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Description

In this example on an SGE cluster, the targets deploy really slowly.

# _targets.R
library(targets)
library(tarchetypes)
options(clustermq.scheduler = "sge")
options(clustermq.template = "cmq.tmpl")
options(crayon.enabled = FALSE)
tar_rep(x, 0, batches = 1000, reps = 1)

# cmq.tmpl
#$ -N {{ job_name }}
#$ -t 1-{{ n_jobs }}
#$ -j y
#$ -cwd
#$ -V
module load R/4.0.3
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'

The profiling study took several minutes.

px <- proffer::pprof(tar_make_clustermq(workers = 10, callr_function = NULL), host = "0.0.0.0")

I saw this flamegraph:

Which tells me exactly where the bottleneck is:

And sure enough, when I changed just retrieval to "worker", everything went much faster.

# _targets.R
library(targets)
library(tarchetypes)
tar_option_set(retrieval = "worker") # worker retrieval
options(clustermq.scheduler = "sge")
options(clustermq.template = "cmq.tmpl")
options(crayon.enabled = FALSE)
tar_rep(x, 0, batches = 1000, reps = 1)

Solution

Apparently, all I need to do is force() all the pre-loaded objects in the subpipeline. It looks like there is an unevaluated promise object that is consuming too much memory. Here I am debugging this example at $run_worker():

> tar_make_clustermq(callr_function = NULL)
● run target x_batch
● run branch x_be02823c
Called from: self$run_worker(target)
Browse[1]> object_size(target) # promise object
9.32 MB # way too big.
Browse[1]> tmp <- force(target$subpipeline$targets$x_batch_084b9b29$value$object)
Browse[1]> object_size(target) # evaluated object
193 kB # much better
Browse[1]>