futurize: Parallelize Common Functions via One Magic Function
TL;DR
The futurize package makes it extremely simple to parallelize your
existing map-reduce calls, but also a growing set of domain-specific
calls. All you need to know is that there is a single function called
futurize() that will take care of everything, e.g.
y <- lapply(x, fcn) |> futurize()
y <- map(x, fcn) |> futurize()
b <- boot(city, ratio, R = 999) |> futurize()The futurize() function parallelizes via futureverse, meaning
your code can take advantage of any supported future backends,
whether it be parallelization on your local computer, across multiple
computers, in the cloud, or on a high-performance compute (HPC) cluster.
The futurize package has only one hard dependency - the
future package. All other dependencies are optional "buy-in"
dependencies as shown in the below tables.
Supported calls
Supported map-reduce packages
The futurize package supports transpilation of functions from multiple packages. The tables below summarize the supported map-reduce and domain-specific functions, respectively. To programmatically see which packages are currently supported, use:
futurize_supported_packages()To see which functions are supported for a specific package, use:
futurize_supported_functions("caret")| Package | Functions | Requires |
|---|---|---|
| base | lapply(), sapply(), tapply(), vapply(), mapply(), .mapply(), Map(), eapply(), apply(), by(), replicate(), Filter() | future.apply |
| stats | kernapply() | future.apply |
| purrr | map() and variants, map2() and variants, pmap() and variants, imap() and variants, modify(), modify_if(), modify_at(), map_if(), map_at(), invoke_map() | furrr |
| crossmap | xmap() and variants, xwalk(), map_vec(), map2_vec(), pmap_vec(), imap_vec() | (itself) |
| foreach | %do%, e.g. foreach() %do% { }, times() %do% { } | doFuture |
| plyr | aaply() and variants, ddply() and variants, llply() and variants, mlply() and variants | doFuture |
| BiocParallel | bplapply(), bpmapply(), bpvec(), bpiterate(), bpaggregate() | doFuture |
Table: Map-reduce functions currently supported by futurize() for parallel transpilation.
Here are some examples:
library(futurize)
plan(multisession)
xs <- 1:10
ys <- lapply(xs, sqrt) |> futurize()
xs <- 1:10
ys <- purrr::map(xs, sqrt) |> futurize()
xs <- 1:10
ys <- crossmap::xmap_dbl(xs, ~ .y * .x) |> futurize()
library(foreach)
xs <- 1:10
ys <- foreach(x = xs) %do% { sqrt(x) } |> futurize()
xs <- 1:10
ys <- plyr::llply(xs, sqrt) |> futurize()
xs <- 1:10
ys <- BiocParallel::bplapply(xs, sqrt) |> futurize()and
ys <- replicate(3, rnorm(1)) |> futurize()
y <- by(warpbreaks, warpbreaks[,"tension"],
function(x) lm(breaks ~ wool, data = x)) |> futurize()
xs <- EuStockMarkets[, 1:2]
k <- kernel("daniell", 50)
xs_smooth <- stats::kernapply(xs, k = k) |> futurize()Supported domain-specific packages
You can also futurize calls from a growing set of domain-specific packages (e.g. boot, caret, glmnet, lme4, mgcv, and tm) that have optional built-in support for parallelization.
| Package | Functions | Requires |
|---|---|---|
| boot | boot(), censboot(), tsboot() | future |
| caret | bag(), gafs(), nearZeroVar(), rfe(), safs(), sbf(), train() | doFuture |
| glmnet | cv.glmnet() | doFuture |
| lme4 | allFit(), bootMer() | future |
| mgcv | bam(), predict.bam() | future |
| tm | TermDocumentMatrix(), tm_index(), tm_map() | future |
Table: Domain-specific functions currently supported by futurize() for parallel transpilation.
Here are some examples:
ctrl <- caret::trainControl(method = "cv", number = 10)
model <- caret::train(Species ~ ., data = iris, method = "rf", trControl = ctrl) |> futurize()
ratio <- function(d, w) sum(d$x * w)/sum(d$u * w)
b <- boot::boot(boot::city, ratio, R = 999) |> futurize()
cv <- glmnet::cv.glmnet(x, y) |> futurize()
m <- lme4::allFit(models) |> futurize()
b <- mgcv::bam(y ~ s(x0, bs = bs) + s(x1, bs = bs), data = dat) |> futurize()
m <- tm::tm_map(crude, content_transformer(tolower)) |> futurize()