- Add matmul_naive function with for-loop implementation for learning
- Update Dense layer to support both NumPy (@) and naive matrix multiplication
- Add comprehensive tests comparing both implementations (correctness & performance)
- Include step-by-step computation visualization for 2x2 matrices
- Fix missing imports in tensor.py and activations.py
- Export both tensor and activations modules to package
This provides students with immediate success using NumPy while allowing them to
understand the underlying computation through explicit for-loops. The scaffolding
includes performance comparisons and educational insights about why NumPy is faster.
- Ported all commands from bin/tito.py to new tito/ CLI architecture
- Added InfoCommand with system info and module status
- Added TestCommand with pytest integration
- Added DoctorCommand with environment diagnosis
- Added SyncCommand for nbdev export functionality
- Added ResetCommand for package cleanup
- Added JupyterCommand for notebook server
- Added NbdevCommand for nbdev development tools
- Added SubmitCommand and StatusCommand (placeholders)
- Fixed missing imports in tinytorch/core/tensor.py
- All commands now work with 'tito' command in shell
- Maintains professional architecture while restoring full functionality
Commands restored:
✅ info - System information and module status
✅ test - Run module tests with pytest
✅ doctor - Environment diagnosis
✅ sync - Export notebooks to package
✅ reset - Clean tinytorch package
✅ nbdev - nbdev development commands
✅ jupyter - Start Jupyter server
✅ submit - Module submission
✅ status - Module status
✅ notebooks - Build notebooks from Python files
The CLI now has both the professional architecture and all original functionality.
Introduces a Tensor class that wraps numpy arrays, enabling
fundamental ML operations like addition, subtraction,
multiplication, and division.
Adds utility methods such as reshape, transpose, sum, mean, max,
min, item, and numpy to the Tensor class.
Updates tests to accommodate both scalar and Tensor results
when checking mean values.
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.
