UncertaintyPlayground: A Fast and Simplified Python Library for
  Uncertainty Estimation
Ilia Azizi
This paper introduces UncertaintyPlayground, a Python library built on
PyTorch and GPyTorch for uncertainty estimation in supervised learning tasks.
The library offers fast training for Gaussian and multi-modal outcome
distributions through Sparse and Variational Gaussian Process Regressions
(SVGPRs) for normally distributed outcomes and Mixed Density Networks (MDN) for
mixed distributions. In addition to model training with various
hyperparameters, UncertaintyPlayground can visualize the prediction intervals
of one or more instances. Due to using tensor operations, the library can be
trained both on CPU and GPU and offers various PyTorch-specific techniques for
speed optimization. The library contains unit tests for each module and ensures
multi-platform continuous integration with GitHub Workflows (online
integration) and Tox (local integration). Finally, the code is documented with
Google-style docstrings and offers a documentation website created with MkDocs
and MkDocStrings.