Essential Tools and Languages for AI Development
Artificial Intelligence (AI) development requires a combination of tools, programming languages, and development environments to build, test, and deploy models effectively. This guide outlines the necessary and optional resources along with a basic setup process to get started.
Required Tools and Languages
Tool/Language | Required/Optional | Version | Description | Link |
WSL (Windows Subsystem for Linux) | Optional | Latest | Enables Linux/Ubuntu distros on Windows OS, avoiding Windows-related package management issues. | LINK |
Python | Required | Latest | Core development language for AI applications. | LINK |
Pip | Required | Latest | Package management tool included by default with Python installations. | LINK |
Conda | Optional | Latest | An alternative package management tool. | LINK |
Jupyter Notebook | Required | Latest | Development environment for writing and testing AI code interactively. | LINK |
Jupyter Lab | Optional | Latest | Enhanced version of Jupyter Notebook with additional features. | LINK |
Visual Studio Code | Required | Latest | Versatile code editor and development environment, highly customizable with extensions. | LINK |
PyLint | Required | Latest | A plugin for linting Python code to ensure quality and avoid bugs. | LINK |
Basic Setup & Installation Guide
Step 1: Enable WSL and Install Ubuntu Distro (Optional)
For Windows users, enabling WSL allows you to run a Linux environment. Install Ubuntu from the Microsoft Store to simplify package management and avoid potential compatibility issues.
Step 2: Install Python 3.11
Download and install Python 3.11 from python.org. Verify the installation with the command:
python -V Step 3: Verify Pip Installation
Ensure pip, Python’s package manager, is installed by running:
pip -V Step 4: Set Up a Virtual Environment
Creating a virtual environment is a best practice to avoid package conflicts across projects. Run the following commands to create and activate a virtual environment:
python -m venv myenv source myenv/bin/activate # For Linux/Mac myenv\Scripts\activate # For Windows Step 5: Install Jupyter Notebook/Lab or Visual Studio Code
Install Jupyter Notebook or Jupyter Lab using pip:
pip install notebook pip install jupyterlab Alternatively, install Visual Studio Code for an integrated development experience.
Step 6: Launch Jupyter Notebook/Lab
Use the following commands to launch Jupyter and access it via your browser:
jupyter notebook # or
jupyter lab If using WSL, run the commands in the Ubuntu terminal and open the provided localhost URL in your browser.
Step 7: Explore AI Frameworks
Start experimenting with AI frameworks like:
• OpenAI: For GPT models and other generative AI tools.
• LangChain: For building language model applications.
• LlamaIndex: For indexing and querying data.
By setting up these tools and environments, you’ll have a solid foundation to begin your AI development journey. Start exploring frameworks and build AI-powered solutions to bring your ideas to life.