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_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.
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
("aws"
, "gcp"
, "local"
). Cloud repository
for the metadata text files in _targets/meta/
, including target
metadata and progress data. Defaults to tar_option_get("repository")
.
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.
"abridge"
: any currently running targets keep running,
but no new targets launch after that.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
"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.
Character of length 1, memory strategy.
If "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).
If "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.
For cloud-based dynamic files
(e.g. format = "file"
with repository = "aws"
),
this memory strategy 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.
Logical, whether to run base::gc()
just before the target runs.
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.
Numeric of length 1 between 0 and 1. Controls which
targets get deployed first when multiple competing targets are ready
simultaneously. Targets with priorities closer to 1 get dispatched earlier
(and polled earlier in tar_make_future()
).
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 of length 1, only relevant to
tar_make_clustermq()
and tar_make_future()
.
Must be one of the following values:
"main"
: the target's return value is sent back to the
host machine and saved/uploaded locally.
"worker"
: the worker saves/uploads the value.
"none"
: almost never recommended. It is only for
niche situations, e.g. the data needs to be loaded
explicitly from another language. If you do use it,
then the return value of the target is totally ignored
when the target ends, but
each downstream target still attempts to load the data file
(except when retrieval = "none"
).
If you select storage = "none"
, then
the return value of the target's command is ignored,
and the data is not saved automatically.
As with dynamic files (format = "file"
) it is the
responsibility of the user to write to
the data store from inside the target.
The distinguishing feature of storage = "none"
(as opposed to format = "file"
)
is that in the general case,
downstream targets will automatically try to load the data
from the data store as a dependency. As a corollary, storage = "none"
is completely unnecessary if format
is "file"
.
Character of length 1, only relevant to
tar_make_clustermq()
and tar_make_future()
.
Must be one of the following values:
"main"
: the target's dependencies are loaded on the host machine
and sent to the worker before the target runs.
"worker"
: the worker loads the targets dependencies.
"none"
: the dependencies are not loaded at all.
This choice is almost never recommended. It is only for
niche situations, e.g. the data needs to be loaded
explicitly from another language.
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 _targets/objects/
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_object_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_object_timestamps
to FALSE
so targets
errs on the safe side and always recomputes the hashes of files in
_targets/objects/
.
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_object_timestamps = FALSE
.
"rds"
: Default, uses saveRDS()
and readRDS()
. Should work for
most objects, but slow.
"qs"
: Uses qs::qsave()
and qs::qread()
. Should work for
most objects, much faster than "rds"
. Optionally set the
preset for qsave()
through tar_resources()
and tar_resources_qs()
.
"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 dynamic file. 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 dynamic files 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.
To check if the file is up to date, targets
avoids timestamps
and always recomputes the hash. If you find this to be too slow,
and if you trust the time stamps on your file system
(see the trust_object_timestamps
argument of tar_option_set()
),
then consider format = "file_fast"
instead.
"file_fast"
: same as format = "file"
, except that targets
uses time stamps to check if a file is up to date. If the time stamp
of the file agrees with the time stamp in the metadata, the
file is considered up to date. Otherwise, targets
recomputes the
hash of the file to make a final determination. Low-precision
timestamps are not reliable for this, and some file systems
have timestamp precision as poor as 2 seconds. See the
trust_object_timestamps
argument of tar_option_set()
for advice on this.
"url"
: A dynamic 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.
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_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({
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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