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Bundling a model prepares it to be saved to a file and later restored for prediction in a new R session. See the 'Value' section for more information on bundles and their usage.

Usage

# S3 method for H2OAutoML
bundle(x, id = NULL, n = NULL, ...)

# S3 method for H2OMultinomialModel
bundle(x, ...)

# S3 method for H2OBinomialModel
bundle(x, ...)

# S3 method for H2ORegressionModel
bundle(x, ...)

Arguments

x

An object returned from modeling functions in the h2o package.

id

A single character. The model_id entry in the leaderboard. Applies to AutoML output only. Supply only one of this argument or n.

n

An integer giving the position in the leaderboard of the model to bundle. Applies to AutoML output only. Will be ignored if id is supplied.

...

Not used in this bundler and included for compatibility with the generic only. Additional arguments passed to this method will return an error.

Value

A bundle object with subclass bundled_h2o.

Bundles are a list subclass with two components:

object

An R object. Gives the output of native serialization methods from the model-supplying package, sometimes with additional classes or attributes that aid portability. This is often a raw object.

situate

A function. The situate() function is defined when bundle() is called, though is a loose analogue of an unbundle() S3 method for that object. Since the function is defined on bundle(), it has access to references and dependency information that can be saved alongside the object component. Calling unbundle() on a bundled object x calls x$situate(x$object), returning the unserialized version of object. situate() will also restore needed references, such as server instances and environmental variables.

Bundles are R objects that represent a "standalone" version of their analogous model object. Thus, bundles are ready for saving to a file; saving with base::saveRDS() is our recommended serialization strategy for bundles, unless documented otherwise for a specific method.

To restore the original model object x in a new environment, load its bundle with base::readRDS() and run unbundle() on it. The output of unbundle() is a model object that is ready to predict() on new data, and other restored functionality (like plotting or summarizing) is supported as a side effect only.

The bundle package wraps native serialization methods from model-supplying packages. Between versions, those model-supplying packages may change their native serialization methods, possibly introducing problems with re-loading objects serialized with previous package versions. The bundle package does not provide checks for these sorts of changes, and ought to be used in conjunction with tooling for managing and monitoring model environments like vetiver or renv.

See vignette("bundle") for more information on bundling and its motivation.

Examples

# fit model and bundle ------------------------------------------------
library(h2o)
#> 
#> ----------------------------------------------------------------------
#> 
#> Your next step is to start H2O:
#>     > h2o.init()
#> 
#> For H2O package documentation, ask for help:
#>     > ??h2o
#> 
#> After starting H2O, you can use the Web UI at http://localhost:54321
#> For more information visit https://docs.h2o.ai
#> 
#> ----------------------------------------------------------------------
#> 
#> Attaching package: ‘h2o’
#> The following objects are masked from ‘package:stats’:
#> 
#>     cor, sd, var
#> The following objects are masked from ‘package:base’:
#> 
#>     %*%, %in%, &&, apply, as.factor, as.numeric, colnames,
#>     colnames<-, ifelse, is.character, is.factor, is.numeric, log,
#>     log10, log1p, log2, round, signif, trunc, ||

set.seed(1)

h2o.init()
#> 
#> H2O is not running yet, starting it now...
#> 
#> Note:  In case of errors look at the following log files:
#>     /tmp/RtmptoU0WC/file3d0843fa1c90/h2o_runner_started_from_r.out
#>     /tmp/RtmptoU0WC/file3d0823b1804d/h2o_runner_started_from_r.err
#> 
#> 
#> Starting H2O JVM and connecting: ...... Connection successful!
#> 
#> R is connected to the H2O cluster: 
#>     H2O cluster uptime:         2 seconds 161 milliseconds 
#>     H2O cluster timezone:       UTC 
#>     H2O data parsing timezone:  UTC 
#>     H2O cluster version:        3.36.1.2 
#>     H2O cluster version age:    3 months and 20 days !!! 
#>     H2O cluster name:           H2O_started_from_R_runner_ydg016 
#>     H2O cluster total nodes:    1 
#>     H2O cluster total memory:   1.70 GB 
#>     H2O cluster total cores:    2 
#>     H2O cluster allowed cores:  2 
#>     H2O cluster healthy:        TRUE 
#>     H2O Connection ip:          localhost 
#>     H2O Connection port:        54321 
#>     H2O Connection proxy:       NA 
#>     H2O Internal Security:      FALSE 
#>     R Version:                  R version 4.2.1 (2022-06-23) 
#> Warning: 
#> Your H2O cluster version is too old (3 months and 20 days)!
#> Please download and install the latest version from http://h2o.ai/download/
#> 

cars_h2o <- as.h2o(mtcars)
#> 
  |                                                                      
  |                                                                |   0%
  |                                                                      
  |================================================================| 100%

cars_fit <-
  h2o.glm(
    x = colnames(cars_h2o)[2:11],
    y = colnames(cars_h2o)[1],
    training_frame = cars_h2o
  )
#> 
  |                                                                      
  |                                                                |   0%
  |                                                                      
  |================================================================| 100%

cars_bundle <- bundle(cars_fit)

# then, after saveRDS + readRDS or passing to a new session ----------
cars_unbundled <- unbundle(cars_fit)

predict(cars_unbundled, cars_h2o[, 2:11])
#> 
  |                                                                      
  |                                                                |   0%
  |                                                                      
  |================================================================| 100%
#>    predict
#> 1 21.94826
#> 2 21.64605
#> 3 25.34547
#> 4 20.44883
#> 5 17.04492
#> 6 20.12585
#> 
#> [32 rows x 1 column] 

h2o.shutdown()
#> Are you sure you want to shutdown the H2O instance running at http://localhost:54321/ (Y/N)?