Document Type
Article
Publication Date
6-30-2024
Publication Title
Revista Colombiana de Computación
Volume
25
Issue
1
Pages
29-38
Publisher Name
Universidad Autónoma de Bucaramanga
Publisher Location
Colombia, South America
Abstract
Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global and local ranks. We also show our dual pipe implementation of a foveator using NVIDIA Data Loading Library (DALI). Finally we conduct a series of strong scaling tests on up to 16 ThetaGPU nodes (128 GPUs), and show some variability trends in parallel scaling efficiency.
Recommended Citation
Dario Dematties, Silvio Rizzi, George K Thiruvathukal, Containerization on a Self-supervised active foveated approach to Computer Vision, Revista Colombiana de Computación, 25(1), pp. 29-38.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Copyright Statement
© Universidad Autónoma de Bucaramanga, 2024.
Comments
Author Posting © Universidad Autónoma de Bucaramanga, 2024. This article was published open access in Revista Colombiana de Computación, Volume 25, Number 1, Jun 2024, https://doi.org/10.29375/25392115.5055.