tar_stan_compile() creates a target
to compile a Stan model on the local file system and return the
original Stan model file. Does not compile the model
if the compilation is already up to date.
Usage
tar_stan_compile(
name,
stan_file,
quiet = TRUE,
stdout = NULL,
stderr = NULL,
dir = NULL,
pedantic = FALSE,
include_paths = NULL,
cpp_options = list(),
stanc_options = list(),
force_recompile = FALSE,
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
Symbol, name of the target. In
tar_target(),nameis an unevaluated symbol, e.g.tar_target(name = data). Intar_target_raw(),nameis a character string, e.g.tar_target_raw(name = "data").A target name must be a valid name for a symbol in R, and it must not start with a dot. 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 nameddownstream_targetwhich depends on a targetupstream_targetand a functionf().In most cases, The target name is the name of its local data file in storage. Some file systems are not case sensitive, which means converting a name to a different case may overwrite a different target. Please ensure all target names have unique names when converted to lower case.
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 runtar_seed_set()on the result to locally recreate the target's initial RNG state.- stan_file
(string) The path to a
.stanfile containing a Stan program. The helper functionwrite_stan_file()is provided for cases when it is more convenient to specify the Stan program as a string. Ifstan_fileis not specified thenexe_filemust be specified.- quiet
(logical) Should the verbose output from CmdStan during compilation be suppressed? The default is
TRUE, but if you encounter an error we recommend trying again withquiet=FALSEto see more of the output.- stdout
Character of length 1, file path to write the stdout stream of the model when it runs. Set to
NULLto print to the console. Set toR.utils::nullfile()to suppress stdout. Does not apply to messages, warnings, or errors.- stderr
Character of length 1, file path to write the stderr stream of the model when it runs. Set to
NULLto print to the console. Set toR.utils::nullfile()to suppress stderr. Does not apply to messages, warnings, or errors.- dir
(string) The path to the directory in which to store the CmdStan executable (or
.hppfile if using$save_hpp_file()). The default is the same location as the Stan program.- pedantic
(logical) Should pedantic mode be turned on? The default is
FALSE. Pedantic mode attempts to warn you about potential issues in your Stan program beyond syntax errors. For details see the Pedantic mode chapter in the Stan Reference Manual. Note: to do a pedantic check for a model without compiling it or for a model that is already compiled the$check_syntax()method can be used instead.- include_paths
(character vector) Paths to directories where Stan should look for files specified in
#includedirectives in the Stan program.- cpp_options
(list) Any makefile options to be used when compiling the model (
STAN_THREADS,STAN_MPI,STAN_OPENCL, etc.). Anything you would otherwise write in themake/localfile. For an example of using threading see the Stan case study Reduce Sum: A Minimal Example.- stanc_options
(list) Any Stan-to-C++ transpiler options to be used when compiling the model. See the Examples section below as well as the
stancchapter of the CmdStan Guide for more details on available options: https://mc-stan.org/docs/cmdstan-guide/stanc.html.- force_recompile
(logical) Should the model be recompiled even if was not modified since last compiled. The default is
FALSE. Can also be set via a globalcmdstanr_force_recompileoption.- 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. In addition, as oftargetsversion 1.8.0.9011, a value ofNULLis given to upstream dependencies witherror = "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: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. Possible values:
"auto": new intargetsversion 1.8.0.9011,memory = "auto"is equivalent tomemory = "transient"for dynamic branching (a non-nullpatternargument) andmemory = "persistent"for targets that do not use dynamic branching."persistent": the target stays in memory until the end of the pipeline (unlessstorageis"worker", in which casetargetsunloads 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"withrepository = "aws"), thememoryoption 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:
TRUEto runbase::gc()just before the target runs,FALSEto 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 ofgarbage_collectionare converted toTRUEorFALSEusingisTRUE(). In other words, non-logical values are convertedFALSE. For example,garbage_collection = 2is equivalent togarbage_collection = FALSE.- deployment
Character of length 1. If
deploymentis"main", then the target will run on the central controlling R process. Otherwise, ifdeploymentis"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 string to control when the output of the target is saved to storage. Only relevant when using
targetswith 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":targetsmakes no attempt to save the result of the target to storage in the location wheretargetsexpects 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
targetswith 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":targetsmakes no attempt to load its dependencies. Withretrieval = "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()andtar_visnetwork(), and they let you select subsets of targets for thenamesargument 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".
Value
tar_stan_compile() returns a target object to compile a Stan file.
The return value of this target is a character vector
containing the Stan model source file and compiled
executable file. A change in either file
will cause the target to rerun in the next run of the pipeline.
See the "Target objects" section for background.
Details
Most of the arguments are passed to the
$compile() method of the CmdStanModel class.
For details, visit https://mc-stan.org/cmdstanr/reference/.
Target objects
Most stantargets 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.
Examples
if (Sys.getenv("TAR_LONG_EXAMPLES") == "true") {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
library(stantargets)
# Do not use temporary storage for stan files in real projects
# or else your targets will always rerun.
path <- tempfile(pattern = "", fileext = ".stan")
tar_stan_example_file(path = path)
list(tar_stan_compile(compiled_model, path))
})
targets::tar_make()
})
}