  In this work, we present {\ae}net-PyTorch, a PyTorch-based implementation for
training artificial neural network-based machine learning interatomic
potentials. Developed as an extension of the atomic energy network ({\ae}net),
{\ae}net-PyTorch provides access to all the tools included in {\ae}net for the
application and usage of the potentials. The package has been designed as an
alternative to the internal training capabilities of {\ae}net, leveraging the
power of graphic processing units to facilitate direct training on forces in
addition to energies. This leads to a substantial reduction of the training
time by one to two orders of magnitude compared to the CPU implementation,
enabling direct training on forces for systems beyond small molecules. Here we
demonstrate the main features of {\ae}net-PyTorch and show its performance on
open databases. Our results show that training on all the force information
within a data set is not necessary, and including between 10% to 20% of the
force information is sufficient to achieve optimally accurate interatomic
potentials with the least computational resources.
