  I show that a software framework intended primarily for training of neural
networks, PyTorch, is easily applied to a general function minimisation problem
in science. The qualities of PyTorch of ease-of-use and very high efficiency
are found to be applicable in this domain and lead to two orders of magnitude
improvement in time-to-solution with very small software engineering effort.
