Version, Share, Deploy, and Monitor Models.

vetiver

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 R and Python. To learn more about vetiver, see:

• the documentation at https://vetiver.rstudio.com/
• the Python package at https://rstudio.github.io/vetiver-python/

You can use vetiver with:

• a tidymodels workflow (including stacks)
• caret
• mlr3
• XGBoost
• ranger
• lm() and glm()
• GAMS fit with mgcv
• keras
• the luz API for torch

Installation

You can install the released version of vetiver from CRAN with:

install.packages("vetiver")

And the development version from GitHub with:

# install.packages("pak")
pak::pak("rstudio/vetiver-r")

Example

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

library(parsnip)
library(workflows)
data(Sacramento, package = "modeldata")

rf_spec <- rand_forest(mode = "regression")
rf_form <- price ~ type + sqft + beds + baths

rf_fit <- 
    workflow(rf_form, rf_spec) %>%
    fit(Sacramento)

library(vetiver)
v <- vetiver_model(rf_fit, "sacramento_rf")
v
#> 
#> ── sacramento_rf ─ <bundled_workflow> model for deployment 
#> A ranger regression modeling workflow using 4 features

You can version and share your vetiver_model() by choosing a pins “board” for it, including a local folder, Posit Connect, Amazon S3, and more.

library(pins)
model_board <- board_temp()
model_board %>% vetiver_pin_write(v)

You can deploy your pinned vetiver_model() via a Plumber API, which can be hosted in a variety of ways.

library(plumber)
pr() %>%
  vetiver_api(v) %>%
  pr_run(port = 8088)

If the deployed model endpoint is running via one R process (either remotely on a server or locally, perhaps via a background job in the RStudio IDE), you can make predictions with that deployed model and new data in another, separate R process. First, create a model endpoint:

library(vetiver)
endpoint <- vetiver_endpoint("http://127.0.0.1:8088/predict")
endpoint
#> 
#> ── A model API endpoint for prediction: 
#> http://127.0.0.1:8088/predict

Such a model API endpoint deployed with vetiver will return predictions for appropriate new data.

library(tidyverse)
new_sac <- Sacramento %>% 
    slice_sample(n = 20) %>% 
    select(type, sqft, beds, baths)

predict(endpoint, new_sac)
#> # A tibble: 20 x 1
#>      .pred
#>      <dbl>
#>  1 165042.
#>  2 212461.
#>  3 119008.
#>  4 201752.
#>  5 223096.
#>  6 115696.
#>  7 191262.
#>  8 211706.
#>  9 259336.
#> 10 206826.
#> 11 234952.
#> 12 221993.
#> 13 204983.
#> 14 548052.
#> 15 151186.
#> 16 299365.
#> 17 213439.
#> 18 287993.
#> 19 272017.
#> 20 226629.

Contributing

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 modeling, machine learning, and MLOps please post on RStudio Community.

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

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.