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arXiv · 2105.08275

ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale

Yuanming Li·Huaizheng Zhang·Shanshan Jiang·Fan Yang·Yonggang Wen·Yong Luo
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Abstract

AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background. In particular, altering these DNN models in the deployment stage posits a tremendous challenge. In this research, we propose and develop a low-code solution, ModelPS (an acronym for "Model Photoshop"), to enable and empower collaborative DNN model editing and intelligent model serving. The ModelPS solution embodies two transformative features: 1) a user-friendly web interface for a developer team to share and edit DNN models pictorially, in a low-code fashion, and 2) a model genie engine in the backend to aid developers in customizing model editing configurations for given deployment requirements or constraints. Our case studies with a wide range of deep learning (DL) models show that the system can tremendously reduce both development and communication overheads with improved productivity.

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Yuanming Li, Huaizheng Zhang, Shanshan Jiang, Fan Yang, Yonggang Wen, Yong Luo. 2021-05-18. ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale. https://arxiv.org/abs/2105.08275

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