ICSE 2020★ Distinguished Paper

An empirical study on program failures of deep learning jobs

Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, Mao Yang

Abstract

Deep learning has made significant achievements in many application areas. To train and test models more efficiently, enterprise developers submit and run their deep learning programs on a shared, multi-tenant platform. However, some of the programs fail after a long execution time due to code/script defects, which reduces the development productivity and wastes expensive resources such as GPU, storage, and network I/O.

BibTeX

@inproceedings{Zhang-al:ICSE20,
  author    = {Ru Zhang and
               Wencong Xiao and
               Hongyu Zhang and
               Yu Liu and
               Haoxiang Lin and
               Mao Yang},
  title     = {An empirical study on program failures of deep learning jobs},
  booktitle = {ICSE},
  pages     = {1159--1170},
  publisher = {{ACM}},
  year      = {2020},
}

An empirical study on program failures of deep learning jobs

Abstract

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