AI in the Myanmar Rainforest
AI in the Myanmar Rainforest: Server Configuration
This document details the server configuration for the "AI in the Myanmar Rainforest" project, designed to process and analyze data collected from remote sensor networks deployed within the rainforest environment. This guide is aimed at new contributors to the project and outlines the hardware and software stack powering the AI-driven analysis.
Project Overview
The “AI in the Myanmar Rainforest” project aims to leverage artificial intelligence to monitor biodiversity, detect illegal logging, and track climate change impacts within a designated region of the Myanmar rainforest. Data from acoustic sensors, camera traps, and environmental monitors is transmitted via satellite links to our central server cluster. This cluster performs real-time data processing, model training, and anomaly detection. The processed data is then made available to researchers via a dedicated web interface. See Data Acquisition for details on the sensor network.
Server Hardware Configuration
Our server infrastructure is hosted in a secure data center with redundant power and cooling. The core components are described below.
| Component | Specification | Quantity |
|---|---|---|
| CPU | Intel Xeon Gold 6248R (3.0 GHz, 24 cores) | 3 |
| RAM | 256GB DDR4 ECC Registered 2933MHz | 3 |
| Storage (OS/Boot) | 500GB NVMe SSD | 3 |
| Storage (Data) | 16TB SAS 7.2k RPM HDD (RAID 6) | 12 |
| Network Interface | Dual 10 Gigabit Ethernet | 3 |
| Power Supply | Redundant 80+ Platinum 1200W | 3 |
The servers are interconnected via a dedicated 40 Gigabit Ethernet backbone. A separate Network Diagram details the network topology. We utilize a clustered file system (see Storage Configuration) to provide high availability and scalability for the large datasets.
Software Stack
The software stack is built around a Linux foundation and incorporates various open-source tools for data processing, machine learning, and web serving.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers |
| Database | PostgreSQL 14 | Data storage and management |
| Message Queue | RabbitMQ 3.9 | Asynchronous task processing |
| Machine Learning Framework | TensorFlow 2.9 | Model training and inference |
| Web Server | Nginx 1.22 | Serving the web application |
| Programming Language | Python 3.10 | Primary language for data processing and AI models |
We employ Docker and Kubernetes for containerization and orchestration, enabling easy deployment and scaling of services. See Deployment Procedures for more information. The Python environment is managed with venv to ensure reproducibility.
Storage Configuration
Given the large volume of data generated by the sensor network, a robust and scalable storage solution is crucial. We employ a distributed file system built on GlusterFS.
| Parameter | Value | Description |
|---|---|---|
| File System | GlusterFS 9.2 | Distributed file system for scalability and redundancy |
| Replication Factor | 3 | Each file is replicated across three different storage nodes. |
| Total Storage Capacity | 192 TB | Aggregate storage capacity across all nodes. |
| Transport Protocol | TCP | Communication protocol used for data transfer. |
| Brick Directory | /data/glusterfs | Location of the data bricks on each server. |
Data is categorized into raw sensor data, processed data, and model outputs. See Data Management Policies for details on data retention and access control. Regular backups are performed using Bacula to ensure data durability.
Security Considerations
Security is paramount, given the sensitive nature of the data collected and the remote location of the sensors.
- Firewall: A strict firewall policy is enforced using `iptables` to restrict network access to authorized services. See Firewall Rules.
- Intrusion Detection: We utilize `fail2ban` to automatically block malicious IP addresses.
- Access Control: Access to the server infrastructure is restricted to authorized personnel via SSH with key-based authentication. See Access Control List.
- Data Encryption: Data is encrypted both in transit (using TLS) and at rest (using LUKS disk encryption).
- Regular Audits: Security audits are conducted quarterly to identify and address potential vulnerabilities. Refer to Security Audit Reports.
Future Enhancements
Planned future enhancements include:
- Implementing a GPU cluster for accelerated machine learning training. See GPU Cluster Proposal.
- Integrating a real-time data streaming platform like Apache Kafka.
- Exploring the use of serverless computing for certain data processing tasks.
- Improving the Monitoring System for proactive issue detection.
Contact Information is available for any questions or issues related to the server configuration.
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 |
Order Your Dedicated Server
Configure and order your ideal server configuration
Need Assistance?
- Telegram: @powervps Servers at a discounted price
⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️