Computer Vision and AI on Slovenian Supercomputing Infrastructure

This paper reviews how computer vision and AI workloads can be executed in Slovenia on the national SLING infrastructure. We focus on two key environments: the EuroHPC petascale system Vega (240 Nvidia A100 GPUs and 960 CPU nodes) and the Arnes HPC cluster, which includes V100 and some H100-based GPU nodes and serves both education and research. We summarise access paths (Test Access, Development Access, Regular/Large Research Access) and the allocation quotas that affect GPU availability for deep learning training. We also note the role of the Slovenian National Competence Centre (EuroCC 2) in training and user support. We discuss software compatibility for CV (PyTorch/TensorFlow, CUDA libraries, containers) and highlight the gap between batch scheduling (Slurm) and modern AI workflows that require interactive development and long-running inference services. Finally, we outline the perspective of the Slovenian AI Factory (SLAIF), which plans an AI-optimised supercomputer and a hybrid HPC-cloud environment with Kubernetes-based orchestration.