# How to Set Up the AI for Beginners Development Environment: 3 Methods Explained

> Easily set up the AI for Beginners development environment using local Conda, VS Code DevContainer, or cloud options like GitHub Codespaces. Get started with AI development today!

- Repository: [Microsoft/AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners)
- Tags: how-to-guide
- Published: 2026-08-02

---

**You can set up the AI for Beginners development environment using three approaches: a local Conda environment created from [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml), a Docker-based VS Code DevContainer, or cloud instances via GitHub Codespaces or Binder.**

The Microsoft **AI-For-Beginners** repository provides a 12-week curriculum of Jupyter notebooks covering neural networks, computer vision, and reinforcement learning. To run the code examples locally or in the cloud, you need a Python runtime with scientific libraries including TensorFlow, PyTorch, OpenCV, and scikit-learn. The repository ships with configuration files that automate dependency management across all three supported installation methods.

## Method 1: Local Conda Setup (Recommended)

The **Conda** approach gives you full control over the environment, supports GPU acceleration, and allows you to install additional packages. This method uses the [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file located in [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) to create an isolated `ai4beg` environment.

### Clone the Repository (Sparse Checkout)

To reduce download size by excluding translation files, use Git’s sparse checkout feature as documented in [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md):

```bash
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

First, install **Miniconda** if you haven’t already. Then create and activate the environment:

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

```

The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) file lists all required dependencies including IPython, Jupyter, matplotlib, numpy, OpenCV, PyTorch, and TensorFlow.

### Verify Core Libraries

Confirm that the major frameworks are correctly installed by running:

```bash
python -c "import torch, tensorflow as tf, cv2; print('PyTorch:', torch.__version__); print('TF:', tf.__version__); print('OpenCV:', cv2.__version__)"

```

### Launch Jupyter

From the repository root, start the notebook server:

```bash
jupyter lab

```

Navigate to any lesson folder (for example, `lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb`) and execute the cells. The [`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) file contains additional configuration details for this workflow.

## Method 2: VS Code DevContainer (Docker)

If you have Docker installed, you can run the entire curriculum inside a container without manually managing Conda. The repository includes a [`.devcontainer/devcontainer.json`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/devcontainer.json) configuration that references the same [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) used for local setups.

To use this method:

1. Open the repository folder in **VS Code**.
2. When prompted, click **"Reopen in Container"**.
3. VS Code automatically builds the Docker image, installs dependencies, and mounts the workspace.

This approach isolates the environment and works identically across Windows, macOS, and Linux. If VS Code suggests reopening in a container while you are using the local Conda setup, you can decline to remain in your host environment.

## Method 3: Cloud-Based Environment

For machines with limited resources or for quick demonstrations, use a cloud-hosted instance.

### GitHub Codespaces

Click the **Code → Codespaces** button on the repository page to launch a pre-configured VS Code instance in the browser. The environment includes the same dependencies defined in [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml), requiring no local installation.

### Binder

Click the Binder badge in the [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) or navigate to `https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD`. Binder spins up a Jupyter server with the curriculum files. Note that Binder has limitations regarding external downloads and compute speed, as documented 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).

For GPU-intensive lessons—such as deep reinforcement learning or computer vision training—consider Azure Data Science VMs or Azure Machine Learning notebooks, which provide dedicated GPU resources.

## VS Code Integration for Local Development

When using the local Conda method, you can enhance the editing experience with VS Code’s Python extension:

1. Open the repository folder in VS Code.
2. Install the **Python extension** from the marketplace.
3. VS Code automatically detects the `ai4beg` Conda environment and prompts you to select it as the interpreter.
4. Open any `.ipynb` file to access the interactive notebook UI with IntelliSense and debugging capabilities.

## Summary

- **Conda (local)** is the most flexible option for learners who need GPU support and custom package management; it uses the `ai4beg` environment defined in [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml).
- **VS Code DevContainer** provides a reproducible Docker environment without local Conda installation, ideal for cross-platform consistency.
- **Cloud options** (GitHub Codespaces or Binder) require zero setup but may have compute limitations for intensive training tasks.
- The repository’s sparse checkout command (`git sparse-checkout set --no-cone '/*' '!translations'`) minimizes download size by excluding translation files.
- Key documentation files include [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md) for overview instructions and [`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) for detailed runtime configuration.

## Frequently Asked Questions

### What Python version does the AI for Beginners environment require?

The environment specifies **Python 3.8 or higher** as defined in the [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) file. The Conda environment automatically installs the compatible Python version when you run `conda env create`.

### Can I use pip instead of Conda to install dependencies?

While the repository officially supports Conda, you can manually install the packages listed in [`.devcontainer/environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/.devcontainer/environment.yml) using `pip`. However, some libraries like OpenCV and PyTorch have platform-specific binary dependencies that Conda handles more reliably than pip.

### How do I access GPU support for the deep learning lessons?

For local setups, ensure you install the CUDA-enabled versions of PyTorch and TensorFlow when creating the Conda environment. The [`environment.yml`](https://github.com/microsoft/AI-For-Beginners/blob/main/environment.yml) includes GPU-compatible package specifications. Alternatively, use Azure Data Science VMs or Azure Machine Learning notebooks for cloud-based GPU resources.

### Why should I use sparse checkout when cloning the repository?

Sparse checkout excludes the `translations` and `translated_images` directories, reducing the download size significantly. This is particularly useful if you only need the English curriculum content, as documented in the sparse checkout instructions in [`README.md`](https://github.com/microsoft/AI-For-Beginners/blob/main/README.md).