# How to Set Up the Development Environment for AI‑For‑Beginners: A Complete Guide

> Set up your AI-For-Beginners development environment easily. Install Miniconda, clone the repo, create the ai4beg environment, and launch Jupyter Lab to start learning AI.

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-24

---

**To set up the development environment for AI‑For‑Beginners, install Miniconda, clone the repository, and create the `ai4beg` environment using the [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file in `.devcontainer/`, then launch Jupyter Lab to run the curriculum notebooks.**

The *AI‑For‑Beginners* curriculum by Microsoft is a comprehensive 12‑week course delivered through Jupyter notebooks, Python scripts, and a Vue.js quiz application. To follow along with the hands‑on lessons covering TensorFlow, PyTorch, and computer vision, you need a properly configured Python environment. This guide walks you through setting up the development environment for AI‑For‑Beginners using the exact dependency specifications found in the repository source code.

## Prerequisites for Local Development

Before cloning the repository, ensure you have the foundational tools installed. The curriculum is optimized for **conda** package management, though Docker provides an isolated alternative.

- **Miniconda**: Download and install the lightweight Miniconda installer for your operating system to access the `conda` command.
- **Docker** (optional): Required only if you plan to use the VS Code devcontainer workflow described later.
- **VS Code** (optional): Provides the best editing experience for Jupyter notebooks when paired with the Python extension.

## Clone the Microsoft AI‑For‑Beginners Repository

The repository contains over 50 language translations, making it large. If you only need the English content, use a sparse checkout to skip the `translations` and `translated_images` folders.

```bash

# Standard clone

git clone https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners

# Or, sparse checkout to exclude translations (faster)

git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
cd AI-For-Beginners
git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'

```

## Create the Conda Environment

The repository includes a complete environment definition at [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml). This file pins specific versions of deep learning frameworks including **TensorFlow 2.17**, **PyTorch**, **Keras 3.5**, **OpenCV**, and **scikit‑learn**.

Run the following commands from the repository root to build and activate the environment:

```bash
conda env create --name ai4beg --file .devcontainer/environment.yml
conda activate ai4beg

```

According to the source code in [`lessons/0-course-setup/how-to-run.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/0-course-setup/how-to-run.md), this creates an isolated Python environment named `ai4beg` that matches the curriculum's exact package requirements.

## Launch Jupyter Locally

Once the environment is active, start the Jupyter server to access the lesson notebooks stored in the `lessons/` directory.

```bash
jupyter notebook

# Alternatively, for the modern interface:

jupyter lab

```

You can now navigate to any `.ipynb` file under `lessons/` and execute the cells interactively.

## VS Code Devcontainer Setup (Docker Alternative)

For a reproducible, containerized environment, the repository ships a `.devcontainer` configuration that lets VS Code automatically build and attach to a Docker image.

The configuration uses:
- [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json): Defines container settings, including the Python path at `/opt/conda/envs/ai4beg/bin/python`.
- `.devcontainer/Dockerfile`: Builds the image with Miniconda and the `ai4beg` environment pre‑installed.

To use this method:

1. Open the cloned folder in VS Code.
2. Install the *Dev Containers* extension if not present.
3. Run the command **"Reopen in Container"**.

VS Code will build the image using the Dockerfile and mount your workspace, ensuring identical dependencies across Windows, macOS, and Linux machines.

## Cloud and GPU Execution Options

While local CPU execution works for introductory lessons, advanced topics like CNN training, GANs, and Transformers benefit from GPU acceleration.

**Cloud environments**:

- **GitHub Codespaces**: Click **Code** → **Codespaces** on the GitHub repository page to launch a browser‑based VS Code instance with the devcontainer pre‑configured.
- **Binder**: Click the *Binder* badge in the [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) to launch a temporary Jupyter server (note: compute‑heavy lessons may run slowly on the free tier).

**GPU acceleration**:

- **Azure Data Science VM** (NC‑series): Provision a VM with NVIDIA GPUs, clone the repository, and run notebooks directly.
- **Azure Machine Learning Workspace**: Use the integrated notebook service with GPU compute targets.
- **Google Colab**: Upload individual notebooks and select **Runtime** → **Change runtime type** → **GPU**.

## Summary

Setting up the development environment for AI‑For‑Beginners requires only a few precise steps:

- Install **Miniconda** to manage Python dependencies isolated from your system.
- Clone the repository using standard Git or sparse checkout to exclude translation files.
- Create and activate the **`ai4beg`** environment using [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml), which includes TensorFlow 2.17, PyTorch, and Keras 3.5.
- Launch **Jupyter Lab** locally, or use the **VS Code devcontainer** for a Docker‑based workflow.
- Leverage **GitHub Codespaces**, **Azure VMs**, or **Google Colab** for cloud‑based or GPU‑accelerated execution.

## Frequently Asked Questions

### Do I need a GPU to complete the AI‑For‑Beginners curriculum?

No. The foundational lessons run entirely on CPU. However, Weeks 4–12 covering Convolutional Neural Networks, GANs, and Transformers execute significantly faster with GPU acceleration. You can use Azure Data Science VMs, Azure Machine Learning, or Google Colab for these compute‑intensive modules.

### Can I install the dependencies with pip instead of conda?

While possible, the repository officially supports **conda** via the [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) file. Using pip may result in version conflicts with TensorFlow 2.17 and Keras 3.5, which are tightly coupled in the curriculum. If you must use pip, manually cross‑reference the package versions listed in the environment file.

### How do I update the environment when the curriculum changes?

If Microsoft updates the dependencies, pull the latest changes with `git pull`, then update your conda environment:

```bash
conda env update --name ai4beg --file .devcontainer/environment.yml --prune

```

The `--prune` flag removes packages no longer listed in the specification, ensuring your environment matches the current source code.

### What is the fastest way to start without installing anything locally?

Click the **Binder** badge in the repository's [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) or open **GitHub Codespaces** directly from the GitHub web interface. Both options provide immediate access to a pre‑configured environment without requiring Miniconda or Docker installations on your machine.