cs249r_book/tinytorch/site/getting-started.md

Getting Started with TinyTorch

You're ahead of the curve. TinyTorch is functional but still being refined. Expect rough edges, incomplete documentation, and things that might change. If you proceed, you're helping us shape this by finding what works and what doesn't.

**Best approach right now:** Browse the code and concepts. For hands-on building, check back when we announce classroom readiness (Summer/Fall 2026).

Questions or feedback? [Join the discussion](https://github.com/harvard-edge/cs249r_book/discussions/1076)

This guide requires **Python programming** (classes, functions, NumPy basics) and **basic linear algebra** (matrix multiplication).

The Journey

TinyTorch follows a simple pattern: build modules, unlock milestones, recreate ML history.

:align: center
graph LR
    A[Install] --> B[Setup]
    B --> C[Start Module]
    C --> D[Complete Module]
    D --> E[Run Milestone]
    E --> C

    style A fill:#e3f2fd
    style B fill:#e3f2fd
    style C fill:#fff3e0
    style D fill:#f0fdf4
    style E fill:#fce4ec

As you complete modules, you unlock milestones that recreate landmark moments in ML history—using YOUR code.

---

Step 1: Install & Setup (2 Minutes)

# Install TinyTorch (run from a project folder like ~/projects)
curl -sSL tinytorch.ai/install | bash

# Activate and verify
cd tinytorch
source .venv/bin/activate
tito setup

What this does:

• Checks your system (Python 3.8+, git)
• Downloads TinyTorch to a tinytorch/ folder
• Creates an isolated virtual environment
• Installs all dependencies
• Verifies installation

---

Step 2: Your First Module (15 Minutes)

Let's build Module 01 (Tensor)—the foundation of all neural networks.

Start the module

tito module start 01

This opens the module notebook and tracks your progress.

Work in the notebook

Edit modules/01_tensor/01_tensor.ipynb in Jupyter:

jupyter lab modules/01_tensor/01_tensor.ipynb

You'll implement:

• N-dimensional array creation
• Mathematical operations (add, multiply, matmul)
• Shape manipulation (reshape, transpose)

Complete the module

When your implementation is ready, export it to the TinyTorch package:

tito module complete 01

Your code is now importable:

from tinytorch.core.tensor import Tensor  # YOUR implementation!
x = Tensor([1, 2, 3])

---

Step 3: Your First Milestone

Now for the payoff! After completing the required modules (01-03), run a milestone:

tito milestone run perceptron

The milestone uses YOUR implementations to recreate Rosenblatt's 1957 Perceptron:

🔍 Checking prerequisites for Milestone 01...
✅ All required modules completed!

🧪 Testing YOUR implementations...
  ✓ Tensor import successful
  ✓ Activations import successful
  ✓ Layers import successful
✅ YOUR Tiny🔥Torch is ready!

╭─────────────────────────── 🏆 Milestone 01 (1957) ───────────────────────────╮
│  🧠 Milestone 01: Perceptron (1957)                                          │
│  Frank Rosenblatt's First Neural Network                                     │
│                                                                              │
│  📂 Running: milestones/01_1957_perceptron/01_rosenblatt_forward.py          │
│  All code uses YOUR Tiny🔥Torch implementations!                             │
╰──────────────────────────────────────────────────────────────────────────────╯

🚀 Starting Milestone 01...

🧠 Assembling perceptron with YOUR Tiny🔥Torch modules...
   ✓ Linear layer: 2 → 1 (YOUR Module 03!)
   ✓ Activation: Sigmoid (YOUR Module 02!)

╭───────────────────────── ✨ Achievement Unlocked ✨ ─────────────────────────╮
│  🏆 MILESTONE ACHIEVED!                                                      │
│                                                                              │
│  You completed Milestone 01: Perceptron (1957)                               │
│  Frank Rosenblatt's First Neural Network                                     │
│                                                                              │
│  What makes this special:                                                    │
│  • Every tensor operation: YOUR Tensor class                                 │
│  • Every layer: YOUR Linear implementation                                   │
│  • Every activation: YOUR Sigmoid function                                   │
╰──────────────────────────────────────────────────────────────────────────────╯

You're recreating ML history with your own code.

---

The Pattern Continues

As you complete more modules, you unlock more milestones:

Modules Completed	Milestone Unlocked	What You Recreate
01-03	perceptron	The 1957 Perceptron
01-05	backprop	1986 Backpropagation
01-07	lenet	1989 LeNet CNN
01-09	alexnet	2012 AlexNet
01-13	transformer	2017 Transformer
01-19	mlperf	MLPerf Benchmarks

See all milestones and their requirements:

tito milestone list

---

Quick Reference

Here are the commands you'll use throughout your journey:

# Module workflow
tito module start <N>       # Start working on module N
tito module complete <N>    # Export module to package
tito module status          # See your progress across all modules

# Milestones
tito milestone list         # See all milestones & requirements
tito milestone run <name>   # Run a milestone with your code

# Utilities
tito setup                  # Verify installation
tito update                 # Update TinyTorch (your work is preserved)
tito --help                 # Full command reference

---

Module Progression

TinyTorch has 20 modules organized in progressive tiers:

Tier	Modules	Focus	Time Estimate
Foundation	01-07	Core ML infrastructure (tensors, autograd, training)	~15-20 hours
Architecture	08-13	Neural architectures (data loading, CNNs, transformers)	~18-24 hours
Optimization	14-19	Production optimization (profiling, quantization)	~18-24 hours
Capstone	20	Torch Olympics Competition	~8-10 hours

Total: ~60-80 hours over 14-18 weeks (4-6 hours/week pace).

See Foundation Tier Overview for detailed module descriptions.

---

Join the Community (Optional)

After setup, join the global TinyTorch community:

tito community login        # Join the community

See Community Guide for complete features.

---

For Instructors & TAs

Classroom support with NBGrader integration is coming (target: Summer/Fall 2026). TinyTorch works for self-paced learning today.

What's Planned:

• Automated assignment generation with solutions removed
• Auto-grading against test suites
• Progress tracking across all 20 modules
• Grade export to CSV for LMS integration

Interested in early adoption? Join the discussion to share your use case.

---

Ready to start? Run tito module start 01 and begin building!