AI in Liberia
AI in Liberia: Server Configuration and Deployment
This article details the server configuration required to support Artificial Intelligence (AI) initiatives within Liberia, focusing on practical deployment considerations for a resource-constrained environment. It is intended as a guide for newcomers to our MediaWiki site and outlines the necessary hardware, software, and networking infrastructure. We will cover everything from basic server specifications to considerations for power and cooling. This deployment assumes a phased approach, starting with core services and scaling as needed. Understanding the limitations of available infrastructure is crucial. See also Data Security Considerations and Network Topology.
Overview
Liberia presents unique challenges for AI deployment, including limited internet bandwidth, unreliable power grids, and a shortage of skilled IT personnel. Therefore, a robust and efficient server configuration is paramount. This configuration prioritizes cost-effectiveness, maintainability, and scalability. Initial deployments will focus on offline processing and data storage, with eventual integration with cloud services as bandwidth improves. This strategy aligns with the national Digital Liberia Strategy. We will be using a hybrid architecture – a combination of on-premise servers and cloud resources.
Hardware Specifications
The initial server deployment will consist of three primary servers: a data storage server, a processing server, and a gateway server. The following tables detail the specifications for each. These specifications are based on current (October 26, 2023) market prices and availability. Remember to consult the Hardware Procurement Guidelines before making any purchases.
| Server Type | Processor | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Data Storage Server | Intel Xeon Silver 4310 (12 Cores) | 64 GB DDR4 ECC | 48 TB RAID 6 configured HDD | Dual 10GbE |
| Processing Server | AMD EPYC 7302P (16 Cores) | 128 GB DDR4 ECC | 2 x 1 TB NVMe SSD (RAID 1) | Dual 10GbE |
| Gateway Server | Intel Core i5-12400 (6 Cores) | 32 GB DDR4 | 1 TB SATA SSD | Dual 1GbE |
These specifications provide a balance between performance and cost. The data storage server uses high-capacity HDDs for cost-effective storage of large datasets. The processing server utilizes NVMe SSDs for fast data access during AI model training and inference. The gateway server facilitates communication between the internal network and external services. Consider the Power Consumption Estimates when planning power infrastructure.
Software Stack
The software stack is designed for flexibility and compatibility with popular AI frameworks. We will be utilizing a Linux-based operating system for its stability and open-source nature. See the Software Licensing Policy for details on licensing requirements.
| Component | Software | Version | Description |
|---|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS | Provides a stable and secure base for the server. |
| Containerization | Docker | 20.10 | Enables packaging and deployment of AI applications in isolated containers. |
| Orchestration | Kubernetes | 1.25 | Manages and scales containerized applications. |
| AI Frameworks | TensorFlow | 2.10 | Popular machine learning framework. |
| AI Frameworks | PyTorch | 1.13 | Another popular machine learning framework. |
| Database | PostgreSQL | 14 | Stores metadata and application data. |
This software stack allows for easy deployment and management of AI applications. Docker and Kubernetes provide a scalable and resilient platform for running AI models. TensorFlow and PyTorch are widely used AI frameworks with extensive community support. Regular updates and security patching are critical – refer to the Security Update Schedule.
Networking Configuration
The network configuration must ensure reliable communication between the servers and external services. A dedicated VLAN will be used to isolate the AI infrastructure from the rest of the network. Bandwidth limitations must be carefully considered.
| Parameter | Value | Description |
|---|---|---|
| VLAN ID | 100 | Dedicated VLAN for AI infrastructure. |
| IP Address Range | 192.168.100.0/24 | IP address range for servers within the VLAN. |
| DNS Server | 8.8.8.8, 8.8.4.4 | Public DNS servers. Consider a local caching DNS server. |
| Gateway | 192.168.100.1 | Gateway IP address. |
| Firewall | iptables | Configured to allow necessary traffic. See Firewall Ruleset. |
The network configuration prioritizes security and isolation. A dedicated VLAN helps to protect the AI infrastructure from unauthorized access. The firewall is configured to allow only necessary traffic. Regular network monitoring is essential – refer to the Network Monitoring Procedures. Consider implementing a VPN for secure remote access. This aligns with the Data Transmission Protocols guidelines.
Future Scalability
As AI initiatives in Liberia expand, the server infrastructure will need to scale accordingly. This can be achieved by adding more servers to the cluster or by leveraging cloud resources. The Kubernetes orchestration platform simplifies the process of scaling applications. The initial deployment is designed to be modular, allowing for easy expansion. See the Disaster Recovery Plan for considerations regarding redundancy and failover. We are also exploring Edge Computing Solutions for remote deployments.
Server Maintenance Schedule Troubleshooting Guide Contact Information Frequently Asked Questions Data Backup Procedures
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.* ⚠️