How to Run the Code in Microsoft AI-For-Beginners: 4 Setup Methods Explained

Clone the repository, create the Conda environment from environment.yml, and launch Jupyter locally—or use Docker, GitHub Codespaces, or cloud GPUs for GPU-accelerated lessons.

The Microsoft AI-For-Beginners repository delivers a comprehensive machine-learning curriculum as Jupyter notebooks, Python scripts, and a Vue-based quiz application. Whether you prefer local development, containerized environments, or cloud execution, the repository provides multiple pathways to run the code with reproducible dependencies.

Prerequisites: The AI-For-Beginners Environment File

All execution methods rely on environment.yml at the repository root. This file pins exact versions of Python 3.8+, scientific libraries (NumPy, SciPy, scikit-learn, OpenCV), deep-learning frameworks (PyTorch, TorchVision, TorchText, TorchData), and visualization tools (Matplotlib, ipywidgets). Creating this environment ensures your runtime matches the curriculum exactly.

Method 1: Local Conda Setup (Recommended)

The most straightforward approach uses conda to replicate the author's environment.

Step 1: Clone and Create Environment

git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
conda env create --name ai4beg --file environment.yml
conda activate ai4beg

Step 2: Launch Jupyter

jupyter notebook

This opens http://localhost:8888 in your browser. Navigate to the lessons/ directory and open any .ipynb file. Notebooks in lessons/*/ are self-contained and download datasets lazily on first execution.

Alternative: VS Code with Python Extension

code .

VS Code detects the Python environment and prompts you to select the ai4beg interpreter for IntelliSense and debugging.

Method 2: VS Code Dev Containers (Docker)

For a containerized, reproducible workspace without manual dependency management:

1. Install Docker Desktop and VS Code with the Remote Containers extension.
2. Open the repository folder:

code .

3. When prompted, click "Reopen in Container". VS Code builds the container from .devcontainer/ configuration and mounts your workspace with all dependencies pre-installed.

This method is ideal if you want to avoid polluting your local Python installation or need to switch between projects with conflicting dependencies.

Method 3: GitHub Codespaces (Cloud Development)

The repository includes a GitHub Codespaces entry point for zero-installation development:

1. Navigate to the repository on GitHub.
2. Click Code → Codespaces → Create codespace on main.
3. A pre-configured Jupyter server launches in your browser with the environment.yml dependencies already resolved.

This runs in Microsoft's cloud infrastructure without consuming local resources.

Method 4: Cloud GPU Platforms (For Deep Learning Lessons)

GPU-intensive lessons—such as CNN training in lessons/4-Computer-Vision/ or transformer fine-tuning—require CUDA-capable hardware. The repository supports three cloud platforms:

Azure Data Science VM

# After provisioning a DSVM with GPU:

ssh user@ds_vm_ip
git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
conda env create --name ai4beg --file environment.yml
conda activate ai4beg
jupyter notebook --ip=0.0.0.0 --no-browser

Forward port 8888 via SSH tunnel to access the notebook interface locally.

Google Colab

1. Open colab.research.google.com.
2. Execute in a cell:

!git clone https://github.com/microsoft/AI-For-Beginners.git
!conda env update -n base -f AI-For-Beginners/environment.yml

3. Upload the target .ipynb from lessons/ and run.

Azure Machine Learning

Deploy the repository as an AML notebook VM with GPU Compute Instance. The lessons/0-course-setup/how-to-run.md file contains Azure-specific provisioning instructions.

Method 5: Binder (No Installation)

For quick experiments without any setup, click the Launch Binder badge on the repository homepage or navigate directly to:

https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD

Binder builds a temporary environment from environment.yml and serves Jupyter. Note: Sessions time out after inactivity and lack persistent storage.

Key Files for Running AI-For-Beginners Code

Path	Purpose
environment.yml	Conda environment specification with pinned dependency versions
lessons/0-course-setup/how-to-run.md	Primary documentation for all execution methods
lessons/0-course-setup/setup.md	Supplementary miniconda installation guide
.devcontainer/	Docker configuration for VS Code Remote Containers
requirements.txt	Pip dependencies referenced by environment.yml
lessons/*/*.ipynb	Executable lesson notebooks with embedded exercises

Troubleshooting Common Issues

• Import errors: Ensure you activated the ai4beg environment (conda activate ai4beg) before launching Jupyter.
• GPU not detected: Verify CUDA drivers or switch to CPU fallback in notebook settings.
• Dataset download failures: Check internet connectivity; notebooks cache downloads to ~/.cache/.

Summary

• Clone the repository from https://github.com/microsoft/AI-For-Beginners.git.
• Create the Conda environment using conda env create --name ai4beg --file environment.yml to lock dependency versions.
• Run locally with jupyter notebook, containerize with VS Code Dev Containers, or deploy to cloud via GitHub Codespaces, Azure DSVM, or Google Colab.
• Use GPU platforms for lessons requiring CUDA acceleration (CNNs, transformers).
• Reference lessons/0-course-setup/how-to-run.md for platform-specific guidance maintained by the Microsoft curriculum team.

Frequently Asked Questions

Does AI-For-Beginners require a GPU to run the code?

No. Most foundational lessons run on CPU-only hardware. GPU acceleration is only necessary for deep-learning modules involving convolutional neural networks, recurrent networks, and transformer fine-tuning. The notebooks detect GPU availability automatically and gracefully degrade to CPU execution.

What Python version does AI-For-Beginners require?

The environment.yml specifies Python 3.8 or newer. Using the Conda environment guarantees compatibility with all curriculum dependencies, including PyTorch and scientific libraries with compiled extensions.

Can I run AI-For-Beginners notebooks in Google Colab?

Yes. Colab supports the repository with minor adaptation: clone the repo, update the base environment using environment.yml, and upload individual notebooks. GPU runtimes in Colab provide free access to CUDA-capable hardware for intensive lessons.

Where are the lesson notebooks located in the repository?

All instructional content resides under lessons/ with subdirectories organized by topic (e.g., 1-Intro/, 4-Computer-Vision/). Each subdirectory contains .ipynb files paired with optional Python scripts and lab instructions. The lessons/0-course-setup/ folder contains execution documentation rather than curriculum material.

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