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✅ Rename all module directories: 00_setup → 01_setup, etc. ✅ Update convert_modules.py mappings for new directory names ✅ Update _toc.yml file paths and titles (1-14 instead of 0-13) ✅ Regenerate all overview pages with new numbering ✅ Fix all broken references in usage-paths and intro ✅ Update chapter references to use natural numbering Benefits: - More intuitive course progression starting from 1 - Matches academic course numbering conventions - Eliminates confusion about 'Module 0' concept - Cleaner mental model for students and instructors - All references and links properly updated Complete transformation: 14 modules now numbered 01-14
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Kernels - Hardware-Optimized ML Operations
Welcome to the Kernels module! This is where we move beyond NumPy to understand how ML operations are optimized for modern hardware. You'll implement custom kernels that run faster than standard library functions.
:class: tip
- Understand why custom kernels matter for ML performance
- Implement vectorized operations using SIMD principles
- Master memory-efficient algorithms for better cache utilization
- Build parallel processing patterns for CPU and GPU-style computing
- Create performance profiling tools to measure and optimize code
- Apply kernel optimizations to compressed model operations
Build → Use → Optimize
- Build: Custom operations, vectorization, and memory optimization
- Use: Apply optimized kernels to real ML workloads
- Optimize: Profile, measure, and improve performance systematically
🚀 Interactive Learning
Choose your preferred way to engage with this module:
```{grid-item-card} 🚀 Launch Binder
:link: https://mybinder.org/v2/gh/mlsysbook/TinyTorch/main?filepath=modules/source/12_kernels/kernels_dev.ipynb
:class-header: bg-light
Run this module interactively in your browser. No installation required!
```
```{grid-item-card} ⚡ Open in Colab
:link: https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/12_kernels/kernels_dev.ipynb
:class-header: bg-light
Use Google Colab for GPU access and cloud compute power.
```
```{grid-item-card} 📖 View Source
:link: https://github.com/mlsysbook/TinyTorch/blob/main/modules/source/12_kernels/kernels_dev.py
:class-header: bg-light
Browse the Python source code and understand the implementation.
```
:class: tip
**Binder sessions are temporary!** Download your completed notebook when done, or switch to local development for persistent work.
Ready for serious development? → [🏗️ Local Setup Guide](../usage-paths/serious-development.md)