documentation](#building-the-documentation) - [Troubleshooting CI Errors](#troubleshooting-ci-errors) - [Building a PDF](#building-a-pdf) - [Previous Versions](#previous-versions) - [Getting Started](#getting-start