cs249r_book/tinytorch/milestones

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:

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

Available Milestones

ID	Name	Year	Required Modules	What You'll Do
01	Perceptron	1958	01-03	Build Rosenblatt's first neural network (forward pass)
02	XOR Crisis	1969	01-03	Experience the XOR limitation that triggered AI Winter
03	MLP Revival	1986	01-08	Train MLPs to solve XOR + recognize digits
04	CNN Revolution	1998	01-09	Build LeNet for image recognition
05	Transformer Era	2017	01-08, 11-13	Prove attention with reversal, copy, and mixed sequence tasks
06	MLPerf Benchmarks	2018	01-08, 14-19	Optimize and benchmark your neural networks

Running Milestones

# 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
└── 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:

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