front matter
When we started the PyTorch project in mid-2016, we were a band of open source hackers who met online and wanted to write better deep learning software. Two of the three authors of this book, Luca Antiga and Thomas Viehmann, were instrumental in developing PyTorch and making it the success that it is today.
Our goal with PyTorch was to build the most flexible framework possible to express deep learning algorithms. We executed with focus and had a relatively short development time to build a polished product for the developer market. This wouldn’t have been possible if we hadn’t been standing on the shoulders of giants. PyTorch derives a significant part of its codebase from the Torch7 project started in 2007 by Ronan Collobert and others, which has roots in the Lush programming language pioneered by Yann LeCun and Leon Bottou. This rich history helped us focus on what needed to change, rather than conceptually starting from scratch.
It is hard to attribute the success of PyTorch to a single factor. The project offers a good user experience and enhanced debuggability and flexibility, ultimately making users more productive. The huge adoption of PyTorch has resulted in a beautiful ecosystem of software and research built on top of it, making PyTorch even richer in its experience.