AI in the Ural Mountains

AI in the Ural Mountains: Server Configuration

This article details the server configuration for our "AI in the Ural Mountains" project, a distributed computing initiative focused on processing geological data using machine learning algorithms. This guide is aimed at new team members responsible for server maintenance and deployment. It covers hardware, software, networking, and security aspects of the system.

Overview

The project utilizes a cluster of servers located in a secure facility within the Ural Mountains. The primary goal is to analyze seismic data, mineral composition scans, and historical geological surveys to identify potential resource deposits and predict geological events. The server architecture is designed for high throughput, scalability, and redundancy. The operating system of choice is Ubuntu Server 22.04 LTS, due to its stability, community support, and compatibility with the necessary machine learning frameworks.

Hardware Configuration

The cluster consists of 20 identical servers, with one designated as the master node. Each server is built with the following specifications:

The master node has slightly enhanced specifications for coordinating the cluster. These are detailed below:

A dedicated Network Attached Storage (NAS) device with 1PB of capacity is used for long-term data archiving. All servers are housed in a temperature and humidity-controlled data center with redundant power and cooling systems. See Data Center Redundancy for more details.

Software Stack

Each server runs a standardized software stack, ensuring consistency and ease of management.

We utilize Docker and Kubernetes for containerization and orchestration, allowing for efficient resource utilization and simplified deployment of machine learning models. The master node also runs a Prometheus instance for monitoring and alerting. Detailed instructions for setting up the software stack are available on the Software Installation Guide page.

Networking Configuration

The servers are connected via a dedicated 100 GbE network. The network topology is a Clos network, providing high bandwidth and low latency. A dedicated VLAN is used for inter-server communication, and another for external access. The master node acts as the network gateway. Firewall rules are configured using iptables to restrict access to essential services. The network configuration details are documented in the Network Diagram. We also employ DNS for service discovery within the cluster.

Security Considerations

Security is paramount. The following measures are in place:

• **Physical Security:** The data center is physically secured with multiple layers of access control, including biometric scanners and surveillance cameras. See Physical Security Protocols for details.
• **Network Security:** Firewalls, intrusion detection systems, and regular security audits are implemented to protect the network. Access to the network is restricted to authorized personnel.
• **Data Encryption:** All sensitive data is encrypted at rest and in transit. We use TLS/SSL for secure communication.
• **User Authentication:** Strong passwords and multi-factor authentication are required for all user accounts.
• **Regular Backups:** Regular backups of all critical data are performed and stored offsite. See the Backup and Recovery Plan for specifics.
• **Vulnerability Scanning**: Regular vulnerability scans using tools like Nessus are performed to identify and remediate security weaknesses.

Data Flow

Raw data is ingested from various sources, including seismic sensors and geological survey databases. This data is initially stored on the NAS device. The master node then distributes tasks to the worker nodes via Kubernetes. Each worker node processes a portion of the data using the designated machine learning algorithms. Results are aggregated on the master node and stored in a centralized database. See the Data Pipeline Diagram for a visual representation of the data flow.

Future Enhancements

We are planning to integrate GPU acceleration to further enhance the performance of our machine learning models. We are also exploring the use of a distributed file system like Hadoop Distributed File System (HDFS) to improve data access speeds.

Category:Server Hardware

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.* ⚠️