Apptainer-Based Containers for Legacy and Platform Dependent Machine Learning Applications on HPC Systems

Apptainer (Formerly known as Singularity) is a secure, portable, and easy-to-use container system that provides absolute trust and security. It is widely used across industry and academia and suitable for filling the gaps in integration between running applications on new software technologies and legacy hardware using the optimized resource utilization of CPU and memory. It runs complex applications on HPC clusters in a simple, reproducible way. In this paper we are discussing about various implementations of Artificial Intelligence and Machine learning container-based applications running on Pegasus Supercomputing Nodes using Singularity, Nextflow. It reduces configuration setup work manually by singularity applications and it increases current workflows of High-Performance Computing (HPC), High Throughput Computing (HTC) and run time performance by 3X. we also incorporated comparative based evaluation analytical results of running an application through normal LSF job with singularity container CPU, GPU utilization and its tradeoffs.