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DOCKER_SETUP_COMPLETE.md

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🚀 Docker Setup Complete!

What Has Been Created

Your root phenotyping project now has a complete Docker-based automation system!

📦 Docker Files

  • Dockerfile.train - Training container with GPU support
  • Dockerfile.inference - Inference container
  • docker-compose.yml - Orchestration configuration
  • .dockerignore - Optimized build context

🛠️ Automation Scripts

  • Makefile - 20+ automation commands
  • download_dataset.sh - Automatic dataset download
  • run_inference.sh - User-friendly inference wrapper
  • example_workflow.sh - Complete example workflow

📚 Documentation

  • README.md - Updated comprehensive guide
  • QUICK_REFERENCE.md - Quick command reference
  • DOCKER_GUIDE.md - Detailed Docker usage
  • IMPLEMENTATION_SUMMARY.md - Implementation details

📁 Directory Structure

models/              ← Trained models saved here
logs/                ← Training logs and checkpoints
test_images/         ← Place your test images here
inference_results/   ← Results appear here

🎯 Quick Start (3 Commands!)

# 1. Build Docker images
make build-all

# 2. Train the model
make train

# 3. Run inference
make inference

That's it! Everything is automated.

📖 Getting Started

For Complete Beginners

Run the interactive example workflow:

./example_workflow.sh

This will guide you through:

  1. Checking prerequisites
  2. Building images
  3. Training
  4. Running inference

For Quick Reference

make help           # See all available commands

For Detailed Information

  • README.md - Main documentation
  • QUICK_REFERENCE.md - Quick commands
  • DOCKER_GUIDE.md - Docker details

💡 Common Use Cases

Use Case 1: Train a New Model

make train
# Model saved to: ./models/root_mask_rcnn_trained.h5

Use Case 2: Run Inference on Your Images

# Add your images
cp /path/to/images/*.jpg ./test_images/

# Run inference
make inference

# Check results
ls ./inference_results/

Use Case 3: Process Custom Directory

make inference TEST_DIR=./my_custom_images

Use Case 4: Interactive Debugging

make shell-train      # or shell-inference
# Now you're inside the container
python Training.py

⚡ Key Features

✅ One-Command Training: make train
✅ One-Command Inference: make inference
✅ Automatic Dataset Download: No manual steps
✅ GPU Acceleration: Full CUDA support
✅ Any Image Directory: Process any folder structure
✅ Persistent Storage: Models and results saved to host
✅ Production Ready: Error handling and logging

🎓 Training Notes

GPU Highly Recommended

  • With GPU: 3-6 hours training time
  • Without GPU: 2-3 days training time

System Requirements

  • NVIDIA GPU with 8+ GB VRAM (for training)
  • 20+ GB disk space
  • 16+ GB RAM (32 GB recommended)

🔍 Inference Features

Flexible Input

  • Single directory with images
  • Nested subdirectories (use --recursive)
  • Any image format (JPG, PNG, etc.)

Rich Output

  • Detailed CSV with metrics
  • Summary JSON statistics
  • Visual overlays with bounding boxes
  • Confidence scores and root lengths

🆘 Need Help?

  1. Quick Commands: make help
  2. Quick Reference: Read QUICK_REFERENCE.md
  3. Docker Guide: Read DOCKER_GUIDE.md
  4. Full Manual: Read README.md
  5. Check Logs: make logs

🎉 You're Ready!

Everything is set up and documented. Just run:

make build-all  # Build images
make train      # Train model
make inference  # Run predictions

Enjoy automated root phenotyping! 🌱

📞 Support

  • Open an issue on GitHub
  • Check documentation in README.md
  • Review DOCKER_GUIDE.md for Docker-specific issues

Created with ❤️ for easy automation