AI in Croatia

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AI in Croatia: A Server Configuration Overview

This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives in Croatia. It is aimed at newcomers to our MediaWiki site and provides a technical overview of the hardware and software used. Understanding this configuration is crucial for those contributing to AI development efforts within the country. We will cover hardware specifications, software stacks, networking considerations, and security protocols.

Hardware Infrastructure

The core infrastructure is distributed across three primary data centers: Zagreb, Split, and Rijeka. This redundancy ensures high availability and disaster recovery. Each data center utilizes a hybrid approach, combining on-premise servers with cloud resources from Amazon Web Services and Microsoft Azure. The on-premise component is crucial for data sovereignty and low-latency applications like real-time image processing.

Here's a breakdown of the primary server specifications for the Zagreb data center, which serves as the main hub:

Component Specification Quantity
CPU AMD EPYC 7763 (64-core) 64
RAM 512GB DDR4 ECC REG 64
Storage (OS) 2 x 1TB NVMe SSD (RAID 1) 64
Storage (Data) 8 x 16TB SAS HDD (RAID 6) 64
GPU NVIDIA A100 (80GB) 32
Network Interface 100GbE Mellanox ConnectX-6 64

The Split and Rijeka data centers mirror this configuration, albeit with slightly fewer servers (32 each) to optimize cost and energy consumption. They primarily function as backup and disaster recovery sites, and also handle regional data processing loads.

Software Stack

The software stack is built around a Linux foundation, specifically Ubuntu Server 22.04 LTS. This provides a stable and well-supported base for the AI applications. Key software components include:

  • Python 3.10: The primary programming language for AI development.
  • TensorFlow 2.12: A leading deep learning framework.
  • PyTorch 2.0: Another popular deep learning framework.
  • CUDA Toolkit 12.2: NVIDIA's parallel computing platform and programming model.
  • Docker 20.10: For containerization and application deployment.
  • Kubernetes 1.27: For container orchestration.
  • PostgreSQL 15: The primary database for storing model metadata and training data.
  • Redis 7: In-memory data structure store used for caching and session management.

Here's a table outlining the software versions as of October 26, 2023:

Software Version Purpose
Ubuntu Server 22.04 LTS Operating System
Python 3.10.6 Programming Language
TensorFlow 2.12.0 Deep Learning Framework
PyTorch 2.0.1 Deep Learning Framework
CUDA Toolkit 12.2 Parallel Computing Platform
Docker 20.10.12 Containerization

Networking and Security

The data centers are interconnected via a dedicated 100GbE fiber optic network. This ensures low-latency communication between the sites. Network traffic is segmented using Virtual LANs (VLANs) to isolate different applications and environments. A robust firewall infrastructure, utilizing iptables and pfSense, protects the network from unauthorized access.

Security is paramount. All servers are hardened according to CIS benchmarks. Regular security audits are conducted to identify and address potential vulnerabilities. Data is encrypted both in transit and at rest using AES-256 encryption. Access control is strictly enforced using [[Role-Based Access Control (RBAC)]. We also employ intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor for malicious activity.

Here’s a summary of the key security measures:

Security Measure Description Status
Firewall iptables & pfSense Active
Encryption AES-256 Enabled
Access Control RBAC Implemented
Intrusion Detection Snort & Suricata Active
Vulnerability Scanning Nessus Scheduled Weekly

Future Considerations

Future upgrades include transitioning to AMD Instinct MI300X GPUs for increased AI processing power. We are also investigating the use of federated learning techniques to enable collaborative AI development while maintaining data privacy. Furthermore, we are exploring integration with Croatian National Cloud for enhanced scalability and cost-effectiveness. Continuous monitoring and optimization of the infrastructure are essential to support the growing demands of AI research and development in Croatia. Data governance is also a priority to ensure ethical and responsible AI practices. Machine learning operations (MLOps) principles are being implemented to streamline the deployment and maintenance of AI models. Server maintenance is scheduled monthly.


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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.* ⚠️

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