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AI in Sussex

```wiki #REDIRECT AI in Sussex

AI in Sussex: Server Configuration Documentation

This document details the server configuration supporting the "AI in Sussex" project, a research initiative leveraging artificial intelligence for local data analysis. This guide is intended for new system administrators and developers contributing to the project. It covers hardware, software, networking, and security aspects of the server infrastructure. Please familiarize yourself with our System Administration Guidelines before making any changes.

Overview

The "AI in Sussex" project utilizes a cluster of servers located within the University of Sussex data center. These servers are responsible for data ingestion, model training, model deployment, and API access for researchers. The architecture emphasizes scalability, reliability, and data security. See Data Security Policy for more information. We utilize a hybrid cloud approach, supplementing on-premise resources with cloud-based services for peak workloads. Refer to Cloud Resource Allocation for details.

Hardware Configuration

The core server infrastructure consists of four primary nodes: three dedicated to computation and one acting as a central data repository. Each node is based on a similar hardware configuration, detailed below.

Node Type CPU RAM Storage Network Interface
Computation Node 1 | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet
Computation Node 2 | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet
Computation Node 3 | Intel Xeon Gold 6248R (24 cores) | 256 GB DDR4 ECC | 2 x 4TB NVMe SSD (RAID 1) | 10 Gbps Ethernet
Data Repository Node | Intel Xeon Silver 4210 (10 cores) | 128 GB DDR4 ECC | 8 x 8TB SATA HDD (RAID 6) | 10 Gbps Ethernet

All servers run on a dedicated power circuit with UPS backup. See Power Management Procedures for details on emergency shutdown procedures.

Software Stack

The software stack is designed for efficient AI development and deployment. It includes the operating system, programming languages, deep learning frameworks, and containerization tools.

Component Version Description
Operating System | Ubuntu Server 22.04 LTS | Provides a stable and secure base for the entire stack.
Python | 3.9 | Primary programming language for data science and machine learning.
TensorFlow | 2.12 | Deep learning framework for model development and training.
PyTorch | 2.0 | Alternative deep learning framework.
Docker | 20.10 | Containerization platform for consistent deployment.
Kubernetes | 1.26 | Container orchestration system for managing the cluster.
PostgreSQL | 15 | Database for metadata and experiment tracking.

All software is managed using Ansible for automated configuration and deployment. Refer to Ansible Playbook Repository for details. Regular security updates are applied using unattended upgrades. See Security Patching Schedule.

Networking Configuration

The server cluster is connected to the University of Sussex network via a dedicated VLAN. Each server has a static IP address assigned within this VLAN.

Server Name IP Address Subnet Mask Gateway
ai-compute-1 | 192.168.10.10 | 255.255.255.0 | 192.168.10.1
ai-compute-2 | 192.168.10.11 | 255.255.255.0 | 192.168.10.1
ai-compute-3 | 192.168.10.12 | 255.255.255.0 | 192.168.10.1
ai-data-repo | 192.168.10.13 | 255.255.255.0 | 192.168.10.1

A firewall is configured to restrict access to the server cluster, allowing only authorized traffic from specific IP addresses. See Firewall Ruleset for details. We implement network segmentation to isolate the AI environment from other university systems. Refer to the Network Diagram.

Security Considerations

Security is paramount for the "AI in Sussex" project. We employ several security measures to protect data and prevent unauthorized access. These include:

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

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