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BREAKTHROUGH IMPLEMENTATION: ✅ Auto-generated warnings now added to ALL exported files automatically ✅ Clear source file paths shown in every tinytorch/ file header ✅ CLAUDE.md updated with crystal clear rules: tinytorch/ = edit modules/ ✅ Export process now runs warnings BEFORE success message SYSTEMATIC PREVENTION: - Every exported file shows: AUTOGENERATED! DO NOT EDIT! File to edit: [source] - THIS FILE IS AUTO-GENERATED FROM SOURCE MODULES - CHANGES WILL BE LOST! - To modify this code, edit the source file listed above and run: tito module complete WORKFLOW ENFORCEMENT: - Golden rule established: If file path contains tinytorch/, DON'T EDIT IT DIRECTLY - Automatic detection of 16 module mappings from tinytorch/ back to modules/source/ - Post-export processing ensures no exported file lacks protection warning VALIDATION: ✅ Tested with multiple module exports - warnings added correctly ✅ All tinytorch/core/ files now protected with clear instructions ✅ Source file paths correctly mapped and displayed This prevents ALL future source/compiled mismatch issues systematically.
152 lines
5.0 KiB
YAML
152 lines
5.0 KiB
YAML
# TinyTorch Milestone Configuration
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# Defines the 3 epic achievements that transform students into ML engineers
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milestones:
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1:
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name: "Machines Can See"
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title: "I taught a computer to recognize real images!"
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emoji: "👁️"
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trigger_module: "05_dense"
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required_modules:
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- "01_setup"
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- "02_tensor"
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- "03_activations"
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- "04_layers"
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- "05_dense"
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required_checkpoints:
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- "00" # Environment
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- "01" # Foundation
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- "02" # Intelligence
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- "03" # Components
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- "04" # Networks
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victory_condition: "45%+ CIFAR-10 accuracy with MLP"
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capability: "I can create neural networks that recognize real RGB images!"
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real_world_impact: "Foundation for any computer vision system"
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dataset: "CIFAR-10"
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model_type: "Multi-Layer Perceptron (MLP)"
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test_file: "foundation/milestone.py"
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demo_description: "Watch YOUR dense layers and activations learn to recognize real-world RGB images"
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2:
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name: "I Can Train Real AI"
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title: "I built and trained a CNN from scratch!"
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emoji: "🏆"
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trigger_module: "11_training"
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required_modules:
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- "01_setup"
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- "02_tensor"
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- "03_activations"
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- "04_layers"
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- "05_dense"
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- "06_spatial"
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- "07_attention"
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- "08_dataloader"
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- "09_autograd"
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- "10_optimizers"
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- "11_training"
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required_checkpoints:
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- "05" # Learning (spatial)
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- "06" # Attention
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- "07" # Stability
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- "08" # Differentiation
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- "09" # Optimization
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- "10" # Training
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victory_condition: "65%+ CIFAR-10 accuracy with CNN"
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capability: "I can train production-quality computer vision models!"
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real_world_impact: "Build vision systems like those used in autonomous vehicles"
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dataset: "CIFAR-10"
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model_type: "Convolutional Neural Network (CNN)"
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test_file: "revolution/milestone.py"
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demo_description: "Watch YOUR complete training pipeline learn to recognize real-world objects"
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3:
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name: "I Built GPT"
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title: "I created an AI that writes Python code!"
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emoji: "🤖"
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trigger_module: "16_tinygpt"
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required_modules:
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- "01_setup"
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- "02_tensor"
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- "03_activations"
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- "04_layers"
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- "05_dense"
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- "06_spatial"
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- "07_attention"
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- "08_dataloader"
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- "09_autograd"
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- "10_optimizers"
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- "11_training"
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- "12_compression"
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- "13_kernels"
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- "14_benchmarking"
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- "15_mlops"
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- "16_tinygpt"
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required_checkpoints:
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- "11" # Regularization
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- "12" # Kernels
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- "13" # Benchmarking
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- "14" # Deployment
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- "15" # Capstone
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victory_condition: "Generate valid Python functions from natural language"
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capability: "I can build the future of AI - language models that write code!"
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real_world_impact: "Foundation technology behind GitHub Copilot and ChatGPT"
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dataset: "Python function dataset"
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model_type: "Transformer (TinyGPT)"
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test_file: "generation/milestone.py"
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demo_description: "Watch YOUR transformer generate Python functions from natural language"
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# Milestone progression path
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progression:
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- milestone: 1
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unlocks_after: "Module 05 completion + Checkpoints 00-04"
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celebrates: "Foundation of neural networks working on real data"
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- milestone: 2
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unlocks_after: "Module 11 completion + Checkpoints 05-10"
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celebrates: "Complete ML training pipeline mastery"
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- milestone: 3
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unlocks_after: "Module 16 completion + Checkpoints 11-15"
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celebrates: "Building the future of AI - language generation"
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# Module exercise tracking
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module_exercise_mapping:
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milestone_1:
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description: "YOUR 5 core modules recognize real RGB images"
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modules_used:
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- module: "02_tensor"
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role: "Core mathematical operations"
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- module: "03_activations"
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role: "Neural network intelligence (ReLU, Sigmoid)"
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- module: "04_layers"
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role: "Building block abstractions"
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- module: "05_dense"
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role: "Multi-layer network architecture"
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milestone_2:
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description: "YOUR 11 modules train a CNN from scratch"
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modules_used:
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- module: "06_spatial"
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role: "Convolutional operations for image processing"
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- module: "08_dataloader"
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role: "CIFAR-10 dataset loading and batching"
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- module: "09_autograd"
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role: "Automatic gradient computation"
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- module: "10_optimizers"
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role: "Adam optimizer for efficient training"
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- module: "11_training"
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role: "Complete training loop orchestration"
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# Plus all modules from milestone 1
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milestone_3:
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description: "YOUR complete 16-module framework generates Python code"
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modules_used:
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- module: "07_attention"
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role: "Transformer attention mechanisms"
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- module: "16_tinygpt"
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role: "Language model architecture"
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- module: "12_compression"
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role: "Model efficiency for deployment"
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- module: "15_mlops"
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role: "Production deployment pipeline"
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# Plus all modules from milestones 1 & 2 |