In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits

Last update: Nov 23, 2022

Overview

Fastapi + MLflow + streamlit

Setup env. I hope I covered all.

pip install -r requirements.txt

Start app

Go in the root dir and run these

Streamlit

streamlit run frontend/streamlit_main.py

FastAPI

uvicorn backend.main:app

MLflow UI

mlflow ui --backend-store-uri sqlite:///db/bakckend.db

Docker

docker-compose build
docker-compose up

Architecture

UI

TODO

Dockerize
Testing
Maybe add celery instead of that background task? (Needs extra configs though)

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Comments

Fix for docker-compose sqlite volume mount path
Hi, nice project and kudos for sharing it ! I wanted to play with your code using the docker-compose file, however it was not working for freshly created project.

Here is what I changed to make it work (for both mlflow and fastapi services):

volumes: - ./db:/db

instead of path to the database file.
opened by kmkolasinski 0

Owner

Computer Science and Engineering undergrad. In my free time I study mathematics, cryptography and machine learning

GitHub

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