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A Lightweight Cloud Execution Stack for Neural Network Simulation

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11306))

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Abstract

This paper presents an execution stack for neural network simulation using Cloud container orchestration and microservices. User (or other systems) can employ it by simple RESTful service calls. This service oriented approach allows easy and user-friendly importing, training and evaluating of arbitrary neural network models. This work is influenced by N2Sky, a framework for the exchange of neural network specific knowledge and is based on ViNNSL, the Vienna Neural Network Specification Language, a domain specific neural network modelling language. The presented execution stack runs on many common cloud platforms. It is scalable and each component is extensible and interchangeable.

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Notes

  1. 1.

    https://kubernetes.io.

  2. 2.

    https://cloud.google.com/kubernetes-engine.

  3. 3.

    https://aws.amazon.com/eks.

  4. 4.

    https://azure.microsoft.com/services/container-service.

  5. 5.

    http://cacm.acm.org/news/171642-neural-nets-now-available-in-the-Cloud/.

  6. 6.

    https://docker.com.

  7. 7.

    https://archive.ics.uci.edu/ml/datasets/iris.

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Author information

Authors and Affiliations

  1. University of Vienna, Währingerstr. 29, 1090, Vienna, Austria

    Benjamin Nussbaum & Erich Schikuta

Authors
  1. Benjamin Nussbaum
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  2. Erich Schikuta
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Corresponding author

Correspondence to Erich Schikuta .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Nussbaum, B., Schikuta, E. (2018). A Lightweight Cloud Execution Stack for Neural Network Simulation. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-04224-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04223-3

  • Online ISBN: 978-3-030-04224-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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