pytorch

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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
• Prerequisites
• NVIDIA CUDA Support
• AMD ROCm Support
• Intel GPU Support
• Get the PyTorch Source

More About PyTorch

Learn the basics of PyTorch

At a granular level, PyTorch is a library that consists of the following components:

| Component | Description | | ---- | --- | | torch | A Tensor library like NumPy, with strong GPU support | | torch.autograd | A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch | | torch.jit | A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code | | torch.nn | A neural networks library deeply integrated with autograd designed for maximum flexibility | | torch.multiprocessing | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training | | torch.utils | DataLoader and other utility functions for convenience |

Usually, PyTorch is used either as:

• A replacement for NumPy to use the power of GPUs.
• A deep learning research platform that provides maximum flexibility and speed.

Elaborating Further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a. ndarray).

Tensor illustration

PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.

Dynamic graph

Python First

PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use NumPy / SciPy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.

Imperative Experiences

PyTorch is designed to be intuitive, linear in thought, and easy to use. When you execute a line of code, it gets executed. There isn't an asynchronous view of the world. When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward. The stack trace points to exactly where your code was defined. We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Fast and Lean

PyTorch has minimal framework overhead. We integrate acceleration libraries such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed. At the core, its CPU and GPU Tensor and neural network backends are mature and have been tested for years.

Hence, PyTorch is quite fast — whether you run small or large neural networks.

The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.

Extensions Without Pain

Writing new neural network modules, or interfacing with PyTorch's Tensor API, was designed to be straightforward and with minimal abstractions.

You can write new neural network layers in Python using the torch API or your favorite NumPy-based libraries such as SciPy.

If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate. No wrapper code needs to be written. You can see a tutorial here and an example here.

Installation

Binaries

Commands to install binaries via Conda or pip wheels are on our website: https://pytorch.org/get-started/locally/

NVIDIA Jetson Platforms

Python wheels for NVIDIA's Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided here and the L4T container is published here

They require JetPack 4.2 and above, and @dusty-nv and @ptrblck are maintaining them.

From Source

Prerequisites

If you are installing from source, you will need:

• Python 3.10 or later
• A compiler that fully supports C++20, such as clang or gcc (gcc 11.3.0 or newer is required, on Linux)
• Visual Studio or Visual Studio Build Tool (Windows only)
• At least 10 GB of free disk space
• 30-60 minutes for the initial build (subsequent rebuilds are much faster)

\ PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise, Professional, or Community Editions. You can also install the build tools from https://visualstudio.microsoft.com/visual-cpp-build-tools/. The build tools do not come with Visual Studio Code by default.

An example of environment setup is shown below:

• Linux:

$ source <CONDA_INSTALL_DIR>/bin/activate
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>

• Windows:

$ source <CONDA_INSTALL_DIR>\Scripts\activate.bat
$ conda create -y -n <CONDA_NAME>
$ conda activate <CONDA_NAME>
$ call "C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat" x64

A conda environment is not required. You can also do a PyTorch build in a standard virtual environment, e.g., created with tools like uv, provided your system has installed all the necessary dependencies unavailable as pip packages (e.g., CUDA, MKL.)

##### NVIDIA CUDA Support If you want to compile with CUDA support, select a supported version of CUDA from our support matrix, then install the following:

• NVIDIA CUDA
• NVIDIA cuDNN v9.0 or above
• Compiler compatible with CUDA

Note: You could refer to the cuDNN Support Matrix for cuDNN versions with the various supported CUDA, C