Note that the differences between the parallel and sequential timings will increase for larger objects as the overhead for starting the parallel workers and collecting the results will decrease.
It is not fully clear why the parallel approach of the {terra} package is slow than its sequential counterpart but it might relate to the single-core performance of the machine the benchmark was run on in combination with the overhead associated with starting the parallel cluster the way it is done in the {terra} package.
library(mlr3)
library(mlr3spatial)
library(future)
library(bench)
library(stars)
#> Loading required package: abind
#> Loading required package: sf
#> Linking to GEOS 3.9.1, GDAL 3.3.1, PROJ 8.1.0
library(rpart)
Small files
- ~ 250k values
- 48 MB on disk
# SpatRaster demo stack
stack_terra = demo_stack_spatraster(50)
value = data.table::data.table(ID = c(0, 1), y = c("negative", "positive"))
terra::setCats(stack_terra, layer = "y", value = value)
colnames = names(stack_terra)
file_terra = tempfile("terra", fileext = ".tif")
terra::writeRaster(stack_terra, file_terra)
# RasterBrick demo stack
stack_raster = demo_stack_rasterbrick(50)
colnames_raster = names(stack_raster)
file_raster = tempfile("raster", fileext = ".tif")
raster::writeRaster(stack_raster, file_raster)
#> Warning in .gd_SetProject(object, ...): NOT UPDATED FOR PROJ >= 6
# tasks
stack_terra = terra::rast(file_terra)
backend_terra = DataBackendRaster$new(stack_terra)
task_terra = as_task_regr(backend_terra, target = "x_1")
stack_raster = raster::brick(file_raster)
names(stack_raster) = colnames_raster
backend_raster = DataBackendRaster$new(stack_raster)
#> Warning in wkt(from): no wkt comment
task_raster = as_task_regr(backend_raster, target = "x_1")
# Train learners
set.seed(42)
row_ids = sample(1:task_terra$nrow, 50)
learner_task_terra = lrn("regr.rpart")
learner_task_terra$parallel_predict = TRUE
learner_task_terra$train(task_terra, row_ids = row_ids)
learner_task_raster = lrn("regr.rpart")
learner_task_terra$parallel_predict = TRUE
learner_task_raster$train(task_raster, row_ids = row_ids)
# non-mlr3 models
rpart_task_terra = rpart::rpart(x_1 ~ ., task_terra$data(rows = row_ids))
rpart_task_raster = rpart::rpart(x_1 ~ ., task_raster$data(rows = row_ids))
bm = bench::mark(
"01-mlr3-terra-4-cores" = {
plan(multicore, workers = 4)
predict_spatial(task_terra, learner_task_terra, chunksize = 2000L)
},
"02-terra-4-cores" = terra::predict(stack_terra, rpart_task_terra, cores = 4, cpkgs = "rpart"),
"03-mlr3-raster-4-cores" = {
plan(multicore, workers = 4)
predict_spatial(task_raster, learner_task_raster, chunksize = 2000L, format = "raster")
},
"04-raster-4-cores" = {
library(raster)
library(rpart)
beginCluster(4, type = "PSOCK")
clusterR(stack_raster, predict, args = list(model = rpart_task_raster))
},
check = FALSE, filter_gc = FALSE, min_iterations = 3,
max_iterations = 3, memory = FALSE)
#> Loading required package: sp
#>
#> Attaching package: 'raster'
#> The following object is masked from 'package:future':
#>
#> values
#> The following object is masked from 'package:mlr3':
#>
#> resample
bm$`itr/sec` = NULL
bm$result = NULL
bm$`gc/sec` = NULL
bm$memory = NULL
bm$mem_alloc = NULL
print(bm)
#> # A tibble: 4 × 8
#> expression min median n_itr n_gc total_time time gc
#> <bch:expr> <bch:tm> <bch:tm> <int> <dbl> <bch:tm> <list> <list>
#> 1 01-mlr3-terra-4-cores 2.44s 2.49s 3 35 7.45s <bench_tm [3]> <tibble [3 × 3]>
#> 2 02-terra-4-cores 17.16s 17.22s 3 26 52.7s <bench_tm [3]> <tibble [3 × 3]>
#> 3 03-mlr3-raster-4-cores 3.25s 3.5s 3 34 10.42s <bench_tm [3]> <tibble [3 × 3]>
#> 4 04-raster-4-cores 8.3s 8.74s 3 1 25.94s <bench_tm [3]> <tibble [3 × 3]>
library(ggplot2)
autoplot(bm, type = "ridge")
#> Loading required namespace: tidyr
#> Picking joint bandwidth of 0.00863
Large files
~ 25 Mio. values
485 MB on disk
# SpatRaster demo stack
stack_terra = demo_stack_spatraster(500)
value = data.table::data.table(ID = c(0, 1), y = c("negative", "positive"))
terra::setCats(stack_terra, layer = "y", value = value)
colnames = names(stack_terra)
file_terra = tempfile("terra", fileext = ".tif")
terra::writeRaster(stack_terra, file_terra)
# RasterBrick demo stack
stack_raster = demo_stack_rasterbrick(500)
colnames_raster = names(stack_raster)
file_raster = tempfile("raster", fileext = ".tif")
raster::writeRaster(stack_raster, file_raster)
#> Warning in .gd_SetProject(object, ...): NOT UPDATED FOR PROJ >= 6
# tasks
stack_terra = terra::rast(file_terra)
backend_terra = DataBackendRaster$new(stack_terra)
task_terra = as_task_regr(backend_terra, target = "x_1")
stack_raster = raster::brick(file_raster)
names(stack_raster) = colnames_raster
backend_raster = DataBackendRaster$new(stack_raster)
#> Warning in wkt(from): no wkt comment
task_raster = as_task_regr(backend_raster, target = "x_1")
# Train learners
set.seed(42)
row_ids = sample(1:task_terra$nrow, 50)
learner_task_terra = lrn("regr.rpart")
learner_task_terra$parallel_predict = TRUE
learner_task_terra$train(task_terra, row_ids = row_ids)
learner_task_raster = lrn("regr.rpart")
learner_task_terra$parallel_predict = TRUE
learner_task_raster$train(task_raster, row_ids = row_ids)
# non-mlr3 models
rpart_task_terra = rpart::rpart(x_1 ~ ., task_terra$data(rows = row_ids))
rpart_task_raster = rpart::rpart(x_1 ~ ., task_raster$data(rows = row_ids))
bm = bench::mark(
"01-mlr3-terra-4-cores" = {
plan(multicore, workers = 4)
predict_spatial(task_terra, learner_task_terra, chunksize = 2000L)
},
"02-terra-4-cores" = terra::predict(stack_terra, rpart_task_terra, cores = 4, cpkgs = "rpart"),
"03-mlr3-raster-4-cores" = {
plan(multicore, workers = 4)
predict_spatial(task_raster, learner_task_raster, chunksize = 2000L, format = "raster")
},
"04-raster-4-cores" = {
library(raster)
library(rpart)
beginCluster(4, type = "PSOCK")
clusterR(stack_raster, predict, args = list(model = rpart_task_raster))
},
check = FALSE, filter_gc = FALSE, min_iterations = 3,
max_iterations = 3, memory = FALSE)
bm$`itr/sec` = NULL
bm$result = NULL
bm$`gc/sec` = NULL
bm$memory = NULL
bm$mem_alloc = NULL
print(bm)
#> # A tibble: 4 × 8
#> expression min median n_itr n_gc total_time time gc
#> <bch:expr> <bch:tm> <bch:tm> <int> <dbl> <bch:tm> <list> <list>
#> 1 01-mlr3-terra-4-cores 20.88s 21.55s 3 170 1.11m <bench_tm [3]> <tibble [3 × 3]>
#> 2 02-terra-4-cores 1.44m 1.45m 3 27 4.37m <bench_tm [3]> <tibble [3 × 3]>
#> 3 03-mlr3-raster-4-cores 30.5s 30.86s 3 38 1.62m <bench_tm [3]> <tibble [3 × 3]>
#> 4 04-raster-4-cores 24.4s 28.1s 3 1 1.35m <bench_tm [3]> <tibble [3 × 3]>
library(ggplot2)
autoplot(bm, type = "ridge")
#> Picking joint bandwidth of 0.0143
