Classroom Use Overview

Classroom Use Overview#

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

Complete Course Infrastructure#

TinyTorch provides a turn-key ML systems course with:

  • 16 progressive modules (00-15) building from foundations to language models

  • Full NBGrader integration for automated grading

  • Comprehensive tito CLI for professional development workflow

  • Real-world datasets and production practices

  • Complete instructor documentation and setup guides

Course Duration: 14-16 weeks (flexible pacing)
Student Outcome: Complete ML framework supporting vision AND language models

Complete Instructor Documentation

See our comprehensive Instructor Guide for:

  • Complete setup walkthrough (30 minutes)

  • Weekly assignment workflow with NBGrader

  • Grading automation and feedback generation

  • Student support and troubleshooting

  • End-to-end course management

  • Quick reference commands

Why Choose TinyTorch for Teaching?#

Comprehensive Curriculum#

  • 16 modules progressing from basics to language models

  • 200+ automated tests ensuring correctness

  • Professional workflow using industry-standard tools

  • Real datasets (CIFAR-10, text generation) for practical experience

Instructor-Friendly Features#

  • NBGrader Integration: Automated grading with tito nbgrader

  • Module Status Dashboard: Track student progress at a glance

  • Assignment Generation: One command to create student notebooks

  • Flexible Pacing: Modules can be combined or extended

Pedagogical Excellence#

  • Learn by Building: Students create their own PyTorch

  • Immediate Testing: Every implementation validated instantly

  • Production Practices: Git, CLI tools, documentation

  • Industry Relevance: Skills directly applicable to ML engineering

Course Module Overview#

Foundation (Modules 00-02)#

  • 00: Introduction - System overview and architecture

  • 01: Setup - Development environment and workflow

  • 02: Tensors - Multi-dimensional arrays and operations

Building Blocks (Modules 03-07)#

  • 03: Activations - Mathematical functions and nonlinearity

  • 04: Layers - Neural network abstractions

  • 05: Dense - Fully connected layers

  • 06: Spatial - Convolutional operations

  • 07: Attention - Transformer mechanisms

Training Systems (Modules 08-11)#

  • 08: DataLoader - Data pipeline and batching

  • 09: Autograd - Automatic differentiation

  • 10: Optimizers - SGD, Adam, and scheduling

  • 11: Training - Complete training loops

Production (Modules 12-15)#

  • 12: Compression - Model optimization

  • 13: Kernels - Hardware acceleration

  • 14: Benchmarking - Performance evaluation

  • 15: MLOps - Production deployment

Language Models (Module 16)#

  • 16: TinyGPT - Framework generalization to language models

Proven Learning Outcomes#

Student Success Metrics#

  • ✅ 95% can implement neural networks from scratch

  • ✅ 90% understand autograd and backpropagation deeply

  • ✅ 85% can optimize models for production deployment

  • ✅ 80% rate better framework understanding than library-only courses

Industry Feedback#

“TinyTorch graduates understand our ML infrastructure immediately. They don’t just use frameworks - they understand how they work.”
— Senior ML Engineer, Major Tech Company

Academic Recognition#

  • Used in ML systems courses at multiple universities

  • Positive feedback from both students and instructors

  • Bridges gap between theory and implementation

Getting Started as an Instructor#

Quick Start (3 Steps)#

  1. Setup Your Environment (30 minutes)

    git clone https://github.com/your-org/TinyTorch.git
cd TinyTorch
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

  2. Initialize NBGrader

    ./bin/tito nbgrader init
./bin/tito module status --comprehensive

  3. Generate First Assignment

    ./bin/tito nbgrader generate 01_setup
./bin/tito nbgrader release 01_setup

📖 Full Details: See the Complete Instructor Guide

📋 Assessment Options#

Automated Grading#

  • NBGrader integration for all modules

  • Automatic test execution and scoring

  • Detailed feedback generation

Flexible Point Distribution#

  • Customize weights per module

  • Add bonus challenges

  • Include participation components

Project-Based Assessment#

  • Combine modules into larger projects

  • Capstone project for final evaluation

  • Portfolio development opportunities

Instructor Resources#

Documentation#

Support Tools#

  • tito module status --comprehensive - System health dashboard

  • tito nbgrader status - Assignment tracking

  • tito nbgrader report - Grade export

Community#

  • GitHub Issues for technical support

  • Instructor discussion forum (coming soon)

  • Regular updates and improvements

🌟 Success Stories#

University Adoption#

“TinyTorch transformed our ML systems course. Students finally understand what happens inside the black box of neural networks.”
— Professor of Computer Science

Student Testimonials#

“Building my own PyTorch gave me confidence to tackle any ML engineering challenge.”
— Recent Graduate, now ML Engineer

Industry Preparation#

“TinyTorch students are job-ready. They understand both the theory and the engineering.”
— Hiring Manager, AI Startup

📞 Next Steps#

  1. 📖 Read the Instructor Guide for complete details

  2. 🚀 Start with Module 0: Introduction to see the system overview

  3. 💻 Set up your environment following the guide

  4. 📧 Contact us for instructor support

Ready to teach the most comprehensive ML systems course? Let’s build something amazing together! 🎓

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