# One command to run (All-in-One image, no downloads needed!)
docker run -d --name depth-pro --gpus all -p 8500:8500 neosun/depth-pro:latest

# Open browser
open http://localhost:8500

# Pull and run (model included in image)
docker run -d \
  --name depth-pro \
  --gpus all \
  -p 8500:8500 \
  -e GPU_IDLE_TIMEOUT=60 \
  neosun/depth-pro:latest

# Create docker-compose.yml
cat > docker-compose.yml << 'EOF'
services:
  depth-pro:
    image: neosun/depth-pro:latest
    container_name: depth-pro
    ports:
      - "8500:8500"
    environment:
      - GPU_IDLE_TIMEOUT=60
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped
EOF

# Start service
docker compose up -d

# Create conda environment
conda create -n depth-pro python=3.9 -y
conda activate depth-pro

# Install dependencies
pip install -e .
pip install flask flask-cors flasgger gunicorn

# Download model
source get_pretrained_models.sh

# Run server
python app.py

services:
  depth-pro:
    image: neosun/depth-pro:latest
    container_name: depth-pro
    ports:
      - "8500:8500"
    environment:
      - PORT=8500
      - GPU_IDLE_TIMEOUT=60
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8500/health"]
      interval: 30s
      timeout: 10s
      retries: 3

curl -X POST http://localhost:8500/api/predict \
  -F "file=@image.jpg" \
  -F "colormap=turbo" \
  -F "focal_length=1000"

{
  "task_id": "abc12345",
  "focal_length_px": 1000.0,
  "min_depth_m": 0.5,
  "max_depth_m": 10.2,
  "mean_depth_m": 3.4,
  "image_size": "1920x1080",
  "depth_image_base64": "...",
  "download_jpg": "/api/download/abc12345/color.jpg",
  "download_npz": "/api/download/abc12345/depth.npz",
  "download_16bit": "/api/download/abc12345/depth16.png"
}

curl http://localhost:8500/api/gpu/status

curl -X POST http://localhost:8500/api/gpu/offload

{
  "mcpServers": {
    "depth-pro": {
      "command": "docker",
      "args": ["exec", "-i", "depth-pro", "python3", "mcp_server.py"]
    }
  }
}

depth-pro-docker/
├── app.py                 # Flask web server
├── mcp_server.py          # MCP server for AI assistants
├── gpu_manager.py         # GPU memory management
├── Dockerfile             # Container build file
├── docker-compose.yml     # Docker Compose config
├── checkpoints/           # Model weights (download separately)
│   └── depth_pro.pt
├── src/depth_pro/         # Core model code
├── templates/             # HTML templates
├── static/                # CSS/JS assets
└── docs/                  # Documentation