Set target options, including default arguments to
tar_target() such as packages, storage format,
iteration type, and cue. Only the non-null arguments are actually
set as options. See currently set options with tar_option_get().
To use tar_option_set() effectively, put it in your workflow's
target script file (default: _targets.R)
before calls to tar_target() or tar_target_raw().
tar_option_set(
tidy_eval = NULL,
packages = NULL,
imports = NULL,
library = NULL,
envir = NULL,
format = NULL,
repository = NULL,
repository_meta = NULL,
iteration = NULL,
error = NULL,
memory = NULL,
garbage_collection = NULL,
deployment = NULL,
priority = NULL,
backoff = NULL,
resources = NULL,
storage = NULL,
retrieval = NULL,
cue = NULL,
description = NULL,
debug = NULL,
workspaces = NULL,
workspace_on_error = NULL,
seed = NULL,
controller = NULL,
trust_timestamps = NULL,
trust_object_timestamps = NULL
)NULL (invisibly).
Logical, whether to enable tidy evaluation
when interpreting command and pattern. If TRUE, you can use the
"bang-bang" operator !! to programmatically insert
the values of global objects.
Character vector of packages to load right before
the target runs or the output data is reloaded for
downstream targets. Use tar_option_set() to set packages
globally for all subsequent targets you define.
Character vector of package names.
For every package listed, targets tracks every
dataset and every object in the package namespace
as if it were part of the global namespace.
As an example, say you have a package called customAnalysisPackage
which contains an object called analysis_function().
If you write tar_option_set(imports = "yourAnalysisPackage") in your
target script file (default: _targets.R),
then a function called "analysis_function" will show up in the
tar_visnetwork() graph, and any targets or functions
referring to the symbol "analysis_function" will depend on the
function analysis_function() from package yourAnalysisPackage.
This is best combined with
tar_option_set(packages = "yourAnalysisPackage") so
that analysis_function() can actually be called in your code.
There are several important limitations:
1. Namespaced calls, e.g. yourAnalysisPackage::analysis_function(),
are ignored because of the limitations in codetools::findGlobals()
which powers the static code analysis capabilities of targets.
2. The imports option only looks at R objects and R code.
It not account for low-level compiled code
such as C/C++ or Fortran.
3. If you supply multiple packages,
e.g. tar_option_set(imports = c("p1", "p2")), then the objects in
p1 override the objects in p2 if there are name conflicts.
4. Similarly, objects in tar_option_get("envir") override
everything in tar_option_get("imports").
Character vector of library paths to try
when loading packages.
Environment containing functions and global objects
common to all targets in the pipeline.
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.
If envir is the global environment, all the promise objects
are diffused before sending the data to parallel workers
in tar_make_future() and tar_make_clustermq(),
but otherwise the environment is unmodified.
This behavior improves performance by decreasing
the size of data sent to workers.
If envir is not the global environment, then it should at least inherit
from the global environment or base environment
so targets can access attached packages.
In the case of a non-global envir, targets attempts to remove
potentially high memory objects that come directly from targets.
That includes tar_target() objects of class "tar_target",
as well as objects of class "tar_pipeline" or "tar_algorithm".
This behavior improves performance by decreasing
the size of data sent to workers.
Package environments should not be assigned to envir.
To include package objects as upstream dependencies in the pipeline,
assign the package to the packages and imports arguments
of tar_option_set().
Optional storage format for the target's return value.
With the exception of format = "file", each target
gets a file in _targets/objects, and each format is a different
way to save and load this file. See the "Storage formats" section
for a detailed list of possible data storage formats.
Character of length 1, remote repository for target storage. Choices:
"local": file system of the local machine.
"aws": Amazon Web Services (AWS) S3 bucket. Can be configured
with a non-AWS S3 bucket using the endpoint argument of
tar_resources_aws(), but versioning capabilities may be lost
in doing so.
See the cloud storage section of
https://books.ropensci.org/targets/data.html
for details for instructions.
"gcp": Google Cloud Platform storage bucket.
See the cloud storage section of
https://books.ropensci.org/targets/data.html
for details for instructions.
A character string from tar_repository_cas() for content-addressable
storage.
Note: if repository is not "local" and format is "file"
then the target should create a single output file.
That output file is uploaded to the cloud and tracked for changes
where it exists in the cloud. The local file is deleted after
the target runs.
Character of length 1 with the same values as
repository but excluding content-addressable storage
("aws", "gcp", "local"). Cloud repository
for the metadata text files in _targets/meta/, including target
metadata and progress data.
Also enables cloud backup of workspace files in _targets/workspaces/
which can be downloaded with tar_workspace_download().
repository_meta defaults to tar_option_get("repository")
except in the case of content-addressable storage (CAS).
When tar_option_get("repository") is a CAS repository,
the default value of repository_meta is "local".
Character of length 1, name of the iteration mode of the target. Choices:
"vector": branching happens with vctrs::vec_slice() and
aggregation happens with vctrs::vec_c().
"list", branching happens with [[]] and aggregation happens with
list().
"group": dplyr::group_by()-like functionality to branch over
subsets of a non-dynamic data frame.
For iteration = "group", the target must not by dynamic
(the pattern argument of tar_target() must be left NULL).
The target's return value must be a data
frame with a special tar_group column of consecutive integers
from 1 through the number of groups. Each integer designates a group,
and a branch is created for each collection of rows in a group.
See the tar_group() function to see how you can
create the special tar_group column with dplyr::group_by().
Character of length 1, what to do if the target stops and throws an error. Options:
"stop": the whole pipeline stops and throws an error.
"continue": the whole pipeline keeps going.
"null": The errored target continues and returns NULL.
The data hash is deliberately wrong so the target is not
up to date for the next run of the pipeline. In addition,
as of targets version 1.8.0.9011, a value of NULL is given
to upstream dependencies with error = "null" if loading fails.
"abridge": any currently running targets keep running,
but no new targets launch after that.
"trim": all currently running targets stay running. A queued
target is allowed to start if:
It is not downstream of the error, and
It is not a sibling branch from the same tar_target() call
(if the error happened in a dynamic branch).
The idea is to avoid starting any new work that the immediate error
impacts. error = "trim" is just like error = "abridge",
but it allows potentially healthy regions of the dependency graph
to begin running.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
Character of length 1, memory strategy. Possible values:
"auto" (default): equivalent to memory = "transient" in almost
all cases. But to avoid superfluous reads from disk,
memory = "auto" is equivalent to memory = "persistent" for
for non-dynamically-branched targets that other targets
dynamically branch over. For example: if your pipeline has
tar_target(name = y, command = x, pattern = map(x)),
then tar_target(name = x, command = f(), memory = "auto")
will use persistent memory in order to avoid rereading all of x
for every branch of y.
"transient": the target gets unloaded
after every new target completes.
Either way, the target gets automatically loaded into memory
whenever another target needs the value.
"persistent": the target stays in memory
until the end of the pipeline (unless storage is "worker",
in which case targets unloads the value from memory
right after storing it in order to avoid sending
copious data over a network).
For cloud-based file targets
(e.g. format = "file" with repository = "aws"),
the memory option applies to the
temporary local copy of the file:
"persistent" means it remains until the end of the pipeline
and is then deleted,
and "transient" means it gets deleted as soon as possible.
The former conserves bandwidth,
and the latter conserves local storage.
A non-negative integer.
If 0, do not run garbage collection.
If a positive integer n, run base::gc() just before
every n'th target that runs.
For the purposes of running garbage collection with this setting,
each R process (whether the local process a parallel worker)
maintains its own independent count of the number of targets
that ran so far.
The garbage_collection option in tar_option_set()
is independent of the
argument of the same name in tar_target().
Character of length 1. If deployment is
"main", then the target will run on the central controlling R process.
Otherwise, if deployment is "worker" and you set up the pipeline
with distributed/parallel computing, then
the target runs on a parallel worker. For more on distributed/parallel
computing in targets, please visit
https://books.ropensci.org/targets/crew.html.
Deprecated on 2025-04-08 (targets version 1.10.1.9013).
targets has moved to a more efficient scheduling algorithm
(https://github.com/ropensci/targets/issues/1458)
which cannot support priorities.
The priority argument of tar_target() no longer has a reliable
effect on execution order.
An object from tar_backoff() configuring the exponential
backoff algorithm of the pipeline. See tar_backoff() for details.
A numeric argument for backoff is still allowed, but deprecated.
Object returned by tar_resources()
with optional settings for high-performance computing
functionality, alternative data storage formats,
and other optional capabilities of targets.
See tar_resources() for details.
Character string to control when the output of the target
is saved to storage. Only relevant when using targets
with parallel workers (https://books.ropensci.org/targets/crew.html).
Must be one of the following values:
"worker" (default): the worker saves/uploads the value.
"main": the target's return value is sent back to the
host machine and saved/uploaded locally.
"none": targets makes no attempt to save the result
of the target to storage in the location where targets
expects it to be. Saving to storage is the responsibility
of the user. Use with caution.
Character string to control when the current target
loads its dependencies into memory before running.
(Here, a "dependency" is another target upstream that the current one
depends on.) Only relevant when using targets
with parallel workers (https://books.ropensci.org/targets/crew.html).
Must be one of the following values:
"auto" (default): equivalent to retrieval = "worker" in almost all
cases. But to avoid unnecessary reads from disk, retrieval = "auto"
is equivalent to retrieval = "main" for dynamic branches that
branch over non-dynamic targets. For example: if your pipeline has
tar_target(x, command = f()), then
tar_target(y, command = x, pattern = map(x), retrieval = "auto")
will use "main" retrieval in order to avoid rereading all of x
for every branch of y.
"worker": the worker loads the target's dependencies.
"main": the target's dependencies are loaded on the host machine
and sent to the worker before the target runs.
"none": targets makes no attempt to load its
dependencies. With retrieval = "none", loading dependencies
is the responsibility of the user. Use with caution.
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
Character of length 1, a custom free-form human-readable
text description of the target. Descriptions appear as target labels
in functions like tar_manifest() and tar_visnetwork(),
and they let you select subsets of targets for the names argument of
functions like tar_make(). For example,
tar_manifest(names = tar_described_as(starts_with("survival model")))
lists all the targets whose descriptions start with the character
string "survival model".
Character vector of names of targets to run in debug mode.
To use effectively, you must set callr_function = NULL and
restart your R session just before running. You should also
tar_make(), tar_make_clustermq(), or tar_make_future().
For any target mentioned in debug, targets will force the target to
run locally (with tar_cue(mode = "always") and deployment = "main"
in the settings) and pause in an interactive debugger to help you diagnose
problems. This is like inserting a browser() statement at the
beginning of the target's expression, but without invalidating any
targets.
Character vector of target names.
Could be non-branching targets, whole dynamic branching targets,
or individual branch names. tar_make() and friends
will save workspace files for these targets even if
the targets are skipped. Workspace files help with debugging.
See tar_workspace() for details about workspaces.
Logical of length 1, whether to save
a workspace file for each target that throws an error.
Workspace files help with debugging.
See tar_workspace() for details about workspaces.
Integer of length 1, seed for generating
target-specific pseudo-random number generator seeds.
These target-specific seeds are deterministic and depend on
tar_option_get("seed") and the target name. Target-specific seeds
are safely and reproducibly applied to each target's command,
and they are stored in the metadata and retrievable with
tar_meta() or tar_seed().
Either the user or third-party packages built on top of targets
may still set seeds inside the command of a target.
For example, some target factories in the
tarchetypes package assigns replicate-specific
seeds for the purposes of reproducible within-target batched replication.
In cases like these, the effect of the target-specific seed saved
in the metadata becomes irrelevant and the seed defined in the command
applies.
The seed option can also be NA to disable
automatic seed-setting. Any targets defined while
tar_option_get("seed") is NA will not set a seed.
In this case, those targets will never be up to date
unless they have cue = tar_cue(seed = FALSE).
A controller or controller group object
produced by the crew R package. crew brings auto-scaled
distributed computing to tar_make().
Logical of length 1, whether to use file system modification timestamps to check whether the target output data files in are up to date. This is an advanced setting and usually does not need to be set by the user except on old or difficult platforms.
If trust_timestamps was reset with tar_option_reset()
or never set at all (recommended)
then targets makes a decision based on the type of file system
of the given file.
If trust_timestamps
is TRUE (default), then targets looks at the timestamp first.
If it agrees with the timestamp recorded in the metadata, then targets
considers the file unchanged. If the timestamps disagree, then targets
recomputes the hash to make a final determination.
This practice reduces the number of hash computations
and thus saves time.
However, timestamp precision varies from a few
nanoseconds at best to 2 entire seconds at worst, and timestamps
with poor precision should not be fully trusted if there is any
possibility that you will manually change the file within 2 seconds
after the pipeline finishes.
If the data store is on a file system with low-precision timestamps,
then you may
consider setting trust_timestamps to FALSE so targets
errs on the safe side and always recomputes the hashes of files.
To check if your
file system has low-precision timestamps, you can run
file.create("x"); nanonext::msleep(1); file.create("y");
from within the directory containing the _targets data store
and then check
difftime(file.mtime("y"), file.mtime("x"), units = "secs").
If the value from difftime() is around 0.001 seconds
(must be strictly above 0 and below 1) then you do not need to set
trust_timestamps = FALSE.
Deprecated. Use trust_timestamps instead.
targets has several built-in storage formats to control how return
values are saved and loaded from disk:
"rds": Default, uses saveRDS() and readRDS(). Should work for
most objects, but slow.
"auto": either "file" or "qs", depending on the return value
of the target. If the return value is a character vector of
existing files (and/or directories), then the format becomes
"file" before tar_make() saves the target. Otherwise,
the format becomes "qs".
NOTE: format = "auto" slows down pipelines with 10000+ targets
because it creates deep copies of 20000+ internal data objects.
Pipelines of this size should use a more explicit format instead of
"auto".
"qs": Uses qs2::qs_save() and qs2::qs_read(). Should work for
most objects, much faster than "rds". Optionally configure settings
through tar_resources() and tar_resources_qs().
Prior to targets version 1.8.0.9014, format = "qs" used the qs
package. qs has since been superseded in favor of qs2, and so
later versions of targets use qs2 to save new data. To read
existing data, targets first attempts qs2::qs_read(), and then if
that fails, it falls back on qs::qread().
"feather": Uses arrow::write_feather() and
arrow::read_feather() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_feather()
through tar_resources() and tar_resources_feather().
Requires the arrow package (not installed by default).
"parquet": Uses arrow::write_parquet() and
arrow::read_parquet() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_parquet()
through tar_resources() and tar_resources_parquet().
Requires the arrow package (not installed by default).
"fst": Uses fst::write_fst() and fst::read_fst().
Much faster than "rds", but the value must be
a data frame. Optionally set the compression level for
fst::write_fst() through tar_resources() and tar_resources_fst().
Requires the fst package (not installed by default).
"fst_dt": Same as "fst", but the value is a data.table.
Deep copies are made as appropriate in order to protect
against the global effects of in-place modification.
Optionally set the compression level the same way as for "fst".
"fst_tbl": Same as "fst", but the value is a tibble.
Optionally set the compression level the same way as for "fst".
"keras": superseded by tar_format() and incompatible
with error = "null" (in tar_target() or tar_option_set()).
Uses keras::save_model_hdf5() and
keras::load_model_hdf5(). The value must be a Keras model.
Requires the keras package (not installed by default).
"torch": superseded by tar_format() and incompatible
with error = "null" (in tar_target() or tar_option_set()).
Uses torch::torch_save() and torch::torch_load().
The value must be an object from the torch package
such as a tensor or neural network module.
Requires the torch package (not installed by default).
"file": A file target. To use this format,
the target needs to manually identify or save some data
and return a character vector of paths
to the data (must be a single file path if repository
is not "local"). (These paths must be existing files
and nonempty directories.)
Then, targets automatically checks those files and cues
the appropriate run/skip decisions if those files are out of date.
Those paths must point to files or directories,
and they must not contain characters | or *.
All the files and directories you return must actually exist,
or else targets will throw an error. (And if storage is "worker",
targets will first stall out trying to wait for the file
to arrive over a network file system.)
If the target does not create any files, the return value should be
character(0).
If repository is not "local" and format is "file",
then the character vector returned by the target must be of length 1
and point to a single file. (Directories and vectors of multiple
file paths are not supported for file targets on the cloud.)
That output file is uploaded to the cloud and tracked for changes
where it exists in the cloud. The local file is deleted after
the target runs.
"url": An input URL. For this storage format,
repository is implicitly "local",
URL format is like format = "file"
except the return value of the target is a URL that already exists
and serves as input data for downstream targets. Optionally
supply a custom curl handle through
tar_resources() and tar_resources_url().
in new_handle(), nobody = TRUE is important because it
ensures targets just downloads the metadata instead of
the entire data file when it checks time stamps and hashes.
The data file at the URL needs to have an ETag or a Last-Modified
time stamp, or else the target will throw an error because
it cannot track the data. Also, use extreme caution when
trying to use format = "url" to track uploads. You must be absolutely
certain the ETag and Last-Modified time stamp are fully updated
and available by the time the target's command finishes running.
targets makes no attempt to wait for the web server.
A custom format can be supplied with tar_format(). For this choice,
it is the user's responsibility to provide methods for (un)serialization
and (un)marshaling the return value of the target.
The formats starting with "aws_" are deprecated as of 2022-03-13
(targets version > 0.10.0). For cloud storage integration, use the
repository argument instead.
Formats "rds", "file", and "url" are general-purpose formats
that belong in the targets package itself.
Going forward, any additional formats should be implemented with
tar_format() in third-party packages like tarchetypes
and geotargets (for example: tarchetypes::tar_format_nanoparquet()).
Formats "qs", "fst", etc. are legacy formats from before the
existence of tar_format(), and they will continue to remain in
targets without deprecation.
Other configuration:
tar_config_get(),
tar_config_projects(),
tar_config_set(),
tar_config_unset(),
tar_config_yaml(),
tar_envvars(),
tar_option_get(),
tar_option_reset(),
tar_option_with()
tar_option_get("format") # default format before we set anything
tar_target(x, 1)$settings$format
tar_option_set(format = "fst_tbl") # new default format
tar_option_get("format")
tar_target(x, 1)$settings$format
tar_option_reset() # reset the format
tar_target(x, 1)$settings$format
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)
tar_option_set(cue = tar_cue(mode = "always")) # All targets always run.
list(tar_target(x, 1), tar_target(y, 2))
})
tar_make()
tar_make()
})
}
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