mirror of https://github.com/MLSysBook/TinyTorch.git synced 2026-03-11 23:03:33 -05:00

Files

Vijay Janapa Reddi 82defeafd3 Refactor notebook generation to use separate files for better architecture

- Restored tools/py_to_notebook.py as a focused, standalone tool
- Updated tito notebooks command to use subprocess to call the separate tool
- Maintains clean separation of concerns: tito.py for CLI orchestration, py_to_notebook.py for conversion logic
- Updated documentation to use 'tito notebooks' command instead of direct tool calls
- Benefits: easier debugging, better maintainability, focused single-responsibility modules

2025-07-10 21:57:09 -04:00

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Setup Module

Welcome to TinyTorch! This is your first module in the Machine Learning Systems course.

Overview

The setup module teaches you the complete TinyTorch development workflow while introducing fundamental programming concepts. You'll learn to write code with NBDev directives, implement classes and functions, and understand the module-to-package export system.

Learning Goals

Understand the nbdev notebook-to-Python workflow
Write your first TinyTorch code with #| export directives
Implement system information collection and developer profiles
Run tests and use the CLI tools
Get comfortable with the development rhythm

Files

setup_dev.py - Main development file (Jupytext format with full educational content)
setup_dev.ipynb - Jupyter notebook version (auto-generated and executed)
tinytorch_flame.txt - ASCII art file containing the TinyTorch flame design
tests/test_setup.py - Comprehensive pytest test suite
README.md - This file

What You'll Implement

1. Basic Functions

hello_tinytorch() - Display ASCII art and welcome message
add_numbers() - Basic arithmetic (foundation of ML operations)

2. System Information Class

SystemInfo - Collect and display Python version, platform, and machine info
Compatibility checking for minimum requirements

3. Developer Profile Class

DeveloperProfile - Personalized developer information and signatures
ASCII art customization and file loading
Professional code attribution system

Usage

Python Script

from setup_dev import hello_tinytorch, add_numbers, SystemInfo, DeveloperProfile

# Display welcome message
hello_tinytorch()

# Basic arithmetic
result = add_numbers(2, 3)

# System information
info = SystemInfo()
print(f"System: {info}")
print(f"Compatible: {info.is_compatible()}")

# Developer profile
profile = DeveloperProfile()
print(profile.get_full_profile())

Jupyter Notebook

Open setup_dev.ipynb and work through the educational content step by step.

Testing

Run the comprehensive test suite using pytest:

# Using the TinyTorch CLI (recommended)
python bin/tito.py test --module setup

# Or directly with pytest
python -m pytest modules/setup/tests/test_setup.py -v

Test Coverage

The test suite includes 20 comprehensive tests covering:

✅ Function execution - All functions run without errors
✅ Output validation - Correct content and formatting
✅ Arithmetic operations - Basic, negative, and floating-point math
✅ System information - Platform detection and compatibility
✅ Developer profiles - Default and custom configurations
✅ ASCII art handling - File loading and fallback behavior
✅ Error recovery - Graceful handling of missing files
✅ Integration testing - All components work together

Development Workflow

This module teaches the core TinyTorch development cycle:

Write code in the notebook using #| export directives
Export code with python bin/tito.py sync --module setup
Run tests with python bin/tito.py test --module setup
Check progress with python bin/tito.py info

Key Concepts

NBDev workflow - Write in notebooks, export to Python packages
Export directives - Use #| export to mark code for export
Module → Package mapping - This module exports to tinytorch/core/utils.py
Teaching vs. Building - Learn by modules, build by function
Student implementation - TODO sections with instructor solutions hidden

Personalization Features

ASCII Art Customization

The ASCII art is loaded from tinytorch_flame.txt. You can customize it by:

Edit the file directly - Modify tinytorch_flame.txt with your own ASCII art
Custom parameter - Pass your own ASCII art to DeveloperProfile
Create your own design - Your initials, logo, or motivational art

Developer Profile Customization

my_profile = DeveloperProfile(
    name="Your Name",
    affiliation="Your University",
    email="your.email@example.com",
    github_username="yourgithub",
    ascii_art="Your custom ASCII art here!"
)

What You'll Learn

This comprehensive module introduces:

NBDev educational patterns - #| export, #| hide directives
File I/O operations - Loading ASCII art with error handling
Object-oriented programming - Classes, methods, and properties
System programming - Platform detection and compatibility
Testing with pytest - Professional test structure and assertions
Code organization - Module structure and package exports
The TinyTorch development workflow - Complete cycle from code to tests

Next Steps

Once you've completed this module and all tests pass, you're ready to move on to the tensor module where you'll build the core data structures that power TinyTorch neural networks!

The skills you learn here - the development workflow, testing patterns, and code organization - will be used throughout every module in TinyTorch.

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