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Dynamic branching over input files or URLs

Source: R/tar_files_input.R, R/tar_files_input_raw.R
tar_files_input.Rd

Dynamic branching over input files or URLs.

tar_files_input() expects a unevaluated symbol for the name argument, whereas tar_files_input_raw() expects a character string for name. See the examples for a demo.

Usage

tar_files_input(
  name,
  files,
  batches = length(files),
  format = c("file", "file_fast", "url", "aws_file"),
  repository = targets::tar_option_get("repository"),
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

tar_files_input_raw(
  name,
  files,
  batches = length(files),
  format = c("file", "file_fast", "url", "aws_file"),
  repository = targets::tar_option_get("repository"),
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Name of the target. tar_files_input() expects a unevaluated symbol for the name argument, whereas tar_files_input_raw() expects a character string for name. See the examples for a demo.

files

Nonempty character vector of known existing input files to track for changes.

batches

Positive integer of length 1, number of batches to partition the files. The default is one file per batch (maximum number of batches) which is simplest to handle but could cause a lot of overhead and consume a lot of computing resources. Consider reducing the number of batches below the number of files for heavy workloads.

format

Character, either "file", "file_fast", or "url". See the format argument of targets::tar_target() for details.

repository

Character of length 1, remote repository for target storage. Choices:

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, iteration method. Must be a method supported by the iteration argument of targets::tar_target(). The iteration method for the upstream target is always "list" in order to support batching.

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.

  • "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 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:

    1. It is not downstream of the error, and

    2. 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.)

memory

Character of length 1, memory strategy. Possible values:

  • "auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.

  • "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).

  • "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"), 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.

garbage_collection

Logical: TRUE to run base::gc() just before the target runs, FALSE to omit garbage collection. In the case of high-performance computing, gc() runs both locally and on the parallel worker. All this garbage collection is skipped if the actual target is skipped in the pipeline. Non-logical values of garbage_collection are converted to TRUE or FALSE using isTRUE(). In other words, non-logical values are converted FALSE. For example, garbage_collection = 2 is equivalent to garbage_collection = FALSE.

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 dispatched earlier (and polled earlier in tar_make_future()).

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.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date. Only applies to the downstream target. The upstream target always runs.

description

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".

Value

A list of two targets, one upstream and one downstream. The upstream one does some work and returns some file paths, and the downstream target is a pattern that applies format = "file" or format = "url". See the "Target objects" section for background.

Details

tar_files_input() is like tar_files() but more convenient when the files in question already exist and are known in advance. Whereas tar_files() always appears outdated (e.g. with tar_outdated()) because it always needs to check which files it needs to branch over, tar_files_input() will appear up to date if the files have not changed since last tar_make(). In addition, tar_files_input() automatically groups input files into batches to reduce overhead and increase the efficiency of parallel processing.

tar_files_input() creates a pair of targets, one upstream and one downstream. The upstream target does some work and returns some file paths, and the downstream target is a pattern that applies format = "file", format = "file_fast", or format = "url". This is the correct way to dynamically iterate over file/url targets. It makes sure any downstream patterns only rerun some of their branches if the files/urls change. For more information, visit https://github.com/ropensci/targets/issues/136 and https://github.com/ropensci/drake/issues/1302.

Target objects

Most tarchetypes functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

See also

Other Dynamic branching over files: tar_files()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
  library(tarchetypes)
  # Do not use temp files in real projects
  # or else your targets will always rerun.
  paths <- unlist(replicate(4, tempfile()))
  file.create(paths)
  list(
    tar_files_input(
      name = x,
      files = paths,
      batches = 2
    ),
    tar_files_input_raw(
      name = "y",
      files = paths,
      batches = 2
    )
  )
})
targets::tar_make()
targets::tar_read(x)
targets::tar_read(x, branches = 1)
})
}