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000861304 037__ $$aFZJ-2019-01799
000861304 1001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b0
000861304 1112_ $$aConference on Big Data from Space (BiDS'19)$$cMunich$$d2019-02-19 - 2019-02-21$$wGermany
000861304 245__ $$aRemote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
000861304 260__ $$aLuxembourg$$bPublications Office of the European Union$$c2019
000861304 29510 $$aProc. of the 2019 conference on Big Data from Space (BiDS’2019), EUR 29660 EN, ISBN 978-92-76-00034-1, doi:10.2760/848593
000861304 300__ $$a177-180
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000861304 520__ $$aMulti-GPU systems are in continuous development to deal with the challenges of intensive computational big data problems. On the one hand, parallel architectures provide a tremendous computation capacity and outstanding scalability. On the other hand, the production path in multi-user environment faces several roadblocks since they do not grant root privileges to the users. Containers provide flexible strategies for packing, deploying and running isolated application processes within multi-user systems and enable scientific reproducibility. This paper describes the usage and advantages that the uDocker container tool offers for the development of deep learning models in the described context. The experimental results show that uDocker is more transparent to deploy for less tech-savvy researchers and allows the application to achieve processing time with negligible overhead compared to an uncontainerized environment.
000861304 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000861304 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x1
000861304 536__ $$0G:(EU-Grant)777435$$aDEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (777435)$$c777435$$fH2020-EINFRA-2017$$x2
000861304 7001_ $$0P:(DE-HGF)0$$aKozlov, Valentin$$b1
000861304 7001_ $$0P:(DE-HGF)0$$aGötz, Markus$$b2
000861304 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b3
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