AI in the Somaliland Rainforest
AI in the Somaliland Rainforest: Server Configuration
This article details the server configuration deployed to support the "AI in the Somaliland Rainforest" project. This project utilizes artificial intelligence for real-time biodiversity monitoring and analysis within the unique ecosystem of the Somaliland rainforest. This guide is intended for newcomers to our MediaWiki platform and provides a technical overview of the underlying infrastructure. Careful planning and robust hardware were crucial for handling the data volume and computational demands.
Project Overview
The "AI in the Somaliland Rainforest" project involves a network of remote sensors (primarily audio and visual) capturing data 24/7. This data is streamed to our central servers where machine learning algorithms process it to identify species, track population movements, and detect potential threats like deforestation or poaching. The system requires high bandwidth, significant storage capacity, and substantial processing power. The Data Acquisition System is a key component, and its outputs feed directly into the server infrastructure. See also Project Goals and Data Security Protocols.
Server Architecture
Our server architecture is a hybrid model, utilizing both on-premise hardware for low-latency processing and cloud resources for scalability and long-term storage. The core on-premise servers are located in a secure, climate-controlled facility in Hargeisa, Somaliland. This is to minimize latency for the real-time analysis. Cloud integration is handled through Amazon Web Services. A detailed network diagram is available at Network Topology. The system employs a distributed processing model, leveraging Apache Spark for data analysis.
On-Premise Server Specifications
The following table details the specifications of the primary on-premise servers:
| Server Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Data Ingestion Server (x2) | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Processing Server (x3) | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 4 x 8TB SAS HDD (RAID 5) + 1TB NVMe SSD (Cache) | 25 Gbps Ethernet |
| Database Server (x1) | Intel Xeon Silver 4310 (12 cores) | 64 GB DDR4 ECC | 8 x 4TB SAS HDD (RAID 6) | 10 Gbps Ethernet |
| Monitoring Server (x1) | Intel Core i7-10700K (8 cores) | 32 GB DDR4 | 1TB NVMe SSD | 1 Gbps Ethernet |
These servers run Ubuntu Server 22.04 LTS with customized kernels optimized for the workload. Regular System Backups are performed to ensure data integrity. We also utilize Intrusion Detection Systems to protect the servers.
Cloud Infrastructure Details
The cloud component, hosted on AWS, is primarily used for long-term data archiving and model training. The following table outlines the AWS services utilized:
| Service | Instance Type (Example) | Purpose | Approximate Cost (Monthly) |
|---|---|---|---|
| S3 | Standard | Long-term Data Archive | $200 |
| EC2 | p3.8xlarge | Model Training (GPU Intensive) | $2,500 |
| RDS (PostgreSQL) | db.r5.large | Backup Database & Reporting | $300 |
| Lambda | N/A | Automated Data Processing Tasks | $50 |
Costs are approximate and subject to change based on usage. The Cloud Security Policy dictates access controls and data encryption. We use Infrastructure as Code via Terraform to manage our AWS resources.
Software Stack
The software stack powering the "AI in the Somaliland Rainforest" project is comprehensive.
| Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base Operating System |
| Database | PostgreSQL 14 | Data Storage and Management |
| Programming Languages | Python 3.9, R 4.2.1 | Data Analysis and Model Development |
| Machine Learning Frameworks | TensorFlow 2.9, PyTorch 1.12 | Model Training and Inference |
| Data Streaming | Apache Kafka 3.2.3 | Real-time Data Ingestion |
| Data Visualization | Grafana 8.5 | Monitoring and Reporting |
We utilize Docker containers to ensure consistent environments across all servers. The Deployment Pipeline utilizes Jenkins for automated builds and deployments. Access to the servers is managed through SSH Keys and multi-factor authentication.
Future Expansion
We anticipate significant growth in data volume as the project expands. Future plans include upgrading the on-premise servers with faster processors and increased RAM. We also plan to explore the use of specialized hardware, such as Tensor Processing Units (TPUs), for accelerated machine learning. Consideration is being given to a dedicated Edge Computing setup closer to the rainforest itself to reduce latency further.
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.* ⚠️