Deployment of a Scikit-Learn model and it's column transformations with a single endpoint. Only a traditional Scikit-Learn model is needed and a ColumnTransformer object (sklearn.compose) to deploy your model. Validation of input data is also supported with pydantic.
See the Examples section of the repository.
The package exists on PyPI (with a different name though) so you can install it directly to your environment by running the command
pip install simple-serve
- pydantic
- fastapi
- pandas
- scikit-learn
Additional packages for development:
- pyright
- pre-commit
If you want to contribute you fork the repository and clone it on your machine
git clone https://github.com/alexliap/sk_serve.git
And after you create you environment (either venv or conda) and activate it then run this command
pip install -e ".[dev]"
That way not only the required dependencies are installed but also the development ones.
Also this makes it so that when you import the code to test it, you can do it like any other module but containing the changes you made locally.
Before you decide to commit, run the following command to reformat code in order to be in the acceptable style.
pre-commit install
pre-commit run --all-files