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AI in the Caribbean

```wiki #REDIRECT AI in the Caribbean

AI in the Caribbean: Server Configuration Overview

This article details the server configuration used to support the “AI in the Caribbean” project. This project focuses on applying Artificial Intelligence and Machine Learning techniques to address challenges specific to the Caribbean region, including climate change modeling, disaster response, and tourism optimization. This document is intended for new system administrators and developers joining the project. It covers hardware specifications, software stack, networking, and security considerations. Refer to MediaWiki Installation Guide for general wiki setup instructions.

Hardware Infrastructure

The server infrastructure is distributed across three geographically diverse locations within the Caribbean – Barbados, Jamaica, and Trinidad & Tobago – to ensure redundancy and minimize latency for regional access. Each location hosts a cluster of servers.

Location Server Role Number of Servers CPU RAM Storage
Barbados Primary AI Training & Model Storage 4 2 x AMD EPYC 7763 (64 cores/128 threads) 512GB DDR4 ECC 8 x 4TB NVMe SSD (RAID 10)
Jamaica Data Ingestion & Pre-processing 3 2 x Intel Xeon Gold 6338 (32 cores/64 threads) 256GB DDR4 ECC 4 x 2TB NVMe SSD (RAID 1)
Trinidad & Tobago API Gateway & Model Serving 2 2 x Intel Xeon Silver 4310 (12 cores/24 threads) 128GB DDR4 ECC 2 x 1TB NVMe SSD (RAID 1)

All servers utilize 10Gbps network interfaces. Power redundancy is achieved through dual power supplies and UPS systems at each location. For detailed information on Server Room Design, see the related article.

Software Stack

The software stack is built around a Linux foundation, specifically Ubuntu Server 22.04 LTS. We leverage containerization technologies for application deployment and management.

Layer Software Version Purpose
Operating System Ubuntu Server 22.04 LTS Base OS for all servers. See Ubuntu Server Documentation
Containerization Docker 20.10 Application packaging and deployment.
Orchestration Kubernetes 1.25 Container orchestration and scaling. Refer to Kubernetes Documentation
AI Frameworks TensorFlow, PyTorch 2.10, 1.13 Machine learning model development and training. See TensorFlow Website and PyTorch Website.
Database PostgreSQL 14 Data storage and management. See PostgreSQL Documentation.
API Gateway Kong 2.8 API management and routing.

All code is version controlled using Git and hosted on a private GitLab instance. Continuous Integration/Continuous Deployment (CI/CD) pipelines are implemented using GitLab CI/CD.

Networking Configuration

Each location is connected to the internet via a dedicated fiber optic connection with a minimum bandwidth of 100Mbps. A Virtual Private Network (VPN) connects the three locations, ensuring secure communication between servers.

Parameter Value Description
VPN Protocol WireGuard Provides fast and secure VPN connections. See WireGuard Documentation.
Internal Network 192.168.0.0/16 Private network address space for internal communication.
DNS Server Bind9 Used for internal DNS resolution. See Bind9 Documentation.
Firewall UFW (Uncomplicated Firewall) Protects servers from unauthorized access. See UFW Documentation.

Network monitoring is performed using Prometheus and Grafana, providing real-time insights into network performance and potential issues. See Prometheus Monitoring for more information.

Security Considerations

Security is paramount. The following measures are in place:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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