TinyTorch/modules/20_capstone

Module 20: Capstone - Complete ML System Integration

Overview

Combine everything you've learned to build a complete, optimized ML system from scratch. This is your masterpiece - demonstrating mastery of both ML algorithms and systems engineering.

Project Options

Option 1: Optimized CIFAR-10 Trainer

Goal: 75% accuracy with minimal resources

• Start with your Module 10 trainer
• Apply all optimizations (acceleration, quantization, pruning)
• Achieve same accuracy with 10x less compute/memory
• Deploy on resource-constrained device

Option 2: Efficient GPT Inference Engine

Goal: Real-time text generation on CPU

• Implement KV caching for transformers
• Quantize model to INT8
• Optimize attention computation
• Generate 100 tokens/second on laptop CPU

Option 3: Custom Challenge

Goal: Define your own optimization challenge

• Pick a problem you care about
• Set performance targets
• Apply systematic optimization
• Document the journey

What You'll Demonstrate

1. Full Stack Understanding

• Build complete training pipeline
• Implement model architecture
• Add optimization layers
• Deploy to production

2. Systems Engineering

• Profile and identify bottlenecks
• Apply appropriate optimizations
• Measure and validate improvements
• Handle resource constraints

3. Scientific Approach

• Baseline measurements
• Systematic optimization
• Ablation studies
• Reproducible results

Capstone Structure

Week 1: Planning & Baseline

# 1. Choose project and define success metrics
metrics = {
    'accuracy_target': 75.0,
    'inference_time': '<10ms',
    'memory_usage': '<100MB',
    'model_size': '<10MB'
}

# 2. Build baseline system
baseline = build_baseline_model()
baseline_metrics = evaluate(baseline)

# 3. Profile and identify opportunities
bottlenecks = profile_system(baseline)

Week 2: Optimization Sprint

# 4. Apply optimizations systematically
optimized = baseline
optimized = apply_acceleration(optimized)
optimized = apply_quantization(optimized)  
optimized = apply_pruning(optimized)
optimized = apply_caching(optimized)

# 5. Measure improvements
for optimization in optimizations:
    metrics = evaluate(optimized)
    speedup = baseline_time / optimized_time
    print(f"{optimization}: {speedup}x faster")

Week 3: Polish & Deploy

# 6. Final optimization pass
final_model = fine_tune_optimizations(optimized)

# 7. Create deployment package
deployment = package_for_production(final_model)

# 8. Document results
write_technical_report(baseline, final_model, metrics)

Deliverables

1. Working System

• Complete codebase on GitHub
• README with setup instructions
• Demonstration video/notebook

2. Technical Report

• Problem statement and approach
• Baseline vs optimized metrics
• Optimization journey and decisions
• Lessons learned

3. Performance Analysis

• Comprehensive benchmarks
• Ablation study results
• Resource utilization graphs
• Comparison with PyTorch/TensorFlow

Evaluation Criteria

Technical Excellence (40%)

• Correctness of implementation
• Quality of optimizations
• Code organization and style

Performance Achievement (30%)

• Meeting stated goals
• Improvement over baseline
• Resource efficiency

Systems Understanding (30%)

• Appropriate optimization choices
• Understanding of tradeoffs
• Scientific methodology

Example Projects from Past Students

"TinyYOLO" - Real-time Object Detection

• 30 FPS on Raspberry Pi
• 90% size reduction through pruning
• Custom INT8 kernels for ARM

"NanoGPT" - Edge Language Model

• 100MB model generates Shakespeare
• KV caching + quantization
• Runs on 2015 laptop

"SwiftCNN" - Instant Image Classification

• <1ms inference on iPhone
• Structured pruning + iOS Metal
• 95% of ResNet accuracy at 10% size

Resources

• All previous module code
• TinyTorch optimization library
• Benchmarking tools
• Community Discord for help

Success Criteria

• ✅ Complete working system with all optimizations
• ✅ 10x+ improvement in speed OR memory
• ✅ Professional documentation and analysis
• ✅ Understanding of when/why to apply each optimization
• ✅ Ready for ML systems engineering roles!

Final Note

This is your chance to show everything you've learned. Build something you're proud of - something that demonstrates not just that you can implement ML algorithms, but that you understand how to build production ML systems.

Remember: The goal isn't perfection, it's demonstrating systematic thinking about performance, memory, and deployment constraints - the real challenges of ML engineering.