AI in History
- AI in History: Server Configuration & Deployment
This article details the server configuration required to support the "AI in History" project on this wiki. This project utilizes artificial intelligence to analyze historical texts, identify patterns, and assist researchers. This guide is aimed at new server engineers and administrators. Understanding these configurations is crucial for maintaining the project’s performance and scalability.
Project Overview
The "AI in History" project relies on several key components: a large historical text database, machine learning models (primarily transformer-based language models), a REST API for querying the models, and a web interface for researchers. All of these components place significant demands on the server infrastructure. We leverage Semantic MediaWiki extensions to structure our data. The project utilizes Lua scripting extensively for complex calculations. The primary goal is to provide fast and accurate historical analysis. We also integrate with Cite extensions for proper source attribution.
Server Hardware Specifications
The following table outlines the specifications for the primary server cluster hosting the AI models and database:
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6338 (32 cores, 64 threads) | 4 |
| RAM | 256 GB DDR4 ECC Registered | 4 |
| Storage (OS & Applications) | 2 x 1 TB NVMe SSD (RAID 1) | 4 |
| Storage (Historical Data) | 8 x 8 TB SATA HDD (RAID 6) | 1 |
| GPU | NVIDIA A100 (80GB) | 4 |
| Network Interface | 100 Gbps Ethernet | 2 |
This configuration is designed for high throughput and low latency, critical for the AI models. We also employ a separate Load balancing server to distribute traffic.
Software Stack
The following table details the software stack used on the servers:
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS |
| Database | PostgreSQL 14 | Historical text storage |
| Web Server | Nginx 1.23 | Serving the web interface and API |
| Programming Language | Python 3.10 | AI model implementation and API |
| Machine Learning Framework | PyTorch 1.13 | Training and inference of AI models |
| API Framework | Flask 2.2 | Building the REST API |
| Version Control | Git | Code management |
We prioritize open-source software for flexibility and cost-effectiveness. Regular Security audits are performed on the software stack. The use of Docker containers is essential for consistent deployments.
Network Configuration
The network is configured with a dedicated VLAN for the AI in History project. This provides isolation and improves security. The following table outlines key network settings:
| Setting | Value |
|---|---|
| VLAN ID | 100 |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.100.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Firewall | UFW (Uncomplicated Firewall) |
| SSH Port | 2222 (Non-standard for security) |
Access to the servers is strictly controlled via SSH with key-based authentication. We use Intrusion detection systems to monitor for malicious activity. Network performance is monitored using Nagios.
Scalability & Future Considerations
The current configuration is designed to handle a moderate load. As the project grows, we anticipate the need for horizontal scaling. This will involve adding more servers to the cluster and utilizing a distributed database solution. We are also investigating the use of Kubernetes for container orchestration. Further research into GPU virtualization may also be necessary to optimize resource utilization. We are also evaluating the use of specialized AI accelerators beyond GPUs. We plan to implement Continuous integration/continuous deployment pipelines for faster updates.
Related Pages
- Server Maintenance
- Database Backups
- Security Protocols
- Troubleshooting Guide
- API Documentation
- Data Storage Policies
- User Access Control
- Performance Monitoring
- Software Updates
- Hardware Inventory
- Network Diagrams
- Disaster Recovery Plan
- Logging and Auditing
- Incident Response
- Wiki Configuration
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.* ⚠️