README

mlr3batchmark

A connector between mlr3 and batchtools. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

The package comes with two core functions for switching between mlr3 and batchtools to perform a benchmark:

After creating a design object (as required for mlr3’s benchmark() function), instead of benchmark() call batchmark() which populates an ExperimentRegistry for the computational jobs of the benchmark. You are now in the world of batchtools where you can selectively submit jobs with different resources, monitor the progress or resubmit as needed.

After the computations are finished, collect the results with reduceResultsBatchmark() to return to mlr3. The resulting object is a regular BenchmarkResult.

Example

library("mlr3")
library("batchtools")
library("mlr3batchmark")
tasks = tsks(c("iris", "sonar"))
learners = lrns(c("classif.featureless", "classif.rpart"))
resamplings = rsmp("cv", folds = 3)

design = benchmark_grid(
  tasks = tasks,
  learners = learners,
  resamplings = resamplings
)

reg = makeExperimentRegistry(NA)

ids = batchmark(design, reg = reg)

## Adding algorithm 'run_learner' ## Adding problem 'abc694dd29a7a8ce' ## Exporting new objects: '2da7eeb80b94fc3b' ... ## Exporting new objects: 'c905990877a775af' ... ## Exporting new objects: '3acc41a799a260d8' ... ## Exporting new objects: 'ecf8ee265ec56766' ... ## Overwriting previously exported object: 'ecf8ee265ec56766' ## Adding 6 experiments ('abc694dd29a7a8ce'[1] x 'run_learner'[2] x repls[3]) ... ## Adding problem 'f9791e97f9813150' ## Exporting new objects: '62ac3bb85aabfbaf' ... ## Adding 6 experiments ('f9791e97f9813150'[1] x 'run_learner'[2] x repls[3]) ...

submitJobs()

## Submitting 12 jobs in 12 chunks using cluster functions 'Interactive' ... ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold) ## Error in workhorse(iteration = job$repl, task = data, learner = learner, : ## unused argument (lgr_threshold = lgr::get_logger("mlr3")$threshold)

getStatus()

## Status for 12 jobs at 2025-05-26 09:23:22: ## Submitted : 12 (100.0%) ## -- Queued : 0 ( 0.0%) ## -- Started : 12 (100.0%) ## ---- Running : 0 ( 0.0%) ## ---- Done : 0 ( 0.0%) ## ---- Error : 12 (100.0%) ## ---- Expired : 0 ( 0.0%)

reduceResultsBatchmark()

mlr3batchmark

Example

Resources