AI in Benin

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  1. AI in Benin: Server Configuration and Deployment

This article details the server configuration required to support Artificial Intelligence (AI) initiatives within Benin. It's geared towards newcomers to our MediaWiki site and provides a technical overview of the necessary hardware, software, and network infrastructure. This documentation assumes a baseline understanding of server administration and Linux operating systems.

Overview

Benin is actively exploring the potential of AI across various sectors, including agriculture, healthcare, and education. A robust and scalable server infrastructure is crucial for supporting these applications. This document outlines the recommended configuration, focusing on cost-effectiveness and maintainability. We will cover hardware specifications, software stack, networking considerations, and security measures. It is important to consult the System Requirements page prior to deployment.

Hardware Specifications

The following table details the recommended hardware specifications for the core AI server cluster. Redundancy is built in for critical components. These servers will primarily run machine learning models and handle data processing.

Component Specification Quantity
CPU Intel Xeon Gold 6248R (24 cores, 3.0 GHz) 4
RAM 256 GB DDR4 ECC Registered 3200MHz 4
Storage (OS) 500 GB NVMe SSD 4
Storage (Data) 8 x 8TB SAS 12Gbps 7.2K RPM HDD (RAID 6) 1 Array
GPU NVIDIA A100 80GB PCIe 4.0 4
Network Interface Dual Port 100GbE QSFP28 4
Power Supply 1600W Redundant Power Supply 4
Chassis 4U Rackmount Server Chassis 4

This configuration provides substantial processing power and storage capacity for demanding AI workloads. Refer to the Hardware Procurement Guide for approved vendors. Consider the Power Consumption implications when planning deployment.

Software Stack

The software stack will be based on Ubuntu Server 22.04 LTS, chosen for its stability, security updates, and extensive package availability. Key components include:

  • Operating System: Ubuntu Server 22.04 LTS
  • Containerization: Docker and Kubernetes (K8s)
  • Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn
  • Data Storage: PostgreSQL with PostGIS extension for geospatial data.
  • Programming Languages: Python 3.10, R
  • Monitoring: Prometheus and Grafana
  • Version Control: Git, hosted on a Git Repository.

The following table outlines the versions of key software packages:

Software Version
Ubuntu Server 22.04 LTS
Docker 24.0.6
Kubernetes 1.28.3
TensorFlow 2.13.0
PyTorch 2.0.1
scikit-learn 1.3.0
PostgreSQL 15.4
Prometheus 2.45.0
Grafana 9.5.2

Installation will be automated using Ansible playbooks, detailed in the Automation Documentation. Regular software updates are paramount; see the Security Patching Schedule.

Network Infrastructure

A dedicated VLAN will be established for the AI server cluster, segmented from the general network for security and performance reasons. The network topology is a flat network with redundant switches.

The following table details the network configuration:

Parameter Value
VLAN ID 100
IP Address Range 192.168.100.0/24
Gateway 192.168.100.1
DNS Servers 8.8.8.8, 8.8.4.4
Switch Redundancy Yes (Pair of Cisco Catalyst 9300 Series Switches)
Firewall pfSense (configured with strict access control rules)

Network monitoring will be performed using Nagios, integrated with Prometheus for alerting. See the Network Monitoring Guide for details. Ensure proper configuration of the Firewall Rules. Consider utilizing a Load Balancer for distribution of workload.


Security Considerations

Security is a top priority. The following measures will be implemented:

  • Firewall: A robust firewall (pfSense) will be configured to restrict access to the server cluster.
  • Intrusion Detection System (IDS): Snort will be deployed to detect and prevent malicious activity.
  • Regular Security Audits: Periodic security audits will be conducted by external security experts.
  • Data Encryption: All sensitive data will be encrypted at rest and in transit.
  • Access Control: Strict access control policies will be enforced using role-based access control (RBAC).
  • Vulnerability Scanning: Automated vulnerability scanning will be performed weekly.

Consult the Security Policy documentation for detailed security guidelines.



Further Documentation


Intel-Based Server Configurations

Configuration Specifications Benchmark
Core i7-6700K/7700 Server 64 GB DDR4, NVMe SSD 2 x 512 GB CPU Benchmark: 8046
Core i7-8700 Server 64 GB DDR4, NVMe SSD 2x1 TB CPU Benchmark: 13124
Core i9-9900K Server 128 GB DDR4, NVMe SSD 2 x 1 TB CPU Benchmark: 49969
Core i9-13900 Server (64GB) 64 GB RAM, 2x2 TB NVMe SSD
Core i9-13900 Server (128GB) 128 GB RAM, 2x2 TB NVMe SSD
Core i5-13500 Server (64GB) 64 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Server (128GB) 128 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Workstation 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000

AMD-Based Server Configurations

Configuration Specifications Benchmark
Ryzen 5 3600 Server 64 GB RAM, 2x480 GB NVMe CPU Benchmark: 17849
Ryzen 7 7700 Server 64 GB DDR5 RAM, 2x1 TB NVMe CPU Benchmark: 35224
Ryzen 9 5950X Server 128 GB RAM, 2x4 TB NVMe CPU Benchmark: 46045
Ryzen 9 7950X Server 128 GB DDR5 ECC, 2x2 TB NVMe CPU Benchmark: 63561
EPYC 7502P Server (128GB/1TB) 128 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/2TB) 128 GB RAM, 2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/4TB) 128 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/1TB) 256 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/4TB) 256 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 9454P Server 256 GB RAM, 2x2 TB NVMe

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