Data Science
- Data Science Server Configuration
This article details the recommended server configuration for a dedicated Data Science environment on our MediaWiki platform. It's geared toward newcomers setting up or maintaining these servers. Data science tasks, including machine learning, statistical modeling, and data analysis, are resource-intensive. Proper configuration is crucial for performance and scalability. This guide will cover hardware, software, and networking considerations.
Hardware Requirements
The hardware forms the foundation of any data science server. Choosing the right components is vital for handling large datasets and complex computations. We recommend a tiered approach based on anticipated workload.
| Component | Minimum Specification | Recommended Specification | High-End Specification |
|---|---|---|---|
| CPU | Intel Xeon E5-2620 v4 or AMD EPYC 7262 | Intel Xeon Gold 6248R or AMD EPYC 7402P | Intel Xeon Platinum 8280 or AMD EPYC 7763 |
| RAM | 64 GB DDR4 ECC | 128 GB DDR4 ECC | 256 GB DDR4 ECC or greater |
| Storage (OS) | 256 GB SSD | 512 GB NVMe SSD | 1 TB NVMe SSD |
| Storage (Data) | 4 TB HDD (RAID 1) | 8 TB HDD (RAID 5) or 4 TB SSD | 16 TB HDD (RAID 6) or 8 TB SSD (RAID 0 or 1) |
| GPU (Optional) | None | NVIDIA Tesla T4 or AMD Radeon Pro VII | NVIDIA A100 or AMD Instinct MI250X |
These specifications are starting points. Consider future growth and the size of expected datasets when making hardware choices. See Server Hardware Maintenance for details on hardware upkeep.
Software Stack
A robust software stack is essential for data science workflows. We standardize on a Linux-based operating system for its flexibility and extensive package availability. Operating System Selection details the approved OS options.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu 22.04 LTS or CentOS Stream 9 | Base Operating System |
| Python | 3.9 or 3.10 | Primary Data Science Language |
| R | 4.2 or 4.3 | Statistical Computing and Graphics |
| Jupyter Notebook | Latest Stable | Interactive Computing Environment |
| TensorFlow | Latest Stable | Machine Learning Framework |
| PyTorch | Latest Stable | Machine Learning Framework |
| Pandas | Latest Stable | Data Analysis and Manipulation |
| NumPy | Latest Stable | Numerical Computing |
| Scikit-learn | Latest Stable | Machine Learning Library |
It's vital to keep all software up-to-date. See Software Update Procedures for instructions. Version control with Git Version Control is strongly recommended for all code. Consider using a containerization technology like Docker Containerization for reproducibility and portability.
Networking Configuration
Efficient networking is critical for data transfer and collaboration.
| Network Parameter | Value |
|---|---|
| Network Interface | 10 Gigabit Ethernet (minimum) |
| IP Addressing | Static IP Address |
| DNS Resolution | Internal DNS Server |
| Firewall | Enabled with appropriate rules (see Firewall Configuration) |
| SSH Access | Enabled with key-based authentication (see Secure Shell Access) |
| Data Transfer Protocol | rsync, scp, or Globus |
Ensure the server has a dedicated network connection to minimize latency and maximize bandwidth. Implement strong security measures, including a firewall and secure SSH access. Consider using a dedicated data transfer protocol like Globus for large dataset transfers. See Network Troubleshooting for common issues and solutions.
Security Considerations
Data science servers often handle sensitive data. Adhering to security best practices is paramount. Implement the following:
- **Regular Security Audits:** Conduct regular audits to identify and address vulnerabilities. Refer to Security Audit Procedures.
- **Data Encryption:** Encrypt data at rest and in transit. See Data Encryption Standards.
- **Access Control:** Implement strict access control policies. Use User Account Management best practices.
- **Intrusion Detection System (IDS):** Deploy an IDS to detect and respond to malicious activity. Intrusion Detection System Configuration provides further details.
- **Regular Backups:** Perform regular backups of all data. Refer to Backup and Recovery Procedures.
Monitoring and Logging
Continuous monitoring and logging are essential for identifying performance bottlenecks and troubleshooting issues.
- **System Monitoring:** Use tools like Nagios Monitoring or Prometheus Monitoring to monitor CPU usage, memory usage, disk I/O, and network traffic.
- **Log Management:** Centralize log collection and analysis using ELK Stack Configuration.
- **Performance Profiling:** Utilize profiling tools to identify performance bottlenecks in your data science code. See Performance Profiling Tools.
Future Scalability
Plan for future growth. Consider using a cluster management system like Kubernetes Cluster Management to easily scale your data science environment. Cloud-based solutions, detailed in Cloud Integration, can also provide scalability and flexibility. Remember to document all configurations thoroughly using Server Documentation Standards.
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.* ⚠️