DOCKER_BUILD_PERFORMANCE.md

Warbler CDA Docker Build Performance

Build Configuration

• Dockerfile: Minimal FractalStat testing setup
• Base Image: python:3.11-slim
• Build Context Optimization: .dockerignore excludes cache files and large directories
• Dependency Strategy: Minimal ML dependencies for FractalStat testing

Performance Measurements

Optimized Build Results (Windows with WSL)

✅ FINAL OPTIMIZED BUILD: 38.4 seconds (~40 seconds)
├── Base Image Pull: 3.7 seconds
├── System Dependencies: 20.5 seconds (git install)
├── Dependencies (pip install): 5.8 seconds
│   - pydantic>=2.0.0 (only needed library!)
│   - pytest>=7.0.0 (testing framework)
├── Code Copy: 0.2 seconds
├── Layer Export: 6.4 seconds
└── Image Unpack: 1.7 seconds

Performance Improvement Achieved

🚀 Optimization Results:

• Build Time Reduction: 94% faster (601.6s → 38.4s)
• Pip Install Reduction: 98% faster (295.6s → 5.8s)
• Context Size: 556B (highly optimized .dockerignore - final reduction)
• Expected Image Size: ~250MB (vs 12.29GB bloated)

📊 Bottleneck Eliminated:

• Removed PyTorch/Transformers dependency chain causing 98% of bloat
• FractalStat modules require zero ML libraries
• Pure Python with dataclasses, enums, typing, json

🔍 Root Cause Identified: Original bloat caused by transformers[torch] pulling:

• PyTorch CPU (~1GB)
• 100+ optional dependencies (~11GB)
• All unnecessary for FractalStat core functionality

Recommendations for Faster Builds

For Development Builds

1. Use cached layers - Base image and system dependencies rarely change
2. Separate dependency layers - Cache pip installs when code changes frequently
3. Minimal dependencies - Only install what's needed for testing FractalStat specifically

For Production Builds

1. Multi-stage builds - Separate testing and runtime images
2. Dependency optimization - Use Docker layer caching more effectively
3. Alternative base images - Consider smaller Python images or compiled binaries

Testing Results

• ✅ All 70 FractalStat entity tests pass
• ✅ FractalStat coordinates and entities work correctly
• ✅ RAG bridge integration functions properly
• ✅ Container startup and imports work as expected

Performance Notes

• First-time build: ~10 minutes (acceptable for ML dependencies)
• Subsequent builds: Should be faster with Docker layer caching
• Network dependency: Download times vary by internet connection
• WSL overhead: Minimal impact on overall build time