AI in Equity
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- AI in Equity: Server Configuration
This article details the server configuration required to support the "AI in Equity" project, a platform leveraging artificial intelligence to analyze financial data for bias detection and equitable lending practices. This guide is aimed at new system administrators and those unfamiliar with the specific requirements of this project. It covers hardware, software, and networking considerations. Understanding these details is crucial for maintaining a stable and performant system.
Overview
The "AI in Equity" platform relies heavily on machine learning models, large datasets, and real-time data processing. Consequently, the server infrastructure must be robust, scalable, and secure. We utilize a distributed architecture, with dedicated servers for data storage, model training, and application serving. This separation of concerns allows for optimized resource allocation and improved fault tolerance. See System Architecture Overview for a broader picture of the entire platform.
Hardware Specifications
The following tables outline the hardware specifications for each server role. These specifications are considered minimal for production deployment; scaling upwards is highly recommended based on anticipated workload. Refer to Hardware Procurement Guidelines for approved vendor lists.
| Server Role | CPU | RAM | Storage | Network Interface |
|---|---|---|---|---|
| Data Storage Server | Intel Xeon Gold 6248R (24 cores) | 256GB DDR4 ECC | 60TB RAID 6 SSD | 100GbE |
| Model Training Server | 2 x AMD EPYC 7763 (64 cores total) | 512GB DDR4 ECC | 4TB NVMe SSD | 100GbE |
| Application Server | Intel Xeon Silver 4210 (10 cores) | 64GB DDR4 ECC | 2TB SSD | 10GbE |
These specifications are based on initial testing and may be adjusted in the future as the project evolves. Regular performance monitoring (see Performance Monitoring Tools) is essential to identify bottlenecks and optimize resource allocation. Consider using a server virtualization platform like Proxmox VE or VMware vSphere for increased flexibility and resource utilization.
Software Stack
The software stack is carefully chosen to provide a stable, secure, and performant environment for the "AI in Equity" platform. Detailed installation guides are available for each component.
| Component | Version | Purpose | Documentation Link |
|---|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system. | Ubuntu Server Documentation |
| Database | PostgreSQL 14 | Stores financial data, model metadata, and application data. | PostgreSQL Documentation |
| Programming Language | Python 3.9 | Used for data processing, model training, and API development. | Python Documentation |
| Machine Learning Framework | TensorFlow 2.9 | Used for building and training machine learning models. | TensorFlow Documentation |
| Web Server | Nginx 1.22 | Serves the application and handles incoming requests. | Nginx Documentation |
All software components are regularly patched and updated to address security vulnerabilities. A robust Continuous Integration/Continuous Deployment (CI/CD) pipeline is in place to automate the deployment process. Using a package manager like APT simplifies software installation and management.
Networking Configuration
A secure and reliable network infrastructure is critical for the "AI in Equity" platform. The following table outlines the key networking components and their configurations. See the Network Security Policy for detailed security guidelines.
| Component | Configuration | Purpose | |
|---|---|---|---|
| Firewall | pfSense 2.5 | Protects the servers from unauthorized access. | pfSense Documentation |
| Load Balancer | HAProxy 2.4 | Distributes traffic across multiple application servers. | HAProxy Documentation |
| DNS Server | Bind9 9.16 | Resolves domain names to IP addresses. | Bind9 Documentation |
| VPN | OpenVPN 2.4 | Provides secure remote access to the servers. | OpenVPN Documentation |
All network traffic is encrypted using TLS/SSL. Regular security audits are conducted to identify and address potential vulnerabilities. Properly configured Virtual LANs (VLANs) segment the network to isolate different components and improve security. A dedicated Intrusion Detection System (IDS) monitors network traffic for malicious activity.
Security Considerations
Security is paramount. The "AI in Equity" platform handles sensitive financial data and must be protected from unauthorized access and cyberattacks. Key security measures include:
- Regular security audits and penetration testing.
- Strong password policies and multi-factor authentication.
- Data encryption at rest and in transit.
- Firewall protection and intrusion detection systems.
- Regular software updates and patching.
- Strict access control policies.
- Review of Data Privacy Regulations
Future Considerations
As the "AI in Equity" project evolves, the server infrastructure will need to be adapted to meet changing demands. Potential future considerations include:
- Scaling the infrastructure to handle increased data volumes and user traffic.
- Implementing a cloud-based deployment model.
- Exploring the use of specialized hardware, such as GPUs, for accelerated model training.
- Adopting a microservices architecture for increased flexibility and scalability. Refer to Microservices Architecture Overview.
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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.* ⚠️