DOCKER_README.md

🐳 Running Optimization with Docker

Yes! You can absolutely use Docker to run the optimization code. This is actually recommended because:

✅ Isolated environment - No conflicts with your system Python
✅ Reproducible - Same results every time
✅ Easy cleanup - Just remove the container when done
✅ Resource control - Limit CPU/memory usage

Quick Start (3 Commands)

# 1. Make script executable (one time)
chmod +x run_optimization_docker.sh

# 2. Run optimization
./run_optimization_docker.sh

# 3. That's it! Results are saved to content/models/

What Gets Created

The Docker setup includes:

1. Dockerfile.optimization - Docker image definition
2. docker-compose.optimization.yml - Easy container management
3. run_optimization_docker.sh - One-command runner script
4. DOCKER_OPTIMIZATION.md - Detailed documentation

Simple Usage Examples

Run Full Optimization

./run_optimization_docker.sh

Takes ~1-2 hours, 100 trials per model

Faster Run (50 trials)

./run_optimization_docker.sh --trials 50

Takes ~30-60 minutes

Run Feature Analysis

./run_optimization_docker.sh --script feature_importance_analysis.py

Takes ~5-10 minutes

Compare Results

./run_optimization_docker.sh --script compare_models.py

Using Docker Compose

If you prefer docker-compose:

# Build and run
docker-compose -f docker-compose.optimization.yml up --build

# View logs
docker-compose -f docker-compose.optimization.yml logs -f

# Stop when done
docker-compose -f docker-compose.optimization.yml down

Using Docker Directly

# Build image
docker build -f Dockerfile.optimization -t heart-optimization .

# Run optimization
docker run --rm \
  -v "$(pwd)/content:/app/content" \
  -v "$(pwd)/model_assets:/app/model_assets:ro" \
  heart-optimization

Results Location

All results are automatically saved to your host machine:

• content/models/model_metrics_optimized.csv - Performance metrics
• content/models/*_optimized.joblib - Optimized models
• content/models/ensemble_info_optimized.json - Ensemble configuration
• content/reports/ - Feature importance visualizations

Resource Requirements

Minimum:

• 4GB RAM
• 2 CPU cores
• 5GB disk space

Recommended:

• 8GB RAM
• 4 CPU cores
• 10GB disk space

Time Estimates

Configuration	Time
30 trials	~20-30 min
50 trials	~30-60 min
100 trials	~1-2 hours
200 trials	~2-4 hours

Troubleshooting

Docker not running

# Check Docker status
docker info

# Start Docker Desktop (if on Mac/Windows)
# Or: sudo systemctl start docker (Linux)

Out of memory

# Reduce trials
./run_optimization_docker.sh --trials 30

# Or reduce timeout
STUDY_TIMEOUT=1800 ./run_optimization_docker.sh

Data file not found

# Verify data exists
ls -lh content/cardio_train_extended.csv

Advanced Options

Custom Resource Limits

Edit docker-compose.optimization.yml:

deploy:
  resources:
    limits:
      cpus: '8'      # Use more CPUs
      memory: 16G    # More RAM

Environment Variables

N_TRIALS=50 STUDY_TIMEOUT=1800 ./run_optimization_docker.sh

Interactive Shell

docker-compose -f docker-compose.optimization.yml run --rm optimization bash

Next Steps

1. ✅ Run ./run_optimization_docker.sh
2. ✅ Wait for completion (1-2 hours)
3. ✅ Check results in content/models/
4. ✅ Compare with baseline using compare_models.py
5. ✅ Deploy optimized models

Full Documentation

For detailed instructions, see:

• DOCKER_OPTIMIZATION.md - Complete Docker guide
• QUICK_START.md - General quick start
• IMPROVEMENTS.md - Improvement details

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Pro Tip: Run optimization overnight or during lunch break. The container will save all results automatically!