vetiver_model.workflow: Create a vetiver object for deployment of a trained...

In juliasilge/deploytidymodels: Version, Pin, and Deploy Modeling Workflows with 'pins' and 'plumber'

Description

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

Usage

## S3 method for class 'workflow'
vetiver_model(
  model,
  model_name,
  board,
  ...,
  desc = NULL,
  metadata = list(),
  save_ptype = TRUE,
  versioned = NULL
)

Arguments

model	A trained model created with workflows::workflow().
model_name	Model name or ID.
board	A pin board to store and version the model, created by pins::board_folder(), pins::board_rsconnect(), or other board_ function from the pins package.
...	Other method-specific arguments passed to vetiver_slice_zero() to compute an input data prototype.
desc	A text description of the model, most important for shared boards so that others can understand what the model is. If omitted, a brief description of the contents will be generated.
metadata	A list containing additional metadata to store with the pin. When retrieving the pin, this will be stored in the user key, to avoid potential clashes with the metadata that pins itself uses.
save_ptype	Should an input data prototype be stored with the model? The options are TRUE (the default, which stores a zero-row slice of the training data), FALSE (no input data prototype for checking), or a dataframe.
versioned	Should the model object be versioned? The default, NULL, will use the default for board.

juliasilge/deploytidymodels documentation built on Dec. 21, 2021, 4:16 a.m.