High-technology computing cluster for artificial intelligence Olympiads

Relevance. The growing popularity of artificial intelligence and machine learning competitions, as well as the increasing number of participants, require specialized computing infrastructure that ensures equal conditions and isolation when working with GPU accelerators. Objective. To describe the architecture and practical implementation of a computing cluster prepared for artificial intelligence and machine learning Olympiads. Research methods. Analysis of the requirements of an onsite competition: participant isolation, identical software environments, equal allocation of GPU accelerators, persistent user data, and controlled network access. Results. The technical solution uses RKE2/Kubernetes, NVIDIA A100 GPUs partitioned with Multi-Instance GPU technology, personal JupyterLab workstations, NFS storage, secure web access, and Prometheus/Grafana monitoring. The target configuration supports 66 isolated workstations on 22 worker nodes and assigns a separate 2g.20gb GPU instance to each participant. Conclusion. The proposed infrastructure provides reproducible deployment and manageable operation of an environment for mass machine learning competitions. The novelty of the case is the adaptation of cloud-native GPU infrastructure management to the tasks of a mass onsite school artificial intelligence Olympiad.