Model retraining
Model Retraining: Server Configuration
This article details the server configuration required for successful model retraining operations. Model retraining is a crucial process for maintaining the accuracy and relevance of our machine learning models used across various services, including Spam Prevention, , and Content Recommendation. This guide is tailored for newcomers to the server infrastructure and assumes a basic understanding of Linux server administration.
Overview
Model retraining involves periodically updating our machine learning models with new data. This process is computationally intensive and requires significant server resources. The configuration outlined below focuses on a dedicated cluster for retraining, separate from the production servers to avoid performance impact. We utilize a distributed training framework, specifically Horovod, to accelerate the process.
Hardware Requirements
The following table outlines the minimum and recommended hardware specifications for each node in the retraining cluster. We currently operate a cluster with 8 nodes, but the architecture is designed for scalability. The nodes are configured using Ansible for consistent deployments.
| Component | Minimum Specification | Recommended Specification |
|---|---|---|
| CPU | Intel Xeon E5-2680 v4 (14 cores) | Intel Xeon Platinum 8280 (28 cores) |
| RAM | 64 GB DDR4 ECC | 128 GB DDR4 ECC |
| Storage | 1 TB SSD (OS and intermediate data) | 2 TB NVMe SSD (OS and intermediate data) |
| GPU | NVIDIA Tesla V100 (16 GB) | NVIDIA A100 (80 GB) |
| Network | 10 Gbps Ethernet | 100 Gbps InfiniBand |
Software Stack
The software stack is carefully managed to ensure compatibility and performance. We use a containerized environment with Docker and Kubernetes to simplify deployment and scaling. All code is version-controlled using Git and hosted on our internal GitLab instance.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu 20.04 LTS | Base OS for all nodes |
| Docker | 20.10.7 | Containerization platform |
| Kubernetes | 1.23.4 | Container orchestration |
| NVIDIA Driver | 510.77.03 | GPU driver support |
| CUDA Toolkit | 11.6 | GPU computing platform |
| Horovod | 0.26.1 | Distributed training framework |
| Python | 3.8 | Primary scripting language |
| TensorFlow | 2.8.0 | Machine learning framework |
Network Configuration
A high-bandwidth, low-latency network is critical for efficient distributed training. We utilize InfiniBand for inter-node communication, significantly reducing the communication overhead. The network is segmented using VLANs to isolate the retraining cluster from other network traffic. Firewall rules, managed by iptables, restrict access to the cluster to authorized personnel and services.
| Parameter | Value |
|---|---|
| Network Type | InfiniBand |
| Bandwidth | 100 Gbps |
| VLAN ID | 1000 |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.1000.1 |
Data Storage
The raw training data is stored in our centralized data lake, built on Hadoop Distributed File System (HDFS). During retraining, data is copied to the local SSDs of each node for faster access. A distributed file system, GlusterFS, is used for staging intermediate results and model checkpoints. Regular backups are performed using rsync to our offsite disaster recovery site.
Monitoring and Logging
Comprehensive monitoring and logging are essential for identifying and resolving issues during retraining. We use Prometheus and Grafana for real-time monitoring of server metrics, including CPU usage, memory consumption, GPU utilization, and network traffic. All logs are collected using Fluentd and centralized in Elasticsearch for analysis and alerting via Kibana. Alerts are configured to notify the on-call team of any critical errors or performance degradation.
Security Considerations
Security is paramount. Access to the retraining cluster is restricted via SSH key authentication and multi-factor authentication. All data in transit is encrypted using TLS/SSL. Regular security audits are conducted to identify and address potential vulnerabilities.
Data Lake
Distributed Training
Horovod Documentation
Kubernetes Best Practices
Ansible Playbooks
GitLab Workflow
Elasticsearch Queries
Prometheus Alerts
HDFS Configuration
GlusterFS Setup
Spam Prevention
Content Recommendation Linux server administration iptables firewall rules SSH key authentication TLS/SSL encryption rsync backups On-call team procedures Fluentd configuration Kibana dashboards TensorFlow documentation CUDA toolkit installation NVIDIA driver updates GPU computing Machine learning framework Docker containers Git version control
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.* ⚠️