mirror of https://github.com/MLSysBook/TinyTorch.git synced 2026-06-03 20:10:51 -05:00

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Vijay Janapa Reddi 8afe207ce5 Renumber modules from 00-13 to 01-14 for natural numbering

✅ 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

2025-07-15 18:51:36 -04:00

2.9 KiB

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Setup - TinyTorch System Configuration

Welcome to TinyTorch! This setup module configures your personal TinyTorch installation and teaches you the NBGrader workflow.

:class: tip
- Configure your personal TinyTorch installation with custom information
- Learn to query system information using Python modules
- Master the NBGrader workflow: implement → test → export
- Create functions that become part of your tinytorch package
- Understand solution blocks, hidden tests, and automated grading

The Big Picture: Why Configuration Matters in ML Systems

Configuration is the foundation of any production ML system. In this module, you'll learn:

1. System Awareness

Real ML systems need to understand their environment:

Hardware constraints: Memory, CPU cores, GPU availability
Software dependencies: Python version, library compatibility
Platform differences: Linux servers, macOS development, Windows deployment

2. Reproducibility

Configuration enables reproducible ML:

Environment documentation: Exactly what system was used
Dependency management: Precise versions and requirements
Debugging support: System info helps troubleshoot issues

3. Professional Development

Proper configuration shows engineering maturity:

Attribution: Your work is properly credited
Collaboration: Others can understand and extend your setup
Maintenance: Systems can be updated and maintained

4. ML Systems Context

This connects to broader ML engineering:

Model deployment: Different environments need different configs
Monitoring: System metrics help track performance
Scaling: Understanding hardware helps optimize training

Let's build the foundation of your ML systems engineering skills!

🚀 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/01_setup/setup_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/01_setup/setup_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/01_setup/setup_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)

2.9 KiB Raw Blame History