AI in the British Virgin Islands Rainforest
AI in the British Virgin Islands Rainforest: Server Configuration
This article details the server configuration utilized for the “AI in the British Virgin Islands Rainforest” project. This project focuses on real-time analysis of audio and visual data collected from remote sensors within the rainforest ecosystem. The goal is to identify and track species, monitor environmental changes, and detect potential threats such as illegal logging or poaching. This documentation is intended for other system administrators and developers contributing to the project. Please review the System Architecture Overview before proceeding.
Project Overview
The project relies on a distributed server infrastructure, with edge processing occurring on-site and centralized analysis performed on servers located in a secure data center. Data is transmitted via Satellite Communication Protocols and secured using Encryption Standards. The data stream is substantial, necessitating high-performance computing and storage solutions. A key component is the Data Pipeline which processes the raw data into usable formats. We utilize Machine Learning Algorithms trained on a comprehensive dataset of rainforest sounds and images. The AI models are continually updated via Model Training Procedures.
Server Infrastructure
The core infrastructure consists of three primary server types: Edge Servers, Processing Servers, and Database Servers. Each type has a specific role and configuration. Understanding the Network Topology is crucial for troubleshooting and maintenance.
Edge Servers
Edge servers are deployed directly within the rainforest, close to the sensor networks. They perform initial data filtering and pre-processing to reduce bandwidth requirements.
| Specification | Value |
|---|---|
| Server Model | Dell PowerEdge R750 |
| CPU | Intel Xeon Silver 4310 (12 Cores) |
| RAM | 64GB DDR4 ECC |
| Storage | 1TB NVMe SSD |
| Operating System | Ubuntu Server 22.04 LTS |
| Network Connectivity | Satellite Link (10 Mbps Downlink/2 Mbps Uplink) |
| Power Supply | Redundant 800W Power Supplies |
These servers run a lightweight version of Linux Distributions optimized for low power consumption. They utilize Containerization Technology (Docker) to deploy the initial processing pipelines.
Processing Servers
Processing servers are located in the data center and are responsible for running the AI models and performing complex data analysis. These servers require significant computational power.
| Specification | Value |
|---|---|
| Server Model | Supermicro SYS-2029U-TR4 |
| CPU | 2 x AMD EPYC 7763 (64 Cores each) |
| RAM | 256GB DDR4 ECC Registered |
| Storage | 4 x 4TB NVMe SSD (RAID 0) |
| GPU | 4 x NVIDIA A100 (80GB) |
| Operating System | CentOS Stream 9 |
| Network Connectivity | 100 Gbps Ethernet |
These servers leverage GPU Acceleration to significantly speed up AI model inference. We utilize Job Scheduling Systems (Slurm) to manage the workload. The servers are monitored using System Monitoring Tools such as Prometheus and Grafana.
Database Servers
Database servers store the processed data, metadata, and AI model results. Data integrity and availability are paramount.
| Specification | Value |
|---|---|
| Server Model | HPE ProLiant DL380 Gen10 |
| CPU | 2 x Intel Xeon Gold 6338 (32 Cores each) |
| RAM | 128GB DDR4 ECC Registered |
| Storage | 16 x 8TB SAS HDD (RAID 10) |
| Database System | PostgreSQL 14 |
| Operating System | Red Hat Enterprise Linux 8 |
| Network Connectivity | 40 Gbps Ethernet |
We employ Database Replication and Backup Strategies to ensure data durability. Access to the database is controlled via Access Control Lists and Authentication Protocols.
Software Stack
The software stack is built around open-source technologies. Key components include:
- Programming Languages: Python, C++
- AI Frameworks: TensorFlow, PyTorch
- Data Storage: PostgreSQL, Object Storage (MinIO)
- [[Message Queue]:] RabbitMQ
- [[Web Server]:] Nginx
Future Considerations
We are currently evaluating the use of Federated Learning to improve model accuracy while preserving data privacy. We are also investigating the feasibility of deploying Serverless Computing to reduce operational costs. The project will benefit from continuous Performance Optimization of the server infrastructure.
Main Page Data Security Policy Sensor Network Deployment AI Model Documentation Troubleshooting Guide System Updates Contact Support Disaster Recovery Plan Network Security Data Validation API Documentation Server Maintenance Schedule User Access Management Configuration Management Software Dependencies Resource Allocation Scalability Planning Cost Analysis Deployment Procedures Backup and Restore 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.* ⚠️