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https://rstudio.github.io/vetiver-r/

Version, share, deploy, and monitor models
https://rstudio.github.io/vetiver-r/

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Version, share, deploy, and monitor models

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# vetiver vetiver website



[![R-CMD-check](https://github.com/rstudio/vetiver-r/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rstudio/vetiver-r/actions/workflows/R-CMD-check.yaml)

[![CRAN status](https://www.r-pkg.org/badges/version/vetiver)](https://CRAN.R-project.org/package=vetiver)

[![Codecov test coverage](https://codecov.io/gh/rstudio/vetiver-r/branch/main/graph/badge.svg)](https://app.codecov.io/gh/rstudio/vetiver-r?branch=main)

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)



> _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 

- the Python package at 



You can use vetiver with:



- a [tidymodels](https://www.tidymodels.org/) workflow (including [stacks](https://stacks.tidymodels.org/))

- [caret](https://topepo.github.io/caret/)

- [mlr3](https://mlr3.mlr-org.com/)

- [XGBoost](https://xgboost.readthedocs.io/en/latest/R-package/)

- [ranger](https://cran.r-project.org/package=ranger)

- [`lm()`](https://stat.ethz.ch/R-manual/R-patched/library/stats/html/lm.html) and [`glm()`](https://stat.ethz.ch/R-manual/R-patched/library/stats/html/glm.html)

- GAMS fit with [mgcv](https://CRAN.R-project.org/package=mgcv)

- [keras](https://tensorflow.rstudio.com/)

- [the luz API for torch](https://torch.mlverse.org/)



## Installation



You can install the released version of vetiver from [CRAN](https://CRAN.R-project.org) with:



``` r

install.packages("vetiver")

```



And the development version from [GitHub](https://github.com/) with:



``` r

# 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. 



```{r}

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

```



You can **version** and **share** your `vetiver_model()` by choosing a [pins](https://pins.rstudio.com) "board" for it, including a local folder, Posit Connect, Amazon S3, and more.



```{r message=FALSE}

library(pins)

model_board <- board_temp()

model_board %>% vetiver_pin_write(v)

```



You can **deploy** your pinned `vetiver_model()` via a [Plumber API](https://www.rplumber.io/), which can be [hosted in a variety of ways](https://www.rplumber.io/articles/hosting.html).



```{r, eval=FALSE}

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](https://docs.posit.co/ide/user/ide/guide/tools/jobs.html)), you can make predictions with that deployed model and new data in another, separate R process. First, create a model endpoint:



```{r}

library(vetiver)

endpoint <- vetiver_endpoint("http://127.0.0.1:8088/predict")

endpoint

```



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



``` r

library(tidyverse)

new_sac <- Sacramento %>% 

    slice_sample(n = 20) %>% 

    select(type, sqft, beds, baths)



predict(endpoint, new_sac)

#> # A tibble: 20 x 1

#>      .pred

#>      

#>  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](https://www.contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). 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](https://community.rstudio.com/new-topic?category_id=15&tags=vetiver,question).



- If you think you have encountered a bug, please [submit an issue](https://github.com/rstudio/vetiver-r/issues).



- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.