AI in Diversity
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- AI in Diversity: Server Configuration
This article details the server configuration required to effectively run "AI in Diversity," a suite of artificial intelligence tools designed to analyze and promote diversity, equity, and inclusion (DEI) within organizational datasets. It's geared towards newcomers to our MediaWiki site and provides a technical overview of the necessary hardware and software. This configuration is optimized for performance, scalability, and security, and is currently deployed across our primary data centers.
Overview
The "AI in Diversity" project leverages several AI models, including Natural Language Processing (NLP) for text analysis, Computer Vision for image and video assessment, and Machine Learning (ML) for predictive analytics. These models demand significant computational resources. This document outlines the server specifications, software stack, and networking requirements to support this workload. We utilize a distributed architecture to handle the large datasets involved and ensure timely results. Familiarity with Server Administration and Linux System Administration is recommended for those deploying or maintaining these servers.
Hardware Specifications
The server infrastructure is divided into three tiers: Data Ingestion, Model Training, and Inference. Each tier utilizes distinct hardware configurations.
Data Ingestion Tier
This tier is responsible for receiving, validating, and pre-processing data from various sources.
| Component | Specification | Quantity per Server |
|---|---|---|
| CPU | Intel Xeon Gold 6338 (32 cores) | 2 |
| RAM | 256 GB DDR4 ECC Registered | - |
| Storage | 4 x 8TB SAS 12Gbps 7.2K RPM HDD (RAID 10) | - |
| Network Interface | 10 Gbps Ethernet | 2 |
| Power Supply | 1600W Redundant | 2 |
Model Training Tier
This tier houses the powerful GPUs necessary for training the AI models.
| Component | Specification | Quantity per Server |
|---|---|---|
| CPU | AMD EPYC 7763 (64 cores) | 2 |
| RAM | 512 GB DDR4 ECC Registered | - |
| GPU | NVIDIA A100 80GB PCIe 4.0 | 8 |
| Storage | 2 x 4TB NVMe PCIe Gen4 SSD (RAID 1) | - |
| Network Interface | 100 Gbps InfiniBand | 2 |
| Power Supply | 2000W Redundant | 2 |
Inference Tier
This tier is optimized for serving trained models and providing real-time predictions.
| Component | Specification | Quantity per Server |
|---|---|---|
| CPU | Intel Xeon Silver 4310 (12 cores) | 2 |
| RAM | 128 GB DDR4 ECC Registered | - |
| GPU | NVIDIA T4 16GB PCIe 3.0 | 4 |
| Storage | 1 x 2TB NVMe PCIe Gen3 SSD | - |
| Network Interface | 25 Gbps Ethernet | 2 |
| Power Supply | 1200W Redundant | 2 |
Software Stack
The software stack is crucial for enabling the AI models and managing the infrastructure.
- Operating System: Ubuntu Server 22.04 LTS. See Ubuntu Server Documentation for more details.
- Containerization: Docker and Kubernetes. We leverage Docker for packaging the AI models and dependencies, and Kubernetes for orchestrating deployments and scaling.
- Programming Languages: Python 3.9, with libraries like TensorFlow, PyTorch, and scikit-learn. Refer to Python Programming for resources.
- Database: PostgreSQL 14 with TimescaleDB extension for time-series data analysis. See PostgreSQL Documentation.
- Message Queue: RabbitMQ for asynchronous task processing. Explore RabbitMQ Documentation.
- Monitoring: Prometheus and Grafana for system and application monitoring. Learn about Prometheus Monitoring and Grafana Dashboards.
- Security: Firewalld and Intrusion Detection Systems (IDS) are implemented. Server Security provides a security overview.
Networking Configuration
The servers are interconnected via a high-speed, low-latency network.
- Network Topology: A flat network topology is used within each tier, with inter-tier communication facilitated through dedicated VLANs.
- Firewall: Stateful firewall rules are implemented to restrict access to only necessary ports and services.
- Load Balancing: HAProxy is used for load balancing across multiple inference servers. See HAProxy Configuration for details.
- DNS: BIND is used for internal DNS resolution.
Security Considerations
Security is paramount. We employ the following measures:
- Regular Security Audits: Periodic security audits are conducted to identify and address vulnerabilities.
- Data Encryption: Data is encrypted both in transit and at rest.
- Access Control: Strict access control policies are enforced based on the principle of least privilege. See Access Control Lists.
- Intrusion Detection: IDS systems monitor for malicious activity.
Scalability and Future Expansion
The architecture is designed for scalability. We can easily add more servers to each tier to accommodate increasing data volumes and model complexity. Future expansion plans include exploring the use of specialized hardware accelerators, such as FPGAs, and adopting a serverless computing model for certain tasks. Refer to Cloud Computing for more information on serverless architectures.
Server Maintenance Data Backup Disaster Recovery Network Troubleshooting AI Model Deployment
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.* ⚠️