Spark ML – Model Persistence




Save/load Spark ML objects


ml_save(x, path, overwrite = FALSE, ...) 

## S3 method for class 'ml_model'
  overwrite = FALSE, 
  type = c("pipeline_model", "pipeline"), 

ml_load(sc, path) 


Arguments Description
x A ML object, which could be a ml_pipeline_stage or a ml_model
path The path where the object is to be serialized/deserialized.
overwrite Whether to overwrite the existing path, defaults to FALSE.
Optional arguments; currently unused.
type Whether to save the pipeline model or the pipeline.
sc A Spark connection.


ml_save() serializes a Spark object into a format that can be read back into sparklyr or by the Scala or PySpark APIs. When called on ml_model objects, i.e. those that were created via the tbl_spark - formula signature, the associated pipeline model is serialized. In other words, the saved model contains both the data processing (RFormulaModel) stage and the machine learning stage.

ml_load() reads a saved Spark object into sparklyr. It calls the correct Scala load method based on parsing the saved metadata. Note that a PipelineModel object saved from a sparklyr ml_model via ml_save() will be read back in as an ml_pipeline_model, rather than the ml_model object.