future workers.Superseded. Use tar_make() with crew:
https://books.ropensci.org/targets/crew.html.
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
)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.
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.
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.
Character of length 1, name of the reporter to user.
Controls how messages are printed as targets run in the pipeline.
Defaults to tar_config_get("reporter_make").
The default of tar_config_get("reporter_make") is "terse"
if running inside a literate programming document
(i.e. the knitr.in.progress global option is TRUE).
Otherwise, the default is "balanced". 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.
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.
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.
Deprecated on 2025-03-31
(targets version 1.10.1.9010).
Deprecated on 2023-08-24
(targets version 1.2.2.9001).
Use seconds_meta_append and seconds_meta_upload instead.
Positive integer, maximum number of transient
future workers allowed to run at any given time.
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.
A list of arguments to callr_function.
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.
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.
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.
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.
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().
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.
Other pipeline:
tar_make(),
tar_make_clustermq()
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()
})
}
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