AI in Portsmouth
- AI in Portsmouth: Server Configuration
This document details the server configuration supporting the "AI in Portsmouth" project. It is intended as a guide for new system administrators and developers contributing to the project. This project utilizes a cluster of servers to handle the computational demands of machine learning models focused on city data. We'll cover hardware, software, network configuration, and security considerations.
Overview
The "AI in Portsmouth" initiative leverages several interconnected servers to process large datasets related to traffic patterns, environmental monitoring, and public service utilization. The system is designed for scalability and redundancy, utilizing a distributed architecture. The core functionality involves training and deploying machine learning models, requiring significant processing power, memory, and storage. The servers are located in a secure data center within Portsmouth city limits. Access is restricted to authorized personnel only. Please review the Security Policy prior to accessing any server resources.
Hardware Configuration
The server cluster consists of three primary node types: Master Nodes, Worker Nodes, and Storage Nodes. Each node type has a specific role and corresponding hardware specifications.
| Node Type | CPU | Memory (RAM) | Storage (SSD) | Network Interface |
|---|---|---|---|---|
| Master Nodes (2) | 2 x Intel Xeon Gold 6248R @ 3.0 GHz | 256 GB DDR4 ECC | 2 x 1 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Worker Nodes (8) | 2 x AMD EPYC 7763 @ 2.45 GHz | 512 GB DDR4 ECC | 4 x 2 TB NVMe SSD (RAID 0) | 10 Gbps Ethernet |
| Storage Nodes (3) | 2 x Intel Xeon Silver 4210 @ 2.1 GHz | 128 GB DDR4 ECC | 12 x 8 TB SAS HDD (RAID 6) | 25 Gbps Ethernet |
All servers are housed in Dell PowerEdge R740xd chassis. Power redundancy is achieved through dual power supplies and a UPS system. Detailed hardware inventory is available on the Hardware Inventory Page. Server monitoring is performed through Nagios.
Software Configuration
The operating system of choice is Ubuntu Server 22.04 LTS. The core software stack includes Python 3.10, TensorFlow 2.12, PyTorch 2.0, and Kubernetes 1.27 for container orchestration. A centralized logging system using the ELK stack (Elasticsearch, Logstash, Kibana) is in place for monitoring and troubleshooting.
| Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers |
| Kubernetes | 1.27 | Container orchestration |
| TensorFlow | 2.12 | Machine Learning Framework |
| PyTorch | 2.0 | Machine Learning Framework |
| Docker | 24.0.5 | Containerization Platform |
| Elasticsearch | 8.8.0 | Log and data analysis |
The Software Deployment Guide provides step-by-step instructions for installing and configuring the software stack. Version control is managed using Git, with all code stored in a private GitLab repository. Continuous integration and continuous deployment (CI/CD) pipelines are established using GitLab CI.
Network Configuration
The server cluster operates on a private network segment (192.168.10.0/24). Connectivity to the external network is provided through a firewall with strict access control rules. A load balancer distributes traffic across the Master Nodes and Worker Nodes. DNS resolution is handled by an internal DNS server.
| Server Role | IP Address | Subnet Mask | Gateway |
|---|---|---|---|
| Master Node 1 | 192.168.10.10 | 255.255.255.0 | 192.168.10.1 |
| Master Node 2 | 192.168.10.11 | 255.255.255.0 | 192.168.10.1 |
| Worker Node 1-8 | 192.168.10.20-27 | 255.255.255.0 | 192.168.10.1 |
| Storage Node 1-3 | 192.168.10.30-32 | 255.255.255.0 | 192.168.10.1 |
Detailed network diagrams are available on the Network Topology Page. Regular network performance testing is conducted as outlined in the Performance Monitoring Plan.
Security Considerations
Security is paramount. All servers are protected by a firewall and intrusion detection system. Access to the servers is restricted to authorized personnel via SSH with key-based authentication. Regular security audits are conducted to identify and address vulnerabilities. Data encryption is employed both in transit and at rest. See the Security Policy for detailed security procedures. Regular backups are performed and stored offsite following the Backup and Disaster Recovery Plan.
Future Expansion
Planned future expansion includes adding GPU acceleration to the Worker Nodes for faster model training. We also plan to integrate a dedicated data lake for storing and processing larger datasets. The Roadmap details these future developments.
Main Page AI Models Data Sources Kubernetes Documentation Troubleshooting Guide Contact Information Server Access Request Form Incident Reporting Process Change Management Process Monitoring Dashboard Backup Schedule Disaster Recovery Procedures Hardware Maintenance Logs Software Patching Schedule
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.* ⚠️