
Configure a backend that controls how and where futures are evaluated
Source:R/backend_api-01-FutureBackend-class.R
, R/backend_api-03.MultiprocessFutureBackend-class.R
, R/backend_api-11.ClusterFutureBackend-class.R
, and 3 more
FutureBackend.Rd
Configure a backend that controls how and where futures are evaluated
launchFuture()
runs a future on the backend.
interruptFuture()
interrupts a future on the backend.
stopWorkers()
stops backend workers
A ClusterFutureBackend resolves futures in parallel using any PSOCK cluster
A MulticoreFutureBackend resolves futures in parallel using forked processing on the current machine
A SequentialFutureBackend resolves futures sequentially in the current R session
A MultisessionFutureBackend resolves futures in parallel using a PSOCK cluster on the current machine
Usage
FutureBackend(
...,
earlySignal = FALSE,
gc = FALSE,
maxSizeOfObjects = getOption("future.globals.maxSize", +Inf),
interrupts = TRUE,
hooks = FALSE
)
launchFuture(backend, future, ...)
listFutures(backend, ...)
interruptFuture(backend, future, ...)
validateFutureGlobals(backend, future, ...)
stopWorkers(backend, ...)
MultiprocessFutureBackend(
...,
wait.timeout = getOption("future.wait.timeout", 24 * 60 * 60),
wait.interval = getOption("future.wait.interval", 0.01),
wait.alpha = getOption("future.wait.alpha", 1.01)
)
ClusterFutureBackend(
workers = availableWorkers(constraints = "connections"),
gc = TRUE,
earlySignal = TRUE,
interrupts = FALSE,
persistent = FALSE,
...
)
MulticoreFutureBackend(
workers = availableCores(constraints = "multicore"),
maxSizeOfObjects = +Inf,
...
)
SequentialFutureBackend(..., maxSizeOfObjects = +Inf)
MultisessionFutureBackend(
workers = availableCores(constraints = "connections"),
interrupts = TRUE,
...
)
Arguments
- earlySignal
Overrides the default behavior on whether futures should resignal ("relay") conditions captured as soon as possible, or delayed, for instance, until
value()
is called on the future. (Default:FALSE
)- gc
Overrides the default behavior of whether futures should trigger garbage collection via
gc()
on the parallel worker after the value has been collected from the worker. This can help to release memory sooner than letting R itself on the parallel worker decided when it is needed. Releasing memory sooner can help to fit more parallel workers on a machine with limited amount of total memory. (Default:FALSE
)- maxSizeOfObjects
The maximum allowed total size, in bytes, of all objects to and from the parallel worker allows. This can help to protect against unexpectedly large data transfers between the parent process and the parallel workers - data that is often transferred over the network, which sometimes also includes the internet. For instance, if you sit at home and have set up a future backend with workers running remotely at your university or company, then you might want to use this protection to avoid transferring giga- or terabytes of data without noticing. (Default: \(500 \cdot 1024^2\) bytes = 500 MiB, unless overridden by a FutureBackend subclass, or by R option future.globals.maxSize (sic!))
- interrupts
If FALSE, attempts to interrupt futures will not take place on this backend, even if the backend supports it. This is useful when, for instance, it takes a long time to interrupt a future.
- backend
a FutureBackend.
- future
a Future to be started.
- wait.timeout
Number of seconds before timing out.
- wait.interval
Baseline number of seconds between retries.
- wait.alpha
Scale factor increasing waiting interval after each attempt.
- workers
...
- persistent
(deprecated) ...
- ...
(optional) not used.
Value
FutureBackend()
returns a FutureBackend object, which inherits an
environment. Specific future backends are defined by subclasses
implementing the FutureBackend API.
launchFuture()
returns the launched Future
object.
interruptFuture()
returns the interrupted Future
object,
if supported, other the unmodified future.
stopWorkers()
returns TRUE if the workers were shut down,
otherwise FALSE.
Details
The ClusterFutureBackend
is selected by
plan(cluster, workers = workers)
.
The MulticoreFutureBackend
backend is selected by
plan(multicore, workers = workers)
.
The SequentialFutureBackend
is selected by plan(sequential)
.
The MultisessionFutureBackend
backend is selected by
plan(multisession, workers = workers)
.