TY  - CONF
AU  - Cavallaro, Gabriele
AU  - Kozlov, Valentin
AU  - Götz, Markus
AU  - Riedel, Morris
TI  - Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
CY  - Luxembourg
PB  - Publications Office of the European Union
M1  - FZJ-2019-01799
SP  - 177-180
PY  - 2019
AB  - Multi-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.
T2  - Conference on Big Data from Space (BiDS'19)
CY  - 19 Feb 2019 - 21 Feb 2019, Munich (Germany)
Y2  - 19 Feb 2019 - 21 Feb 2019
M2  - Munich, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - https://juser.fz-juelich.de/record/861304
ER  -