parallelism_choicesList the types of supported parallel computing.
parallelism_choices()Character vector listing the types of parallel computing supported.
Run make(..., parallelism = x, jobs = n) for any of
the following values of x to distribute targets over parallel
units of execution.
launches multiple processes in a single R session
using parallel::parLapply().
This is single-node, (potentially) multicore computing.
It requires more overhead than the 'mclapply' option,
but it works on Windows. If jobs is 1 in
make(), then no 'cluster' is created and
no parallelism is used.
uses multiple processes in a single R session.
This is single-node, (potentially) multicore computing.
Does not work on Windows for jobs > 1
because mclapply() is based on forking.
uses multiple R sessions
by creating and running a Makefile.
For distributed computing on a cluster or supercomputer,
try make(..., parallelism = 'Makefile',
prepend = 'SHELL=./shell.sh').
You need an auxiliary shell.sh file for this,
and shell_file()
writes an example.
Here, Makefile-level parallelism is only used for
targets in your workflow plan
data frame, not imports. To process imported objects and files,
drake selects the best parallel
backend for your system and uses
the number of jobs you give to the jobs
argument to make().
To use at most 2 jobs for imports and at most 4 jobs
for targets, run
make(..., parallelism = 'Makefile', jobs = 2, args = '--jobs=4')
Caution: the Makefile generated by
make(..., parallelism = 'Makefile')
is NOT standalone. DO NOT run it outside of
make() or make().
Also, Windows users will need to download and intall Rtools.
# NOT RUN {
parallelism_choices()
# }
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