multisession {future}

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

Description

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.

Usage

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

Arguments

...

Additional arguments passed to Future().

workers

The number of parallel processes to use. If a function, it is called without arguments when the future is created and its value is used to configure the workers.

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.

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.

Value

A MultisessionFuture. If workers == 1, then all processing is 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).

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

## 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)

v <- value(f)
print(v)
stopifnot(v == 0)

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