Create comprehensive guidelines for git commits, code quality, testing, and development workflow that apply to Cursor, Claude, and any other AI assistants
- Document two-tier testing approach (inline vs integration)
- Explain purpose and scope of each test type
- Provide test coverage matrix for all 20 modules
- Include testing workflow for students and instructors
- Add best practices and common patterns
- Show current status: 11/15 inline tests passing, all 20 modules have test infrastructure
- Add tests/16_quantization with run_all_tests.py and integration test
- Add tests/17_compression with run_all_tests.py and integration test
- Add tests/18_acceleration with run_all_tests.py and integration test
- Add tests/19_benchmarking with run_all_tests.py and integration test
- Add tests/20_capstone with run_all_tests.py and integration test
- All test files marked as pending implementation with TODO markers
- Completes test directory structure for all 20 modules
- Rename tests/14_kvcaching to tests/14_profiling
- Rename tests/15_profiling to tests/15_memoization
- Aligns test structure with optimization tier reorganization
- Import Module base class from core.layers
- Fix embeddings import path (text.embeddings not core.embeddings)
- Fix attention import (MultiHeadAttention not SelfAttention)
- Fix transformer import path (models.transformer not core.transformers)
- Handle missing functional module gracefully with try/except
- Update __all__ exports to match available components
- Delete kvcaching_dev.py (superseded by memoization_dev.py)
- Delete kvcaching_dev.ipynb (superseded by memoization_dev.ipynb)
- memoization_dev files are the current versions with complete content
- Move logo-tinytorch-grey.png to _static/logos/
- Move logo-tinytorch-simple.png to _static/logos/
- Move logo-tinytorch-white.png to _static/logos/
- Move tensortorch.png to _static/logos/
- Update _config.yml to reference new logo path
- Keeps all logo versions organized in standard static assets location
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
- tinytorch/benchmarking/: Benchmark class for Module 19
- tinytorch/competition/: Submission utilities for Module 20
- tinytorch/data/: Data loading utilities
- tinytorch/utils/data/: Additional data helpers
Exported from modules 19-20 and module 08
- Update module path from 15_profiling to 14_profiling
- Add quick_profile helper for quick bottleneck discovery
- Add analyze_weight_distribution for pruning analysis
- Export new helper functions in __all__
Comprehensive summary of all changes made:
- Module reorganization complete
- Chapter updates complete
- All commits made in logical pieces
- Ready for testing and review
Cleanup of renamed files:
- Deleted old module source files (14_kvcaching, 15_profiling, 16_acceleration, etc.)
- Deleted old chapter markdown files
- These have been replaced by reorganized versions in previous commits
- Shows O(n²) latency growth in transformer generation
- Demonstrates problem before teaching solution
- Prepares module for reorganization to Module 15
- Add quick_profile() for simplified profiling interface
- Add analyze_weight_distribution() for compression module
- Both functions will be used by modules 15-18
Changed tier name from 'Intelligence' to 'Architecture' for clarity:
- Updated TOC: 🏛️ Architecture Tier (08-14)
- Updated all tier badges in modules 08-14
- Changed emoji from 🧠 to 🏛️ (building/columns)
Rationale:
- 'Intelligence' was vague and didn't describe content
- 'Architecture' accurately describes what students learn
- Professional terminology used in ML industry
- Clearer pedagogical narrative: different neural architectures
(data infrastructure, CNNs for vision, Transformers for language)
Content remains unchanged, only naming improved.
Vijay Janapa Reddi