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Docker Deployment

Deploy Duckling using Docker for quick setup and isolation.

TL;DR - One Command Start

curl -O https://raw.githubusercontent.com/davidgs/duckling/main/docker-compose.prebuilt.yml && docker-compose -f docker-compose.prebuilt.yml up -d
Then open http://localhost:3000 🎉

Prerequisites

  • Docker 20.10+
  • Docker Compose 2.0+

Quick Start

Option 1: Build Locally

# Clone the repository
git clone https://github.com/davidgs/duckling.git
cd duckling

# Build and start (development mode)
docker-compose up --build

# Or run in background
docker-compose up -d --build

Option 2: Use Pre-built Images

# Download docker-compose.prebuilt.yml
curl -O https://raw.githubusercontent.com/davidgs/duckling/main/docker-compose.prebuilt.yml

# Start with pre-built images
docker-compose -f docker-compose.prebuilt.yml up -d

Access the application at http://localhost:3000

Docker Compose Files

Duckling provides several Docker Compose configurations:

File Purpose
docker-compose.yml Development with local builds
docker-compose.prod.yml Production overrides
docker-compose.prebuilt.yml Pre-built images from registry

Development

docker-compose up --build

Production

docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

Pre-built Images

# Using default registry (davidgs)
docker-compose -f docker-compose.prebuilt.yml up -d

# Using custom registry
DOCKER_REGISTRY=ghcr.io/yourusername docker-compose -f docker-compose.prebuilt.yml up -d

# Using specific version
VERSION=1.0.0 docker-compose -f docker-compose.prebuilt.yml up -d

Building Docker Images

Build Script

Use the provided build script for easy image building. The script automatically builds the MkDocs documentation before building Docker images:

# Build images locally (includes documentation build)
./scripts/docker-build.sh

# Build and push to Docker Hub
./scripts/docker-build.sh --push

# Build with specific version
./scripts/docker-build.sh --version 1.0.0

# Build for multiple platforms (requires buildx)
./scripts/docker-build.sh --multi-platform --push

# Push to custom registry
./scripts/docker-build.sh --push --registry ghcr.io/yourusername

# Skip documentation build (use existing site/)
./scripts/docker-build.sh --skip-docs

Documentation Build

The build script automatically runs mkdocs build to ensure documentation is available in the Docker containers. If MkDocs is not installed, it will attempt to install it from requirements-docs.txt.

Manual Build

# Backend (production target)
cd backend
docker build --target production -t duckling-backend:latest .

# Frontend
cd frontend
docker build --target production -t duckling-frontend:latest .

Environment Variables

Create a .env file in the project root:

# Security (required for production)
SECRET_KEY=your-very-secure-random-key-at-least-32-chars

# Flask configuration
FLASK_ENV=production
DEBUG=False

# Optional: Custom registry for pre-built images
DOCKER_REGISTRY=davidgs
VERSION=latest

Security

Always set a strong SECRET_KEY in production. Generate one with: 

python -c "import secrets; print(secrets.token_hex(32))"

Managing Containers

View Status

# Container status
docker-compose ps

# Resource usage
docker stats

View Logs

# All services
docker-compose logs -f

# Specific service
docker-compose logs -f backend

# Last 100 lines
docker-compose logs --tail=100 backend

Stop Services

# Stop containers
docker-compose down

# Stop and remove volumes
docker-compose down -v

# Stop and remove images
docker-compose down --rmi all

Restart Services

# Restart all
docker-compose restart

# Restart specific service
docker-compose restart backend

GPU Support

For GPU-accelerated OCR with NVIDIA GPUs:

# docker-compose.gpu.yml
version: '3.8'

services:
  backend:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - NVIDIA_VISIBLE_DEVICES=all

Run with:

docker-compose -f docker-compose.yml -f docker-compose.gpu.yml up

NVIDIA Container Toolkit

GPU support requires the NVIDIA Container Toolkit.

Persistent Storage

Default (Bind Mounts)

volumes:
  - ./uploads:/app/uploads
  - ./outputs:/app/outputs

services:
  backend:
    volumes:
      - duckling-uploads:/app/uploads
      - duckling-outputs:/app/outputs
      - duckling-data:/app/data

volumes:
  duckling-uploads:
  duckling-outputs:
  duckling-data:

Backup Data

# Backup volumes
docker run --rm -v duckling-outputs:/data -v $(pwd):/backup alpine tar cvf /backup/outputs-backup.tar /data

# Restore volumes
docker run --rm -v duckling-outputs:/data -v $(pwd):/backup alpine tar xvf /backup/outputs-backup.tar -C /

Health Checks

Both containers include health checks:

# Check backend health
curl http://localhost:5001/api/health
# Response: {"status": "healthy", "service": "duckling-backend"}

# Check frontend (returns HTML)
curl -I http://localhost:3000
# Response: HTTP/1.1 200 OK

Docker Compose waits for health checks:

frontend:
  depends_on:
    backend:
      condition: service_healthy

Resource Limits

Production configuration includes resource limits:

services:
  backend:
    deploy:
      resources:
        limits:
          cpus: '2'
          memory: 4G
        reservations:
          cpus: '0.5'
          memory: 1G

  frontend:
    deploy:
      resources:
        limits:
          cpus: '0.5'
          memory: 256M

Networking

Services communicate over a bridge network:

networks:
  duckling-network:
    driver: bridge

The frontend proxies API requests to the backend:

Browser → Frontend (nginx:3000) → Backend (flask:5001)

Troubleshooting

Container Won't Start

# Check logs
docker-compose logs backend

# Check container status
docker-compose ps

# Inspect container
docker inspect duckling-backend

Port Conflicts

Change ports in docker-compose.yml:

services:
  backend:
    ports:
      - "5002:5001"  # Change external port
  frontend:
    ports:
      - "8080:3000"  # Change external port

Build Failures

# Clean build cache
docker builder prune

# Rebuild without cache
docker-compose build --no-cache

Memory Issues

# Check memory usage
docker stats

# Increase Docker memory limit (Docker Desktop)
# Settings → Resources → Memory

Network Issues

# List networks
docker network ls

# Inspect network
docker network inspect duckling_duckling-network

# Recreate network
docker-compose down
docker network prune
docker-compose up

Next Steps