In addition to providing a foreach adapter to be used with the
%dopar% operator of foreach, the doFuture package provides
an alternative foreach() operator called %dofuture% that ties more
directly into the future framework. For example,
library(doFuture)
plan(multisession)
cutoff <- 0.10
y <- foreach(x = mtcars, .export = c("cutoff")) %dofuture% {
mean(x, trim = cutoff)
}
names(y) <- colnames(mtcars)
There are several advantages of using %dofuture% instead of
%dopar%. When you use %dofuture%,
there is no need to use registerDoFuture()
there is no need to use %dorng% of the doRNG package
(but you need to specify .options.future = list(seed = TRUE)
whenever using random numbers in the expr expression)
global variables and packages are identified automatically by the future framework
errors are relayed as-is (with %dopar% they captured and modified)
This makes foreach(...) %dofuture% { ... } more in line with how
sibling packages future.apply and furrr work.
When using %dofuture%, the future framework identifies globals and
packages automatically (via static code inspection).
However, there are cases where it fails to find some of the globals or
packages. When this happens, one can specify the future() arguments
globals and packages via foreach argument .options.future. For
example, if you specify argument .options.future = list(globals = structure(TRUE, ignore = "b", add = "a")) then globals are
automatically identified (TRUE), but it ignores b and always adds
a.
An alternative to specifying the globals and the packages options
via .options.future, is to use the %globals% and %packages%
operators.
For further details and instructions, see help("future", package = "future").
The %dofuture% uses the future ecosystem to generate proper random
numbers in parallel in the same way they are generated in, for
instance, future.apply and furrr. For this to work, you need
to specify .options.future = list(seed = TRUE). For example,
y <- foreach(i = 1:3, .options.future = list(seed = TRUE)) %dofuture% {
rnorm(1)
}
An alternative to specifying the seed option via .options.future,
is to use the %seed% operator.
y <- foreach(i = 1:3) %dofuture% {
rnorm(1)
} %seed% TRUE
For further details and instructions, see help("future", package = "future").
Whether load balancing (“chunking”) should take place or not can be
controlled by specifying either argument .options.future = list(scheduling = <ratio>) or .options.future = list(chunk.size = <count>) to foreach(). For example,
y <- foreach(x = 1:10, .options.future = list(scheduling = 2.0)) %dofuture% {
slow_fcn(x)
}
For further details and instructions, see help("future_lapply", package = "future.apply").