A target is a single step of computation in a pipeline. It runs an R command and returns a value. This value gets treated as an R object that can be used by the commands of targets downstream. Targets that are already up to date are skipped. See the user manual for more details.
tar_target(
name,
command,
pattern = NULL,
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")
)Symbol, name of the target. Subsequent targets
can refer to this name symbolically to induce a dependency relationship:
e.g. tar_target(downstream_target, f(upstream_target)) is a
target named downstream_target which depends on a target
upstream_target and a function f(). In addition, a target's
name determines its random number generator seed. In this way,
each target runs with a reproducible seed so someone else
running the same pipeline should get the same results,
and no two targets in the same pipeline share the same seed.
(Even dynamic branches have different names and thus different seeds.)
You can recover the seed of a completed target
with tar_meta(your_target, seed) and run set.seed() on the result
to locally recreate the target's initial RNG state.
R code to run the target.
Language to define branching for a target.
For example, in a pipeline with numeric vector targets x and y,
tar_target(z, x + y, pattern = map(x, y)) implicitly defines
branches of z that each compute x[1] + y[1], x[2] + y[2],
and so on. See the user manual for details.
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.
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.
Character vector of library paths to try
when loading packages.
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.
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 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().
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.
"abridge": any currently running targets keep running,
but no new targets launch after that.
(Visit https://books.ropensci.org/targets/debugging.html
to learn how to debug targets using saved workspaces.)
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 strategy 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.
Logical, whether to run base::gc()
just before the target runs.
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.
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
(and polled earlier in tar_make_future()).
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.
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.
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.
An optional object from tar_cue() to customize the
rules that decide whether the target is up to date.
A target object. Users should not modify these directly,
just feed them to list() in your target script file
(default: _targets.R).
"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 tar_resources() and tar_resources_qs().
"feather": Uses arrow::write_feather() and
arrow::read_feather() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_feather()
through tar_resources() and tar_resources_feather().
Requires the arrow package (not installed by default).
"parquet": Uses arrow::write_parquet() and
arrow::read_parquet() (version 2.0). Much faster than "rds",
but the value must be a data frame. Optionally set
compression and compression_level in arrow::write_parquet()
through tar_resources() and tar_resources_parquet().
Requires the arrow package (not installed by default).
"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 tar_resources() and tar_resources_fst().
Requires the fst package (not installed by default).
"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.
Requires the keras package (not installed by default).
"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.
Requires the torch package (not installed by default).
"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. (These paths must be existing files
and nonempty directories.)
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
tar_resources() and tar_resources_url().
in new_handle(), nobody = TRUE is important because it
ensures targets just downloads the metadata instead of
the entire data file when it checks time stamps and hashes.
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_parquet", "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 tar_resources_aws(). 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.
Other targets:
tar_cue(),
tar_target_raw()
# NOT RUN {
# Defining targets does not run them.
data <- tar_target(target_name, get_data(), packages = "tidyverse")
analysis <- tar_target(analysis, analyze(x), pattern = map(x))
# Pipelines accept targets.
pipeline <- list(data, analysis)
# Tidy evaluation
tar_option_set(envir = environment())
n_rows <- 30L
data <- tar_target(target_name, get_data(!!n_rows))
print(data)
# Disable tidy evaluation:
data <- tar_target(target_name, get_data(!!n_rows), tidy_eval = FALSE)
print(data)
tar_option_reset()
# In a pipeline:
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
tar_dir({ # tar_dir() runs code from a temporary directory.
tar_script(tar_target(x, 1 + 1), ask = FALSE)
tar_make()
tar_read(x)
})
}
# }
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