AI in the Wallis and Futuna Rainforest
- AI in the Wallis and Futuna Rainforest: Server Configuration
This document details the server configuration for the "AI in the Wallis and Futuna Rainforest" project, a remote data collection and analysis initiative. It is intended as a guide for new system administrators and developers contributing to the project. This project utilizes Artificial intelligence to analyze data collected from sensors deployed within the rainforests of Wallis and Futuna.
Project Overview
The project aims to monitor biodiversity, track climate change impacts, and detect illegal logging activities within the fragile rainforest ecosystem. Data is gathered from a network of low-power sensors and processed both locally at the edge and centrally on our server infrastructure. The central server handles model training, long-term data storage, and complex analysis. Efficient and reliable server operation is critical to the success of the project. We leverage machine learning algorithms for image recognition (identifying species) and anomaly detection (logging activity). The entire system relies on a robust network infrastructure.
Server Hardware Specifications
The central server is housed in a secure, climate-controlled facility in Papeete, Tahiti, providing reliable power and internet connectivity. The hardware is specifically chosen for its balance of performance, power efficiency, and reliability in a potentially humid environment.
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6338 (32 cores, 64 threads) | 2 |
| RAM | 256GB DDR4 ECC Registered 3200MHz | 1 |
| Storage (OS & Applications) | 2 x 1TB NVMe PCIe Gen4 SSD (RAID 1) | 1 |
| Storage (Data Archive) | 16 x 18TB SATA HDD (RAID 6) | 1 |
| Network Interface | 10 Gigabit Ethernet | 2 |
| Power Supply | 1600W Redundant Power Supplies (80+ Platinum) | 2 |
Software Stack
The server utilizes a Linux-based operating system and a suite of open-source software for data management, processing, and analysis. We prioritize software stability and security. Consider reviewing our security protocols before making any changes to the system.
Operating System
- Operating System: Ubuntu Server 22.04 LTS
- Kernel Version: 5.15.0-86-generic
Database
- Database System: PostgreSQL 14
- Database Extensions: PostGIS, TimescaleDB (for time-series data)
Programming Languages
- Python 3.10 (primary language for AI models and data processing)
- R 4.3.1 (for statistical analysis and data visualization)
AI Frameworks
- TensorFlow 2.12
- PyTorch 2.0
Web Server
- Apache 2.4 (for serving web-based dashboards and APIs)
Network Configuration
The server is connected to the internet via a dedicated 10 Gigabit Ethernet connection. Network security is paramount, with multiple layers of protection in place. Detailed information on network security can be found on the internal wiki.
| Parameter | Value |
|---|---|
| IP Address | 192.168.1.10 (internal) / 203.0.113.5 (external - example) |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.1.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Firewall | UFW (Uncomplicated Firewall) with strict ruleset |
Data Flow and Processing Pipeline
Data from the rainforest sensors is transmitted via a LoRaWAN network to a local gateway. The gateway forwards the data to the central server in Papeete. The data processing pipeline consists of the following stages:
1. **Data Ingestion:** Data is received and validated. 2. **Data Storage:** Raw data is stored in the PostgreSQL database (TimescaleDB extension). 3. **Data Preprocessing:** Data is cleaned, transformed, and prepared for analysis. 4. **AI Model Execution:** Pre-trained AI models are used to analyze the data (e.g., species identification from camera trap images). 5. **Data Visualization:** Results are displayed on a web-based dashboard (using Apache and custom Python scripts).
Server Monitoring and Maintenance
Regular monitoring and maintenance are crucial for ensuring server uptime and data integrity. We utilize a combination of tools for monitoring system performance and identifying potential issues. Consult the maintenance schedule for details.
| Monitoring Tool | Metrics Monitored |
|---|---|
| Nagios | CPU Usage, Memory Usage, Disk Space, Network Traffic, Service Status |
| Prometheus | Time-series data for performance analysis |
| Grafana | Data visualization and dashboarding |
| Logwatch | Log file analysis and reporting |
Future Considerations
- **GPU Acceleration:** Adding a GPU to the server could significantly accelerate AI model training and inference.
- **Distributed Computing:** Exploring the use of a distributed computing framework (e.g., Apache Spark) to handle larger datasets. See the distributed computing guidelines.
- **Edge Computing:** Expanding the use of edge computing to perform more data processing locally at the sensor sites. This can reduce latency and bandwidth requirements.
Help:Contents MediaWiki FAQ Manual:Configuration Server administration Database management Network configuration Security policy Troubleshooting guide
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.* ⚠️