AI in Luton
```wiki
- REDIRECT AI in Luton
AI in Luton: Server Configuration Overview
This article details the server configuration supporting the "AI in Luton" project. It is intended for newcomers to the MediaWiki site and outlines the hardware, software, and network infrastructure. Understanding these components is critical for system administration, troubleshooting, and future scalability. This project utilizes a distributed system architecture to handle the intensive computational demands of machine learning models. We will cover the core server specifications, network topology, and software stack employed. This documentation assumes a basic understanding of Linux server administration and networking concepts.
Hardware Specifications
The core of the "AI in Luton" infrastructure consists of three primary server types: Compute Nodes, Storage Nodes, and a Management Node. Each node type is configured with specific hardware to optimize performance for its designated role.
| Node Type | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Compute Node | 2 x Intel Xeon Gold 6248R (24 cores/48 threads per CPU) | 256 GB DDR4 ECC Registered RAM | 2 x 1.92TB NVMe SSD (RAID 1) + 4 x 8TB SAS HDD (RAID 5) | 100 Gbps Ethernet |
| Storage Node | 2 x Intel Xeon Silver 4210 (10 cores/20 threads per CPU) | 128 GB DDR4 ECC Registered RAM | 16 x 16TB SAS HDD (RAID 6) | 40 Gbps Ethernet |
| Management Node | 2 x Intel Xeon E-2224 (6 cores/12 threads per CPU) | 64 GB DDR4 ECC Registered RAM | 2 x 480GB SATA SSD (RAID 1) | 1 Gbps Ethernet |
These servers are housed in a dedicated data center facility with redundant power and cooling systems. Detailed hardware inventory lists are available on the internal asset management system.
Network Topology
The network infrastructure is designed for high bandwidth and low latency communication between the nodes. A dedicated VLAN is used for all "AI in Luton" traffic, isolated from the general corporate network.
| Component | IP Address Range | Role | Notes |
|---|---|---|---|
| Compute Nodes | 192.168.10.10 - 192.168.10.19 | Model Training & Inference | Connected via 100Gbps switches. |
| Storage Nodes | 192.168.20.10 - 192.168.20.19 | Data Storage & Retrieval | Connected via 40Gbps switches. |
| Management Node | 192.168.30.10 | System Monitoring & Control | Accessible via SSH and web interface. |
| Load Balancer | 192.168.40.10 | Distributes traffic to Compute Nodes | HAProxy configured for failover. |
The network is monitored using Nagios and Zabbix for performance and availability. Firewall rules are in place to restrict access to authorized personnel and services. The network diagram is available on the network documentation page.
Software Stack
The software stack is built around a Linux distribution, specifically Ubuntu Server 22.04 LTS. This provides a stable and secure base for the application.
| Software | Version | Purpose | Notes |
|---|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base Operating System | Security patches applied regularly. |
| Programming Language | Python 3.9 | Primary Development Language | Used for model training and inference. |
| Machine Learning Framework | TensorFlow 2.10 | Machine Learning Library | Optimized for GPU acceleration. |
| Database | PostgreSQL 14 | Data Storage | Stores model metadata and training data. |
| Containerization | Docker 20.10 | Application Packaging | Simplifies deployment and scaling. |
| Orchestration | Kubernetes 1.24 | Container Management | Automates deployment, scaling, and management of containers. |
The code repository is hosted on GitLab, and continuous integration/continuous deployment (CI/CD) pipelines are implemented using Jenkins. Logging is centralized using the ELK stack (Elasticsearch, Logstash, Kibana) for efficient analysis and troubleshooting. Security audits are performed quarterly to ensure the integrity of the system. Detailed software documentation can be found on the software wiki page.
Future Considerations
Future plans include upgrading the GPU infrastructure on the Compute Nodes to enhance model training performance. We are also evaluating the use of edge computing to deploy models closer to the data source. Further documentation on scalability testing and disaster recovery are planned.
Help:Contents
Manual:Configuration
Manual:Installation
Manual:Troubleshooting
Networking
Linux
Ubuntu
Python
TensorFlow
PostgreSQL
Docker
Kubernetes
GitLab
Jenkins
Nagios
Zabbix
ELK stack
Security
```
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.* ⚠️