AI in the Europe Rainforest
- AI in the Europe Rainforest: Server Configuration
This article details the server configuration supporting the "AI in the Europe Rainforest" project, a research initiative utilizing artificial intelligence to monitor and analyze biodiversity within the European rainforest ecosystem. This guide is intended for new contributors to the MediaWiki site and provides a technical overview of the infrastructure. Understanding this setup is crucial for development, maintenance, and troubleshooting.
Project Overview
The "AI in the Europe Rainforest" project relies on a distributed server architecture to process data collected from a network of sensors deployed throughout several European rainforest locations. These sensors capture audio, visual, and environmental data, which is then analyzed by AI models to identify species, track their movements, and assess the overall health of the ecosystem. The system utilizes machine learning, deep learning, and computer vision techniques. Data storage is critical to the project's success.
Server Architecture
The server infrastructure is composed of three primary tiers: Data Acquisition, Processing, and Storage. Each tier utilizes a specific set of servers with tailored configurations. Network topology is a key aspect of system reliability. The servers are hosted in geographically diverse data centers to ensure redundancy and minimize latency. Security considerations are paramount, given the sensitive nature of the data.
Data Acquisition Servers
These servers are located near the sensor networks and are responsible for collecting and pre-processing data before transmitting it to the processing tier. They are designed for high throughput and reliability. Sensor integration is a crucial function.
| Server Role | Quantity | CPU | RAM | Storage | Operating System |
|---|---|---|---|---|---|
| Data Ingestor | 6 | Intel Xeon Silver 4310 (12 cores) | 64 GB DDR4 ECC | 2 x 4TB SSD (RAID 1) | Ubuntu Server 22.04 LTS |
| Pre-Processor | 6 | AMD EPYC 7302P (16 cores) | 128 GB DDR4 ECC | 4 x 2TB NVMe SSD (RAID 0) | CentOS Stream 9 |
Processing Servers
These servers perform the core AI computations, including model training, inference, and data analysis. They require significant processing power and memory. AI model deployment is a frequent task. GPU acceleration is heavily utilized.
| Server Role | Quantity | CPU | RAM | GPU | Storage | Operating System |
|---|---|---|---|---|---|---|
| AI Inference | 12 | Intel Xeon Gold 6338 (32 cores) | 256 GB DDR4 ECC | 4 x NVIDIA A100 (80GB) | 2 x 8TB NVMe SSD (RAID 1) | Ubuntu Server 22.04 LTS |
| Model Training | 4 | AMD EPYC 7763 (64 cores) | 512 GB DDR4 ECC | 8 x NVIDIA A100 (80GB) | 4 x 8TB NVMe SSD (RAID 1) | Rocky Linux 9 |
Storage Servers
These servers provide long-term storage for the collected data and AI model outputs. They are designed for high capacity and data integrity. Backup strategies are essential. Data archiving is regularly performed.
| Server Role | Quantity | CPU | RAM | Storage | Operating System |
|---|---|---|---|---|---|
| Raw Data Storage | 8 | Intel Xeon Silver 4314 (10 cores) | 128 GB DDR4 ECC | 16 x 16TB SAS HDD (RAID 6) | SUSE Linux Enterprise Server 15 SP4 |
| Model Output Storage | 4 | Intel Xeon E-2336 (8 cores) | 64 GB DDR4 ECC | 8 x 8TB SAS HDD (RAID 6) | Ubuntu Server 22.04 LTS |
Software Stack
The project utilizes a variety of software tools and frameworks. Software upgrades are carefully planned.
- **Programming Languages:** Python, R
- **AI Frameworks:** TensorFlow, PyTorch, scikit-learn
- **Database:** PostgreSQL with PostGIS extension
- **Data Pipeline:** Apache Kafka, Apache Spark
- **Monitoring:** Prometheus, Grafana
- **Version Control:** Git
Network Configuration
The servers are connected via a dedicated high-bandwidth network. Firewall configuration is critical. Load balancing is used to distribute traffic across the processing servers. The network utilizes a combination of 10GbE and 40GbE connections. DNS management is handled centrally. VPN access is restricted to authorized personnel.
Future Considerations
Future upgrades may include the integration of edge computing capabilities to reduce latency and bandwidth requirements. Scalability planning is an ongoing process. We are also exploring the use of newer GPU architectures for improved performance. Cost optimization is always a consideration. Disaster recovery planning is regularly reviewed and updated.
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.* ⚠️