deploytidymodels

This repo is archived in favor of the vetiver package.

The goal of deploytidymodels is to provide fluent tooling to version, share, and deploy a trained model workflow using the vetiver framework. Functions handle both recording and checking the model’s input data prototype, and loading the packages needed for prediction.

Installation

~~You can install the released version of deploytidymodels from CRAN with:~~

install.packages("deploytidymodels") ## not yet

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("juliasilge/deploytidymodels")

Example

You can use the tidymodels ecosystem to train a model, with a wide variety of preprocessing and model estimation options.

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)

You can version and share your model by pinning it, to a local folder, RStudio Connect, Amazon S3, and more.

library(deploytidymodels)
library(pins)

model_board <- board_temp()
m <- vetiver_model(rf_fit, "sacramento_rf", model_board)
vetiver_pin_write(m)
#> Creating new version '20211008T150541Z-21d32'
#> Writing to pin 'sacramento_rf'

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

library(plumber)

pr() %>%
    vetiver_pr_predict(m) %>%
    pr_run(port = 8088)

Make predictions with your deployed model by creating an endpoint object:

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

A model API endpoint deployed with vetiver_pr_predict() will return predictions with 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 tidymodels packages, modeling, and machine learning, 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.
Check out further details on contributing guidelines for tidymodels packages and how to get help.

jmcoyro / deploytidymodels Goto Github PK