TinyTorch for Instructors: Complete ML Systems Course

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.html))
  • 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)

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!

📋 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#

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 for complete curriculum overview

  2. 🚀 Explore Student Workflow to understand the development cycle

  3. 💻 Set up your environment using the Quick Start Guide

  4. 📧 Contact us via GitHub Issues for instructor support

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

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