SINGA: A distributed deep learning platform

Beng Chin Ooi, Kian Lee Tan, Sheng Wang, Wei Wang, Qingchao Cai, Gang Chen, Jinyang Gao, Zhaojing Luo, Anthony K.H. Tung, Yuan Wang, Zhongle Xie, Meihui Zhang, Kaiping Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

110 Citations (Scopus)

Abstract

Deep learning has shown outstanding performance in various machine learning tasks. However, the deep complex model structure and massive training data make it expensive to train. In this paper, we present a distributed deep learning system, called SINGA, for training big models over large datasets. An intuitive programming model based on the layer abstraction is provided, which supports a variety of popular deep learning models. SINGA architecture supports both synchronous and asynchronous training frameworks. Hybrid training frameworks can also be customized to achieve good scalability. SINGA provides different neural net partitioning schemes for training large models. SINGA is an Apache Incubator project released under Apache License 2.

Original languageEnglish
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages685-688
Number of pages4
ISBN (Electronic)9781450334594
DOIs
Publication statusPublished - 13 Oct 2015
Externally publishedYes
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: 26 Oct 201530 Oct 2015

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Conference

Conference23rd ACM International Conference on Multimedia, MM 2015
Country/TerritoryAustralia
CityBrisbane
Period26/10/1530/10/15

Keywords

  • Deep learning
  • Distributed training

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