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Publication type: Conference paper
Type of review: Peer review (publication)
Title: Cloud native robotic applications with GPU sharing on Kubernetes
Authors: Toffetti, Giovanni
Militano, Leonardo
Murphy, Seán
Maurer, Remo
Straub, Mark
et. al: No
DOI: 10.48550/arXiv.2210.03936
Conference details: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23-27 October 2022
Issue Date: 8-Oct-2022
Publisher / Ed. Institution: arXiv
Other identifiers: arXiv:2210.03936
Language: English
Subjects: Robotics; Artificial intelligence; Computer vision; Pattern recognition; Distributed computing; Parallel computing; Cluster computing; Networking; Internet architecture
Subject (DDC): 006: Special computer methods
621.3: Electrical, communications, control engineering
Abstract: In this paper we discuss our experience in teaching the Robotic Applications Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and real, heterogeneous, robotic hardware. We discuss the main advantages of our solutions in terms of seamless "simulation to real'' experience for students and the main shortcomings we encountered with networking and sharing GPUs to support deep learning workloads. We describe the current and foreseen alternatives to avoid these drawbacks in future course editions and propose a more cloud-native approach to deploying multiple robotics applications on a k8s cluster.
Further description: Accepted submission at the Workshop "Cloud and Fog Robotics In The Age of Deep Learning".
Fulltext version: Submitted version
License (according to publishing contract): CC BY-NC 4.0: Attribution - Non commercial 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in collections:Publikationen School of Engineering

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