Tensorflow-tracing: A performance tuning framework for production

Sayed Hadi Hashemi, Paul Rausch, Benjamin Rabe, Kuan Yen Chou, Simeng Liu, Volodymyr Kindratenko, Roy H. Campbell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019
PublisherUSENIX Association
Pages31-33
Number of pages3
ISBN (Electronic)9781939133007
StatePublished - Jan 1 2019
Event2019 USENIX Conference on Operational Machine Learning, OpML 2019 - Santa Clara, United States
Duration: May 20 2019 → …

Publication series

NameProceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019

Conference

Conference2019 USENIX Conference on Operational Machine Learning, OpML 2019
CountryUnited States
CitySanta Clara
Period5/20/19 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Hashemi, S. H., Rausch, P., Rabe, B., Chou, K. Y., Liu, S., Kindratenko, V., & Campbell, R. H. (2019). Tensorflow-tracing: A performance tuning framework for production. In Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019 (pp. 31-33). (Proceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019). USENIX Association.