Essential TITO Commands

Essential TITO Commands#

Master the TinyTorch CLI in Minutes

Everything you need to build ML systems efficiently

Purpose: Complete command reference for the TITO CLI. Master the essential commands for development workflow, progress tracking, and system management.

The Core Workflow#

TinyTorch follows a simple three-step cycle: Edit modules → Export to package → Validate with milestones

The essential command: tito module complete MODULE_NUMBER - exports your code to the TinyTorch package.

See Student Workflow for the complete development cycle, best practices, and troubleshooting.

This page documents all available TITO commands. The checkpoint system (tito checkpoint status) is optional for progress tracking.

Most Important Commands#

The commands you’ll use most often:

Check Your Environment

tito system doctor

Verify your setup is ready for development

Export Module to Package (Essential)

tito module complete 01

Export your module to the TinyTorch package - use this after editing modules

Track Your Progress (Optional)

tito checkpoint status

See which capabilities you've mastered (optional capability tracking)

Typical Development Session#

Here’s what a typical session looks like:

Edit modules:

cd modules/source/03_layers
jupyter lab 03_layers_dev.py
# Make your implementation...

Export to package:

# From repository root
tito module complete 03

Validate with milestones:

cd milestones/01_1957_perceptron
python 01_rosenblatt_forward.py  # Uses YOUR implementation!

Optional progress tracking:

tito checkpoint status  # See what you've completed

See Student Workflow for complete development cycle details.

Complete Command Reference#

System & Health#

System Check

tito system doctor

Diagnose environment issues before they block you

System Info

tito system info

View configuration details

Module Management#

Export Module to Package (Essential)

tito module complete MODULE_NUMBER

Export your implementation to the TinyTorch package - the key command in the workflow

Example:

tito module complete 05  # Export Module 05 (Autograd)

After exporting, your code is importable:

from tinytorch.autograd import backward  # YOUR implementation!

Progress Tracking (Optional)#

Capability Overview

tito checkpoint status

Quick view of your capabilities (optional tracking)

Detailed Progress

tito checkpoint status --detailed

Module-by-module breakdown

Visual Timeline

tito checkpoint timeline

See your learning journey in visual format

Test Specific Capability

tito checkpoint test CHECKPOINT_NUMBER

Verify you’ve mastered a specific capability

Instructor Commands (Coming Soon)#

TinyTorch includes NBGrader integration for classroom use. Full documentation for instructor workflows (assignment generation, autograding, etc.) will be available in future releases.

For now, focus on the student workflow: edit modules → export → validate with milestones.

For current instructor capabilities, see Classroom Use Guide

Troubleshooting Commands#

When things go wrong, these commands help:

Environment Issues:

tito system doctor          # Diagnose problems
tito system info           # Show configuration details

Progress Tracking (Optional):

tito checkpoint status --detailed    # See exactly where you are
tito checkpoint timeline            # Visualize your progress

Ready to Build?#

Start Your TinyTorch Journey

Follow the 2-minute setup and begin building ML systems from scratch

2-Minute Setup → Student Workflow →

Master these commands and you’ll build ML systems with confidence. Every command is designed to accelerate your learning and keep you focused on what matters: building production-quality ML frameworks from scratch.

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