The itemize environment parameters [leftmargin=*, itemsep=1pt, parsep=0pt]
were appearing as visible text in the PDF because the enumitem package
wasn't loaded. This fix adds \usepackage{enumitem} to the preamble.
All itemized lists now format correctly with proper spacing and margins.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Improve site navigation and content structure
- Update development testing documentation
- Enhance site styling and visual consistency
- Update release notes and milestone templates
- Improve site rebuild script functionality
- Add TESTING_QUICK_REFERENCE.md for quick access to common testing commands
- Add comprehensive-module-testing-plan.md with module-by-module test requirements
- Add gradient-flow-testing-strategy.md for gradient flow test coverage analysis
- Add testing-architecture.md explaining two-tier testing approach
- Update TEST_STRATEGY.md to reference master testing plan
These documents define clear boundaries between unit tests (modules/),
integration tests (tests/), and milestones, with comprehensive coverage
analysis and implementation roadmap.
Documentation updates across the codebase:
Root documentation:
- README.md: Updated references from book/ to site/
- CONTRIBUTING.md: Updated build and workflow instructions
- .shared-ai-rules.md: Updated AI assistant rules for new structure
GitHub configuration:
- Issue templates updated for new module locations
- Workflow references updated from book/ to site/
docs/ updates:
- STUDENT_QUICKSTART.md: New paths and structure
- module-rules.md: Updated module development guidelines
- NBGrader documentation: Updated for module restructuring
- Archive documentation: Updated references
Module documentation:
- modules/17_memoization/README.md: Updated after reordering
All documentation now correctly references:
- site/ instead of book/
- modules/XX_name/ instead of modules/source/
✅ Phase 1-2 Complete: Modules 1-10 aligned with tutorial master plan
✅ CNN Training Pipeline: Autograd → Spatial → Optimizers → DataLoader → Training
✅ Technical Validation: All modules import and function correctly
✅ CIFAR-10 Ready: Multi-channel Conv2D, BatchNorm, MaxPool2D, complete pipeline
Key Achievements:
- Fixed module sequence alignment (spatial now Module 7, not 6)
- Updated tutorial master plan for logical pedagogical flow
- Phase 2 milestone achieved: Students can train CNNs on CIFAR-10
- Complete systems engineering focus throughout all modules
- Production-ready CNN pipeline with memory profiling
Next Phase: Language models (Modules 11-15) for TinyGPT milestone
Implements comprehensive demo system showing AI capabilities unlocked by each module export:
- 8 progressive demos from tensor math to language generation
- Complete tito demo CLI integration with capability matrix
- Real AI demonstrations including XOR solving, computer vision, attention mechanisms
- Educational explanations connecting implementations to production ML systems
Repository reorganization:
- demos/ directory with all demo files and comprehensive README
- docs/ organized by category (development, nbgrader, user guides)
- scripts/ for utility and testing scripts
- Clean root directory with only essential files
Students can now run 'tito demo' after each module export to see their framework's
growing intelligence through hands-on demonstrations.
- 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
- Replaced 3 overlapping documentation files with 1 authoritative source
- Set modules/source/08_optimizers/optimizers_dev.py as reference implementation
- Created comprehensive module-rules.md with complete patterns and examples
- Added living-example approach: use actual working code as template
- Removed redundant files: module-structure-design.md, module-quick-reference.md, testing-design.md
- Updated cursor rules to point to consolidated documentation
- All module development now follows single source of truth
- Remove all tests/ directories under modules/source/
- Keep main tests/ directory for testing exported functionality
- Update status command to check tests in main tests/ directory
- Update documentation to reflect new test structure
- Reduce maintenance burden by eliminating duplicate test systems
- Focus on inline NBGrader tests for development, main tests for package validation
- Prioritize student learning effectiveness over context switching
- Define three-tier architecture: Inline → Module → Integration
- Emphasize comprehensive inline testing with educational context
- Maintain professional module tests for grading
- Preserve flow state by keeping students in notebooks
- Provide immediate, encouraging feedback with visual indicators
- Define clear goals for each testing tier: Unit → Module → Integration → System
- Implement mock-based module testing to avoid dependency cascades
- Provide comprehensive examples for each testing level
- Establish clear interface contracts through visible mocks
- Enable independent module development and grading
- Ensure realistic integration testing with vetted solutions
- 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
- Remove 5 outdated development guides that contradicted clean NBGrader/nbdev architecture
- Update all documentation to reflect assignments/ directory structure
- Remove references to deprecated #| hide approach and old command patterns
- Ensure clean separation: NBGrader for assignments, nbdev for package export
- Update README, Student Guide, and Instructor Guide with current workflows
- 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
- 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
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.
- 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
- 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
- Move all documentation to docs/ directory with clear organization
- Use lowercase-with-dashes naming convention (modern standard)
- Organize by audience: students/, development/, pedagogy/
- Create comprehensive docs/README.md index
- Clean up root directory (only README.md and quickstart.md remain)
Structure:
docs/
├── pedagogy/ # Educational philosophy
├── development/ # Module development guides
├── students/ # Student-facing documentation
└── README.md # Documentation index
This makes the project more professional and easier to navigate.
