spark objects are created from the sparklyr package, a R interface for Apache Spark. The axe methods available for spark objects are designed such that interoperability is maintained. In other words, for a multilingual machine learning team, butchered spark objects instantiated from sparklyr can still be serialized to disk, work in Python, be deployed on Scala, etc. It is also worth noting here that spark objects created from sparklyr have a lot of metadata attached to it, including but not limited to the formula, dataset, model, index labels, etc. The axe functions provided are for parsing down the model object both prior saving to disk, or loading from disk. Traditional R save functions are not available for these objects, so functionality is provided in sparklyr::ml_save. This function gives the user the option to keep either the pipeline_model or the pipeline, so both of these objects are retained from butchering, yet removal of one or the other might be conducive to freeing up memory on disk.

# S3 method for ml_model
axe_call(x, verbose = FALSE, ...)

# S3 method for ml_model
axe_ctrl(x, verbose = FALSE, ...)

# S3 method for ml_model
axe_data(x, verbose = FALSE, ...)

# S3 method for ml_model
axe_fitted(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed spark object.

Examples

if (FALSE) { if (FALSE) { suppressWarnings(suppressMessages(library(sparklyr))) sc <- spark_connect(master = "local") iris_tbls <- sdf_copy_to(sc, iris, overwrite = TRUE) %>% sdf_random_split(train = 2/3, validation = 2/3, seed = 2018) train <- iris_tbls$train spark_fit <- ml_logistic_regression(train, Species ~ .) out <- butcher(spark_fit, verbose = TRUE) spark_disconnect(sc) } }