- Remove redundant fields from module.yaml files: exports_to, files, components
- Keep only essential system metadata: name, title, description, dependencies
- Export command now reads actual export targets from dev files (#| default_exp directive)
- Status command updated to use dev files as source of truth for export targets
- Export command shows detailed source → target mapping for better clarity
- Dependencies field retained as it's useful for CLI module ordering and prerequisites
- Eliminates duplication between YAML and dev files - dev files are the real truth
- Rename SyncCommand to ExportCommand and sync.py to export.py
- Update all CLI references from 'tito package sync' to 'tito package export'
- Update help text and internal messages to use 'Export' terminology
- Update imports across all command files
- Update help text in main CLI, reset, clean, info, and notebooks commands
- Command now clearly communicates that it exports notebook code to Python package
- Maintains same functionality but with clearer naming for user experience
- Add Module Info sections with difficulty ratings to all README.md files
- Use consistent 4-star difficulty scale: ⭐ Beginner, ⭐⭐ Intermediate, ⭐⭐⭐ Advanced, ⭐⭐⭐⭐ Expert
- Include time estimates, prerequisites, and next steps for each module
- Maintain clear separation: README.md = student experience, module.yaml = system metadata
- Difficulty progression: Setup (⭐) → Tensor/Activations/Layers (⭐⭐) → Networks/CNN/DataLoader (⭐⭐⭐) → Transformer (⭐⭐⭐⭐)
- Help students plan their learning journey and set appropriate expectations
- Add new CleanCommand for cleaning up module directories
- Supports cleaning notebooks (*.ipynb) and cache files (__pycache__, *.pyc)
- Can clean specific modules or all modules
- Provides preview of files to be cleaned with confirmation
- Includes --force flag to skip confirmation
- Integrates with module command group as 'tito module clean'
- Preserves Python source files (*_dev.py) and other important files
- Fixes issue with duplicate file removal from __pycache__ directories
- Remove outdated documentation files (cli-reorganization, command-cleanup-summary, module-metadata-system, testing-separation)
- Update all CLI commands to use current hierarchical structure (tito system/module/package)
- Align documentation with simplified metadata system
- Update student project guide with current module structure
- Modernize development guides and quick reference
- Remove references to removed features (py_to_notebook, complex metadata)
- Ensure all documentation reflects current system state
Documentation now focuses on:
- Current CLI structure and commands
- Simplified module development workflow
- Real data and production patterns
- Clean educational progression
- Delete bin/py_to_notebook.py and tito/tools/py_to_notebook.py
- Update notebooks command to use Jupytext directly
- Jupytext is already configured in all *_dev.py files
- Simpler, more standard workflow using established tools
- Better integration with NBDev ecosystem
Benefits:
- Eliminates duplicate conversion tools
- Uses industry-standard Jupytext instead of custom tool
- Reduces maintenance burden
- Better error handling and compatibility
- Skip environment validation for 'tito system doctor' command
- Fix dependency detection in doctor command for packages without __version__
- Doctor command now works correctly and shows comprehensive system diagnosis
- Remove version field from all module.yaml files
- Update template generator to exclude version field
- Further simplify metadata to focus on system information only
- Status remains dynamically determined by test results
- Reduce module.yaml files from 100+ lines to ~25 lines focused on system needs
- Remove pedagogical details (learning objectives, difficulty, time estimates)
- Keep only essential fields: name, title, description, status, dependencies, exports, files, components
- Update status command to work with simplified metadata format
- Update metadata generation script to create simplified templates
- Focus on system metadata for CLI tools and build systems, not educational content
Before: Verbose pedagogical metadata with 20+ fields
After: Concise system metadata with 8 core fields
This aligns with the principle that module.yaml should be for systems, not pedagogy.
- Change doctor command check from 'bin/tito.py' to 'bin/tito'
- The actual CLI script is 'bin/tito' (without .py extension)
- Doctor command now correctly shows CLI script as found instead of missing
- Resolves false positive error in environment diagnosis
- Remove legacy flat commands (info, test, sync, etc.) from main parser
- Keep only hierarchical command groups (system, module, package)
- Eliminate confusing positional arguments showing both flat and hierarchical commands
- Update help text to remove references to deprecated commands
- CLI now shows clean 3-command structure: system, module, package
- Old flat commands like 'tito info' now properly error with helpful message
- Maintains all functionality through hierarchical structure:
- tito info → tito system info
- tito status → tito module status
- tito sync → tito package sync
Result: Clean, focused CLI with clear command organization
- Add brief descriptions to YAML frontmatter for all rule files
- Descriptions explain the purpose and content of each rule
- Follow consistent format matching ml-systems-course-context.mdc
- Improve rule discoverability and understanding
Files updated:
- user-preferences.mdc: User preferences and development conventions
- tinytorch-project-structure.mdc: Dual-structure architecture guide
- testing-patterns.mdc: Testing standards with pytest and real data
- nbdev-educational-pattern.mdc: Educational NBDev patterns
- module-development-best-practices.mdc: Real Data, Real Systems principles
- git-workflow.mdc: Git workflow guidelines for incremental commits
- development-workflow.mdc: Complete development workflow with tito CLI
- cli-patterns.mdc: CLI development patterns for tito tool
- Changed all module titles to be short and clean (e.g., 'Autograd' not 'Autograd - Automatic Differentiation')
- Updated metadata generation template to use concise titles by default
- Fixed CLI reference in metadata generator to use new hierarchical structure
- Titles are now consistent: just the module name capitalized
- Detailed descriptions remain in the description field where they belong
- Add system, module, and package command groups for clear subsystem separation
- Create SystemCommand, ModuleCommand, and PackageCommand classes
- Maintain backward compatibility with existing flat commands
- Enhanced help system with contextual guidance at each level
- Updated main CLI to show organized command groups
- Added comprehensive documentation for CLI reorganization
New structure:
- tito system (info, doctor, jupyter)
- tito module (status, test, notebooks)
- tito package (sync, reset, nbdev)
Benefits:
- Clear subsystem separation
- Intuitive command discovery
- Better extensibility for future commands
- Reduced cognitive load for users
- Add module.yaml files for setup, tensor, activations, layers, and autograd modules
- Enhanced tito status command with --metadata flag for rich information display
- Created metadata schema with learning objectives, dependencies, components, and more
- Added metadata generation script (bin/generate_module_metadata.py)
- Comprehensive documentation in docs/development/module-metadata-system.md
- Status command now shows module status, difficulty, time estimates, and detailed metadata
- Supports dependency tracking, component-level status, and educational information
- Enables rich CLI experience with structured module information
- Rename 'modules' command to 'status' for intuitive module status checking
- Consolidate all testing functionality into 'test' command:
- 'tito test --module X' for individual module testing with detailed output
- 'tito test --all' for all modules with progress bar
- Remove confusing redirection from test to modules
- Simplify 'info' command to focus on system information and course navigation:
- Remove module implementation status table (moved to status command)
- Add quick command reference panel
- Clean separation between system info and module status
- Update all imports and registrations for renamed command
Result: Clean, intuitive CLI with no duplication:
- 'tito status' → Module development status
- 'tito test' → All testing functionality
- 'tito info' → System info and navigation
No more confusing overlaps or redirections between commands.
- Fix Networks and MLP status checks to use actual available components
- Networks: test Sequential composition with layers
- MLP: test multi-layer perceptron using Sequential + Dense + ReLU
- CNN: simplified test for convolution concepts
- Focus on functional capabilities rather than specific package organization
This addresses the fundamental issue that status checks were trying to match
pedagogical module organization with production package structure.
- Fix CLI test to use Tensor objects instead of raw integers
- DataLoader now correctly shows as ✅ Implemented in status
- Test creates proper Tensor data for DataLoader compatibility
- Update module → package mapping in pedagogy/vision.md
- Update project guide module references
- Update cursor rules for testing patterns
- Update all documentation paths and references
Ensures all documentation is consistent with the new module name.
- Update valid modules list in test command: data → dataloader
- Update module display name in info command: Data → DataLoader
- Update CLI references to use new module name
- All CLI commands now recognize 'dataloader' instead of 'data'
Ensures CLI tools work seamlessly with the renamed module.
- 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.
- Added pytest-timeout configuration with 5-minute timeout for all tests
- Added timeout handling to test command with proper error messages
- Created small local test dataset (50 train + 20 test samples) that mimics CIFAR-10 structure
- Updated data module tests to use local test data instead of downloading CIFAR-10
- Tests now run much faster (~0.1s vs ~30s) and don't require internet connection
- Added TestCIFAR10Dataset class that loads from local pickle files
- All test functionality preserved but using local data for speed and reliability
- Fixed plt.show() call in data module to respect _should_show_plots() check
- Protected test code that calls visualization functions in data module
- Protected test code that calls visualization functions in networks module
- All visualization functions now properly skip during testing
- Tests should no longer block waiting for user interaction
Introduces documentation for TinyTorch module development, including guides for developers and AI assistants.
Provides comprehensive resources for creating high-quality, educational modules, focusing on real-world applications and systems thinking.
- Updated pedagogical principles with refined engagement patterns:
- Build → Use → Reflect (design & systems thinking)
- Build → Use → Analyze (technical depth & debugging)
- Build → Use → Optimize (systems iteration & performance)
- Added pattern selection guide for module developers
- Updated development workflow to choose pattern first
- Created specific module assignments for each pattern
- Enhanced quick reference with pattern-specific activities
This evolution moves beyond passive 'understanding' to active,
specific engagement that matches professional ML engineering skills.
Initializes the networks module, enabling the composition of layers into complete neural network architectures.
It introduces sequential networks, MLP creation, and network visualization tools to facilitate architecture understanding and analysis.
Adds practical classification and regression network implementations and network behavior analysis capabilities.
Adds ReLU, Sigmoid, and Tanh activation functions, enabling
non-linearity in neural networks.
Includes testing and visualization of each function to ensure
correct behavior and understanding of their properties.
Introduces a comprehensive module for 2D Convolutional Neural Networks.
This module provides a foundational understanding of CNNs through:
- Implementation of a naive Conv2D layer with sliding window convolution
- Visualization of kernel operations and feature map construction
- Composition of Conv2D layers with other layers to build a simple ConvNet
This structure provides a step-by-step guide to building and understanding CNNs, with clear examples and tests.
This commit introduces the core building blocks for neural networks,
including a naive matrix multiplication implementation and a Dense layer.
It provides a foundation for constructing and experimenting with
neural networks, emphasizing the concept of layers as tensor
transformations and function composition.
The module includes thorough testing and performance comparisons
to demonstrate the importance of optimized operations.
Implements the core Tensor class with data handling and properties, including initialization, shape, size, dtype, and string representation.
Adds element-wise addition and multiplication functions for tensors.
Implements tensor addition and multiplication as methods within the Tensor class.
Defines the structure and guidelines for creating educational modules in TinyTorch using NBDev.
This includes file format, key NBDev directives, educational structure, implementation guidance, testing, naming conventions, and educational principles to promote effective learning.
Enhances the notebook by replacing some unicode characters with more standard and universally compatible symbols, improving the overall readability and user experience.
- Add template section to tensor, layers, activations, and cnn modules
- Create docs/development/module-template.md for future reference
- Clarify learning vs building structure consistently
- Show students where their code will live in the final package
- Decouple learning modules from production organization
- Add cnn_dev.py with NBDev educational pattern, Conv2D for-loop TODO, and all scaffolding
- Add README.md explaining learning goals, what is implemented vs provided, and rationale
- Add tests/test_cnn.py for basic correctness and shape tests
- Generate cnn_dev.ipynb for notebook workflow
- 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.
- Add modules/networks/networks_dev.py and networks_dev.ipynb (Jupytext/nbdev educational pattern)
- Add comprehensive visualizations: architecture, data flow, layer analysis, network comparison
- Add modules/networks/README.md with learning goals, usage, and visualization docs
- Add modules/networks/tests/test_networks.py with thorough tests for composition, MLPs, and visualizations
- Register 'networks' in CLI info and test commands
- Update CLI info command to check layers/networks status
- This module focuses on forward pass only (no training yet)
- Remove 14 empty/unused directories from tinytorch/ package
- Keep only essential directories: core/, datasets/, configs/
- All directories removed contained only empty __init__.py files or were completely empty
- CLI functionality preserved and tested working
- Cleaner package structure for development
- Delete _proc/ directory (duplicate of modules/ with processed files)
- Delete bin/tito.py (old CLI implementation, replaced by tito/ structure)
- Delete temporary log files (tito-cli.log, tinytorch-cli.log)
- Update bin/tito wrapper to use new CLI structure
- All CLI functionality preserved with new architecture
- Updated git-workflow.mdc with proper metadata and focused content
- Updated development-workflow.mdc with proper metadata
- Added cli-patterns.mdc for CLI development best practices
- Followed Cursor's best practices: focused, actionable, scoped
- Added proper globs for auto-attachment to relevant files
- Improved rule descriptions for better AI context
Key improvements:
- Proper metadata structure with description, globs, alwaysApply
- Focused content under 500 lines per rule
- Concrete examples and patterns
- Clear, actionable guidance for AI
- Better scoping for when rules should apply
- Created comprehensive Git workflow guidelines for ad-hoc development
- Focus on small, focused commits for easy reverts
- Added practical examples for different development scenarios
- Updated development workflow to reference new Git guidelines
- Updated CLI commands to use 'tito' instead of 'python bin/tito.py'
- Includes commit message format, branch strategies, and revert procedures
Key principles:
- Incremental commits for easy reverts
- Test before committing to avoid broken commits
- Use feature branches for larger changes
- Descriptive commit messages that explain what changed
- 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.
- Moved tools/py_to_notebook.py to bin/py_to_notebook.py
- Updated tito.py to reference the new location
- Made py_to_notebook.py executable for direct invocation
- Removed empty tools/ directory
- Updated documentation to reflect new location
- All tools now consolidated in bin/ directory for consistency
Benefits:
- Conventional organization (bin/ for executables)
- Can invoke tools directly: ./bin/py_to_notebook.py
- Cleaner project structure
- Consistent with other tools (tito.py, generate_student_notebooks.py)
- Restored tools/py_to_notebook.py as a focused, standalone tool
- Updated tito notebooks command to use subprocess to call the separate tool
- Maintains clean separation of concerns: tito.py for CLI orchestration, py_to_notebook.py for conversion logic
- Updated documentation to use 'tito notebooks' command instead of direct tool calls
- Benefits: easier debugging, better maintainability, focused single-responsibility modules
