DragDL:An Easy-to-Use Graphical DL Model Construction System

Abstract

Deep learning has broad applications in various fields.However,users still need to face problems from two aspects when applying deep learning.First,deep learning has a complex theoretical background,non-professional users lack background knowledge in modeling and tuning.It is difficult for them to build performance-optimized models.Second,modules such as data preprocessing,model training,and prediction often involve more complicated programming implementations,which bring some difficulties in getting started for non-professional users who have no programming skill background.In view of the above two issues of usability,this paper proposes an easy-to-use graphical deep learning model construction system,DragDL.The purpose of DragDL is to reduce the difficulty of data preprocessing,model training,monitoring,online prediction and other tasks for users.The system is based on the PaddlePaddle framework and supports building a deep learning network structure on the canvas by dragging graphical operators,supporting inference and prediction functions,and abstracting the data preprocessing operation process into a dataflow graph,which is convenient for users to understand and debug.The system also provides visualization functions for performance monitoring during the training process.At the same time,DragDL provides a classic model library,which allows users to build new DL network by tuning the existing classic model network.DragDL is deployed based on a centralized server and Web client.The server provides a virtual machine service for submitted tasks and supports large-scale asynchronous task scheduling to have concurrent processing capabilities.

Keywords

• deep learning|graphical programming|dataflow graph|pre-trained model|paddlepaddle