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.
Topics & Concepts
Computer scienceDeep learningProductivityCode (set theory)Empirical researchArtificial intelligenceSoftware engineeringProgramming languageSet (abstract data type)MacroeconomicsPhilosophyEpistemologyEconomicsSoftware System Performance and ReliabilityCloud Computing and Resource ManagementAdvanced Neural Network Applications