Unified Container Environments for Scientific Cluster Scenarios

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URI: http://hdl.handle.net/10900/87666
Dokumentart: ConferencePaper
Date: 2019-04
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
DDC Classifikation: 004 - Data processing and computer science
Keywords: Hochleistungsrechnen
Other Keywords: bwHPC Symposium
unified container environments
machine learning
computational workflow
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Providing runtime dependencies for computational workflows in shared environments, like HPC clusters, requires appropriate management efforts from users and administrators. Users of a cluster define the software stack required for a workflow to execute successfully, while administrators maintain the mechanisms to offer libraries and applications in different versions and combinations for the users to have maximum flexibility. The Environment Modules system is the tool of choice on bwForCluster BinAC for this purpose. In this paper, we present a solution to execute a workflow which relies on a software stack not available via Environment Modules on BinAC. The paper describes the usage of a containerized, user-defined software stack for this particular problem using the Singularity and Docker container platforms. Additionally, we present a solution for the reproducible provisioning of identical software stacks across HPC and non-HPC environments. The approach uses a Docker image as the basis for a Singularity container. This allows users to define arbitrary software stacks giving them the ability to execute their workflows across different environments, from local workstations to HPC clusters. This approach provides identical versions of software and libraries across all environments.

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