Andrew Mao

Machine Learning Engineer at DRW

2 minute read

We walk through building a modern AI chat application that supports both OpenAI and local LLM models, with Kubernetes deployment and GPU acceleration.

Table of Contents

  1. Project Overview
  2. Architecture
  3. Development Setup
  4. Kubernetes Deployment
  5. CI/CD Pipeline
  6. Best Practices

Project Overview

Our AI chat application is a full-stack solution that demonstrates modern software development practices:

  • Multiple LLM Support: Integration with OpenAI’s GPT models and local models using vLLM
  • Microservices Architecture: Separate services for frontend, backend, and inference
  • Container Orchestration: Kubernetes deployment with GPU support
  • CI/CD Pipeline: Automated testing and deployment using GitHub Actions

Architecture

Components

  1. Frontend (Streamlit)
    • Modern chat interface
    • Real-time response streaming
    • Model selection and configuration
  2. Backend (FastAPI)
    • API gateway
    • Request routing
    • Model management
  3. Inference Service (vLLM)
    • GPU-accelerated inference
    • Model loading and caching
    • Efficient resource utilization

Infrastructure

graph TD
    A[User] --> B[Frontend Service]
    B --> C[Backend Service]
    C --> D[OpenAI API]
    C --> E[Inference Service]
    E --> F[GPU Resources]

Development Setup

Prerequisites

  • Python 3.10+
  • Docker
  • Kubernetes cluster
  • NVIDIA GPU with drivers

Local Development

  1. Clone the Repository 
    git clone https://github.com/yourusername/ai-chat.git
cd ai-chat
  2. Set Up Environment 
    python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
  3. Run Services 
    # Terminal 1 - Backend
cd backend && uvicorn main:app --reload
   
# Terminal 2 - Frontend
cd frontend && streamlit run app.py
   
# Terminal 3 - Inference
cd inference && uvicorn main:app --reload

Kubernetes Deployment

Cluster Setup

  1. Enable GPU Support 
    # Install NVIDIA device plugin
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/master/nvidia-device-plugin.yml
  2. Create Namespace 
    kubectl create namespace ai-chat
  3. Apply Configurations 
    kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/pvc.yaml
kubectl apply -f k8s/backend-deployment.yaml
kubectl apply -f k8s/frontend-deployment.yaml
kubectl apply -f k8s/inference-deployment.yaml

Resource Management

  • GPU allocation through Kubernetes device plugins
  • Persistent volume for model storage
  • Resource limits and requests for each service

CI/CD Pipeline

GitHub Actions Workflow

  1. Build and Test
    • Run unit tests
    • Build Docker images
    • Push to container registry
  2. Deploy
    • Update Kubernetes manifests
    • Apply configurations
    • Verify deployment

Security Considerations

  • Secrets management
  • Image scanning
  • Access control

Best Practices

Development

  1. Code Organization
    • Modular architecture
    • Clear separation of concerns
    • Comprehensive testing
  2. Performance
    • Efficient resource utilization
    • Caching strategies
    • Load balancing
  3. Security
    • API key management
    • Input validation
    • Error handling

Deployment

  1. Monitoring
    • Health checks
    • Resource usage
    • Error tracking
  2. Scaling
    • Horizontal pod autoscaling
    • Resource optimization
    • Load distribution
  3. Maintenance
    • Regular updates
    • Backup strategies
    • Disaster recovery

Conclusion

This project demonstrates how to build and deploy a modern AI application using best practices in software development and DevOps. The combination of microservices architecture, container orchestration, and GPU acceleration provides a scalable and efficient solution for AI-powered applications.

Resources

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