TinyTorch/site/quickstart-guide.md

Quick Start Guide

From Zero to Building Neural Networks

Complete setup + first module in 15 minutes

Purpose: Get hands-on experience building ML systems in 15 minutes. Complete setup verification and build your first neural network component from scratch.

2-Minute Setup

Let's get you ready to build ML systems:

Step 1: One-Command Setup

# Clone repository
git clone https://github.com/mlsysbook/TinyTorch.git
cd TinyTorch

# Automated setup (handles everything!)
./setup-environment.sh

# Activate environment
source activate.sh

What this does:

• Creates optimized virtual environment (arm64 on Apple Silicon)
• Installs all dependencies (NumPy, Jupyter, Rich, PyTorch for validation)
• Configures TinyTorch in development mode
• Verifies installation

See TITO CLI Reference for detailed workflow and troubleshooting.

Step 2: Verify Setup

# Run system diagnostics
tito system doctor

You should see all green checkmarks. This confirms your environment is ready for hands-on ML systems building.

See Module Workflow for detailed commands and Troubleshooting if needed.

Step 3: Join the Community & Benchmark

After setup, join the global TinyTorch community and validate your setup:

# Join the community (optional)
tito community join

# Run baseline benchmark to validate setup
tito benchmark baseline

Community Features:

• Join with optional information (country, institution, course type)
• Track your progress automatically
• See your cohort (Fall 2024, Spring 2025, etc.)
• All data stored locally in .tinytorch/ directory

Baseline Benchmark:

• Quick validation that everything works
• Your "Hello World" moment!
• Generates score and saves results locally

See Community Guide for complete features.

15-Minute First Module Walkthrough

Let's build your first neural network component following the TinyTorch workflow:

graph TD
    Start[Clone & Setup] --> Edit[Edit Module<br/>tensor_dev.ipynb]
    Edit --> Export[Export to Package<br/>tito module complete 01]
    Export --> Test[Test Import<br/>from tinytorch import Tensor]
    Test --> Next[Continue to Module 02]

    style Start fill:#e3f2fd
    style Edit fill:#fffbeb
    style Export fill:#f0fdf4
    style Test fill:#fef3c7
    style Next fill:#f3e5f5

See Student Workflow for the complete development cycle.

Module 01: Tensor Foundations

Learning Goal: Build N-dimensional arrays - the foundation of all neural networks

Time: 15 minutes

Action: Start with Module 01 to build tensor operations from scratch.

# Step 1: Edit the module source
cd modules/01_tensor
jupyter lab tensor_dev.ipynb

You'll implement core tensor operations:

• N-dimensional array creation
• Basic mathematical operations (add, multiply, matmul)
• Shape manipulation (reshape, transpose)
• Memory layout understanding

Key Implementation: Build the Tensor class that forms the foundation of all neural networks

# Step 2: Export to package when ready
tito module complete 01

This makes your implementation importable: from tinytorch import Tensor

See Student Workflow for the complete edit → export → validate cycle.

Achievement Unlocked: Foundation capability - "Can I create and manipulate the building blocks of ML?"

Next Step: Module 02 - Activations

Learning Goal: Add nonlinearity - the key to neural network intelligence

Time: 10 minutes

Action: Continue with Module 02 to add activation functions.

# Step 1: Edit the module
cd modules/02_activations
jupyter lab activations_dev.ipynb

You'll implement essential activation functions:

• ReLU (Rectified Linear Unit) - the workhorse of deep learning
• Softmax - for probability distributions
• Understand gradient flow and numerical stability
• Learn why nonlinearity enables learning

Key Implementation: Build activation functions that allow neural networks to learn complex patterns

# Step 2: Export when ready
tito module complete 02

See Student Workflow for the complete edit → export → validate cycle.

Achievement Unlocked: Intelligence capability - "Can I add nonlinearity to enable learning?"

Track Your Progress

After completing your first modules:

Check your new capabilities: Use the optional checkpoint system to track your progress:

tito checkpoint status  # View your completion tracking

This is helpful for self-assessment but not required for the core workflow.

See Student Workflow for the essential edit → export → validate cycle.

Validate with Historical Milestones

After exporting your modules, prove what you've built by running milestone scripts:

After Module 07: Build Rosenblatt's 1957 Perceptron - the first trainable neural network
After Module 07: Solve the 1969 XOR Crisis with multi-layer networks
After Module 08: Achieve 95%+ accuracy on MNIST with 1986 backpropagation
After Module 09: Hit 75%+ on CIFAR-10 with 1998 CNNs
After Module 13: Generate text with 2017 Transformers
After Module 18: Optimize for production with 2018 Torch Olympics

See Journey Through ML History for complete timeline, requirements, and expected results.

The Workflow: Edit modules → Export with tito module complete N → Run milestone scripts to validate

See Student Workflow for the complete cycle.

What You Just Accomplished

In 15 minutes, you've:

Setup Complete

Installed TinyTorch and verified your environment

Created Foundation

Implemented core tensor operations from scratch

First Capability

Earned your first ML systems capability checkpoint

Your Next Steps

Immediate Next Actions (Choose One):

Continue Building (Recommended): Begin Module 03 to add layers to your network.

Master the Workflow:

• See Student Workflow for the complete edit → export → validate cycle
• See TITO CLI Reference for complete command reference

For Instructors:

• See Classroom Setup Guide for NBGrader integration

Notebook Platforms:

• Online (Viewing): Jupyter/MyBinder, Google Colab, Marimo - great for exploring notebooks
• ⚠️ Important: Online notebooks are for viewing only. For full package experiments, milestone validation, and CLI tools, you need local installation (see Student Workflow)

Pro Tips for Continued Success

The TinyTorch Development Cycle:

1. Edit module sources in modules/NN_name/ (e.g., modules/01_tensor/tensor_dev.ipynb)
2. Export with tito module complete N
3. Validate by running milestone scripts

See Student Workflow for detailed workflow guide and best practices.

You're Now a TinyTorch Builder

Ready to Build Production ML Systems

You've proven you can build ML components from scratch. Time to keep going!

Continue Building → TITO CLI Reference →

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What makes TinyTorch different: You're not just learning about neural networks—you're building them from fundamental mathematical operations. Every line of code you write builds toward complete ML systems mastery.

Next milestone: After Module 08, you'll train real neural networks on actual datasets using 100% your own code!