AI in the Bonaire Rainforest
AI in the Bonaire Rainforest: Server Configuration
This article details the server configuration powering the "AI in the Bonaire Rainforest" project. This project utilizes machine learning to analyze audio and visual data collected from remote sensors within the Bonaire rainforest, aiming to identify and track species, monitor ecosystem health, and detect illegal activity. This guide is geared towards new contributors to our MediaWiki site and assumes a basic understanding of server administration.
Project Overview
The "AI in the Bonaire Rainforest" initiative relies on a distributed server architecture to handle the substantial data processing demands. Data is collected from a network of sensor nodes deployed throughout the rainforest. This data is transmitted to a central hub, pre-processed, and then distributed to a cluster of servers for model training and inference. Data pipelines are critical for ensuring data integrity and efficient processing. The project utilizes TensorFlow, PyTorch, and scikit-learn for machine learning tasks. Real-time analytics are a key feature, requiring low-latency processing. The project also leverages a database system to store metadata and analysis results.
Server Hardware Specifications
The core processing is handled by a cluster of eight identical servers. The following table outlines the specifications for each server:
| Component | Specification |
|---|---|
| CPU | AMD EPYC 7763 (64-Core) |
| RAM | 512GB DDR4 ECC Registered |
| Storage (OS/Boot) | 1TB NVMe SSD |
| Storage (Data) | 16TB RAID 6 (SAS 7.2k RPM) |
| GPU | 4x NVIDIA A100 (80GB) |
| Network Interface | 100GbE Ethernet |
| Power Supply | 2000W Redundant |
These servers are housed in a secure, climate-controlled data center.
Network Infrastructure
The network is designed for high bandwidth and low latency. Key components are defined below.
| Component | Specification |
|---|---|
| Core Switch | Arista 7508R |
| Edge Switches | Cisco Catalyst 9300 Series |
| Inter-Server Network | 100GbE Fabric |
| Internet Connectivity | Redundant 10GbE connections |
| Firewall | Palo Alto Networks PA-820 |
| Load Balancer | HAProxy |
Network security is paramount, with multiple layers of protection in place. We utilize Virtual LANs to segment the network and isolate sensitive data. Intrusion detection systems are also deployed.
Software Stack
The servers run a customized Ubuntu 22.04 LTS operating system. The following table details the key software components:
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu 22.04 LTS | Base OS |
| Containerization | Docker 24.0.5 | Application deployment and isolation |
| Orchestration | Kubernetes 1.27 | Container management and scaling |
| Programming Languages | Python 3.10, CUDA 12.2 | ML development and execution |
| Database | PostgreSQL 15 | Metadata storage |
| Monitoring | Prometheus & Grafana | System and application monitoring |
| Logging | ELK Stack (Elasticsearch, Logstash, Kibana) | Log aggregation and analysis |
Containerization is used extensively to ensure portability and reproducibility. Kubernetes automatically scales resources based on demand. Continuous integration and continuous delivery (CI/CD) pipelines are used to automate the deployment process. We are also exploring serverless computing options for certain tasks. API gateways manage access to the machine learning models. Version control is handled using Git.
Future Considerations
We are actively investigating the integration of edge computing to reduce latency and bandwidth requirements. Additionally, we plan to explore the use of federated learning to train models on data distributed across multiple locations without centralizing the data. Further improvements to data compression techniques are also planned to optimize storage and network usage.
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.* ⚠️