AI in the South America Rainforest
AI in the South America Rainforest: Server Configuration
This article details the server configuration for the "AI in the South America Rainforest" project. This project utilizes artificial intelligence to analyze data collected from remote sensors deployed within the Amazon rainforest, focusing on biodiversity monitoring, deforestation detection, and climate change impact assessment. This document is intended for new system administrators and developers joining the project.
Project Overview
The core of the project revolves around processing large volumes of data – images from camera traps, audio recordings, environmental sensor data (temperature, humidity, CO2 levels), and satellite imagery. This requires a robust and scalable server infrastructure capable of handling both real-time data ingestion and complex AI model training and inference. The servers are geographically distributed, with a primary cluster located in São Paulo, Brazil, and a secondary, geographically redundant cluster in Miami, Florida. The choice of locations minimizes latency to data sources and provides disaster recovery capabilities. We heavily rely on Special:Search/Data Pipelines to manage data flow.
Hardware Specifications
The server hardware is standardized across both clusters to simplify maintenance and deployment. All servers utilize a bare-metal deployment model for optimal performance, eschewing virtualization where possible. The following table details the key hardware components:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores, 64 threads per CPU) |
| RAM | 512GB DDR4 ECC Registered RAM (32 x 16GB modules) |
| Storage (Primary) | 2 x 4TB NVMe PCIe Gen4 SSD (RAID 1) - Operating System & Applications |
| Storage (Secondary) | 8 x 16TB SAS HDD (RAID 6) - Data Storage |
| Network Interface | Dual 100GbE Network Interface Cards (NICs) |
| Power Supply | Redundant 1600W Platinum Power Supplies |
These specifications allow for efficient processing of the massive datasets generated by the project. We use Special:Search/Storage Management to oversee the data storage infrastructure.
Software Stack
The software stack is carefully curated to provide a stable and performant environment for AI development and deployment.
- Operating System: Ubuntu Server 22.04 LTS (Long Term Support)
- Containerization: Docker and Kubernetes are used for application deployment and orchestration. See our Special:Search/Kubernetes Configuration document for details.
- Programming Languages: Python 3.9 is the primary language used for AI model development and data processing. R is used for statistical analysis.
- AI Frameworks: TensorFlow and PyTorch are the primary AI frameworks. We also use scikit-learn for traditional machine learning tasks.
- Database: PostgreSQL with the PostGIS extension is used for storing spatial data and metadata. Special:Search/Database Schema provides a schema overview.
- Message Queue: RabbitMQ is used for asynchronous task processing and communication between different services.
- Monitoring: Prometheus and Grafana are used for system monitoring and alerting.
Network Configuration
The network infrastructure is crucial for ensuring data transfer speeds and system reliability. Here's a breakdown of key network components:
| Component | Specification |
|---|---|
| Network Topology | Spine-Leaf Architecture |
| Core Switches | Arista 7050X Series |
| Leaf Switches | Arista 7280E Series |
| Inter-Cluster Connectivity | Dedicated 10Gbps fiber optic link |
| Firewall | Palo Alto Networks PA-5220 |
All network traffic is encrypted using TLS/SSL. We utilize Special:Search/Firewall Rules for security and access control. Proper network segmentation is implemented to isolate different components of the system.
AI Model Deployment & Inference
AI models are deployed using Kubernetes. Each model is containerized and scaled based on demand. We use NVIDIA Triton Inference Server to optimize inference performance. The following table details the GPU configuration for the AI inference servers:
| Component | Specification |
|---|---|
| GPU | NVIDIA A100 (80GB) x 4 |
| GPU Driver | NVIDIA Driver 535.104.05 |
| CUDA Toolkit | CUDA 12.2 |
| cuDNN | cuDNN 8.9.2 |
We employ techniques such as model quantization and pruning to reduce model size and improve inference speed. Special:Search/Model Optimization details these techniques. Monitoring inference latency and accuracy is critical, and we use custom dashboards in Grafana for this purpose. The entire process is documented in our Special:Search/Deployment Pipeline.
Security Considerations
Security is paramount, especially given the sensitive nature of the data collected. Key security measures include:
- Regular security audits and vulnerability scanning.
- Multi-factor authentication for all server access.
- Strict access control policies based on the principle of least privilege.
- Data encryption at rest and in transit.
- Intrusion detection and prevention systems.
- Regular backups and disaster recovery procedures.
- Compliance with relevant data privacy regulations (e.g., LGPD in Brazil).
See our Special:Search/Security Policy for detailed information.
Future Enhancements
Future enhancements include:
- Implementing a federated learning framework to allow for collaborative model training without sharing raw data.
- Exploring the use of edge computing to process data closer to the source, reducing latency and bandwidth requirements.
- Investigating the integration of new AI frameworks and technologies.
Special:Search/Data Acquisition Special:Search/Data Preprocessing Special:Search/Model Training Special:Search/System Monitoring Special:Search/Disaster Recovery Special:Search/Backup Procedures Special:Search/User Management Special:Search/Access Control Special:Search/Network Security Special:Search/Logging and Auditing Special:Search/Performance Tuning Special:Search/Resource Allocation Special:Search/API Documentation Special:Search/Troubleshooting Guide
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.* ⚠️