pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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Quick Overview

PyTorch is an open-source machine learning library developed by Facebook's AI Research lab. It provides a flexible and efficient framework for building and training neural networks, with a focus on deep learning. PyTorch is known for its dynamic computational graph and intuitive Python-like syntax.

Pros

Dynamic computational graph allowing for easier debugging and more flexible model architectures
Excellent GPU acceleration and distributed training support
Strong community support and extensive ecosystem of tools and libraries
Seamless integration with Python libraries and scientific computing tools

Cons

Steeper learning curve compared to some other deep learning frameworks
Less optimized for production deployment compared to TensorFlow
Smaller ecosystem of pre-trained models compared to some competitors
Documentation can be inconsistent or lacking for some advanced features

Code Examples

Creating a simple neural network:

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 2)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = SimpleNet()

Training a model:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

for epoch in range(num_epochs):
    for inputs, labels in dataloader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

Loading and using a pre-trained model:

import torchvision.models as models

resnet = models.resnet50(pretrained=True)
resnet.eval()

input_tensor = torch.rand(1, 3, 224, 224)
with torch.no_grad():
    output = resnet(input_tensor)

Getting Started

To get started with PyTorch, follow these steps:

Install PyTorch:

pip install torch torchvision torchaudio

Import PyTorch and verify the installation:

import torch
print(torch.__version__)
print(torch.cuda.is_available())

Create a simple tensor and perform operations:

x = torch.rand(5, 3)
y = torch.rand(5, 3)
z = x + y
print(z)

Competitor Comparisons

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Pros of TensorFlow

More mature ecosystem with extensive tools for production deployment
Better support for mobile and embedded devices
Stronger visualization capabilities with TensorBoard

Cons of TensorFlow

Steeper learning curve and more complex API
Less intuitive debugging process
Slower adoption of new research ideas compared to PyTorch

Code Comparison

PyTorch:

import torch

x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])
z = x + y
print(z)

TensorFlow:

import tensorflow as tf

x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.add(x, y)
print(z)

Both frameworks offer similar functionality, but PyTorch's syntax is often considered more Pythonic and intuitive. TensorFlow's approach is more verbose, reflecting its roots in static computational graphs. However, TensorFlow 2.x has made significant strides in simplifying its API and improving ease of use.

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Pros of ONNX Runtime

Optimized for inference performance across multiple hardware platforms
Supports a wide range of ML frameworks, not limited to PyTorch models
Provides a unified runtime for deploying models in production environments

Cons of ONNX Runtime

Less flexible for model development and training compared to PyTorch
May require additional steps to convert models from PyTorch to ONNX format
Smaller community and ecosystem compared to PyTorch

Code Comparison

PyTorch:

import torch

model = torch.nn.Linear(10, 5)
input = torch.randn(3, 10)
output = model(input)

ONNX Runtime:

import onnxruntime as ort

session = ort.InferenceSession("model.onnx")
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: input.numpy()})

The PyTorch code shows direct model definition and inference, while ONNX Runtime requires a pre-exported ONNX model and uses a session-based approach for inference.

mxnet

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Pros of MXNet

More efficient memory usage and faster training on large datasets
Better support for distributed and multi-GPU training out of the box
Hybrid programming model allowing both imperative and symbolic coding styles

Cons of MXNet

Smaller community and ecosystem compared to PyTorch
Less intuitive API and steeper learning curve for beginners
Fewer pre-trained models and libraries available

Code Comparison

MXNet:

import mxnet as mx
from mxnet import nd, autograd, gluon

x = nd.array([[1, 2], [3, 4]])
y = nd.array([[5, 6], [7, 8]])
z = x + y

PyTorch:

import torch

x = torch.tensor([[1, 2], [3, 4]])
y = torch.tensor([[5, 6], [7, 8]])
z = x + y

Both frameworks offer similar functionality for basic tensor operations, but MXNet uses the nd module for array manipulation, while PyTorch uses the torch module directly. PyTorch's syntax is generally considered more Pythonic and easier to read, especially for those familiar with NumPy.

jax

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Pros of JAX

Better performance for large-scale numerical computing and machine learning
Seamless integration with NumPy and automatic differentiation
Supports both CPU and GPU acceleration out of the box

Cons of JAX

Steeper learning curve, especially for those familiar with PyTorch
Smaller ecosystem and community compared to PyTorch
Limited support for dynamic neural networks

Code Comparison

PyTorch example:

import torch

x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)
y = torch.sum(x**2)
y.backward()
print(x.grad)

JAX example:

import jax
import jax.numpy as jnp

def f(x):
    return jnp.sum(x**2)

x = jnp.array([1.0, 2.0, 3.0])
grad_f = jax.grad(f)
print(grad_f(x))

Both examples compute the gradient of the sum of squared elements. PyTorch uses an imperative approach with automatic differentiation, while JAX employs a functional programming style with explicit gradient computation.

keras

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Pros of Keras

Higher-level API, making it easier for beginners to get started
Seamless integration with TensorFlow backend
Modular and user-friendly design for rapid prototyping

Cons of Keras

Less flexibility for advanced users compared to PyTorch
Slower development of new features
Limited support for dynamic computational graphs

Code Comparison

Keras:

from keras.models import Sequential
from keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(10,)),
    Dense(1, activation='sigmoid')
])

PyTorch:

import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(10, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        return self.layers(x)

model = Model()

The Keras example demonstrates its simplicity and high-level API, while the PyTorch code showcases its object-oriented approach and flexibility in defining custom models.

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README

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
Installation
Getting Started
Resources
Communication
Releases and Contributing
The Team
License

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 â 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 3.9 or later
A compiler that fully supports C++17, such as clang or gcc (gcc 9.4.0 or newer is required, on Linux)
Visual Studio or Visual Studio Build Tool (Windows only)

* 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.

* We highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.

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

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 v8.5 or above
Compiler compatible with CUDA

Note: You could refer to the cuDNN Support Matrix for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware

If you want to disable CUDA support, export the environment variable USE_CUDA=0. Other potentially useful environment variables may be found in setup.py.

If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are available here

AMD ROCm Support

If you want to compile with ROCm support, install

AMD ROCm 4.0 and above installation
ROCm is currently supported only for Linux systems.

By default the build system expects ROCm to be installed in /opt/rocm. If ROCm is installed in a different directory, the ROCM_PATH environment variable must be set to the ROCm installation directory. The build system automatically detects the AMD GPU architecture. Optionally, the AMD GPU architecture can be explicitly set with the PYTORCH_ROCM_ARCH environment variable AMD GPU architecture

If you want to disable ROCm support, export the environment variable USE_ROCM=0. Other potentially useful environment variables may be found in setup.py.

Intel GPU Support

If you want to compile with Intel GPU support, follow these

PyTorch Prerequisites for Intel GPUs instructions.
Intel GPU is supported for Linux and Windows.

If you want to disable Intel GPU support, export the environment variable USE_XPU=0. Other potentially useful environment variables may be found in setup.py.

Get the PyTorch Source

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
# if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive

Install Dependencies

Common

conda install cmake ninja
# Run this command from the PyTorch directory after cloning the source code using the âGet the PyTorch Sourceâ section below
pip install -r requirements.txt

On Linux

pip install mkl-static mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda121  # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo

# (optional) If using torch.compile with inductor/triton, install the matching version of triton
# Run from the pytorch directory after cloning
# For Intel GPU support, please explicitly `export USE_XPU=1` before running command.
make triton

On MacOS

# Add this package on intel x86 processor machines only
pip install mkl-static mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv

On Windows

pip install mkl-static mkl-include
# Add these packages if torch.distributed is needed.
# Distributed package support on Windows is a prototype feature and is subject to changes.
conda install -c conda-forge libuv=1.39

Install PyTorch

On Linux

If you would like to compile PyTorch with new C++ ABI enabled, then first run this command:

export _GLIBCXX_USE_CXX11_ABI=1

Please note that starting from PyTorch 2.5, the PyTorch build with XPU supports both new and old C++ ABIs. Previously, XPU only supported the new C++ ABI. If you want to compile with Intel GPU support, please follow Intel GPU Support.

If you're compiling for AMD ROCm then first run this command:

# Only run this if you're compiling for ROCm
python tools/amd_build/build_amd.py

Install PyTorch

export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
python setup.py develop

On macOS

python3 setup.py develop

On Windows

If you want to build legacy python code, please refer to Building on legacy code and CUDA

CPU-only builds

In this mode PyTorch computations will run on your CPU, not your GPU

python setup.py develop

Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you'll need to manually download the library and set up the building environment by tweaking CMAKE_INCLUDE_PATH and LIB. The instruction here is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.

CUDA based build

In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching

NVTX is needed to build Pytorch with CUDA. NVTX is a part of CUDA distributive, where it is called "Nsight Compute". To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox. Make sure that CUDA with Nsight Compute is installed after Visual Studio.

Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If ninja.exe is detected in PATH, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.
If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.

Additional libraries such as Magma, oneDNN, a.k.a. MKLDNN or DNNL, and Sccache are often needed. Please refer to the installation-helper to install them.

You can refer to the build_pytorch.bat script for some other environment variables configurations

cmd

:: Set the environment variables after you have downloaded and unzipped the mkl package,
:: else CMake would throw an error as `Could NOT find OpenMP`.
set CMAKE_INCLUDE_PATH={Your directory}\mkl\include
set LIB={Your directory}\mkl\lib;%LIB%

:: Read the content in the previous section carefully before you proceed.
:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.
:: "Visual Studio 2019 Developer Command Prompt" will be run automatically.
:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.
set CMAKE_GENERATOR_TOOLSET_VERSION=14.27
set DISTUTILS_USE_SDK=1
for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%i\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%

:: [Optional] If you want to override the CUDA host compiler
set CUDAHOSTCXX=C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX64\x64\cl.exe

python setup.py develop

Adjust Build Options (Optional)

You can adjust the configuration of cmake variables optionally (without building first), by doing the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done with such a step.

On Linux

export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
python setup.py build --cmake-only
ccmake build  # or cmake-gui build

On macOS

export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}"
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only
ccmake build  # or cmake-gui build

Docker Image

Using pre-built images

You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+

docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

Building the image yourself

NOTE: Must be built with a docker version > 18.06

The Dockerfile is supplied to build images with CUDA 11.1 support and cuDNN v8. You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it unset to use the default.

make -f docker.Makefile
# images are tagged as docker.io/${your_docker_username}/pytorch

You can also pass the CMAKE_VARS="..." environment variable to specify additional CMake variables to be passed to CMake during the build. See setup.py for the list of available variables.

make -f docker.Makefile

Building the Documentation

To build documentation in various formats, you will need Sphinx and the readthedocs theme.

cd docs/
pip install -r requirements.txt
make html
make serve

Run make to get a list of all available output formats.

If you get a katex error run npm install katex. If it persists, try npm install -g katex

Note: if you installed nodejs with a different package manager (e.g., conda) then npm will probably install a version of katex that is not compatible with your version of nodejs and doc builds will fail. A combination of versions that is known to work is node@6.13.1 and katex@0.13.18. To install the latter with npm you can run npm install -g katex@0.13.18

Previous Versions

Installation instructions and binaries for previous PyTorch versions may be found on our website.

Getting Started

Three-pointers to get you started:

Resources

Communication

Forums: Discuss implementations, research, etc. https://discuss.pytorch.org
GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.
Slack: The PyTorch Slack hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is PyTorch Forums. If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1
Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: https://eepurl.com/cbG0rv
Facebook Page: Important announcements about PyTorch. https://www.facebook.com/pytorch
For brand guidelines, please visit our website at pytorch.org

Releases and Contributing

Typically, PyTorch has three minor releases a year. Please let us know if you encounter a bug by filing an issue.

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.

To learn more about making a contribution to Pytorch, please see our Contribution page. For more information about PyTorch releases, see Release page.

The Team

PyTorch is a community-driven project with several skillful engineers and researchers contributing to it.

PyTorch is currently maintained by Soumith Chintala, Gregory Chanan, Dmytro Dzhulgakov, Edward Yang, and Nikita Shulga with major contributions coming from hundreds of talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Koepf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.

Note: This project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.

License

PyTorch has a BSD-style license, as found in the LICENSE file.

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