Dynamic batched computation downstream of tar_rep()

Batching is important for optimizing the efficiency of heavily dynamically-branched workflows: https://books.ropensci.org/targets/dynamic.html#batching. tar_rep2() uses dynamic branching to iterate over the batches and reps of existing upstream targets.

tar_rep2() expects unevaluated language for the name, command, and ... arguments (e.g. tar_rep2(name = sim, command = simulate(), data1, data2)) whereas tar_rep2_raw() expects an evaluated string for name, an evaluated expression object for command, and a character vector for targets (e.g. tar_rep2_raw("sim", quote(simulate(x, y)), targets = c("x', "y"))).

Usage

tar_rep2(
  name,
  command,
  ...,
  rep_workers = 1,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = targets::tar_option_get("format"),
  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"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

tar_rep2_raw(
  name,
  command,
  targets,
  rep_workers = 1,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = targets::tar_option_get("format"),
  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"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Name of the target. tar_rep2() expects unevaluated language for the name, command, and ... arguments (e.g. tar_rep2(name = sim, command = simulate(), data1, data2)) whereas tar_rep2_raw() expects an evaluated string for name, an evaluated expression object for command, and a character vector for targets (e.g. tar_rep2_raw("sim", quote(simulate(x, y)), targets = c("x', "y"))).

command

R code to run multiple times. Must return a list or data frame because tar_rep() will try to append new elements/columns tar_batch and tar_rep to the output to denote the batch and rep-within-batch IDs, respectively.

tar_rep2() expects unevaluated language for the name, command, and ... arguments (e.g. tar_rep2(name = sim, command = simulate(), data1, data2)) whereas tar_rep2_raw() expects an evaluated string for name, an evaluated expression object for command, and a character vector for targets (e.g. tar_rep2_raw("sim", quote(simulate(x, y)), targets = c("x', "y"))).

...

Symbols to name one or more upstream batched targets created by tar_rep(). If you supply more than one such target, all those targets must have the same number of batches and reps per batch. And they must all return either data frames or lists. List targets must use iteration = "list" in tar_rep().

rep_workers

Positive integer of length 1, number of local R processes to use to run reps within batches in parallel. If 1, then reps are run sequentially within each batch. If greater than 1, then reps within batch are run in parallel using a PSOCK cluster.

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 runs or the output data is reloaded for downstream targets. Use tar_option_set() to set packages globally for all subsequent targets you define.

library

Character vector of library paths to try when loading packages.

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.

• 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.

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 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().

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.

deployment

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.

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.

storage

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:

• "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": 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.

retrieval

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:

• "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 target's dependencies.

• "none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

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".

targets

Character vector of names of upstream batched targets created by tar_rep(). If you supply more than one such target, all those targets must have the same number of batches and reps per batch. And they must all return either data frames or lists. List targets must use iteration = "list" in tar_rep().

Value

A new target object to perform batched computation. See the "Target objects" section for background.

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.

Replicate-specific seeds

In ordinary pipelines, each target has its own unique deterministic pseudo-random number generator seed derived from its target name. In batched replicate, however, each batch is a target with multiple replicate within that batch. That is why tar_rep() and friends give each replicate its own unique seed. Each replicate-specific seed is created based on the dynamic parent target name, tar_option_get("seed") (for targets version 0.13.5.9000 and above), batch index, and rep-within-batch index. The seed is set just before the replicate runs. Replicate-specific seeds are invariant to batching structure. In other words, tar_rep(name = x, command = rnorm(1), batches = 100, reps = 1, ...) produces the same numerical output as tar_rep(name = x, command = rnorm(1), batches = 10, reps = 10, ...) (but with different batch names). Other target factories with this seed scheme are tar_rep2(), tar_map_rep(), tar_map2_count(), tar_map2_size(), and tar_render_rep(). For the tar_map2_*() functions, it is possible to manually supply your own seeds through the command1 argument and then invoke them in your custom code for command2 (set.seed(), withr::with_seed, or withr::local_seed()). For tar_render_rep(), custom seeds can be supplied to the params argument and then invoked in the individual R Markdown reports. Likewise with tar_quarto_rep() and the execute_params argument.

See also

Other branching: tar_map2(), tar_map2_count(), tar_map2_size(), tar_map_rep(), tar_rep(), tar_rep_map(), tar_rep_map_raw()

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)
  list(
    tar_rep(
      data1,
      data.frame(value = rnorm(1)),
      batches = 2,
      reps = 3
    ),
    tar_rep(
      data2,
      list(value = rnorm(1)),
      batches = 2, reps = 3,
      iteration = "list" # List iteration is important for batched lists.
    ),
    tar_rep2(
      aggregate,
      data.frame(value = data1$value + data2$value),
      data1,
      data2
    ),
    tar_rep2_raw(
      "aggregate2",
      quote(data.frame(value = data1$value + data2$value)),
      targets = c("data1", "data2")
    )
  )
})
targets::tar_make()
targets::tar_read(aggregate)
})
}