- Remove unnecessary module_paths.txt file for cleaner architecture
- Update export command to discover modules dynamically from modules/source/
- Simplify nbdev command to support --all and module-specific exports
- Use single source of truth: nbdev settings.ini for module paths
- Clean up import structure in setup module for proper nbdev export
- Maintain clean separation between module discovery and export logic
This implements a proper software engineering approach with:
- Single source of truth (settings.ini)
- Dynamic discovery (no hardcoded paths)
- Clean CLI interface (tito package nbdev --export [--all|module])
- Robust error handling with helpful feedback
- Migrated all Python source files to assignments/source/ structure
- Updated nbdev configuration to use assignments/source as nbs_path
- Updated all tito commands (nbgrader, export, test) to use new structure
- Fixed hardcoded paths in Python files and documentation
- Updated config.py to use assignments/source instead of modules
- Fixed test command to use correct file naming (short names vs full module names)
- Regenerated all notebook files with clean metadata
- Verified complete workflow: Python source → NBGrader → nbdev export → testing
All systems now working: NBGrader (14 source assignments, 1 released), nbdev export (7 generated files), and pytest integration.
The modules/ directory has been retired and replaced with standard NBGrader structure.
- Rename modules/data/ → modules/dataloader/
- Rename data_dev.py → dataloader_dev.py
- Update NBDev export target: core.data → core.dataloader
- Rename test files: test_data.py → test_dataloader.py
- Update package exports to tinytorch.core.dataloader
- Update module imports and internal references
This makes the module name more descriptive and aligned with ML industry standards.
Introduces the foundational CLI structure and core components for the TinyTorch project.
This initial commit establishes the command-line interface (CLI) using `argparse` for training, evaluation, benchmarking, and system information. It also lays out the basic directory structure and essential modules, including tensor operations, autograd, neural network layers, optimizers, data loading, and MLOps components.
