  Gravitational time delays provide a powerful one step measurement of $H_0$,
independent of all other probes. One key ingredient in time delay cosmography
are high accuracy lens models. Those are currently expensive to obtain, both,
in terms of computing and investigator time (10$^{5-6}$ CPU hours and $\sim$
0.5-1 year, respectively). Major improvements in modeling speed are therefore
necessary to exploit the large number of lenses that are forecast to be
discovered over the current decade. In order to bypass this roadblock, building
on the work by Shajib et al. (2019), we develop an automated modeling pipeline
and apply it to a sample of 30 quadruply imaged quasars and one lensed compact
galaxy, observed by the Hubble Space Telescope in multiple bands. Our automated
pipeline can derive models for 30/31 lenses with few hours of human time and
<100 CPU hours of computing time for a typical system. For each lens, we
provide measurements of key parameters and predictions of magnification as well
as time delays for the multiple images. We characterize the
cosmography-readiness of our models using the stability of differences in
Fermat potential (proportional to time delay) w.r.t. modeling choices. We find
that for 10/30 lenses our models are cosmography or nearly cosmography grade
(<3% and 3-5% variations). For 6/30 lenses the models are close to cosmography
grade (5-10%). These results are based on informative priors and will need to
be confirmed by further analysis. However, they are also likely to improve by
extending the pipeline modeling sequence and options. In conclusion, we show
that uniform cosmography grade modeling of large strong lens samples is within
reach.
