3 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	d892a10492	Simplify export workflow: remove module_paths.txt, use dynamic discovery ... - 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	2025-07-12 17:19:22 -04:00
Vijay Janapa Reddi	7c6f0e5681	Complete migration from modules/ to assignments/source/ structure ... - 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.	2025-07-12 12:06:56 -04:00
Vijay Janapa Reddi	a51bfa0ab2	MAJOR: Separate CLI from framework - proper architectural separation ... BREAKING CHANGE: CLI moved from tinytorch/cli/ to tito/ Perfect Senior Engineer Architecture: - tinytorch/ = Pure ML framework (production) - tito/ = Development/management CLI tool - modules/ = Educational content Benefits: ✅ Clean separation of concerns ✅ Framework stays lightweight (no CLI dependencies) ✅ Clear mental model for users ✅ Professional project organization ✅ Proper dependency management Structure: tinytorch/ # 🧠 Core ML Framework ├── core/ # Tensors, layers, operations ├── training/ # Training loops, optimizers ├── models/ # Model architectures └── ... # Pure ML functionality tito/ # 🔧 Development CLI Tool ├── main.py # CLI entry point ├── core/ # CLI configuration & console ├── commands/ # Command implementations └── tools/ # CLI utilities Key Changes: - Moved all CLI code from tinytorch/cli/ to tito/ - Updated imports and entry points - Separated dependencies (Rich only for dev tools) - Updated documentation to reflect proper separation - Maintained backward compatibility with bin/tito wrapper This demonstrates how senior engineers separate: - Production code (framework) from development tools (CLI) - Core functionality from management utilities - User-facing APIs from internal tooling Educational Value: - Shows proper software architecture - Teaches separation of concerns - Demonstrates dependency management - Models real-world project organization	2025-07-10 22:08:56 -04:00