Running Machine Learning Projects in cPanel

Running Machine Learning Projects in cPanel

You can deploy Machine Learning (ML) projects in cPanel, but cPanel is mainly designed for web hosting, not ML computing. However, you can still host ML models, APIs, and web apps with some adjustments.

⚙️ Steps to Run an ML Project in cPanel

1️⃣ Prepare Your ML Model

  • Train your ML model using Python (TensorFlow, Scikit-Learn, PyTorch, etc.) on your local machine.
  • Save the trained model as a .pkl (Pickle), .h5 (Keras), or .pt (PyTorch) file.

2️⃣ Upload Your Model to cPanel

  • Go to cPanel File Manager → Upload your ML model file (model.pkl, model.h5, etc.) inside public_html/ or a secure folder.

3️⃣ Set Up a Python Environment

  • In cPanel, go to "Setup Python App" (if your hosting supports Python).
  • Choose Python 3.x and create a virtual environment.

4️⃣ Install Required ML Libraries

  • Inside the Python environment, install dependencies using pip:
    bash
    pip install flask numpy pandas scikit-learn tensorflow torch

5️⃣ Create a Flask API for the ML Model

  • Write a app.py file in File Manager inside public_html/ (or in your Python app directory).
  • Example Flask API to load and use the model:
python
from flask import Flask, request, jsonify import pickle import numpy as np app = Flask(__name__) # Load the model model = pickle.load(open('model.pkl', 'rb')) @app.route('/predict', methods=['POST']) def predict(): data = request.get_json() input_features = np.array(data['features']).reshape(1, -1) prediction = model.predict(input_features) return jsonify({'prediction': prediction.tolist()}) if __name__ == '__main__': app.run()

6️⃣ Deploy the Flask App with Passenger (WSGI Support)

  • In Setup Python App, select your app folder and add Passenger WSGI file (passenger_wsgi.py):
python
from app import app as application
  • Restart the Python app. Your API will be live at:
    arduino
    https://yourdomain.com/predict

7️⃣ Use Your ML Model in a Website

  • Create an HTML/JS frontend to send requests to your API (/predict).
  • Example JavaScript fetch request:
    js
    fetch("https://yourdomain.com/predict", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ features: [5.1, 3.5, 1.4, 0.2] }) }).then(response => response.json()) .then(data => console.log(data));

✅ What You Can Do

✔️ Host an ML model as an API.
✔️ Integrate ML predictions into a website.
✔️ Process CSV/JSON datasets stored in File Manager.
✔️ Run lightweight ML tasks (e.g., classification, regression).

???? Limitations of cPanel for ML

⚠️ No GPU support (only CPU processing).
⚠️ Limited RAM & processing power (shared hosting may be slow).
⚠️ Not ideal for training ML models, but fine for running predictions.

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