Machine Learning
- Machine Learning Server Configuration
This article details the recommended server configuration for deploying machine learning workloads within our MediaWiki environment. It is intended for system administrators and engineers responsible for setting up and maintaining the infrastructure. We will cover hardware specifications, software requirements, and key configuration considerations. This builds upon the existing Server Infrastructure Overview and complements the documentation on Database Configuration.
Hardware Requirements
Machine learning tasks are often computationally intensive, particularly during training. Therefore, robust hardware is crucial. The following table outlines the recommended specifications for different tiers of machine learning servers. These specifications assume a primary focus on deep learning applications using frameworks like TensorFlow and PyTorch.
| Tier | CPU | RAM | GPU | Storage | Network |
|---|---|---|---|---|---|
| Development | Intel Xeon E5-2680 v4 or AMD EPYC 7302P | 64 GB DDR4 | NVIDIA GeForce RTX 3060 (12GB VRAM) | 1 TB NVMe SSD | 1 Gbps Ethernet |
| Production (Small) | Intel Xeon Gold 6248R or AMD EPYC 7402P | 128 GB DDR4 ECC | NVIDIA Tesla T4 (16GB VRAM) | 2 TB NVMe SSD (RAID 1) | 10 Gbps Ethernet |
| Production (Large) | Dual Intel Xeon Platinum 8280 or Dual AMD EPYC 7763 | 256 GB DDR4 ECC | 4x NVIDIA A100 (80GB VRAM each) | 4 TB NVMe SSD (RAID 10) | 25 Gbps Ethernet |
These are baseline recommendations; specific requirements will vary based on the complexity of the models and the size of the datasets. Consider scaling storage and GPU resources as needed. Refer to the Storage Solutions Guide for more detailed information on storage options.
Software Stack
The software stack for a machine learning server typically includes an operating system, a containerization platform, a machine learning framework, and supporting libraries. We standardize on the following:
- **Operating System:** Ubuntu Server 22.04 LTS. This provides a stable and well-supported environment. See the Operating System Standards page.
- **Containerization:** Docker and Kubernetes. Containerization allows for easy deployment, scaling, and reproducibility of machine learning models.
- **Machine Learning Frameworks:** TensorFlow, PyTorch, and scikit-learn. These frameworks provide the tools and libraries necessary for building and training machine learning models.
- **Programming Language:** Python 3.9 or higher is the preferred language for machine learning development.
- **Data Science Libraries:** NumPy, Pandas, Matplotlib, and Seaborn are essential libraries for data manipulation, analysis, and visualization.
Configuration Details
Proper configuration is vital for maximizing performance and ensuring stability. Key areas to focus on include GPU drivers, CUDA toolkit, and network settings.
GPU Configuration
The NVIDIA drivers and CUDA toolkit must be installed correctly to enable GPU acceleration. The following table details the recommended versions:
| Component | Recommended Version |
|---|---|
| NVIDIA Driver | 535.104.05 |
| CUDA Toolkit | 12.2 |
| cuDNN | 8.9.2 |
Ensure that the NVIDIA drivers are compatible with the CUDA toolkit version. Refer to the GPU Driver Installation Guide for detailed installation instructions.
Network Configuration
Low latency and high bandwidth are critical for machine learning workloads, especially when dealing with large datasets. Configure the network interface with a static IP address and ensure that DNS resolution is working correctly. Consider using a dedicated network for machine learning traffic to isolate it from other network activity. See the Network Configuration Best Practices for more information.
Storage Configuration
Using NVMe SSDs is crucial for fast data access. Employ RAID configurations (RAID 1 or RAID 10) for data redundancy and improved performance. Mount the storage volumes with appropriate permissions and quotas, as described in the File System Management documentation. Regular backups are essential; utilize the Backup and Recovery Procedures.
Kubernetes Configuration
When utilizing Kubernetes, ensure adequate resource requests and limits are set for each pod. Utilize GPU scheduling policies to ensure that machine learning workloads are scheduled on nodes with available GPUs. Monitor resource utilization closely using tools like Prometheus and Grafana.
Monitoring and Maintenance
Regular monitoring is crucial for identifying and resolving performance bottlenecks and ensuring system stability. Monitor CPU utilization, memory usage, GPU utilization, disk I/O, and network traffic. Establish automated alerts to notify administrators of critical issues. Scheduled maintenance, including software updates and security patches, is also essential. Refer to the System Monitoring Guide for details on setting up monitoring tools and alerts.
Security Considerations
Machine learning systems often handle sensitive data. Implement appropriate security measures to protect this data, including access control, encryption, and vulnerability scanning. Follow the Security Policies and Procedures outlined by the security team.
Server Infrastructure Overview
Database Configuration
TensorFlow
PyTorch
scikit-learn
Python
NumPy
Pandas
Matplotlib
Seaborn
Docker
Kubernetes
Operating System Standards
Storage Solutions Guide
GPU Driver Installation Guide
Network Configuration Best Practices
File System Management
Backup and Recovery Procedures
System Monitoring Guide
Security Policies and Procedures
Prometheus
Grafana
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.* ⚠️