Title: | Apply Function to Elements in Parallel using Futures |
---|---|
Description: | Implementations of apply(), by(), eapply(), lapply(), Map(), .mapply(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster. These future_*apply() functions come with the same pros and cons as the corresponding base-R *apply() functions but with the additional feature of being able to be processed via the future framework. |
Authors: | Henrik Bengtsson [aut, cre, cph] , R Core Team [cph, ctb] |
Maintainer: | Henrik Bengtsson <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.11.2 |
Built: | 2024-06-26 04:45:38 UTC |
Source: | https://github.com/HenrikBengtsson/future.apply |
future_apply()
implements base::apply()
using future with perfect
replication of results, regardless of future backend used.
It returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.
future_apply( X, MARGIN, FUN, ..., simplify = TRUE, future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_apply-%d" )
future_apply( X, MARGIN, FUN, ..., simplify = TRUE, future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_apply-%d" )
X |
an array, including a matrix. |
MARGIN |
A vector giving the subscripts which the function will be
applied over. For example, for a matrix |
FUN |
A function taking at least one argument. |
simplify |
a logical indicating whether results should be simplified if possible. |
future.envir |
An environment passed as argument |
future.stdout |
If |
future.conditions |
A character string of conditions classes to be
captured and relayed. The default is the same as the |
future.globals |
A logical, a character vector, or a named list for controlling how globals are handled. For details, see below section. |
future.packages |
(optional) a character vector specifying packages to be attached in the R environment evaluating the future. |
future.seed |
A logical or an integer (of length one or seven),
or a list of |
future.scheduling |
Average number of futures ("chunks") per worker.
If |
future.chunk.size |
The average number of elements per future ("chunk").
If |
future.label |
If a character string, then each future is assigned
a label |
... |
(optional) Additional arguments passed to |
Returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.
See base::apply()
for details.
The implementations of future_apply()
is adopted from the source code
of the corresponding base R function, which is licensed under GPL (>= 2)
with 'The R Core Team' as the copyright holder.
## --------------------------------------------------------- ## apply() ## --------------------------------------------------------- X <- matrix(c(1:4, 1, 6:8), nrow = 2L) Y0 <- apply(X, MARGIN = 1L, FUN = table) Y1 <- future_apply(X, MARGIN = 1L, FUN = table) print(Y1) stopifnot(all.equal(Y1, Y0, check.attributes = FALSE)) ## FIXME Y0 <- apply(X, MARGIN = 1L, FUN = stats::quantile) Y1 <- future_apply(X, MARGIN = 1L, FUN = stats::quantile) print(Y1) stopifnot(all.equal(Y1, Y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical X <- matrix(c(1:4, 1, 6:8), nrow = 2L) plan(multisession) set.seed(0xBEEF) Y1 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y1) plan(sequential) set.seed(0xBEEF) Y2 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y2) stopifnot(all.equal(Y1, Y2))
## --------------------------------------------------------- ## apply() ## --------------------------------------------------------- X <- matrix(c(1:4, 1, 6:8), nrow = 2L) Y0 <- apply(X, MARGIN = 1L, FUN = table) Y1 <- future_apply(X, MARGIN = 1L, FUN = table) print(Y1) stopifnot(all.equal(Y1, Y0, check.attributes = FALSE)) ## FIXME Y0 <- apply(X, MARGIN = 1L, FUN = stats::quantile) Y1 <- future_apply(X, MARGIN = 1L, FUN = stats::quantile) print(Y1) stopifnot(all.equal(Y1, Y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical X <- matrix(c(1:4, 1, 6:8), nrow = 2L) plan(multisession) set.seed(0xBEEF) Y1 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y1) plan(sequential) set.seed(0xBEEF) Y2 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE) print(Y2) stopifnot(all.equal(Y1, Y2))
Apply a Function to a Data Frame Split by Factors via Futures
future_by( data, INDICES, FUN, ..., simplify = TRUE, future.envir = parent.frame() )
future_by( data, INDICES, FUN, ..., simplify = TRUE, future.envir = parent.frame() )
data |
An R object, normally a data frame, possibly a matrix. |
INDICES |
A factor or a list of factors, each of length |
FUN |
a function to be applied to (usually data-frame) subsets of |
simplify |
logical: see base::tapply. |
future.envir |
An environment passed as argument |
... |
Additional arguments pass to |
Internally, data
is grouped by INDICES
into a list of data
subset elements which is then processed by future_lapply()
.
When the groups differ significantly in size, the processing time
may differ significantly between the groups.
To correct for processing-time imbalances, adjust the amount of chunking
via arguments future.scheduling
and future.chunk.size
.
An object of class "by", giving the results for each subset.
This is always a list if simplify is false, otherwise a list
or array (see base::tapply).
See also base::by()
for details.
The future_by()
is modeled as closely as possible to the
behavior of base::by()
. Both functions have "default" S3 methods that
calls data <- as.data.frame(data)
internally. This call may in turn call
an S3 method for as.data.frame()
that coerces strings to factors or not
depending on whether it has a stringsAsFactors
argument and what its
default is.
For example, the S3 method of as.data.frame()
for lists changed its
(effective) default from stringsAsFactors = TRUE
to
stringsAsFactors = TRUE
in R 4.0.0.
## --------------------------------------------------------- ## by() ## --------------------------------------------------------- library(datasets) ## warpbreaks library(stats) ## lm() y0 <- by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x)) plan(multisession) y1 <- future_by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x)) plan(sequential) y2 <- future_by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x))
## --------------------------------------------------------- ## by() ## --------------------------------------------------------- library(datasets) ## warpbreaks library(stats) ## lm() y0 <- by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x)) plan(multisession) y1 <- future_by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x)) plan(sequential) y2 <- future_by(warpbreaks, warpbreaks[,"tension"], function(x) lm(breaks ~ wool, data = x))
future_lapply()
implements base::lapply()
using futures with perfect
replication of results, regardless of future backend used.
Analogously, this is true for all the other future_nnn()
functions.
future_eapply( env, FUN, ..., all.names = FALSE, USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_eapply-%d" ) future_lapply( X, FUN, ..., future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_lapply-%d" ) future_replicate( n, expr, simplify = "array", future.seed = TRUE, ..., future.envir = parent.frame(), future.label = "future_replicate-%d" ) future_sapply( X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_sapply-%d" ) future_tapply( X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE, future.envir = parent.frame(), future.label = "future_tapply-%d" ) future_vapply( X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_vapply-%d" )
future_eapply( env, FUN, ..., all.names = FALSE, USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_eapply-%d" ) future_lapply( X, FUN, ..., future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_lapply-%d" ) future_replicate( n, expr, simplify = "array", future.seed = TRUE, ..., future.envir = parent.frame(), future.label = "future_replicate-%d" ) future_sapply( X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_sapply-%d" ) future_tapply( X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE, future.envir = parent.frame(), future.label = "future_tapply-%d" ) future_vapply( X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE, future.envir = parent.frame(), future.label = "future_vapply-%d" )
env |
An R environment. |
FUN |
A function taking at least one argument. |
all.names |
If |
USE.NAMES |
See |
future.envir |
An environment passed as argument |
future.label |
If a character string, then each future is assigned
a label |
X |
An R object for which a |
future.stdout |
If |
future.conditions |
A character string of conditions classes to be
captured and relayed. The default is the same as the |
future.globals |
A logical, a character vector, or a named list for controlling how globals are handled. For details, see below section. |
future.packages |
(optional) a character vector specifying packages to be attached in the R environment evaluating the future. |
future.seed |
A logical or an integer (of length one or seven),
or a list of |
future.scheduling |
Average number of futures ("chunks") per worker.
If |
future.chunk.size |
The average number of elements per future ("chunk").
If |
n |
The number of replicates. |
expr |
An R expression to evaluate repeatedly. |
simplify |
See |
INDEX |
A list of one or more factors, each of same length as |
default |
See |
FUN.VALUE |
A template for the required return value from
each |
... |
(optional) Additional arguments passed to |
A named (unless USE.NAMES = FALSE
) list.
See base::eapply()
for details.
For future_lapply()
, a list with same length and names as X
.
See base::lapply()
for details.
future_replicate()
is a wrapper around future_sapply()
and return
simplified object according to the simplify
argument.
See base::replicate()
for details.
Since future_replicate()
usually involves random number generation (RNG),
it uses future.seed = TRUE
by default in order produce sound random
numbers regardless of future backend and number of background workers used.
For future_sapply()
, a vector with same length and names as X
.
See base::sapply()
for details.
future_tapply()
returns an array with mode "list"
, unless
simplify = TRUE
(default) and FUN
returns a scalar, in which
case the mode of the array is the same as the returned scalars.
See base::tapply()
for details.
For future_vapply()
, a vector with same length and names as X
.
See base::vapply()
for details.
Argument future.globals
may be used to control how globals
should be handled similarly how the globals
argument is used with
future()
.
Since all function calls use the same set of globals, this function can do
any gathering of globals upfront (once), which is more efficient than if
it would be done for each future independently.
If TRUE
(default), then globals are automatically identified and gathered.
If a character vector of names is specified, then those globals are gathered.
If a named list, then those globals are used as is.
In all cases, FUN
and any \ldots
arguments are automatically
passed as globals to each future created as they are always needed.
Unless future.seed
is FALSE
or NULL
, this function guarantees to
generate the exact same sequence of random numbers given the same initial
seed / RNG state - this regardless of type of futures, scheduling
("chunking") strategy, and number of workers.
RNG reproducibility is achieved by pregenerating the random seeds for all
iterations (over X
) by using L'Ecuyer-CMRG RNG streams. In each
iteration, these seeds are set before calling FUN(X[[ii]], ...)
.
Note, for large length(X)
this may introduce a large overhead.
If future.seed = TRUE
, then .Random.seed
is used if it holds a L'Ecuyer-CMRG RNG seed, otherwise one is created
randomly.
If future.seed = FALSE
, it is expected that none of the
FUN(X[[ii]], ...)
function calls use random number generation.
If they do, then an informative warning or error is produces depending
on settings. See future::future for more details.
Using future.seed = NULL
, is like future.seed = FALSE
but without
the check whether random numbers were generated or not.
As input, future.seed
may also take a fixed initial seed (integer),
either as a full L'Ecuyer-CMRG RNG seed (vector of 1+6 integers), or
as a seed generating such a full L'Ecuyer-CMRG seed. This seed will
be used to generated length(X)
L'Ecuyer-CMRG RNG streams.
In addition to the above, it is possible to specify a pre-generated
sequence of RNG seeds as a list such that
length(future.seed) == length(X)
and where each element is an
integer seed vector that can be assigned to
.Random.seed
. One approach to generate a
set of valid RNG seeds based on fixed initial seed (here 42L
) is:
seeds <- future_lapply(seq_along(X), FUN = function(x) .Random.seed, future.chunk.size = Inf, future.seed = 42L)
Note that as.list(seq_along(X))
is not a valid set of such
.Random.seed
values.
In all cases but future.seed = FALSE
and NULL
, the RNG state of the
calling R processes after this function returns is guaranteed to be
"forwarded one step" from the RNG state that was before the call and
in the same way regardless of future.seed
, future.scheduling
and future strategy used. This is done in order to guarantee that an R
script calling future_lapply()
multiple times should be numerically
reproducible given the same initial seed.
Attribute ordering
of future.chunk.size
or future.scheduling
can
be used to control the ordering the elements are iterated over, which
only affects the processing order and not the order values are returned.
This attribute can take the following values:
index vector - an numeric vector of length length(X)
function - an function taking one argument which is called as
ordering(length(X))
and which must return an
index vector of length length(X)
, e.g.
function(n) rev(seq_len(n))
for reverse ordering.
"random"
- this will randomize the ordering via random index
vector sample.int(length(X))
.
For example, future.scheduling = structure(TRUE, ordering = "random")
.
Note, when elements are processed out of order, then captured standard
output and conditions are also relayed in that order, that is out of order.
The implementations of future_replicate()
, future_sapply()
, and
future_tapply()
are adopted from the source code of the corresponding
base R functions, which are licensed under GPL (>= 2) with
'The R Core Team' as the copyright holder.
## --------------------------------------------------------- ## lapply(), sapply(), tapply() ## --------------------------------------------------------- x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE, FALSE, FALSE, TRUE)) y0 <- lapply(x, FUN = quantile, probs = 1:3/4) y1 <- future_lapply(x, FUN = quantile, probs = 1:3/4) print(y1) stopifnot(all.equal(y1, y0)) y0 <- sapply(x, FUN = quantile) y1 <- future_sapply(x, FUN = quantile) print(y1) stopifnot(all.equal(y1, y0)) y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L)) y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L)) print(y1) stopifnot(all.equal(y1, y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical plan(multisession) set.seed(0xBEEF) y1 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE) str(y1) plan(sequential) set.seed(0xBEEF) y2 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE) str(y2) stopifnot(all.equal(y1, y2)) ## --------------------------------------------------------- ## Process chunks of data.frame rows in parallel ## --------------------------------------------------------- iris <- datasets::iris chunks <- split(iris, seq(1, nrow(iris), length.out = 3L)) y0 <- lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y0 <- do.call(sum, y0) y1 <- future_lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y1 <- do.call(sum, y1) print(y1) stopifnot(all.equal(y1, y0))
## --------------------------------------------------------- ## lapply(), sapply(), tapply() ## --------------------------------------------------------- x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE, FALSE, FALSE, TRUE)) y0 <- lapply(x, FUN = quantile, probs = 1:3/4) y1 <- future_lapply(x, FUN = quantile, probs = 1:3/4) print(y1) stopifnot(all.equal(y1, y0)) y0 <- sapply(x, FUN = quantile) y1 <- future_sapply(x, FUN = quantile) print(y1) stopifnot(all.equal(y1, y0)) y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L)) y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L)) print(y1) stopifnot(all.equal(y1, y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical plan(multisession) set.seed(0xBEEF) y1 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE) str(y1) plan(sequential) set.seed(0xBEEF) y2 <- future_lapply(1:5, FUN = rnorm, future.seed = TRUE) str(y2) stopifnot(all.equal(y1, y2)) ## --------------------------------------------------------- ## Process chunks of data.frame rows in parallel ## --------------------------------------------------------- iris <- datasets::iris chunks <- split(iris, seq(1, nrow(iris), length.out = 3L)) y0 <- lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y0 <- do.call(sum, y0) y1 <- future_lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y1 <- do.call(sum, y1) print(y1) stopifnot(all.equal(y1, y0))
future_mapply()
implements base::mapply()
using futures with perfect
replication of results, regardless of future backend used.
Analogously to mapply()
, future_mapply()
is a multivariate version of
future_sapply()
.
It applies FUN
to the first elements of each \ldots
argument,
the second elements, the third elements, and so on.
Arguments are recycled if necessary.
future_Map( f, ..., future.envir = parent.frame(), future.label = "future_Map-%d" ) future_mapply( FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE, future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_mapply-%d" ) future_.mapply(FUN, dots, MoreArgs, ..., future.label = "future_.mapply-%d")
future_Map( f, ..., future.envir = parent.frame(), future.label = "future_Map-%d" ) future_mapply( FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE, future.envir = parent.frame(), future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_mapply-%d" ) future_.mapply(FUN, dots, MoreArgs, ..., future.label = "future_.mapply-%d")
f |
A function of the arity |
future.envir |
An environment passed as argument |
future.label |
If a character string, then each future is assigned
a label |
FUN |
A function to apply, found via |
MoreArgs |
A list of other arguments to |
SIMPLIFY |
A logical or character string; attempt to reduce the
result to a vector, matrix or higher dimensional array; see the simplify
argument of |
USE.NAMES |
A logical; use names if the first |
future.stdout |
If |
future.conditions |
A character string of conditions classes to be
captured and relayed. The default is the same as the |
future.globals |
A logical, a character vector, or a named list for
controlling how globals are handled.
For details, see |
future.packages |
(optional) a character vector specifying packages to be attached in the R environment evaluating the future. |
future.seed |
A logical or an integer (of length one or seven), or
a list of |
future.scheduling |
Average number of futures ("chunks") per worker.
If |
future.chunk.size |
The average number of elements per future ("chunk").
If |
dots |
A list of arguments to vectorize over (vectors or lists of strictly positive length, or all of zero length). |
... |
Arguments to vectorize over, will be recycled to common length, or zero if one of them is of length zero. |
Note that base::.mapply()
, which future_.mapply()
is modeled after
is listed as an "internal" function in R despite being exported.
future_Map()
is a simple wrapper to future_mapply()
which does not
attempt to simplify the result.
See base::Map()
for details.
future_mapply()
returns a list, or for SIMPLIFY = TRUE
, a vector,
array or list. See base::mapply()
for details.
future_.mapply()
returns a list. See base::.mapply()
for details.
The implementations of future_Map()
is adopted from the source code
of the corresponding base R function Map()
, which is licensed under
GPL (>= 2) with 'The R Core Team' as the copyright holder.
## --------------------------------------------------------- ## mapply() ## --------------------------------------------------------- y0 <- mapply(rep, 1:4, 4:1) y1 <- future_mapply(rep, 1:4, 4:1) stopifnot(identical(y1, y0)) y0 <- mapply(rep, times = 1:4, x = 4:1) y1 <- future_mapply(rep, times = 1:4, x = 4:1) stopifnot(identical(y1, y0)) y0 <- mapply(rep, times = 1:4, MoreArgs = list(x = 42)) y1 <- future_mapply(rep, times = 1:4, MoreArgs = list(x = 42)) stopifnot(identical(y1, y0)) y0 <- mapply(function(x, y) seq_len(x) + y, c(a = 1, b = 2, c = 3), # names from first c(A = 10, B = 0, C = -10)) y1 <- future_mapply(function(x, y) seq_len(x) + y, c(a = 1, b = 2, c = 3), # names from first c(A = 10, B = 0, C = -10)) stopifnot(identical(y1, y0)) word <- function(C, k) paste(rep.int(C, k), collapse = "") y0 <- mapply(word, LETTERS[1:6], 6:1, SIMPLIFY = FALSE) y1 <- future_mapply(word, LETTERS[1:6], 6:1, SIMPLIFY = FALSE) stopifnot(identical(y1, y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical plan(multisession) set.seed(0xBEEF) y1 <- future_mapply(stats::runif, n = 1:4, max = 2:5, MoreArgs = list(min = 1), future.seed = TRUE) print(y1) plan(sequential) set.seed(0xBEEF) y2 <- future_mapply(stats::runif, n = 1:4, max = 2:5, MoreArgs = list(min = 1), future.seed = TRUE) print(y2) stopifnot(all.equal(y1, y2))
## --------------------------------------------------------- ## mapply() ## --------------------------------------------------------- y0 <- mapply(rep, 1:4, 4:1) y1 <- future_mapply(rep, 1:4, 4:1) stopifnot(identical(y1, y0)) y0 <- mapply(rep, times = 1:4, x = 4:1) y1 <- future_mapply(rep, times = 1:4, x = 4:1) stopifnot(identical(y1, y0)) y0 <- mapply(rep, times = 1:4, MoreArgs = list(x = 42)) y1 <- future_mapply(rep, times = 1:4, MoreArgs = list(x = 42)) stopifnot(identical(y1, y0)) y0 <- mapply(function(x, y) seq_len(x) + y, c(a = 1, b = 2, c = 3), # names from first c(A = 10, B = 0, C = -10)) y1 <- future_mapply(function(x, y) seq_len(x) + y, c(a = 1, b = 2, c = 3), # names from first c(A = 10, B = 0, C = -10)) stopifnot(identical(y1, y0)) word <- function(C, k) paste(rep.int(C, k), collapse = "") y0 <- mapply(word, LETTERS[1:6], 6:1, SIMPLIFY = FALSE) y1 <- future_mapply(word, LETTERS[1:6], 6:1, SIMPLIFY = FALSE) stopifnot(identical(y1, y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical plan(multisession) set.seed(0xBEEF) y1 <- future_mapply(stats::runif, n = 1:4, max = 2:5, MoreArgs = list(min = 1), future.seed = TRUE) print(y1) plan(sequential) set.seed(0xBEEF) y2 <- future_mapply(stats::runif, n = 1:4, max = 2:5, MoreArgs = list(min = 1), future.seed = TRUE) print(y2) stopifnot(all.equal(y1, y2))
The future.apply packages provides parallel implementations of common "apply" functions provided by base R. The parallel processing is performed via the future ecosystem, which provides a large number of parallel backends, e.g. on the local machine, a remote cluster, and a high-performance compute cluster.
Currently implemented functions are:
future_apply()
: a parallel version of apply()
future_by()
: a parallel version of by()
future_eapply()
: a parallel version of eapply()
future_lapply()
: a parallel version of lapply()
future_mapply()
: a parallel version of mapply()
future_sapply()
: a parallel version of sapply()
future_tapply()
: a parallel version of tapply()
future_vapply()
: a parallel version of vapply()
future_Map()
: a parallel version of Map()
future_replicate()
: a parallel version of replicate()
future_.mapply()
: a parallel version of .mapply()
Reproducibility is part of the core design, which means that perfect, parallel random number generation (RNG) is supported regardless of the amount of chunking, type of load balancing, and future backend being used.
Since these future_*()
functions have the same arguments as the
corresponding base R function, start using them is often as simple as
renaming the function in the code. For example, after attaching the package:
library(future.apply)
code such as:
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE)) y <- lapply(x, quantile, probs = 1:3/4)
can be updated to:
y <- future_lapply(x, quantile, probs = 1:3/4)
The default settings in the future framework is to process code sequentially. To run the above in parallel on the local machine (on any operating system), use:
plan(multisession)
first. That's it!
To go back to sequential processing, use plan(sequential)
.
If you have access to multiple machines on your local network, use:
plan(cluster, workers = c("n1", "n2", "n2", "n3"))
This will set up four workers, one on n1
and n3
, and two on n2
.
If you have SSH access to some remote machines, use:
plan(cluster, workers = c("m1.myserver.org", "m2.myserver.org))
See the future package and future::plan()
for more examples.
The future.batchtools package provides support for high-performance compute (HPC) cluster schedulers such as SGE, Slurm, and TORQUE / PBS. For example,
plan(batchtools_slurm)
:
Process via a Slurm scheduler job queue.
plan(batchtools_torque)
:
Process via a TORQUE / PBS scheduler job queue.
This builds on top of the queuing framework that the batchtools package provides. For more details on backend configuration, please see the future.batchtools and batchtools packages.
These are just a few examples of parallel/distributed backend for the future ecosystem. For more alternatives, see the 'Reverse dependencies' section on the future CRAN package page.
Henrik Bengtsson, except for the implementations of future_apply()
,
future_Map()
, future_replicate()
, future_sapply()
, and
future_tapply()
, which are adopted from the source code of the
corresponding base R functions, which are licensed under GPL (>= 2)
with 'The R Core Team' as the copyright holder.
Because of these dependencies, the license of this package is GPL (>= 2).
Useful links:
Report bugs at https://github.com/HenrikBengtsson/future.apply/issues
Below are the R options and environment variables that are used by the
future.apply 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.
For settings specific to the future package, see future::future.options page.
(logical) If TRUE
, extensive debug messages are generated. (Default: FALSE
)
All of the above R future.apply.* options can be set by
corresponding environment variable R_FUTURE_APPLY_* when the
future.apply package is loaded.
For example, if R_FUTURE_APPLY_DEBUG=TRUE
, then option
future.apply.debug is set to TRUE
(logical).
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 configuring R's startup process.
## Not run: options(future.apply.debug = TRUE) ## End(Not run)
## Not run: options(future.apply.debug = TRUE) ## End(Not run)