AI in Teacher Support
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- AI in Teacher Support: Server Configuration
This article details the server configuration necessary to support an AI-powered teacher support system. This system aims to assist educators with tasks like grading, lesson planning, and student performance analysis. We will cover hardware requirements, software stack, and network considerations. This guide is intended for newcomers to our MediaWiki site and assumes basic familiarity with server administration.
Overview
The core of the AI system relies on substantial computational resources for model training and inference. The server infrastructure must be scalable to accommodate growing datasets and user demands. We utilize a distributed architecture, separating data storage, processing, and application services. Server architecture principles are fundamental to this design. This setup provides redundancy and ensures high availability. High availability systems are critical for educational tools.
Hardware Specifications
The following table outlines the hardware requirements for the primary server components. We have three main role types: Data Storage, Processing (AI Model), and Application Server.
| Component | Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| Server 1 | Data Storage | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 100 TB RAID 6 HDD | 10 GbE |
| Server 2-5 | AI Model Processing | AMD EPYC 7763 (64 cores) | 512 GB DDR4 ECC | 2 x 1 TB NVMe SSD (RAID 1) | 25 GbE |
| Server 6-8 | Application Server | Intel Xeon Silver 4210 (10 cores) | 64 GB DDR4 ECC | 1 TB NVMe SSD | 1 GbE |
Further details on RAID configurations can be found on the internal wiki. The choice of NVMe SSDs for the AI processing servers is crucial for minimizing I/O latency during model training and inference. Solid-state drives provide significant performance benefits.
Software Stack
The software stack is built around a Linux operating system, specifically Ubuntu Server 22.04 LTS. This provides a stable and secure foundation for our applications. We leverage containerization using Docker and orchestration with Kubernetes for efficient resource management and deployment.
| Layer | Software | Version | Purpose |
|---|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS | Base OS and kernel |
| Containerization | Docker | 24.0.5 | Package and run applications in containers |
| Orchestration | Kubernetes | 1.27 | Manage and scale containerized applications |
| AI Framework | TensorFlow | 2.12 | Machine learning framework |
| Database | PostgreSQL | 15 | Data storage and management |
| Programming Language | Python | 3.10 | Primary development language |
PostgreSQL database administration is covered in a separate article. The AI models are developed using TensorFlow and Python, allowing for flexibility and access to a wide range of machine learning libraries. We utilize version control systems (Git) extensively during the development process.
Network Configuration
The server network is segmented into three zones: Public, DMZ, and Private. The Application Servers are exposed to the public via a reverse proxy (Nginx). The AI Processing Servers and Data Storage Servers reside in the private network, accessible only from within the cluster. Network security is paramount in this design.
| Zone | Servers | Access Control | Security Measures |
|---|---|---|---|
| Public | Nginx (Reverse Proxy) | Public access (HTTPS) | Firewall, Intrusion Detection System |
| DMZ | None | Limited access from Public | DMZ Firewall |
| Private | Data Storage, AI Processing, Application Servers | Restricted access (Kubernetes network policies) | Internal Firewall, Encryption |
We employ a firewall configuration that strictly controls incoming and outgoing traffic. All communication between servers is encrypted using TLS. Monitoring tools like Prometheus and Grafana are used to track network performance and identify potential issues. Regular security audits are conducted to ensure the integrity of the system.
Scalability and Future Considerations
The Kubernetes-based architecture allows for horizontal scalability, enabling us to add more AI Processing Servers as needed to handle increased workloads. We are also exploring the use of GPU acceleration to further improve model training and inference speeds. Future development will focus on integrating with other educational platforms and expanding the range of AI-powered features. Cloud computing options are also being evaluated for long-term scalability and cost optimization.
Server maintenance is a critical ongoing process, along with disaster recovery planning.
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.* ⚠️