- Delete outdated site/ directory
- Rename docs/ → site/ to match original architecture intent
- Update all GitHub workflows to reference site/:
- publish-live.yml: Update paths and build directory
- publish-dev.yml: Update paths and build directory
- build-pdf.yml: Update paths and artifact locations
- Update README.md:
- Consolidate site/ documentation (website + PDF)
- Update all docs/ links to site/
- Test successful: Local build works with all 40 pages
The site/ directory now clearly represents the course website
and documentation, making the repository structure more intuitive.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Shows 🚧 Under Construction badge next to brand
- Bar hides on scroll down, reappears on scroll up
- Always visible at top of page
- Badge hidden on mobile to save space
This commit implements a comprehensive quality assurance system and removes
outdated backup files from the repository.
## Release Check Workflow
Added GitHub Actions workflow for systematic release validation:
- Manual-only workflow (workflow_dispatch) - no automatic PR triggers
- 6 sequential quality gates: educational, implementation, testing, package, documentation, systems
- 13 validation scripts (4 fully implemented, 9 stubs for future work)
- Comprehensive documentation in .github/workflows/README.md
- Release process guide in .github/RELEASE_PROCESS.md
Implemented validators:
- validate_time_estimates.py - Ensures consistency between LEARNING_PATH.md and ABOUT.md files
- validate_difficulty_ratings.py - Validates star rating consistency across modules
- validate_testing_patterns.py - Checks for test_unit_* and test_module() patterns
- check_checkpoints.py - Recommends checkpoint markers for long modules (8+ hours)
## Pedagogical Improvements
Added checkpoint markers to Module 05 (Autograd):
- Checkpoint 1: After computational graph construction (~40% progress)
- Checkpoint 2: After automatic differentiation implementation (~80% progress)
- Helps students track progress through the longest foundational module (8-10 hours)
## Codebase Cleanup
Removed 20 legacy *_dev.py files across all modules:
- Confirmed via export system analysis: only *.py files (without _dev suffix) are used
- Export system explicitly reads from {name}.py (see tito/commands/export.py line 461)
- All _dev.py files were outdated backups not used by the build/export pipeline
- Verified all active .py files contain current implementations with optimizations
This cleanup:
- Eliminates confusion about which files are source of truth
- Reduces repository size
- Makes development workflow clearer (work in modules/XX_name/name.py)
## Formatting Standards Documentation
Documents formatting and style standards discovered through systematic
review of all 20 TinyTorch modules.
### Key Findings
Overall Status: 9/10 (Excellent consistency)
- All 20 modules use correct test_module() naming
- 18/20 modules have proper if __name__ guards
- All modules use proper Jupytext format (no JSON leakage)
- Strong ASCII diagram quality
- All 20 modules missing 🧪 emoji in test_module() docstrings
### Standards Documented
1. Test Function Naming: test_unit_* for units, test_module() for integration
2. if __name__ Guards: Immediate guards after every test/analysis function
3. Emoji Protocol: 🔬 for unit tests, 🧪 for module tests, 📊 for analysis
4. Markdown Formatting: Jupytext format with proper section hierarchy
5. ASCII Diagrams: Box-drawing characters, labeled dimensions, data flow arrows
6. Module Structure: Standard template with 9 sections
### Quick Fixes Identified
- Add 🧪 emoji to test_module() in all 20 modules (~5 min)
- Fix Module 16 if __name__ guards (~15 min)
- Fix Module 08 guard (~5 min)
Total quick fixes: 25 minutes to achieve 10/10 consistency
- 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
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/
Add build scripts and GitHub Actions workflow to support PDF generation:
- build_pdf.sh: LaTeX-based PDF build for professional quality
- build_pdf_simple.sh: HTML-to-PDF build without LaTeX requirement
- Makefile: convenient shortcuts for common build tasks
- GitHub Actions workflow: automated PDF builds on demand
Supports multiple output formats:
- HTML website (default, via jupyter-book)
- PDF via HTML-to-PDF (pyppeteer, no LaTeX needed)
- PDF via LaTeX (professional typography, requires LaTeX)
Usage:
make html - Build HTML website
make pdf-simple - Build PDF without LaTeX
make pdf - Build PDF with LaTeX
- This workflow was testing notebook conversion features
- Not required for website deployment
- Website deploys via deploy-book.yml on main branch
- Can re-enable later if needed for CI testing
- Change 'tito module notebooks' to 'tito notebooks'
- The notebooks command is a top-level command, not a module subcommand
- Fixes workflow test failures
- Create .claude directory with team structure and guidelines
- Add MODULE_DEVELOPMENT_GUIDELINES.md for educational patterns
- Add EDUCATIONAL_PATTERN_TEMPLATE.md for consistent module structure
- Add GIT_WORKFLOW_STANDARDS.md for branch management
- Create setup-dev.sh for automated environment setup
- Add notebook workflow documentation
- Add CI/CD workflow for notebook testing
This commit establishes consistent development standards and documentation
for the TinyTorch educational ML framework development.
- Combine venv setup and tito execution in same step
- Add pytest installation for tito environment validation
- Add explanatory comments about GitHub Actions shell behavior
- Remove environment skipping hack in favor of proper setup
- Workflow now uses tito CLI consistently for book generation
✅ Remove unnecessary nesting: book/tinytorch-course/ → book/
✅ Update all path references in scripts and workflows
✅ Cleaner development experience with shorter paths
✅ Book builds successfully with simplified structure
Changes:
- Move all book files up one directory level
- Update convert_modules.py paths
- Update GitHub Actions workflow paths
- Update book configuration paths
- Test confirms everything works correctly
✅ Add learning goals extraction with beautiful admonition blocks
✅ Create hybrid book approach (overview pages + interactive buttons)
✅ Generate both notebooks (Binder/Colab) and book pages
✅ Update GitHub Actions for main branch deployment
✅ All 14 modules now have consistent formatting
Features:
- Extract content from source files (no duplication)
- Interactive launch buttons (GitHub/Binder/Colab)
- Learning objectives as 🎯 tip admonitions
- Notebooks generated in-place for direct access
- Clean separation: dev → main → website deployment
- Changed trigger from main to dev branch (repository default)
- Updated deploy condition to check for dev branch
- Ensures workflow runs on correct branch for this repository
- Add module conversion step before book building
- Install jupytext dependency for conversion script
- Maintains existing GitHub Pages deployment pipeline
- Automatically converts modules/source/ to notebooks on deploy
- Created interactive Jupyter Book structure in book/tinytorch-course/
- Configured for GitHub Pages, Binder, and Colab integration
- Set up automated deployment with GitHub Actions
- Designed comprehensive course structure with 4 main parts:
* Foundation (Setup, Tensors, Activations)
* Building Blocks (Layers, Networks, CNNs)
* Training Systems (DataLoader, Autograd, Optimizers, Training)
* Production & Performance (Compression, Kernels, Benchmarking, MLOps)
- Added engaging introduction with interactive features
- Ready for module conversion from *_dev.py to executable notebooks
Next: Convert modules/source/*_dev.py files to interactive chapters
