vetiver (version 0.2.7)

vetiver_api: Create a Plumber API to predict with a deployable vetiver_model() object

Description

Use vetiver_api() to add a POST endpoint for predictions from a trained vetiver_model() to a Plumber router.

Usage

vetiver_api(
  pr,
  vetiver_model,
  path = "/predict",
  debug = is_interactive(),
  ...
)
vetiver_pr_post(
  pr,
  vetiver_model,
  path = "/predict",
  debug = is_interactive(),
  ...,
  check_prototype = TRUE,
  check_ptype = deprecated()
)
vetiver_pr_docs(pr, vetiver_model, path = "/predict", all_docs = TRUE)

Value

A Plumber router with the prediction endpoint added.

Arguments

pr

A Plumber router, such as from plumber::pr().

vetiver_model

A deployable vetiver_model() object

path

The endpoint path

debug

TRUE provides more insight into your API errors.

...

Other arguments passed to predict(), such as prediction type

check_prototype

Should the input data prototype stored in vetiver_model (used for visual API documentation) also be used to check new data at prediction time? Defaults to TRUE.

check_ptype

all_docs

Should the interactive visual API documentation be created for all POST endpoints in the router pr? This defaults to TRUE, and assumes that all POST endpoints use the vetiver_model() input data prototype.

Details

You can first store and version your vetiver_model() with vetiver_pin_write(), and then create an API endpoint with vetiver_api().

Setting debug = TRUE may expose any sensitive data from your model in API errors.

Several GET endpoints will also be added to the router pr, depending on the characteristics of the model object:

• a /pin-url endpoint to return the URL of the pinned model

• a /metadata endpoint to return any metadata stored with the model

• a /ping endpoint for the API health

• a /prototype endpoint for the model's input data prototype (use cereal::cereal_from_json()) to convert this back to a vctrs ptype

The function vetiver_api() uses:

• vetiver_pr_post() for endpoint definition and

• vetiver_pr_docs() to create visual API documentation

These modular functions are available for more advanced use cases.

Examples

if (FALSE) { # rlang::is_installed("plumber")

cars_lm <- lm(mpg ~ ., data = mtcars)
v <- vetiver_model(cars_lm, "cars_linear")

library(plumber)
pr() |> vetiver_api(v)
## is the same as:
pr() |> vetiver_pr_post(v) |> vetiver_pr_docs(v)
## for either, next, pipe to `pr_run()`
}