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257 lines
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---
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title: "Getting Started with TinyTorch"
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---
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:::{.callout-note title="Prerequisites Check"}
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This guide requires **Python programming** (classes, functions, NumPy basics) and **basic linear algebra** (matrix multiplication).
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:::
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## The Journey
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TinyTorch follows a simple pattern: **build modules, unlock milestones, recreate ML history**.
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::: {#fig-journey fig-env="figure" fig-pos="htb" fig-cap="**Your TinyTorch Journey**: Install once, then loop through start → complete → milestone for each of the 20 modules — every milestone runs on the code you just wrote." fig-alt="Five-stage horizontal pipeline: Install (install.sh) → Setup (tito setup) → Start (begin module) → Complete (export code) → Milestone (run YOUR code), with a feedback arrow from Milestone back to Start labeled 'next module'."}
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:::
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As you complete modules, you unlock milestones that recreate landmark moments in ML history---using YOUR code.
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## Step 1: Install & Setup (2 Minutes)
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:::{.panel-tabset}
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## macOS / Linux
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```bash
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# Install TinyTorch (run from a project folder like ~/projects)
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curl -sSL mlsysbook.ai/tinytorch/install.sh | bash
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# Activate and verify
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cd tinytorch
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source .venv/bin/activate
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tito setup
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```
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## Windows
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TinyTorch works on Windows using **Git Bash** (included with Git for Windows).
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**Step 1: Install Git for Windows** (if you don't have it)
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- Download from [git-scm.com/download/win](https://git-scm.com/download/win)
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- Run the installer with default options
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**Step 2: Open Git Bash**
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- Search "Git Bash" in the Start menu and open it
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**Step 3: Install TinyTorch**
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```bash
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# In Git Bash (run from a project folder like ~/projects)
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curl -sSL mlsysbook.ai/tinytorch/install.sh | bash
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# Activate and verify
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cd tinytorch
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source .venv/Scripts/activate
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tito setup
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```
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:::
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**What this does:**
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- Checks your system (Python 3.10+, git)
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- Downloads TinyTorch to a `tinytorch/` folder
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- Creates an isolated virtual environment
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- Installs all dependencies
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- Verifies installation
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**Check your version:**
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```bash
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tito --version
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```
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**Update TinyTorch:**
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```bash
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tito system update
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```
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## Step 2: Your First Module (15 Minutes)
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Let's build Module 01 (Tensor)---the foundation of all neural networks.
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### Start the module
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```bash
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tito module start 01
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```
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This opens the module notebook and tracks your progress.
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### Work in the notebook
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Edit `modules/01_tensor/tensor.ipynb` in Jupyter:
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```bash
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jupyter lab modules/01_tensor/tensor.ipynb
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```
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You'll implement:
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- N-dimensional array creation
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- Mathematical operations (add, multiply, matmul)
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- Shape manipulation (reshape, transpose)
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### Complete the module
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When your implementation is ready, export it to the TinyTorch package:
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```bash
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tito module complete 01
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```
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Your code is now importable:
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```python
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from tinytorch.core.tensor import Tensor # YOUR implementation!
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x = Tensor([1, 2, 3])
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```
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## Step 3: Your First Milestone
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Now for the payoff! After completing the required modules (01-03), run a milestone:
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```bash
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tito milestone run perceptron
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```
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The milestone uses YOUR implementations to recreate Rosenblatt's 1958 Perceptron:
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```text
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Checking prerequisites for Milestone 01...
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All required modules completed!
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Testing YOUR implementations...
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* Tensor import successful
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* Activations import successful
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* Layers import successful
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YOUR TinyTorch is ready!
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+----------------------- Milestone 01 (1958) -----------------------+
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| Milestone 01: Perceptron (1958) |
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| Frank Rosenblatt's First Neural Network |
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| Running: milestones/01_1958_perceptron/01_rosenblatt_forward.py |
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| All code uses YOUR TinyTorch implementations! |
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+-------------------------------------------------------------------+
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Starting Milestone 01...
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Assembling perceptron with YOUR TinyTorch modules...
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* Linear layer: 2 -> 1 (YOUR Module 03!)
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* Activation: Sigmoid (YOUR Module 02!)
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+-------------------- Achievement Unlocked --------------------+
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| MILESTONE ACHIEVED! |
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| You completed Milestone 01: Perceptron (1958) |
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| Frank Rosenblatt's First Neural Network |
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| What makes this special: |
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| - Every tensor operation: YOUR Tensor class |
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| - Every layer: YOUR Linear implementation |
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| - Every activation: YOUR Sigmoid function |
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+--------------------------------------------------------------+
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```
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You're recreating ML history with your own code. *By Module 19, you'll benchmark against MLPerf---the industry standard for ML performance.*
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## The Pattern Continues
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As you complete more modules, you unlock more milestones:
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@tbl-getting-started-milestone-unlocks shows which milestones unlock as you complete more modules.
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| Modules Completed | Milestone | What You Recreate |
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|-------------------|-----------|-------------------|
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| 01-03 | Perceptron (1958) | First neural network (forward pass) |
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| 01-03 | XOR Crisis (1969) | The limitation that triggered AI Winter |
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| 01-08 | MLP Revival (1986) | Backprop solves XOR + real digit recognition |
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| 01-09 | CNN Revolution (1998) | Convolutions for spatial understanding |
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| 01-08 + 11-13 | Transformers (2017) | Language generation with attention |
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| 01-08 + 14-19 | MLPerf (2018) | Production optimization pipeline |
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: **Milestones unlocked as learners complete more modules.** {#tbl-getting-started-milestone-unlocks}
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See all milestones and their requirements:
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```bash
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tito milestone list
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```
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## Quick Reference
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Here are the commands you'll use throughout your journey:
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```bash
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# Module workflow
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tito module start <N> # Start working on module N
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tito module complete <N> # Export module to package
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tito module status # See your progress across all modules
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# Milestones
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tito milestone list # See all milestones & requirements
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tito milestone run <name> # Run a milestone with your code
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# Utilities
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tito setup # First-time setup (safe to re-run)
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tito system update # Update TinyTorch (your work is preserved)
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tito --help # Full command reference
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```
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## Module Progression
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TinyTorch has 20 modules organized in progressive tiers:
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@tbl-getting-started-module-tiers groups the modules into tiers with scope and time estimates.
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| Tier | Modules | Focus | Time Estimate |
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|------|---------|-------|---------------|
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| **Foundation** | 01-08 | Core ML infrastructure (tensors, dataloader, autograd, training) | ~18-24 hours |
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| **Architecture** | 09-13 | Neural architectures (CNNs, transformers) | ~15-20 hours |
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| **Optimization** | 14-19 | Production optimization (profiling, quantization) | ~18-24 hours |
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| **Capstone** | 20 | Torch Olympics Competition | ~8-10 hours |
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: **TinyTorch module tiers with scope and time estimate.** {#tbl-getting-started-module-tiers}
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**Total: ~60-80 hours** over 14-18 weeks (4-6 hours/week pace).
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See the module descriptions in this guide for detailed prerequisites and learning objectives.
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## Join the Community (Optional)
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After setup, join the global TinyTorch community:
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```bash
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tito community login # Join the community
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```
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The community features include progress tracking and connecting with other builders.
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## For Instructors & TAs
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:::{.callout-note}
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Classroom support with NBGrader integration is coming (target: Summer/Fall 2026). TinyTorch works for self-paced learning today.
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:::
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**What's Planned:**
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- Automated assignment generation with solutions removed
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- Auto-grading against test suites
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- Progress tracking across all 20 modules
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- Grade export to CSV for LMS integration
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**Interested in early adoption?** [Join the discussion](https://github.com/harvard-edge/cs249r_book/discussions/1076) to share your use case.
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**Ready to start?** Run `tito module start 01` and begin building!
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