DLSpec: A deep learning task exchange specification

Abdul Dakkak, Cheng Li, Jinjun Xiong, Wen Mei Hwu

Research output: Contribution to conferencePaperpeer-review

Abstract

Deep Learning (DL) innovations are being introduced at a rapid pace. However, the current lack of standard specification of DL tasks makes sharing, running, reproducing, and comparing these innovations difficult. To address this problem, we propose DLSpec, a model-, dataset-, software-, and hardware-agnostic DL specification that captures the different aspects of DL tasks. DLSpec has been tested by specifying and running hundreds of DL tasks.

Original languageEnglish (US)
StatePublished - 2020
Event2020 USENIX Conference on Operational Machine Learning, OpML 2020 - Virtual, Online
Duration: Jul 28 2020Aug 7 2020

Conference

Conference2020 USENIX Conference on Operational Machine Learning, OpML 2020
CityVirtual, Online
Period7/28/208/7/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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