# TinyTorch for Instructors: Complete ML Systems Course

✅ Classroom Integration Available

TinyTorch includes complete NBGrader integration with automated grading workflows. See the Complete Instructor Guide for setup, grading rubrics, and sample solutions.

📖 Course Vision: This page describes the planned TinyTorch classroom experience.
📖 For Current Usage: Students should follow the Student Workflow guide.

🏫 Planned: Turn-Key ML Systems Education

Transform students from framework users to systems engineers

**Vision:** Replace black-box API courses with deep systems understanding. Students will build neural networks from scratch, understand every operation, and graduate job-ready for ML engineering roles. --- ## 🎯 Planned Course Infrastructure

Planned Features: Production-Ready Course Materials

Three-tier progression (20 modules) with [NBGrader](https://nbgrader.readthedocs.io/) integration
Automated grading for immediate feedback
Professional CLI tools for development workflow
Real datasets (CIFAR-10, text generation)

Complete instructor guide with setup & grading ([available now](../instructor-guide.md))
Flexible pacing (14-18 weeks depending on depth)
Industry practices (Git, testing, documentation)
Academic foundation from university research

**Planned Course Duration:** 14-16 weeks (flexible pacing) **Student Outcome:** Complete ML framework supporting vision AND language models **Current Status:** Complete NBGrader integration available! See the [Instructor Guide](../instructor-guide.md) for setup, grading workflows, and sample solutions. --- ## 🌟 Why TinyTorch for Your Classroom

🎯 Deep Learning Outcomes

Students build neural networks from scratch

Graduates understand deep systems architecture
Can debug ML issues from first principles
Prepared for ML engineering roles
Confident implementing novel architectures

⚡ Zero-Setup Teaching

30-minute instructor setup, then focus on teaching

NBGrader integration: Automated grading & feedback
One-command workflows: Generate, release, collect assignments
Progress dashboards: Track all students at a glance
Flexible pacing: Adapt to your semester schedule

🏆 Industry-Standard Workflow

Students learn professional ML engineering practices

Git workflow: Feature branches, commits, merges
CLI tools: Professional development environment
Testing culture: Every implementation immediately validated
Documentation: Clear code, explanations, insights

🔬 Deep Systems Understanding

Beyond APIs: Students understand how ML really works

Memory analysis: Profile and optimize resource usage
Performance insights: Understand computational complexity
Production context: How PyTorch/TensorFlow actually work
Systems thinking: Architecture, scaling, optimization

--- ## Course Module Overview The TinyTorch course consists of 20 progressive modules organized into learning stages. **📖 See [Complete Course Structure](../chapters/00-introduction.md)** for detailed module descriptions, learning objectives, and prerequisites for each module. --- ## Academic Learning Goals **What Students Will Achieve:** - Build deep systems understanding through implementation - Bridge gap between ML theory and engineering practice - Prepare for real-world ML systems challenges - Enable research into novel architectures and optimizations **Core Capabilities Developed:** - Implement neural networks from scratch - Understand autograd and backpropagation deeply - Optimize models for production deployment - Build complete frameworks supporting vision and language --- ## 🚀 Quick Start for Instructors

⏱️ 30 Minutes to Teaching-Ready Course

Three simple steps to transform your ML teaching

1️⃣ Clone & Setup (10 min)

git clone TinyTorch

cd TinyTorch

source .venv/bin/activate

pip install -r requirements.txt

One-time environment setup

2️⃣ Initialize Course (10 min)

tito nbgrader init

tito module status --comprehensive

NBGrader integration & health check

3️⃣ First Assignment (10 min)

tito nbgrader generate 01_tensor

tito nbgrader release 01_tensor

Ready to distribute to students!

📖 Complete Instructor Guide 👥 TA Guide 🧪 Testing Framework 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 ### Essential Documentation - **[Complete Instructor Guide](../instructor-guide.md)** - 30-minute setup, grading rubrics, sample solutions, common errors - **[TA Guide](ta-guide.md)** - Common student errors, debugging strategies, office hour patterns - Module-specific teaching notes in each ABOUT.md file - [Course Structure](../chapters/00-introduction.md) - Full curriculum overview - [Student Workflow](../student-workflow.md) - Essential development cycle ### 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 --- ## 📞 Next Steps 1. **📖 Review [Course Structure](../chapters/00-introduction.md)** for complete curriculum overview 2. **🚀 Explore [Student Workflow](../student-workflow.md)** to understand the development cycle 3. **💻 Set up your environment** using the [Quick Start Guide](../quickstart-guide.md) 4. **📧 Contact us** via GitHub Issues for instructor support --- *Ready to teach the most comprehensive ML systems course? Let's build something amazing together!* 🎓