Streamlit-Fastapi-Model-Serving

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streamlit-fastapi-model-serving is an open-source tool for serving PyTorch models. It combines Streamlit and FastAPI to create both a backend with API documentation and a frontend for user experimentation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is streamlit-fastapi-model-serving?

streamlit-fastapi-model-serving is an open-source project designed to simplify the deployment of machine learning models. It leverages FastAPI for creating a robust backend with automatic API documentation and Streamlit for building an interactive, user-friendly frontend. This combination allows developers to quickly serve PyTorch models, providing both a programmatic interface for other applications and a visual interface for direct user experimentation. The project uses Docker Compose to orchestrate these two services, ensuring seamless communication and easy setup. It's an ideal solution for developers looking to deploy ML models with a complete web application stack.

Best used for

Ideal for developers who need to serve PyTorch models, create interactive web applications for machine learning, and provide both API and UI access to their models. Especially valuable for quickly deploying and experimenting with ML functionality.

Common actions

serve machine learning models

deploy PyTorch models

create ML web apps

build interactive ML demos

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Capabilities

Key features

PyTorch model serving
FastAPI backend
Streamlit frontend
Docker Compose orchestration
API documentation

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of machine learning models can be served with this tool?

This tool is specifically designed for serving PyTorch models. The example provided demonstrates its use with an image semantic segmentation model, but it can be adapted for other PyTorch-based machine learning models.

How does streamlit-fastapi-model-serving handle the backend and frontend?

It uses FastAPI to create the backend service, which includes automatic API documentation. For the frontend, it utilizes Streamlit to build an interactive web user interface, allowing users to experiment with the model's functionality.

What is required to run this example project?

To run the example, you need a machine with Docker and Docker Compose installed. The project uses Docker Compose to orchestrate the FastAPI backend and Streamlit frontend services, simplifying deployment and communication.

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