Vijay Janapa Reddi
2f23f757e7
MAJOR: Implement beautiful module progression through strategic reordering
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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.
2025-09-24 15:56:47 -04:00
Vijay Janapa Reddi
20850b2d40
Implement brilliant tiered numbering system: 0x → 1x → 2x levels
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Revolutionary tiered system that makes learning progression crystal clear:
## 0x Series: Foundation & Building Blocks 🏗️
- 00_setup: Development environment
- 01_tensor: Core data structures
- 02_activations: Mathematical functions
- 03_layers: Neural network primitives
- 04_networks: Architecture composition
## 1x Series: ML Systems & Training 🎓
- 10_dataloader: Data pipeline systems
- 11_cnn: Advanced architectures
- 12_autograd: Automatic differentiation
- 13_optimizers: Learning algorithms
- 14_training: Training orchestration
## 2x Series: Production & Optimization 🚀
- 20_compression: Model optimization
- 21_kernels: Hardware optimization
- 22_benchmarking: Performance measurement
- 23_mlops: Production deployment
- 24_transformer: Advanced architectures
Benefits:
- Clear conceptual levels (primitives → systems → production)
- Natural dependencies (1x needs 0x, 2x needs 1x)
- Scalable system (room for 3x, 4x, etc.)
- Educational clarity (students immediately understand their level)
- Perfect for ML Systems course progression
2025-07-12 02:31:42 -04:00
