Abdul Dakkak and Cheng Li, University of Illinois at Urbana-Champaign; Jinjun Xiong, IBM; Wen-mei Hwu, University of Illinois at Urbana-Champaign
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.
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author = {Abdul Dakkak and Cheng Li and Jinjun Xiong and Wen-mei Hwu},
title = {{DLSpec}: A Deep Learning Task Exchange Specification},
booktitle = {2020 USENIX Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/dakkak},
publisher = {USENIX Association},
month = jul
}
