A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
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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01:00 - 01:30: ML In PracticePaper Presentations / Industry Papers / Research Papers at Virtual room 1 | |||
| 01:00 - 01:02 Talk | A Comprehensive Study on Challenges in Deploying Deep Learning Based Software Research Papers Zhenpeng ChenPeking University, China, Yanbin CaoPeking University, China, Yuanqiang LiuPeking University, China, Haoyu WangBeijing University of Posts and Telecommunications, Tao XiePeking University, Xuanzhe LiuPeking University, China DOI Pre-print | ||
| 01:03 - 01:04 Talk | A First Look at the Integration of Machine Learning Models in Complex Autonomous Driving Systems: A Case Study on Apollo Industry Papers pengzi Concordia University, Canada, Jinqiu YangConcordia University, Montreal, Canada, Tse-Hsun (Peter) ChenConcordia University, Lei MaKyushu University DOI | ||
| 01:05 - 01:06 Talk | Enhancing the Interoperability between Deep Learning Frameworks by Model Conversion Industry Papers Yu David LiuSUNY Binghamton, USA, Cheng ChenByteDance, China, Ru ZhangMicrosoft Research, Tingting QinMicrosoft Research, China, Xiang JiMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research DOI | ||
| 01:07 - 01:08 Talk | Estimating GPU Memory Consumption of Deep Learning Models Industry Papers Yanjie GaoMicrosoft Research, China, Yu David LiuSUNY Binghamton, USA, Hongyu ZhangUniversity of Newcastle, Australia, lizhengxian Microsoft Research, China, Yonghao ZhuMicrosoft Research, China, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research DOI | ||
| 01:09 - 01:10 Talk | IntelliCode Compose: Code Generation using Transformer Industry Papers Alexey SvyatkovskiyMicrosoft, Shao Kun DengMicrosoft Corporation, Shengyu FuMicrosoft, USA, Neel SundaresanMicrosoft Corporation DOI Pre-print | ||
| 01:11 - 01:12 Talk | Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving Industry Papers Jinhan KimKAIST, Jeongil JuHyundai Motor Group, South Korea, Robert FeldtChalmers University of Technology, Sweden, Shin YooKorea Advanced Institute of Science and Technology DOI Pre-print | ||
| 01:13 - 01:30 | Conversations on ML In Practice Research Papers Sidong FengAustralian National University, Australia, Tse-Hsun (Peter) ChenConcordia University, Yanbin CaoPeking University, China, Yanjie GaoMicrosoft Research, China, Zhenpeng ChenPeking University, China, M: Joshua GarciaUniversity of California, Irvine | ||