TinyTorch/docs/INSTRUCTOR_GUIDE.md

👩‍🏫 TinyTorch Instructor Guide

Complete guide for teaching ML Systems Engineering with TinyTorch.

🎯 Course Overview

TinyTorch teaches ML systems engineering through building, not just using. Students construct a complete ML framework from tensors to transformers, understanding memory, performance, and scaling at each step.

🛠️ Instructor Setup

1. Initial Setup

# Clone and setup
git clone https://github.com/MLSysBook/TinyTorch.git
cd TinyTorch

# Virtual environment (MANDATORY)
python -m venv .venv
source .venv/bin/activate

# Install with instructor tools
pip install -r requirements.txt
pip install nbgrader

# Setup grading infrastructure
tito grade setup

2. Verify Installation

tito system doctor
# Should show all green checkmarks

tito grade
# Should show available grade commands

📝 Assignment Workflow

Simplified with Tito CLI

We've wrapped NBGrader behind simple tito grade commands so you don't need to learn NBGrader's complex interface.

1. Prepare Assignments

# Generate instructor version (with solutions)
tito grade generate 01_setup

# Create student version (solutions removed)
tito grade release 01_setup

# Student version will be in: release/tinytorch/01_setup/

2. Distribute to Students

# Option A: GitHub Classroom (recommended)
# 1. Create assignment repository from TinyTorch
# 2. Remove solutions from modules
# 3. Students clone and work

# Option B: Direct distribution
# Share the release/ directory contents

3. Collect Submissions

# Collect all students
tito grade collect 01_setup

# Or specific student
tito grade collect 01_setup --student student_id

4. Auto-Grade

# Grade all submissions
tito grade autograde 01_setup

# Grade specific student
tito grade autograde 01_setup --student student_id

5. Manual Review

# Open grading interface (browser-based)
tito grade manual 01_setup

# This launches a web interface for:
# - Reviewing ML Systems question responses
# - Adding feedback comments
# - Adjusting auto-grades

6. Generate Feedback

# Create feedback files for students
tito grade feedback 01_setup

7. Export Grades

# Export all grades to CSV
tito grade export

# Or specific module
tito grade export --module 01_setup --output grades_module01.csv

📊 Grading Components

Auto-Graded (70%)

• Code implementation correctness
• Test passing
• Function signatures
• Output validation

Manually Graded (30%)

• ML Systems Thinking questions (3 per module)
• Each question: 10 points
• Focus on understanding, not perfection

Grading Rubric for ML Systems Questions

Points	Criteria
9-10	Demonstrates deep understanding, references specific code, discusses systems implications
7-8	Good understanding, some code references, basic systems thinking
5-6	Surface understanding, generic response, limited systems perspective
3-4	Attempted but misses key concepts
0-2	No attempt or completely off-topic

What to Look For:

• References to actual implemented code
• Memory/performance analysis
• Scaling considerations
• Production system comparisons
• Understanding of trade-offs

📚 Module Teaching Notes

Module 01: Setup

• Focus: Environment configuration, systems thinking mindset
• Key Concept: Development environments matter for ML systems
• Common Issues: Virtual environment confusion

Module 02: Tensor

• Focus: Memory layout, data structures
• Key Concept: Understanding memory is crucial for ML performance
• Demo: Show memory profiling, copying behavior

Module 03: Activations

• Focus: Vectorization, numerical stability
• Key Concept: Small details matter at scale
• Demo: Gradient vanishing/exploding

Module 04-05: Layers & Networks

• Focus: Composition, parameter management
• Key Concept: Building blocks combine into complex systems
• Project: Build a small CNN

Module 06-07: Spatial & Attention

• Focus: Algorithmic complexity, memory patterns
• Key Concept: O(N²) operations become bottlenecks
• Demo: Profile attention memory usage

Module 08-11: Training Pipeline

• Focus: End-to-end system integration
• Key Concept: Many components must work together
• Project: Train a real model

Module 12-15: Production

• Focus: Deployment, optimization, monitoring
• Key Concept: Academic vs production requirements
• Demo: Model compression, deployment

Module 16: TinyGPT

• Focus: Framework generalization
• Key Concept: 70% component reuse from vision to language
• Capstone: Build a working language model

🎯 Learning Objectives

By course end, students should be able to:

1. Build complete ML systems from scratch
2. Analyze memory usage and computational complexity
3. Debug performance bottlenecks
4. Optimize for production deployment
5. Understand framework design decisions
6. Apply systems thinking to ML problems

📈 Tracking Progress

Individual Progress

# Check specific student progress
tito checkpoint status --student student_id

Class Overview

# Export all checkpoint achievements
tito checkpoint export --output class_progress.csv

Identify Struggling Students

Look for:

• Missing checkpoint achievements
• Low scores on ML Systems questions
• Incomplete module submissions

💡 Teaching Tips

1. Emphasize Building Over Theory

• Have students type every line of code
• Run tests immediately after implementation
• Break and fix things intentionally

2. Connect to Production Systems

• Show PyTorch/TensorFlow equivalents
• Discuss real-world bottlenecks
• Share production war stories

3. Make Performance Visible

# Use profilers liberally
with TimeProfiler("operation"):
    result = expensive_operation()
    
# Show memory usage
print(f"Memory: {get_memory_usage():.2f} MB")

4. Encourage Systems Questions

• "What would break at 1B parameters?"
• "How would you distributed this?"
• "What's the bottleneck here?"

🔧 Troubleshooting

Common Student Issues

Environment Problems

# Student fix:
tito system doctor
tito system reset

Module Import Errors

# Rebuild package
tito export --all

Test Failures

# Detailed test output
tito module test MODULE --verbose

NBGrader Issues

Database Locked

# Clear NBGrader database
rm gradebook.db
tito grade setup

Missing Submissions

# Check submission directory
ls submitted/*/MODULE/

📊 Sample Schedule (16 Weeks)

Week	Module	Focus
1	01 Setup	Environment, Tools
2	02 Tensor	Data Structures
3	03 Activations	Functions
4	04 Layers	Components
5	05 Dense	Networks
6	06 Spatial	Convolutions
7	07 Attention	Transformers
8	Midterm Project	Build CNN
9	08 Dataloader	Data Pipeline
10	09 Autograd	Differentiation
11	10 Optimizers	Training Algorithms
12	11 Training	Complete Pipeline
13	12 Compression	Optimization
14	13 Kernels	Performance
15	14-15 MLOps	Production
16	16 TinyGPT	Capstone

🎓 Assessment Strategy

Continuous Assessment (70%)

• Module completion: 4% each × 16 = 64%
• Checkpoint achievements: 6%

Projects (30%)

• Midterm: Build and train CNN (15%)
• Final: Extend TinyGPT (15%)

📚 Additional Resources

• MLSys Book - Companion textbook
• Course Discussions
• Issue Tracker

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Need help? Open an issue or contact the TinyTorch team!