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This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
33 lines
927 B
Python
33 lines
927 B
Python
"""
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TinyTorch: Build ML Systems from Scratch
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🚧 COMING SOON 🚧
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TinyTorch is an educational deep learning framework being developed at Harvard University.
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This package is currently under active development.
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Full release coming soon with:
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- Complete tensor operations and autograd
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- Neural network layers and optimizers
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- Educational modules for learning ML systems
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- Production-ready training pipelines
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Stay tuned! 🔥
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For updates, visit: https://github.com/VJ/TinyTorch
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"""
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__version__ = "0.0.1"
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__author__ = "Vijay Janapa Reddi"
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__email__ = "vj@eecs.harvard.edu"
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def coming_soon():
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"""Display coming soon message."""
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print("🔥 TinyTorch: Build ML Systems from Scratch")
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print("🚧 Coming Soon from Harvard University!")
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print("📚 Educational deep learning framework in development")
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print("🌟 Visit https://github.com/VJ/TinyTorch for updates")
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# Show message on import
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coming_soon()
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