Shorthand for a pattern that replicates a command
using batches. Batches reduce the number of targets
and thus reduce overhead.
tar_rep(
name,
command,
batches = 1,
reps = 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"),
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")
)
Arguments
| name |
Symbol, name of the target. |
| 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. |
| 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. |
| 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 builds. 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.
Possible 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 the resources argument, e.g.
tar_target(..., resources = list(preset = "archive")).
"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 the resources argument, e.g.
tar_target(..., resources = list(compress = 100)).
"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": Uses keras::save_model_hdf5() and
keras::load_model_hdf5(). The value must be a Keras model.
"torch": 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.
"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. 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.)
"url": A dynamic input URL. It works 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 the resources argument, e.g.
tar_target(..., resources = list(handle = curl::new_handle())).
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.
"aws_rds", "aws_qs", "aws_fst", "aws_fst_dt",
"aws_fst_tbl", "aws_keras": AWS-powered versions of the
respective formats "rds", "qs", etc. The only difference
is that the data file is uploaded to the AWS S3 bucket
you supply to resources. See the cloud computing chapter
of the manual for details.
"aws_file": arbitrary dynamic files on AWS S3. The target
should return a path to a temporary local file, then
targets will automatically upload this file to an S3
bucket and track it for you. Unlike format = "file",
format = "aws_file" can only handle one single file,
and that file must not be a directory.
tar_read() and downstream targets
download the file to _targets/scratch/ locally and return the path.
_targets/scratch/ gets deleted at the end of tar_make().
Requires the same resources and other configuration details
as the other AWS-powered formats. See the cloud computing
chapter of the manual for details.
|
| 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 tar_batch(), 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 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
runs into an error. If "stop", the whole pipeline stops
and throws an error. If "continue", the error is recorded,
but the pipeline keeps going. |
| 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 such as format = "aws_file",
this memory policy applies to
temporary local copies of the file in _targets/scratch/":
"persistent" means they remain until the end of the pipeline,
and "transient" means they get deleted from the file system
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.
Only applies to tar_make_future() and tar_make_clustermq()
(not tar_make()). tar_make_future() with no extra settings is
a drop-in replacement for tar_make() in this case. |
| resources |
A named list of computing resources. Uses:
Template file wildcards for future::future() in tar_make_future(). Template file wildcards clustermq::workers() in tar_make_clustermq(). Custom target-level future::plan(), e.g.
resources = list(plan = future.callr::callr). Custom curl handle if format = "url",
e.g. resources = list(handle = curl::new_handle(nobody = TRUE)).
In custom handles, most users should manually set nobody = TRUE
so targets does not download the entire file when it
only needs to check the time stamp and ETag. Custom preset for qs::qsave() if format = "qs", e.g.
resources = list(handle = "archive"). Custom compression level for fst::write_fst() if
format is "fst", "fst_dt", or "fst_tbl", e.g.
resources = list(compress = 100). AWS bucket and prefix for the "aws_" formats, e.g.
resources = list(bucket = "your-bucket", prefix = "folder/name").
bucket is required for AWS formats. See the cloud computing chapter
of the manual for details. |
| storage |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future().
If "main", the target's return value is sent back to the
host machine and saved locally. If "worker", the worker
saves the value. |
| retrieval |
Character of length 1, only relevant to
tar_make_clustermq() and tar_make_future().
If "main", the target's dependencies are loaded on the host machine
and sent to the worker before the target builds.
If "worker", the worker loads the targets dependencies. |
| cue |
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date. |
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 and tar_rep to the output
to denote the batch and rep-within-batch IDs, respectively.
Target objects represent skippable steps of the analysis pipeline
as described at https://books.ropensci.org/targets/.
Please see the design specification at
https://books.ropensci.org/targets-design/
to learn about the structure and composition of target objects.
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).
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 and tar_rep to the output
to denote the batch and rep-within-batch IDs, 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().
Examples
