Spark ML – Model Persistence
Save/load Spark ML objects
ml_save(x, path, overwrite = FALSE, ...)
## S3 method for class 'ml_model'
ml_save(
x,
path,
overwrite = FALSE,
type = c("pipeline_model", "pipeline"),
...
)
ml_load(sc, path)x |
A ML object, which could be a |
path |
The path where the object is to be serialized/deserialized. |
overwrite |
Whether to overwrite the existing path, defaults to |
... |
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.
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