Package 'future'

Description

Back trace the expressions evaluated when an error was caught

Usage

backtrace(future, envir = parent.frame(), ...)

Arguments

Value

A list with the future's call stack that led up to the error.

Examples

my_log <- function(x) log(x)
foo <- function(...) my_log(...)

f <- future({ foo("a") })
res <- tryCatch({
  v <- value(f)
}, error = function(ex) {
  t <- backtrace(f)
  print(t)
})

Description

A cluster future is a future that uses cluster evaluation, which means that its value is computed and resolved in parallel in another process.

Usage

cluster(
  ...,
  workers = availableWorkers(),
  gc = FALSE,
  earlySignal = FALSE,
  passthrough = FALSE,
  persistent = FALSE,
  envir = parent.frame()
)

Arguments

Details

This function is not meant to be called directly. Instead, the typical usages are:

# Evaluate futures via a single background R process on the local machine
plan(cluster, workers = 1)

# Evaluate futures via two background R processes on the local machine
plan(cluster, workers = 2)

# Evaluate futures via a single R process on another machine on on the
# local area network (LAN)
plan(cluster, workers = "raspberry-pi")

# Evaluate futures via a single R process running on a remote machine
plan(cluster, workers = "pi.example.org")

# Evaluate futures via four R processes, one running on the local machine,
# two running on LAN machine 'n1' and one on a remote machine
plan(cluster, workers = c("localhost", "n1", "n1", "pi.example.org"))

Value

A ClusterFuture.

Examples

## Use cluster futures
cl <- parallel::makeCluster(2, timeout = 60)
plan(cluster, workers = cl)

## A global variable
a <- 0

## Create future (explicitly)
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## A cluster future is evaluated in a separate process.
## Regardless, 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)

## CLEANUP
parallel::stopCluster(cl)

Description

Creates a future that evaluates an R expression or a future that calls an R function with a set of arguments. How, when, and where these futures are evaluated can be configured using plan() such that it is evaluated in parallel on, for instance, the current machine, on a remote machine, or via a job queue on a compute cluster. Importantly, any R code using futures remains the same regardless on these settings and there is no need to modify the code when switching from, say, sequential to parallel processing.

Usage

future(
  expr,
  envir = parent.frame(),
  substitute = TRUE,
  lazy = FALSE,
  seed = FALSE,
  globals = TRUE,
  packages = NULL,
  stdout = TRUE,
  conditions = "condition",
  label = NULL,
  gc = FALSE,
  earlySignal = FALSE,
  ...
)

futureCall(
  FUN,
  args = list(),
  envir = parent.frame(),
  lazy = FALSE,
  seed = FALSE,
  globals = TRUE,
  packages = NULL,
  stdout = TRUE,
  conditions = "condition",
  earlySignal = FALSE,
  label = NULL,
  gc = FALSE,
  ...
)

minifuture(
  expr,
  substitute = TRUE,
  globals = NULL,
  packages = NULL,
  stdout = NA,
  conditions = NULL,
  seed = NULL,
  ...,
  envir = parent.frame()
)

Arguments

Details

The state of a future is either unresolved or resolved. The value of a future can be retrieved using v <- value(f). Querying the value of a non-resolved future will block the call until the future is resolved. It is possible to check whether a future is resolved or not without blocking by using resolved(f).

The futureCall() function works analogously to do.call(), which calls a function with a set of arguments. The difference is that do.call() returns the value of the call whereas futureCall() returns a future.

Value

future() returns Future that evaluates expression expr.

futureCall() returns a Future that calls function FUN with arguments args.

minifuture(expr) creates a future with minimal overhead, by disabling user-friendly behaviors, e.g. automatic identification of global variables and packages needed, and relaying of output.

Eager or lazy evaluation

By default, a future is resolved using eager evaluation (lazy = FALSE). This means that the expression starts to be evaluated as soon as the future is created.

As an alternative, the future can be resolved using lazy evaluation (lazy = TRUE). This means that the expression will only be evaluated when the value of the future is requested. Note that this means that the expression may not be evaluated at all - it is guaranteed to be evaluated if the value is requested.

Globals used by future expressions

Global objects (short globals) are objects (e.g. variables and functions) that are needed in order for the future expression to be evaluated while not being local objects that are defined by the future expression. For example, in

  a <- 42
  f <- future({ b <- 2; a * b })

variable a is a global of future assignment f whereas b is a local variable. In order for the future to be resolved successfully (and correctly), all globals need to be gathered when the future is created such that they are available whenever and wherever the future is resolved.

The default behavior (globals = TRUE), is that globals are automatically identified and gathered. More precisely, globals are identified via code inspection of the future expression expr and their values are retrieved with environment envir as the starting point (basically via get(global, envir = envir, inherits = TRUE)). In most cases, such automatic collection of globals is sufficient and less tedious and error prone than if they are manually specified.

However, for full control, it is also possible to explicitly specify exactly which the globals are by providing their names as a character vector. In the above example, we could use

  a <- 42
  f <- future({ b <- 2; a * b }, globals = "a")

Yet another alternative is to explicitly specify also their values using a named list as in

  a <- 42
  f <- future({ b <- 2; a * b }, globals = list(a = a))

or

  f <- future({ b <- 2; a * b }, globals = list(a = 42))

Specifying globals explicitly avoids the overhead added from automatically identifying the globals and gathering their values. Furthermore, if we know that the future expression does not make use of any global variables, we can disable the automatic search for globals by using

  f <- future({ a <- 42; b <- 2; a * b }, globals = FALSE)

Future expressions often make use of functions from one or more packages. As long as these functions are part of the set of globals, the future package will make sure that those packages are attached when the future is resolved. Because there is no need for such globals to be frozen or exported, the future package will not export them, which reduces the amount of transferred objects. For example, in

  x <- rnorm(1000)
  f <- future({ median(x) })

variable x and median() are globals, but only x is exported whereas median(), which is part of the stats package, is not exported. Instead it is made sure that the stats package is on the search path when the future expression is evaluated. Effectively, the above becomes

  x <- rnorm(1000)
  f <- future({
    library(stats)
    median(x)
  })

To manually specify this, one can either do

  x <- rnorm(1000)
  f <- future({
    median(x)
  }, globals = list(x = x, median = stats::median)

or

  x <- rnorm(1000)
  f <- future({
    library(stats)
    median(x)
  }, globals = list(x = x))

Both are effectively the same.

Although rarely needed, a combination of automatic identification and manual specification of globals is supported via attributes add (to add false negatives) and ignore (to ignore false positives) on value TRUE. For example, with globals = structure(TRUE, ignore = "b", add = "a") any globals automatically identified except b will be used in addition to global a.

Author(s)

The future logo was designed by Dan LaBar and tweaked by Henrik Bengtsson.

See Also

How, when and where futures are resolved is given by the future strategy, which can be set by the end user using the plan() function. The future strategy must not be set by the developer, e.g. it must not be called within a package.

Examples

## Evaluate futures in parallel
plan(multisession)

## Data
x <- rnorm(100)
y <- 2 * x + 0.2 + rnorm(100)
w <- 1 + x ^ 2


## EXAMPLE: Regular assignments (evaluated sequentially)
fitA <- lm(y ~ x, weights = w)      ## with offset
fitB <- lm(y ~ x - 1, weights = w)  ## without offset
fitC <- {
  w <- 1 + abs(x)  ## Different weights
  lm(y ~ x, weights = w)
}
print(fitA)
print(fitB)
print(fitC)


## EXAMPLE: Future assignments (evaluated in parallel)
fitA %<-% lm(y ~ x, weights = w)      ## with offset
fitB %<-% lm(y ~ x - 1, weights = w)  ## without offset
fitC %<-% {
  w <- 1 + abs(x)
  lm(y ~ x, weights = w)
}
print(fitA)
print(fitB)
print(fitC)


## EXAMPLE: Explicitly create futures (evaluated in parallel)
## and retrieve their values
fA <- future( lm(y ~ x, weights = w) )
fB <- future( lm(y ~ x - 1, weights = w) )
fC <- future({
  w <- 1 + abs(x)
  lm(y ~ x, weights = w)
})
fitA <- value(fA)
fitB <- value(fB)
fitC <- value(fC)
print(fitA)
print(fitB)
print(fitC)


## EXAMPLE: futureCall() and do.call()
x <- 1:100
y0 <- do.call(sum, args = list(x))
print(y0)

f1 <- futureCall(sum, args = list(x))
y1 <- value(f1)
print(y1)

Description

x %<-% value (also known as a "future assignment") and futureAssign("x", value) create a Future that evaluates the expression (value) and binds it to variable x (as a promise). The expression is evaluated in parallel in the background. Later on, when x is first queried, the value of future is automatically retrieved as it were a regular variable and x is materialized as a regular value.

Usage

futureAssign(
  x,
  value,
  envir = parent.frame(),
  substitute = TRUE,
  lazy = FALSE,
  seed = FALSE,
  globals = TRUE,
  packages = NULL,
  stdout = TRUE,
  conditions = "condition",
  earlySignal = FALSE,
  label = NULL,
  gc = FALSE,
  ...,
  assign.env = envir
)

x %<-% value

fassignment %globals% globals
fassignment %packages% packages

fassignment %seed% seed

fassignment %stdout% capture

fassignment %conditions% capture

fassignment %lazy% lazy

fassignment %label% label

fassignment %plan% strategy

fassignment %tweak% tweaks

Arguments

Details

For a future created via a future assignment, x %<-% value or futureAssign("x", value), the value is bound to a promise, which when queried will internally call value() on the future and which will then be resolved into a regular variable bound to that value. For example, with future assignment x %<-% value, the first time variable x is queried the call blocks if, and only if, the future is not yet resolved. As soon as it is resolved, and any succeeding queries, querying x will immediately give the value.

The future assignment construct x %<-% value is not a formal assignment per se, but a binary infix operator on objects x and expression value. However, by using non-standard evaluation, this constructs can emulate an assignment operator similar to x <- value. Due to R's precedence rules of operators, future expressions often need to be explicitly bracketed, e.g. x %<-% { a + b }.

Value

futureAssign() and x %<-% expr returns the Future invisibly, e.g. f <- futureAssign("x", expr) and f <- (x %<-% expr).

Adjust future arguments of a future assignment

future() and futureAssign() take several arguments that can be used to explicitly specify what global variables and packages the future should use. They can also be used to override default behaviors of the future, e.g. whether output should be relayed or not. When using a future assignment, these arguments can be specified via corresponding assignment expression. For example, x %<-% { rnorm(10) } %seed% TRUE corresponds to futureAssign("x", { rnorm(10) }, seed = TRUE). Here are a several examples.

To explicitly specify variables and functions that a future assignment should use, use ⁠%globals%⁠. To explicitly specify which packages need to be attached for the evaluate to success, use ⁠%packages%⁠. For example,

> x <- rnorm(1000)
> y %<-% { median(x) } %globals% list(x = x) %packages% "stats"
> y
[1] -0.03956372

The median() function is part of the 'stats' package.

To declare that you will generate random numbers, use ⁠%seed%⁠, e.g.

> x %<-% { rnorm(3) } %seed% TRUE
> x
[1] -0.2590562 -1.2262495  0.8858702

To disable relaying of standard output (e.g. print(), cat(), and str()), while keeping relaying of conditions (e.g. message() and

> x %<-% { cat("Hello\n"); message("Hi there"); 42 } %stdout% FALSE
> y <- 13
> z <- x + y
Hi there
> z
[1] 55

To disable relaying of conditions, use ⁠%conditions%⁠, e.g.

> x %<-% { cat("Hello\n"); message("Hi there"); 42 } %conditions% character(0)
> y <- 13
> z <- x + y
Hello
> z
[1] 55

> x %<-% { print(1:10); message("Hello"); 42 } %stdout% FALSE
> y <- 13
> z <- x + y
Hello
> z
[1] 55

To create a future without launching in such that it will only be processed if the value is really needed, use ⁠%lazy%⁠, e.g.

> x %<-% { Sys.sleep(5); 42 } %lazy% TRUE
> y <- sum(1:10)
> system.time(z <- x + y)
  user  system elapsed 
  0.004   0.000   5.008
> z
[1] 97

Error handling

Because future assignments are promises, errors produced by the the future expression will not be signaled until the value of the future is requested. For example, if you create a future assignment that produce an error, you will not be affected by the error until you "touch" the future-assignment variable. For example,

> x %<-% { stop("boom") }
> y <- sum(1:10)
> z <- x + y
Error in eval(quote({ : boom

Use alternative future backend for future assignment

Futures are evaluated on the future backend that the user has specified by plan(). With regular futures, we can temporarily use another future backend by wrapping our code in ⁠[withPlan()]⁠, of temporarily inside a function using ⁠[localPlan()]⁠. To achieve the same for a specific future assignment, use ⁠%plan%⁠, e.g.

> plan(multisession)
> x %<-% { 42 }
> y %<-% { 13 } %plan% sequential
> z <- x + y
> z
[1] 55

Here x is resolved in the background via the multisession backend, whereas y is resolved sequentially in the main R session.

Getting the future object of a future assignment

The underlying Future of a future variable x can be retrieved without blocking using f <- futureOf(x), e.g.

> x %<-% { stop("boom") }
> f_x <- futureOf(x)
> resolved(f_x)
[1] TRUE
> x
Error in eval(quote({ : boom
> value(f_x)
Error in eval(quote({ : boom

Technically, both the future and the variable (promise) are assigned at the same time to environment assign.env where the name of the future is ⁠.future_<name>⁠.

Description

Get the future of a future variable that has been created directly or indirectly via future().

Usage

futureOf(
  var = NULL,
  envir = parent.frame(),
  mustExist = TRUE,
  default = NA,
  drop = FALSE
)

Arguments

Value

A Future (or default). If var is NULL, then a named list of Future:s are returned.

Examples

a %<-% { 1 }

f <- futureOf(a)
print(f)

b %<-% { 2 }

f <- futureOf(b)
print(f)

## All futures
fs <- futureOf()
print(fs)


## Futures part of environment
env <- new.env()
env$c %<-% { 3 }

f <- futureOf(env$c)
print(f)

f2 <- futureOf(c, envir = env)
print(f2)

f3 <- futureOf("c", envir = env)
print(f3)

fs <- futureOf(envir = env)
print(fs)

Description

Gets all futures in an environment, a list, or a list environment and returns an object of the same class (and dimensions). Non-future elements are returned as is.

Usage

futures(x, ...)

Arguments

Details

This function is useful for retrieve futures that were created via future assignments (⁠%<-%⁠) and therefore stored as promises. This function turns such promises into standard Future objects.

Value

An object of same type as x and with the same names and/or dimensions, if set.

Description

Get future-specific session information and validate current backend

Usage

futureSessionInfo(test = TRUE, anonymize = TRUE)

Arguments

Value

Nothing.

Examples

plan(multisession, workers = 2)
futureSessionInfo()
plan(sequential)

Description

Attempts to interrupt a running future. If the backend does not support interrupting futures, nothing is done.

Usage

interrupt(x, ...)

Arguments

Value

interrupt() returns the Future flagged as "interrupted", if the backend supports interrupting futures.

Description

A multicore future is a future that uses multicore evaluation, which means that its value is computed and resolved in parallel in another process.

Usage

multicore(
  ...,
  workers = availableCores(constraints = "multicore"),
  gc = FALSE,
  earlySignal = FALSE,
  envir = parent.frame()
)

Arguments

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 forked
# processes as available to the current R process
plan(multicore)

# Evaluate futures in parallel on the local machine via two forked processes
plan(multicore, workers = 2)

Value

A Future. If workers == 1, then all processing using done in the current/main R session and we therefore fall back to using a sequential future. To override this fallback, use workers = I(1). This is also the case whenever multicore processing is not supported, e.g. on Windows.

Support for forked ("multicore") processing

Not all operating systems support process forking and thereby not multicore futures. For instance, forking is not supported on Microsoft Windows. Moreover, process forking may break some R environments such as RStudio. Because of this, the future package disables process forking also in such cases. See parallelly::supportsMulticore() for details. Trying to create multicore futures on non-supported systems or when forking is disabled will result in multicore futures falling back to becoming sequential futures. If used in RStudio, there will be an informative warning:

> plan(multicore)
Warning message:
In supportsMulticoreAndRStudio(...) :
  [ONE-TIME WARNING] Forked processing ('multicore') is not supported when
running R from RStudio because it is considered unstable. For more details,
how to control forked processing or not, and how to silence this warning in
future R sessions, see ?parallelly::supportsMulticore

See Also

For processing in multiple background R sessions, see multisession futures.

Use parallelly::availableCores() to see the total number of cores that are available for the current R session. Use availableCores("multicore") > 1L to check whether multicore futures are supported or not on the current system.

Examples

## Use multicore futures
plan(multicore)

## A global variable
a <- 0

## Create future (explicitly)
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## A multicore future is evaluated in a separate forked
## process.  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)

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(),
  gc = FALSE,
  earlySignal = FALSE,
  envir = parent.frame()
)

Arguments

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)

Description

Get the number of workers available

Usage

nbrOfWorkers(evaluator = NULL)

nbrOfFreeWorkers(evaluator = NULL, background = FALSE, ...)

Arguments

Value

nbrOfWorkers() returns a positive number in ${1, 2, 3, ...}$, which for some future backends may also be +Inf.

nbrOfFreeWorkers() returns a non-negative number in ${0, 1, 2, 3, ...}$ which is less than or equal to nbrOfWorkers().

Examples

plan(multisession)
nbrOfWorkers()  ## == availableCores()

plan(sequential)
nbrOfWorkers()  ## == 1

Description

This function allows the user to plan the future, more specifically, it specifies how future():s are resolved, e.g. sequentially or in parallel.

Usage

plan(
  strategy = NULL,
  ...,
  substitute = TRUE,
  .skip = FALSE,
  .call = TRUE,
  .cleanup = NA,
  .init = TRUE
)

tweak(strategy, ..., penvir = parent.frame())

withPlan(
  strategy = NULL,
  expr,
  envir = parent.frame(),
  .cleanup = NA,
  substitute = TRUE,
  ...
)

localPlan(
  strategy = NULL,
  .cleanup = NA,
  envir = parent.frame(),
  substitute = TRUE,
  ...
)

Arguments

Details

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").

Value

plan() returns a the previous plan invisibly if a new strategy is chosen, otherwise it returns the current one visibly.

a future function.

withPlan() returns the value of the expression evaluated invisibly.

localPlan() returns the current future plan before applying the temporary one.

Built-in evaluation strategies

The future package provides the following built-in backends:

sequential:

Resolves futures sequentially in the current R process, e.g. plan(sequential).

multisession:

Resolves futures asynchronously (in parallel) in separate R sessions running in the background on the same machine, e.g. plan(multisession) and plan(multisession, workers = 2).

multicore:

Resolves futures asynchronously (in parallel) in separate forked R processes running in the background on the same machine, e.g. plan(multicore) and plan(multicore, workers = 2). This backend is not supported on Windows.

cluster:

Resolves futures asynchronously (in parallel) in separate R sessions running typically on one or more machines, e.g. plan(cluster), plan(cluster, workers = 2), and plan(cluster, workers = c("n1", "n1", "n2", "server.remote.org")).

Other package provide additional evaluation strategies. For example, the future.callr package implements an alternative to the multisession backend on top of the callr package, e.g. plan(future.callr::callr, workers = 2). Another example is the future.batchtools package, which implements, on top of the batchtools package, e.g. plan(future.batchtools::batchtools_slurm). These types of futures are resolved via job schedulers, which typically are 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.

For package developers

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 archived by using localPlan(), e.g.

  my_fcn <- function(x) {
    localPlan(multisession)
    y <- analyze(x)
    summarize(y)
  }

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.

Using plan() in scripts and vignettes

When writing scripts or vignettes that use futures, try to place any call to plan() as far up (i.e. 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.

See Also

Use plan() to set a future to become the new default strategy.

Examples

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 specifying number of workers
## (default is parallelly::availableCores())
plan(multicore, workers = 2)
message("Number of parallel workers: ", nbrOfWorkers())


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

Description

A future that has successfully completed, has been interrupted, or failed due to an error, can be relaunched after resetting it.

Usage

reset(x, ...)

Arguments

Details

A lazy, vanilla Future can be reused in another R session. For instance, if we do:

library(future)
a <- 2
f <- future(42 * a, lazy = TRUE)
saveRDS(f, "myfuture.rds")

Then we can read and evaluate the future in another R session using:

library(future)
f <- readRDS("myfuture.rds")
v <- value(f)
print(v)
#> [1] 84

Value

reset() returns a lazy, vanilla Future that can be relaunched. Resetting a running future results in a FutureError.

Examples

## Like mean(), but fails 90% of the time
shaky_mean <- function(x) {
  if (as.double(Sys.time()) %% 1 < 0.90) stop("boom")
  mean(x)
}

x <- rnorm(100)

## Calculate the mean of 'x' with a risk of failing randomly
f <- future({ shaky_mean(x) })

## Relaunch until success
repeat({
  v <- tryCatch(value(f), error = identity)
  if (!inherits(v, "error")) break
  message("Resetting failed future, and retry in 0.1 seconds")
  f <- reset(f)
  Sys.sleep(0.1)
})
cat("mean:", v, "\n")

Description

This function provides an efficient mechanism for waiting for multiple futures in a container (e.g. list or environment) to be resolved while in the meanwhile retrieving values of already resolved futures.

Usage

resolve(
  x,
  idxs = NULL,
  recursive = 0,
  result = FALSE,
  stdout = FALSE,
  signal = FALSE,
  force = FALSE,
  sleep = getOption("future.wait.interval", 0.01),
  ...
)

Arguments

Details

This function is resolves synchronously, i.e. it blocks until x and any containing futures are resolved.

Value

Returns x (regardless of subsetting or not). If signal is TRUE and one of the futures produces an error, then that error is produced.

See Also

To resolve a future variable, first retrieve its Future object using futureOf(), e.g. resolve(futureOf(x)).

Description

Check whether a future is resolved or not

Usage

resolved(x, ...)

Arguments

Details

This method needs to be implemented by the class that implement the Future API. The implementation should return either TRUE or FALSE and must never throw an error (except for FutureError:s which indicate significant, often unrecoverable infrastructure problems). It should also be possible to use the method for polling the future until it is resolved (without having to wait infinitely long), e.g. while (!resolved(future)) Sys.sleep(5).

Value

A logical of the same length and dimensions as x. Each element is TRUE unless the corresponding element is a non-resolved future in case it is FALSE.

Description

A sequential future is a future that is evaluated sequentially in the current R session similarly to how R expressions are evaluated in R. The only difference to R itself is that globals are validated by default just as for all other types of futures in this package.

Usage

sequential(..., gc = FALSE, earlySignal = FALSE, envir = parent.frame())

Arguments

Details

This function is not meant to be called directly. Instead, the typical usages are:

# Evaluate futures sequentially in the current R process
plan(sequential)

Value

A Future.

Examples

## Use sequential futures
plan(sequential)

## A global variable
a <- 0

## Create a sequential future
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## Since 'a' is a global variable in future 'f' which
## is eagerly resolved (default), this global has already
## been resolved / incorporated, and any changes to 'a'
## at this point will _not_ affect the value of 'f'.
a <- 7
print(a)

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

Description

Gets the value of a future or the values of all elements (including futures) in a container such as a list, an environment, or a list environment. If one or more futures is unresolved, then this function blocks until all queried futures are resolved.

Usage

value(...)

## S3 method for class 'Future'
value(future, stdout = TRUE, signal = TRUE, drop = FALSE, ...)

## S3 method for class 'list'
value(
  x,
  idxs = NULL,
  recursive = 0,
  reduce = NULL,
  stdout = TRUE,
  signal = TRUE,
  interrupt = TRUE,
  inorder = TRUE,
  drop = FALSE,
  force = TRUE,
  sleep = getOption("future.wait.interval", 0.01),
  ...
)

## S3 method for class 'listenv'
value(
  x,
  idxs = NULL,
  recursive = 0,
  reduce = NULL,
  stdout = TRUE,
  signal = TRUE,
  interrupt = TRUE,
  inorder = TRUE,
  drop = FALSE,
  force = TRUE,
  sleep = getOption("future.wait.interval", 0.01),
  ...
)

## S3 method for class 'environment'
value(x, ...)

Arguments

Value

value() of a Future object returns the value of the future, which can be any type of R object.

value() of a list, an environment, or a list environment returns an object with the same number of elements and of the same class. Names and dimension attributes are preserved, if available. All future elements are replaced by their corresponding value() values. For all other elements, the existing object is kept as-is.

If signal is TRUE and one of the futures produces an error, then that error is relayed. Any remaining, non-resolved futures in x are interrupted, prior to signaling such an error.

Examples

## ------------------------------------------------------
## A single future
## ------------------------------------------------------
x <- sample(100, size = 50)
f <- future(mean(x))
v <- value(f)
message("The average of 50 random numbers in [1,100] is: ", v)



## ------------------------------------------------------
## Ten futures
## ------------------------------------------------------
xs <- replicate(10, { list(sample(100, size = 50)) })
fs <- lapply(xs, function(x) { future(mean(x)) })

## The 10 values as a list (because 'fs' is a list)
vs <- value(fs)
message("The ten averages are:")
str(vs)

## The 10 values as a vector (by manually unlisting)
vs <- value(fs)
vs <- unlist(vs)
message("The ten averages are: ", paste(vs, collapse = ", "))

## The values as a vector (by reducing)
vs <- value(fs, reduce = `c`)
message("The ten averages are: ", paste(vs, collapse = ", "))

## Calculate the sum of the averages (by reducing)
total <- value(fs, reduce = `sum`)
message("The sum of the ten averages is: ", total)

Description

Below are the R options and environment variables that are used by the future package and packages enhancing it.

WARNING: Note that the names and the default values of these options may change in future versions of the package. Please use with care until further notice.

Packages must not change future options

Just like for other R options, as a package developer you must not change any of the below ⁠future.*⁠ options. Only the end-user should set these. If you find yourself having to tweak one of the options, make sure to undo your changes immediately afterward. For example, if you want to bump up the future.globals.maxSize limit when creating a future, use something like the following inside your function:

oopts <- options(future.globals.maxSize = 1.0 * 1e9)  ## 1.0 GB
on.exit(options(oopts))
f <- future({ expr })  ## Launch a future with large objects

Settings moved to the 'parallelly' package

Several functions have been moved to the parallelly package:

• parallelly::availableCores()

• parallelly::availableWorkers()

• parallelly::makeClusterMPI()

• parallelly::makeClusterPSOCK()

• parallelly::makeNodePSOCK()

• parallelly::supportsMulticore()

The options and environment variables controlling those have been adjusted accordingly to have different prefixes. For example, option future.fork.enable has been renamed to parallelly.fork.enable and the corresponding environment variable R_FUTURE_FORK_ENABLE has been renamed to R_PARALLELLY_FORK_ENABLE. For backward compatibility reasons, the parallelly package will support both versions for a long foreseeable time. See the parallelly::parallelly.options page for the settings.

Options for controlling futures

future.plan:

(character string or future function) Default future strategy plan used unless otherwise specified via plan(). This will also be the future plan set when calling plan("default"). If not specified, this option may be set when the future package is loaded if command-line option --parallel=ncores (short ⁠-p ncores⁠) is specified; if ncores > 1, then option future.plan is set to multisession otherwise sequential (in addition to option mc.cores being set to ncores, if ncores >= 1). (Default: sequential)

future.globals.maxSize:

(numeric) Maximum allowed total size (in bytes) of global variables identified. This is used to protect against exporting too large objects to parallel workers by mistake. Transferring large objects over a network, or over the internet, can be slow and therefore introduce a large bottleneck that increases the overall processing time. It can also result in large egress or ingress costs, which may exist on some systems. If set of +Inf, then the check for large globals is skipped. (Default: 500 * 1024 ^ 2 = 500 MiB)

future.globals.onReference: (beta feature - may change)

(character string) Controls whether the identified globals should be scanned for so called references (e.g. external pointers and connections) or not. It is unlikely that another R process ("worker") can use a global that uses a internal reference of the master R process—we call such objects non-exportable globals. If this option is "error", an informative error message is produced if a non-exportable global is detected. If "warning", a warning is produced, but the processing will continue; it is likely that the future will be resolved with a run-time error unless processed in the master R process (e.g. plan(sequential) and plan(multicore)). If "ignore", no scan is performed. (Default: "ignore" but may change)

future.resolve.recursive:

(integer) An integer specifying the maximum recursive depth to which futures should be resolved. If negative, nothing is resolved. If 0, only the future itself is resolved. If 1, the future and any of its elements that are futures are resolved, and so on. If +Inf, infinite search depth is used. (Default: 0)

future.rng.onMisuse: (beta feature - may change)

(character string) If random numbers are used in futures, then parallel (L'Ecuyer-CMRG) RNG should be used in order to get statistical sound RNGs. The defaults in the future framework assume that no random number generation (RNG) is taken place in the future expression because L'Ecuyer-CMRG RNGs come with an unnecessary overhead if not needed. To protect against mistakes, the future framework attempts to detect when random numbers are used despite L'Ecuyer-CMRG RNGs are not in place. If this is detected, and future.rng.onMisuse = "error", then an informative error message is produced. If "warning", then a warning message is produced. If "ignore", no check is performed. (Default: "warning")

future.connections.onMisuse: (beta feature - may change)

(character string) A future must close any connections it opens and must not close connections it did not open. If such misuse is detected and this option is set to "error", value() will produce an error with details. If it is set to "warning", a warning is produced. If "ignore", no check is performed. (Default: "warning")

future.globalenv.onMisuse: (beta feature - may change)

(character string) Assigning variables to the global environment for the purpose of using the variable at a later time makes no sense with futures, because the next future may be evaluated in different R process. To protect against mistakes, the future framework attempts to detect when variables are added to the global environment. If this is detected, and future.globalenv.onMisuse = "error", then an informative error message is produced. If "warning", then a warning message is produced. If "ignore", no check is performed. (Default: "ignore")

future.onFutureCondition.keepFuture:

(logical) If TRUE, a FutureCondition keeps a copy of the Future object that triggered the condition. If FALSE, it is dropped. (Default: TRUE)

future.wait.timeout:

(numeric) Maximum waiting time (in seconds) for a future to resolve or for a free worker to become available before a timeout error is generated. (Default: 30 * 24 * 60 * 60 (= 30 days))

future.wait.interval:

(numeric) Initial interval (in seconds) between polls. This controls the polling frequency for finding an available worker when all workers are currently busy. It also controls the polling frequency of resolve(). (Default: 0.01 = 1 ms)

future.wait.alpha:

(numeric) Positive scale factor used to increase the interval after each poll. (Default: 1.01)

Options for debugging futures

future.debug:

(logical) If TRUE, extensive debug messages are generated. (Default: FALSE)

Options for controlling package startup

future.startup.script:

(character vector or a logical) Specifies zero of more future startup scripts to be sourced when the future package is attached. It is only the first existing script that is sourced. If none of the specified files exist, nothing is sourced—there will be neither a warning nor an error. If this option is not specified, environment variable R_FUTURE_STARTUP_SCRIPT is considered, where multiple scripts may be separated by either a colon (:) or a semicolon (⁠;⁠). If neither is set, or either is set to TRUE, the default is to look for a ‘.future.R’ script in the current directory and then in the user's home directory. To disable future startup scripts, set the option or the environment variable to FALSE. Importantly, this option is always set to FALSE if the future package is loaded as part of a future expression being evaluated, e.g. in a background process. In order words, they are sourced in the main R process but not in future processes. (Default: TRUE in main R process and FALSE in future processes / during future evaluation)

future.cmdargs:

(character vector) Overrides commandArgs() when the future package is loaded.

Options for configuring low-level system behaviors

future.fork.multithreading.enable (beta feature - may change):

(logical) Enable or disable multi-threading while using forked parallel processing. If FALSE, different multi-thread library settings are overridden such that they run in single-thread mode. Specifically, multi-threading will be disabled for OpenMP (which requires the RhpcBLASctl package) and for RcppParallel. If TRUE, or not set (the default), multi-threading is allowed. Parallelization via multi-threaded processing (done in native code by some packages and external libraries) while at the same time using forked (aka "multicore") parallel processing is known to unstable. Note that this is not only true when using plan(multicore) but also when using, for instance, mclapply() of the parallel package. (Default: not set)

future.output.windows.reencode:

(logical) Enable or disable re-encoding of UTF-8 symbols that were incorrectly encoded while captured. In R (< 4.2.0) and on older versions of MS Windows, R cannot capture UTF-8 symbols as-is when they are captured from the standard output. For examples, a UTF-8 check mark symbol ("\u2713") would be relayed as "<U+2713>" (a string with eight ASCII characters). Setting this option to TRUE will cause value() to attempt to recover the intended UTF-8 symbols from ⁠<U+nnnn>⁠ string components, if, and only if, the string was captured by a future resolved on MS Windows. (Default: TRUE)

See also parallelly::parallelly.options.

Options for demos

future.demo.mandelbrot.region:

(integer) Either a named list of mandelbrot() arguments or an integer in {1, 2, 3} specifying a predefined Mandelbrot region. (Default: 1L)

future.demo.mandelbrot.nrow:

(integer) Number of rows and columns of tiles. (Default: 3L)

Deprecated or for internal prototyping

The following options exists only for troubleshooting purposes and must not be used in production. If used, there is a risk that the results are non-reproducible if processed elsewhere. To lower the risk of them being used by mistake, they are marked as deprecated and will produce warnings if set.

future.globals.onMissing:

(character string) Action to take when non-existing global variables ("globals" or "unknowns") are identified when the future is created. If "error", an error is generated immediately. If "ignore", no action is taken and an attempt to evaluate the future expression will be made. The latter is useful when there is a risk for false-positive globals being identified, e.g. when future expression contains non-standard evaluation (NSE). (Default: "ignore")

future.globals.method:

(character string) Method used to identify globals. For details, see globalsOf(). (Default: "ordered")

future.globals.resolve:

(logical) If TRUE, globals that are Future objects (typically created as explicit futures) will be resolved and have their values (using value()) collected. Because searching for unresolved futures among globals (including their content) can be expensive, the default is not to do it and instead leave it to the run-time checks that assert proper ownership when resolving futures and collecting their values. (Default: FALSE)

Environment variables that set R options

All of the above R future.* options can be set by corresponding environment variable R_FUTURE_* when the future package is loaded. This means that those environment variables must be set before the future package is loaded in order to have an effect. For example, if R_FUTURE_RNG_ONMISUSE="ignore", then option future.rng.onMisuse is set to "ignore" (character string). Similarly, if R_FUTURE_GLOBALS_MAXSIZE="50000000", then option future.globals.maxSize is set to 50000000 (numeric).

See Also

To set R options or environment variables when R starts (even before the future package is loaded), see the Startup help page. The startup package provides a friendly mechanism for configurating R's startup process.

Examples

# Allow at most 5 MB globals per futures
options(future.globals.maxSize = 5e6)

# Be strict; catch all RNG mistakes
options(future.rng.onMisuse = "error")