MAJOR IMPROVEMENT: Simplified test discovery logic
- Removed restrictive valid_patterns requirement from testing framework
- Any function starting with 'test_' is now automatically discovered
- Follows standard pytest conventions - no maintenance overhead
- Eliminates need to manually add patterns for new test functions
CLEANED UP: Test function names across all 10 modules
- Removed redundant '_comprehensive' suffix from all test functions
- Updated 40+ test function names to be more concise and readable:
* 00_setup: 6 functions (test_personal_info, test_system_info, etc.)
* 01_tensor: 4 functions (test_tensor_creation, test_tensor_properties, etc.)
* 02_activations: 1 function (test_activations)
* 03_layers: 3 functions (test_matrix_multiplication, test_dense_layer, etc.)
* 04_networks: 4 functions (test_sequential_networks, test_mlp_creation, etc.)
* 05_cnn: 3 functions (test_convolution_operation, test_conv2d_layer, etc.)
* 06_dataloader: 4 functions (test_dataset_interface, test_dataloader, etc.)
* 07_autograd: 6 functions (test_variable_class, test_add_operation, etc.)
* 08_optimizers: 5 functions (test_gradient_descent_step, test_sgd_optimizer, etc.)
* 09_training: 6 functions (test_mse_loss, test_crossentropy_loss, etc.)
* 10_compression: 6 functions (already cleaned up)
VERIFICATION: All tests still pass
- All 10 modules tested successfully with new discovery logic
- Total test count maintained: 47 inline tests across all modules
- No functionality lost, only improved maintainability
RESULT: Much cleaner, more maintainable testing framework following standard conventions
- Updated all _dev.py files to use 'comprehensive test' instead of 'integration test'
- Changed function names: test_*_integration() → test_*_comprehensive()
- Updated markdown headers, print statements, success/error messages
- Clarifies that these are comprehensive tests of single modules, not cross-module integration
- Real cross-module integration tests remain in tests/ directory
- Updated modules: 00_setup, 01_tensor, 02_activations, 03_layers, 04_networks, 05_cnn, 06_dataloader, 07_autograd
- Move testing utilities from tinytorch/utils/testing.py to tito/tools/testing.py
- Update all module imports to use tito.tools.testing
- Remove testing utilities from core TinyTorch package
- Testing utilities are development tools, not part of the ML library
- Maintains clean separation between library code and development toolchain
- All tests continue to work correctly with improved architecture
- Enhanced tensor module documentation with mathematical foundations
- Improved explanations for scalars, vectors, and matrices
- Added NBGrader workflow documentation to activations module
- Cleaned up .cursor/rules/ directory structure
- Updated user preferences for better development workflow
These changes improve the educational content and developer experience
while maintaining the core functionality of all modules.
- Implement 'explain → code → test → repeat' structure across all modules
- Replace comprehensive end-of-module tests with progressive unit tests
- Add rich scaffolding with detailed implementation guidance
- Transform generic TODOs into step-by-step learning instructions
- Connect educational content to real-world ML systems and PyTorch
- Reduce overall codebase by 37% while enhancing learning experience
- Ensure immediate feedback and skill building for students
Modules transformed:
- 01_tensor: Tensor operations and broadcasting
- 02_activations: Activation functions and derivatives
- 03_layers: Linear layers and forward/backward propagation
- 04_networks: Network building and multi-layer composition
- 05_cnn: Convolution operations and CNN architecture
- 06_dataloader: Data pipeline and batch processing
- 07_autograd: Automatic differentiation and computational graphs
- Fixed indentation error in tensor module add method
- Updated networks test import to use correct function name
- Most tests now passing with only minor edge case failures
- Added detailed mathematical progression from scalars to higher-order tensors
- Enhanced conceptual explanations with real-world ML applications
- Improved tensor class design with comprehensive requirements analysis
- Added extensive arithmetic operations section with broadcasting and performance considerations
- Connected to industry frameworks (PyTorch, TensorFlow, JAX)
- Improved learning scaffolding with step-by-step implementation guidance
- Add 17 intermediate test points across 6 modules for immediate student feedback
- Tensor module: Tests after creation, properties, arithmetic, and operators
- Activations module: Tests after each activation function (ReLU, Sigmoid, Tanh, Softmax)
- Layers module: Tests after matrix multiplication and Dense layer implementation
- Networks module: Tests after Sequential class and MLP creation
- CNN module: Tests after convolution, Conv2D layer, and flatten operations
- DataLoader module: Tests after Dataset interface and DataLoader class
- All tests include visual progress indicators and behavioral explanations
- Maintains NBGrader compliance with proper metadata and point allocation
- Enables steady forward progress and better debugging for students
- 100% test success rate across all modules and integration testing
- Added package structure documentation explaining modules/source/ vs tinytorch.core.
- Enhanced mathematical foundations with linear algebra refresher and Universal Approximation Theorem
- Added real-world applications for each activation function (ReLU, Sigmoid, Tanh, Softmax)
- Included mathematical properties, derivatives, ranges, and computational costs
- Added performance considerations and numerical stability explanations
- Connected to production ML systems (PyTorch, TensorFlow, JAX equivalents)
- Implemented streamlined 'tito export' command with automatic .py → .ipynb conversion
- All functionality preserved: scripts run correctly, tests pass, package integration works
- Ready to continue with remaining modules (layers, networks, cnn, dataloader)
- 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
