PyTorch Continuous Learning Example

This project demonstrates a simple PyTorch application that continuously learns from streaming data. It showcases how to:

• Create a neural network using PyTorch
• Fetch real-time data from an API
• Preprocess and feed data to the model
• Continuously update the model with new data
• Save model checkpoints

Project Structure

• main.py: Main application entry point
• model.py: Neural network model definition
• data_fetcher.py: Handles data fetching and preprocessing
• requirements.txt: Project dependencies

Setup

1. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

Running the Application

To start the continuous learning process:

python main.py

The application will:

• Fetch data from a public API every minute
• Preprocess the data
• Update the neural network model
• Save checkpoints periodically
• Print progress information

Press Ctrl+C to stop the process. The final model state will be saved to 'final_model.pth'.

Customization

You can modify the following aspects:

• Data source: Edit data_fetcher.py to use a different API
• Model architecture: Modify model.py to change the neural network structure
• Training parameters: Adjust learning rates and other hyperparameters in model.py
• Fetch interval: Change the sleep duration in main.py

Notes

• The current implementation uses a simple example API. You can replace it with your own data source.
• The model architecture is basic and can be enhanced based on your specific needs.
• Error handling and logging can be improved for production use.