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AI in Blackburn

# AI in Blackburn: Server Configuration

This document details the server configuration for the "AI in Blackburn" project, a local initiative utilizing artificial intelligence for community benefit. This guide is intended for new system administrators and developers contributing to the project. It covers hardware specifications, software stack, networking, and security considerations.

Overview

The "AI in Blackburn" project relies on a cluster of servers hosted at the Blackburn Technology Centre. These servers are responsible for data processing, model training, and serving AI-powered applications to local businesses and residents. The goal is to provide accessible AI solutions tailored to the needs of the Blackburn community. This document outlines the technical details of the infrastructure supporting this initiative. For information on the project's goals, see Project Goals.

Hardware Specifications

The server cluster consists of five primary servers, each with a specific role. Below are the specifications for each.

Server Name CPU RAM Storage Network Interface - Server 1 (Master Node) Intel Xeon Gold 6248R (24 cores) 128 GB DDR4 ECC 2 x 2TB NVMe SSD (RAID 1) 10 Gigabit Ethernet
Server 2 (Data Processing) AMD EPYC 7763 (64 cores) 256 GB DDR4 ECC 4 x 4TB SATA HDD (RAID 10) 10 Gigabit Ethernet
Server 3 (Model Training) 2x NVIDIA Tesla V100 GPUs 128 GB DDR4 ECC 2 x 8TB SATA HDD (RAID 1) 10 Gigabit Ethernet
Server 4 (Serving – API) Intel Xeon Silver 4210 (10 cores) 64 GB DDR4 ECC 1 x 1TB NVMe SSD 1 Gigabit Ethernet
Server 5 (Database) Intel Xeon Gold 5218 (16 cores) 64 GB DDR4 ECC 2 x 4TB SATA HDD (RAID 1) 1 Gigabit Ethernet

These servers are housed in a dedicated rack with redundant power supplies and cooling. Refer to the Data Centre Location page for more details. Power consumption is monitored via Power Monitoring System.

Software Stack

Each server runs a customized version of Ubuntu Server 22.04 LTS. The software stack is designed for scalability, reliability, and ease of maintenance.

Server Role Operating System Core Software Version - Master Node Ubuntu Server 22.04 LTS Kubernetes 1.27
Data Processing Ubuntu Server 22.04 LTS Apache Spark 3.4.1
Model Training Ubuntu Server 22.04 LTS TensorFlow 2.12 PyTorch 2.0
Serving (API) Ubuntu Server 22.04 LTS Flask 2.3.2 Gunicorn 20.1.0
Database Ubuntu Server 22.04 LTS PostgreSQL 15

All code is managed using Git Version Control, hosted on a private GitLab instance. Continuous Integration and Continuous Deployment (CI/CD) pipelines are implemented using Jenkins Automation. For detailed software installation instructions, see Software Installation Guide.

Networking Configuration

The server cluster is connected to the internal network via a 10 Gigabit Ethernet switch. Each server has a static IP address assigned within the 192.168.1.0/24 subnet. The Master Node acts as the central point of communication for the Kubernetes cluster. External access to the API server is provided through a reverse proxy configured on a separate firewall appliance.

Server Name IP Address Subnet Mask Gateway - Server 1 (Master Node) 192.168.1.10 255.255.255.0 192.168.1.1
Server 2 (Data Processing) 192.168.1.11 255.255.255.0 192.168.1.1
Server 3 (Model Training) 192.168.1.12 255.255.255.0 192.168.1.1
Server 4 (Serving – API) 192.168.1.13 255.255.255.0 192.168.1.1
Server 5 (Database) 192.168.1.14 255.255.255.0 192.168.1.1

DNS resolution is handled by an internal DNS server. Network monitoring is performed using Network Monitoring Tools. Detailed network diagrams can be found on the Network Infrastructure page.

Security Considerations

Security is a paramount concern for the "AI in Blackburn" project. The following security measures are in place:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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