  We introduce TyXe, a Bayesian neural network library built on top of Pytorch
and Pyro. Our leading design principle is to cleanly separate architecture,
prior, inference and likelihood specification, allowing for a flexible workflow
where users can quickly iterate over combinations of these components. In
contrast to existing packages TyXe does not implement any layer classes, and
instead relies on architectures defined in generic Pytorch code. TyXe then
provides modular choices for canonical priors, variational guides, inference
techniques, and layer selections for a Bayesian treatment of the specified
architecture. Sampling tricks for variance reduction, such as local
reparameterization or flipout, are implemented as effect handlers, which can be
applied independently of other specifications. We showcase the ease of use of
TyXe to explore Bayesian versions of popular models from various libraries: toy
regression with a pure Pytorch neural network; large-scale image classification
with torchvision ResNets; graph neural networks based on DGL; and Neural
Radiance Fields built on top of Pytorch3D. Finally, we provide convenient
abstractions for variational continual learning. In all cases the change from a
deterministic to a Bayesian neural network comes with minimal modifications to
existing code, offering a broad range of researchers and practitioners alike
practical access to uncertainty estimation techniques. The library is available
at https://github.com/TyXe-BDL/TyXe.
