An empirical study on program failures of deep learning jobs

Authors: Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, Mao Yang - IEEE/ACM International Conference on Software Engineering, ICSE 2020 cited by 96

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.