TITO Command Reference#
Master the TinyTorch CLI
Complete command reference for building ML systems efficiently
Purpose: Quick reference for all TITO commands. Find the right command for every task in your ML systems engineering journey.
Quick Start: Three Commands You Need#
1. Check Your Environment
tito system health
Verify your setup is ready for development
2. Build & Export Modules
tito module complete 01
Export your module to the TinyTorch package
3. Run Historical Milestones
tito milestone run 03
Recreate ML history with YOUR code
Commands by User Role#
TinyTorch serves three types of users. Choose your path:
Student / Learner
You're learning ML systems by building from scratch
Your Workflow:
# Start learning
tito module start 01
# Complete modules
tito module complete 01
# Validate with history
tito milestone run 03
# Track progress
tito module status
Key Commands:
tito module- Build componentstito milestone- Validatetito module status- Track progress
Instructor
You're teaching ML systems engineering
Your Workflow:
# Generate assignments
tito nbgrader generate 01
# Distribute to students
tito nbgrader release 01
# Collect & grade
tito nbgrader collect 01
tito nbgrader autograde 01
# Provide feedback
tito nbgrader feedback 01
Key Commands:
tito nbgrader- Assignment managementtito module- Test implementationstito milestone- Validate setups
👩💻 Developer / Contributor
You're contributing to TinyTorch modules
Your Workflow:
# Edit source code
# src/01_tensor/01_tensor.py
# Export to notebooks & package
tito src export 01_tensor
tito src export --all
# Test implementations
tito src test 01_tensor
# Validate changes
tito milestone run 03
Key Commands:
tito src- Developer workflowtito module- Test as studenttito milestone- Validate
Complete Command Reference#
System Commands#
Purpose: Environment health, validation, and configuration
Command |
Description |
Guide |
|---|---|---|
|
Environment health check and validation |
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|
System resources (paths, disk, memory) |
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|
Start Jupyter Lab server |
Module Commands#
Purpose: Build-from-scratch workflow (your main development cycle)
Command |
Description |
Guide |
|---|---|---|
|
Begin working on a module (first time) |
|
|
Continue working on a module |
|
|
Test, export, and track module completion |
|
|
View module completion progress |
|
|
Reset module to clean state |
See: Module Workflow Guide for complete details
Milestone Commands#
Purpose: Run historical ML recreations with YOUR implementations
Command |
Description |
Guide |
|---|---|---|
|
Show all 6 historical milestones (1957-2018) |
|
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Run milestone with prerequisite checking |
|
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Get detailed milestone information |
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View milestone progress and achievements |
|
|
Visual timeline of your journey |
See: Milestone System Guide for complete details
Progress & Data Commands#
Purpose: Track progress and manage user data
Command |
Description |
Guide |
|---|---|---|
|
View module completion progress |
|
|
View milestone achievements |
|
|
Reset a specific module |
See: Progress & Data Management for complete details
Community Commands#
Purpose: Join the global TinyTorch community and track your progress
Command |
Description |
Guide |
|---|---|---|
|
Log in to the community via web browser |
|
|
Log out of the community |
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|
View/edit your community profile |
|
|
Show login status and user info |
|
|
Open global community map |
See: Community Guide for complete details
Benchmark Commands#
Purpose: Validate setup and measure performance
Command |
Description |
Guide |
|---|---|---|
|
Quick setup validation (“Hello World”) |
|
|
Full Module 20 performance evaluation |
See: Community Guide for complete details
Developer Commands#
Purpose: Source code development and contribution (for developers only)
Command |
Description |
Use Case |
|---|---|---|
|
Export src/ → modules/ → tinytorch/ |
After editing source files |
|
Export all modules |
After major refactoring |
|
Run tests on source files |
During development |
Note: These commands work with src/XX_name/XX_name.py files and are for TinyTorch contributors/developers.
Students use tito module commands to work with generated notebooks.
Directory Structure:
src/ ← Developers edit here (Python source)
modules/ ← Students use these (generated notebooks)
tinytorch/ ← Package code (auto-generated)
Command Groups by Task#
First-Time Setup#
# Clone and setup
git clone https://github.com/harvard-edge/cs249r_book.git
cd TinyTorch
./setup-environment.sh
source activate.sh
# Verify environment
tito system health
Student Workflow (Learning)#
# Start or continue a module
tito module start 01 # First time
tito module resume 01 # Continue later
# Export when complete
tito module complete 01
# Check progress
tito module status
Developer Workflow (Contributing)#
# Edit source files in src/
vim src/01_tensor/01_tensor.py
# Export to notebooks + package
tito src export 01_tensor
# Test implementation
python -c "from tinytorch import Tensor; print(Tensor([1,2,3]))"
# Validate with milestones
tito milestone run 03
Achievement & Validation#
# See available milestones
tito milestone list
# Get details
tito milestone info 03
# Run milestone
tito milestone run 03
# View achievements
tito milestone status
Progress Management#
# View all progress
tito module status
tito milestone status
Typical Session Flow#
Here’s what a typical TinyTorch session looks like:
1. Start Session
cd TinyTorch
source activate.sh
tito system health # Verify environment
2. Work on Module
tito module start 03 # Or: tito module resume 03
# Edit in Jupyter Lab...
3. Export & Test
tito module complete 03
4. Run Milestone (when prerequisites met)
tito milestone list # Check if ready
tito milestone run 03 # Run with YOUR code
5. Track Progress
tito module status # See module progress
tito milestone status # See milestone achievements
Command Help#
Every command has detailed help text:
# Top-level help
tito --help
# Command group help
tito module --help
tito milestone --help
# Specific command help
tito module complete --help
tito milestone run --help
Detailed Guides#
Module Workflow - Complete guide to building and exporting modules
Milestone System - Running historical ML recreations
Progress & Data - Managing your learning journey
Troubleshooting - Common issues and solutions