AI Deployment Best Practices

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  1. AI Deployment Best Practices

This article outlines best practices for deploying Artificial Intelligence (AI) models on our server infrastructure. It is geared towards system administrators and developers responsible for maintaining and scaling AI applications. Deploying AI effectively requires careful consideration of hardware, software, and network configurations. This guide provides a foundation for successful AI deployment within our environment. Refer to Server Administration Guide for general server management procedures.

1. Hardware Considerations

AI workloads are resource-intensive. Selecting appropriate hardware is crucial for performance and scalability. We primarily support deployments utilizing GPU acceleration.

Component Specification Recommended Quantity (per server)
CPU Intel Xeon Gold 6338 or AMD EPYC 7763 2
RAM 256GB DDR4 ECC Registered 1
GPU NVIDIA A100 80GB or AMD Instinct MI250X 4-8 (depending on model size)
Storage 4TB NVMe PCIe Gen4 SSD (OS & Model Storage) 1
Network 100Gbps Ethernet 1

It’s important to note that the specific hardware requirements will vary depending on the complexity and size of the AI model. Refer to the Hardware Compatibility List for validated configurations. Regular monitoring of resource utilization using Server Monitoring Tools is essential.

2. Software Stack

The software stack needs to be carefully selected to support AI model serving and management. We standardize on a specific set of technologies to ensure compatibility and maintainability.

Software Component Version Purpose
Operating System Ubuntu Server 22.04 LTS Base OS for the server. See Operating System Installation Guide.
CUDA Toolkit 12.2 NVIDIA's parallel computing platform and API.
cuDNN 8.9.2 NVIDIA's Deep Neural Network library.
Docker 24.0.5 Containerization platform for packaging and deploying AI models. Refer to Docker Usage Guidelines.
Kubernetes 1.27 Container orchestration system for scalable deployments. See Kubernetes Cluster Management.
TensorFlow/PyTorch 2.12 / 2.0 Deep learning frameworks.

Ensure all software components are kept up to date with the latest security patches. Automated patching is configured through Automated Patch Management System.

3. Network Configuration

AI deployments often involve transferring large datasets and model files. A high-bandwidth, low-latency network is crucial.

Network Area Configuration Notes
Internal Network 100Gbps Ethernet Dedicated network segments for AI workloads are recommended.
Load Balancing HAProxy or Nginx Distribute traffic across multiple AI model servers. See Load Balancing Configuration.
Firewall iptables/nftables Secure the AI deployment with appropriate firewall rules. Refer to Firewall Management.
DNS Internal DNS Server Ensure proper DNS resolution for all AI services.
Monitoring Prometheus & Grafana Monitor network traffic and latency. See Network Monitoring.

Consider using a Content Delivery Network (CDN) for serving model outputs to end-users, especially if geographically distributed. Details on CDN integration can be found in the CDN Integration Guide.

4. Model Serving Best Practices

Efficient model serving is critical for delivering a responsive user experience.

  • **Model Optimization:** Optimize models for inference using techniques like quantization and pruning. Refer to Model Optimization Techniques.
  • **Batching:** Process multiple requests in a single batch to improve throughput.
  • **Caching:** Cache frequently accessed model outputs to reduce latency.
  • **Monitoring:** Monitor model performance metrics such as latency, throughput, and accuracy using Model Monitoring Tools.
  • **Versioning:** Implement a robust model versioning system to facilitate rollbacks and A/B testing. See Model Versioning Strategy.
  • **Security:** Secure model endpoints with authentication and authorization. Review Security Best Practices for AI Models.

5. Scaling and High Availability

To ensure high availability and scalability, utilize Kubernetes to orchestrate the AI deployments.

  • **Horizontal Pod Autoscaling (HPA):** Automatically scale the number of pods based on resource utilization.
  • **Load Balancing:** Distribute traffic across multiple pods using Kubernetes Services.
  • **Replication:** Ensure multiple replicas of each pod are running to provide redundancy.
  • **Rolling Updates:** Deploy new model versions without downtime using rolling updates.
  • **Disaster Recovery:** Implement a disaster recovery plan to protect against data loss and service interruptions. See Disaster Recovery Procedures.

6. Security Considerations

AI deployments are subject to various security threats.

  • **Data Security:** Protect sensitive data used for training and inference.
  • **Model Security:** Prevent unauthorized access to and modification of AI models.
  • **API Security:** Secure model APIs with authentication and authorization.
  • **Vulnerability Management:** Regularly scan for and patch vulnerabilities in the AI software stack. See Vulnerability Scanning Procedures.
  • **Access Control:** Implement strict access control policies to limit access to AI resources.

7. Documentation and Support

  • Maintain comprehensive documentation of the AI deployment configuration.
  • Provide clear instructions for troubleshooting common issues.
  • Establish a dedicated support channel for AI-related issues. Contact AI Support Team. Refer to Troubleshooting Common AI Issues.


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.* ⚠️

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