Vetiver, the oil of tranquility, is used as a stabilizing ingredient in perfumery to preserve more volatile fragrances.

The goal of vetiver is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The vetiver package is extensible, with generics that can support many kinds of models, and available for both Python and R. To learn more about vetiver, see:

• the documentation at https://vetiver.rstudio.com/
• the R package at https://rstudio.github.io/vetiver-r/

You can use vetiver with:

• scikit-learn
• PyTorch

You can install the released version of vetiver from PyPI:

python -m pip install vetiver

And the development version from GitHub with:

python -m pip install git+https://github.com/rstudio/vetiver-python

A VetiverModel() object collects the information needed to store, version, and deploy a trained model.

from vetiver import mock, VetiverModel

X, y = mock.get_mock_data()
model = mock.get_mock_model().fit(X, y)

v = VetiverModel(model, save_ptype=True, ptype_data=X)

You can version and share your VetiverModel() by choosing a pins "board" for it, including a local folder, RStudio Connect, Amazon S3, and more.

from pins import board_temp
from vetiver import vetiver_pin_write

model_board = board_temp(versioned = True, allow_pickle_read = True)
vetiver_pin_write(model_board, v)

You can deploy your pinned VetiverModel() using VetiverAPI(), an extension of FastAPI.

from vetiver import VetiverAPI
app = VetiverAPI(v, check_ptype = True)

To start a server using this object, use app.run(port = 8080) or your port of choice.

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about deploying models, statistical modeling, and machine learning, please post on RStudio Community.

• If you think you have encountered a bug, please submit an issue.