Target resources: clustermq high-performance computing

Create the clustermq argument of tar_resources() to specify optional high-performance computing settings for tar_make_clustermq(). For details, see the documentation of the clustermq R package and the corresponding argument names in this help file.

Usage

tar_resources_clustermq(
  template = targets::tar_option_get("resources")$clustermq$template
)

Arguments

template

Named list, template argument to clustermq::workers(). Defaults to an empty list.

Value

Object of class "tar_resources_clustermq", to be supplied to the clustermq argument of tar_resources().

Details

clustermq workers are persistent, so there is not a one-to-one correspondence between workers and targets. The clustermq resources apply to the workers, not the targets. So the correct way to assign clustermq resources is through tar_option_set(), not tar_target(). clustermq resources in individual tar_target() calls will be ignored.

Resources

Functions tar_target() and tar_option_set() each takes an optional resources argument to supply non-default settings of various optional backends for data storage and high-performance computing. The tar_resources() function is a helper to supply those settings in the correct manner.

In targets version 0.12.2 and above, resources are inherited one-by-one in nested fashion from tar_option_get("resources"). For example, suppose you set tar_option_set(resources = tar_resources(aws = my_aws)), where my_aws equals tar_resources_aws(bucket = "x", prefix = "y"). Then, tar_target(data, get_data() will have bucket "x" and prefix "y". In addition, if new_resources equals tar_resources(aws = tar_resources_aws(bucket = "z"))), then tar_target(data, get_data(), resources = new_resources) will use the new bucket "z", but it will still use the prefix "y" supplied through tar_option_set(). (In targets 0.12.1 and below, options like prefix do not carry over from tar_option_set() if you supply non-default resources to tar_target().)

See also

Other resources: tar_resources(), tar_resources_aws(), tar_resources_crew(), tar_resources_custom_format(), tar_resources_feather(), tar_resources_fst(), tar_resources_future(), tar_resources_gcp(), tar_resources_network(), tar_resources_parquet(), tar_resources_qs(), tar_resources_url()

Examples

# Somewhere in you target script file (usually _targets.R):
tar_target(
  name,
  command(),
  resources = tar_resources(
    clustermq = tar_resources_clustermq(template = list(n_cores = 2))
  )
)
#> <tar_stem> 
#>   name: name 
#>   description:  
#>   command:
#>     command() 
#>   format: rds 
#>   repository: local 
#>   iteration method: vector 
#>   error mode: stop 
#>   memory mode: persistent 
#>   storage mode: main 
#>   retrieval mode: main 
#>   deployment mode: worker 
#>   priority: 0 
#>   resources:
#>     clustermq: <environment> 
#>   cue:
#>     mode: thorough
#>     command: TRUE
#>     depend: TRUE
#>     format: TRUE
#>     repository: TRUE
#>     iteration: TRUE
#>     file: TRUE
#>     seed: TRUE 
#>   packages:
#>     targets
#>     stats
#>     graphics
#>     grDevices
#>     utils
#>     datasets
#>     methods
#>     base 
#>   library:
#>     NULL