AI Project Documentation
- AI Project Documentation
Introduction
This document details the server configuration for the "AI Project Documentation" system. This system is designed to host and serve a comprehensive knowledge base relating to our ongoing Artificial Intelligence research and development projects. The core function of this documentation platform is to provide a centralized, easily searchable, and version-controlled repository for all project-related information, including model specifications, training data details, experimental results, code documentation, and operational procedures. The system utilizes a robust MediaWiki installation, optimized for both performance and scalability. A key feature of this implementation is the integration with our internal Version Control System, allowing for seamless linking between documentation and code revisions. This ensures that documentation always reflects the current state of the project. The documentation is intended for use by researchers, engineers, data scientists, and other stakeholders involved in the AI projects. Access control is managed through MediaWiki’s built-in user and group permissions, coupled with an integration with our central Authentication Service. We've prioritized a highly available and resilient infrastructure to minimize downtime and ensure continuous access to critical project information. The "AI Project Documentation" platform is critical for knowledge sharing, collaboration, and adherence to best practices within the AI development team. Furthermore, the consistent documentation format enforced by MediaWiki aids in long-term maintainability and understanding of complex projects. This article will cover the technical specifications, performance metrics, and detailed configuration of the server infrastructure supporting this vital resource. We will also touch upon the Disaster Recovery Plan in place to protect against data loss.
Technical Specifications
The server infrastructure supporting the "AI Project Documentation" platform is composed of several key components. These include web servers, database servers, and caching layers – all interconnected to provide a responsive and reliable service. The following table outlines the core technical specifications:
| Component | Specification | Quantity | Notes |
|---|---|---|---|
| Web Server | Operating System: Ubuntu Server 22.04 LTS | 3 | Load balanced using HAProxy |
| Web Server | CPU: Intel Xeon Gold 6248R (24 cores) | 3 | CPU Architecture details available. |
| Web Server | Memory: 128 GB DDR4 ECC RAM | 3 | Memory Specifications detailed. |
| Web Server | Storage: 1 TB NVMe SSD | 3 | RAID 1 configuration for redundancy. |
| Database Server | Operating System: CentOS Stream 9 | 2 | Utilizing MariaDB Galera Cluster for high availability. |
| Database Server | CPU: AMD EPYC 7543P (32 cores) | 2 | CPU Comparison available. |
| Database Server | Memory: 256 GB DDR4 ECC RAM | 2 | Optimized for database workloads. |
| Database Server | Storage: 2 TB NVMe SSD | 2 | RAID 10 configuration for performance and redundancy. |
| Caching Server | Operating System: Alpine Linux 3.18 | 2 | Utilizing Redis for caching frequently accessed data. |
| Caching Server | CPU: Intel Xeon E-2336 (8 cores) | 2 | Low power consumption. |
| Caching Server | Memory: 64 GB DDR4 ECC RAM | 2 | Optimized for in-memory caching. |
| Load Balancer | Operating System: Ubuntu Server 22.04 LTS | 2 | Active-Passive configuration. |
| Load Balancer | Software: HAProxy 2.6 | 2 | HAProxy Configuration documentation. |
| "AI Project Documentation" | MediaWiki Version | N/A | 1.40 (Latest stable release) |
Performance Metrics
Maintaining optimal performance is crucial for user satisfaction and efficient knowledge management. We continuously monitor key performance indicators (KPIs) to identify and address potential bottlenecks. The following table summarizes the observed performance metrics during peak usage:
| Metric | Value | Unit | Notes |
|---|---|---|---|
| Average Page Load Time | 0.8 | seconds | Measured using WebPageTest. |
| Concurrent Users | 500 | Number of active users. | Based on historical data and Load Testing results. |
| Database Query Time (Average) | 15 | milliseconds | Monitored using Database Performance Monitor. |
| CPU Utilization (Web Server) | 40 | percentage | Average across all web servers. |
| Memory Utilization (Web Server) | 60 | percentage | Average across all web servers. |
| Disk I/O (Database Server) | 200 | IOPS | Average across all database servers. |
| Network Latency | < 5 | milliseconds | Between web servers and database servers. |
| Cache Hit Ratio | 95 | percentage | Redis Caching Strategy employed. |
| Error Rate | < 0.1 | percentage | Monitored using Error Tracking System. |
| Search Query Time | < 2 | seconds | Using the built-in MediaWiki search functionality. Optimized with Search Indexing. |
Configuration Details
The "AI Project Documentation" system relies on a carefully configured software stack to ensure stability, security, and optimal performance. This section details the key configuration aspects.
| Parameter | Value | Description | Notes |
|---|---|---|---|
| MediaWiki Configuration File | /etc/mediawiki/LocalSettings.php | Contains core MediaWiki settings. | MediaWiki Configuration detailed documentation. |
| Database Type | MariaDB | Using MariaDB Galera Cluster. | MariaDB Installation Guide. |
| Database Name | ai_documentation | The database used to store MediaWiki data. | Requires appropriate Database Permissions. |
| Web Server Configuration | /etc/apache2/sites-available/ai_documentation.conf | Apache configuration file for the "AI Project Documentation" website. | Apache Configuration Best Practices. |
| PHP Version | 8.2 | The PHP version used by MediaWiki. | PHP Security Considerations. |
| PHP Modules | pdo_mysql, mbstring, xml, json, curl | Required PHP modules for MediaWiki functionality. | PHP Module Management. |
| HAProxy Configuration | /etc/haproxy/haproxy.cfg | HAProxy configuration file for load balancing. | HAProxy Load Balancing Algorithms. |
| Redis Configuration | /etc/redis/redis.conf | Redis configuration file for caching. | Redis Persistence Options. |
| Firewall Rules | UFW (Uncomplicated Firewall) | Firewall rules to protect the server. | Firewall Configuration Guide. |
| SSL/TLS Certificate | Let's Encrypt | SSL/TLS certificate for secure HTTPS connections. | SSL Certificate Renewal Process. |
| MediaWiki Extensions | Cite, Interwiki, Semantic MediaWiki | Enabled MediaWiki extensions for enhanced functionality. | MediaWiki Extension Installation. |
| Cron Jobs | Daily maintenance tasks | Used for database backups and cache clearing. | Cron Job Scheduling. |
| Logging | Systemd Journald, Apache Access Logs | Logging for troubleshooting and monitoring. | Log Analysis Tools. |
| Security Hardening | SELinux, Fail2Ban | Security measures to protect against attacks. | Server Security Best Practices. |
Scalability and Future Considerations
The current infrastructure is designed to handle the present workload, but we anticipate future growth in both the volume of documentation and the number of users. To ensure continued performance and scalability, we are evaluating several options:
- **Horizontal Scaling:** Adding more web servers and database servers to the cluster.
- **Database Sharding:** Distributing the database load across multiple servers. Requires careful planning and implementation of a Database Sharding Strategy.
- **Content Delivery Network (CDN):** Utilizing a CDN to cache static content closer to users. CDN Integration is under evaluation.
- **Improved Search Indexing:** Exploring more advanced search indexing techniques to improve search query performance. Elasticsearch Integration is being considered.
- **Automated Scaling:** Implementing automated scaling based on real-time performance metrics using Kubernetes.
Conclusion
The "AI Project Documentation" platform is a critical component of our AI development workflow. The detailed server configuration outlined in this document ensures a reliable, performant, and secure environment for managing and sharing critical project knowledge. Continuous monitoring, optimization, and planning for future scalability are essential to maintain the effectiveness of this vital resource. Regular reviews of the Security Audit Logs are essential for maintaining a secure system. We will continue to adapt and improve the infrastructure to meet the evolving needs of our AI projects.
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.* ⚠️