One challenge I've consistently encountered while working with machine learning and deep learning is the gap between creating powerful models and deploying them effectively. Many researchers and scientists excel at building complex models but may lack expertise in frontend or backend web development. This often results in innovative software that is difficult to test, use, or integrate into practical applications. Consequently, groundbreaking research risks being underutilized or overlooked.
This lightweight repository is designed to bridge that gap. Built on top of FastAPI, it provides an easy-to-use framework for deploying machine learning models (currently optimized for PyTorch, with support for other frameworks like TensorFlow and Keras coming soon).
With this template, you can quickly set up:
- A model definition and description
- A frontend interface
- A REST API for interacting with one or multiple models
While the current setup focuses on a convolutional neural network (CNN), it is highly flexible and can be adapted for text predictions or other machine learning tasks.
- FastAPI-based: Lightweight and efficient backend framework.
- Flexible Model Integration: Designed for PyTorch, but easily extensible for Keras, TensorFlow, or other frameworks.
- REST API Documentation: Automatically generated API documentation available at
/docs. - Quick Deployment: Minimal configuration required to get your models up and running.
Ensure you have Python installed, and install the required dependencies:
pip install -r requirements.txt
- The application will be accessible at: http://127.0.0.1:8000
- API documentation can be found at: http://127.0.0.1:8000/docs
Contributions are welcome! If you’d like to add new features, extend framework support, or improve usability, feel free to submit a pull request.