DIY AI Part 3: Setting Up Your Environment and Installing Libraries
If you’ve been following our DIY AI series, you’re ready to move onto the next step—setting up your environment and installing the necessary libraries. This part of the process is both fast and essential, providing a lot of useful information to get your AI project up and running.
Installing Visual Studio Code and Setting Up a Virtual Environment
For this project, we recommend using Visual Studio Code (VS Code), a free and powerful editor that is well-suited for Python programming. VS Code is lightweight yet fully featured, making it an ideal choice for managing our AI project.
Steps to set up Visual Studio Code:
- Download and install VS Code from the official website.
- Create a project folder with a suitable name (e.g., “My_DIY_AI”).
- Set up a virtual environment to manage your Python dependencies effectively.
For detailed guidance on setting up your virtual environment, refer to our guide.
Installing the Libraries
With your virtual environment ready, the next step is installing the libraries crucial for our AI project. Below are the libraries we will use:
Pre-installed Libraries
- os: Used for interacting with the operating system, handling file and directory management.
- shutil: Facilitates file operations like copying, moving, and deleting.
Data Handling and File Management
- Pandas: Excellent for handling large data sets and supports a variety of data formats. Integrates seamlessly with other Python libraries.
- PyPDF2 and Fitz: Used for reading and writing PDF files.
- python-magic: Detects file types by examining their contents for accurate identification.
- Whoosh: Ideal for creating search functionalities within local or embedded applications.
Natural Language Processing and Machine Learning
- Transformers and Torch: Vital for natural language processing tasks like summarization and sentiment analysis, supporting pre-trained models.
Mathematical Libraries
- Numpy and Scipy: Essential for performing calculations, handling arrays, matrices, and large datasets in data science or machine learning projects.
API Development
- FastAPI and Uvicorn: Used for building fast, lightweight APIs to serve data or machine learning models offline.
Installation Command
To install these libraries, enter the following command in your terminal while inside your virtual environment:
pip install pandas pypdf2 fitz python-magic whoosh transformers torch numpy scipy fastapi uvicornWith these libraries in place, the backbone of our AI project is established. In the next guide, we’ll focus on creating the file structure and advancing to practical implementation.
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Source: https://geeksided.com/diy-ai-part-3-setting-up-your-environment-and-installing-libraries