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The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
library(futurize)
plan(multisession)
library(caret)
ctrl <- trainControl(method = "cv", number = 10)
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()
This vignette demonstrates how to use this approach to parallelize caret
functions such as train().
The caret package provides a rich set of machine-learning tools
with a unified API. The train() function fits models using
cross-validation or bootstrap resampling, making it an excellent
candidate for parallelization.
The train() function fits models across multiple resampling
iterations:
library(caret)
## Set up 10-fold cross-validation
ctrl <- trainControl(method = "cv", number = 10)
## Train a random forest model
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl)
Here train() evaluates sequentially, but we can easily make it
evaluate in parallel by piping to futurize():
library(futurize)
library(caret)
ctrl <- trainControl(method = "cv", number = 10)
model <- train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()
This will distribute the cross-validation folds across the available parallel workers, given that we have set up parallel workers, e.g.
plan(multisession)
The built-in multisession backend parallelizes on your local
computer and works on all operating systems. There are other
parallel backends to choose from, including alternatives to
parallelize locally as well as distributed across remote machines,
e.g.
plan(future.mirai::mirai_multisession)
and
plan(future.batchtools::batchtools_slurm)
The following caret functions are supported by futurize():
bag()gafs()nearZeroVar()rfe()safs()sbf()train()