TinyTorch/site/student-workflow.md

Student Workflow

This guide explains the actual day-to-day workflow for building your ML framework with TinyTorch.

The Core Workflow

TinyTorch follows a simple three-step cycle:

graph LR
    A[Edit Modules<br/>modules/NN_name/] --> B[Export to Package<br/>tito module complete N]
    B --> C[Validate with Milestones<br/>Run milestone scripts]
    C --> A

    style A fill:#e3f2fd
    style B fill:#f0fdf4
    style C fill:#fef3c7

Step 1: Edit Modules

Work on module notebooks in modules/:

# Example: Working on Module 03 (Layers)
cd modules/03_layers
jupyter lab layers_dev.ipynb

Each module is a Jupyter notebook that you edit interactively. You'll:

• Implement the required functionality
• Add docstrings and comments
• Run and test your code inline
• See immediate feedback

Step 2: Export to Package

Once your module implementation is complete, export it to the main TinyTorch package:

tito module complete MODULE_NUMBER

This command:

• Converts your source files to the tinytorch/ package
• Validates NBGrader metadata
• Makes your implementation available for import

Example:

tito module complete 03  # Export Module 03 (Layers)

After export, your code is importable:

from tinytorch.layers import Linear  # YOUR implementation!

Step 3: Validate with Milestones

Run milestone scripts to prove your implementation works:

cd milestones/01_1957_perceptron
python 01_rosenblatt_forward.py  # Uses YOUR Tensor (M01)
python 02_rosenblatt_trained.py  # Uses YOUR layers (M01-M07)

Each milestone has a README explaining:

• Required modules
• Historical context
• Expected results
• What you're learning

See Milestones Guide for the full progression.

Module Progression

TinyTorch has 20 modules organized in three tiers:

Foundation (Modules 01-07)

Core ML infrastructure - tensors, autograd, training loops

Milestones unlocked:

• M01: Perceptron (after Module 07)
• M02: XOR Crisis (after Module 07)

Architecture (Modules 08-13)

Neural network architectures - data loading, CNNs, transformers

Milestones unlocked:

• M03: MLPs (after Module 08)
• M04: CNNs (after Module 09)
• M05: Transformers (after Module 13)

Optimization (Modules 14-19)

Production optimization - profiling, quantization, benchmarking

Milestones unlocked:

• M06: Torch Olympics (after Module 18)

Capstone Competition (Module 20)

Apply all optimizations in the Torch Olympics Competition

Typical Development Session

Here's what a typical session looks like:

# 1. Work on a module
cd modules/05_autograd
jupyter lab autograd_dev.ipynb
# Edit your implementation interactively

# 2. Export when ready
tito module complete 05

# 3. Validate with existing milestones
cd ../milestones/01_1957_perceptron
python 01_rosenblatt_forward.py  # Should still work!

# 4. Continue to next module or milestone

TITO Commands Reference

The most important commands you'll use:

# Export module to package
tito module complete MODULE_NUMBER

# Check module status (optional capability tracking)
tito checkpoint status

# System information
tito system info

For complete command documentation, see TITO CLI Reference.

Checkpoint System (Optional)

TinyTorch includes an optional checkpoint system for tracking progress:

tito checkpoint status  # View completion tracking

This is helpful for self-assessment but not required for the core workflow. The essential cycle remains: edit → export → validate.

Instructor Integration (Coming Soon)

TinyTorch supports NBGrader for classroom use. Documentation for instructors using the autograding features will be available in future releases.

For now, focus on the student workflow: building your implementations and validating them with milestones.

What's Next?

1. Start with Module 01: See Getting Started
2. Follow the progression: Each module builds on previous ones
3. Run milestones: Prove your implementations work
4. Build intuition: Understand ML systems from first principles

The goal isn't just to write code - it's to understand how modern ML frameworks work by building one yourself.