What Are the Dependencies for AI-For-Beginners?

The AI-For-Beginners curriculum relies on two primary dependency specifications: a Conda environment definition (environment.yml) for base packages and a pip requirements file (requirements.txt) for specific PyPI versions.

The microsoft/AI-For-Beginners repository provides a comprehensive 12-week curriculum covering artificial intelligence fundamentals. To ensure reproducibility across symbolic AI, neural networks, computer vision, and natural language processing lessons, the project defines exact dependency versions in configuration files at the repository root.

Core Dependency Files

The dependencies for AI-For-Beginners are split between Conda and pip to handle both system-level libraries and Python-specific packages.

Conda Environment Definition

The environment.yml file defines the base computational environment using the Conda package manager. According to the microsoft/AI-For-Beginners source code, this file specifies:

• Python interpreter: Latest stable Python release
• Jupyter ecosystem: ipykernel, ipython, ipywidgets, and jupyter for notebook execution
• Core scientific stack: numpy (1.26), scipy (1.13), matplotlib (3.9), and pandas
• Machine learning: scikit-learn for classical algorithms
• Deep learning frameworks: pytorch, torchtext, torchvision, and torchdata from the pytorch channel
• Computer vision: opencv from conda-forge
• Utilities: requests (2.32), pip, and setuptools

Python Package Requirements

The requirements.txt file enumerates exact PyPI versions required for the notebook examples. Key dependencies include:

• TensorFlow ecosystem: tensorflow (2.17.0), keras (3.13.2), tensorflow-datasets (4.9.6), tensorflow-hub (0.16.1), tensorboard (2.17.1), and tensorboard-data-server (0.7.2)
• Natural language processing: nltk (3.10.0), gensim (4.3.3), huggingface (0.0.1), and tokenizers (0.20.0)
• Computer vision: imageio (2.35.0), pillow (12.2.0), and scikit-image (0.24.0)
• Reinforcement learning: gym (0.26.2) and pygame (2.6.0)
• Visualization and monitoring: seaborn (0.13.2), tqdm (4.66.5), and torchinfo (1.8.0)
• Data access: smart-open (7.0.4)

Dependency Categories by Curriculum Topic

The dependencies for AI-For-Beginners support distinct AI domains covered in the lessons.

Deep Learning Frameworks

Both PyTorch and TensorFlow/Keras are required to support the neural network and transformer lessons. The curriculum uses torch, torchvision, and torchtext for PyTorch implementations, while tensorflow (2.17.0) and keras (3.13.2) handle the TensorFlow-based tutorials.

Natural Language Processing

NLP lessons depend on nltk (3.10.0) for tokenization and corpora, gensim (4.3.3) for word embeddings and topic modeling, and tokenizers (0.20.0) for transformer model preprocessing. The huggingface (0.0.1) package provides access to the Hugging Face Hub for downloading pre-trained models.

Computer Vision

Vision modules require opencv for image loading and preprocessing, supplemented by pillow (12.2.0) for image manipulation and scikit-image (0.24.0) for advanced image processing functions. The imageio (2.35.0) library handles simple image I/O operations.

Reinforcement Learning

RL environments utilize gym (0.26.2) for standardized environment interfaces and pygame (2.6.0) for interactive graphics and event handling in custom environment implementations.

Installation Instructions

To install the dependencies for AI-For-Beginners, execute the following commands from the repository root.

First, create and activate the Conda environment defined in environment.yml:

conda env create -f environment.yml
conda activate ai4beg

Then install the pip-based requirements:

pip install -r requirements.txt

Verify the installation by importing core libraries in a Jupyter notebook:

import numpy as np
import torch
import tensorflow as tf
import matplotlib.pyplot as plt

print("All core libraries loaded successfully!")

Environment Variations

The repository provides additional configuration files for specific deployment scenarios:

• binder/environment.yml: Trimmed version for Binder deployments, providing an online Jupyter environment without local installation
• binder/requirements.txt: Mirrors the root requirements for Binder's isolated environment
• .devcontainer/environment.yml: Configuration for VS Code devcontainers, ensuring fully reproducible development setups in containerized environments

Summary

• The dependencies for AI-For-Beginners are defined in environment.yml (Conda) and requirements.txt (pip) at the repository root.
• Conda packages include Python, Jupyter, NumPy (1.26), SciPy (1.13), PyTorch ecosystem, and OpenCV.
• Pip packages specify exact versions for TensorFlow (2.17.0), Keras (3.13.2), NLP libraries (NLTK, Gensim, Hugging Face), and RL tools (Gym, Pygame).
• Installation requires two steps: conda env create -f environment.yml followed by pip install -r requirements.txt.
• Alternative configurations exist for Binder (binder/) and VS Code devcontainers (.devcontainer/).

Frequently Asked Questions

What Python version does AI-For-Beginners require?

The curriculum targets the latest stable Python release as specified in environment.yml. The Conda environment definition handles the Python interpreter installation automatically, ensuring compatibility with all downstream packages.

Can I install AI-For-Beginners dependencies without Conda?

While possible, using Conda is strongly recommended. The environment.yml file specifies channels like conda-forge and pytorch that provide optimized binaries for system-level dependencies such as OpenCV and PyTorch. Pure pip installation may fail to resolve these system dependencies correctly.

What is the difference between environment.yml and requirements.txt?

The environment.yml file defines the base Conda environment including the Python interpreter, Jupyter, and compiled libraries like OpenCV and PyTorch. The requirements.txt file contains PyPI-specific packages that require pip installation, including exact version pins for TensorFlow, Keras, and specialized AI libraries. Both files are required for a complete setup.

How do I verify that all dependencies installed correctly?

After activating the environment (conda activate ai4beg), run the sanity check by importing core libraries: numpy, torch, tensorflow, and matplotlib. Successful imports without errors confirm that the dependencies for AI-For-Beginners are properly configured.

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