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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.
📖 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 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 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)
cd TinyTorch
source .venv/bin/activate
pip install -r requirements.txt
One-time environment setup
2️⃣ Initialize Course (10 min)
tito module status --comprehensive
NBGrader integration & health check
3️⃣ First Assignment (10 min)
tito nbgrader release 01_tensor
Ready to distribute to students!
📋 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 - 30-minute setup, grading rubrics, sample solutions, common errors
- TA Guide - Common student errors, debugging strategies, office hour patterns
- Module-specific teaching notes in each ABOUT.md file
- Course Structure - Full curriculum overview
- Student Workflow - Essential development cycle
Support Tools
tito module status --comprehensive- System health dashboardtito nbgrader status- Assignment trackingtito nbgrader report- Grade export
Community
- GitHub Issues for technical support
- Instructor discussion forum (coming soon)
- Regular updates and improvements
📞 Next Steps
- 📖 Review Course Structure for complete curriculum overview
- 🚀 Explore Student Workflow to understand the development cycle
- 💻 Set up your environment using the Quick Start Guide
- 📧 Contact us via GitHub Issues for instructor support
Ready to teach the most comprehensive ML systems course? Let's build something amazing together! 🎓