Configure a backend that controls how and where futures are evaluated

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(),
  rscript_libs = .libPaths(),
  interrupts = TRUE,
  gc = FALSE,
  earlySignal = FALSE,
  ...
)

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).

The FutureBackend API

The FutureBackend class specifies FutureBackend API, that all backends must implement and comply to. Specifically,