TinyTorch/docs/nbgrader/NBGrader_Quick_Reference.md

📋 NBGrader Quick Reference Card

TinyTorch + NBGrader Essential Commands for Instructors

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🚀 One-Time Setup

# 1. Setup environment
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 2. Initialize NBGrader
./bin/tito nbgrader init

# 3. Verify setup
./bin/tito system health

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📝 Weekly Assignment Workflow

Monday: Release New Assignment

# Generate assignment from TinyTorch module
./bin/tito nbgrader generate 03_activations

# Create student version
./bin/tito nbgrader release 03_activations

# Upload assignments/release/03_activations/03_activations.ipynb to LMS

Friday: Grade Submissions

# After downloading student submissions to assignments/submitted/
./bin/tito nbgrader autograde 03_activations
./bin/tito nbgrader feedback 03_activations

# Return assignments/feedback/ files to students

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🔧 Essential Commands

Status & Monitoring

./bin/tito module status --comprehensive    # System health
./bin/tito nbgrader status                  # Assignment status
./bin/tito nbgrader analytics MODULE_NAME   # Student progress

Batch Operations

./bin/tito nbgrader generate --all          # All assignments
./bin/tito nbgrader generate --range 01-04  # Module range
./bin/tito nbgrader autograde --all         # Grade everything
./bin/tito nbgrader feedback --all          # Generate all feedback

Export & Cleanup

./bin/tito nbgrader report --format csv     # Export gradebook
./bin/tito clean                            # Clean temp files

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📁 Directory Structure

assignments/
├── source/        # Generated assignments (git tracked)
├── release/       # Student versions (git tracked)
├── submitted/     # Student submissions (git ignored)
├── autograded/    # Graded submissions (git ignored)
└── feedback/      # Student feedback (git ignored)

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🆘 Quick Troubleshooting

# Environment issues
source .venv/bin/activate
./bin/tito system health

# Module not found
ls modules/                          # Check available modules
./bin/tito nbgrader generate 02_tensor      # Use exact name

# Validation failures (normal for student notebooks)
# Students have unimplemented functions = expected behavior

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📚 Course Planning

17 TinyTorch Modules:

• 00-02: Foundation (intro, setup, tensors)
• 03-07: Building Blocks (activations, layers, dense, spatial, attention)
• 08-11: Training (dataloader, autograd, optimizers, training)
• 12-16: Production (compression, kernels, benchmarking, mlops, capstone)

Recommended Pacing: 1 module per week = 16-week semester

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📖 For complete details: See Instructor Guide