TinyTorch Milestones

Milestones are capstone experiences that bring together everything you've built in the TinyTorch modules. Each milestone recreates a pivotal moment in ML history using YOUR implementations.

How Milestones Work

After completing a set of modules, you unlock the ability to run a milestone. Each milestone:

Uses YOUR code - Every tensor operation, gradient computation, and layer runs on code YOU wrote
Recreates history - Experience the same breakthroughs researchers achieved decades ago
Proves understanding - If it works, you truly understand how these systems function

Available Milestones

<table> <thead> <tr> <th width="5%">ID</th> <th width="15%">Name</th> <th width="8%">Year</th> <th width="20%">Required Modules</th> <th width="52%">What You'll Do</th> </tr> </thead> <tbody> <tr> <td>01</td> <td><b>Perceptron</b></td> <td>1958</td> <td>01-03</td> <td>Build Rosenblatt's first neural network (forward pass)</td> </tr> <tr> <td>02</td> <td><b>XOR Crisis</b></td> <td>1969</td> <td>01-03</td> <td>Experience the XOR limitation that triggered AI Winter</td> </tr> <tr> <td>03</td> <td><b>MLP Revival</b></td> <td>1986</td> <td>01-08</td> <td>Train MLPs to solve XOR + recognize digits</td> </tr> <tr> <td>04</td> <td><b>CNN Revolution</b></td> <td>1998</td> <td>01-09</td> <td>Build LeNet for image recognition</td> </tr> <tr> <td>05</td> <td><b>Transformer Era</b></td> <td>2017</td> <td>01-08, 11-13</td> <td>Build attention and generate text</td> </tr> <tr> <td>06</td> <td><b>MLPerf Benchmarks</b></td> <td>2018</td> <td>01-08, 14-19</td> <td>Optimize and benchmark your neural networks</td> </tr> </tbody> </table>

Running Milestones

bash

# List available milestones and your progress
tito milestone list

# Run a specific milestone (all parts)
tito milestone run 03

# Run a specific part of a multi-part milestone
tito milestone run 03 --part 1  # Part 1: XOR Solved
tito milestone run 03 --part 2  # Part 2: TinyDigits

# Get detailed info about a milestone
tito milestone info 05

Directory Structure

milestones/
├── 01_1958_perceptron/     # Milestone 01: Rosenblatt's Perceptron
├── 02_1969_xor/            # Milestone 02: XOR Problem
├── 03_1986_mlp/            # Milestone 03: Backpropagation MLP
├── 04_1998_cnn/            # Milestone 04: LeNet CNN
├── 05_2017_transformer/    # Milestone 05: Attention Mechanism
├── 06_2018_mlperf/         # Milestone 06: Optimization Olympics
├── extras/                 # Additional demos and variants (see extras/README.md)
└── data_manager.py         # Shared dataset management utility

The Journey

Success Criteria

Each milestone has specific success criteria. Passing means your implementation is correct:

Milestone 01: Forward pass produces reasonable outputs
Milestone 02: Demonstrates XOR is unsolvable with single layer (75% max accuracy)
Milestone 03: Part 1 solves XOR (100% accuracy), Part 2 achieves 85%+ on TinyDigits
Milestone 04: TinyDigits achieves 90%+ accuracy with CNN
Milestone 05: Pass all three attention challenges (95%+ accuracy)
Milestone 06: Part 1 completes optimization pipeline, Part 2 shows KV cache speedup

Troubleshooting

If a milestone fails:

Check that all required modules are completed: tito module status
Run the module tests: tito test <module_number>
Look at the specific error message for debugging hints
Review the milestone's docstring for implementation requirements