AI in Colchester
- AI in Colchester: Server Configuration
This article details the server configuration supporting the "AI in Colchester" project. It is intended for newcomers to the MediaWiki site and provides a technical overview of the hardware and software utilized. This project focuses on utilizing artificial intelligence to analyze traffic patterns within Colchester, improving transportation efficiency. Understanding the server infrastructure is crucial for anyone contributing to this initiative.
Overview
The "AI in Colchester" project relies on a cluster of servers located within the Colchester data center. These servers are responsible for data ingestion, model training, inference, and data storage. The architecture is designed for scalability and redundancy, ensuring high availability and performance. We leverage a hybrid cloud approach, utilizing both on-premise hardware and cloud computing resources for specific tasks. The primary operating system is Ubuntu Server 22.04, chosen for its stability and extensive package availability.
Hardware Specifications
The core infrastructure consists of four primary server types: Data Ingestion Servers, Model Training Servers, Inference Servers, and Database Servers. Below are the detailed specifications for each.
| Server Type | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Data Ingestion Servers (x3) | Intel Xeon Gold 6248R (24 cores) | 128GB DDR4 ECC | 4 x 4TB NVMe SSD (RAID 10) | 10GbE |
| Model Training Servers (x2) | AMD EPYC 7763 (64 cores) | 256GB DDR4 ECC | 8 x 8TB NVMe SSD (RAID 0) | 100GbE |
| Inference Servers (x4) | Intel Xeon Silver 4310 (12 cores) | 64GB DDR4 ECC | 2 x 2TB NVMe SSD (RAID 1) | 10GbE |
| Database Servers (x2 - Primary/Replica) | Intel Xeon Gold 6338 (32 cores) | 256GB DDR4 ECC | 16 x 4TB SAS HDD (RAID 6) | 10GbE |
These specifications are subject to change based on project requirements and hardware availability. Regular server maintenance is performed to ensure optimal performance.
Software Stack
The software stack is carefully chosen to support the project's AI/ML workflows. We utilize a combination of open-source and commercial tools. Python is the primary programming language, with TensorFlow and PyTorch being the main machine learning frameworks.
| Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Server OS |
| Programming Language | Python 3.10 | Core application logic |
| Machine Learning Frameworks | TensorFlow 2.12, PyTorch 2.0 | Model training and inference |
| Database | PostgreSQL 15 | Data storage and retrieval |
| Message Queue | RabbitMQ 3.9 | Asynchronous task processing |
| Containerization | Docker 20.10 | Application packaging and deployment |
| Orchestration | Kubernetes 1.26 | Container management and scaling |
All code is version controlled using Git and hosted on a private GitLab instance. Continuous integration/continuous deployment (CI/CD) pipelines are implemented to automate the build, test, and deployment processes.
Networking Configuration
The servers are connected via a dedicated VLAN within the Colchester data center network. A firewall, utilizing iptables, protects the servers from unauthorized access. Load balancing is implemented using HAProxy to distribute traffic across the Inference Servers. Internal DNS resolution is managed by a local BIND9 server.
| Parameter | Value |
|---|---|
| VLAN ID | 100 |
| Firewall | iptables |
| Load Balancer | HAProxy |
| DNS Server | BIND9 |
| Internal Subnet | 192.168.100.0/24 |
| Gateway | 192.168.100.1 |
Regular network monitoring is performed using tools like Nagios to identify and resolve network issues. Security audits are conducted quarterly to ensure the network remains secure. We utilize VPN access for remote administration.
Future Considerations
Future plans include migrating some of the model training workload to GPU instances in the cloud to accelerate training times. We are also exploring the use of Kafka as a more scalable message queue solution. Further optimization of the database schema is planned to improve query performance. Consideration is being given to implementing a monitoring dashboard using Grafana to provide real-time visibility into server performance.
Special:Search/AI Special:Search/Colchester Special:Search/Server Special:Search/Ubuntu Special:Search/TensorFlow Special:Search/PostgreSQL Special:Search/Kubernetes Special:Search/iptables Special:Search/HAProxy Special:Search/Git Special:Search/Docker Special:Search/RabbitMQ Special:Search/BIND9 Special:Search/GitLab Special:Search/GPU Special:Search/Kafka Special:Search/Grafana Help:Contents MediaWiki:MainPage
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.* ⚠️