The spectroscopic features of white dwarfs are formed in the thin upper layer of their stellar photosphere. These features carry information about the white dwarfs 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.
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