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

**Authors:** Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai  
**arXiv:** [1912.01703](https://arxiv.org/abs/1912.01703)  
**Date:** 2019-12-03  
**Summary page:** https://www.summarizepaper.com/en/paper/1912.01703v1/

## Key points

- PyTorch is a cutting-edge library that combines imperative and Pythonic programming styles, challenging the conventional belief that these two goals are mutually exclusive.
- Developed by a team of experts including Adam Paszke, Sam Gross, Francisco Massa, and others.
- PyTorch simplifies code as a model, facilitates easy debugging, and seamlessly integrates with other popular scientific computing libraries.
- Despite its user-friendly interface, PyTorch remains highly efficient and supports hardware accelerators like GPUs.
- Every aspect of PyTorch operates as a regular Python program, granting users full control over its functionalities.
- PyTorch achieves remarkable performance by enabling seamless collaboration between various subsystems.
- The authors showcase the effectiveness of individual subsystems within PyTorch as well as its overall speed through rigorous testing on multiple standard benchmarks.

## Summary

PyTorch is a cutting-edge  library that successfully combines  and , challenging the conventional belief that these two goals are mutually exclusive. Developed by a team of experts including Adam Paszke, Sam Gross, Francisco Massa, and others, PyTorch offers an imperative and Pythonic programming style that simplifies code as a model, facilitates easy debugging, and seamlessly integrates with other popular scientific computing libraries. Despite its user-friendly interface, PyTorch remains highly efficient and supports hardware accelerators like GPUs. In their paper titled "PyTorch: An Imperative Style, High-Performance Deep Learning Library," the authors delve into the core principles that underpin PyTorch's design and how these principles manifest in its architecture. They highlight the fact that every aspect of PyTorch operates as a regular Python program, granting users full control over its functionalities. By meticulously implementing key components of its runtime system, PyTorch achieves remarkable performance by enabling seamless collaboration between various subsystems. The authors showcase the effectiveness of individual subsystems within PyTorch as well as its overall speed through rigorous testing on multiple standard benchmarks. With 12 pages and 3 figures presented at NeurIPS 2019, this comprehensive exploration of PyTorch underscores its status as a versatile deep learning framework that excels in both  and .