- Standardize module.yaml files (11-13) to match concise format of early modules
- Remove verbose sections, keep essential metadata only
- Update kernels README to match TinyTorch module style standards
- Add comprehensive integration tests for kernels module
- Test hardware-optimized operations with real TinyTorch components
- Prepare for systematic integration testing across all modules
- Update kernels_dev.py with any modifications made during testing
- Add test_report.md generated by benchmarking module
- Ensure all changes from comprehensive testing are committed
✅ **Pedagogical Improvements:**
- Removed complex SimpleProfiler dependency
- Added simple time_kernel() function using time.perf_counter()
- Displays timing in microseconds (realistic for kernel operations)
- Focused learning on kernel optimization vs profiling complexity
✅ **Clean Learning Progression:**
- Module 11 (Kernels): Simple timing - 'Can I make this faster?'
- Module 12 (Benchmarking): Professional profiling - 'How do I measure systematically?'
- Module 13 (MLOps): Production monitoring - 'How do I track in production?'
✅ **Implementation Details:**
- Fixed imports to use matmul_naive from TinyTorch layers
- Simplified baseline implementation using NumPy dot product
- Reduced cognitive load by removing measurement complexity
- Maintained all kernel optimization concepts
⚠️ **Note:** Cache-friendly implementation needs debugging but core timing functionality works
🎯 **Impact:** Students can now focus on building optimized kernels with immediate microsecond-level performance feedback, setting up perfect progression to comprehensive benchmarking in Module 12.
- Added locked standardized testing sections to autograd and optimizers modules
- Fixed kernels module structure to match optimizers/training pattern
- Added comprehensive VS Code setup guide for Jupytext editing
- All 12 TinyTorch modules now have consistent testing framework
- Cleaned up temporary development files
- Add tinytorch.utils.profiler following PyTorch's utils pattern
- Includes SimpleProfiler class for educational performance measurement
- Provides timing, memory usage, and system metrics
- Follows PyTorch's torch.utils.* organizational pattern
- Module 11: Kernels uses profiler for performance demonstrations
Features:
- Wall time and CPU time measurement
- Memory usage tracking (peak, delta, percentages)
- Array information (shape, size, dtype)
- CPU and system metrics
- Clean educational interface for ML performance learning
Import pattern:
from tinytorch.utils.profiler import SimpleProfiler
