  Modeling of strongly gravitationally lensed galaxies is often required in
order to use them as astrophysical or cosmological probes. With current and
upcoming wide-field imaging surveys, the number of detected lenses is
increasing significantly such that automated and fast modeling procedures for
ground-based data are urgently needed. This is especially pertinent to
short-lived lensed transients in order to plan follow-up observations.
Therefore, we present in a companion paper (submitted) a neural network
predicting the parameter values with corresponding uncertainties of a Singular
Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we
present a newly-developed pipeline glee_auto.py to model consistently any
galaxy-scale lensing system. In contrast to previous automated modeling
pipelines that require high-resolution images, glee_auto.py is optimized for
ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the
upcoming Rubin Observatory Legacy Survey of Space and Time. We further present
glee_tools.py, a flexible automation code for individual modeling that has no
direct decisions and assumptions implemented. Both pipelines, in addition to
our modeling network, minimize the user input time drastically and thus are
important for future modeling efforts. We apply the network to 31 real
galaxy-scale lenses of HSC and compare the results to the traditional models.
In the direct comparison, we find a very good match for the Einstein radius
especially for systems with $\theta_E \gtrsim 2$". The lens mass center and
ellipticity show reasonable agreement. The main discrepancies are on the
external shear as expected from our tests on mock systems. In general, our
study demonstrates that neural networks are a viable and ultra fast approach
for measuring the lens-galaxy masses from ground-based data in the upcoming era
with $\sim10^5$ lenses expected.
