mlflow: mlflow_save_model – R documentation

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mlflow_save_model

Save Model for MLflow

Description

Saves model in MLflow format that can later be used for prediction and serving. This method is generic to allow package authors to save custom model types.

Usage

## S3 method for class 'crate'
mlflow_save_model(model, path, model_spec = list(), ...)

mlflow_save_model(model, path, model_spec = list(), ...)

## S3 method for class 'H2OModel'
mlflow_save_model(model, path, model_spec = list(), conda_env = NULL, ...)

## S3 method for class 'keras.engine.training.Model'
mlflow_save_model(model, path, model_spec = list(), conda_env = NULL, ...)

## S3 method for class 'ml_pipeline_model'
mlflow_save_model(
  model,
  path,
  model_spec = list(),
  conda_env = NULL,
  sample_input = NULL,
  ...
)

## S3 method for class 'xgb.Booster'
mlflow_save_model(model, path, model_spec = list(), conda_env = NULL, ...)

Arguments

`model`	The model that will perform a prediction.
`path`	Destination path where this MLflow compatible model will be saved.
`model_spec`	MLflow model config this model flavor is being added to.
`...`	Optional additional arguments.
`conda_env`	Path to Conda dependencies file.
`sample_input`	Sample Spark DataFrame input that the model can evaluate. This is required by MLeap for data schema inference.

mlflow

Interface to 'MLflow'

v1.17.0

Apache License 2.0

Authors

Matei Zaharia [aut, cre], Javier Luraschi [aut], Kevin Kuo [aut] (<https://orcid.org/0000-0001-7803-7901>), RStudio [cph]

Initial release

mlflow_save_model

Description

Usage

Arguments

mlflow

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