Commit Graph

4 Commits

Author SHA1 Message Date
Vijay Janapa Reddi
bfadc82ce6 Update generated notebooks and package exports
- Regenerate all .ipynb files from fixed .py modules
- Update tinytorch package exports with corrected implementations
- Sync package module index with current 16-module structure

These generated files reflect all the module fixes and ensure consistent
.py ↔ .ipynb conversion with the updated module implementations.
2025-09-18 16:42:57 -04:00
Vijay Janapa Reddi
8a101cf52d Add tito grade command for simplified NBGrader interface
Implement comprehensive grading workflow wrapped behind tito CLI:
• tito grade setup - Initialize NBGrader course structure
• tito grade generate - Create instructor version with solutions
• tito grade release - Create student version without solutions
• tito grade collect - Collect student submissions
• tito grade autograde - Automatically grade submissions
• tito grade manual - Open manual grading interface
• tito grade feedback - Generate student feedback
• tito grade export - Export grades to CSV

This allows users to only learn tito commands without needing to
understand NBGrader's complex interface. All grading functionality
is accessible through simple, consistent tito commands.
2025-09-17 19:22:02 -04:00
Vijay Janapa Reddi
771ed98a80 🧹 Remove Jupyter notebooks from modules/source - Python-first workflow
- Delete all 15 .ipynb files from modules/source directories
- Align with TinyTorch's Python-first development philosophy
- .py files are the source of truth, .ipynb files are temporary outputs
- Prevents version control conflicts with notebook metadata
- Students work directly with .py files using Jupytext format
- Notebooks can be regenerated when needed via 'tito nbdev generate'

Removed files:
- All *_dev.ipynb files across modules 01-15
- Keeps repository clean and focused on source code
2025-07-20 08:41:26 -04:00
Vijay Janapa Reddi
59d58718f9 refactor: Implement learner-focused module progression with better naming
 Renamed modules for clearer pedagogical flow:
- 05_networks → 05_dense (multi-layer dense/fully connected networks)
- 06_cnn → 06_spatial (convolutional networks for spatial patterns)
- 06_attention → 07_attention (attention mechanisms for sequences)

 Shifted remaining modules down by 1:
- 07_dataloader → 08_dataloader
- 08_autograd → 09_autograd
- 09_optimizers → 10_optimizers
- 10_training → 11_training
- 11_compression → 12_compression
- 12_kernels → 13_kernels
- 13_benchmarking → 14_benchmarking
- 14_mlops → 15_mlops
- 15_capstone → 16_capstone

 Updated module metadata (module.yaml files):
- Updated names, descriptions, dependencies
- Fixed prerequisite chains and enables relationships
- Updated export paths to match new names

New learner progression:
Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline

Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
2025-07-18 00:12:50 -04:00