AI in Woking
- AI in Woking: Server Configuration and Deployment
This article details the server configuration used to support the "AI in Woking" project, a local initiative exploring applications of Artificial Intelligence within the borough. This document is intended for new system administrators and developers contributing to the project. It outlines the hardware, software, and network setup necessary for successful operation. Please review this document thoroughly before making any changes to the production environment. Refer to the System Administration Manual for general site policies.
Project Overview
The "AI in Woking" project involves several key components: data collection from local sources (e.g., traffic sensors, environmental monitors), model training using a cluster of GPU servers, and a web-based interface for accessing AI-powered insights. The project relies heavily on Python for scripting and data processing, and TensorFlow and PyTorch for machine learning tasks. Data storage is managed using PostgreSQL, and the web interface is built with PHP. Security is paramount; see Security Policies for detailed guidance.
Hardware Configuration
The server infrastructure is hosted in a dedicated rack at a local data center. The primary server roles are divided across several physical machines. The entire infrastructure is monitored using Nagios.
Primary Server Specs
| Server Role | Model | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|---|
| Data Collection Server | Dell PowerEdge R750 | Intel Xeon Gold 6338 | 128 GB DDR4 | 2 x 1 TB NVMe SSD (RAID 1) | 10 GbE |
| Model Training Cluster (Node 1-4) | Supermicro SYS-2029U-TR4 | AMD EPYC 7763 | 256 GB DDR4 | 4 x 4 TB SATA HDD (RAID 5) + 1 x 512GB NVMe SSD | 25 GbE |
| Web Server | HP ProLiant DL380 Gen10 | Intel Xeon Silver 4210 | 64 GB DDR4 | 2 x 480 GB SATA SSD (RAID 1) | 1 GbE |
| Database Server | Dell PowerEdge R650 | Intel Xeon Gold 6330 | 64 GB DDR4 | 4 x 2 TB SATA HDD (RAID 10) | 10 GbE |
GPU Specifications (Model Training Cluster)
Each node in the model training cluster is equipped with four NVIDIA A100 GPUs.
| GPU Model | Memory | CUDA Cores | Tensor Cores | Power Consumption |
|---|---|---|---|---|
| NVIDIA A100 | 80 GB HBM2e | 6912 | 432 | 400W |
Software Configuration
The operating system across all servers is Ubuntu Server 22.04 LTS. Specific software packages and versions are detailed below. Configuration management is handled using Ansible.
Software Stack
| Server Role | Operating System | Key Software | Versions |
|---|---|---|---|
| Data Collection Server | Ubuntu Server 22.04 LTS | Python, RabbitMQ, InfluxDB | 3.10, 3.9, 2.0 |
| Model Training Cluster | Ubuntu Server 22.04 LTS | Python, TensorFlow, PyTorch, CUDA, NCCL | 3.10, 2.12, 2.0, 12.1, 2.12 |
| Web Server | Ubuntu Server 22.04 LTS | PHP, Apache, MariaDB Client | 8.1, 2.4, 10.6 |
| Database Server | Ubuntu Server 22.04 LTS | PostgreSQL, pgAdmin | 14, 4 |
Network Configuration
The servers are connected to the data center network via a dedicated VLAN. Firewall rules are configured using iptables to restrict access to only necessary ports. A reverse proxy, Nginx, is used on the web server to handle SSL termination and load balancing. Internal DNS is managed using Bind9.
- **VLAN ID:** 100
- **Subnet:** 192.168.100.0/24
- **Gateway:** 192.168.100.1
- **DNS Servers:** 192.168.100.2, 8.8.8.8
Security Considerations
All servers are regularly patched and updated. Access to the servers is restricted to authorized personnel only, using SSH keys and strong passwords. Regular security audits are conducted. See the Incident Response Plan for details on handling security breaches. Data encryption is employed both in transit and at rest.
Future Expansion
Planned future expansion includes adding more GPUs to the model training cluster and implementing a distributed caching layer using Redis. We also plan to investigate the use of Kubernetes for container orchestration.
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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.* ⚠️