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000861288 037__ $$aFZJ-2019-01785
000861288 1001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b0
000861288 1112_ $$aConference on Big Data from Space (BiDS'19)$$cMunich$$d2019-02-19 - 2019-02-21$$wGermany
000861288 245__ $$aRemote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
000861288 260__ $$c2019
000861288 3367_ $$033$$2EndNote$$aConference Paper
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000861288 520__ $$aMulti-GPU systems are in continuous development todeal with the challenges of intensive computational big dataproblems. On the one hand, parallel architectures provide atremendous computation capacity and outstanding scalability.On the other hand, the production path in multi-user environmentsfaces several roadblocks since they do not grant rootprivileges to the users. Containers provide flexible strategiesfor packing, deploying and running isolated applicationprocesses within multi-user systems and enable scientific reproducibility.This paper describes the usage and advantagesthat the uDocker container tool offers for the developmentof deep learning models in the described context. The experimentalresults show that uDocker is more transparent todeploy for less tech-savvy researchers and allows the applicationto achieve processing time with negligible overheadcompared to an uncontainerized environment.
000861288 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000861288 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x1
000861288 536__ $$0G:(EU-Grant)777435$$aDEEP-HybridDataCloud - Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (777435)$$c777435$$fH2020-EINFRA-2017$$x2
000861288 7001_ $$0P:(DE-HGF)0$$aKozlov, Valentin$$b1
000861288 7001_ $$0P:(DE-HGF)0$$aGötz, Markus$$b2
000861288 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b3
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000861288 9141_ $$y2019
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