A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
release_rnspsvdv3fcc7k37xy3yxobe74
by
Zhenpeng Chen and Yanbin Cao and Yuanqiang Liu and Haoyu Wang and Tao Xie and Xuanzhe Liu
2020
Abstract
Deep learning (DL) becomes increasingly pervasive, being used in a wide range
of software applications. These software applications, named as DL based
software (in short as DL software), integrate DL models trained using a large
data corpus with DL programs written based on DL frameworks such as TensorFlow
and Keras. A DL program encodes the network structure of a desirable DL model
and the process by which the model is trained using the training data. To help
developers of DL software meet the new challenges posed by DL, enormous
research efforts in software engineering have been devoted. Existing studies
focus on the development of DL software and extensively analyze faults in DL
programs. However, the deployment of DL software has not been comprehensively
studied. To fill this knowledge gap, this paper presents a comprehensive study
on understanding challenges in deploying DL software. We mine and analyze 3,023
relevant posts from Stack Overflow, a popular Q&A website for developers, and
show the increasing popularity and high difficulty of DL software deployment
among developers. We build a taxonomy of specific challenges encountered by
developers in the process of DL software deployment through manual inspection
of 769 sampled posts and report a series of actionable implications for
researchers, developers, and DL framework vendors.
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