Interactive streamlit application to use the python library mloptimizer
Before you start, make sure you have Docker and, of course, your favorite IDE installed.
- Clone this repo to your local machine
- Go to mloptimizer-app and build a Docker image from the Dockerfile provided:
cd ./mloptimizer-app/
docker build -t mloptimizer-img .
- Run a new container and start using the app
docker run -dp 127.0.0.1:8000:8501 --name mloptimizer-app mloptimizer-img
Take into account that Streamlit app uses port 8501 of your new container, and it is mapped to your localhost 8000 port. You can edit command above to use a different port of your local machine. 4. You can now view the MLOptimizer App in your browser. Open your favorite browser and go to http://localhost:8000/
Forget about huge python scripts, maintenance of libraries on your machine and difficult commands of a CLI. With this GUI, you can easily upload your CSV dataset and search for the best hyper-parameters for different cases. You'll be able, for example, to choose between different algorithms, specify amount of generations and individuals, set ranges of values for the hyper-paramaters or even decide which ones you want to keep fixed.