Deploying AI Frameworks on Secure HPC Systems with Containers
This solves deployment problems for data scientists working in restricted HPC environments, but it is incremental as it applies existing container technology to a specific domain.
The paper addresses the challenge of deploying AI frameworks on secure HPC systems, where typical data scientists face compatibility and security issues, and reports successful deployment on SuperMUC-NG using Charliecloud.
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.