library(mlr3verse)Scope
This is the fourth part of the practical tuning series. The other parts can be found here:
- Part I - Tune a Support Vector Machine
- Part II - Tune a Preprocessing Pipeline
- Part III - Build an Automated Machine Learning System
In this post, we teach how to run various jobs in mlr3 in parallel. The goal is to map computational jobs (e.g. evaluation of one configuration) to a pool of workers (usually physical CPU cores, sometimes remote computational nodes) to reduce the run time needed for tuning.
Prerequisites
We load the mlr3verse package which pulls in the most important packages for this example. Additionally, make sure you have installed the packages future and future.apply.
We decrease the verbosity of the logger to keep the output clearly represented. The lgr package is used for logging in all mlr3 packages. The mlr3 logger prints the logging messages from the base package, whereas the bbotk logger is responsible for logging messages from the optimization packages (e.g. mlr3tuning ).
set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")Parallel Backend
The workers are specified by the parallel backend which orchestrates starting up, shutting down, and communication with the workers. On a single machine, multisession and multicore are common backends. The multisession backend spawns new background R processes. It is available on all platforms.
future::plan("multisession")The multicore backend uses forked R processes which allows the workers to access R objects in a shared memory. This reduces the overhead since R objects are only copied in memory if they are modified. Unfortunately, forking processes is not supported on Windows and when running R from within RStudio.
future::plan("multicore")Both backends support the workers argument that specifies the number of used cores.
Use this code if your code should run with the multicore backend when possible.
if (future::supportsMulticore()) {
future::plan(future::multicore)
} else {
future::plan(future::multisession)
}Resampling
The resample() and benchmark() functions in mlr3 can be executed in parallel. The parallelization is triggered by simply declaring a plan via future::plan().
future::plan("multisession")
task = tsk("pima")
learner = lrn("classif.rpart") # classification tree
resampling = rsmp("cv", folds = 3)
resample(task, learner, resampling)
── <ResampleResult> with 3 resampling iterations ───────────────────────────────────────────────────────────────────────
task_id learner_id resampling_id iteration prediction_test warnings errors
pima classif.rpart cv 1 <PredictionClassif> 0 0
pima classif.rpart cv 2 <PredictionClassif> 0 0
pima classif.rpart cv 3 <PredictionClassif> 0 0The 3-fold cross-validation gives us 3 jobs since each resampling iteration is executed in parallel.
The benchmark() function accepts a design of experiments as input where each experiment is defined as a combination of a task, a learner, and a resampling strategy. For each experiment, resampling is performed. The nested loop over experiments and resampling iterations is flattened so that all resampling iterations of all experiments can be executed in parallel.
future::plan("multisession")
tasks = list(tsk("pima"), tsk("iris"))
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 3)
grid = benchmark_grid(tasks, learner, resampling)
benchmark(grid)
── <BenchmarkResult> of 6 rows with 2 resampling run ───────────────────────────────────────────────────────────────────
nr task_id learner_id resampling_id iters warnings errors
1 pima classif.rpart cv 3 0 0
2 iris classif.rpart cv 3 0 0The 2 experiments and the 3-fold cross-validation result in 6 jobs which are executed in parallel.
Tuning
The mlr3tuning package internally calls benchmark() during tuning. If the tuner is capable of suggesting multiple configurations per iteration (such as random search, grid search, or hyperband), these configurations represent individual experiments, and the loop flattening of benchmark() is triggered. E.g., all resampling iterations of all hyperparameter configurations on a grid can be executed in parallel.
future::plan("multisession")
learner = lrn("classif.rpart")
learner$param_set$values$cp = to_tune(0.001, 0.1)
learner$param_set$values$minsplit = to_tune(1, 10)
instance = tune(
tuner = tnr("random_search", batch_size = 5), # random search suggests 5 configurations per batch
task = tsk("pima"),
learner = learner,
resampling = rsmp("cv", folds = 3),
measure = msr("classif.ce"),
term_evals = 10
)The batch size of 5 and the 3-fold cross-validation gives us 15 jobs. This is done twice because of the limit of 10 evaluations in total.
Nested Resampling
Nested resampling results in two nested resampling loops. For this, an AutoTuner is passed to resample() or benchmark(). We can choose different parallelization backends for the inner and outer resampling loop, respectively. We just have to pass a list of backends.
# Runs the outer loop in parallel and the inner loop sequentially
future::plan(list("multisession", "sequential"))
# Runs the outer loop sequentially and the inner loop in parallel
future::plan(list("sequential", "multisession"))
learner = lrn("classif.rpart")
learner$param_set$values$cp = to_tune(0.001, 0.1)
learner$param_set$values$minsplit = to_tune(1, 10)
rr = tune_nested(
tuner = tnr("random_search", batch_size = 5), # random search suggests 5 configurations per batch
task = tsk("pima"),
learner = learner,
inner_resampling = rsmp ("cv", folds = 3),
outer_resampling = rsmp("cv", folds = 3),
measure = msr("classif.ce"),
term_evals = 10
)Warning: SequentialFuture ('future_mapply-1') added, removed, or modified connections. A future expression must close
any opened connections and must not close connections it did not open. Details: 12 connection added ([index= 5,
description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened, can.read=yes, can.write=yes];
[index= 6, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened, can.read=yes,
can.write=yes]; [index= 7, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened,
can.read=yes, can.write=yes]; [index= 8, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary,
opened=opened, can.read=yes, can.write=yes]; [index= 9, description=<-localhost:11967, class=sockconn, mode=a+b,
text=binary, opened=opened, can.read=yes, can.write=yes]; [index=10, description=<-localhost:11967, class=sockconn,
mode=a+b, text=binary, opened=opened, can.read=yes, can.write=yes]; [index=11, description=<-localhost:11967,
class=sockconn, mode=a+b, text=binary, opened=opened, can.read=yes, can.write=yes]; [index=12,
description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened, can.read=yes, can.write=yes];
[index=13, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened, can.read=yes,
can.write=yes]; [index=14, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary, opened=opened,
can.read=yes, can.write=yes]; [index=15, description=<-localhost:11967, class=sockconn, mode=a+b, text=binary,
opened=opened, can.read=yes, can.write=yes]; [index=16, description=<-localhost:11967, class=sockconn, mode=a+b,
text=binary, opened=opened, can.read=yes, can.write=yes]), 0 connection removed (<none>), 0 connection replaced
(<none>). See also help("future.options", package = "future") [future 'future_mapply-1'
(f8f4958a0ee670a5f4b85e9bd400ff97-40); on f8f4958a0ee670a5f4b85e9bd400ff97@5be2c2c9e0c1<31471>]While nesting real parallelization backends is often unintended and causes unnecessary overhead, it is useful in some distributed computing setups. It can be achieved with future by forcing a fixed number of workers for each loop.
# Runs both loops in parallel
future::plan(list(future::tweak("multisession", workers = 2),
future::tweak("multisession", workers = 4)))This example would run on 8 cores (= 2 * 4) on the local machine.
Resources
The mlr3book includes a chapters on parallelization. The mlr3cheatsheets contain frequently used commands and workflows of mlr3.
Session Information
sessioninfo::session_info(info = "packages")═ Session info ═══════════════════════════════════════════════════════════════════════════════════════════════════════
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
backports 1.5.0 2024-05-23 [1] RSPM
bbotk 1.8.1 2025-11-26 [1] RSPM
checkmate 2.3.4 2026-02-03 [1] RSPM
class 7.3-23 2025-01-01 [2] CRAN (R 4.5.2)
cli 3.6.5 2025-04-23 [1] RSPM
cluster 2.1.8.1 2025-03-12 [2] CRAN (R 4.5.2)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.5.2)
crayon 1.5.3 2024-06-20 [1] RSPM
data.table * 1.18.2.1 2026-01-27 [1] RSPM
DEoptimR 1.1-4 2025-07-27 [1] RSPM
digest 0.6.39 2025-11-19 [1] RSPM
diptest 0.77-2 2025-08-20 [1] RSPM
dplyr 1.2.0 2026-02-03 [1] RSPM
evaluate 1.0.5 2025-08-27 [1] RSPM
farver 2.1.2 2024-05-13 [1] RSPM
fastmap 1.2.0 2024-05-15 [1] RSPM
flexmix 2.3-20 2025-02-28 [1] RSPM
fpc 2.2-14 2026-01-14 [1] RSPM
future * 1.69.0 2026-01-16 [1] RSPM
future.apply 1.20.2 2026-02-20 [1] RSPM
generics 0.1.4 2025-05-09 [1] RSPM
ggplot2 4.0.2 2026-02-03 [1] RSPM
globals 0.19.0 2026-02-02 [1] RSPM
glue 1.8.0 2024-09-30 [1] RSPM
gtable 0.3.6 2024-10-25 [1] RSPM
htmltools 0.5.9 2025-12-04 [1] RSPM
htmlwidgets 1.6.4 2023-12-06 [1] RSPM
jsonlite 2.0.0 2025-03-27 [1] RSPM
kernlab 0.9-33 2024-08-13 [1] RSPM
knitr 1.51 2025-12-20 [1] RSPM
lattice 0.22-7 2025-04-02 [2] CRAN (R 4.5.2)
lgr 0.5.2 2026-01-30 [1] RSPM
lifecycle 1.0.5 2026-01-08 [1] RSPM
listenv 0.10.0 2025-11-02 [1] RSPM
magrittr 2.0.4 2025-09-12 [1] RSPM
MASS 7.3-65 2025-02-28 [2] CRAN (R 4.5.2)
Matrix 1.7-4 2025-08-28 [2] CRAN (R 4.5.2)
mclust 6.1.2 2025-10-31 [1] RSPM
mlr3 * 1.4.0 2026-02-19 [1] RSPM
mlr3cluster 0.2.0 2026-02-04 [1] RSPM
mlr3cmprsk 0.0.1 2026-02-27 [1] Github (mlr-org/mlr3cmprsk@5a04c29)
mlr3data 0.9.0 2024-11-08 [1] RSPM
mlr3extralearners 1.4.0 2026-01-26 [1] https://m~
mlr3filters 0.9.0 2025-09-12 [1] RSPM
mlr3fselect * 1.5.0 2025-11-27 [1] RSPM
mlr3hyperband 1.0.0 2025-07-10 [1] RSPM
mlr3inferr 0.2.1 2025-11-26 [1] RSPM
mlr3learners 0.14.0 2025-12-13 [1] RSPM
mlr3mbo 0.3.3 2025-10-10 [1] RSPM
mlr3measures 1.2.0 2025-11-25 [1] RSPM
mlr3misc 0.21.0 2026-02-26 [1] RSPM
mlr3pipelines 0.10.0 2025-11-07 [1] RSPM
mlr3tuning 1.5.1 2025-12-14 [1] RSPM
mlr3tuningspaces 0.6.0 2025-05-16 [1] RSPM
mlr3verse * 0.3.1 2025-01-14 [1] RSPM
mlr3viz 0.11.0 2026-02-22 [1] RSPM
mlr3website * 0.0.0.9000 2026-02-27 [1] Github (mlr-org/mlr3website@f6e32a7)
modeltools 0.2-24 2025-05-02 [1] RSPM
nnet 7.3-20 2025-01-01 [2] CRAN (R 4.5.2)
otel 0.2.0 2025-08-29 [1] RSPM
palmerpenguins 0.1.1 2022-08-15 [1] RSPM
paradox 1.0.1 2024-07-09 [1] RSPM
parallelly 1.46.1 2026-01-08 [1] RSPM
pillar 1.11.1 2025-09-17 [1] RSPM
pkgconfig 2.0.3 2019-09-22 [1] RSPM
prabclus 2.3-5 2026-01-14 [1] RSPM
R6 2.6.1 2025-02-15 [1] RSPM
RColorBrewer 1.1-3 2022-04-03 [1] RSPM
Rcpp 1.1.1 2026-01-10 [1] RSPM
rlang 1.1.7 2026-01-09 [1] RSPM
rmarkdown 2.30 2025-09-28 [1] RSPM
robustbase 0.99-7 2026-02-05 [1] RSPM
rpart 4.1.24 2025-01-07 [2] CRAN (R 4.5.2)
S7 0.2.1 2025-11-14 [1] RSPM
scales 1.4.0 2025-04-24 [1] RSPM
sessioninfo 1.2.3 2025-02-05 [1] RSPM
spacefillr 0.4.0 2025-02-24 [1] RSPM
stringi 1.8.7 2025-03-27 [1] RSPM
survdistr 0.0.1 2026-02-27 [1] Github (mlr-org/survdistr@d7babd1)
survival 3.8-3 2024-12-17 [2] CRAN (R 4.5.2)
tibble 3.3.1 2026-01-11 [1] RSPM
tidyselect 1.2.1 2024-03-11 [1] RSPM
uuid 1.2-2 2026-01-23 [1] RSPM
vctrs 0.7.1 2026-01-23 [1] RSPM
withr 3.0.2 2024-10-28 [1] RSPM
xfun 0.56 2026-01-18 [1] RSPM
yaml 2.3.12 2025-12-10 [1] RSPM
[1] /usr/local/lib/R/site-library
[2] /usr/local/lib/R/library
* ── Packages attached to the search path.
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────