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Deploying AI Frameworks on Secure HPC Systems with Containers
- Source :
- HPEC
- Publication Year :
- 2019
-
Abstract
- The increasing interest in the usage of Artificial Intelligence techniques (AI) from the research community and industry to tackle "real world" problems, requires High Performance Computing (HPC) resources to efficiently compute and scale complex algorithms across thousands of nodes. Unfortunately, typical data scientists are not familiar with the unique requirements and characteristics of HPC environments. They usually develop their applications with high-level scripting languages or frameworks such as TensorFlow and the installation process often requires connection to external systems to download open source software during the build. HPC environments, on the other hand, are often based on closed source applications that incorporate parallel and distributed computing API's such as MPI and OpenMP, while users have restricted administrator privileges, and face security restrictions such as not allowing access to external systems. In this paper we discuss the issues associated with the deployment of AI frameworks in a secure HPC environment and how we successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.<br />6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing Conference
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Science - Artificial Intelligence
Distributed computing
Scale (chemistry)
020206 networking & telecommunications
02 engineering and technology
Open source software
Supercomputer
computer.software_genre
Artificial Intelligence (cs.AI)
Computer Science - Distributed, Parallel, and Cluster Computing
Software deployment
Scripting language
Research community
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Distributed, Parallel, and Cluster Computing (cs.DC)
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- HPEC
- Accession number :
- edsair.doi.dedup.....c4068fe1d946e13fa068b8dc16d94537