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AI in Åland Islands

# 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:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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