  The spectroscopic features of white dwarfs are formed in the thin upper layer
of their stellar photosphere. These features carry information about the white
dwarf's surface temperature, surface gravity, and chemical composition
(hereafter 'labels'). Existing methods to determine these labels rely on
complex ab-initio theoretical models which are not always publicly available.
Here we present two techniques to determine atmospheric labels from white dwarf
spectra: a generative fitting pipeline that interpolates theoretical spectra
with artificial neural networks, and a random forest regression model using
parameters derived from absorption line features. We test and compare our
methods using a large catalog of white dwarfs from the Sloan Digital Sky Survey
(SDSS), achieving the same accuracy and negligible bias compared to previous
studies. We package our techniques into an open-source Python module 'wdtools'
that provides a computationally inexpensive way to determine stellar labels
from white dwarf spectra observed from any facility. We will actively develop
and update our tool as more theoretical models become publicly available. We
discuss applications of our tool in its present form to identify interesting
outlier white dwarf systems including those with magnetic fields, helium-rich
atmospheres, and double-degenerate binaries.
