AI in the Faroe Islands Rainforest
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- AI in the Faroe Islands Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Faroe Islands Rainforest" project. This project aims to analyze real-time data from sensors deployed within the unique ecosystem of the Faroe Islands' rainforests, leveraging Artificial Intelligence for conservation efforts. This document is geared towards new members of the team involved in server maintenance and expansion. Please familiarize yourself with MediaWiki Help before contributing.
Project Overview
The Faroe Islands, despite their northern latitude, boast unique rainforest environments sustained by persistent moisture and sheltered locations. These ecosystems are fragile and sensitive to climate change. Our project utilizes a network of sensors collecting data on temperature, humidity, soil composition, audio (for identifying bird species), and visual imagery (for plant health monitoring). This data is processed using AI models to detect anomalies, predict potential threats, and inform conservation strategies. See also Data Acquisition Systems. Understanding the server infrastructure is crucial for maintaining the project's functionality.
Server Hardware Specifications
The core of our AI processing is a cluster of servers hosted in a dedicated facility on Streymoy, Faroe Islands. The following table details the specifications of each server node:
| Server Node | CPU | RAM | Storage | GPU |
|---|---|---|---|---|
| Node 1 (Primary AI Processing) | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 4 x 4TB NVMe SSD (RAID 0) | NVIDIA Tesla V100 (32GB) |
| Node 2 (Secondary AI Processing/Backup) | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 4 x 4TB NVMe SSD (RAID 0) | NVIDIA Tesla V100 (32GB) |
| Node 3 (Data Storage & Ingestion) | AMD EPYC 7763 (64 cores) | 512 GB DDR4 ECC | 8 x 8TB SATA HDD (RAID 6) | None |
| Node 4 (Database Server) | Intel Xeon Silver 4210 (10 cores) | 128 GB DDR4 ECC | 2 x 2TB NVMe SSD (RAID 1) | None |
These specifications are subject to change as the project evolves. Always consult the Hardware Inventory before making any modifications. Regular Server Monitoring is essential.
Software Stack
The servers run a customized Linux distribution based on Ubuntu Server 22.04 LTS. The following software components are critical to the project:
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker & Kubernetes. See Kubernetes Documentation for details.
- AI Framework: TensorFlow 2.12.0 and PyTorch 2.0.1. Refer to AI Framework Comparison.
- Database: PostgreSQL 15. See Database Administration Guide.
- Message Queue: RabbitMQ 3.9. Used for asynchronous task processing. Refer to Message Queue Configuration.
- Monitoring: Prometheus and Grafana. Essential for performance analysis and alerting. See Monitoring System Setup.
- Version Control: Git, hosted on a private GitLab instance. See Version Control Best Practices.
Network Configuration
The server cluster is connected to the internet via a dedicated 10 Gbps fiber connection. Internal communication between servers utilizes a private VLAN.
| Component | IP Address | Subnet Mask | Gateway |
|---|---|---|---|
| Node 1 | 192.168.1.10 | 255.255.255.0 | 192.168.1.1 |
| Node 2 | 192.168.1.11 | 255.255.255.0 | 192.168.1.1 |
| Node 3 | 192.168.1.12 | 255.255.255.0 | 192.168.1.1 |
| Node 4 | 192.168.1.13 | 255.255.255.0 | 192.168.1.1 |
| Gateway | 192.168.1.1 | 255.255.255.0 | N/A |
Firewall rules are configured using `iptables` to restrict access to necessary ports only. Review the Firewall Configuration document for detailed settings. Regular Network Security Audits are conducted.
Data Flow & Processing Pipeline
The data flow follows these steps:
1. Sensor data is ingested by Node 3. 2. Data is pre-processed and queued using RabbitMQ. 3. AI models running on Node 1 and Node 2 consume data from the queue and perform analysis. 4. Results are stored in the PostgreSQL database on Node 4. 5. Data Visualization dashboards are powered by Grafana, querying the database.
This pipeline is orchestrated using Kubernetes, ensuring scalability and resilience. See Kubernetes Deployment Guide for more details.
Future Expansion
Planned future expansions include:
- Adding additional GPU nodes to increase AI processing capacity. See GPU Upgrade Planning.
- Implementing a distributed storage system for handling larger datasets. Consider Ceph Cluster Configuration.
- Exploring edge computing solutions to reduce latency and bandwidth usage. Refer to Edge Computing Strategy.
Troubleshooting Resources
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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.* ⚠️