Setup Guide

📌 Note: Before proceeding, make sure you have collected and populated the data into MongoDB as specified in HPE-Data-Generator. This is required for the system to work correctly.

I. Backend

Requirements

• Python 3.10 - 3.12
• MongoDB (local or cloud)
• pip for installing Python packages

1. Install Python

Download and install Python from the official site:
🔗 https://www.python.org/downloads/

• Use Python 3.10 or 3.11, or 3.12. TensorFlow does not yet support Python 3.13.

2. Clone the Repository

git clone https://github.com/VarshithPawarHR/HPE-StoragePrediction

3. Open Project in VS Code

• Launch Visual Studio Code
• Open the folder you just cloned

4. Select Python Interpreter (VS Code GUI)

• Press Ctrl + Shift + P
• Select: Python: Select Interpreter
• Choose a Python version between 3.10 – 3.12
• Avoid selecting Python 3.13

5. Create and Activate Virtual Environment

Option 1: Using VS Code GUI

• Press Ctrl + Shift + P
• Select: Python: Create Environment
• Choose:
  • Environment Type: venv
  • Python Interpreter: 3.10, 3.11, or 3.12

VS Code will create and activate the environment automatically.

Option 2: Using Terminal

# On Windows
py -3.12 -m venv venv

# On Linux/macOS
python3.12 -m venv venv

# Activate the virtual environment
# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

6. Install Dependencies

cd backend
pip install -r requirenments.txt

7. Set Environment Variables

Create a .env file inside the backend folder: (refer .env.example)

TF_ENABLE_ONEDNN_OPTS=0
MONGO_URL=mongodb+srv://<username>:<password>@<cluster-url>/<database>
MONGO_DB=your_db_name
MONGO_COLLECTION=your_collection

8. Run the Backend

fastapi dev main.py

FastAPI will be live at: http://127.0.0.1:8000/

9. Test API and Train Models (Swagger UI)

To access the API interface, open your browser and go to:
http://127.0.0.1:8000/docs

Trigger Training

Use the following endpoints to trigger model training:

Endpoint	Description
POST /retrain/daily	Train Daily Model
POST /retrain/weekly	Train Weekly Model
POST /retrain/monthly	Train Monthly Model
POST /retrain/quarterly	Train Quarterly Model

After hitting any of these endpoints, training will begin using the data in your MongoDB collection.

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Model & Scaler Output

Once training is complete:

• The trained model files will be saved in the models/ directory.
• The scalers used for preprocessing will be stored in the scalers/ directory.

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Next Step: Setup Frontend

After your models are trained and saved, proceed to set up the frontend dashboard to visualize predictions and analytics.

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