  We present DLKoopman -- a software package for Koopman theory that uses deep
learning to learn an encoding of a nonlinear dynamical system into a linear
space, while simultaneously learning the linear dynamics. While several
previous efforts have either restricted the ability to learn encodings, or been
bespoke efforts designed for specific systems, DLKoopman is a generalized tool
that can be applied to data-driven learning and optimization of any dynamical
system. It can either be trained on data from individual states (snapshots) of
a system and used to predict its unknown states, or trained on data from
trajectories of a system and used to predict unknown trajectories for new
initial states. DLKoopman is available on the Python Package Index (PyPI) as
'dlkoopman', and includes extensive documentation and tutorials. Additional
contributions of the package include a novel metric called Average Normalized
Absolute Error for evaluating performance, and a ready-to-use hyperparameter
search module for improving performance.
