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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# vetiver <a href="https://rstudio.github.io/vetiver-r/"><img src="man/figures/logo.png" align="right" height="138" alt="vetiver website" /></a>
<!-- badges: start -->
[](https://github.com/rstudio/vetiver-r/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=vetiver)
[](https://app.codecov.io/gh/rstudio/vetiver-r?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
<!-- badges: end -->
> _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](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
#> <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](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.