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AI in the Basque Country Rainforest

AI in the Basque Country Rainforest: Server Configuration

This article details the server configuration supporting the "AI in the Basque Country Rainforest" project, a research initiative utilizing artificial intelligence to analyze biodiversity data collected from the rainforest region. This documentation is intended for new system administrators and developers contributing to the project. It covers hardware specifications, software stack, networking, and security considerations. This project utilizes a distributed system for handling the large datasets involved. See also Data Acquisition Procedures and Project Overview.

Hardware Overview

The project utilizes a cluster of servers located in a secure, climate-controlled facility near Bilbao. The cluster is designed for high throughput and redundancy. We utilize a mix of compute and storage nodes.

Server Role Server Name CPU RAM Storage
Compute Node 1 basque-ai-compute-01 Intel Xeon Gold 6248R (24 cores) 256 GB DDR4 ECC 2 x 1 TB NVMe SSD (RAID 1)
Compute Node 2 basque-ai-compute-02 Intel Xeon Gold 6248R (24 cores) 256 GB DDR4 ECC 2 x 1 TB NVMe SSD (RAID 1)
Storage Node 1 basque-ai-storage-01 Intel Xeon Silver 4210 (10 cores) 128 GB DDR4 ECC 8 x 4 TB SATA HDD (RAID 6)
Storage Node 2 basque-ai-storage-02 Intel Xeon Silver 4210 (10 cores) 128 GB DDR4 ECC 8 x 4 TB SATA HDD (RAID 6)
Master Node basque-ai-master Intel Xeon E-2288G (8 cores) 64 GB DDR4 ECC 1 x 500 GB NVMe SSD

The master node manages the cluster using Kubernetes. All data is backed up nightly to an offsite location, utilizing rsync and a secure VPN connection. Power redundancy is provided by a UPS system with battery backup for at least 30 minutes. See also Disaster Recovery Plan.

Software Stack

The software stack is built around a Linux foundation and utilizes various open-source tools for AI development and data management.

Component Version Description
Operating System Ubuntu Server 22.04 LTS Provides the base operating system for all servers.
Container Orchestration Kubernetes 1.27 Manages the deployment and scaling of containerized applications.
Programming Language Python 3.10 The primary language used for AI model development. See Python Best Practices.
AI Framework TensorFlow 2.12 Used for building and training deep learning models.
Data Storage PostgreSQL 15 Stores metadata and processed data. See Database Schema.
Message Queue RabbitMQ 3.9 Facilitates asynchronous communication between services.
Monitoring Prometheus & Grafana Monitors server performance and application metrics.

All software is managed using Ansible for automated configuration and deployment. Version control is handled using Git and hosted on a private GitLab instance. Regular security audits are conducted to ensure the system is protected against vulnerabilities. Refer to Security Protocols.

Networking Configuration

The server cluster is connected to the internet via a dedicated 1 Gbps fiber optic connection. Internal networking is handled by a private VLAN. Firewalls are configured to restrict access to only necessary ports.

Network Interface IP Address Subnet Mask Gateway
eth0 (External) 192.0.2.100 255.255.255.0 192.0.2.1
eth1 (Internal) 10.0.0.10 255.255.255.0 10.0.0.1
eth2 (Storage) 10.0.1.10 255.255.255.0 10.0.1.1

DNS is managed internally using Bind9. All communication between servers is encrypted using TLS/SSL. Access to the servers is restricted to authorized personnel via SSH with key-based authentication. See Network Diagram for a visual representation of the network topology. The network is segmented using VLANs to improve security.

Security Considerations

Security is paramount to the success of the project. Several layers of security are implemented to protect the data and infrastructure.

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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