Understanding PyTorch's history

As more and more people started migrating to the fascinating world of machine learning, different universities and organizations began building their own frameworks to support their daily research, and Torch was one of the early members of that family. Ronan Collobert, Koray Kavukcuoglu, and Clement Farabet released Torch in 2002 and, later, it was picked up by Facebook AI Research and many other people from several universities and research groups. Lots of start-ups and researchers accepted Torch, and companies started productizing their Torch models to serve millions of users. Twitter, Facebook, DeepMind, and more are part of that list. As per the official Torch7 paper [1] published by the core team, Torch was designed with three key features in mind:

1. It should ease the development of numerical algorithms.
2. It should be easily extended.
3. It should be fast.

Although Torch gives flexibility to the bone, and the Lua + C combo satisfied all the preceding requirements, the major drawback the community faced was the learning curve to the new language, Lua. Although Lua wasn't difficult to grasp and had been used in the industry for a while for highly efficient product development, it did not have widespread acceptance like several other popular languages.

The widespread acceptance of Python in the deep learning community made some researchers and developers rethink the decision made by core authors to choose Lua over Python. It wasn't just the language: the absence of an imperative-styled framework with easy debugging capability also triggered the ideation of PyTorch.

The frontend developers of deep learning find the idea of the symbolic graph difficult. Unfortunately, almost all the deep learning frameworks were built on this foundation. In fact, a few developer groups tried to change this approach with dynamic graphs. Autograd from the Harvard Intelligent Probabilistic Systems Group was the first popular framework that did so. Then the Torch community on Twitter took the idea and implemented torch-autograd.

Next, a research group from Carnegie Mellon University (CMU) came up with DyNet, and then Chainer came up with the capability of dynamic graphs and an interpretable development environment.

All these events were a great inspiration for starting the amazing framework PyTorch, and, in fact, PyTorch started as a fork of Chainer. It began as an internship project by Adam Paszke, who was working under Soumith Chintala, a core developer of Torch. PyTorch then got two more core developers on board and around 100 alpha testers from different companies and universities.

The whole team pulled the chain together in six months and released the beta to the public in January 2017. A big chunk of the research community accepted PyTorch, although the product developers did not initially. Several universities started running courses on PyTorch, including New York University (NYU), Oxford University, and some other European universities.