AI in the Pyrenees

AI in the Pyrenees: Server Configuration

This document details the server configuration powering the "AI in the Pyrenees" project. This project utilizes artificial intelligence for environmental monitoring and predictive analysis within the Pyrenees mountain range. This article is aimed at newcomers to our MediaWiki infrastructure and provides a technical overview of the hardware and software deployed.

Overview

The "AI in the Pyrenees" project relies on a distributed server infrastructure to process data from a network of sensors deployed throughout the mountain range. These sensors collect data on temperature, humidity, wind speed, snow depth, and wildlife activity. The data is transmitted to regional hubs, which then forward it to the central processing servers located in a secure data center. The servers employ machine learning algorithms to identify patterns, predict environmental changes, and provide alerts to relevant authorities. We utilize a hybrid cloud approach, combining on-premise servers for low-latency processing with cloud resources for scalability and long-term storage. See also Data Acquisition Systems and Sensor Networks.

Hardware Configuration

The core of our infrastructure consists of three tiers of servers: edge servers, regional hubs, and central processing servers.

Edge Servers

These servers are located near the sensor networks to provide initial data processing and filtering. They are ruggedized for harsh environmental conditions.

Specification	Value
Processor	Intel Xeon E-2388G (8 cores, 3.2 GHz)
RAM	64 GB DDR4 ECC
Storage	1 TB NVMe SSD
Network Interface	Dual Gigabit Ethernet
Operating System	Ubuntu Server 22.04 LTS

Regional Hubs

These servers aggregate data from multiple edge servers and perform preliminary analysis. They act as a gateway to the central processing servers. Consider reviewing Network Topology for more details.

Specification	Value
Processor	AMD EPYC 7302P (16 cores, 3.0 GHz)
RAM	128 GB DDR4 ECC
Storage	2 x 2 TB NVMe SSD (RAID 1)
Network Interface	Quad Gigabit Ethernet
Operating System	CentOS Stream 9

Central Processing Servers

These servers handle the bulk of the data processing, model training, and analysis. They are housed in a secure data center with redundant power and cooling. See Data Center Security for detailed information.

Specification	Value
Processor	Dual Intel Xeon Platinum 8380 (40 cores per processor, 2.3 GHz)
RAM	512 GB DDR4 ECC
Storage	8 x 4 TB NVMe SSD (RAID 6) + 100 TB HDD Array
Network Interface	Dual 10 Gigabit Ethernet
GPU	4 x NVIDIA A100 (80GB)
Operating System	Red Hat Enterprise Linux 8

Software Configuration

The software stack is built around Python and various machine learning libraries. Review Software Dependencies for a complete list.

Programming Language: Python 3.9
Machine Learning Libraries: TensorFlow, PyTorch, scikit-learn
Data Storage: PostgreSQL 14 with PostGIS extension
Data Visualization: Grafana, Jupyter Notebook
Message Queue: RabbitMQ
Containerization: Docker, Kubernetes (for scalability and deployment)
Monitoring: Prometheus, Nagios

Network Architecture

The network is a hybrid VPN and direct connection setup. Edge servers communicate with regional hubs via encrypted VPN tunnels. Regional hubs have direct, high-bandwidth connections to the central processing servers. Firewall rules are strictly enforced to ensure data security. Please consult Firewall Configuration for details. The network also utilizes a Content Delivery Network (CDN) to distribute processed data and visualizations. See CDN Implementation for additional information.

Security Considerations

Security is paramount. All data transmission is encrypted using TLS/SSL. Access to servers is restricted using SSH keys and multi-factor authentication. Regular security audits are conducted to identify and address vulnerabilities. We adhere to the principles outlined in Security Best Practices. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are deployed throughout the network. A detailed security report is available on the Security Reports page.

Future Expansion

We plan to expand the infrastructure to include more edge servers and enhance the capabilities of the central processing servers. We are also exploring the use of edge computing to reduce latency and improve responsiveness. Future plans are documented in Project Roadmap.

Related Pages

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.* ⚠️