RESEARCH

COMPUTER VISION

PyTorch: An Imperative Style, High-Performance Deep Learning Library

December 02, 2019

Abstract

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.

Download the Paper

AUTHORS

Written by

Soumith Chintala

Adam Lerer

Benoit Steiner

Edward Yang

Francisco Massa

Gregory Chanan

Junjie Bai

Lu Fang

Sam Gross

Zachary DeVito

Zeming Lin

Adam Paszke

Alban Desmaison

Alykhan Tejani

Andreas Köpf

James Bradbury

Luca Antiga

Martin Raison

Natalia Gimelshein

Sasank Chilamkurthy

Trevor Killeen

Publisher

NeurIPS

Research Topics

Computer Vision

Related Publications

April 18, 2024

COMPUTER VISION

Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation

Jonas Kohler, Albert Pumarola, Edgar Schoenfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali Thabet

April 18, 2024

March 20, 2024

COMPUTER VISION

SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model

Armen Avetisyan, Chris Xie, Henry Howard-Jenkins, Tsun-Yi Yang, Samir Aroudj, Suvam Patra, Fuyang Zhang, Duncan Frost, Luke Holland, Campbell Orme, Jakob Julian Engel, Edward Miller, Richard Newcombe, Vasileios Balntas

March 20, 2024

February 13, 2024

GRAPHICS

COMPUTER VISION

IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos

February 13, 2024

January 25, 2024

COMPUTER VISION

LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks

Felix Xu, Di Lin, Jianjun Zhao, Jianlang Chen, Lei Ma, Qing Guo, Wei Feng, Xuhong Ren

January 25, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.