tar_rep: Batched replication with dynamic branching.

In tarchetypes: Archetypes for Targets

Batched replication with dynamic branching.

Description

Batching is important for optimizing the efficiency of heavily dynamically-branched workflows: https://books.ropensci.org/targets/dynamic.html#batching. tar_rep() replicates a command in strategically sized batches.

tar_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).

Usage

tar_rep(
  name,
  command,
  batches = 1,
  reps = 1,
  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_rep_raw(
  name,
  command,
  batches = 1,
  reps = 1,
  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_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).
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_rep() expects unevaluated name and command arguments (e.g. tar_rep(name = sim, command = simulate())) whereas tar_rep_raw() expects an evaluated string for name and an evaluated expression object for command (e.g. tar_rep_raw(name = "sim", command = quote(simulate()))).
batches	Number of batches. This is also the number of dynamic branches created during tar_make().
reps	Number of replications in each batch. The total number of replications is batches * reps.
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	Whether to invoke tidy evaluation (e.g. the ⁠!!⁠ operator from rlang) as soon as the target is defined (before tar_make()). Applies to the command argument.
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 vectors::vec_slice() and aggregation happens with vctrs::vec_c(). "list", branching happens with ⁠[[]]⁠ and aggregation happens with list(). In the case of list iteration, tar_read(your_target) will return a list of lists, where the outer list has one element per batch and each inner list has one element per rep within batch. To un-batch this nested list, call tar_read(your_target, recursive = FALSE). "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 in targets 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. "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: It is not downstream of the error, and 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. 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. 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 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 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.
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".

Details

tar_rep() and tar_rep_raw() each create two targets: an upstream local stem with an integer vector of batch ids, and a downstream pattern that maps over the batch ids. (Thus, each batch is a branch.) Each batch/branch replicates the command a certain number of times. If the command returns a list or data frame, then the targets from tar_rep() will try to append new elements/columns tar_batch, tar_rep, and tar_seed to the output to denote the batch, rep-within-batch index, and rep-specific seed, respectively.

Both batches and reps within each batch are aggregated according to the method you specify in the iteration argument. If "list", reps and batches are aggregated with list(). If "vector", then vctrs::vec_c(). If "group", then vctrs::vec_rbind().

Value

A list of two targets, one upstream and one downstream. The upstream target returns a numeric index of batch ids, and the downstream one dynamically maps over the batch ids to run the command multiple times. If the command returns a list or data frame, then the targets from tar_rep() will try to append new elements/columns tar_batch, tar_rep, and tar_seed to the output to denote the batch, rep-within-batch ID, and random number generator seed, respectively.

tar_read(your_target) (on the downstream target with the actual work) will return a list of lists, where the outer list has one element per batch and each inner list has one element per rep within batch. To un-batch this nested list, call tar_read(your_target, recursive = FALSE).

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.

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 branching: tar_map2(), tar_map2_count(), tar_map2_size(), tar_map_rep(), tar_rep2(), 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({
  list(
    tarchetypes::tar_rep(
      x,
      data.frame(x = sample.int(1e4, 2)),
      batches = 2,
      reps = 3
    )
  )
})
targets::tar_make()
targets::tar_read(x)
targets::tar_script({
  list(
    tarchetypes::tar_rep_raw(
      "x",
      quote(data.frame(x = sample.int(1e4, 2))),
      batches = 2,
      reps = 3
    )
  )
})
targets::tar_make()
targets::tar_read(x)
})
}

tarchetypes documentation built on Sept. 30, 2024, 9:18 a.m.