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DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure

Elias De Coninck (UGent) , Steven Bohez (UGent) , Sam Leroux (UGent) , Tim Verbelen (UGent) , Bert Vankeirsbilck (UGent) , Pieter Simoens (UGent) and Bart Dhoedt (UGent)
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Abstract
Deep learning has shown tremendous results on various machine learning tasks, but the nature of the problems being tackled and the size of state-of-the-art deep neural networks often require training and deploying models on distributed infrastructure. DIANNE is a modular framework designed for dynamic (re)distribution of deep learning models and procedures. Besides providing elementary network building blocks as well as various training and evaluation routines, DIANNE focuses on dynamic deployment on heterogeneous distributed infrastructure, abstraction of Internet of Things (loT) sensors, integration with external systems and graphical user interfaces to build and deploy networks, while retaining the performance of similar deep learning frameworks. In this paper the DIANNE framework is proposed as an all-in-one solution for deep learning, enabling data and model parallelism though a modular design, offloading to local compute power, and the ability to abstract between simulation and real environment. (C) 2018 Elsevier Inc. All rights reserved.
Keywords
Artificial neural networks, Distributed applications, Machine learning, Internet of Things

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Please use this url to cite or link to this publication:

MLA
De Coninck, Elias et al. “DIANNE: a Modular Framework for Designing, Training and Deploying Deep Neural Networks on Heterogeneous Distributed Infrastructure.” JOURNAL OF SYSTEMS AND SOFTWARE 141 (2018): 52–65. Print.
APA
De Coninck, E., Bohez, S., Leroux, S., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2018). DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure. JOURNAL OF SYSTEMS AND SOFTWARE, 141, 52–65.
Chicago author-date
De Coninck, Elias, Steven Bohez, Sam Leroux, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt. 2018. “DIANNE: a Modular Framework for Designing, Training and Deploying Deep Neural Networks on Heterogeneous Distributed Infrastructure.” Journal of Systems and Software 141: 52–65.
Chicago author-date (all authors)
De Coninck, Elias, Steven Bohez, Sam Leroux, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt. 2018. “DIANNE: a Modular Framework for Designing, Training and Deploying Deep Neural Networks on Heterogeneous Distributed Infrastructure.” Journal of Systems and Software 141: 52–65.
Vancouver
1.
De Coninck E, Bohez S, Leroux S, Verbelen T, Vankeirsbilck B, Simoens P, et al. DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure. JOURNAL OF SYSTEMS AND SOFTWARE. New york: Elsevier Science Inc; 2018;141:52–65.
IEEE
[1]
E. De Coninck et al., “DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure,” JOURNAL OF SYSTEMS AND SOFTWARE, vol. 141, pp. 52–65, 2018.
@article{8566055,
  abstract     = {Deep learning has shown tremendous results on various machine learning tasks, but the nature of the problems being tackled and the size of state-of-the-art deep neural networks often require training and deploying models on distributed infrastructure. DIANNE is a modular framework designed for dynamic (re)distribution of deep learning models and procedures. Besides providing elementary network building blocks as well as various training and evaluation routines, DIANNE focuses on dynamic deployment on heterogeneous distributed infrastructure, abstraction of Internet of Things (loT) sensors, integration with external systems and graphical user interfaces to build and deploy networks, while retaining the performance of similar deep learning frameworks. In this paper the DIANNE framework is proposed as an all-in-one solution for deep learning, enabling data and model parallelism though a modular design, offloading to local compute power, and the ability to abstract between simulation and real environment. (C) 2018 Elsevier Inc. All rights reserved.},
  author       = {De Coninck, Elias and Bohez, Steven and Leroux, Sam and Verbelen, Tim and Vankeirsbilck, Bert and Simoens, Pieter and Dhoedt, Bart},
  issn         = {0164-1212},
  journal      = {JOURNAL OF SYSTEMS AND SOFTWARE},
  keywords     = {Artificial neural networks,Distributed applications,Machine learning,Internet of Things},
  language     = {eng},
  pages        = {52--65},
  publisher    = {Elsevier Science Inc},
  title        = {DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure},
  url          = {http://dx.doi.org/10.1016/j.jss.2018.03.032},
  volume       = {141},
  year         = {2018},
}

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