AI in Åland Islands

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  1. AI in Åland Islands: Server Configuration and Deployment

This article details the server configuration used to support Artificial Intelligence (AI) initiatives within the Åland Islands. It is intended as a guide for new system administrators and developers contributing to these projects. This deployment focuses on providing a scalable and reliable platform for machine learning model training, inference, and data processing. We will cover hardware, software, networking and security aspects. Please refer to the Main Page for overall project information.

Overview

The Åland Islands AI infrastructure is built around a hybrid cloud approach, leveraging both on-premise servers and cloud resources provided by Cloud Providers. This allows for flexibility, cost optimization, and data sovereignty. The on-premise infrastructure is located in a secure data center in Mariehamn. This deployment prioritizes data privacy in accordance with Åland Islands Data Protection Regulations. Initial deployments focused on Natural Language Processing and Computer Vision applications. Future expansion will include Reinforcement Learning capabilities.

Hardware Specifications

The core on-premise AI server infrastructure consists of the following:

Component Specification Quantity
CPU Intel Xeon Gold 6338 (32 cores, 64 threads) 4
RAM 512 GB DDR4 ECC Registered 3200MHz 4
GPU NVIDIA A100 80GB PCIe 4.0 8
Storage (OS/Boot) 1TB NVMe PCIe 4.0 SSD 4
Storage (Data) 16TB SAS 12Gbps 7.2K RPM HDD (RAID 6) 2 Arrays
Network Interface 100 Gigabit Ethernet (Dual Port) 4

These servers are housed in a redundant power and cooling environment. Detailed rack diagrams are available on the Data Center Documentation page. We utilize a Server Monitoring System for proactive issue detection.

Software Stack

The software stack is designed for flexibility and ease of management. We prioritize open-source technologies where possible, adhering to Open Source Policy.

Layer Software Version
Operating System Ubuntu Server 22.04 LTS 22.04
Containerization Docker 24.0.5
Orchestration Kubernetes 1.27.3
Machine Learning Frameworks TensorFlow, PyTorch, scikit-learn Latest Stable Releases
Data Storage Ceph Quincy
Monitoring Prometheus, Grafana Latest Stable Releases

All machine learning models are deployed using Model Deployment Pipeline. We also utilize a dedicated Data Versioning System for reproducibility. This setup allows for easy scaling and deployment of new AI models. Regular security updates are applied as per the Security Update Schedule.

Networking Configuration

The network infrastructure is critical for performance and security. We employ a layered approach to network security.

Aspect Configuration
Network Topology VLAN-segmented network with separate zones for management, data, and public access.
Firewall pfSense
Load Balancing HAProxy
DNS Bind9
Internal Network 10.0.0.0/16
External Network Provided by Internet Service Provider

All communication between servers is encrypted using TLS/SSL Encryption. Network traffic is monitored using a Network Intrusion Detection System. Firewall rules are reviewed quarterly as per the Firewall Management Policy. Detailed network diagrams can be found on the Network Documentation page. We utilize Virtual Private Networks for secure remote access.

Security Considerations

Security is paramount. The following measures are in place:

Future Expansion

We plan to expand the infrastructure to include more specialized hardware, such as TPUs, and to integrate with additional cloud providers. We will continue to evaluate and adopt new AI technologies as they become available. The roadmap for future expansion is detailed in the AI Infrastructure Roadmap. We are also investigating Federated Learning techniques to further enhance data privacy.



Main Page Data Center Documentation Server Monitoring System Åland Islands Data Protection Regulations Natural Language Processing Computer Vision Reinforcement Learning Cloud Providers Mariehamn Open Source Policy Model Deployment Pipeline Data Versioning System Security Update Schedule TLS/SSL Encryption Network Intrusion Detection System Firewall Management Policy Network Documentation Virtual Private Networks Vulnerability Scanning Tools Security Audit Team GDPR Regulations Incident Response Plan AI Infrastructure Roadmap Federated Learning


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.* ⚠️