[AI from Scratch] Episode 212: Installing and Setting Up Anaconda — Building a Development Environment

Recap and Today’s Theme

Hello! In the previous episode, we explored the basics of Python, covering essential knowledge for AI development such as Python syntax, data types, lists, conditional statements, loops, and functions. Today, we will move on to setting up a development environment to effectively utilize Python for AI. We will explain how to install and set up Anaconda, one of the most popular development environments for Python.

Setting up an efficient environment is crucial in AI development. Anaconda simplifies the management of libraries and development environments, making it a powerful tool for AI developers. Let’s go through the installation and setup process of Anaconda.

What Is Anaconda?

Anaconda is a package and environment management system designed for data science and AI development using Python and R. It simplifies the installation of libraries and management of development environments, offering several benefits:

Key Features of Anaconda

1. Package Management: Easily install AI libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.
2. Environment Management: Manage different environments (Python versions and installed packages) for each project.
3. Jupyter Notebook: Comes pre-installed with interactive development tools, making it convenient for data analysis and AI model development.

These features make Anaconda an essential tool for AI developers.

Steps to Install Anaconda

Let’s walk through the steps to install Anaconda.

1. Download Anaconda

Visit the official Anaconda website (https://www.anaconda.com) and download the version for Python 3.x. Select the appropriate installer for your operating system (Windows, macOS, or Linux).

2. Running the Installer (Windows)

1. Double-click the downloaded installer to run it.
2. Specify the installation location (the default is fine).
3. Uncheck “Add Anaconda to my PATH environment variable”. It’s safer to manage the Python environment using Anaconda Prompt rather than modifying the PATH variable.
4. Check “Register Anaconda as my default Python 3.7” (or the latest version) and complete the installation.

3. Running the Installer (macOS)

1. Double-click the downloaded installer (.pkg file) to run it.
2. Follow the on-screen instructions, confirming the installation location and options as you proceed.

4. Running the Installer (Linux)

Open the terminal and enter the following command:

bash ~/Downloads/Anaconda3-2024.XX-Linux-x86_64.sh

Follow the installation guide. When asked whether to add it to PATH, choose yes.

Setting Up Anaconda Environment

After installing Anaconda, the next step is to set up the environment. Anaconda recommends creating virtual environments for each project, allowing different versions of libraries to coexist.

1. Launching Anaconda Prompt (for Windows)

Open Anaconda Prompt from the Start menu. On macOS or Linux, confirm that the terminal reflects the Anaconda environment.

2. Creating a Virtual Environment

To create a virtual environment, use the following command:

conda create -n myenv python=3.9

• myenv: The name of the environment (you can specify any name).
• python=3.9: The Python version to use.

This command creates a new virtual environment.

3. Activating the Virtual Environment

To activate the virtual environment, use the following command:

conda activate myenv

When the environment is activated, its name (myenv in this example) will appear in the terminal or command prompt, indicating that you can now run Python or install packages within this environment.

4. Installing Packages

Once the virtual environment is activated, you can install the required libraries. For example, to install NumPy and Pandas, run:

conda install numpy pandas

Anaconda automatically installs the specified libraries and their dependencies.

5. Deactivating the Virtual Environment

When you’re done working, deactivate the virtual environment with the following command:

conda deactivate

This returns you to the base environment.

6. Deleting a Virtual Environment

If you no longer need a virtual environment, you can delete it with the following command:

conda remove -n myenv --all

Replace myenv with the name of the environment you want to delete.

Launching Jupyter Notebook

Anaconda includes Jupyter Notebook, which can be easily launched from within a virtual environment.

1. Activate your virtual environment.
2. Enter the following command:

jupyter notebook

This opens Jupyter Notebook in your browser, providing an interface where you can execute Python code, visualize data, and develop models interactively.

Summary

In this episode, we explained how to install and set up Anaconda. Anaconda is a powerful tool for Python development, allowing you to manage different environments for each project. It simplifies Python and library installation, making it an ideal starting environment for AI development.

Next Episode Preview

Next time, we will explain how to use Jupyter Notebook, a crucial tool for interactive data science and AI development. We’ll cover basic operations and helpful tips for efficient usage!

---

Annotations

• Virtual Environment: An isolated environment where different versions of Python and libraries can be managed for each project.
• Anaconda Prompt: A command-line tool provided by Anaconda for managing Python environments.
• Jupyter Notebook: An interactive tool for executing Python code and visualizing data within a browser interface.