AI in Senegal
- AI in Senegal: Server Configuration and Infrastructure
This article details the server configuration for supporting Artificial Intelligence (AI) initiatives in Senegal. It’s targeted towards newcomers to our MediaWiki site and provides a technical overview of the hardware and software used, as well as considerations for future scalability. Understanding this infrastructure is crucial for System Administrators and Data Scientists working on projects within the country.
Overview
The increasing adoption of AI in Senegal necessitates robust and scalable server infrastructure. This infrastructure supports a variety of applications, including Machine Learning, Natural Language Processing, and Computer Vision. The current setup is a hybrid model, leveraging both on-premise servers and cloud resources provided by AWS. This approach balances cost-effectiveness with the need for data sovereignty and low latency for certain applications. The primary goal is to provide accessible and reliable computing power for researchers and developers across Senegal. Network Security is a paramount concern.
Hardware Configuration
The core on-premise infrastructure is located in a secure data center in Dakar. Redundancy is built into every layer, from power supplies to network connections.
| Component | Specification | Quantity |
|---|---|---|
| Server Type | Dell PowerEdge R750 | 12 |
| CPU | Intel Xeon Gold 6338 (32 cores) | 12 |
| RAM | 256GB DDR4 ECC REG | 12 |
| Storage | 4 x 4TB NVMe SSD (RAID 10) | 12 |
| Network Interface | Dual 100GbE | 12 |
| Power Supply | 1100W Redundant | 12 |
The AWS component primarily utilizes EC2 instances for burst capacity and specialized AI workloads. We use a mix of instance types, detailed below. Virtualization is key to our cloud strategy.
| AWS Instance Type | Specification | Usage |
|---|---|---|
| p3.8xlarge | 4 NVIDIA V100 GPUs, 32 vCPUs, 244GB RAM | Deep Learning Training |
| g4dn.xlarge | 1 NVIDIA T4 GPU, 4 vCPUs, 16GB RAM | Machine Learning Inference |
| c5.2xlarge | 8 vCPUs, 16GB RAM | General Purpose Computing |
| r5.large | 2 vCPUs, 8GB RAM | Data Storage & Processing |
Software Stack
The software stack is designed for flexibility and ease of management. Operating Systems are a critical component.
- Operating System: Ubuntu Server 22.04 LTS (on-premise & AWS AMIs)
- Containerization: Docker and Kubernetes are used for application deployment and orchestration. Docker Images are managed through a private registry.
- Programming Languages: Python (3.9), R (4.2)
- Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn
- Database: PostgreSQL 14 with PostGIS extension for geospatial data. Database Administration is handled by a dedicated team.
- Data Storage: MinIO object storage for unstructured data.
- Monitoring: Prometheus and Grafana for system monitoring and alerting. System Monitoring is essential for proactive maintenance.
- Version Control: Git and GitLab for code management. Git Workflow is enforced for all projects.
- Security: Fail2ban, UFW firewall, and regular security audits. Firewall Configuration is documented thoroughly.
Network Infrastructure
The network infrastructure is designed for high bandwidth and low latency.
| Component | Specification | Details |
|---|---|---|
| Core Switch | Cisco Catalyst 9500 Series | Provides high-speed switching and routing. |
| Distribution Switches | Cisco Catalyst 9300 Series | Connects servers and network devices. |
| Firewall | Fortinet FortiGate 600F | Protects the network from external threats. |
| Internet Connectivity | 10Gbps Dedicated Link | Provides high-speed internet access. |
| Internal Network | 10Gbps Ethernet | Connects all servers and network devices. |
We also utilize a VPN for secure remote access.
Scalability and Future Considerations
The current infrastructure is designed to be scalable. We plan to:
- Increase the number of on-premise servers as demand grows.
- Expand our use of AWS cloud resources, particularly for specialized AI workloads.
- Implement a more sophisticated data pipeline using tools like Apache Kafka and Apache Spark. Data Pipeline Design is a key priority.
- Invest in GPU clusters for accelerated deep learning training.
- Explore the use of edge computing for applications requiring ultra-low latency. Edge Computing could be vital for certain applications.
Security Considerations
Security is paramount. Regular Security Audits are performed. Access control is strictly enforced. Data encryption is used both in transit and at rest. Incident response plans are in place to address potential security breaches. Data Backup procedures are followed rigorously.
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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.* ⚠️