AI in the Ireland Rainforest
- AI in the Ireland Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Ireland Rainforest" project, a research initiative utilizing artificial intelligence to monitor and analyze biodiversity within the unique rainforest ecosystem of Ireland (yes, it exists – a microclimate phenomenon!). This guide is intended for newcomers to the MediaWiki platform and provides a detailed overview of the server infrastructure.
Project Overview
The "AI in the Ireland Rainforest" project leverages a network of sensors deployed within the rainforest to collect data on various environmental factors and species presence. This data is then processed using machine learning algorithms to identify trends, predict potential threats, and inform conservation efforts. The project requires significant computational resources for data ingestion, model training, and real-time analysis. See also Data Acquisition Protocols and Machine Learning Algorithms Used.
Server Infrastructure Overview
The server infrastructure is comprised of three primary tiers: Data Ingestion, Processing/AI, and Presentation. Each tier utilizes dedicated server hardware and software components. We aim for high availability and scalability. Server Redundancy is a key design consideration.
Data Ingestion Tier
This tier is responsible for receiving data from the sensor network. It's designed for high throughput and reliability. Sensor Network Architecture
| Server Role | Server Name | Operating System | RAM | Storage |
|---|---|---|---|---|
| Data Receiver | iris-dr01 | Ubuntu Server 22.04 LTS | 64 GB | 2 TB SSD |
| Data Receiver (Backup) | iris-dr02 | Ubuntu Server 22.04 LTS | 64 GB | 2 TB SSD |
| Database Server | iris-db01 | PostgreSQL 15 | 128 GB | 4 TB RAID 1 |
This tier utilizes PostgreSQL for data storage, chosen for its reliability and support for complex data types. Data is received via a secure MQTT protocol. MQTT Configuration Details.
Processing/AI Tier
This tier houses the computational resources required for data processing and AI model execution. This is the most resource-intensive tier. AI Model Training Pipeline
| Server Role | Server Name | Operating System | CPU | GPU | RAM | Storage |
|---|---|---|---|---|---|---|
| AI Model Training | iris-ai01 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6338 | NVIDIA A100 (80GB) | 256 GB | 8 TB NVMe SSD |
| AI Model Training (Backup) | iris-ai02 | Ubuntu Server 22.04 LTS | Intel Xeon Gold 6338 | NVIDIA A100 (80GB) | 256 GB | 8 TB NVMe SSD |
| Real-time Inference | iris-rt01 | Ubuntu Server 22.04 LTS | Intel Xeon Silver 4310 | NVIDIA T4 | 128 GB | 4 TB NVMe SSD |
The AI models are developed using Python and the TensorFlow framework. TensorFlow Version Details. GPU acceleration is crucial for training and inference speed. GPU Driver Installation Guide.
Presentation Tier
This tier serves the processed data and insights to users via a web interface. It prioritizes responsiveness and security. Web Application Security Protocols.
| Server Role | Server Name | Operating System | Web Server | RAM | Storage |
|---|---|---|---|---|---|
| Web Server | iris-web01 | Ubuntu Server 22.04 LTS | Nginx | 32 GB | 1 TB SSD |
| Web Server (Load Balanced) | iris-web02 | Ubuntu Server 22.04 LTS | Nginx | 32 GB | 1 TB SSD |
| API Server | iris-api01 | Python (Flask) | 64 GB | 2 TB SSD |
The web interface is built using HTML, CSS, and JavaScript. Data visualization is achieved using libraries like Chart.js. Data Visualization Standards. The API server handles requests from the web interface and retrieves data from the database. API Documentation.
Network Configuration
All servers are connected via a dedicated Gigabit Ethernet network. A firewall is in place to restrict access to authorized personnel and services. Firewall Configuration Details. Virtual LANs (VLANs) are used to segment the network for security and performance. VLAN Configuration.
Software Stack
- Operating System: Ubuntu Server 22.04 LTS
- Database: PostgreSQL 15
- Web Server: Nginx
- Programming Languages: Python 3.10
- Machine Learning Framework: TensorFlow 2.10
- Data Visualization: Chart.js
- API Framework: Flask
- MQTT Broker: Mosquitto
- Monitoring: Prometheus and Grafana Server Monitoring Setup
Future Considerations
- Scaling the AI tier to accommodate larger datasets and more complex models.
- Implementing a distributed database solution for improved performance and scalability.
- Exploring the use of cloud-based services for specific tasks. Cloud Integration Plan.
- Automating server provisioning and configuration using tools like Ansible. Ansible Playbooks.
Server Backup Strategy is routinely checked and updated. Disaster Recovery Plan is also maintained.
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.* ⚠️