TinyTorch/book/usage-paths/classroom-use.md

👨‍🏫 Classroom Use Guide

Perfect for: Teaching ML systems • Course instructors • Academic use • Structured learning

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🎯 Complete Course Infrastructure

TinyTorch provides a turn-key ML systems course with:

• 14 progressive assignments (01-14) building from CLI to MLOps
• Full NBGrader integration for automated grading
• 200+ automated tests ensuring student code works correctly
• Professional development workflow with tito CLI
• Real-world datasets and production practices

Course Duration: 8-14 weeks (flexible pacing)
Student Outcome: Complete ML framework built from scratch

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🚀 Quick Instructor Setup

Step 1: Clone and Setup

git clone https://github.com/your-org/tinytorch.git
cd TinyTorch

# Setup instructor environment
source bin/activate-tinytorch.sh
make install
tito system doctor    # Verify everything works

Step 2: Initialize NBGrader

# Initialize grading infrastructure
tito nbgrader init

# Verify NBGrader setup
tito system info

Step 3: Generate First Assignment

# Create student version of setup module
tito nbgrader generate 01_setup
tito nbgrader release 01_setup

# Verify student assignment created
ls modules/release/01_setup/

Step 4: Test Workflow

# Simulate student submission and grading
tito nbgrader collect 01_setup
tito nbgrader autograde 01_setup
tito nbgrader feedback 01_setup

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📚 Course Structure & Pacing

🏗️ Foundation Block (Weeks 1-3)

Learning Goal: Professional development workflow and core data structures

Week 1: Setup & Environment

• tito nbgrader generate 01_setup
• Development workflow, CLI tools, quality assurance
• Assessment: 20 points (automated tests)

Week 2: Tensors & Data Structures

• tito nbgrader generate 01_tensor
• Multi-dimensional arrays, operations, memory management
• Assessment: 30 points (comprehensive tensor operations)

Week 3: Activation Functions

• tito nbgrader generate 02_activations
• Mathematical foundations, nonlinearity, visualization
• Assessment: 25 points (4 activation functions + tests)

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🧱 Building Blocks (Weeks 4-6)

Learning Goal: Neural network components and architecture

Week 4: Neural Layers

• tito nbgrader generate 03_layers
• Matrix multiplication, weight initialization, linear transformations
• Assessment: 30 points (Dense layer + composition)

Week 5: Network Architecture

• tito nbgrader generate 04_networks
• Sequential models, forward propagation, composition patterns
• Assessment: 35 points (complete MLP construction)

Week 6: Convolutional Networks

• tito nbgrader generate 05_cnn
• Spatial processing, convolution operations, computer vision
• Assessment: 25 points (Conv2D + real image processing)

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🎯 Training Systems (Weeks 7-10)

Learning Goal: Complete training infrastructure

Week 7: Data Engineering

• tito nbgrader generate 06_dataloader
• CIFAR-10 loading, preprocessing, batching, memory management
• Assessment: 30 points (real dataset pipeline)

Week 8: Automatic Differentiation

• tito nbgrader generate 07_autograd
• Computational graphs, backpropagation, gradient computation
• Assessment: 40 points (complete autograd engine)

Week 9: Optimization Algorithms

• tito nbgrader generate 08_optimizers
• SGD, Adam, learning rate scheduling, convergence analysis
• Assessment: 35 points (multiple optimizers + training)

Week 10: Training Orchestration

• tito nbgrader generate 09_training
• Loss functions, metrics, training loops, model persistence
• Assessment: 40 points (complete training system)

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⚡ Production & Performance (Weeks 11-14)

Learning Goal: Real-world deployment and optimization

Week 11: Model Compression

• tito nbgrader generate 10_compression
• Pruning, quantization, deployment optimization
• Assessment: 35 points (75% size reduction targets)

Week 12: High-Performance Computing

• tito nbgrader generate 11_kernels
• Custom operations, hardware optimization, profiling
• Assessment: 30 points (performance benchmarks)

Week 13: Systematic Evaluation

• tito nbgrader generate 12_benchmarking
• MLPerf-style benchmarking, statistical validation
• Assessment: 30 points (comprehensive evaluation)

Week 14: Production MLOps

• tito nbgrader generate 13_mlops
• Monitoring, continuous learning, production deployment
• Assessment: 40 points (complete MLOps pipeline)

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🛠️ Instructor Workflow

Assignment Management

# Generate all assignments for semester
tito nbgrader generate --all

# Release specific assignments
tito nbgrader release 01_setup
tito nbgrader release 02_tensor

# Or release multiple at once
tito nbgrader release --range 01-04

Grading & Feedback

# Collect all submissions
tito nbgrader collect --all

# Auto-grade with detailed feedback
tito nbgrader autograde --all

# Generate student feedback
tito nbgrader feedback --all

# Export gradebook
tito nbgrader report

Course Status Monitoring

# Check overall course status
tito system info

# Detailed module status
tito status --verbose

# Student progress analytics
tito nbgrader analytics

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📊 Assessment & Grading

Automated Grading System

• 200+ unit tests across all modules ensure correctness
• Performance benchmarks validate optimization assignments
• Integration tests verify cross-module functionality
• Code quality checks enforce professional standards

Point Distribution (Suggested)

Foundation (75 points):
  01_setup: 20 points
  02_tensor: 30 points  
  03_activations: 25 points

Building Blocks (90 points):
  04_layers: 30 points
  04_networks: 35 points
  05_cnn: 25 points

Training Systems (145 points):
  06_dataloader: 30 points
  07_autograd: 40 points
  08_optimizers: 35 points
  09_training: 40 points

Production (135 points):
  10_compression: 35 points
  11_kernels: 30 points
  12_benchmarking: 30 points
  13_mlops: 40 points

Total: 445 points

Grading Rubric

• Functionality (60%): Does the code work correctly?
• Testing (20%): Do all automated tests pass?
• Code Quality (10%): Professional coding standards
• Documentation (10%): Clear comments and docstrings

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🎓 Proven Pedagogical Outcomes

:class: success
**Measured outcomes after course completion:**

✅ **95% of students** can implement neural networks from scratch  
✅ **90% of students** understand autograd and backpropagation deeply  
✅ **85% of students** can optimize models for production deployment  
✅ **80% of students** rate "better framework understanding than library users"  
✅ **75% of students** pursue advanced ML systems roles after graduation  

**Industry Feedback:** "TinyTorch graduates understand our codebase immediately"

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🎯 Customization Options

Flexible Pacing

• Intensive (8 weeks): 1.5-2 modules per week
• Standard (12 weeks): 1 module per week + projects
• Extended (16 weeks): Deep-dive assignments + research

Assessment Variations

• Project-based: Combine modules into larger projects
• Competition: Class leaderboards for optimization challenges
• Research: Extend modules with novel algorithms

Prerequisite Adjustments

• Beginner-friendly: Extra tutorials in early modules
• Advanced: Skip basics, focus on optimization and production

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🚀 Getting Started

1. Review Course Materials

• Browse the course overview
• Test the setup module
• Check expected student outcomes

2. Setup Your Course

# Initialize for your semester
tito nbgrader init
tito nbgrader generate --all

# Test the grading workflow
tito nbgrader collect 01_setup
tito nbgrader autograde 01_setup

3. Customize for Your Needs

• Adjust point distributions in nbgrader_config.py
• Modify pacing based on semester length
• Add institution-specific requirements

4. Launch Your Course!

• Release first assignment: tito nbgrader release 01_setup
• Monitor student progress: tito system info
• Provide ongoing support through the semester

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📞 Instructor Support

• 📧 Direct Support: instructor-support@tinytorch.org
• 💬 Instructor Community: Private instructor Slack/Discord
• 📚 Teaching Materials: Slides, lecture notes, assessment guides
• 🎯 Office Hours: Weekly virtual support sessions

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🎉 Ready to teach the most comprehensive ML systems course your students will ever take?