AI in the Saba Rainforest
- AI in the Saba Rainforest: Server Configuration
This article details the server configuration powering the "AI in the Saba Rainforest" project, a research initiative utilizing artificial intelligence to monitor and analyze the biodiversity of the Saba rainforest ecosystem. This document is intended as a guide for new server administrators joining the project. It covers hardware specifications, software stack, network configuration, and security considerations. Understanding these details is crucial for maintaining the system's stability and performance. Refer to MediaWiki Help for general wiki formatting assistance.
Project Overview
The "AI in the Saba Rainforest" project employs a network of sensor nodes deployed throughout the rainforest, collecting data on various environmental factors like temperature, humidity, soundscapes, and visual data. This data is transmitted to a central server cluster for processing using machine learning algorithms. The primary goals include species identification, anomaly detection (e.g., illegal logging), and long-term environmental monitoring. Data storage and retrieval are managed through a robust database system. See Data Acquisition Systems for details on the sensor network.
Hardware Specifications
The server cluster consists of three primary server types: Ingestion Servers, Processing Servers, and Database Servers. Each type is optimized for its specific role.
| Server Type | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Ingestion Servers (x2) | Intel Xeon Silver 4310 (12 cores) | 64 GB DDR4 ECC | 2 x 2 TB NVMe SSD (RAID 1) | 10 GbE |
| Processing Servers (x4) | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 4 x 4 TB NVMe SSD (RAID 0) | 100 GbE |
| Database Servers (x2) | Intel Xeon Gold 6338 (32 cores) | 128 GB DDR4 ECC | 8 x 8 TB SAS HDD (RAID 6) | 25 GbE |
All servers are housed in a climate-controlled data center with redundant power supplies and network connections. The data center utilizes a UPS (Uninterruptible Power Supply) for short-term power outages and a backup generator for extended outages. See Data Center Infrastructure for more details.
Software Stack
The software stack is built around a Linux-based operating system, providing a stable and secure platform for the AI applications.
| Component | Software | Version |
|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS |
| Programming Languages | Python, C++ | 3.10, 11 |
| Machine Learning Framework | TensorFlow, PyTorch | 2.12, 2.0 |
| Database Management System | PostgreSQL | 15 |
| Web Server | Nginx | 1.22 |
| Containerization | Docker, Kubernetes | 20.10, 1.26 |
The AI models are developed and trained using Python and the TensorFlow and PyTorch frameworks. Docker and Kubernetes are used for containerization and orchestration, simplifying deployment and scaling. PostgreSQL serves as the primary database for storing sensor data, model metadata, and analysis results. See Software Version Control for information on managing software versions.
Network Configuration
The server cluster is connected to the internet via a dedicated fiber optic connection. The network is segmented into three VLANs: one for the Ingestion Servers, one for the Processing Servers, and one for the Database Servers. This segmentation enhances security and isolates traffic. A firewall is in place to restrict access to the servers and protect against unauthorized access.
| VLAN | IP Range | Subnet Mask | Gateway |
|---|---|---|---|
| Ingestion | 192.168.1.0/24 | 255.255.255.0 | 192.168.1.1 |
| Processing | 192.168.2.0/24 | 255.255.255.0 | 192.168.2.1 |
| Database | 192.168.3.0/24 | 255.255.255.0 | 192.168.3.1 |
DNS resolution is handled by an internal DNS server. Network monitoring is performed using Prometheus and Grafana. See Network Security Protocols for a detailed explanation of network security measures.
Security Considerations
Security is paramount for the "AI in the Saba Rainforest" project. The following security measures are in place:
- **Firewall:** A stateful firewall protects the server cluster from unauthorized access.
- **Intrusion Detection System (IDS):** An IDS monitors network traffic for malicious activity.
- **Regular Security Audits:** Periodic security audits are conducted to identify and address vulnerabilities.
- **Access Control:** Access to the servers is restricted to authorized personnel only, using strong passwords and multi-factor authentication.
- **Data Encryption:** Sensitive data is encrypted both in transit and at rest.
- **Software Updates:** All software is kept up-to-date with the latest security patches.
- **Vulnerability Scanning:** Regular vulnerability scans are performed to identify and remediate potential security weaknesses. See Security Best Practices for more details.
Future Expansion
Planned future expansion includes adding GPU-accelerated servers to the Processing Server cluster to accelerate machine learning model training and inference. We also plan to implement a distributed file system for storing large datasets. See Scalability Planning for details on future infrastructure growth. Further details on the AI models used can be found at AI Model Documentation. Consider reviewing Troubleshooting Common Issues for known problems.
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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.* ⚠️