AI Education

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AI Education Server Configuration

This document details the server configuration for the "AI Education" project, designed to support a suite of tools for learning and experimenting with Artificial Intelligence. This guide is intended for new system administrators and developers contributing to the platform. It covers hardware specifications, software stack, and key configuration details.

Overview

The AI Education server is built to provide a robust and scalable environment for users to access and utilize AI-related resources. The primary goals are to support interactive tutorials, code execution, and model training, all within a secure and manageable infrastructure. We utilize a distributed architecture to maximize performance and availability. See Server Architecture Overview for a broader context. This server is distinct from the Data Analysis Server and the Content Delivery Network.

Hardware Specifications

The core server utilizes the following hardware components. Redundancy is built in at multiple levels to ensure high availability.

Component Specification Quantity
CPU Intel Xeon Gold 6338 (32 cores, 64 threads) 2
RAM 256 GB DDR4 ECC Registered 1
Storage (OS/Boot) 500 GB NVMe SSD 1
Storage (Data) 8 x 4TB SAS HDD (RAID 6) 1 Array
Network Interface 10 Gigabit Ethernet 2
GPU NVIDIA A100 (80GB) 4

We also utilize a separate storage cluster detailed in the Storage Cluster Documentation. This cluster is accessed via NFS.

Software Stack

The AI Education server is built on a Linux foundation, utilizing a combination of open-source and commercially supported software.

Software Version Purpose
Operating System Ubuntu Server 22.04 LTS Base OS and System Management
Containerization Docker 24.0.5 Application Isolation and Deployment
Container Orchestration Kubernetes 1.27 Automating deployment, scaling, and management of containerized applications
Programming Languages Python 3.10, R 4.3.1 Core languages for AI development and scripting. See Supported Languages for details.
Machine Learning Frameworks TensorFlow 2.13, PyTorch 2.0, scikit-learn 1.3 Libraries for building and training AI models. Refer to Framework Compatibility.
Database PostgreSQL 15 Metadata storage and user data management. See Database Schema.
Web Server Nginx 1.25 Reverse proxy and load balancer. Configuration details are in Nginx Configuration.

Configuration Details

Several key configuration elements are critical to the operation of the AI Education server.

Network Configuration

  • The server utilizes a static IP address within the 192.168.1.0/24 subnet.
  • DNS resolution is handled by internal DNS servers (see DNS Server Configuration).
  • Firewall rules are managed using `ufw` and configured to allow only necessary traffic.
  • Port 80 (HTTP) and 443 (HTTPS) are open for web access, and port 22 (SSH) is restricted to authorized users.

Security Considerations

  • All user data is encrypted at rest and in transit.
  • Regular security audits are performed. See Security Audit Logs.
  • User authentication is managed through a centralized identity provider (LDAP). (See LDAP Integration).
  • Intrusion detection and prevention systems are in place.

Storage Configuration

The primary data storage is a RAID 6 array providing redundancy and data protection. The storage cluster is mounted via NFS at `/mnt/data`. Permissions are carefully managed to ensure data integrity and security. See NFS Mount Options for specific settings.

Mount Point Filesystem Permissions
/mnt/data NFS (from Storage Cluster) 755 (for directories), 644 (for files)
/var/log ext4 755
/home ext4 700

Monitoring and Logging

Comprehensive monitoring and logging are essential for maintaining the stability and performance of the AI Education server. We use Prometheus for metrics collection and Grafana for visualization. Logs are aggregated using the ELK stack (Elasticsearch, Logstash, Kibana). See Monitoring Dashboard and Log Analysis Procedures for more details. Regularly checking the System Event Logs is crucial.

Future Enhancements

Planned future enhancements include:

  • Integration with a cloud-based GPU service for increased scalability.
  • Support for additional machine learning frameworks.
  • Implementation of a more sophisticated resource management system.
  • Automated scaling based on demand. See Scalability Roadmap.


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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