Plan how to resolve a future
This function allows the user to plan the future, more specifically,
it specifies how future():s are resolved,
e.g. sequentially or in parallel.
plan( strategy = NULL, ..., substitute = TRUE, .skip = FALSE, .call = TRUE, .cleanup = TRUE, .init = TRUE )
strategy |
The evaluation function (or name of it) to use for resolving a future. If NULL, then the current strategy is returned. |
... |
Additional arguments overriding the default arguments
of the evaluation function. Which additional arguments are supported
depends on what evaluation function is used, e.g. several support
argument |
substitute |
If TRUE, the |
.skip |
(internal) If |
.call |
(internal) Used for recording the call to this function. |
.cleanup |
(internal) Used to stop implicitly started clusters. |
.init |
(internal) Used to initiate workers. |
The default strategy is sequential, but the default can be
configured by option future.plan and, if that is not set,
system environment variable R_FUTURE_PLAN.
To reset the strategy back to the default, use plan("default").
If a new strategy is chosen, then the previous one is returned (invisible), otherwise the current one is returned (visibly).
sequential:
Resolves futures sequentially in the current R process.
transparent:
Resolves futures sequentially in the current R process and
assignments will be done to the calling environment.
Early stopping is enabled by default.
multisession:
Resolves futures asynchronously (in parallel) in separate
R sessions running in the background on the same machine.
multicore:
Resolves futures asynchronously (in parallel) in separate
forked R processes running in the background on
the same machine. Not supported on Windows.
multiprocess:(DEPRECATED)
If multicore evaluation is supported, that will be used,
otherwise multisession evaluation will be used.
cluster:
Resolves futures asynchronously (in parallel) in separate
R sessions running typically on one or more machines.
remote:
Resolves futures asynchronously in a separate R session
running on a separate machine, typically on a different
network.
Other package may provide additional evaluation strategies. Notably, the future.batchtools package implements a type of futures that will be resolved via job schedulers that are typically available on high-performance compute (HPC) clusters, e.g. LSF, Slurm, TORQUE/PBS, Sun Grid Engine, and OpenLava.
To "close" any background workers (e.g. multisession), change
the plan to something different; plan(sequential) is recommended
for this.
Please refrain from modifying the future strategy inside your packages /
functions, i.e. do not call plan() in your code. Instead, leave
the control on what backend to use to the end user. This idea is part of
the core philosophy of the future framework - as a developer you can never
know what future backends the user have access to. Moreover, by not making
any assumptions about what backends are available, your code will also work
automatically with any new backends developed after you wrote your code.
If you think it is necessary to modify the future strategy within a function, then make sure to undo the changes when exiting the function. This can be done using:
oplan <- plan(new_set_of_strategies) on.exit(plan(oplan), add = TRUE) [...]
This is important because the end-user might have already set the future strategy elsewhere for other purposes and will most likely not known that calling your function will break their setup. Remember, your package and its functions might be used in a greater context where multiple packages and functions are involved and those might also rely on the future framework, so it is important to avoid stepping on others' toes.
When writing scripts or vignettes that uses futures, try to place any
call to plan() as far up (as early on) in the code as possible.
This will help users to quickly identify where the future plan is set up
and allow them to modify it to their computational resources.
Even better is to leave it to the user to set the plan() prior to
source():ing the script or running the vignette.
If a ‘.future.R’ exists in the current directory and / or in
the user's home directory, it is sourced when the future package is
loaded. Because of this, the ‘.future.R’ file provides a
convenient place for users to set the plan().
This behavior can be controlled via an R option - see
future options for more details.
a <- b <- c <- NA_real_
# An sequential future
plan(sequential)
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
})
y <- value(f)
print(y)
str(list(a = a, b = b, c = c)) ## All NAs
# A sequential future with lazy evaluation
plan(sequential)
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
}, lazy = TRUE)
y <- value(f)
print(y)
str(list(a = a, b = b, c = c)) ## All NAs
# A multicore future (specified as a string)
plan("multicore")
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
})
y <- value(f)
print(y)
str(list(a = a, b = b, c = c)) ## All NAs
## Multisession futures gives an error on R CMD check on
## Windows (but not Linux or macOS) for unknown reasons.
## The same code works in package tests.
# A multisession future (specified via a string variable)
plan("future::multisession")
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
})
y <- value(f)
print(y)
str(list(a = a, b = b, c = c)) ## All NAs
## Explicitly close multisession workers by switching plan
plan(sequential)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.