Commit Graph

6 Commits

Author SHA1 Message Date
Vijay Janapa Reddi
e82bc8ba97 Complete comprehensive system validation and cleanup
🎯 Major Accomplishments:
•  All 15 module dev files validated and unit tests passing
•  Comprehensive integration tests (11/11 pass)
•  All 3 examples working with PyTorch-like API (XOR, MNIST, CIFAR-10)
•  Training capability verified (4/4 tests pass, XOR shows 35.8% improvement)
•  Clean directory structure (modules/source/ → modules/)

🧹 Repository Cleanup:
• Removed experimental/debug files and old logos
• Deleted redundant documentation (API_SIMPLIFICATION_COMPLETE.md, etc.)
• Removed empty module directories and backup files
• Streamlined examples (kept modern API versions only)
• Cleaned up old TinyGPT implementation (moved to examples concept)

📊 Validation Results:
• Module unit tests: 15/15 
• Integration tests: 11/11 
• Example validation: 3/3 
• Training validation: 4/4 

🔧 Key Fixes:
• Fixed activations module requires_grad test
• Fixed networks module layer name test (Dense → Linear)
• Fixed spatial module Conv2D weights attribute issues
• Updated all documentation to reflect new structure

📁 Structure Improvements:
• Simplified modules/source/ → modules/ (removed unnecessary nesting)
• Added comprehensive validation test suites
• Created VALIDATION_COMPLETE.md and WORKING_MODULES.md documentation
• Updated book structure to reflect ML evolution story

🚀 System Status: READY FOR PRODUCTION
All components validated, examples working, training capability verified.
Test-first approach successfully implemented and proven.
2025-09-23 10:00:33 -04:00
Vijay Janapa Reddi
791d1e3153 Update generated notebooks and package exports
- Regenerate all .ipynb files from fixed .py modules
- Update tinytorch package exports with corrected implementations
- Sync package module index with current 16-module structure

These generated files reflect all the module fixes and ensure consistent
.py ↔ .ipynb conversion with the updated module implementations.
2025-09-18 16:42:57 -04:00
Vijay Janapa Reddi
f9309e8b9d 🔧 Complete module restructuring and integration fixes
📦 Module File Organization:
- Renamed networks_dev.py → dense_dev.py in 05_dense module
- Renamed cnn_dev.py → spatial_dev.py in 06_spatial module
- Added new 07_attention module with attention_dev.py
- Updated module.yaml files to reference correct filenames
- Updated #| default_exp directives for proper package exports

🔄 Core Package Updates:
- Added tinytorch.core.dense (Sequential, MLP architectures)
- Added tinytorch.core.spatial (Conv2D, pooling operations)
- Added tinytorch.core.attention (self-attention mechanisms)
- Updated all core modules with latest implementations
- Fixed tensor assignment issues in compression module

🧪 Test Integration Fixes:
- Updated integration tests to use correct module imports
- Fixed tensor activation tests for new module structure
- Ensured compatibility with renamed components
- Maintained 100% individual module test success rate

Result: Complete 14-module TinyTorch framework with proper organization,
working integrations, and comprehensive test coverage ready for production use.
2025-07-18 02:10:49 -04:00
Vijay Janapa Reddi
05391eb550 feat: Restructure integration tests and optimize module timing
- Flattened tests/ directory structure (removed integration/ and system/ subdirectories)
- Renamed all integration tests with _integration.py suffix for clarity
- Created test_utils.py with setup_integration_test() function
- Updated integration tests to use ONLY tinytorch package imports
- Ensured all modules are exported before running tests via tito export --all
- Optimized module test timing for fast execution (under 5 seconds each)
- Fixed MLOps test reliability and reduced timing parameters across modules
- Exported all modules (compression, kernels, benchmarking, mlops) to tinytorch package
2025-07-14 23:37:50 -04:00
Vijay Janapa Reddi
5fc55f8cbe Been refactoring the structure, got setup working 2025-07-10 11:13:45 -04:00
Vijay Janapa Reddi
c16c81484c Adds initial TinyTorch CLI and core structure
Introduces the foundational CLI structure and core components for the TinyTorch project.

This initial commit establishes the command-line interface (CLI) using `argparse` for training, evaluation, benchmarking, and system information. It also lays out the basic directory structure and essential modules, including tensor operations, autograd, neural network layers, optimizers, data loading, and MLOps components.
2025-07-09 00:23:19 -04:00