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Progress updates for 'future.apply' functions

The progressify package allows you to easily add progress reporting to sequential and parallel map-reduce code by piping to the progressify() function. Easy!

TL;DR

library(progressify)
handlers(global = TRUE)
library(future.apply)
plan(multisession)

slow_fcn <- function(x) {
  Sys.sleep(0.1)  # emulate work
  x^2
}

xs <- 1:100
ys <- future_lapply(xs, slow_fcn) |> progressify()

Introduction

This vignette demonstrates how to use this approach to add progress reporting to future.apply functions such as future_lapply(), future_tapply(), future_apply(), and future_replicate().

The future.apply future_lapply() function is commonly used to apply a function to the elements of a vector or a list in parallel. For example,

library(future.apply)
plan(multisession)

xs <- 1:100
ys <- future_lapply(xs, slow_fcn)

Here future_lapply() provides no feedback on how far it has progressed, but we can easily add progress reporting by using:

library(future.apply)
plan(multisession)

library(progressify)
handlers(global = TRUE)

ys <- future_lapply(xs, slow_fcn) |> progressify()

Using the default progress handler, the progress reporting will appear as:

  |=====                    |  20%

Supported Functions

The progressify() function supports the following future.apply functions:

• future_lapply(), future_vapply(), future_sapply(), future_tapply()
• future_mapply(), future_.mapply(), future_Map()
• future_eapply()
• future_apply()
• future_replicate()
• future_by()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.