Docker Guide

# Start the API server (production)
docker compose up api

# Start development server with hot reload
docker compose --profile dev up dev

# Run CLI commands
docker compose --profile cli run --rm cli check "မြန်မာစာ"

# Run tests
docker compose --profile test run --rm test

# Build the image
docker build -t myspellchecker:latest .

# Run API server
docker run -p 8000:8000 -v ./data:/app/data:ro myspellchecker:latest

# Run CLI
docker run --rm -v ./data:/app/data:ro myspellchecker:cli --help

# Build production image
docker build --target runtime -t myspellchecker:latest .

# Build development image
docker build --target development -t myspellchecker:dev .

# Build CLI-only image
docker build --target cli -t myspellchecker:cli .

docker compose up api

docker compose --profile dev up dev

docker compose --profile gpu up api-gpu

# Check text
docker compose --profile cli run --rm cli check "သွားပါမယ်"

# Build dictionary
docker compose --profile cli run --rm cli build --input /app/input/corpus.txt --output /app/output/dictionary.db

# Get help
docker compose --profile cli run --rm cli --help

docker compose --profile test run --rm test

# Place your dictionary in ./data/
cp my_dictionary.db ./data/dictionary.db

# Start API with custom dictionary
docker compose up api

# Override environment variables
LOG_LEVEL=DEBUG docker compose up api

# Or use .env file
echo "LOG_LEVEL=DEBUG" > .env
docker compose up api

# Build production image
docker compose build api

# Run in detached mode
docker compose up -d api

# Check status
docker compose ps
docker compose logs api

# docker-compose.override.yml
services:
  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
      - ./certs:/etc/nginx/certs:ro
    depends_on:
      - api

# Example health endpoint in your FastAPI app
@app.get("/health")
def health():
    return {"status": "ok"}

# Docker health check runs automatically every 30s
docker inspect --format='{{.State.Health.Status}}' myspellchecker-api

# Place corpus in input directory
cp my_corpus.txt ./input/

# Build dictionary
docker compose --profile cli run --rm cli build \
  --input /app/input/my_corpus.txt \
  --output /app/output/dictionary.db

# Copy output to data directory
cp ./output/dictionary.db ./data/

# Build sample dictionary for testing
docker compose --profile cli run --rm cli build --sample

# Output is saved to /app/data/dictionary.db

# Add NVIDIA Container Toolkit repository
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

# Restart Docker
sudo systemctl restart docker

# Start GPU-enabled service
docker compose --profile gpu up api-gpu

# Verify GPU access
docker compose run --rm api-gpu python -c "import torch; print(torch.cuda.is_available())"

# Check logs
docker compose logs api

# Check if port is in use
lsof -i :8000

# Rebuild image
docker compose build --no-cache api

# Ensure data directory exists and contains dictionary
ls -la ./data/

# Build sample dictionary if needed
docker compose --profile cli run --rm cli build --sample

# Fix volume permissions (Linux)
sudo chown -R 1000:1000 ./data ./output

# Or run as root (not recommended for production)
docker compose --profile cli run --rm --user root cli build --sample

# Verify NVIDIA runtime
docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

# Check Docker GPU configuration
docker info | grep -i gpu

# Increase memory limit in docker-compose.yml
services:
  api:
    deploy:
      resources:
        limits:
          memory: 2G  # Increase from 1G

docker run --read-only \
  --tmpfs /tmp \
  -v ./data:/app/data:ro \
  myspellchecker:latest