AI in the Kosovo Rainforest
AI in the Kosovo Rainforest: Server Configuration Guide
Welcome to the server configuration documentation for the "AI in the Kosovo Rainforest" project. This article provides a detailed overview of the server infrastructure required to support the data analysis and machine learning workloads associated with this initiative. This guide is intended for newcomers to our MediaWiki site and will cover hardware specifications, software stack, and network considerations. Please familiarize yourself with our Server Administration Policy before making any changes.
Project Overview
The "AI in the Kosovo Rainforest" project focuses on analyzing acoustic data collected from the rainforest to identify and track endangered species. This involves significant computational resources for real-time processing, model training, and data storage. We leverage Machine Learning algorithms to differentiate between species, and the server infrastructure is designed to handle the demands of these computationally intensive tasks. Understanding the Data Flow is crucial for troubleshooting.
Hardware Specifications
The core of our infrastructure consists of three primary server types: Data Acquisition Servers, Processing Servers, and Database Servers. Below are detailed specifications for each. It's important to consult the Hardware Procurement Guide for approved vendors.
| Server Type | CPU | RAM | Storage | Network Interface | |
|---|---|---|---|---|---|
| Data Acquisition Server | Intel Xeon Silver 4310 (12 Cores) | 64 GB DDR4 ECC | 4TB NVMe SSD (RAID 1) | 10 Gigabit Ethernet | |
| Processing Server | AMD EPYC 7763 (64 Cores) | 256 GB DDR4 ECC | 8TB NVMe SSD (RAID 0) + 32TB HDD (RAID 5) | 100 Gigabit Ethernet | |
| Database Server | Intel Xeon Gold 6338 (32 Cores) | 128 GB DDR4 ECC | 16TB SAS HDD (RAID 6) | 10 Gigabit Ethernet |
These specifications are subject to change based on ongoing performance monitoring. See the Performance Monitoring Dashboard for current metrics.
Software Stack
The software stack is built around a Linux foundation, with specific distributions chosen for stability and security. We utilize a containerized approach using Docker and Kubernetes for application deployment and orchestration. Familiarity with Linux System Administration is essential.
| Component | Version | Purpose | |
|---|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers | |
| Containerization | Docker 20.10.12 | Application packaging and isolation | |
| Orchestration | Kubernetes 1.24 | Container management and scaling | |
| Programming Language | Python 3.9 | Core language for AI models | |
| Machine Learning Framework | TensorFlow 2.9 | Deep learning framework | |
| Database | PostgreSQL 14 | Data storage and retrieval |
All code is managed using Git and hosted on our internal GitLab instance. Regular Security Audits are performed to ensure system integrity. Refer to the Software Deployment Procedure for detailed instructions.
Network Configuration
The servers are interconnected via a dedicated VLAN with a high-bandwidth backbone. Security is paramount, and we employ firewalls and intrusion detection systems. Understanding our Network Topology is crucial for troubleshooting connectivity issues.
| Network Segment | IP Range | Purpose | Security | |
|---|---|---|---|---|
| Data Acquisition | 192.168.1.0/24 | Connecting to sensor networks | Firewall restricted to specific ports | |
| Processing | 192.168.2.0/24 | AI model training and inference | Strict access control lists (ACLs) | |
| Database | 192.168.3.0/24 | Data storage and management | Database firewall enabled | |
| Management | 192.168.4.0/24 | Server administration and monitoring | Multi-factor authentication required |
All network traffic is monitored using Nagios for performance and security alerts. Refer to the Network Security Policy for detailed information.
Future Considerations
We are currently evaluating the integration of GPU acceleration for faster model training. Additionally, we are exploring the use of a Distributed File System to improve data scalability. The Capacity Planning Document outlines our projected growth and resource needs. Regular review of the Disaster Recovery Plan is also essential.
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.* ⚠️