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()
.
Usage
tar_option_set(
tidy_eval = NULL,
packages = NULL,
imports = NULL,
library = NULL,
envir = NULL,
format = NULL,
repository = NULL,
iteration = NULL,
error = NULL,
memory = NULL,
garbage_collection = NULL,
deployment = NULL,
priority = NULL,
backoff = NULL,
resources = NULL,
storage = NULL,
retrieval = NULL,
cue = NULL,
debug = NULL,
workspaces = NULL,
workspace_on_error = NULL,
seed = NULL
)
Arguments
- tidy_eval
Logical, whether to enable tidy evaluation when interpreting
command
andpattern
. IfTRUE
, you can use the "bang-bang" operator!!
to programmatically insert the values of global objects.- packages
Character vector of packages to load right before the target builds or the output data is reloaded for downstream targets. Use
tar_option_set()
to set packages globally for all subsequent targets you define.- imports
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 calledcustomAnalysisPackage
which contains an object calledanalysis_function()
. If you writetar_option_set(imports = "yourAnalysisPackage")
in your target script file (default:_targets.R
), then a function called"analysis_function"
will show up in thetar_visnetwork()
graph, and any targets or functions referring to the symbol"analysis_function"
will depend on the functionanalysis_function()
from packageyourAnalysisPackage
. This is best combined withtar_option_set(packages = "yourAnalysisPackage")
so thatanalysis_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 incodetools::findGlobals()
which powers the static code analysis capabilities oftargets
. 2. Theimports
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 inp1
override the objects inp2
if there are name conflicts. 4. Similarly, objects intar_option_get("envir")
override everything intar_option_get("imports")
.- library
Character vector of library paths to try when loading
packages
.- envir
Environment containing functions and global objects common to all targets in the pipeline. The
envir
argument oftar_make()
and related functions always overrides the current value oftar_option_get("envir")
in the current R session just before running the target script file, so whenever you need to set an alternativeenvir
, you should always set it withtar_option_set()
from within the target script file. In other words, if you calltar_option_set(envir = envir1)
in an interactive session and thentar_make(envir = envir2, callr_function = NULL)
, thenenvir2
will be used.If
envir
is the global environment, all the promise objects are diffused before sending the data to parallel workers intar_make_future()
andtar_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 sotargets
can access attached packages. In the case of a non-globalenvir
,targets
attempts to remove potentially high memory objects that come directly fromtargets
. That includestar_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 thepackages
andimports
arguments oftar_option_set()
.- format
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.- repository
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 theendpoint
argument oftar_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"
andformat
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.- iteration
Character of length 1, name of the iteration mode of the target. Choices:
"vector"
: branching happens withvctrs::vec_slice()
and aggregation happens withvctrs::vec_c()
."list"
, branching happens with[[]]
and aggregation happens withlist()
."group"
:dplyr::group_by()
-like functionality to branch over subsets of a data frame. The target's return value must be a data frame with a specialtar_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 thetar_group()
function to see how you can create the specialtar_group
column withdplyr::group_by()
.
- error
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 returnsNULL
. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline.
- memory
Character of length 1, memory strategy. If
"persistent"
, the target stays in memory until the end of the pipeline (unlessstorage
is"worker"
, in which casetargets
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"
withrepository = "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.- garbage_collection
Logical, whether to run
base::gc()
just before the target runs.- deployment
Character of length 1, only relevant to
tar_make_clustermq()
andtar_make_future()
. If"worker"
, the target builds on a parallel worker. If"main"
, the target builds on the host machine / process managing the pipeline.- priority
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 built earlier (and polled earlier in
tar_make_future()
).- backoff
Numeric of length 1, must be greater than or equal to 0.01. Maximum upper bound of the random polling interval for the priority queue (seconds). In high-performance computing (e.g.
tar_make_clustermq()
andtar_make_future()
) it can be expensive to repeatedly poll the priority queue if no targets are ready to process. The number of seconds between polls isrunif(1, 0.001, max(backoff, 0.001 * 1.5 ^ index))
, whereindex
is the number of consecutive polls so far that found no targets ready to skip or run. (If no target is ready,index
goes up by 1. If a target is ready,index
resets to 0. For more information on exponential, backoff, visit https://en.wikipedia.org/wiki/Exponential_backoff). Raisingbackoff
is kinder to the CPU etc. but may incur delays in some instances.- resources
Object returned by
tar_resources()
with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities oftargets
. Seetar_resources()
for details.- storage
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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 whenretrieval = "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 toformat = "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 ifformat
is"file"
.
- retrieval
Character of length 1, only relevant to
tar_make_clustermq()
andtar_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 builds."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.
- cue
An optional object from
tar_cue()
to customize the rules that decide whether the target is up to date.- debug
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 alsotar_make()
,tar_make_clustermq()
, ortar_make_future()
. For any target mentioned indebug
,targets
will force the target to build locally (withtar_cue(mode = "always")
anddeployment = "main"
in the settings) and pause in an interactive debugger to help you diagnose problems. This is like inserting abrowser()
statement at the beginning of the target's expression, but without invalidating any targets.- workspaces
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. Seetar_workspace()
for details about workspaces.- workspace_on_error
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.- seed
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 applied to each target's command usingwithr::with_seed()
, and they are stored in the metadata and retrievable withtar_meta()
ortar_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 thetarchetypes
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 beNA
to disable automatic seed-setting. Any targets defined whiletar_option_get("seed")
isNA
will not set a seed. In this case, those targets will never be up to date unless they havecue = tar_cue(seed = FALSE)
.
Storage formats
"rds"
: Default, usessaveRDS()
andreadRDS()
. Should work for most objects, but slow."qs"
: Usesqs::qsave()
andqs::qread()
. Should work for most objects, much faster than"rds"
. Optionally set the preset forqsave()
throughtar_resources()
andtar_resources_qs()
."feather"
: Usesarrow::write_feather()
andarrow::read_feather()
(version 2.0). Much faster than"rds"
, but the value must be a data frame. Optionally setcompression
andcompression_level
inarrow::write_feather()
throughtar_resources()
andtar_resources_feather()
. Requires thearrow
package (not installed by default)."parquet"
: Usesarrow::write_parquet()
andarrow::read_parquet()
(version 2.0). Much faster than"rds"
, but the value must be a data frame. Optionally setcompression
andcompression_level
inarrow::write_parquet()
throughtar_resources()
andtar_resources_parquet()
. Requires thearrow
package (not installed by default)."fst"
: Usesfst::write_fst()
andfst::read_fst()
. Much faster than"rds"
, but the value must be a data frame. Optionally set the compression level forfst::write_fst()
throughtar_resources()
andtar_resources_fst()
. Requires thefst
package (not installed by default)."fst_dt"
: Same as"fst"
, but the value is adata.table
. Optionally set the compression level the same way as for"fst"
."fst_tbl"
: Same as"fst"
, but the value is atibble
. Optionally set the compression level the same way as for"fst"
."keras"
: superseded bytar_format()
and incompatible witherror = "null"
(intar_target()
ortar_option_set()
). Useskeras::save_model_hdf5()
andkeras::load_model_hdf5()
. The value must be a Keras model. Requires thekeras
package (not installed by default)."torch"
: superseded bytar_format()
and incompatible witherror = "null"
(intar_target()
ortar_option_set()
). Usestorch::torch_save()
andtorch::torch_load()
. The value must be an object from thetorch
package such as a tensor or neural network module. Requires thetorch
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 ifrepository
is not"local"
). (These paths must be existing files and nonempty directories.) Then,targets
automatically checks those files and cues the appropriate build 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 elsetargets
will throw an error. (And ifstorage
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 becharacter(0)
.If
repository
is not"local"
andformat
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."url"
: A dynamic input URL. For this storage format,repository
is implicitly"local"
, URL format is likeformat = "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 customcurl
handle throughtar_resources()
andtar_resources_url()
. innew_handle()
,nobody = TRUE
is important because it ensurestargets
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 useformat = "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.
See also
Other configuration:
tar_config_get()
,
tar_config_set()
,
tar_config_unset()
,
tar_envvars()
,
tar_option_get()
,
tar_option_reset()
Examples
tar_option_get("format") # default format before we set anything
#> [1] "rds"
tar_target(x, 1)$settings$format
#> [1] "rds"
tar_option_set(format = "fst_tbl") # new default format
tar_option_get("format")
#> [1] "fst_tbl"
tar_target(x, 1)$settings$format
#> [1] "fst_tbl"
tar_option_reset() # reset the format
tar_target(x, 1)$settings$format
#> [1] "rds"
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
tar_dir({ # tar_dir() runs code from a temporary directory.
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()
})
}