Machine learning
- Machine Learning Server Configuration
This article details the server configuration best suited for running machine learning workloads within our infrastructure. It is aimed at newcomers to the system and provides a technical overview of the hardware and software components required for optimal performance. This guide assumes a basic understanding of Server Administration and Linux Command Line.
Introduction
Machine learning (ML) tasks demand significant computational resources. Effective deployment requires careful consideration of CPU, GPU, memory, and storage. This document outlines a recommended configuration, focusing on balancing cost and performance. We’ll cover hardware specifications, software requirements, and essential configuration steps. This server will primarily be used for Model Training and Inference Serving.
Hardware Specifications
The following table outlines the recommended hardware components. Note that these are *minimum* specifications; scaling up based on workload demands is strongly encouraged. Further details on Hardware Procurement can be found on the internal wiki.
| Component | Specification | Notes |
|---|---|---|
| CPU | Intel Xeon Gold 6338 (32 cores) or AMD EPYC 7763 (64 cores) | Higher core counts are beneficial for parallel processing. |
| RAM | 256 GB DDR4 ECC Registered | Crucial for handling large datasets and complex models. |
| GPU | NVIDIA A100 80GB or AMD Instinct MI250X | The GPU is the most critical component for ML workloads. |
| Storage (OS) | 500GB NVMe SSD | For fast boot times and system responsiveness. |
| Storage (Data) | 4TB NVMe SSD RAID 0 or 8TB SATA SSD RAID 10 | Fast storage is essential for data loading and processing. RAID configuration impacts performance and redundancy. |
| Network Interface | 100 GbE | High bandwidth is needed for data transfer. |
Software Configuration
The operating system of choice is Ubuntu Server 22.04 LTS. This provides a stable and well-supported platform. The following software packages are required:
- CUDA Toolkit: For GPU acceleration. Ensure compatibility with the chosen GPU.
- cuDNN: A library for deep neural networks. Requires a compatible CUDA toolkit version.
- Python 3.10: The primary programming language for ML.
- TensorFlow or PyTorch: ML frameworks. Choose based on project requirements.
- Docker: For containerization and deployment.
- NVIDIA Container Toolkit: Enables GPU access within Docker containers.
Detailed Storage Configuration
The data storage configuration is critical. The following table details considerations for different storage options:
| Storage Type | Capacity | Performance | Redundancy | Cost |
|---|---|---|---|---|
| NVMe SSD (RAID 0) | 4TB - 8TB | Very High | None | Moderate |
| SATA SSD (RAID 10) | 8TB - 16TB | High | High | High |
| HDD (RAID 5/6) | 16TB+ | Low | Moderate - High | Low |
RAID 0 provides the best performance but no redundancy. RAID 10 offers a good balance of performance and redundancy. HDD arrays are cost-effective for large datasets but significantly slower. Detailed instructions on RAID Configuration can be found elsewhere in the documentation.
Networking Considerations
High-speed networking is crucial for distributing data and models. A 100 GbE connection allows for efficient communication with other servers and data storage systems. Consider using RDMA over Converged Ethernet (RoCE) for even lower latency. Proper Network Configuration is paramount for optimal performance.
Software Stack Versioning
Maintaining consistent software versions is vital for reproducibility and stability. The following table outlines recommended versions as of October 26, 2023. These versions should be updated regularly based on security patches and performance improvements.
| Software | Recommended Version | Notes |
|---|---|---|
| Ubuntu Server | 22.04 LTS | Long Term Support release |
| CUDA Toolkit | 12.1 | Compatible with NVIDIA A100 |
| cuDNN | 8.9.2 | Requires CUDA 12.1 |
| Python | 3.10.6 | Stable and widely used |
| TensorFlow | 2.13.0 | Latest stable release |
| PyTorch | 2.0.1 | Latest stable release |
| Docker | 24.0.5 | Latest stable release |
Monitoring and Maintenance
Regular monitoring of server resources is essential. Use tools like Prometheus and Grafana to track CPU usage, GPU utilization, memory consumption, and disk I/O. Implement a regular Backup Strategy to protect against data loss. Review System Logs regularly for errors and warnings.
Server Documentation Machine Learning Workflow GPU Troubleshooting Data Storage Best Practices Security Considerations
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.* ⚠️