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@INPROCEEDINGS{Cavallaro:861288,
      author       = {Cavallaro, Gabriele and Kozlov, Valentin and Götz, Markus
                      and Riedel, Morris},
      title        = {{R}emote {S}ensing {D}ata {A}nalytics with the {U}docker
                      {C}ontainer {T}ool using {M}ulti-{GPU} {D}eep {L}earning
                      {S}ystems},
      reportid     = {FZJ-2019-01785},
      year         = {2019},
      abstract     = {Multi-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.},
      month         = {Feb},
      date          = {2019-02-19},
      organization  = {Conference on Big Data from Space
                       (BiDS'19), Munich (Germany), 19 Feb
                       2019 - 21 Feb 2019},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / DEEP-EST - DEEP - Extreme Scale Technologies
                      (754304) / DEEP-HybridDataCloud - Designing and Enabling
                      E-infrastructures for intensive Processing in a Hybrid
                      DataCloud (777435)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304 /
                      G:(EU-Grant)777435},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/861288},
}