AI in the Kurdistan Rainforest
AI in the Kurdistan Rainforest: Server Configuration
This article details the server configuration deployed to support the "AI in the Kurdistan Rainforest" project. This project utilizes artificial intelligence to analyze audio and visual data collected from remote sensors within the Kurdistan rainforest region, focusing on biodiversity monitoring and illegal logging detection. This document is intended for new system administrators and engineers contributing to the project's infrastructure.
Project Overview
The "AI in the Kurdistan Rainforest" project requires significant computational resources for real-time data processing, model training, and long-term data storage. The server infrastructure is designed for scalability, reliability, and security, given the remote location of the data sources and the sensitive nature of the information collected. Data is collected via a network of sensor nodes and transmitted to a central server cluster. The AI models employed include deep learning algorithms for audio classification (identifying animal calls and machinery) and computer vision techniques for image analysis (detecting deforestation and identifying species). This data is then stored in a database server for long-term analysis.
Server Hardware Specifications
The core of the infrastructure consists of three primary server types: Ingestion Servers, Processing Servers, and Storage Servers. Each type is detailed below.
| Server Type | Quantity | CPU | Memory (RAM) | Storage | Network Interface |
|---|---|---|---|---|---|
| Ingestion Server | 2 | Intel Xeon Gold 6248R (24 cores) | 128 GB DDR4 ECC | 2 x 1 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Processing Server | 4 | AMD EPYC 7763 (64 cores) | 256 GB DDR4 ECC | 4 x 4 TB SAS HDD (RAID 10) + 1 x 500 GB NVMe SSD (OS) | 25 Gbps Ethernet + 10 Gbps Ethernet |
| Storage Server | 3 | Intel Xeon Silver 4210 (10 cores) | 64 GB DDR4 ECC | 12 x 8 TB SAS HDD (RAID 6) | 10 Gbps Ethernet |
These servers are housed in a secure data center with redundant power and cooling systems. The data center also features a robust firewall and intrusion detection system. Power is provided by a combination of grid electricity and a backup generator.
Software Stack
The software stack is built around a Linux foundation, specifically Ubuntu Server 22.04 LTS. Key components include:
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes are used for application deployment and orchestration.
- Data Ingestion: Apache Kafka is used as a message broker to handle the high volume of data streaming from the sensor network.
- Database: PostgreSQL with the PostGIS extension is used for storing and querying geospatial data.
- AI Frameworks: TensorFlow and PyTorch are used for developing and deploying the AI models.
- Monitoring: Prometheus and Grafana are used for system monitoring and alerting.
- Web Server: Nginx serves as a reverse proxy and web server for the project's web interface.
- Version Control: Git is used for managing source code.
Network Configuration
The server network is segmented into three zones: public, private, and management.
| Zone | Purpose | Access Restrictions |
|---|---|---|
| Public | External access (web interface, API) | Limited to specific ports through the firewall. |
| Private | Internal communication between servers (data ingestion, processing, storage) | Restricted to authorized servers only. |
| Management | Remote administration of servers | Secured with SSH keys and VPN access. |
All communication between servers is encrypted using TLS/SSL. The network is monitored for security threats using an Intrusion Detection System. A DNS server manages internal name resolution.
Data Flow
Data flows from the sensor nodes through the ingestion servers, to the processing servers for AI analysis, and finally to the storage servers for long-term archiving. This process is outlined below:
1. Sensor nodes transmit data to the ingestion servers via a secure wireless network. 2. Ingestion servers validate and format the data and then publish it to an Apache Kafka topic. 3. Processing servers subscribe to the Kafka topic and consume the data. 4. AI models are applied to the data to identify relevant events (e.g., animal calls, deforestation). 5. Processed data and metadata are stored in the PostgreSQL database. 6. Users can access the data and analysis results through the web interface served by Nginx.
Scalability and Future Considerations
The infrastructure is designed to scale horizontally by adding more processing and storage servers as needed. Load balancing is implemented to distribute traffic across multiple servers. Future considerations include:
- Implementing a distributed file system (e.g., Hadoop Distributed File System (HDFS)) for even larger datasets.
- Exploring the use of GPU acceleration for faster AI model training and inference.
- Integrating with cloud-based services for disaster recovery and backup.
- Investigating the use of edge computing to process data closer to the source.
Security Considerations
Security is paramount. Regular security audits are conducted. All software is kept up to date with the latest security patches. Access control lists (ACLs) are used to restrict access to sensitive data. A comprehensive backup and recovery plan is in place.
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.* ⚠️