AI in Nagorno-Karabakh
# AI in Nagorno-Karabakh: A Server Configuration Overview
This article details the server infrastructure required to support Artificial Intelligence (AI) applications focused on analyzing data related to the Nagorno-Karabakh region. It's designed for newcomers to our MediaWiki site and provides a technical deep-dive into the necessary components and configurations. Understanding these requirements is crucial for anyone contributing to or maintaining these systems. This project requires significant computational resources due to the complex nature of the data, including satellite imagery, sensor data, and open-source intelligence (OSINT).
Project Overview
The core goal of this project is to develop and deploy AI models for several applications:
- Conflict Prediction: Analyzing historical data and current events to predict potential escalations.
- Damage Assessment: Utilizing satellite imagery to assess damage to infrastructure.
- Disinformation Detection: Identifying and flagging potentially false or misleading information circulating online.
- Geospatial Analysis: Creating detailed maps and visualizations of the region.
- Operating System: Ubuntu Server 22.04 LTS (all servers) – Refer to the OS Installation Guide.
- Database: PostgreSQL 14 with PostGIS extension for geospatial data – See Database Configuration.
- AI Framework: PyTorch 2.0 with CUDA Toolkit 11.8 – Detailed installation instructions are available on the PyTorch Installation page.
- Data Ingestion: Apache Kafka for real-time data streams. Consult the Kafka Setup documentation.
- Model Serving: TensorFlow Serving for deploying trained models. Refer to the TensorFlow Serving Guide.
- Monitoring: Prometheus and Grafana for system monitoring and alerting – See Monitoring Setup.
- Version Control: Git with GitLab for collaborative development. Refer to the Git Workflow.
- Containerization: Docker and Kubernetes for application deployment and scaling. See Kubernetes Deployment.
- Federated Learning: Exploring the use of federated learning to train models on distributed datasets while preserving data privacy.
- Edge Computing: Deploying AI models to edge devices for real-time analysis and reduced latency.
- Automated Model Retraining: Implementing automated pipelines for retraining models as new data becomes available.
- Explainable AI (XAI): Integrating XAI techniques to improve the transparency and interpretability of AI models.
- Telegram: @powervps Servers at a discounted price
These applications necessitate a robust and scalable server infrastructure, detailed below. Data security is paramount, given the sensitive nature of the information. Refer to our Data Security Policy for more information.
Server Hardware Specifications
The following table outlines the hardware specifications for the primary servers:
| Server Role | CPU | RAM | Storage | GPU |
|---|
| Data Ingestion Server | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 20 TB RAID 6 HDD | None | |
| AI Training Server | 2 x AMD EPYC 7763 (64 cores each) | 512 GB DDR4 ECC | 100 TB NVMe SSD RAID 0 | 4 x NVIDIA A100 (80GB) | |
| Model Deployment Server | Intel Xeon Silver 4210 (10 cores) | 64 GB DDR4 ECC | 4 TB NVMe SSD | NVIDIA Tesla T4 | |
| Database Server | Intel Xeon Gold 5218 (16 cores) | 256 GB DDR4 ECC | 40 TB RAID 10 SSD | None | |
These specifications are subject to change based on performance testing and evolving project requirements. See Hardware Revision History for updates.
Software Stack
The software stack is built around open-source technologies to maximize flexibility and minimize costs.
Network Configuration
The server infrastructure is deployed within a Virtual Private Cloud (VPC) on a major cloud provider (AWS, Azure, or GCP – specific provider determined by cost and availability). The network configuration is as follows:
| Component | IP Address Range | Subnet Mask | Security Group Rules |
|---|
| Data Ingestion Server | 10.0.1.0/24 | 255.255.255.0 | Allow inbound SSH (port 22) from Management Network, allow inbound Kafka traffic (port 9092). | |
| AI Training Server | 10.0.2.0/24 | 255.255.255.0 | Allow inbound SSH (port 22) from Management Network, allow inbound NFS traffic (ports 111, 2049). | |
| Model Deployment Server | 10.0.3.0/24 | 255.255.255.0 | Allow inbound HTTP/HTTPS (ports 80, 443) from Public Network, allow inbound gRPC traffic (port 8500). | |
| Database Server | 10.0.4.0/24 | 255.255.255.0 | Allow inbound PostgreSQL traffic (port 5432) from all internal servers. | |
A dedicated Management Network is used for administrative access to the servers. All traffic is encrypted using TLS/SSL where applicable. Network segmentation is implemented to isolate the different components of the infrastructure.
Data Storage and Backup
Data is stored redundantly across multiple availability zones to ensure high availability and durability.
| Data Type | Storage Location | Backup Strategy | Retention Policy |
|---|
| Raw Data (Satellite Imagery, Sensor Data) | Object Storage (AWS S3, Azure Blob Storage, GCP Cloud Storage) | Daily incremental backups, weekly full backups | 1 year | |
| Processed Data (Geospatial Data, Feature Vectors) | Database Server (PostgreSQL) | Daily full backups, transaction log archiving | 6 months | |
| AI Models | Model Registry (MLflow) | Versioned model artifacts | Indefinite (with version control) | |
All backups are encrypted at rest and in transit. Regular disaster recovery drills are conducted to ensure the effectiveness of the backup and recovery procedures. See Disaster Recovery Plan for details.
Future Considerations
Server Administration Network Security Database Management AI Development Data Analysis Cloud Computing Disaster Recovery Monitoring Tools Version Control Systems Security Protocols Data Privacy Infrastructure as Code Deployment Strategies System Documentation Troubleshooting Guide Performance Optimization
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.* ⚠️