AI in the Guam Rainforest

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AI in the Guam Rainforest: Server Configuration

This article details the server configuration supporting the "AI in the Guam Rainforest" project. This project utilizes artificial intelligence to analyze data collected from remote sensors deployed throughout the Guam rainforest, focusing on biodiversity monitoring and rapid environmental change detection. This document is intended for new contributors and system administrators familiar with basic Linux server administration.

Project Overview

The "AI in the Guam Rainforest" project depends on real-time data processing from a network of sensor nodes. These nodes collect data on temperature, humidity, acoustic signatures (for animal identification), and visual data (for plant species identification). This data is transmitted wirelessly to a central server cluster for analysis. The AI models, primarily convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are deployed and managed on this cluster. Data Acquisition is a critical component, as is Sensor Calibration.

Server Hardware

The server cluster consists of three primary servers: a data ingestion server, a processing server, and a database server. Each server is physically located in a secure, climate-controlled facility at the University of Guam.

Server Role Hardware Specification Operating System
CPU: Intel Xeon Silver 4210R
RAM: 64GB DDR4 ECC
Storage: 2 x 4TB SATA SSD (RAID 1) | Ubuntu Server 22.04 LTS
CPU: 2 x AMD EPYC 7763
RAM: 256GB DDR4 ECC
GPU: 2 x NVIDIA A100 (80GB)
Storage: 4 x 8TB SAS HDD (RAID 10) | CentOS Stream 9
CPU: Intel Xeon Gold 6248R
RAM: 128GB DDR4 ECC
Storage: 8 x 4TB SAS HDD (RAID 6) | Debian 11

Software Stack

The software stack is designed for scalability, reliability, and ease of maintenance. We rely heavily on containerization for consistent deployments. Containerization Best Practices are followed rigorously.

  • Data Ingestion Server: Nginx (web server), RabbitMQ (message queue), Python 3.10 with Flask (API endpoints).
  • Processing Server: Docker, NVIDIA Container Toolkit, CUDA Toolkit 11.8, TensorFlow 2.12, PyTorch 1.13.
  • Database Server: PostgreSQL 14, pgAdmin 4.

Network Configuration

The servers are connected via a dedicated 10 Gigabit Ethernet network. Firewall rules are configured using `iptables` to restrict access to only necessary ports. Network Security Protocols are implemented to secure data transmission. The network is segmented into three zones: public (for external access to the API), internal (for communication between servers), and management (for remote administration).

Server IP Address Network Zone Purpose
192.168.1.10 | Internal | API Gateway & Data Receiver
192.168.1.20 | Internal | AI Model Execution
192.168.1.30 | Internal | Data Storage
203.0.113.5 | Public | Public Access to Data

AI Model Deployment

AI models are deployed as Docker containers on the Processing Server. We utilize NVIDIA's Triton Inference Server to optimize model serving and handle concurrent requests. Model Versioning is a crucial aspect of this process. The models are trained remotely on more powerful hardware and then pushed to the server cluster for inference. Continuous integration and continuous deployment (CI/CD) pipelines automate the model deployment process. CI/CD Pipeline Details are documented separately.

Database Schema

The database schema is designed to efficiently store and query the sensor data. PostgreSQL's JSONB data type is used to store the raw sensor readings. Time-series data is indexed using appropriate data types and indexes for fast retrieval.

Table Name Description Key Columns
Stores raw sensor readings | sensor_id, timestamp, data (JSONB)
Stores metadata about each sensor | sensor_id, location, sensor_type
Stores predictions made by the AI models | sensor_id, timestamp, prediction_type, prediction_value

Monitoring and Alerting

Server performance and application health are monitored using Prometheus and Grafana. Alerts are configured to notify administrators of any issues, such as high CPU usage, low disk space, or failed model deployments. Monitoring Dashboard Link provides access to real-time metrics. Alerting Configuration details defines the alerting thresholds.

Future Considerations

  • Implementing a distributed database solution for increased scalability and resilience.
  • Exploring the use of edge computing to perform some data processing closer to the sensors.
  • Integrating with other data sources, such as satellite imagery. Remote Sensing Data will be a valuable addition.



Server Administration Data Analysis AI Model Training Sensor Networks Database Management Network Troubleshooting Security Hardening System Updates Backup and Recovery Disaster Recovery Plan Performance Tuning User Management Log Analysis API Documentation


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.* ⚠️

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