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

Authors
  • De Coninck, Elias
  • Bohez, Steven
  • Leroux, Sam
  • Verbelen, Tim
  • Vankeirsbilck, Bert
  • Simoens, Pieter
  • Dhoedt, Bart
Publication Date
Jan 01, 2018
Source
Ghent University Institutional Archive
Keywords
Language
English
License
Unknown
External links

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.

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