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Superseded. Run a pipeline of targets in parallel with transient future workers.

Source: R/tar_make_future.R
tar_make_future.Rd

Superseded. Use tar_make() with crew: https://books.ropensci.org/targets/crew.html.

Usage

tar_make_future(
  names = NULL,
  shortcut = targets::tar_config_get("shortcut"),
  reporter = targets::tar_config_get("reporter_make"),
  seconds_meta_append = targets::tar_config_get("seconds_meta_append"),
  seconds_meta_upload = targets::tar_config_get("seconds_meta_upload"),
  seconds_reporter = targets::tar_config_get("seconds_reporter"),
  seconds_interval = targets::tar_config_get("seconds_interval"),
  workers = targets::tar_config_get("workers"),
  callr_function = callr::r,
  callr_arguments = targets::tar_callr_args_default(callr_function, reporter),
  envir = parent.frame(),
  script = targets::tar_config_get("script"),
  store = targets::tar_config_get("store"),
  garbage_collection = NULL
)

Arguments

names

Names of the targets to run or check. Set to NULL to check/run all the targets (default). The object supplied to names should be a tidyselect expression like any_of() or starts_with() from tidyselect itself, or tar_described_as() to select target names based on their descriptions.

shortcut

Logical of length 1, how to interpret the names argument. If shortcut is FALSE (default) then the function checks all targets upstream of names as far back as the dependency graph goes. shortcut = TRUE increases speed if there are a lot of up-to-date targets, but it assumes all the dependencies are up to date, so please use with caution. It relies on stored metadata for information about upstream dependencies. shortcut = TRUE only works if you set names.

reporter

Character of length 1, name of the reporter to user. Controls how messages are printed as targets run in the pipeline.

The default value of reporter is the value returned by tar_config_get("reporter_make"). The default of tar_config_get("reporter_make") is "terse" if the calling R session is either:

  1. Non-interactive (`interactive()` returns `FALSE`), or
  2. Inside a literate programming document
    (the `knitr.in.progress` global option is `TRUE`).

Otherwise, the default is "balanced". You can always set the reporter manually. Choices:

  • "balanced": a reporter that balances efficiency with informative detail. Uses a cli progress bar instead of printing messages for individual dynamic branches. To the right of the progress bar is a text string like "22.6s, 4510+, 124-" (22.6 seconds elapsed, 4510 targets completed successfully so far, 124 targets skipped so far).

    For best results with the balanced reporter, you may need to adjust your cli settings. See global options cli.num_colors and cli.dynamic at https://cli.r-lib.org/reference/cli-config.html. On that page is also the CLI_TICK_TIME environment variable which controls the time delay between progress bar updates. If the delay is too low, then overhead from printing to the console may slow down the pipeline.

  • "terse": like the "balanced" reporter, but without a progress bar.

  • "silent": print nothing.

  • "timestamp": same as the "verbose" reporter except that each message begins with a time stamp.

  • "verbose": print messages for individual targets as they dispatch or complete. Each individual target-specific time (e.g. "3.487 seconds") is strictly the elapsed runtime of the target and does not include steps like data retrieval and output storage.

seconds_meta_append

Positive numeric of length 1 with the minimum number of seconds between saves to the local metadata and progress files in the data store. his is an aggressive optimization setting not recommended for most users: higher values generally make the pipeline run faster, but unsaved work (in the event of a crash) is not up to date. When the pipeline ends, all the metadata and progress data is saved immediately, regardless of seconds_meta_append.

When the pipeline is just skipping targets, the actual interval between saves is max(1, seconds_meta_append) to reduce overhead.

seconds_meta_upload

Positive numeric of length 1 with the minimum number of seconds between uploads of the metadata and progress data to the cloud (see https://books.ropensci.org/targets/cloud-storage.html). Higher values generally make the pipeline run faster, but unsaved work (in the event of a crash) may not be backed up to the cloud. When the pipeline ends, all the metadata and progress data is uploaded immediately, regardless of seconds_meta_upload.

seconds_reporter

Deprecated on 2025-03-31 (targets version 1.10.1.9010).

seconds_interval

Deprecated on 2023-08-24 (targets version 1.2.2.9001). Use seconds_meta_append and seconds_meta_upload instead.

workers

Positive integer, maximum number of transient future workers allowed to run at any given time.

callr_function

A function from callr to start a fresh clean R process to do the work. Set to NULL to run in the current session instead of an external process (but restart your R session just before you do in order to clear debris out of the global environment). callr_function needs to be NULL for interactive debugging, e.g. tar_option_set(debug = "your_target"). However, callr_function should not be NULL for serious reproducible work.

callr_arguments

A list of arguments to callr_function.

envir

An environment, where to run the target R script (default: _targets.R) if callr_function is NULL. Ignored if callr_function is anything other than NULL. callr_function should only be NULL for debugging and testing purposes, not for serious runs of a pipeline, etc.

The envir argument of tar_make() and related functions always overrides the current value of tar_option_get("envir") in the current R session just before running the target script file, so whenever you need to set an alternative envir, you should always set it with tar_option_set() from within the target script file. In other words, if you call tar_option_set(envir = envir1) in an interactive session and then tar_make(envir = envir2, callr_function = NULL), then envir2 will be used.

script

Character of length 1, path to the target script file. Defaults to tar_config_get("script"), which in turn defaults to _targets.R. When you set this argument, the value of tar_config_get("script") is temporarily changed for the current function call. See tar_script(), tar_config_get(), and tar_config_set() for details about the target script file and how to set it persistently for a project.

store

Character of length 1, path to the targets data store. Defaults to tar_config_get("store"), which in turn defaults to _targets/. When you set this argument, the value of tar_config_get("store") is temporarily changed for the current function call. See tar_config_get() and tar_config_set() for details about how to set the data store path persistently for a project.

garbage_collection

Deprecated. Use the garbage_collection argument of tar_option_set() instead to run garbage collection at regular intervals in a pipeline, or use the argument of the same name in tar_target() to activate garbage collection for a specific target.

Value

NULL except if callr_function = callr::r_bg(), in which case a handle to the callr background process is returned. Either way, the value is invisibly returned.

Details

This function is like tar_make() except that targets run in parallel with transient future workers. It requires that you declare your future::plan() inside the target script file (default: _targets.R). future is not a strict dependency of targets, so you must install future yourself.

To configure tar_make_future() with a computing cluster, see the future.batchtools package documentation.

Storage access

Several functions like tar_make(), tar_read(), tar_load(), tar_meta(), and tar_progress() read or modify the local data store of the pipeline. The local data store is in flux while a pipeline is running, and depending on how distributed computing or cloud computing is set up, not all targets can even reach it. So please do not call these functions from inside a target as part of a running pipeline. The only exception is literate programming target factories in the tarchetypes package such as tar_render() and tar_quarto().

See also

Other pipeline: tar_make(), tar_make_clustermq()

Examples

if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) { # for CRAN
tar_dir({ # tar_dir() runs code from a temp dir for CRAN.
tar_script({
  library(targets)
  library(tarchetypes)
  future::plan(future::multisession, workers = 2)
  list(
    tar_target(x, 1 + 1),
    tar_target(y, 1 + 1)
  )
}, ask = FALSE)
tar_make_future()
})
}