Overview

The Triton Server HPA project provides a comprehensive solution for implementing Horizontal Pod Autoscaling (HPA) in a GPU-based AI inference environment using the NVIDIA Triton Inference Server on Kubernetes. This guide details the creation of a scalable system that adapts dynamically to fluctuating workloads.

Key Features

• Dynamic Scaling: Ensure reliable handling of varying AI request volumes through automated scaling of Triton inference server pods.
• GPU Utilization: Leverage NVIDIA GPUs for efficient processing of AI models, enhancing performance significantly.
• Comprehensive Documentation: Step-by-step instructions available for installation and configuration, making it accessible for users of varying skill levels.

Architecture

The architecture builds on several technologies:

• Docker for containerization.
• Kubernetes for orchestration and management of containerized applications.
• NVIDIA Triton Inference Server for hosting AI models and serving inference requests.

Getting Started

1. Create Vision-Based AI Model Application: A straightforward guide to developing a vision AI model using YOLOv7.
2. Setup Kubernetes Environment: Utilize Minikube and install necessary tools such as kubectl and Helm.
3. Deploy Triton Inference Server: Instructions provided for deployment configurations and service settings.
4. Horizontal Pod Autoscaler Configuration: Define HPA settings based on GPU utilization metrics.

Example Commands

To start the deployment, follow these commands:

# Install NVIDIA Container Toolkit for Docker GPU support
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

# Start Minikube with GPU support
minikube start --driver docker --container-runtime docker --gpus all

# Verify GPU setup
docker run --rm --gpus all nvidia/cuda:12.2.0-devel-ubuntu22.04 nvidia-smi

Monitoring and Metrics

Integration with DCGM (Data Center GPU Manager) and Prometheus allows for monitoring GPU metrics, ensuring real-time insights into resource utilization and scaling needs.

Acknowledgements

Acknowledgment of team contributions enhances the project's collaborative spirit and recognizes valuable support received during its development.

References

• Learn about NVIDIA NLP Scaling Documentation for further insights.
• Explore the Kubernetes Horizontal Pod Autoscaler documentation for advanced scaling strategies.
• Additional information on the YOLOv7 Vision Model can facilitate understanding of the AI model used in this project.