AI in the Vietnamese Rainforest
- AI in the Vietnamese Rainforest: Server Configuration
This article details the server configuration powering the "AI in the Vietnamese Rainforest" project, a research initiative utilizing artificial intelligence to analyze biodiversity and track environmental changes within the Vietnamese rainforest ecosystem. This documentation is intended for new server administrators joining the project and those interested in the technical aspects of our infrastructure.
Project Overview
The "AI in the Vietnamese Rainforest" project involves deploying a network of sensor nodes throughout the rainforest, collecting data on soundscapes, visual imagery, and environmental conditions. This data is transmitted to a central server cluster for processing and analysis using machine learning algorithms. Our primary goals are species identification, anomaly detection (e.g., illegal logging), and long-term ecological monitoring. See Data Acquisition for more details on data collection. The project relies heavily on Machine Learning Algorithms for data processing.
Server Infrastructure
Our server infrastructure is hosted in a secure data center with redundant power and network connectivity. The core components consist of three primary server types: Data Ingestion Servers, Processing Servers, and Database Servers. This tiered architecture allows for scalability and efficient resource allocation. Understanding Network Topology is crucial for troubleshooting. We utilize a Load Balancing System to distribute traffic.
Data Ingestion Servers
These servers are responsible for receiving data streams from the sensor nodes. They perform initial data validation and buffering before forwarding the data to the processing servers.
| Data Ingestion Server Specifications | Value |
|---|---|
| Server Model | Dell PowerEdge R750 |
| CPU | 2 x Intel Xeon Gold 6338 |
| RAM | 128 GB DDR4 ECC |
| Storage | 4 TB NVMe SSD (RAID 1) |
| Network Interface | 2 x 10 Gbps Ethernet |
| Operating System | Ubuntu Server 22.04 LTS |
These servers run a custom data ingestion script written in Python using the ZeroMQ messaging library. See Data Validation Procedures for details on the validation process. They are monitored by Nagios for uptime and performance.
Processing Servers
These servers are the workhorses of the system, performing the computationally intensive tasks of data analysis and machine learning. They utilize specialized hardware like GPUs to accelerate these processes. Familiarity with GPU Configuration is essential.
| Processing Server Specifications | Value |
|---|---|
| Server Model | Supermicro SYS-220U-TN8R |
| CPU | 2 x AMD EPYC 7763 |
| RAM | 256 GB DDR4 ECC |
| Storage | 8 TB NVMe SSD (RAID 0) |
| GPU | 4 x NVIDIA A100 (40GB) |
| Network Interface | 2 x 100 Gbps InfiniBand |
| Operating System | CentOS Stream 9 |
The primary software stack on these servers includes TensorFlow, PyTorch, and CUDA. We employ Docker Containers to isolate and manage different machine learning models. Kubernetes handles orchestration of these containers.
Database Servers
These servers store the processed data, metadata, and model outputs. Data integrity and efficient query performance are critical.
| Database Server Specifications | Value |
|---|---|
| Server Model | HP ProLiant DL380 Gen10 |
| CPU | 2 x Intel Xeon Silver 4310 |
| RAM | 192 GB DDR4 ECC |
| Storage | 16 TB SAS HDD (RAID 6) + 2 TB NVMe SSD (for caching) |
| Network Interface | 2 x 10 Gbps Ethernet |
| Operating System | Red Hat Enterprise Linux 8 |
We utilize a PostgreSQL database with the PostGIS extension for geospatial data management. Regular Database Backups are performed to ensure data recovery. Database Schema Documentation provides a detailed overview of the database structure. We also leverage a Caching Layer using Redis for frequently accessed data.
Software Stack and Dependencies
The entire system relies on a complex software stack. Here's a summary of key components:
- Operating Systems: Ubuntu Server 22.04 LTS, CentOS Stream 9, Red Hat Enterprise Linux 8
- Programming Languages: Python, C++
- Machine Learning Frameworks: TensorFlow, PyTorch
- Database: PostgreSQL with PostGIS
- Messaging: ZeroMQ, Kafka
- Containerization: Docker
- Orchestration: Kubernetes
- Monitoring: Nagios, Prometheus, Grafana
Understanding these dependencies is crucial for troubleshooting and maintenance. See Software Version Control for our versioning policies.
Security Considerations
Security is paramount. We employ several measures to protect the system from unauthorized access and data breaches:
- Firewalls: Strict firewall rules are in place to restrict network access.
- Intrusion Detection System: An IDS System monitors for malicious activity.
- Regular Security Audits: Periodic security audits are conducted to identify and address vulnerabilities.
- Data Encryption: Data is encrypted both in transit and at rest.
- Access Control: Role-based access control is implemented to limit user privileges. Refer to Access Control Policies for details.
Future Enhancements
We are continuously working on improving the system. Planned enhancements include:
- Implementing a more sophisticated Anomaly Detection Algorithm.
- Integrating data from additional sensor types (e.g., weather stations).
- Scaling the infrastructure to handle larger data volumes.
- Automating the deployment process using Ansible.
Contact Information is available for questions and support.
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.* ⚠️