AI in Cognitive Science
# AI in Cognitive Science: Server Configuration Guide
This document details the server configuration recommended for running applications supporting Artificial Intelligence (AI) research within the domain of Cognitive Science. This guide is intended for system administrators and researchers new to deploying these workloads on our MediaWiki-hosted infrastructure. It covers hardware, software, and networking considerations.
Introduction
The intersection of AI and Cognitive Science places unique demands on server infrastructure. Tasks such as neural network training, natural language processing (NLP), and computational modeling require substantial computational power, memory, and storage. This guide provides a baseline configuration and scaling recommendations. We will focus on a server capable of handling medium-scale research projects. Larger projects will necessitate a distributed computing cluster, a topic outside the scope of this initial guide. Please consult the Distributed Computing Resources page for more information.
Hardware Configuration
The following table outlines the minimum recommended hardware specifications for a single server dedicated to AI/Cognitive Science research.
| Component | Specification | Notes |
|---|---|---|
| CPU | Intel Xeon Gold 6248R (24 cores/48 threads) or AMD EPYC 7543 (32 cores/64 threads) | Higher core counts are beneficial for parallel processing. |
| RAM | 256 GB DDR4 ECC REG 3200MHz | Crucial for handling large datasets and complex models. Consider 512GB for larger projects. |
| Storage (OS & Applications) | 1 TB NVMe SSD | Fast storage for the operating system and frequently accessed applications. |
| Storage (Data) | 8 TB RAID 5 HDD array or larger | Sufficient storage for datasets and model checkpoints. RAID configuration provides redundancy. See Data Backup Procedures. |
| GPU | NVIDIA GeForce RTX 3090 (24 GB VRAM) or NVIDIA A40 (48 GB VRAM) | Essential for accelerating deep learning tasks. Consider multiple GPUs for larger models. Refer to GPU Driver Installation. |
| Network Interface | 10 Gigabit Ethernet | High bandwidth for data transfer and communication with other servers. |
| Power Supply | 1200W 80+ Platinum | Sufficient power for all components, with headroom for expansion. |
Software Configuration
The recommended operating system is Ubuntu Server 22.04 LTS. This provides a stable and well-supported platform with excellent package management.
Operating System & Core Packages
- Operating System: Ubuntu Server 22.04 LTS (64-bit) - Instructions for installation can be found on the Ubuntu Server Installation page.
- Python: Version 3.9 or 3.10 – Managed via `conda` or `venv`. See Python Environment Management.
- CUDA Toolkit: Latest compatible version for the chosen GPU. Important for GPU acceleration. See CUDA Toolkit Installation.
- cuDNN: Latest compatible version for the chosen CUDA Toolkit. Required for deep learning frameworks. See cuDNN Installation.
- Deep Learning Frameworks: TensorFlow, PyTorch, Keras – Install using `pip` or `conda`. See Deep Learning Framework Installation.
- NLP Libraries: spaCy, NLTK, Transformers – Install using `pip` or `conda`. See NLP Library Installation.
- Data Science Libraries: NumPy, Pandas, Scikit-learn – Install using `pip` or `conda`. See Data Science Library Installation.
- Static IP Address: Assign a static IP address to the server. Consult the Networking Guide for details.
- Firewall: Configure a firewall (e.g., `ufw`) to restrict access to necessary ports. See Firewall Configuration.
- SSH Access: Enable secure shell (SSH) access for remote administration. Disable password authentication and use SSH keys. See Secure SSH Access.
- Data Transfer: Utilize secure data transfer protocols such as `scp` or `sftp`. Consider using `rsync` for efficient file synchronization.
- Regularly update the operating system and all installed software.
- Implement strong passwords and multi-factor authentication.
- Monitor system logs for suspicious activity.
- Back up data regularly. Refer to Data Backup Procedures.
- Consider using intrusion detection and prevention systems. See Security Best Practices.
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Software Stack Details
The following table outlines a recommended software stack and versions.
| Software | Version (Recommended) | Purpose |
|---|---|---|
| Ubuntu Server | 22.04 LTS | Operating System |
| Python | 3.10 | Programming Language |
| Anaconda | Latest | Package and Environment Management |
| TensorFlow | 2.12.0 | Deep Learning Framework |
| PyTorch | 2.0.1 | Deep Learning Framework |
| spaCy | 3.5.0 | NLP Library |
| CUDA Toolkit | 12.2 | GPU Acceleration |
Networking Configuration
Proper network configuration is crucial for data access, collaboration, and remote access.
Network Settings
The following table illustrates the required open ports.
| Port | Protocol | Description |
|---|---|---|
| 22 | TCP | SSH (Secure Shell) – For remote administration. |
| 80 | TCP | HTTP (Web Server) – If serving web-based applications. |
| 443 | TCP | HTTPS (Secure Web Server) – If serving secure web-based applications. |
| 2224 | TCP | Jupyter Notebook (if used) |
| 5432 | TCP | PostgreSQL (if used) |
Security Considerations
Further Resources
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.* ⚠️