How to Run Tests for AI For Beginners: A Complete Guide to Validating Notebooks and the Quiz App

AI For Beginners does not include a traditional automated test suite; instead, you validate the repository by executing Jupyter notebooks manually and verifying the Vue quiz application through local serving and linting.

The microsoft/AI-For-Beginners repository is an educational curriculum rather than a software library. Its testing approach reflects this purpose: notebooks serve as executable specifications, and the quiz app relies on manual UI validation. This guide covers the complete workflow for running tests for AI For Beginners, from environment setup through verification.

Test the Jupyter Notebooks

The core educational content lives in Python notebooks under lessons/. Each notebook contains its own validation logic through executed cells that print metrics, visualize outputs, and raise exceptions on failure.

Step 1: Create the Conda Environment

The environment.yml file pins exact dependency versions for TensorFlow, PyTorch, Keras, OpenCV, and other libraries used throughout the curriculum.

conda env create -f environment.yml
conda activate ai4beg

This ensures reproducible execution across different systems, as noted in the repository's setup documentation.

Step 2: Launch Jupyter and Run Cells

Start Jupyter Lab or Notebook from the repository root:

jupyter lab

Navigate to any lesson folder under lessons/ and execute every cell sequentially. According to AGENTS.md, each notebook's cells act as "executable specifications"—if a cell raises an exception or produces unexpected output, that lesson has failed its test.

Key validation indicators within notebooks include:

• Printed model accuracy scores
• Generated visualizations (confusion matrices, training curves)
• Assertion cells that verify expected tensor shapes or values

Test the Vue Quiz Application

The interactive quiz app resides in etc/quiz-app/ and requires manual UI verification plus optional static analysis.

Manual UI Testing

cd etc/quiz-app
npm install
npm run serve

This launches a development server at http://localhost:8080. Navigate through quiz questions to verify:

• Questions render correctly
• Answer selection functions
• Progress tracking works
• Score calculation displays properly

Static Analysis and Linting

npm run lint

This runs ESLint with auto-fix enabled, catching style errors and basic code issues. For a production-ready build, use:

npm run build

The generated dist/ folder can be served locally or deployed to Azure Static Web Apps.

Why No Automated Test Suite Exists

The AGENTS.md file explicitly states that the curriculum "focuses on learning content rather than software testing." This architectural decision reflects the repository's educational mission:

• Notebook cells are designed to be run interactively, with outputs inspected by learners
• Manual verification encourages deeper engagement with the material
• Self-contained lessons reduce maintenance overhead across 24+ separate topics

Traditional unit or integration tests would add complexity without improving the learning experience, given that notebooks already fail loudly on execution errors.

Key Files for Testing Reference

File	Purpose
environment.yml	Exact dependency specification for reproducible notebook execution
lessons/**/*.ipynb	Core educational content—each notebook is its own test case
etc/quiz-app/README.md	Quiz app setup, serve, build, and lint instructions
AGENTS.md	Architecture overview confirming absence of traditional test suite

Troubleshooting Common Issues

Notebook Kernel Failures

If cells fail to execute, verify your Conda environment is activated and matches the Python version specified in environment.yml. Reinstall with:

conda env update -f environment.yml --prune

Quiz App npm Errors

Clear node_modules and lock files if dependencies conflict:

cd etc/quiz-app
rm -rf node_modules package-lock.json
npm install

CUDA/GPU Warnings

Many notebooks include optional GPU acceleration. CPU-only execution is fully supported; warnings about missing CUDA can be safely ignored for testing purposes.

Summary

• AI For Beginners testing is manual by design—no pytest, jest, or similar frameworks are included
• Validate notebooks via conda env create -f environment.yml → jupyter lab → execute all cells
• Verify the quiz app through npm run serve for UI checks and npm run lint for static analysis
• Each notebook's output serves as its own pass/fail indicator—exceptions and incorrect results signal test failure
• The repository's educational focus, documented in AGENTS.md, explains this intentional departure from conventional software testing practices

Frequently Asked Questions

Does AI For Beginners have unit tests?

No. According to AGENTS.md, the repository "focuses on learning content rather than software testing." The Jupyter notebooks themselves function as executable verification—if cells run without exceptions and produce expected outputs, the lesson passes.

How do I know if a notebook test failed?

A notebook fails its test if any cell raises an exception, produces incorrect output, or generates warnings about deprecated APIs. Successful execution means all cells complete and final output cells show expected metrics or visualizations.

Can I automate notebook testing in CI/CD?

Technically possible with tools like nbmake or nbconvert --execute, but not officially supported. The environment.yml dependencies are heavy (full TensorFlow/PyTorch stacks), making automated runs resource-intensive and slow.

What Node.js version works with the quiz app?

The etc/quiz-app/ directory uses Vue 2.x. Node.js 14.x or 16.x LTS versions are recommended for best compatibility with the existing package.json dependencies.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}