Artificial Intelligence models (AI models) require a proper setup to ensure smooth execution.
Whether you are working with AI Chatbot, image search, semantic search, NLP to SQL , image background removal, etc, the installation steps remain the same.
Setting up an AI model correctly prevents dependency conflicts, ensures stable execution, and improves overall project maintainability.
This guide provides a structured approach to setting up AI Python model efficiently.
Prerequisites for AI models
Before starting, ensure that you have:
- Python 3.10 installed on your system.
- Basic command-line knowledge to execute shell commands.
- pip package manager (comes with Python) to install dependencies.
- Sufficient disk space for storing dependencies and models.
- A stable internet connection to download required packages.
It is recommended to check your Python version before proceeding:
python --version
If Python is not installed, download it from the official Python website.
Step 1: Create a Virtual Environment
A virtual environment helps isolate dependencies and avoid conflicts between projects. It ensures that each AI model operates within its required environment without affecting other projects.
Creating Virtual Environment for AI models using virtualenv
virtualenv .venv
Using venv (Built-in Python module)
python3 -m venv .venv
Creating a virtual environment is a best practice for AI development because different modules may require different package versions.
Step 2: Activate the Virtual Environment
Once created, activate the virtual environment. Activation ensures that all installed dependencies remain contained within the environment.
source .venv/bin/activate
To confirm that the environment is activated, check the terminal prompt. You should see (.venv) at the beginning of the line.
Step 3: Install Dependencies
After activation, install all required dependencies. The dependencies required by AI model are usually listed in a requirements.txt file.
pip install -r requirements.txt
Step 4: Run the AI model
Once everything is set up, run your AI module. The exact command may vary depending on the module, but a common way to start an AI module is:
python app.py
Optional Step: Deploying the Application with Gunicorn and HTTPS
To enhance the security and performance of your AI models server, you can deploy your application using Gunicorn with HTTPS support.
Follow these steps to set up Gunicorn with a single worker and SSL certificates:
Install Gunicorn First, install Gunicorn using pip:
pip install gunicorn
Obtain SSL Certificates Ensure you have your SSL certificate (server.crt) and private key (server.key) files ready.
You can obtain these from a trusted Certificate Authority (CA) or generate self-signed certificates for testing purposes.
Run Gunicorn with HTTPS Execute the following command to start your application with Gunicorn, utilizing a single worker and enabling HTTPS:
gunicorn --certfile=server.crt --keyfile=server.key --workers=1 --bind 0.0.0.0:5000 your_application:app
Replace your_application:app with the appropriate module and application name for your project.
This command binds Gunicorn to port 5000 and specifies the SSL certificate and key files.
The API is deployed on https://{your_server_ip}:5000/ .
By following these steps, you can securely serve your AI models server over HTTPS using Gunicorn.
Common Errors & Fixes while setup AI models
1. Virtual Environment Not Found
Error: source: command not found Fix: Ensure you created the virtual environment and use the correct activation command for your OS.
2. Missing Dependencies
Error: ModuleNotFoundError: No module named 'X' Fix: Run pip install -r requirements.txt again.
3. Python Version Incompatibility
Error: This package requires Python 3.10 Fix: Upgrade Python or create a new virtual environment with a compatible version.
4. Port Already in Use
Error: OSError: [Errno 98] Address already in use Fix: Identify the process using the port and stop it:
lsof -i :5000 kill -9 <PID>
Replace <PID> with the actual process ID.
Final Thoughts
Following these steps ensures a smooth setup for any AI module. Using a virtual environment keeps your projects organized and secure, while dependency management helps prevent conflicts.
Key Takeaways
- Use a virtual environment for isolation.
- Install dependencies properly.
- Verify setup with sample tests.
- Run and test the model correctly.
- Troubleshoot common issues effectively.
By following these steps, you ensure a reliable and smooth AI module setup, ready for development and deployment.
Happy Coding! 🚀
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