Set target options.

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 and pattern. If TRUE, 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 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").

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 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().

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 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.

iteration

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 data frame. 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().

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 returns NULL. 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 (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.

garbage_collection

Logical, whether to run base::gc() just before the target runs.

deployment

Character of length 1, only relevant to tar_make_clustermq() and tar_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() and tar_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 is runif(1, 0.001, max(backoff, 0.001 * 1.5 ^ index)), where index 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). Raising backoff 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 of targets. See tar_resources() for details.

storage

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".

retrieval

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 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 also tar_make(), tar_make_clustermq(), or tar_make_future(). For any target mentioned in debug, targets will force the target to build 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.

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. See tar_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 using withr::with_seed(), 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).

Value

NULL (invisibly).

Storage formats

• "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. 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 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 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.

• "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.

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()
})
}