View source: R/backend_api-13.MultisessionFutureBackend-class.R
multisession | R Documentation |
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(),
gc = FALSE,
earlySignal = FALSE,
envir = parent.frame()
)
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 |
gc |
If TRUE, the garbage collector run (in the process that
evaluated the future) only after the value of the future is collected.
Exactly when the values are collected may depend on various factors such
as number of free workers and whether |
earlySignal |
Specified whether conditions should be signaled as soon as possible or not. |
envir |
The environment from where global objects should be identified. |
... |
Additional arguments passed to |
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.
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)
.
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.
## 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)
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