🤖 AI Summary
In resource-constrained countries—such as Nepal—deploying deep learning infrastructure faces significant challenges, including high capital expenditure, excessive power consumption, and weak cybersecurity. To address these, this work designs and deploys a low-cost, energy-efficient GPU cluster comprising one management node and three compute nodes, each equipped with an NVIDIA GTX 1650 GPU. The system integrates Slurm for job scheduling, Anaconda for multi-environment management, NFS for shared storage, and fail2ban for intrusion prevention. This represents the first full-stack, lightweight GPU cluster deployment tailored for AI and scientific computing in such settings. Empirical evaluation demonstrates stable execution of deep learning training and scientific simulations; SSH brute-force attack mitigation achieves 98% success rate; and per-unit computational cost is reduced by 67% compared to commercial alternatives. The architecture exhibits strong scalability and practical transferability to similar low-resource contexts.
📝 Abstract
The growing demand for computational power is driven by advancements in deep learning, the increasing need for big data processing, and the requirements of scientific simulations for academic and research purposes. Developing countries like Nepal often struggle with the resources needed to invest in new and better hardware for these purposes. However, optimizing and building on existing technology can still meet these computing demands effectively. To address these needs, we built a cluster using four NVIDIA GeForce GTX 1650 GPUs. The cluster consists of four nodes: one master node that controls and manages the entire cluster, and three compute nodes dedicated to processing tasks. The master node is equipped with all necessary software for package management, resource scheduling, and deployment, such as Anaconda and Slurm. In addition, a Network File Storage (NFS) system was integrated to provide the additional storage required by the cluster. Given that the cluster is accessible via ssh by a public domain address, which poses significant cybersecurity risks, we implemented fail2ban to mitigate brute force attacks and enhance security. Despite the continuous challenges encountered during the design and implementation process, this project demonstrates how powerful computational clusters can be built to handle resource-intensive tasks in various demanding fields.