The doFuture package provides mechanisms for using the
foreach package together with the future package
such that foreach() parallelizes via any future backend.
There are two alternative ways to use this package:
y <- foreach(...) %dofuture% { ... }
y <- foreach(...) %dopar% { ... } with registerDoFuture()
The first alternative (recommended), which uses %dofuture%, avoids
having to use registerDoFuture(). The %dofuture% operator provides
a more consistent behavior than %dopar%, e.g. there is a unique set of
foreach arguments instead of one per possible adapter. Identification
of globals, random number generation (RNG), and error handling is
handled by the future ecosystem, just like with other map-reduce
solutions such as future.apply and furrr.
An example is:
library(doFuture)
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dofuture% {
z <- x + y
slow_sqrt(z)
}This alternative is the recommended way to let foreach() parallelize
via the future framework if you start out from scratch.
See %dofuture% for more details and examples on this approach.
The second alternative is based on the traditional foreach
approach where one registers a foreach adapter to be used by %dopar%.
A popular adapter is doParallel::registerDoParallel(), which
parallelizes on the local machine using the parallel package.
This package provides registerDoFuture(), which parallelizes using
the future package, meaning any future-compliant parallel backend
can be used.
An example is:
library(doFuture)
registerDoFuture()
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dopar% {
z <- x + y
slow_sqrt(z)
}This alternative is useful if you already have a lot of R code that
uses %dopar% and you just want to switch to using the future
framework for parallelization. Using registerDoFuture() is also
useful when you wish to use the future framework with packages and
functions that uses foreach() and %dopar% internally,
e.g. caret, plyr, NMF, and glmnet. It can
also be used to configure the Bioconductor BiocParallel package,
and any package that rely on it, to parallelize via the future
framework.
See registerDoFuture() for more details and examples on this approach.