AI Research
AI Research Server Configuration
Welcome to the documentation for the AI Research server! This article details the hardware and software configuration for our dedicated AI research environment. This guide is intended for newcomers to the system and provides a comprehensive overview of the server’s capabilities and specifications. Understanding these details will be crucial for utilizing the server effectively and troubleshooting any potential issues. Please refer to our Server Access Guide before attempting to connect.
Overview
The AI Research server is a high-performance computing (HPC) platform designed to support demanding machine learning and deep learning workloads. It is equipped with powerful GPUs, a large amount of RAM, and fast storage to facilitate rapid experimentation and model training. The server runs a customized Linux distribution optimized for AI tasks. This document will cover the hardware components, software environment, and key configuration details.
Hardware Specifications
The following table outlines the core hardware specifications of the AI Research server:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores per CPU, 64 total) |
| RAM | 512GB DDR4 ECC Registered Memory |
| GPU | 4 x NVIDIA A100 80GB GPUs |
| Storage (OS) | 500GB NVMe SSD |
| Storage (Data) | 4 x 8TB SAS HDD (RAID 0) |
| Network Interface | Dual 100GbE Network Adapters |
| Power Supply | 2 x 2000W Redundant Power Supplies |
Software Environment
The server utilizes a customized Linux environment built on Ubuntu Server 22.04 LTS. Several key software packages are pre-installed and configured for AI research. Please see the Software Installation Guide for details on additional packages.
Core Packages
The following table lists the core software packages installed on the server:
| Package | Version |
|---|---|
| CUDA Toolkit | 12.2 |
| cuDNN | 8.9.2 |
| Python | 3.10 |
| TensorFlow | 2.13.0 |
| PyTorch | 2.0.1 |
| Jupyter Notebook | 6.4.5 |
| Docker | 24.0.5 |
| NVIDIA Driver | 535.104.05 |
Containerization
We heavily utilize Docker for managing dependencies and ensuring reproducibility. Pre-built Docker images with common AI frameworks are available on our internal Docker Registry. Using containers is strongly recommended for all research projects. This ensures a consistent environment across different users and prevents conflicts between software versions. Refer to the Docker Tutorial for more information.
Server Configuration Details
The AI Research server is configured with several specific settings to optimize performance. These configurations are managed by our System Administration Team.
Network Configuration
The server is accessible through two 100GbE network interfaces. The primary interface is used for general network access, while the secondary interface is dedicated to storage traffic. Users can access the server via SSH using the address `ai-research.example.com`. Please consult the Network Security Policy for information on acceptable use.
Storage Configuration
The data storage is configured in a RAID 0 array for maximum performance. While this provides fast read/write speeds, it also means there is no redundancy. Therefore, it is *critical* to regularly back up your data using our provided Backup System. The data storage is mounted at `/data`.
GPU Configuration
The NVIDIA A100 GPUs are configured to maximize memory utilization and computational throughput. Users can select the desired GPU using environment variables within their Docker containers. For example, `CUDA_VISIBLE_DEVICES=0,1` will make GPUs 0 and 1 available to the container. See the GPU Usage Guidelines for best practices.
User Accounts
User accounts are managed through LDAP authentication. New users can request an account through the Account Request Form. Access to specific directories and resources is granted based on group membership.
Troubleshooting
If you encounter issues while using the AI Research server, please consult the following resources:
- Frequently Asked Questions
- Error Log Location
- Contact Support (for critical issues)
Related Links
- Server Access Guide
- Software Installation Guide
- Docker Tutorial
- Network Security Policy
- Backup System
- GPU Usage Guidelines
- Account Request Form
- Frequently Asked Questions
- Error Log Location
- Contact Support
- Linux distribution
- System Administration Team
- Docker Registry
- LDAP authentication
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.* ⚠️