AI in the Baltic Sea

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  1. AI in the Baltic Sea: Server Configuration

This article details the server configuration utilized for the "AI in the Baltic Sea" project, a research initiative focused on real-time data analysis and predictive modeling of environmental conditions within the Baltic Sea region. This guide is intended for newcomers to our server environment and provides a comprehensive overview of the hardware and software stack.

Project Overview

The "AI in the Baltic Sea" project ingests data from a network of underwater sensors, satellite imagery, and historical datasets. This data is processed using machine learning algorithms to predict algal blooms, monitor water quality, and track marine life migration patterns. The server infrastructure is designed for high throughput, low latency, and scalability. Data Acquisition is a crucial component, and Data Preprocessing prepares the data for analysis. The core of the project revolves around Machine Learning Models and their deployment. We utilize a distributed system to handle the large data volumes. See also Project Goals for a high-level overview.

Hardware Infrastructure

The server infrastructure consists of three primary tiers: Data Ingestion, Processing, and Storage. Each tier is built with redundancy and scalability in mind.

Data Ingestion Tier

This tier handles the reception of data from various sources. It’s designed for high availability and rapid data transfer.

Component Specification Quantity
Server Type Dell PowerEdge R750 2
CPU Intel Xeon Gold 6338 (32 Cores) 2 per server
RAM 256 GB DDR4 ECC REG 2 per server
Network Interface 100 Gbps Ethernet 2 per server
Storage (Temporary) 2 x 1 TB NVMe SSD (RAID 1) 2 per server

These servers utilize Network Protocols like MQTT and HTTP/S for data reception. Security Considerations are paramount in this tier.

Processing Tier

This tier performs the computationally intensive tasks of data cleaning, transformation, and model training/inference.

Component Specification Quantity
Server Type Supermicro SYS-2029U-TR4 4
CPU AMD EPYC 7763 (64 Cores) 2 per server
GPU NVIDIA A100 (80GB) 2 per server
RAM 512 GB DDR4 ECC REG 2 per server
Storage (Local) 4 x 4 TB NVMe SSD (RAID 10) 4 per server

GPU acceleration is essential for our Deep Learning Frameworks, specifically TensorFlow and PyTorch. We employ Containerization using Docker and Kubernetes for efficient resource management.

Storage Tier

The Storage Tier provides persistent storage for raw data, processed data, and model artifacts.

Component Specification Capacity
Storage System Dell EMC PowerScale F600 1 PB (Scalable to 5 PB)
File System Lustre N/A
Network Connectivity 200 Gbps InfiniBand N/A
Redundancy Triple Parity RAID N/A

Data Backup Strategies are crucial for data integrity and disaster recovery. We use a tiered storage approach, utilizing faster storage for frequently accessed data and slower, cheaper storage for archival purposes.

Software Stack

The software stack is designed to support the entire data pipeline, from ingestion to model deployment.

  • Operating System: Ubuntu Server 22.04 LTS
  • Containerization: Docker 20.10.7, Kubernetes 1.23
  • Programming Languages: Python 3.9, R 4.2.1
  • Databases: PostgreSQL 14, TimescaleDB 2.7
  • Message Queue: Kafka 3.2.0
  • Machine Learning Frameworks: TensorFlow 2.9, PyTorch 1.12
  • Monitoring: Prometheus, Grafana
  • Version Control: Git, GitLab
  • CI/CD: Jenkins

We leverage Cloud Integration for certain tasks, such as model deployment and remote access. Regular Software Updates are performed to ensure system security and stability. The API Documentation provides details on accessing the processed data.


Network Topology

The servers are interconnected via a high-speed network utilizing a spine-leaf architecture. This provides low latency and high bandwidth between all tiers. Network Security is a key concern and is addressed through firewalls, intrusion detection systems, and regular security audits. See also Network Monitoring.

This configuration provides a robust and scalable platform for the "AI in the Baltic Sea" project. Future enhancements will focus on increasing storage capacity and incorporating new machine learning algorithms. For more information, please refer to the Internal Documentation.


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.* ⚠️

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