Dynamic batched computation downstream of tar_rep()
Source: R/tar_rep2.R
, R/tar_rep2_raw.R
tar_rep2.Rd
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 thename
,command
, and...
arguments (e.g.tar_rep2(name = sim, command = simulate(), data1, data2)
) whereastar_rep2_raw()
expects an evaluated string forname
, an evaluated expression object forcommand
, and a character vector fortargets
(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/columnstar_batch
andtar_rep
to the output to denote the batch and rep-within-batch IDs, respectively.tar_rep2()
expects unevaluated language for thename
,command
, and...
arguments (e.g.tar_rep2(name = sim, command = simulate(), data1, data2)
) whereastar_rep2_raw()
expects an evaluated string forname
, an evaluated expression object forcommand
, and a character vector fortargets
(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 useiteration = "list"
intar_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
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 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 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.A character string from
tar_repository_cas()
for content-addressable storage.
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 non-dynamic data frame. Foriteration = "group"
, the target must not by dynamic (thepattern
argument oftar_target()
must be leftNULL
). 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."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."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 likeerror = "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 (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. If
deployment
is"main"
, then the target will run on the central controlling R process. Otherwise, ifdeployment
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 intargets
, 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 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 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()
andtar_visnetwork()
, and they let you select subsets of targets for thenames
argument of functions liketar_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 useiteration = "list"
intar_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)
})
}