- Fix syntax errors in setup_dev.py test cells (proper indentation)
- Add comprehensive test suite for setup module (test_setup.py)
- Test coverage: functions, SystemInfo class, integration tests
- Custom test runner (no pytest dependency required)
- All 9 tests pass successfully
- Update .gitignore to allow modules/ directory for educational structure
- Fix CLI tool to look for tests in modules/{module}/tests/ instead of tests/
- Update test imports to use parent directory module imports
- Update Cursor rules to reflect new test structure:
* Project structure shows tests/ subdirectory
* Testing patterns show correct paths and import patterns
* Development workflow shows updated test locations
- Test imports now work: from tensor_dev import Tensor
- CLI commands now find tests in correct locations
Tests are now properly organized and discoverable
- Move test_tensor.py to modules/tensor/tests/test_tensor.py
- Create tests/ directories in setup and tensor modules
- Clean up setup module: remove setup_dev.html, setup_dev_files/, __init__.py
- Establish clean module structure pattern:
* {module}_dev.py - Main development file
* {module}_dev.ipynb - Generated notebook
* README.md - Module documentation
* tests/ - Test directory with test_{module}.py
This makes it crystal clear to students where tests are located.
- Fix frontmatter for all rules to include proper descriptions
- Update rule types according to Cursor guidelines:
* alwaysApply: project-structure, user-preferences
* globs: nbdev-educational-pattern, testing-patterns
* description only: development-workflow, ml-systems-course-context
- All rules under 500 lines, focused and actionable
- Follow MDC format with proper metadata
- Ready for proper Cursor rule functionality
- Project structure guide (always applied)
- NBDev educational pattern for module development files
- Development workflow and CLI usage
- Testing patterns and conventions
- ML Systems course context and learning approach
- User preferences and conventions
These rules will help with codebase navigation, understanding the educational approach,
and maintaining consistency across the TinyTorch framework.
🎯 Student Version (Visible):
- All functions raise NotImplementedError('Student implementation required')
- Clear TODO instructions with specific guidance
- Test cells handle NotImplementedError gracefully
- Students must implement everything from scratch
🔧 Instructor Version (Hidden with #|hide):
- Complete working implementations
- Same function signatures as student version
- Production-ready code for package export
📚 Added MODULE_GENERATION_GUIDE.md:
- Clear instructions for creating student/instructor modules
- NBDev workflow documentation
- Module structure patterns and templates
- Generation commands and checklist
🔄 Generation Flow:
1. Write .py with student stubs + hidden instructor solutions
2. Convert to notebook: jupytext --to notebook module_dev.py
3. Export package: python bin/tito.py sync --module name
4. Package gets instructor solutions, students see exercises
Perfect foundation for all TinyTorch modules
- Removed modules/setup/setup_nbdev_educational.* files
- Keep only the clean setup_dev.* files
- Setup module now focused and clean
- NBDev features work behind scenes without clutter
- Focused on original purpose: just setting up development environment
- Students don't see NBDev educational features explanations
- Simple workflow: hello world, basic class, export, test, progress
- NBDev #|hide directive works behind scenes for instructor solutions
- Clean and simple, just like the original but with hidden solutions
Back to basics: setup is about setup, not teaching NBDev features
SINGLE SOURCE approach: One notebook serves both instructors and students
🎯 Key Features:
- #|hide directive hides complete solutions from students
- #|code-fold creates collapsible sections for details
- #|export with educational metadata
- Progressive learning: simple → ML-relevant complexity
- Vector operations (add, dot product) as ML foundations
- ML-aware SystemInfo class with library checking
- Comprehensive testing for both student/instructor versions
🔄 Workflow:
- Students see exercises with TODOs and hints
- Instructors see complete solutions (hidden by default)
- Package exports get instructor (complete) implementations
- Single notebook maintains both audiences
This establishes the pattern for all TinyTorch modules
- Create tensor_nbdev_educational.py with NBDev directives
- Demonstrate #|hide, #|code-fold, #|filter_stream features
- Convert using standard Jupytext (# %% markers)
- Add comprehensive guides and comparisons
- Show superior approach vs custom generator
Key insight: NBDev already has mature educational capabilities!
No need to build custom tools when industry standards exist.
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 a README with project overview, setup instructions,
and course structure.
Adds a VISION document outlining the project's goals, conventions,
and architecture.
Includes updates to the setup module's README to clarify module to
package mapping.
✅ Setup Module Implementation:
- Created comprehensive setup_dev.ipynb with TinyTorch workflow tutorial
- Added hello_tinytorch(), add_numbers(), and SystemInfo class
- Updated README with clear learning objectives and development workflow
- All 11 tests passing for complete workflow validation
🔧 CLI Enhancements:
- Added --module flag to 'tito sync' for module-specific exports
- Implemented 'tito reset' command with --force option
- Smart auto-generated file detection and cleanup
- Interactive confirmation with safety preservations
📚 Documentation Updates:
- Updated all references to use [module]_dev.ipynb naming convention
- Enhanced test coverage for new functionality
- Clear error handling and user guidance
This establishes the foundation workflow that students will use throughout TinyTorch development.
Extends the sync command to allow users to synchronize
specific modules instead of the entire project. This
improves efficiency by reducing the scope of the nbdev
export process. Adds argument parsing for module selection.
Adds a comprehensive VISION.md document outlining the philosophy, learning approach, module design principles, and target learning outcomes for TinyTorch.
This document serves as a canonical guide for module development, emphasizing a systems-first approach to understanding ML frameworks, and provides a framework for thinking about design trade-offs in accuracy, runtime, and memory footprint.
Introduces the VISION.md document, which outlines the core philosophy, learning approach, module design principles, target learning outcomes, module progression logic, and success metrics for TinyTorch.
This document serves as the "ground truth" for course design decisions, emphasizing a systems-first approach to understanding machine learning frameworks. It details the "Loops to Systems" learning approach, focusing on the systems engineering challenges that arise when implementing ML concepts at scale.
Updates references to the development notebook naming convention from `[module].ipynb` to `[module]_dev.ipynb` in documentation. This change ensures consistency across the project and aligns with the intended naming scheme for development notebooks.
Introduces a standardized module structure with README, notebooks, tutorials, tests, and solutions.
Refactors the project to emphasize a modular learning path, enhancing clarity and consistency across the TinyTorch course.
Changes the virtual environment path to ".venv".
Updates the project to use `.venv` as the standard virtual environment directory. This change:
- Updates `.gitignore` to ignore `.venv/`.
- Modifies the activation script to create and activate `.venv`.
- Adjusts the `tito.py` script to check for `.venv`'s existence and activation.
- Updates documentation and setup scripts to reflect the new virtual environment naming convention.
This change streamlines environment management and aligns with common Python practices.
Adds a comprehensive .gitignore file to exclude common build artifacts, logs, temporary files, and other project-specific files that should not be tracked by Git.
This ensures a cleaner repository and prevents accidental inclusion of sensitive or unnecessary files.
Introduces a command-line interface (CLI) named 'tito'
to streamline project setup, testing, and information retrieval.
Includes a setup script to automate virtual environment
creation and dependency installation.
Improves the user experience by providing clear instructions
and status indicators within the CLI.
Introduces the TinyTorch project, a pedagogical tool for building ML systems from scratch.
Provides an overview of the project's goals, system architecture, curriculum integration, and getting started guide.
Sets up the foundational project structure for the TinyTorch ML system, including the CLI entry point, project directories, and setup scripts.
This commit introduces the `tito` CLI for project management, testing, and information display.
It also includes setup scripts to automate environment creation and verification, along with initial documentation.
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.
