AI in the Vanuatuan Rainforest
AI in the Vanuatuan Rainforest: Server Configuration
This article details the server infrastructure supporting the "AI in the Vanuatuan Rainforest" project. This project utilizes artificial intelligence to analyze audio data collected from remote sensors within the rainforests of Vanuatu, focusing on biodiversity monitoring and early detection of illegal logging activities. This guide is intended for new contributors and system administrators assisting with the project.
Project Overview
The "AI in the Vanuatuan Rainforest" project relies on a distributed server architecture to handle the large volume of audio data and the computationally intensive machine learning models. Data is collected from a network of acoustic sensors deployed throughout the rainforest. This data is transmitted via satellite link to a central ingestion server, processed, and analyzed. The results are then made available to researchers and conservationists through a web-based interface. See Data Acquisition for details on the sensor network. Understanding Audio Processing Pipelines is also critical.
Server Architecture
The system is comprised of three primary tiers: Ingestion, Processing, and Presentation. We utilize a hybrid cloud approach, leveraging both on-premise hardware and cloud-based services. Network Topology provides a visual representation of the entire system. The on-premise components are located in a secure data center in Port Vila, Vanuatu, with redundant power and network connectivity. Cloud resources are primarily hosted on Amazon Web Services (AWS). Security Considerations are paramount throughout the entire infrastructure.
Ingestion Server
The Ingestion Server is the first point of contact for data arriving from the rainforest sensors. Its primary function is to receive, validate, and store the raw audio data. It also handles initial data preprocessing, such as format conversion and noise reduction. The server is built for high availability and scalability. Details below:
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6248R (24 cores) | 2 |
| RAM | 128 GB DDR4 ECC | 1 |
| Storage | 60 TB RAID 6 (SATA SSD) | 1 |
| Network Interface | 10 Gbps Ethernet | 2 |
| Operating System | Ubuntu Server 22.04 LTS | 1 |
This server utilizes rsync for data synchronization with the cloud storage (AWS S3). Data Validation Procedures are crucial for ensuring data integrity.
Processing Cluster
The Processing Cluster is responsible for running the machine learning models that analyze the audio data. This cluster consists of multiple GPU-accelerated servers, allowing for parallel processing and faster turnaround times. The core of the analysis relies on Deep Learning Models specifically trained on Vanuatuan rainforest sounds.
| Component | Specification | Quantity |
|---|---|---|
| CPU | AMD EPYC 7763 (64 cores) | 4 |
| GPU | NVIDIA A100 (80GB) | 8 |
| RAM | 256 GB DDR4 ECC | 4 |
| Storage | 2 TB NVMe SSD | 4 |
| Network Interface | 100 Gbps InfiniBand | 4 |
| Operating System | CentOS Stream 9 | 4 |
This cluster utilizes Kubernetes for container orchestration and resource management. Monitoring Tools provide real-time insights into cluster performance. We also use Job Scheduling to prioritize tasks.
Presentation Server
The Presentation Server hosts the web-based interface that allows researchers and conservationists to access the results of the AI analysis. It provides tools for visualizing data, generating reports, and managing user accounts. The interface is built using Python Flask and JavaScript.
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Core i7-12700K (12 cores) | 2 |
| RAM | 64 GB DDR5 | 2 |
| Storage | 1 TB NVMe SSD | 2 |
| Web Server | Nginx | 1 |
| Database | PostgreSQL 14 | 1 |
| Operating System | Debian 11 | 1 |
Database Management practices are essential for maintaining data accuracy and availability. The server is protected by a Firewall Configuration. User Access Control is strictly enforced.
Software Stack
The entire system relies on a robust software stack. Key components include:
- Python 3.9
- TensorFlow 2.8
- PyTorch 1.10
- PostgreSQL 14
- Nginx
- Kubernetes
- rsync
- Prometheus for System Monitoring
Future Considerations
Future development will focus on improving the accuracy of the AI models, expanding the sensor network, and enhancing the user interface. We are also exploring the use of Edge Computing to perform some of the data processing closer to the sensors, reducing latency and bandwidth requirements.
Contact Information for the project team is available on the main project page.
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.* ⚠️