✅ 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
MAJOR FEATURE: Multi-channel convolutions for real CNN architectures
Key additions:
- MultiChannelConv2D class with in_channels/out_channels support
- Handles RGB images (3 channels) and arbitrary channel counts
- He initialization for stable training
- Optional bias parameters
- Batch processing support
Testing & Validation:
- Comprehensive unit tests for single/multi-channel
- Integration tests for complete CNN pipelines
- Memory profiling and parameter scaling analysis
- QA approved: All mandatory tests passing
CIFAR-10 CNN Example:
- Updated train_cnn.py to use MultiChannelConv2D
- Architecture: Conv(3→32) → Pool → Conv(32→64) → Pool → Dense
- Demonstrates why convolutions matter for vision
- Shows parameter reduction vs MLPs (18KB vs 12MB)
Systems Analysis:
- Parameter scaling: O(in_channels × out_channels × kernel²)
- Memory profiling shows efficient scaling
- Performance characteristics documented
- Production context with PyTorch comparisons
This enables proper CNN training on CIFAR-10 with ~60% accuracy target.
Clean, dependency-driven organization:
- Part I (1-5): MLPs for XORNet
- Part II (6-10): CNNs for CIFAR-10
- Part III (11-15): Transformers for TinyGPT
Key improvements:
- Dropped modules 16-17 (regularization/systems) to maintain scope
- Moved normalization to module 13 (Part III where it's needed)
- Created three CIFAR-10 examples: random, MLP, CNN
- Each part introduces ONE major innovation (FC → Conv → Attention)
CIFAR-10 now showcases progression:
- test_random_baseline.py: ~10% (random chance)
- train_mlp.py: ~55% (no convolutions)
- train_cnn.py: ~60%+ (WITH Conv2D - shows why convolutions matter!)
This follows actual ML history and each module is needed for its capstone.
- Part I: Foundations (Modules 1-5) - Build MLPs, solve XOR
- Part II: Computer Vision (Modules 6-11) - Build CNNs, classify CIFAR-10
- Part III: Language Models (Modules 12-17) - Build transformers, generate text
Key changes:
- Renamed 05_dense to 05_networks for clarity
- Moved 08_dataloader to 07_dataloader (swap with attention)
- Moved 07_attention to 13_attention (Part III)
- Renamed 12_compression to 16_regularization
- Created placeholder dirs for new language modules (12,14,15,17)
- Moved old modules 13-16 to temp_holding for content migration
- Updated README with three-part structure
- Added comprehensive documentation in docs/three-part-structure.md
This structure gives students three natural exit points with concrete achievements at each level.
Major changes:
- Moved TinyGPT from Module 16 to examples/tinygpt (capstone demo)
- Fixed Module 10 (optimizers) and Module 11 (training) bugs
- All 16 modules now passing tests (100% health)
- Added comprehensive testing with 'tito test --comprehensive'
- Renamed example files for clarity (train_xor_network.py, etc.)
- Created working TinyGPT example structure
- Updated documentation to reflect 15 core modules + examples
- Added KISS principle and testing framework documentation
- Create professional examples directory showcasing TinyTorch as real ML framework
- Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison
- Fix import paths in exported modules (training.py, dense.py)
- Update training module with autograd integration for loss functions
- Add progressive integration tests for all 16 modules
- Document framework capabilities and usage patterns
This commit establishes the examples gallery that demonstrates TinyTorch
works like PyTorch/TensorFlow, validating the complete framework.
This commit adds complete documentation for the 5-milestone system that transforms
TinyTorch from module-based to capability-driven learning:
📚 Documentation Suite:
- milestone-system.md: Student-facing guide with milestone descriptions
- instructor-milestone-guide.md: Complete assessment framework for instructors
- milestone-troubleshooting.md: Comprehensive debugging guide for common issues
- milestone-implementation-guide.md: Technical implementation specifications
- milestone-system-overview.md: Executive summary tying everything together
🎯 The Five Milestones:
1. Basic Inference (Module 04) - Neural networks work (85%+ MNIST)
2. Computer Vision (Module 06) - MNIST recognition (95%+ CNN accuracy)
3. Full Training (Module 11) - Complete training loops (CIFAR-10 training)
4. Advanced Vision (Module 13) - CIFAR-10 classification (75%+ accuracy)
5. Language Generation (Module 16) - GPT text generation (coherent output)
🚀 Key Features:
- Capability-based achievement system replacing traditional module completion
- Visual progress tracking with Rich CLI visualizations
- Victory conditions aligned with industry-relevant skills
- Comprehensive troubleshooting for each milestone challenge
- Instructor assessment framework with automated testing
- Technical implementation roadmap for CLI integration
💡 Educational Impact:
- Students develop portfolio-worthy capabilities rather than just completing assignments
- Clear progression from basic neural networks to production AI systems
- Motivation through achievement and concrete skill development
- Industry alignment with real ML engineering competencies
Ready for implementation phase with complete technical specifications.
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.
- Add comprehensive README section showcasing 75% accuracy goal
- Update dataloader module README with CIFAR-10 support details
- Update training module README with checkpointing features
- Create complete CIFAR-10 training guide for students
- Document all north star implementations in CLAUDE.md
Students can now train real CNNs on CIFAR-10 using 100% TinyTorch code.
- 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
✅ PYTHON-FIRST DEVELOPMENT:
- Always work in raw Python files (modules/XX/XX_dev.py)
- Generate Jupyter notebooks on demand using Jupytext
- NBGrader compliance through automated cell metadata
- nbdev for package building and exports
🔧 WORKFLOW IMPROVEMENTS:
- Fixed file priority: use XX_dev.py over XX_dev_enhanced.py
- Clean up enhanced files to use standard files as source of truth
- Updated documentation to highlight Python-first approach
📚 COMPLETE INSTRUCTOR WORKFLOW:
1. Edit modules/XX/XX_dev.py (Python source of truth)
2. Export to package: tito module export XX (nbdev)
3. Generate assignment: tito nbgrader generate XX (Python→Jupyter→NBGrader)
4. Release to students: tito nbgrader release XX
5. Auto-grade with pytest: tito nbgrader autograde XX
✅ VERIFIED WORKING:
- Python file editing ✅
- nbdev export to tinytorch package ✅
- Jupytext conversion to notebooks ✅
- NBGrader assignment generation ✅
- pytest integration for auto-grading ✅🎯 TOOLS INTEGRATION:
- Raw Python development (version control friendly)
- Jupytext (Python ↔ Jupyter conversion)
- nbdev (package building and exports)
- NBGrader (student assignments and auto-grading)
- pytest (testing within notebooks)
Perfect implementation of user's ideal workflow
- Move development artifacts to development/archived/ directory
- Remove NBGrader artifacts (assignments/, testing/, gradebook.db, logs)
- Update root README.md to match actual repository structure
- Provide clear navigation paths for instructors and students
- Remove outdated documentation references
- Clean root directory while preserving essential files
- Maintain all functionality while improving organization
Repository is now optimally structured for classroom use with clear entry points:
- Instructors: docs/INSTRUCTOR_GUIDE.md
- Students: docs/STUDENT_GUIDE.md
- Developers: docs/development/
✅ All functionality verified working after restructuring
- Create comprehensive INSTRUCTOR_GUIDE.md with verified modules, teaching sequence, and practical commands
- Create new STUDENT_GUIDE.md based on actual working state with correct module numbering
- Update main docs/README.md to reflect current capabilities and clean structure
- Remove outdated docs/students/project-guide.md that had incorrect information
- Focus on 6+ weeks of proven curriculum content currently ready for classroom use
- Base all documentation on verified test results and working module status
- 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
- 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.
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.
