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- Create standalone verify_vectorization_speedup() function (Section 4) - Measures ACTUAL timing of loop-based vs vectorized operations - Uses time.perf_counter() for precise measurements - Includes warmup runs for accurate timing - Verifies >10× speedup (typical for NumPy/BLAS) - test_module() calls verification function cleanly - Returns dict with speedup, times, and verification status - Includes example usage in __main__ block - Update section numbering: Systems Analysis now Section 5 Verification shows: - Loop-based: ~100ms for 100 iterations - Vectorized: ~1ms for 100 iterations - Demonstrates SIMD parallelization benefits
TinyTorch Modules Directory
This directory contains student-facing Jupyter notebooks for learning ML systems from scratch.
📦 Module Structure
Each module directory contains:
{module}_dev.py- Jupytext Python file (source of truth){module}.ipynb- Jupyter notebook (auto-generated)README.md- Module overview and learning objectives
🔄 How Modules Are Created
Modules are automatically exported from src/ using the following workflow:
- Source notebooks live in
src/{module}/as.ipynbfiles - Run export:
tito system export {module}ornbdev_export - Auto-generated files appear in
modules/{module}/
The src/ directory is where development happens. The modules/ directory is what students use.
📚 Available Modules
Modules will be populated as you complete the TinyTorch learning path:
- ✅
01_tensor- Tensor fundamentals and operations - ✅
02_activations- Activation functions (ReLU, Sigmoid, etc.) - ✅
04_losses- Loss functions for training - ✅
06_optimizers- Optimization algorithms (SGD, Adam, etc.) - 🔒 Additional modules unlock as you progress...
🚀 Getting Started
- Check module status:
tito module status - Start a module:
tito module start 01 - Work on the module: Opens Jupyter Lab automatically
- Complete the module:
tito module complete 01
Each module builds on previous ones, creating a complete ML framework from scratch!