ModelFoundry: A Tool for DNN Modularization and On-Demand Model Reuse Inspired by the Wisdom of Software Engineering

Reusing DNN models provides an efficient way to meet new requirements without training models from scratch. Recently, inspired by the wisdom of software reuse, on-demand model reuse has drawn much attention, which aims to reduce the overhead and security risk of model reuse via decomposing models into modules and reusing modules according to user’s requirements. However, existing efforts for on-demand model reuse mainly provide algorithm implementations without tool support. These implementations involve ad-hoc decomposition in experiments and require considerable manual efforts to adapt to new models; thus obstructing the practicality of on-demand model reuse. In this paper, we introduce ModelFoundry, a tool that systematically integrates two modularization approaches proposed in our prior work. ModelFoundry supports automated model decomposition and module reuse, making it more practical and easily integrated into model-sharing platforms. Evaluations conducted on widely used models sourced from PyTorch and GitHub platforms demonstrate that ModelFoundry achieves effective model decomposition and module reuse, as well as good generalizability to various models. A demonstration is available at https://youtu.be/dXHeQ0fGldk.