Estimates the runtimes of jobs using the random forest implemented in ranger.
Observed runtimes are retrieved from the Registry and runtimes are
predicted for unfinished jobs.
The estimated remaining time is calculated in the print method.
You may also pass n here to determine the number of parallel jobs which is then used
in a simple Longest Processing Time (LPT) algorithm to give an estimate for the parallel runtime.
Usage
estimateRuntimes(tab, ..., reg = getDefaultRegistry())
# S3 method for class 'RuntimeEstimate'
print(x, n = 1L, ...)Arguments
- tab
[
data.table]
Table with column “job.id” and additional columns to predict the runtime. Observed runtimes will be looked up in the registry and serve as dependent variable. All columns intabexcept “job.id” will be passed torangeras independent variables to fit the model.- ...
[ANY]
Additional parameters passed toranger. Ignored for theprintmethod.- reg
[
Registry]
Registry. If not explicitly passed, uses the default registry (seesetDefaultRegistry).- x
[
RuntimeEstimate]
Object to print.- n
[
integer(1)]
Number of parallel jobs to assume for runtime estimation.
Value
[RuntimeEstimate] which is a list with two named elements:
“runtimes” is a data.table with columns “job.id”,
“runtime” (in seconds) and “type” (“estimated” if runtime is estimated,
“observed” if runtime was observed).
The other element of the list named “model”] contains the fitted random forest object.
Examples
# Create a simple toy registry
set.seed(1)
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE, seed = 1)
#> No readable configuration file found
#> Created registry in '/tmp/batchtools-example/reg' using cluster functions 'Interactive'
addProblem(name = "iris", data = iris, fun = function(data, ...) nrow(data), reg = tmp)
#> Adding problem 'iris'
addAlgorithm(name = "nrow", function(instance, ...) nrow(instance), reg = tmp)
#> Adding algorithm 'nrow'
addAlgorithm(name = "ncol", function(instance, ...) ncol(instance), reg = tmp)
#> Adding algorithm 'ncol'
addExperiments(algo.designs = list(nrow = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
#> Adding 250 experiments ('iris'[1] x 'nrow'[250] x repls[1]) ...
addExperiments(algo.designs = list(ncol = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
#> Adding 250 experiments ('iris'[1] x 'ncol'[250] x repls[1]) ...
# We use the job parameters to predict runtimes
tab = unwrap(getJobPars(reg = tmp))
# First we need to submit some jobs so that the forest can train on some data.
# Thus, we just sample some jobs from the registry while grouping by factor variables.
library(data.table)
ids = tab[, .SD[sample(nrow(.SD), 5)], by = c("problem", "algorithm", "y")]
setkeyv(ids, "job.id")
submitJobs(ids, reg = tmp)
#> Submitting 50 jobs in 50 chunks using cluster functions 'Interactive' ...
waitForJobs(reg = tmp)
#> [1] TRUE
# We "simulate" some more realistic runtimes here to demonstrate the functionality:
# - Algorithm "ncol" is 5 times more expensive than "nrow"
# - x has no effect on the runtime
# - If y is "a" or "b", the runtimes are really high
runtime = function(algorithm, x, y) {
ifelse(algorithm == "nrow", 100L, 500L) + 1000L * (y %in% letters[1:2])
}
tmp$status[ids, done := done + tab[ids, runtime(algorithm, x, y)]]
#> Key: <job.id>
#> job.id def.id submitted started done error mem.used resource.id
#> <int> <int> <num> <num> <num> <char> <num> <int>
#> 1: 1 1 NA NA NA <NA> NA NA
#> 2: 2 2 NA NA NA <NA> NA NA
#> 3: 3 3 NA NA NA <NA> NA NA
#> 4: 4 4 NA NA NA <NA> NA NA
#> 5: 5 5 NA NA NA <NA> NA NA
#> ---
#> 496: 496 496 NA NA NA <NA> NA NA
#> 497: 497 497 NA NA NA <NA> NA NA
#> 498: 498 498 NA NA NA <NA> NA NA
#> 499: 499 499 1764152569 1764152569 1764153069 <NA> NA 1
#> 500: 500 500 NA NA NA <NA> NA NA
#> batch.id log.file job.hash job.name repl
#> <char> <char> <char> <char> <int>
#> 1: <NA> <NA> <NA> <NA> 1
#> 2: <NA> <NA> <NA> <NA> 1
#> 3: <NA> <NA> <NA> <NA> 1
#> 4: <NA> <NA> <NA> <NA> 1
#> 5: <NA> <NA> <NA> <NA> 1
#> ---
#> 496: <NA> <NA> <NA> <NA> 1
#> 497: <NA> <NA> <NA> <NA> 1
#> 498: <NA> <NA> <NA> <NA> 1
#> 499: cfInteractive <NA> job9a2771e603043c31788ff87c02c0103d <NA> 1
#> 500: <NA> <NA> <NA> <NA> 1
rjoin(sjoin(tab, ids), getJobStatus(ids, reg = tmp)[, c("job.id", "time.running")])
#> Key: <job.id>
#> job.id problem algorithm x y time.running
#> <int> <char> <char> <int> <char> <difftime>
#> 1: 32 iris nrow 7 b 1100.0330 secs
#> 2: 42 iris nrow 9 b 1100.0321 secs
#> 3: 47 iris nrow 10 b 1100.0322 secs
#> 4: 66 iris nrow 14 a 1100.0322 secs
#> 5: 73 iris nrow 15 c 100.0322 secs
#> 6: 75 iris nrow 15 e 100.0319 secs
#> 7: 86 iris nrow 18 a 1100.0318 secs
#> 8: 100 iris nrow 20 e 100.0319 secs
#> 9: 101 iris nrow 21 a 1100.0322 secs
#> 10: 103 iris nrow 21 c 100.0325 secs
#> 11: 123 iris nrow 25 c 100.0322 secs
#> 12: 125 iris nrow 25 e 100.0320 secs
#> 13: 161 iris nrow 33 a 1100.0321 secs
#> 14: 165 iris nrow 33 e 100.0320 secs
#> 15: 169 iris nrow 34 d 100.0319 secs
#> 16: 183 iris nrow 37 c 100.0320 secs
#> 17: 184 iris nrow 37 d 100.0319 secs
#> 18: 203 iris nrow 41 c 100.0320 secs
#> 19: 207 iris nrow 42 b 1100.0318 secs
#> 20: 209 iris nrow 42 d 100.0318 secs
#> 21: 220 iris nrow 44 e 100.0319 secs
#> 22: 227 iris nrow 46 b 1100.0319 secs
#> 23: 229 iris nrow 46 d 100.0317 secs
#> 24: 231 iris nrow 47 a 1100.0319 secs
#> 25: 244 iris nrow 49 d 100.0317 secs
#> 26: 260 iris ncol 2 e 500.0318 secs
#> 27: 276 iris ncol 6 a 1500.0320 secs
#> 28: 278 iris ncol 6 c 500.0333 secs
#> 29: 279 iris ncol 6 d 500.0325 secs
#> 30: 296 iris ncol 10 a 1500.0326 secs
#> 31: 320 iris ncol 14 e 500.0324 secs
#> 32: 340 iris ncol 18 e 500.0323 secs
#> 33: 347 iris ncol 20 b 1500.0325 secs
#> 34: 363 iris ncol 23 c 500.0337 secs
#> 35: 369 iris ncol 24 d 500.0329 secs
#> 36: 373 iris ncol 25 c 500.0328 secs
#> 37: 387 iris ncol 28 b 1500.0329 secs
#> 38: 410 iris ncol 32 e 500.0325 secs
#> 39: 421 iris ncol 35 a 1500.0325 secs
#> 40: 436 iris ncol 38 a 1500.0324 secs
#> 41: 444 iris ncol 39 d 500.0327 secs
#> 42: 448 iris ncol 40 c 500.0325 secs
#> 43: 456 iris ncol 42 a 1500.0324 secs
#> 44: 459 iris ncol 42 d 500.0333 secs
#> 45: 467 iris ncol 44 b 1500.0334 secs
#> 46: 468 iris ncol 44 c 500.0330 secs
#> 47: 475 iris ncol 45 e 500.0331 secs
#> 48: 482 iris ncol 47 b 1500.0332 secs
#> 49: 492 iris ncol 49 b 1500.0383 secs
#> 50: 499 iris ncol 50 d 500.0337 secs
#> job.id problem algorithm x y time.running
# Estimate runtimes:
est = estimateRuntimes(tab, reg = tmp)
print(est)
#> Runtime Estimate for 500 jobs with 1 CPUs
#> Done : 0d 09h 43m 21.6s
#> Remaining: 3d 17h 35m 47.4s
#> Total : 4d 03h 19m 9.0s
rjoin(tab, est$runtimes)
#> Key: <job.id>
#> job.id problem algorithm x y type runtime
#> <int> <char> <char> <int> <char> <fctr> <num>
#> 1: 1 iris nrow 1 a estimated 1104.0461
#> 2: 2 iris nrow 1 b estimated 1088.0005
#> 3: 3 iris nrow 1 c estimated 338.5989
#> 4: 4 iris nrow 1 d estimated 318.2378
#> 5: 5 iris nrow 1 e estimated 317.9085
#> ---
#> 496: 496 iris ncol 50 a estimated 1387.8280
#> 497: 497 iris ncol 50 b estimated 1394.9184
#> 498: 498 iris ncol 50 c estimated 618.9306
#> 499: 499 iris ncol 50 d observed 500.0337
#> 500: 500 iris ncol 50 e estimated 580.0825
print(est, n = 10)
#> Runtime Estimate for 500 jobs with 10 CPUs
#> Done : 0d 09h 43m 21.6s
#> Remaining: 3d 17h 35m 47.4s
#> Parallel : 0d 08h 58m 16.3s
#> Total : 4d 03h 19m 9.0s
# Submit jobs with longest runtime first:
ids = est$runtimes[type == "estimated"][order(runtime, decreasing = TRUE)]
print(ids)
#> job.id type runtime
#> <int> <fctr> <num>
#> 1: 466 estimated 1424.2072
#> 2: 461 estimated 1420.8138
#> 3: 472 estimated 1417.8669
#> 4: 462 estimated 1416.9134
#> 5: 457 estimated 1416.9133
#> ---
#> 446: 185 estimated 133.4590
#> 447: 204 estimated 132.9087
#> 448: 189 estimated 132.4590
#> 449: 174 estimated 131.4416
#> 450: 179 estimated 130.2949
if (FALSE) { # \dontrun{
submitJobs(ids, reg = tmp)
} # }
# Group jobs into chunks with runtime < 1h
ids = est$runtimes[type == "estimated"]
ids[, chunk := binpack(runtime, 3600)]
#> Key: <job.id>
#> job.id type runtime chunk
#> <int> <fctr> <num> <int>
#> 1: 1 estimated 1104.0461 48
#> 2: 2 estimated 1088.0005 52
#> 3: 3 estimated 338.5989 37
#> 4: 4 estimated 318.2378 33
#> 5: 5 estimated 317.9085 70
#> ---
#> 446: 495 estimated 585.0171 15
#> 447: 496 estimated 1387.8280 17
#> 448: 497 estimated 1394.9184 13
#> 449: 498 estimated 618.9306 4
#> 450: 500 estimated 580.0825 17
print(ids)
#> Key: <job.id>
#> job.id type runtime chunk
#> <int> <fctr> <num> <int>
#> 1: 1 estimated 1104.0461 48
#> 2: 2 estimated 1088.0005 52
#> 3: 3 estimated 338.5989 37
#> 4: 4 estimated 318.2378 33
#> 5: 5 estimated 317.9085 70
#> ---
#> 446: 495 estimated 585.0171 15
#> 447: 496 estimated 1387.8280 17
#> 448: 497 estimated 1394.9184 13
#> 449: 498 estimated 618.9306 4
#> 450: 500 estimated 580.0825 17
print(ids[, list(runtime = sum(runtime)), by = chunk])
#> chunk runtime
#> <int> <num>
#> 1: 48 3486.521
#> 2: 52 3598.806
#> 3: 37 3595.674
#> 4: 33 3598.121
#> 5: 70 3493.071
#> 6: 49 3480.762
#> 7: 54 3589.919
#> 8: 51 3594.607
#> 9: 71 3491.029
#> 10: 53 3596.295
#> 11: 55 3585.848
#> 12: 68 3499.083
#> 13: 72 3488.600
#> 14: 56 3576.969
#> 15: 69 3495.780
#> 16: 73 3482.256
#> 17: 46 3518.845
#> 18: 50 3599.764
#> 19: 42 3577.707
#> 20: 64 3512.924
#> 21: 66 3509.266
#> 22: 43 3574.094
#> 23: 65 3511.738
#> 24: 67 3507.024
#> 25: 38 3599.959
#> 26: 35 3599.586
#> 27: 60 3535.969
#> 28: 47 3494.137
#> 29: 39 3598.195
#> 30: 36 3598.731
#> 31: 41 3587.235
#> 32: 58 3548.470
#> 33: 59 3543.101
#> 34: 40 3593.835
#> 35: 57 3564.195
#> 36: 44 3540.210
#> 37: 62 3521.418
#> 38: 61 3527.278
#> 39: 45 3538.939
#> 40: 63 3515.904
#> 41: 34 3599.823
#> 42: 27 3598.451
#> 43: 23 3599.621
#> 44: 24 3594.031
#> 45: 25 3597.491
#> 46: 29 3564.062
#> 47: 26 3582.342
#> 48: 75 3599.964
#> 49: 20 3517.893
#> 50: 74 3478.383
#> 51: 8 3593.415
#> 52: 31 3599.868
#> 53: 6 3592.027
#> 54: 7 3595.832
#> 55: 12 3558.434
#> 56: 5 3593.649
#> 57: 11 3572.833
#> 58: 10 3578.199
#> 59: 32 3599.610
#> 60: 83 3599.773
#> 61: 82 3599.642
#> 62: 76 3590.200
#> 63: 81 3472.972
#> 64: 3 3599.877
#> 65: 79 3491.740
#> 66: 80 3481.754
#> 67: 77 3522.931
#> 68: 86 3573.502
#> 69: 91 2163.640
#> 70: 89 3547.870
#> 71: 78 3507.950
#> 72: 88 3556.205
#> 73: 4 3596.032
#> 74: 90 3529.595
#> 75: 84 3591.850
#> 76: 85 3580.713
#> 77: 2 3599.972
#> 78: 87 3564.720
#> 79: 9 3587.892
#> 80: 22 3598.898
#> 81: 18 3572.611
#> 82: 16 3588.081
#> 83: 21 3599.813
#> 84: 19 3568.880
#> 85: 17 3582.218
#> 86: 30 3551.126
#> 87: 13 3598.089
#> 88: 28 3572.702
#> 89: 15 3598.504
#> 90: 14 3599.101
#> 91: 1 3470.710
#> chunk runtime
if (FALSE) { # \dontrun{
submitJobs(ids, reg = tmp)
} # }
# Group jobs into 10 chunks with similar runtime
ids = est$runtimes[type == "estimated"]
ids[, chunk := lpt(runtime, 10)]
#> Key: <job.id>
#> job.id type runtime chunk
#> <int> <fctr> <num> <int>
#> 1: 1 estimated 1104.0461 8
#> 2: 2 estimated 1088.0005 3
#> 3: 3 estimated 338.5989 8
#> 4: 4 estimated 318.2378 3
#> 5: 5 estimated 317.9085 1
#> ---
#> 446: 495 estimated 585.0171 8
#> 447: 496 estimated 1387.8280 3
#> 448: 497 estimated 1394.9184 7
#> 449: 498 estimated 618.9306 5
#> 450: 500 estimated 580.0825 5
print(ids[, list(runtime = sum(runtime)), by = chunk])
#> chunk runtime
#> <int> <num>
#> 1: 8 32219.58
#> 2: 3 32224.97
#> 3: 1 32278.64
#> 4: 5 32279.15
#> 5: 6 32219.58
#> 6: 9 32219.46
#> 7: 10 32295.54
#> 8: 4 32294.65
#> 9: 2 32296.33
#> 10: 7 32219.47