Create a multisession future whose value will be resolved asynchronously in a parallel R session

A multisession future is a future that uses multisession evaluation, which means that its value is computed and resolved in parallel in another R session.

multisession(
  ...,
  workers = availableCores(),
  lazy = FALSE,
  rscript_libs = .libPaths(),
  envir = parent.frame()
)

Arguments

...

Additional arguments passed to Future().

workers

Ignored in future (>= 1.31.0).

lazy

If FALSE (default), the future is resolved eagerly (starting immediately), otherwise not.

rscript_libs

A character vector of R package library folders that the workers should use. The default is .libPaths() so that multisession workers inherits the same library path as the main R session. To avoid this, use plan(multisession, ..., rscript_libs = NULL). Important: Note that the library path is set on the workers when they are created, i.e. when plan(multisession) is called. Any changes to .libPaths() in the main R session after the workers have been created will have no effect. This is passed down as-is to parallelly::makeClusterPSOCK().

envir

The environment from where global objects should be identified.

Value

A MultisessionFuture. If workers == 1, then all processing using done in the current/main R session and we therefore fall back to using a lazy future. To override this fallback, use workers = I(1).

Details

This function is not meant to be called directly. Instead, the typical usages are:

# Evaluate futures in parallel on the local machine via as many background
# processes as available to the current R process
plan(multisession)

# Evaluate futures in parallel on the local machine via two background
# processes
plan(multisession, workers = 2)

The background R sessions (the "workers") are created using makeClusterPSOCK().

For the total number of R sessions available including the current/main R process, see parallelly::availableCores().

A multisession future is a special type of cluster future.

See also

For processing in multiple forked R sessions, see multicore futures.

Use parallelly::availableCores() to see the total number of cores that are available for the current R session.

Examples

# \donttest{

## Use multisession futures
plan(multisession)

## A global variable
a <- 0

## Create future (explicitly)
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## A multisession future is evaluated in a separate R session.
## Changing the value of a global variable will not affect
## the result of the future.
a <- 7
print(a)
#> [1] 7

v <- value(f)
print(v)
#> [1] 0
stopifnot(v == 0)

## Explicitly close multisession workers by switching plan
plan(sequential)
# }