name: "10_compression"
title: "Compression & Optimization"
description: "Making AI models efficient for real-world deployment"
version: "1.0.0"
author: "TinyTorch Team"
dependencies:
  - "00_setup"
  - "01_tensor"
  - "03_layers"
  - "04_networks"
  - "09_training"

learning_goals:
  - "Understand model size and deployment constraints"
  - "Implement magnitude-based pruning for weight reduction"
  - "Master quantization for memory efficiency"
  - "Build knowledge distillation for compact models"
  - "Create structured pruning for architecture optimization"
  - "Compare compression techniques and their trade-offs"

components:
  - name: "CompressionMetrics"
    description: "Model size analysis and parameter counting"
    type: "class"
    
  - name: "prune_weights_by_magnitude"
    description: "Remove unimportant weights from layers"
    type: "function"
    
  - name: "calculate_sparsity"
    description: "Calculate fraction of zero weights"
    type: "function"
    
  - name: "prune_model_by_magnitude"
    description: "Apply pruning to entire models"
    type: "function"
    
  - name: "quantize_layer_weights"
    description: "Reduce parameter precision for memory savings"
    type: "function"
    
  - name: "DistillationLoss"
    description: "Train compact models with teacher guidance"
    type: "class"
    
  - name: "prune_layer_neurons"
    description: "Remove entire neurons/channels"
    type: "function"

tests:
  - name: "test_compression_metrics_comprehensive"
    description: "Test model size analysis functionality"
    
  - name: "test_magnitude_pruning_comprehensive"
    description: "Test weight pruning algorithms"
    
  - name: "test_quantization_comprehensive"
    description: "Test precision reduction techniques"
    
  - name: "test_distillation_comprehensive"
    description: "Test knowledge distillation training"
    
  - name: "test_structured_pruning_comprehensive"
    description: "Test neuron/channel removal"
    
  - name: "test_compression_integration_comprehensive"
    description: "Test combined compression techniques"

educational_flow:
  - step: 1
    title: "Understanding Model Size"
    description: "Learn to analyze and measure neural network parameters"
    
  - step: 2
    title: "Magnitude-Based Pruning"
    description: "Remove unimportant weights based on magnitude"
    
  - step: 3
    title: "Quantization Experiments"
    description: "Reduce precision for memory efficiency"
    
  - step: 4
    title: "Knowledge Distillation"
    description: "Train compact models with teacher guidance"
    
  - step: 5
    title: "Structured Pruning"
    description: "Remove entire neurons and channels"
    
  - step: 6
    title: "Comprehensive Comparison"
    description: "Compare all techniques and combine for maximum benefit"

real_world_applications:
  - "Mobile AI deployment (smartphone apps)"
  - "Edge computing (IoT devices, sensors)"
  - "Real-time inference (autonomous vehicles)"
  - "Cost optimization (cloud inference)"
  - "Battery efficiency (wearable devices)"

industry_connections:
  - "MobileNet: Mobile-optimized architectures"
  - "DistilBERT: Compressed language models"
  - "TinyML: Microcontroller deployment"
  - "Neural Architecture Search: Automated optimization"

assessment_criteria:
  - "Implement 4 compression techniques correctly"
  - "Understand accuracy vs efficiency trade-offs"
  - "Measure compression effectiveness quantitatively"
  - "Apply techniques to real neural networks"
  - "Compare different compression strategies"

next_steps:
  - "Module 11: Kernels - Hardware-aware optimization"
  - "Module 12: Benchmarking - Performance measurement"
  - "Module 13: MLOps - Production deployment" 

# File Structure - What files exist in this module
files:
  dev_file: "compression_dev.py"
  readme: "README.md"
  tests: "inline"

# Educational Metadata
difficulty: "⭐⭐⭐⭐"
time_estimate: "8-10 hours"