  Currently, most machine learning models are trained by centralized teams and
are rarely updated. In contrast, open-source software development involves the
iterative development of a shared artifact through distributed collaboration
using a version control system. In the interest of enabling collaborative and
continual improvement of machine learning models, we introduce Git-Theta, a
version control system for machine learning models. Git-Theta is an extension
to Git, the most widely used version control software, that allows fine-grained
tracking of changes to model parameters alongside code and other artifacts.
Unlike existing version control systems that treat a model checkpoint as a blob
of data, Git-Theta leverages the structure of checkpoints to support
communication-efficient updates, automatic model merges, and meaningful
reporting about the difference between two versions of a model. In addition,
Git-Theta includes a plug-in system that enables users to easily add support
for new functionality. In this paper, we introduce Git-Theta's design and
features and include an example use-case of Git-Theta where a pre-trained model
is continually adapted and modified. We publicly release Git-Theta in hopes of
kickstarting a new era of collaborative model development.
