AI in Social Justice
- AI in Social Justice: Server Configuration
This article details the server configuration necessary to support applications focused on Artificial Intelligence (AI) within the context of Social Justice initiatives. It’s geared towards newcomers to our MediaWiki site and provides a technical overview of hardware and software requirements. This infrastructure is designed to handle large datasets, complex model training, and real-time inference, while prioritizing ethical considerations and data privacy.
Overview
The intersection of AI and Social Justice presents unique computational challenges. Many applications require processing sensitive data, addressing biases in algorithms, and ensuring equitable access to resources. This necessitates a robust and scalable server infrastructure. We will cover the key components, including hardware specifications, operating system choices, software dependencies, and security considerations. Understanding these requirements is crucial for deploying and maintaining reliable and ethical AI systems. See also Data Privacy Considerations and Ethical AI Development.
Hardware Requirements
The hardware foundation is critical for performance and scalability. The following table outlines the recommended specifications for a baseline server node. Multiple nodes are typically deployed in a clustered configuration for redundancy and increased processing power. Consider Server Clustering for details.
| Component | Specification | Notes |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores/64 threads per CPU) | Higher core count is beneficial for parallel processing. |
| RAM | 256 GB DDR4 ECC Registered RAM | Crucial for handling large datasets and complex models. |
| Storage | 4 x 4TB NVMe SSD (RAID 0) + 8 x 16TB SAS HDD (RAID 6) | NVMe for fast model loading and training. SAS for large-scale data storage. |
| GPU | 4 x NVIDIA A100 (80GB) | Essential for deep learning tasks. Consider GPU Acceleration. |
| Network Interface | 100 Gbps Ethernet | High bandwidth for data transfer within the cluster. |
| Power Supply | 2 x 1600W Redundant Power Supplies | Ensures high availability. |
Software Stack
The software stack consists of the operating system, deep learning frameworks, data science libraries, and supporting tools. We prioritize open-source technologies to promote transparency and collaboration. Detailed instructions for installation and configuration can be found on the Software Installation Guide.
Operating System
Ubuntu Server 22.04 LTS is the recommended operating system due to its stability, extensive package repository, and strong community support. Alternatives include CentOS Stream 9 and Debian 11. Proper Operating System Hardening is essential for security.
Deep Learning Frameworks
- TensorFlow: A widely used framework for building and deploying machine learning models. See the TensorFlow Documentation.
- PyTorch: Another popular framework, known for its flexibility and ease of use. Refer to PyTorch Tutorials.
- JAX: A high-performance numerical computation library, often used for research and experimentation. Explore JAX Examples.
Data Science Libraries
- NumPy: Fundamental package for numerical computing in Python. See NumPy Reference.
- Pandas: Data manipulation and analysis library. Learn more at Pandas Documentation.
- Scikit-learn: Machine learning library with various algorithms. Check out Scikit-learn Tutorials.
Supporting Tools
- Docker: Containerization platform for packaging and deploying applications. Read the Docker Guide.
- Kubernetes: Orchestration platform for managing containerized applications. See Kubernetes Basics.
- MLflow: Platform for managing the machine learning lifecycle. Refer to MLflow Documentation.
Network Configuration
A secure and high-bandwidth network is critical for data transfer and communication between server nodes. The following table details the network configuration.
| Parameter | Value | Description |
|---|---|---|
| Network Topology | Spine-Leaf | Provides high bandwidth and low latency. See Network Topology Guide. |
| IP Addressing | Private IP Range (10.0.0.0/16) | Security best practice. |
| DNS | Internal DNS Server | Resolves internal hostnames. |
| Firewall | iptables/nftables | Protects the server from unauthorized access. Refer to Firewall Configuration. |
| Load Balancing | HAProxy/Nginx | Distributes traffic across multiple server nodes. |
Security Considerations
Security is paramount when dealing with sensitive data. The following measures are essential:
- Data Encryption: Encrypt all data at rest and in transit. See Data Encryption Methods.
- Access Control: Implement strict access control policies. Use Role-Based Access Control.
- Regular Security Audits: Conduct regular security audits to identify and address vulnerabilities. See the Security Audit Checklist.
- Intrusion Detection System (IDS): Deploy an IDS to detect and respond to malicious activity. Learn about Intrusion Detection Systems.
- Vulnerability Scanning: Regularly scan for vulnerabilities in software and hardware. Refer to Vulnerability Scanning Tools.
Scalability and Monitoring
The system should be designed for scalability to accommodate growing data volumes and increasing computational demands. Monitoring tools are essential for identifying performance bottlenecks and ensuring system health.
| Component | Tool | Description |
|---|---|---|
| System Monitoring | Prometheus & Grafana | Collects and visualizes system metrics. See Prometheus Setup. |
| Application Performance Monitoring (APM) | Datadog/New Relic | Monitors application performance and identifies bottlenecks. |
| Log Management | Elasticsearch, Logstash, Kibana (ELK Stack) | Collects, indexes, and analyzes logs. Read the ELK Stack Guide. |
| Resource Allocation | Kubernetes Horizontal Pod Autoscaler | Automatically scales the number of pods based on resource utilization. |
Further Resources
- Server Maintenance Schedule
- Backup and Disaster Recovery Plan
- Troubleshooting Common Issues
- Contact Support
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.* ⚠️