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

• URL: http://arxiv.org/abs/2105.08275v1
• Date: Tue, 18 May 2021 04:51:56 GMT
• Title: ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
• Authors: Yuanming Li, Huaizheng Zhang, Shanshan Jiang, Fan Yang, Yonggang Wen and Yong Luo
• Abstract summary: We propose and develop a low-code solution, ModelPS, to enable 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.
• Score: 30.333660470820604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The code has been released as an open-source package at GitHub.

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