  Ever since the Multilayered Perceptron was first introduced the connectionist
community has struggled with the concept of uncertainty and how this could be
represented in these types of models. This past decade has seen a lot of effort
in trying to join the principled approach of probabilistic modeling with the
scalable nature of deep neural networks. While the theoretical benefits of this
consolidation are clear, there are also several important practical aspects of
these endeavors; namely to force the models we create to represent, learn, and
report uncertainty in every prediction that is made. Many of these efforts have
been based on extending existing frameworks with additional structures. We
present Borch, a scalable deep universal probabilistic programming language,
built on top of PyTorch. The code is available for download and use in our
repository https://gitlab.com/desupervised/borch.
