Experiment overview
| Setting | Value |
|---|
| Model for non-random steps | BOTORCH_MODULAR |
| Max. nr. evaluations | 1000 |
| Number random steps | 20 |
| Nr. of workers (parameter) | 20 |
| Main process memory (GB) | 20 |
| Worker memory (GB) | 40 |
Best VAL_ACC, max (total: 125)
| OO_Info_SLURM_JOB_ID | epochs | lr | batch_size | hidden_size | dropout | num_dense_layers | filter | num_conv_layers | VAL_ACC |
|---|
| 1217394.0 | 150 | 0.001 | 8 | 1853 | 0.5 | 1 | 80 | 7 | 65.7 |
Job Summary per Generation Node
| Generation Node | Total | ABANDONED | COMPLETED | RUNNING |
|---|
| SOBOL | 40 | 0 | 29 | 11 |
| BOTORCH_MODULAR | 130 | 34 | 96 | 0 |
Experiment parameters
| Name | Type | Lower bound | Upper bound | Type | Log Scale? |
|---|
| epochs | range | 20 | 150 | int | No |
| lr | range | 0.0001 | 0.001 | float | No |
| batch_size | range | 8 | 1024 | int | No |
| hidden_size | range | 8 | 4096 | int | No |
| dropout | range | 0 | 0.5 | float | No |
| num_dense_layers | range | 1 | 2 | int | No |
| filter | range | 4 | 80 | int | No |
| num_conv_layers | range | 4 | 7 | int | No |
Number of evaluations
| Failed |
Succeeded |
Running |
Total |
| 0 |
125 |
11 |
170 |
Result names and types
Last progressbar status
2025-11-05 07:00:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, 50 empty jobs (>= 50)
Git-Version
Commit: 86ec80a2d91b6b5a1d9c417f907f93a644d0ab96 (9060)
trial_index,submit_time,queue_time,worker_generator_uuid,start_time,end_time,run_time,program_string,exit_code,signal,hostname,OO_Info_SLURM_JOB_ID,arm_name,trial_status,generation_node,VAL_ACC,epochs,lr,batch_size,hidden_size,dropout,num_dense_layers,filter,num_conv_layers
0,1762269700,719,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270419,1762270960,541,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 50 --learning_rate 0.00028504498302936551 --batch_size 511 --hidden_size 492 --dropout 0.47350972890853881836 --num_dense_layers 2 --filter 38 --num_conv_layers 4,0,,c53,1216297,0_0,COMPLETED,SOBOL,36.78000000000000113686837721616,50,0.00028504498302936551352992911,511,492,0.473509728908538818359375,2,38,4
1,1762269700,714,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270414,1762271748,1334,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 123 --learning_rate 0.0005635558089241387 --batch_size 636 --hidden_size 2806 --dropout 0.03501166775822639465 --num_dense_layers 1 --filter 78 --num_conv_layers 6,0,,c75,1216295,1_0,COMPLETED,SOBOL,60.67000000000000170530256582424,123,0.000563555808924138699686490206,636,2806,0.0350116677582263946533203125,1,78,6
2,1762269700,714,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270414,1762273392,2978,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 104 --learning_rate 0.00052950892057269808 --batch_size 13 --hidden_size 1789 --dropout 0.16381247295066714287 --num_dense_layers 1 --filter 16 --num_conv_layers 7,0,,c137,1216293,2_0,COMPLETED,SOBOL,50.25,104,0.000529508920572698080914131324,13,1789,0.163812472950667142868041992188,1,16,7
3,,,,,,,,,,,,3_0,RUNNING,SOBOL,,64,0.00082183483112603433404230735,905,3569,0.344466467853635549545288085938,2,56,5
4,1762269700,714,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270414,1762271197,783,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 78 --learning_rate 0.00037567342175170776 --batch_size 755 --hidden_size 3771 --dropout 0.2774448692798614502 --num_dense_layers 2 --filter 6 --num_conv_layers 7,0,,c127,1216294,4_0,COMPLETED,SOBOL,26.98999999999999843680598132778,78,0.000375673421751707755505966801,755,3771,0.2774448692798614501953125,2,6,7
5,1762269700,678,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270378,1762271291,913,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 85 --learning_rate 0.00097561588278040291 --batch_size 376 --hidden_size 1459 --dropout 0.21398603357374668121 --num_dense_layers 1 --filter 42 --num_conv_layers 5,0,,c85,1216291,5_0,COMPLETED,SOBOL,59.28999999999999914734871708788,85,0.000975615882780402911798878218,376,1459,0.21398603357374668121337890625,1,42,5
6,1762269700,658,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270358,1762271787,1429,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 141 --learning_rate 0.00010308470716699958 --batch_size 785 --hidden_size 2475 --dropout 0.09324374934658408165 --num_dense_layers 1 --filter 32 --num_conv_layers 4,0,,c143,1216290,6_0,COMPLETED,SOBOL,44.03999999999999914734871708788,141,0.00010308470716699957760859041,785,2475,0.093243749346584081649780273438,1,32,4
7,,,,,,,,,,,,7_0,RUNNING,SOBOL,,35,0.000745461639296263493467686878,147,695,0.398921723943203687667846679688,2,69,6
8,,,,,,,,,,,,8_0,RUNNING,SOBOL,,20,0.000488729313760995838673839575,842,1200,0.004283570684492588043212890625,2,10,7
9,,,,,,,,,,,,9_0,RUNNING,SOBOL,,143,0.000866130609437823343645102003,208,4030,0.440823736600577831268310546875,1,48,5
10,1762269700,701,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270401,1762271429,1028,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 101 --learning_rate 0.00021287195198237894 --batch_size 697 --hidden_size 957 --dropout 0.3193478812463581562 --num_dense_layers 1 --filter 27 --num_conv_layers 4,0,,c26,1216292,10_0,COMPLETED,SOBOL,43.619999999999997442046151263639,101,0.000212871951982378942742207828,697,957,0.319347881246358156204223632812,1,27,4
11,1762269700,775,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270475,1762271329,854,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 75 --learning_rate 0.0006322137631475926 --batch_size 313 --hidden_size 2212 --dropout 0.12502485746517777443 --num_dense_layers 2 --filter 65 --num_conv_layers 6,0,,c95,1216298,11_0,COMPLETED,SOBOL,59.1499999999999985789145284798,75,0.000632213763147592604577096331,313,2212,0.125024857465177774429321289062,2,65,6
12,1762269700,714,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762270414,1762271215,801,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 57 --learning_rate 0.00017153613129630685 --batch_size 75 --hidden_size 3064 --dropout 0.24673401471227407455 --num_dense_layers 2 --filter 33 --num_conv_layers 4,0,,c61,1216296,12_0,COMPLETED,SOBOL,56.03000000000000113686837721616,57,0.000171536131296306853730171404,75,3064,0.246734014712274074554443359375,2,33,4
13,,,,,,,,,,,,13_0,RUNNING,SOBOL,,115,0.000673494063783437041381674248,963,234,0.308236879296600818634033203125,1,72,6
14,,,,,,,,,,,,14_0,RUNNING,SOBOL,,130,0.000419268285203725095920679333,450,3306,0.437279215548187494277954101562,1,21,7
15,,,,,,,,,,,,15_0,RUNNING,SOBOL,,39,0.000935536117944866488400912363,579,2052,0.117934149224311113357543945312,2,61,5
16,,,,,,,,,,,,16_0,RUNNING,SOBOL,,40,0.000393409935384988757988561359,246,2355,0.361060642171651124954223632812,1,79,7
17,,,,,,,,,,,,17_0,RUNNING,SOBOL,,131,0.000901741011068224961565176212,879,575,0.178452553693205118179321289062,2,40,5
18,,,,,,,,,,,,18_0,RUNNING,SOBOL,,113,0.000205414230749011053438202179,280,3636,0.049652718938887119293212890625,2,53,4
19,,,,,,,,,,,,19_0,RUNNING,SOBOL,,55,0.000699490324407815917441555431,663,1323,0.490102949552237987518310546875,1,14,6
20,1762273435,946,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274381,1762275108,727,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 69 --learning_rate 0.00024675031462684276 --batch_size 997 --hidden_size 1653 --dropout 0.38426541397348046303 --num_dense_layers 1 --filter 45 --num_conv_layers 4,0,,c32,1216534,20_0,COMPLETED,SOBOL,49.5,69,0.000246750314626842761397268466,997,1653,0.384265413973480463027954101562,1,45,4
21,1762273435,976,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274411,1762275591,1180,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 95 --learning_rate 0.00065820858376100663 --batch_size 109 --hidden_size 3434 --dropout 0.07663528295233845711 --num_dense_layers 2 --filter 7 --num_conv_layers 6,0,,c47,1216538,21_0,COMPLETED,SOBOL,39.28000000000000113686837721616,95,0.000658208583761006626490497329,109,3434,0.076635282952338457107543945312,2,7,6
22,1762273435,1010,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274445,1762276096,1651,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00046287124054506418 --batch_size 542 --hidden_size 372 --dropout 0.1973766004666686058 --num_dense_layers 2 --filter 67 --num_conv_layers 7,0,,c75,1216536,22_0,COMPLETED,SOBOL,56.369999999999997442046151263639,150,0.000462871240545064180620798977,542,372,0.197376600466668605804443359375,2,67,7
23,1762273435,976,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274411,1762274687,276,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.0008323340491391719 --batch_size 413 --hidden_size 2686 --dropout 0.26278952974826097488 --num_dense_layers 1 --filter 29 --num_conv_layers 5,0,,c84,1216535,23_0,COMPLETED,SOBOL,53.10999999999999943156581139192,26,0.000832334049139171901730838155,413,2686,0.262789529748260974884033203125,1,29,5
24,1762273435,976,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274411,1762274733,322,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 29 --learning_rate 0.00013259519804269075 --batch_size 608 --hidden_size 3170 --dropout 0.14161951793357729912 --num_dense_layers 1 --filter 49 --num_conv_layers 4,0,,c43,1216539,24_0,COMPLETED,SOBOL,49.049999999999997157829056959599,29,0.000132595198042690753763295231,608,3170,0.141619517933577299118041992188,1,49,4
25,1762273435,1036,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274471,1762275860,1389,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 135 --learning_rate 0.00076879318561404945 --batch_size 483 --hidden_size 1916 --dropout 0.3339884481392800808 --num_dense_layers 2 --filter 13 --num_conv_layers 6,0,,c143,1216545,25_0,COMPLETED,SOBOL,43.28000000000000113686837721616,135,0.000768793185614049449753959475,483,1916,0.333988448139280080795288085938,2,13,6
26,1762273435,1036,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274471,1762275472,1001,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 92 --learning_rate 0.00035242379512637851 --batch_size 932 --hidden_size 2944 --dropout 0.45546527020633220673 --num_dense_layers 2 --filter 61 --num_conv_layers 7,0,,c50,1216547,26_0,COMPLETED,SOBOL,53.700000000000002842170943040401,92,0.000352423795126378513326015351,932,2944,0.45546527020633220672607421875,2,61,7
27,1762273435,1006,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274441,1762275767,1326,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 85 --learning_rate 0.00094624222982674837 --batch_size 40 --hidden_size 114 --dropout 0.0208772607147693634 --num_dense_layers 1 --filter 25 --num_conv_layers 5,0,,c85,1216543,27_0,COMPLETED,SOBOL,52.450000000000002842170943040401,85,0.000946242229826748369216071755,40,114,0.0208772607147693634033203125,1,25,5
28,1762273435,948,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274383,1762275150,767,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 66 --learning_rate 0.00050626063002273442 --batch_size 348 --hidden_size 837 --dropout 0.1032773568294942379 --num_dense_layers 1 --filter 75 --num_conv_layers 7,0,,c32,1216533,28_0,COMPLETED,SOBOL,59.880000000000002557953848736361,66,0.000506260630022734416447183214,348,837,0.103277356829494237899780273438,1,75,7
29,1762273435,1006,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274441,1762275560,1119,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 106 --learning_rate 0.0007924608110450209 --batch_size 728 --hidden_size 2093 --dropout 0.42067026672884821892 --num_dense_layers 2 --filter 35 --num_conv_layers 5,0,,c42,1216544,29_0,COMPLETED,SOBOL,56,106,0.000792460811045020896078994177,728,2093,0.420670266728848218917846679688,2,35,5
30,1762273435,1006,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274441,1762275833,1392,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 120 --learning_rate 0.00031455682506784799 --batch_size 176 --hidden_size 1064 --dropout 0.29162696003913879395 --num_dense_layers 2 --filter 58 --num_conv_layers 4,0,,c119,1216541,30_0,COMPLETED,SOBOL,55.75,120,0.000314556825067847988051245034,176,1064,0.2916269600391387939453125,2,58,4
31,1762273436,1035,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274471,1762274974,503,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 48 --learning_rate 0.00058688697470352059 --batch_size 813 --hidden_size 3895 --dropout 0.23207818903028964996 --num_dense_layers 1 --filter 18 --num_conv_layers 6,0,,c114,1216546,31_0,COMPLETED,SOBOL,45.689999999999997726263245567679,48,0.000586886974703520591239991333,813,3895,0.23207818903028964996337890625,1,18,6
32,1762273435,1006,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274441,1762274923,482,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 45 --learning_rate 0.0005439870707690716 --batch_size 684 --hidden_size 2600 --dropout 0.39841875620186328888 --num_dense_layers 1 --filter 52 --num_conv_layers 6,0,,c114,1216542,32_0,COMPLETED,SOBOL,56.07000000000000028421709430404,45,0.000543987070769071600316590942,684,2600,0.39841875620186328887939453125,1,52,6
33,1762273437,1064,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274501,1762275760,1259,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 118 --learning_rate 0.00080735028050839903 --batch_size 304 --hidden_size 314 --dropout 0.07909660786390304565 --num_dense_layers 2 --filter 15 --num_conv_layers 4,0,,c141,1216549,33_0,COMPLETED,SOBOL,45.85999999999999943156581139192,118,0.00080735028050839903381724616,304,314,0.079096607863903045654296875,2,15,4
34,1762273435,976,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274411,1762275580,1169,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 108 --learning_rate 0.0002714324738830328 --batch_size 856 --hidden_size 3386 --dropout 0.20762202469632029533 --num_dense_layers 2 --filter 79 --num_conv_layers 5,0,,c48,1216537,34_0,COMPLETED,SOBOL,58.64000000000000056843418860808,108,0.000271432473883032802756953838,856,3386,0.207622024696320295333862304688,2,79,5
35,1762273435,1007,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274442,1762275227,785,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 68 --learning_rate 0.0005771756522357464 --batch_size 219 --hidden_size 1588 --dropout 0.26916306605562567711 --num_dense_layers 1 --filter 42 --num_conv_layers 7,0,,c152,1216540,35_0,COMPLETED,SOBOL,55.78999999999999914734871708788,68,0.000577175652235746398932558776,219,1588,0.269163066055625677108764648438,1,42,7
36,1762273454,1078,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274532,1762275429,897,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 82 --learning_rate 0.00011583894798532129 --batch_size 424 --hidden_size 1407 --dropout 0.35863555595278739929 --num_dense_layers 1 --filter 68 --num_conv_layers 5,0,,c151,1216548,36_0,COMPLETED,SOBOL,53.07000000000000028421709430404,82,0.000115838947985321289455354354,424,1407,0.3586355559527873992919921875,1,68,5
37,1762273454,1047,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274501,1762275453,952,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 90 --learning_rate 0.00073271387265995142 --batch_size 549 --hidden_size 3695 --dropout 0.16428967379033565521 --num_dense_layers 2 --filter 28 --num_conv_layers 7,0,,c133,1216550,37_0,COMPLETED,SOBOL,49.770000000000003126388037344441,90,0.000732713872659951424674840137,549,3695,0.16428967379033565521240234375,2,28,7
38,1762273459,1043,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274502,1762276278,1776,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 137 --learning_rate 0.00036026833700016141 --batch_size 100 --hidden_size 622 --dropout 0.04333449667319655418 --num_dense_layers 2 --filter 47 --num_conv_layers 6,0,,c69,1216552,38_0,COMPLETED,SOBOL,57.090000000000003410605131648481,137,0.000360268337000161406168813327,100,622,0.043334496673196554183959960938,2,47,6
39,1762273459,1042,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762274501,1762274837,336,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 32 --learning_rate 0.00099101370880380277 --batch_size 992 --hidden_size 2420 --dropout 0.4798365454189479351 --num_dense_layers 1 --filter 6 --num_conv_layers 4,0,,c88,1216551,39_0,COMPLETED,SOBOL,34.189999999999997726263245567679,32,0.000991013708803802767510759963,992,2420,0.479836545418947935104370117188,1,6,4
40,1762276376,357,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276733,1762278143,1410,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 107 --learning_rate 0.00097193650915942519 --batch_size 78 --hidden_size 2766 --dropout 0.36253459487226308289 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c86,1216651,40_0,COMPLETED,BOTORCH_MODULAR,62.14000000000000056843418860808,107,0.000971936509159425192361236867,78,2766,0.362534594872263082887542395838,1,42,4
41,1762276376,448,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276824,1762277870,1046,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 95 --learning_rate 0.00100000000000000002 --batch_size 320 --hidden_size 3282 --dropout 0.43233642997539467601 --num_dense_layers 1 --filter 43 --num_conv_layers 4,0,,c81,1216652,41_0,COMPLETED,BOTORCH_MODULAR,59.439999999999997726263245567679,95,0.001000000000000000020816681712,320,3282,0.432336429975394676006317240535,1,43,4
42,1762276376,448,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276824,1762280592,3768,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 96 --learning_rate 0.00093381643359516993 --batch_size 8 --hidden_size 2981 --dropout 0.36078627230770582424 --num_dense_layers 1 --filter 41 --num_conv_layers 4,0,,c30,1216653,42_0,COMPLETED,BOTORCH_MODULAR,63.42000000000000170530256582424,96,0.000933816433595169932864454854,8,2981,0.360786272307705824236023772755,1,41,4
43,1762276375,178,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276553,1762278513,1960,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 131 --learning_rate 0.00100000000000000002 --batch_size 56 --hidden_size 2190 --dropout 0.2764195414818511054 --num_dense_layers 2 --filter 46 --num_conv_layers 5,0,,c114,1216647,43_0,COMPLETED,BOTORCH_MODULAR,64.14000000000000056843418860808,131,0.001000000000000000020816681712,56,2190,0.276419541481851105402256507659,2,46,5
44,1762276376,478,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276854,1762281072,4218,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 108 --learning_rate 0.00096743627429108075 --batch_size 8 --hidden_size 2714 --dropout 0.34476631790779155295 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c134,1216654,44_0,COMPLETED,BOTORCH_MODULAR,63.689999999999997726263245567679,108,0.000967436274291080746008664182,8,2714,0.344766317907791552954677172238,1,42,4
45,1762276378,507,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276885,1762279406,2521,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 106 --learning_rate 0.0009566639581949359 --batch_size 17 --hidden_size 2745 --dropout 0.34422853974504563634 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c125,1216659,45_0,COMPLETED,BOTORCH_MODULAR,64.019999999999996020960679743439,106,0.000956663958194935897319521878,17,2745,0.344228539745045636344400463713,1,42,4
46,1762276375,118,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276493,1762277716,1223,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 110 --learning_rate 0.0009932633496190107 --batch_size 346 --hidden_size 1498 --dropout 0.11179140221767250307 --num_dense_layers 1 --filter 67 --num_conv_layers 6,0,,c114,1216645,46_0,COMPLETED,BOTORCH_MODULAR,62.729999999999996873611962655559,110,0.000993263349619010697116872066,346,1498,0.111791402217672503072165568483,1,67,6
47,1762276375,27,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276402,1762277401,999,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 94 --learning_rate 0.00093388465575099599 --batch_size 766 --hidden_size 1574 --dropout 0.1201497638208971902 --num_dense_layers 1 --filter 76 --num_conv_layers 5,0,,c38,1216643,47_0,COMPLETED,BOTORCH_MODULAR,62.92000000000000170530256582424,94,0.000933884655750995989510754303,766,1574,0.120149763820897190202963145111,1,76,5
48,1762276375,27,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276402,1762277566,1164,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 105 --learning_rate 0.00100000000000000002 --batch_size 276 --hidden_size 2773 --dropout 0.39959512118516965495 --num_dense_layers 1 --filter 43 --num_conv_layers 4,0,,c44,1216642,48_0,COMPLETED,BOTORCH_MODULAR,59.35999999999999943156581139192,105,0.001000000000000000020816681712,276,2773,0.399595121185169654953739382108,1,43,4
49,1762276376,478,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276854,1762278038,1184,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 104 --learning_rate 0.00099971944819541854 --batch_size 205 --hidden_size 2940 --dropout 0.39877672810681175086 --num_dense_layers 1 --filter 43 --num_conv_layers 4,0,,c34,1216657,49_0,COMPLETED,BOTORCH_MODULAR,59.46999999999999886313162278384,104,0.00099971944819541853737010495,205,2940,0.398776728106811750862448207045,1,43,4
50,1762276375,58,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276433,1762277651,1218,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 93 --learning_rate 0.00092168315112595362 --batch_size 78 --hidden_size 3452 --dropout 0.40794078891666579745 --num_dense_layers 1 --filter 41 --num_conv_layers 4,0,,c107,1216644,50_0,COMPLETED,BOTORCH_MODULAR,62.21000000000000085265128291212,93,0.000921683151125953620784458931,78,3452,0.407940788916665797447791419472,1,41,4
51,1762276375,208,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276583,1762278035,1452,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 107 --learning_rate 0.00098531540859647291 --batch_size 68 --hidden_size 2795 --dropout 0.36815487126989909772 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c31,1216649,51_0,COMPLETED,BOTORCH_MODULAR,62.729999999999996873611962655559,107,0.000985315408596472907740149161,68,2795,0.368154871269899097718791836087,1,42,4
52,1762276376,177,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276553,1762278093,1540,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 103 --learning_rate 0.00093353775518535242 --batch_size 51 --hidden_size 3093 --dropout 0.37615434376405254557 --num_dense_layers 1 --filter 41 --num_conv_layers 4,0,,c145,1216646,52_0,COMPLETED,BOTORCH_MODULAR,62.909999999999996589394868351519,103,0.000933537755185352420388145145,51,3093,0.37615434376405254557340640531,1,41,4
53,1762276376,207,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276583,1762278591,2008,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 93 --learning_rate 0.00089055556849197508 --batch_size 21 --hidden_size 3312 --dropout 0.39100878709329028871 --num_dense_layers 1 --filter 41 --num_conv_layers 4,0,,c43,1216648,53_0,COMPLETED,BOTORCH_MODULAR,64.090000000000003410605131648481,93,0.000890555568491975080118450148,21,3312,0.391008787093290288705560442395,1,41,4
54,1762276381,503,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276884,1762280736,3852,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 100 --learning_rate 0.00093595601688869514 --batch_size 8 --hidden_size 2906 --dropout 0.35930141924844222201 --num_dense_layers 1 --filter 41 --num_conv_layers 4,0,,c114,1216660,54_0,COMPLETED,BOTORCH_MODULAR,63.770000000000003126388037344441,100,0.000935956016888695135728937213,8,2906,0.35930141924844222200974286352,1,41,4
55,1762276375,479,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276854,1762278149,1295,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 112 --learning_rate 0.00093192730829618116 --batch_size 241 --hidden_size 1360 --dropout 0.06722034454425859784 --num_dense_layers 1 --filter 67 --num_conv_layers 6,0,,c95,1216656,55_0,COMPLETED,BOTORCH_MODULAR,61.96999999999999886313162278384,112,0.00093192730829618115623996788,241,1360,0.067220344544258597840880042895,1,67,6
56,1762276377,477,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276854,1762277953,1099,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 99 --learning_rate 0.00100000000000000002 --batch_size 281 --hidden_size 3052 --dropout 0.41011154129072091568 --num_dense_layers 1 --filter 43 --num_conv_layers 4,0,,c13,1216658,56_0,COMPLETED,BOTORCH_MODULAR,59.57999999999999829469743417576,99,0.001000000000000000020816681712,281,3052,0.410111541290720915675649393961,1,43,4
57,1762276377,296,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276673,1762281282,4609,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 109 --learning_rate 0.00097046910959242244 --batch_size 8 --hidden_size 2687 --dropout 0.33868772375713879219 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c50,1216650,57_0,COMPLETED,BOTORCH_MODULAR,63.770000000000003126388037344441,109,0.000970469109592422439145020796,8,2687,0.338687723757138792191057063974,1,42,4
58,1762276377,477,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276854,1762278046,1192,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 105 --learning_rate 0.00100000000000000002 --batch_size 213 --hidden_size 2827 --dropout 0.39005370435901420834 --num_dense_layers 1 --filter 43 --num_conv_layers 4,0,,c131,1216655,58_0,COMPLETED,BOTORCH_MODULAR,59.689999999999997726263245567679,105,0.001000000000000000020816681712,213,2827,0.390053704359014208336020601564,1,43,4
59,1762276381,509,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762276890,1762281088,4198,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 108 --learning_rate 0.00095128909150736355 --batch_size 8 --hidden_size 2745 --dropout 0.34983236734869266327 --num_dense_layers 1 --filter 42 --num_conv_layers 4,0,,c38,1216661,59_0,COMPLETED,BOTORCH_MODULAR,63.340000000000003410605131648481,108,0.000951289091507363554663101457,8,2745,0.34983236734869266326697356817,1,42,4
60,1762281418,157,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281575,1762283146,1571,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 662 --hidden_size 1217 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c29,1216839,60_0,COMPLETED,BOTORCH_MODULAR,57.549999999999997157829056959599,150,0.001000000000000000020816681712,662,1217,0,2,60,5
61,1762281417,9,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281426,1762283030,1604,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 661 --hidden_size 559 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c46,1216824,61_0,COMPLETED,BOTORCH_MODULAR,58.25,150,0.001000000000000000020816681712,661,559,0,2,60,5
62,1762281417,8,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281425,1762282975,1550,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 661 --hidden_size 367 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c84,1216823,62_0,COMPLETED,BOTORCH_MODULAR,58.240000000000001989519660128281,150,0.001000000000000000020816681712,661,367,0,2,60,5
63,1762281418,72,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281490,1762283070,1580,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 652 --hidden_size 1355 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c32,1216833,63_0,COMPLETED,BOTORCH_MODULAR,56.630000000000002557953848736361,150,0.001000000000000000020816681712,652,1355,0,2,60,5
64,1762281418,37,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281455,1762283014,1559,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 652 --hidden_size 1215 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c50,1216828,64_0,COMPLETED,BOTORCH_MODULAR,57.17999999999999971578290569596,150,0.001000000000000000020816681712,652,1215,0,2,60,5
65,1762281418,98,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281516,1762283075,1559,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 650 --hidden_size 479 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c32,1216836,65_0,COMPLETED,BOTORCH_MODULAR,58.049999999999997157829056959599,150,0.001000000000000000020816681712,650,479,0,2,60,5
66,1762281418,37,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281455,1762283038,1583,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 661 --hidden_size 1070 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c112,1216827,66_0,COMPLETED,BOTORCH_MODULAR,57.299999999999997157829056959599,150,0.001000000000000000020816681712,661,1070,0,2,60,5
67,1762281418,36,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281454,1762283026,1572,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 654 --hidden_size 1317 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c46,1216829,67_0,COMPLETED,BOTORCH_MODULAR,57.479999999999996873611962655559,150,0.001000000000000000020816681712,654,1317,0,2,60,5
68,1762281418,98,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281516,1762283082,1566,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 658 --hidden_size 508 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c151,1216834,68_0,COMPLETED,BOTORCH_MODULAR,59.119999999999997442046151263639,150,0.001000000000000000020816681712,658,508,0,2,60,5
69,1762281418,7,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281425,1762283014,1589,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 655 --hidden_size 787 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c46,1216825,69_0,COMPLETED,BOTORCH_MODULAR,57.67000000000000170530256582424,150,0.001000000000000000020816681712,655,787,0,2,60,5
70,1762281418,189,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281607,1762283212,1605,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 658 --hidden_size 1560 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c69,1216840,70_0,COMPLETED,BOTORCH_MODULAR,57.57999999999999829469743417576,150,0.001000000000000000020816681712,658,1560,0,2,60,5
71,1762281418,37,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281455,1762283020,1565,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 651 --hidden_size 821 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c146,1216826,71_0,COMPLETED,BOTORCH_MODULAR,58.009999999999998010480339871719,150,0.001000000000000000020816681712,651,821,0,2,60,5
72,1762281418,99,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281517,1762283095,1578,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 659 --hidden_size 1612 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c146,1216835,72_0,COMPLETED,BOTORCH_MODULAR,57.57000000000000028421709430404,150,0.001000000000000000020816681712,659,1612,0,2,60,5
73,1762281418,157,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281575,1762283155,1580,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 650 --hidden_size 812 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c43,1216838,73_0,COMPLETED,BOTORCH_MODULAR,57.520000000000003126388037344441,150,0.001000000000000000020816681712,650,812,0,2,60,5
74,1762281418,157,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281575,1762283143,1568,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 658 --hidden_size 1068 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c131,1216837,74_0,COMPLETED,BOTORCH_MODULAR,57.32999999999999829469743417576,150,0.001000000000000000020816681712,658,1068,0,2,60,5
75,1762281418,189,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281607,1762283164,1557,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 656 --hidden_size 1381 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c40,1216841,75_0,COMPLETED,BOTORCH_MODULAR,57.200000000000002842170943040401,150,0.001000000000000000020816681712,656,1381,0,2,60,5
76,1762281419,36,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281455,1762283044,1589,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 656 --hidden_size 667 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c41,1216830,76_0,COMPLETED,BOTORCH_MODULAR,58.020000000000003126388037344441,150,0.001000000000000000020816681712,656,667,0,2,60,5
77,1762281419,66,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281485,1762283057,1572,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 657 --hidden_size 609 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c90,1216831,77_0,COMPLETED,BOTORCH_MODULAR,58.159999999999996589394868351519,150,0.001000000000000000020816681712,657,609,0,2,60,5
78,1762281419,66,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281485,1762283075,1590,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 651 --hidden_size 1074 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c32,1216832,78_0,COMPLETED,BOTORCH_MODULAR,57.71000000000000085265128291212,150,0.001000000000000000020816681712,651,1074,0,2,60,5
79,1762281424,183,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762281607,1762283176,1569,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 651 --hidden_size 829 --dropout 0 --num_dense_layers 2 --filter 60 --num_conv_layers 5,0,,c26,1216842,79_0,COMPLETED,BOTORCH_MODULAR,58.21999999999999886313162278384,150,0.001000000000000000020816681712,651,829,0,2,60,5
80,1762283301,19,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283320,1762283551,231,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00098472358770261494 --batch_size 572 --hidden_size 8 --dropout 0.02665267332559044175 --num_dense_layers 1 --filter 71 --num_conv_layers 7,0,,c113,1216905,80_0,COMPLETED,BOTORCH_MODULAR,2.719999999999999751310042483965,20,0.000984723587702614937613998514,572,8,0.026652673325590441749666226201,1,71,7
81,1762283301,19,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283320,1762287276,3956,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 83 --learning_rate 0.00080195600201239745 --batch_size 8 --hidden_size 3622 --dropout 0.2821492795914870011 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c46,1216907,81_0,COMPLETED,BOTORCH_MODULAR,61.909999999999996589394868351519,83,0.00080195600201239745485198851,8,3622,0.282149279591487001095373443604,2,49,4
82,1762283302,48,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283350,1762286980,3630,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 81 --learning_rate 0.00081408389449048955 --batch_size 8 --hidden_size 3047 --dropout 0.28346141901231203253 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c38,1216914,82_0,COMPLETED,BOTORCH_MODULAR,61.759999999999998010480339871719,81,0.000814083894490489549770895206,8,3047,0.283461419012312032528200234083,2,49,4
83,1762283301,19,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283320,1762288083,4763,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 99 --learning_rate 0.00079291266047019351 --batch_size 8 --hidden_size 4096 --dropout 0.29013647236780598648 --num_dense_layers 2 --filter 48 --num_conv_layers 4,0,,c131,1216904,83_0,COMPLETED,BOTORCH_MODULAR,61.60999999999999943156581139192,99,0.000792912660470193510903191836,8,4096,0.290136472367805986483091373884,2,48,4
84,1762283301,79,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283380,1762284730,1350,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 48 --learning_rate 0.00084618140293645839 --batch_size 16 --hidden_size 1894 --dropout 0.34159150816902594805 --num_dense_layers 2 --filter 51 --num_conv_layers 4,0,,c60,1216919,84_0,COMPLETED,BOTORCH_MODULAR,62.07999999999999829469743417576,48,0.000846181402936458390909302274,16,1894,0.341591508169025948049579710641,2,51,4
85,1762283301,50,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283351,1762287521,4170,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 89 --learning_rate 0.0008067566470074158 --batch_size 8 --hidden_size 3308 --dropout 0.2977463388109030662 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c41,1216910,85_0,COMPLETED,BOTORCH_MODULAR,62.67999999999999971578290569596,89,0.00080675664700741579575538065,8,3308,0.297746338810903066196544841659,2,49,4
86,1762283302,78,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283380,1762284876,1496,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 72 --learning_rate 0.00080942588557131464 --batch_size 30 --hidden_size 3526 --dropout 0.21503110205811204469 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c93,1216917,86_0,COMPLETED,BOTORCH_MODULAR,64.439999999999997726263245567679,72,0.000809425885571314640033746546,30,3526,0.215031102058112044694837550196,2,50,4
87,1762283301,20,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283321,1762287202,3881,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 87 --learning_rate 0.00081431974217619711 --batch_size 8 --hidden_size 2968 --dropout 0.25798113699318647729 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c46,1216906,87_0,COMPLETED,BOTORCH_MODULAR,61.71999999999999886313162278384,87,0.000814319742176197107312585199,8,2968,0.257981136993186477290862512746,2,49,4
88,1762283302,78,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283380,1762287552,4172,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 94 --learning_rate 0.00081627620784967606 --batch_size 8 --hidden_size 2623 --dropout 0.29449238771592939345 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c111,1216916,88_0,COMPLETED,BOTORCH_MODULAR,61.689999999999997726263245567679,94,0.000816276207849676057985710553,8,2623,0.294492387715929393454672435837,2,50,4
89,1762283302,48,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283350,1762287815,4465,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 93 --learning_rate 0.00079173874786372099 --batch_size 8 --hidden_size 4096 --dropout 0.26348919296241896104 --num_dense_layers 2 --filter 48 --num_conv_layers 4,0,,c38,1216913,89_0,COMPLETED,BOTORCH_MODULAR,61.509999999999998010480339871719,93,0.000791738747863720985012414566,8,4096,0.263489192962418961041493048469,2,48,4
90,1762283302,78,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283380,1762288021,4641,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 91 --learning_rate 0.00080672650513277004 --batch_size 8 --hidden_size 3153 --dropout 0.28923444301320988536 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c149,1216915,90_0,COMPLETED,BOTORCH_MODULAR,61.310000000000002273736754432321,91,0.000806726505132770044942391863,8,3153,0.289234443013209885364744877734,2,49,4
91,1762283302,20,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283322,1762285819,2497,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 119 --learning_rate 0.0008081480872830333 --batch_size 29 --hidden_size 3597 --dropout 0.10578414898974008995 --num_dense_layers 2 --filter 54 --num_conv_layers 4,0,,c46,1216908,91_0,COMPLETED,BOTORCH_MODULAR,62.630000000000002557953848736361,119,0.00080814808728303330100095625,29,3597,0.105784148989740089952249491034,2,54,4
92,1762283302,78,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283380,1762284335,955,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 57 --learning_rate 0.00081055736239002277 --batch_size 49 --hidden_size 4042 --dropout 0 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c26,1216920,92_0,COMPLETED,BOTORCH_MODULAR,56.03999999999999914734871708788,57,0.000810557362390022773622477725,49,4042,0,2,50,4
93,1762283302,50,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283352,1762285254,1902,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 73 --learning_rate 0.00083296863172916953 --batch_size 16 --hidden_size 2242 --dropout 0.29967751322165359085 --num_dense_layers 2 --filter 52 --num_conv_layers 4,0,,c41,1216911,93_0,COMPLETED,BOTORCH_MODULAR,64.730000000000003979039320256561,73,0.00083296863172916952959556891,16,2242,0.299677513221653590846926817903,2,52,4
94,1762283302,109,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283411,1762285643,2232,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 46 --learning_rate 0.00079776201031476481 --batch_size 8 --hidden_size 4038 --dropout 0.27220057516375106843 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c133,1216921,94_0,COMPLETED,BOTORCH_MODULAR,58.770000000000003126388037344441,46,0.000797762010314764812880794498,8,4038,0.272200575163751068430428858846,2,50,4
95,1762283302,48,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283350,1762287747,4397,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 89 --learning_rate 0.00081819835557899422 --batch_size 8 --hidden_size 2594 --dropout 0.28567340466324725456 --num_dense_layers 2 --filter 49 --num_conv_layers 4,0,,c49,1216909,95_0,COMPLETED,BOTORCH_MODULAR,62.35999999999999943156581139192,89,0.000818198355578994215853150251,8,2594,0.285673404663247254564595323245,2,49,4
96,1762283303,47,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283350,1762287742,4392,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 92 --learning_rate 0.00079114498434115993 --batch_size 8 --hidden_size 4072 --dropout 0.27567055170849658063 --num_dense_layers 2 --filter 48 --num_conv_layers 4,0,,c41,1216912,96_0,COMPLETED,BOTORCH_MODULAR,62.090000000000003410605131648481,92,0.000791144984341159932667020271,8,4072,0.275670551708496580634033534807,2,48,4
97,1762283303,78,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283381,1762284154,773,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.00080665362446011258 --batch_size 14 --hidden_size 3070 --dropout 0.22845031862677039891 --num_dense_layers 2 --filter 48 --num_conv_layers 4,0,,c69,1216918,97_0,COMPLETED,BOTORCH_MODULAR,54.10999999999999943156581139192,26,0.00080665362446011258309996883,14,3070,0.228450318626770398910963422168,2,48,4
98,1762283308,102,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283410,1762287693,4283,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 89 --learning_rate 0.00079013604065808073 --batch_size 8 --hidden_size 4069 --dropout 0.26202594282015051474 --num_dense_layers 2 --filter 48 --num_conv_layers 4,0,,c42,1216922,98_0,COMPLETED,BOTORCH_MODULAR,61.990000000000001989519660128281,89,0.00079013604065808072836329945,8,4069,0.262025942820150514744881320439,2,48,4
99,1762283308,132,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762283440,1762284956,1516,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 34 --learning_rate 0.00081770651889285714 --batch_size 8 --hidden_size 2813 --dropout 0.24663688542483019894 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c137,1216923,99_0,COMPLETED,BOTORCH_MODULAR,57.090000000000003410605131648481,34,0.000817706518892857142644103874,8,2813,0.246636885424830198942913739302,2,50,4
100,1762288170,113,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288283,1762289021,738,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 62 --learning_rate 0.00089096559571914303 --batch_size 147 --hidden_size 725 --dropout 0.30786390222464932176 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c129,1217119,100_0,COMPLETED,BOTORCH_MODULAR,59.42000000000000170530256582424,62,0.000890965595719143033277831467,147,725,0.307863902224649321759386566555,1,60,4
101,1762288170,143,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288313,1762290839,2526,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.0008909299670202905 --batch_size 8 --hidden_size 8 --dropout 0.30356708337359211702 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c53,1217121,101_0,COMPLETED,BOTORCH_MODULAR,18.48000000000000042632564145606,63,0.000890929967020290497319023881,8,8,0.303567083373592117023775927009,1,60,4
102,1762288170,53,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288223,1762290668,2445,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.0008894262828023433 --batch_size 8 --hidden_size 8 --dropout 0.30300871254234684748 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c36,1217113,102_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000889426282802343302381908874,8,8,0.303008712542346847484964200703,1,60,4
103,1762288171,81,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288252,1762290636,2384,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 62 --learning_rate 0.00088353380092808995 --batch_size 8 --hidden_size 837 --dropout 0.30411483369302538815 --num_dense_layers 1 --filter 61 --num_conv_layers 4,0,,c70,1217117,103_0,COMPLETED,BOTORCH_MODULAR,63.82999999999999829469743417576,62,0.00088353380092808994888259333,8,837,0.304114833693025388150488197425,1,61,4
104,1762288170,173,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288343,1762290627,2284,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00088968993289935091 --batch_size 8 --hidden_size 8 --dropout 0.30366111501296422226 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c127,1217122,104_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000889689932899350908145075234,8,8,0.303661115012964222259483904054,1,60,4
105,1762288170,173,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288343,1762290700,2357,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089037123166621295 --batch_size 8 --hidden_size 8 --dropout 0.30409840691547274094 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c21,1217125,105_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000890371231666212950214833732,8,8,0.304098406915472740941197571374,1,60,4
106,1762288170,83,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288253,1762290515,2262,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00088990314770653108 --batch_size 8 --hidden_size 8 --dropout 0.3031598750931604469 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c38,1217118,106_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000889903147706531082172798364,8,8,0.303159875093160446901663362951,1,60,4
107,1762288170,28,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288198,1762288950,752,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 67 --learning_rate 0.00087531857290229931 --batch_size 286 --hidden_size 259 --dropout 0.30552832075838098902 --num_dense_layers 1 --filter 66 --num_conv_layers 4,0,,c76,1217110,107_0,COMPLETED,BOTORCH_MODULAR,47.71999999999999886313162278384,67,0.000875318572902299314730800717,286,259,0.305528320758380989019542539609,1,66,4
108,1762288170,83,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288253,1762290498,2245,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089020053772497973 --batch_size 8 --hidden_size 8 --dropout 0.30373023079580518946 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c125,1217115,108_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000890200537724979731706020925,8,8,0.303730230795805189458747008757,1,60,4
109,1762288170,82,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288252,1762290600,2348,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00088598655082246887 --batch_size 8 --hidden_size 861 --dropout 0.30128296015341493597 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c134,1217114,109_0,COMPLETED,BOTORCH_MODULAR,63.689999999999997726263245567679,63,0.000885986550822468865024061913,8,861,0.301282960153414935966509347054,1,60,4
110,1762288170,83,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288253,1762290524,2271,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089199896929136947 --batch_size 8 --hidden_size 8 --dropout 0.30372598432661651602 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c79,1217116,110_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000891998969291369465071939171,8,8,0.303725984326616516018049196646,1,60,4
111,1762288170,22,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288192,1762288975,783,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 65 --learning_rate 0.00088866903543670308 --batch_size 131 --hidden_size 1182 --dropout 0.30152347856655065117 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c13,1217112,111_0,COMPLETED,BOTORCH_MODULAR,61.64000000000000056843418860808,65,0.000888669035436703076864806139,131,1182,0.301523478566550651169819730057,1,60,4
112,1762288170,113,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288283,1762289372,1089,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 64 --learning_rate 0.00088758259097303444 --batch_size 32 --hidden_size 417 --dropout 0.3013139484529960832 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c13,1217120,112_0,COMPLETED,BOTORCH_MODULAR,63.32000000000000028421709430404,64,0.000887582590973034438880384656,32,417,0.301313948452996083204880051198,1,60,4
113,1762288170,22,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288192,1762290478,2286,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089146799599996301 --batch_size 8 --hidden_size 8 --dropout 0.30403329269778450739 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c41,1217111,113_0,COMPLETED,BOTORCH_MODULAR,1,63,0.00089146799599996301433080248,8,8,0.304033292697784507385705410343,1,60,4
114,1762288170,174,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288344,1762290590,2246,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089235140076309534 --batch_size 8 --hidden_size 8 --dropout 0.3043882454623407785 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c124,1217123,114_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000892351400763095338984631777,8,8,0.304388245462340778502152716101,1,60,4
115,1762288170,174,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288344,1762290609,2265,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089233341366565732 --batch_size 8 --hidden_size 8 --dropout 0.30403429801106590658 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c122,1217124,115_0,COMPLETED,BOTORCH_MODULAR,20.48999999999999843680598132778,63,0.000892333413665657320702762068,8,8,0.304034298011065906575112194332,1,60,4
116,1762288181,162,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288343,1762289080,737,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 65 --learning_rate 0.00088750297939424829 --batch_size 246 --hidden_size 1467 --dropout 0.30323422122909665655 --num_dense_layers 1 --filter 61 --num_conv_layers 4,0,,c13,1217126,116_0,COMPLETED,BOTORCH_MODULAR,60.39000000000000056843418860808,65,0.000887502979394248292868696559,246,1467,0.303234221229096656546886379147,1,61,4
117,1762288181,252,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288433,1762290816,2383,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089386047772392033 --batch_size 8 --hidden_size 8 --dropout 0.30411202444068952122 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c131,1217127,117_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000893860477723920328688156367,8,8,0.304112024440689521220804181212,1,60,4
118,1762288182,251,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288433,1762290736,2303,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00089153161540035859 --batch_size 8 --hidden_size 8 --dropout 0.30389553126113261206 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c106,1217128,118_0,COMPLETED,BOTORCH_MODULAR,1,63,0.000891531615400358587800178078,8,8,0.303895531261132612055320123545,1,60,4
119,1762288187,246,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762288433,1762290744,2311,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 63 --learning_rate 0.00088894988720021324 --batch_size 8 --hidden_size 11 --dropout 0.30134712995305357186 --num_dense_layers 1 --filter 60 --num_conv_layers 4,0,,c45,1217129,119_0,COMPLETED,BOTORCH_MODULAR,22.14000000000000056843418860808,63,0.000888949887200213241175705825,8,11,0.301347129953053571860266401927,1,60,4
120,1762291025,62,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762291087,1762296846,5759,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 566 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c87,1217233,120_0,COMPLETED,BOTORCH_MODULAR,65.67000000000000170530256582424,150,0.001000000000000000020816681712,8,565,0,1,80,4
121,1762291025,92,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762291117,1762296941,5824,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 567 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c111,1217234,121_0,COMPLETED,BOTORCH_MODULAR,62.92000000000000170530256582424,150,0.001000000000000000020816681712,8,564,0,1,80,4
122,1762291025,92,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762291117,1762296823,5706,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 565 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c35,1217235,122_0,COMPLETED,BOTORCH_MODULAR,64.489999999999994884092302527279,150,0.001000000000000000020816681712,8,566,0,1,80,4
123,,,,,,,,,,,,122_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,566,0,1,80,4
124,,,,,,,,,,,,120_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,565,0,1,80,4
125,,,,,,,,,,,,122_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,566,0,1,80,4
126,,,,,,,,,,,,120_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,565,0,1,80,4
127,,,,,,,,,,,,122_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,566,0,1,80,4
128,1762291025,152,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762291177,1762297869,6692,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 567 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c48,1217237,128_0,COMPLETED,BOTORCH_MODULAR,64.64000000000000056843418860808,150,0.001000000000000000020816681712,8,567,0,1,80,4
129,,,,,,,,,,,,120_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,565,0,1,80,4
130,1762291025,122,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762291147,1762296867,5720,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 568 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c119,1217236,130_0,COMPLETED,BOTORCH_MODULAR,64.459999999999993747223925311118,150,0.001000000000000000020816681712,8,568,0,1,80,4
131,,,,,,,,,,,,120_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,565,0,1,80,4
132,,,,,,,,,,,,121_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,564,0,1,80,4
133,,,,,,,,,,,,120_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,565,0,1,80,4
134,,,,,,,,,,,,128_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,567,0,1,80,4
135,,,,,,,,,,,,122_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,566,0,1,80,4
136,1762298079,303,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762298382,1762305329,6947,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3158 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c139,1217394,136_0,COMPLETED,BOTORCH_MODULAR,65.700000000000002842170943040401,150,0.001000000000000000020816681712,8,1853,0.5,1,80,7
137,1762298079,273,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762298352,1762305275,6923,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3123 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c76,1217390,137_0,COMPLETED,BOTORCH_MODULAR,65.150000000000005684341886080801,150,0.001000000000000000020816681712,8,3162,0.5,1,80,7
138,1762298079,273,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762298352,1762305233,6881,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3175 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c70,1217391,138_0,COMPLETED,BOTORCH_MODULAR,65.189999999999997726263245567679,150,0.001000000000000000020816681712,8,3172,0.5,1,80,7
139,1762298079,274,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762298353,1762305571,7218,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3162 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c13,1217393,139_0,COMPLETED,BOTORCH_MODULAR,64.64000000000000056843418860808,150,0.001000000000000000020816681712,8,3137,0.5,1,80,7
140,1762298079,273,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762298352,1762305217,6865,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3152 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c31,1217392,140_0,COMPLETED,BOTORCH_MODULAR,65.010000000000005115907697472721,150,0.001000000000000000020816681712,8,3192,0.5,1,80,7
141,,,,,,,,,,,,141_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2610,0.5,1,80,7
142,,,,,,,,,,,,142_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,3531,0.5,1,63,7
143,,,,,,,,,,,,143_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,3511,0.5,1,63,7
144,,,,,,,,,,,,144_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,3506,0.5,1,62,7
145,,,,,,,,,,,,145_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2627,0.5,1,80,4
146,,,,,,,,,,,,146_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,3676,0.5,1,80,7
147,1762305745,15,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762305760,1762312801,7041,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3540 --dropout 0.5 --num_dense_layers 1 --filter 65 --num_conv_layers 7,0,,c38,1217542,147_0,COMPLETED,BOTORCH_MODULAR,64.260000000000005115907697472721,150,0.001000000000000000020816681712,8,2600,0.5,1,80,7
148,,,,,,,,,,,,148_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2566,0.5,1,80,4
149,1762305745,15,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762305760,1762312040,6280,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2581 --dropout 0.5 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c68,1217541,149_0,COMPLETED,BOTORCH_MODULAR,63.689999999999997726263245567679,150,0.001000000000000000020816681712,8,2581,0.5,1,80,4
150,,,,,,,,,,,,150_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2548,0.5,1,69,7
151,,,,,,,,,,,,151_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2597,0.5,1,80,4
152,,,,,,,,,,,,152_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,56,7
153,,,,,,,,,,,,152_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,56,7
154,,,,,,,,,,,,154_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,75,7
155,,,,,,,,,,,,155_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,54,7
156,,,,,,,,,,,,156_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,59,7
157,,,,,,,,,,,,157_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,8,0,1,4,7
158,,,,,,,,,,,,158_0,ABANDONED,BOTORCH_MODULAR,,20,0.001000000000000000020816681712,8,1748,0,1,80,7
159,,,,,,,,,,,,159_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1015,4096,0.5,1,56,7
160,,,,,,,,,,,,160_0,ABANDONED,BOTORCH_MODULAR,,150,0.000100000000000000004792173602,1024,4096,0.5,1,52,4
161,1762312986,11,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762312997,1762314559,1562,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.0001 --batch_size 1024 --hidden_size 4096 --dropout 0.5 --num_dense_layers 1 --filter 62 --num_conv_layers 7,0,,c152,1217626,161_0,COMPLETED,BOTORCH_MODULAR,42.310000000000002273736754432321,150,0.001000000000000000020816681712,1024,4096,0.5,1,64,7
162,1762312986,11,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762312997,1762313822,825,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 1751 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 4,0,,c152,1217625,157_0,COMPLETED,BOTORCH_MODULAR,53.240000000000001989519660128281,150,0.001000000000000000020816681712,8,8,0,1,4,7
163,1762312988,9,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762312997,1762313909,912,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 1724 --dropout 0 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c149,1217627,163_0,COMPLETED,BOTORCH_MODULAR,54.979999999999996873611962655559,20,0.001000000000000000020816681712,8,1724,0,1,80,7
164,,,,,,,,,,,,164_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2091,0.5,1,59,4
165,,,,,,,,,,,,165_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2103,0.5,1,60,4
166,,,,,,,,,,,,166_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,1024,1995,0.5,1,74,4
167,,,,,,,,,,,,167_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,598,0.5,1,70,7
168,,,,,,,,,,,,164_0,ABANDONED,BOTORCH_MODULAR,,150,0.001000000000000000020816681712,8,2091,0.5,1,59,4
169,1762314782,3,e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3,1762314785,1762316871,2086,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 150 --learning_rate 0.00100000000000000002 --batch_size 68 --hidden_size 2073 --dropout 0.5 --num_dense_layers 1 --filter 59 --num_conv_layers 4,0,,c152,1217650,169_0,COMPLETED,BOTORCH_MODULAR,65.17000000000000170530256582424,150,0.001000000000000000020816681712,227,2081,0.5,1,61,4
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
get_ax_client_trial: trial_index 143 failed
get_ax_client_trial: trial_index 141 failed
get_ax_client_trial: trial_index 144 failed
execute_evaluation: _trial was not in execute_evaluation for params [143, {'epochs': 150, 'lr': 0.001, 'batch_size': 8, 'hidden_size': 3140, 'dropout': 0.5, 'num_dense_layers': 1, 'filter': 80, 'num_conv_layers': 7}, 8, 'systematic']
execute_evaluation: _trial was not in execute_evaluation for params [141, {'epochs': 150, 'lr': 0.001, 'batch_size': 8, 'hidden_size': 3153, 'dropout': 0.5, 'num_dense_layers': 1, 'filter': 80, 'num_conv_layers': 7}, 6, 'systematic']
execute_evaluation: _trial was not in execute_evaluation for params [144, {'epochs': 150, 'lr': 0.001, 'batch_size': 8, 'hidden_size': 3155, 'dropout': 0.5, 'num_dense_layers': 1, 'filter': 80, 'num_conv_layers': 7}, 9, 'systematic']
get_ax_client_trial: trial_index 148 failed
get_ax_client_trial: trial_index 142 failed
execute_evaluation: _trial was not in execute_evaluation for params [148, {'epochs': 150, 'lr': 0.001, 'batch_size': 8, 'hidden_size': 3151, 'dropout': 0.5, 'num_dense_layers': 1, 'filter': 80, 'num_conv_layers': 7}, 12, 'systematic']
get_ax_client_trial: trial_index 146 failed
execute_evaluation: _trial was not in execute_evaluation for params [142, {'epochs': 150, 'lr': 0.001, 'batch_size': 8, 'hidden_size': 3176, 'dropout': 0.5, 'num_dense_layers': 1, 'filter': 80, 'num_conv_layers': 7}, 7, 'systematic']
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It seems like the search space was exhausted. You were able to get 12% of the jobs you requested (got: 125, submitted: 125, failed: 0, requested: 1000) after main ran
To cancel, press CTRL c, then run 'scancel 1216265'
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[WARNING 11-04 16:20:48] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
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Run-UUID: dd580c09-c757-4b09-b23e-a638fad1be6e
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⠋ Writing worker creation log...
omniopt --partition=alpha --experiment_name=mnist_normalized_runtime_mono --mem_gb=40 --time=2880 --worker_timeout=120 --max_eval=1000 --num_parallel_jobs=20 --gpus=1 --num_random_steps=20 --follow --live_share --send_anonymized_usage_stats --result_names VAL_ACC=max --run_program=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdfcmF0ZSAlbHIgLS1iYXRjaF9zaXplICViYXRjaF9zaXplIC0taGlkZGVuX3NpemUgJWhpZGRlbl9zaXplIC0tZHJvcG91dCAlZHJvcG91dCAtLW51bV9kZW5zZV9sYXllcnMgJW51bV9kZW5zZV9sYXllcnMgLS1maWx0ZXIgJShmaWx0ZXIpIC0tbnVtX2NvbnZfbGF5ZXJzICUobnVtX2NvbnZfbGF5ZXJzKQo= --cpus_per_task=1 --nodes_per_job=1 --revert_to_random_when_seemingly_exhausted --model=BOTORCH_MODULAR --n_estimators_randomforest=100 --run_mode=local --occ_type=euclid --main_process_gb=20 --max_nr_of_zero_results=50 --slurm_signal_delay_s=0 --max_failed_jobs=0 --username=conv_test_non_normalized --max_attempts_for_generation=20 --num_restarts=20 --raw_samples=1024 --max_abandoned_retrial=20 --max_num_of_parallel_sruns=16 --number_of_generators=1 --generate_all_jobs_at_once --parameter epochs range 20 150 int false --parameter lr range 0.0001 0.001 float false --parameter batch_size range 8 1024 int false --parameter hidden_size range 8 4096 int false --parameter dropout range 0 0.5 float false --parameter num_dense_layers range 1 2 int false --parameter filter range 4 80 int false --parameter num_conv_layers range 4 7 int false
⠋ Disabling logging...
⠋ Setting run folder...
⠋ Creating folder /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime_mono/1...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
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⠋ Writing result names file...
⠋ Writing result min/max file...
⠋ Saving state files...
Run-folder: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime_mono/1
⠋ Writing live_share file if it is present...
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⠼ Writing git information
⠋ Checking max_eval...
⠋ Calculating number of steps...
⠋ Adding excluded nodes...
⠋ Initializing ax_client...
⠋ Setting orchestrator...
See https://imageseg.scads.de/omniax/share?user_id=conv_test_non_normalized&experiment_name=mnist_normalized_runtime_mono&run_nr=0 for live-results.
You have 1 CPUs available for the main process. Using CUDA device NVIDIA H100.
Generation strategy: SOBOL for 20 steps and then BOTORCH_MODULAR for 980 steps.
Run-Program: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs %epochs --learning_rate %lr --batch_size %batch_size --hidden_size %hidden_size --dropout %dropout --num_dense_layers %num_dense_layers --filter %(filter) --num_conv_layers %(num_conv_layers)
Experiment parameters
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Name ┃ Type ┃ Lower bound ┃ Upper bound ┃ Type ┃ Log Scale? ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ epochs │ range │ 20 │ 150 │ int │ No │
│ lr │ range │ 0.0001 │ 0.001 │ float │ No │
│ batch_size │ range │ 8 │ 1024 │ int │ No │
│ hidden_size │ range │ 8 │ 4096 │ int │ No │
│ dropout │ range │ 0 │ 0.5 │ float │ No │
│ num_dense_layers │ range │ 1 │ 2 │ int │ No │
│ filter │ range │ 4 │ 80 │ int │ No │
│ num_conv_layers │ range │ 4 │ 7 │ int │ No │
└──────────────────┴───────┴─────────────┴─────────────┴───────┴────────────┘
Result-Names
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Result-Name ┃ Min or max? ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ VAL_ACC │ max │
└─────────────┴─────────────┘
⠋ Write files and show overview
SOBOL, pending/unknown 8/1 = ∑9/20, started new job : 0%|░░░░░░░░░░| 0/1000 [01:51
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
BOTORCH_MODULAR, best VAL_ACC: 65.7, found 50 zero-jobs (max: 50) : 12%|█░░░░░░░░░| 125/1000 [14:39:02<102:33:14, 421.94s/it]
It seems like the search space was exhausted. You were able to get 12% of the jobs you requested (got: 125, submitted: 125, failed: 0, requested: 1000) after main ran
Best VAL_ACC, max (total: 125)
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ OO_Info_SLURM_JOB_ID ┃ epochs ┃ lr ┃ batch_size ┃ hidden_size ┃ dropout ┃ num_dense_layers ┃ filter ┃ num_conv_layers ┃ VAL_ACC ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ 1217394.0 │ 150 │ 0.001 │ 8 │ 1853 │ 0.5 │ 1 │ 80 │ 7 │ 65.7 │
└──────────────────────┴────────┴───────┴────────────┴─────────────┴─────────┴──────────────────┴────────┴─────────────────┴─────────┘
Runtime Infos
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Number of evaluations ┃ Min time ┃ Max time ┃ Average time ┃ Median time ┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ 125 │ 231.00 sec (3m51s) │ 7218.00 sec (2h18s) │ 2283.12 sec (38m3s) │ 1572.00 sec (26m12s) │
└───────────────────────┴────────────────────┴─────────────────────┴─────────────────────┴──────────────────────┘
2025-11-04 16:20:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, Started OmniOpt2 run...
2025-11-04 16:21:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #1/20
2025-11-04 16:21:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #2/20
2025-11-04 16:21:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #3/20
2025-11-04 16:21:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #4/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #5/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #6/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #7/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #8/20
2025-11-04 16:21:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #9/20
2025-11-04 16:21:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #10/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #11/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #12/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #13/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #14/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #15/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #16/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #17/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #18/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #19/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #20/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, requested 20 jobs, got 20, 0.85 s/job
2025-11-04 16:21:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #1/20 start
2025-11-04 16:21:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #2/20 start
2025-11-04 16:21:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #3/20 start
2025-11-04 16:21:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #4/20 start
2025-11-04 16:21:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #5/20 start
2025-11-04 16:21:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #6/20 start
2025-11-04 16:21:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #7/20 start
2025-11-04 16:21:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #8/20 start
2025-11-04 16:21:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #9/20 start
2025-11-04 16:21:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #10/20 start
2025-11-04 16:21:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #11/20 start
2025-11-04 16:21:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #12/20 start
2025-11-04 16:21:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #13/20 start
2025-11-04 16:21:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #14/20 start
2025-11-04 16:21:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #15/20 start
2025-11-04 16:21:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #16/20 start
2025-11-04 16:21:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #17/20 start
2025-11-04 16:21:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #18/20 start
2025-11-04 16:21:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #19/20 start
2025-11-04 16:21:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #20/20 start
2025-11-04 16:21:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, starting new job
2025-11-04 16:22:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, unknown 1 = ∑1/20, started new job
2025-11-04 16:22:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, unknown 1 = ∑1/20, starting new job
2025-11-04 16:22:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 16:22:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/1 = ∑3/20, started new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/1 = ∑3/20, starting new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/2 = ∑4/20, started new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 16:22:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 4/1 = ∑5/20, started new job
2025-11-04 16:22:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 5/1 = ∑6/20, started new job
2025-11-04 16:22:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 6/1 = ∑7/20, started new job
2025-11-04 16:22:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 7/1 = ∑8/20, started new job
2025-11-04 16:22:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 8/1 = ∑9/20, started new job
2025-11-04 16:23:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending 9 = ∑9/20, waiting for 9 jobs
2025-11-04 16:40:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 16:42:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, running 9 = ∑9/20, new result: VAL_ACC: 36.780000
2025-11-04 16:42:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 16:42:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 16:46:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 8 = ∑8/20, new result: VAL_ACC: 26.990000
2025-11-04 16:46:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 16:46:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 16:46:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 36.78, running 7 = ∑7/20, new result: VAL_ACC: 56.030000
2025-11-04 16:47:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 56.03, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 16:47:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 56.03, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 16:48:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 56.03, running 6 = ∑6/20, new result: VAL_ACC: 59.290000
2025-11-04 16:48:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 16:48:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 16:48:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 5 = ∑5/20, new result: VAL_ACC: 59.150000
2025-11-04 16:48:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 16:48:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 16:50:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 4 = ∑4/20, new result: VAL_ACC: 43.620000
2025-11-04 16:50:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 16:50:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 16:55:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 59.29, running 3 = ∑3/20, new result: VAL_ACC: 60.670000
2025-11-04 16:55:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 16:55:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 16:56:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, new result: VAL_ACC: 44.040000
2025-11-04 16:56:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 16:56:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, waiting for 1 job
2025-11-04 17:23:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, new result: VAL_ACC: 50.250000
2025-11-04 17:23:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, waiting for 1 job, finished 1 job
2025-11-04 17:23:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #1/20
2025-11-04 17:23:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #2/20
2025-11-04 17:23:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #3/20
2025-11-04 17:23:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #4/20
2025-11-04 17:23:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #5/20
2025-11-04 17:23:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #6/20
2025-11-04 17:23:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #7/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #8/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #9/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #10/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #11/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #12/20
2025-11-04 17:23:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #13/20
2025-11-04 17:23:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #14/20
2025-11-04 17:23:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #15/20
2025-11-04 17:23:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #16/20
2025-11-04 17:23:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #17/20
2025-11-04 17:23:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #18/20
2025-11-04 17:23:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #19/20
2025-11-04 17:23:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, getting new HP set #20/20
2025-11-04 17:23:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, requested 20 jobs, got 20, 0.46 s/job
2025-11-04 17:23:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #1/20 start
2025-11-04 17:23:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #2/20 start
2025-11-04 17:23:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #3/20 start
2025-11-04 17:23:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #4/20 start
2025-11-04 17:23:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #5/20 start
2025-11-04 17:23:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #6/20 start
2025-11-04 17:23:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #7/20 start
2025-11-04 17:23:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #8/20 start
2025-11-04 17:23:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #9/20 start
2025-11-04 17:23:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #10/20 start
2025-11-04 17:23:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #11/20 start
2025-11-04 17:23:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #12/20 start
2025-11-04 17:23:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #13/20 start
2025-11-04 17:23:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #14/20 start
2025-11-04 17:23:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #15/20 start
2025-11-04 17:23:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #16/20 start
2025-11-04 17:23:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #17/20 start
2025-11-04 17:23:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #18/20 start
2025-11-04 17:23:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #19/20 start
2025-11-04 17:23:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, eval #20/20 start
2025-11-04 17:23:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, starting new job
2025-11-04 17:24:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, unknown 2 = ∑2/20, started new job
2025-11-04 17:24:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, unknown 2 = ∑2/20, starting new job
2025-11-04 17:24:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 2/2 = ∑4/20, started new job
2025-11-04 17:24:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 17:24:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 4/3 = ∑7/20, started new job
2025-11-04 17:24:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 7/2 = ∑9/20, started new job
2025-11-04 17:24:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 7/3 = ∑10/20, started new job
2025-11-04 17:24:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 10/1 = ∑11/20, started new job
2025-11-04 17:24:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 11/3 = ∑14/20, started new job
2025-11-04 17:24:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 14/2 = ∑16/20, started new job
2025-11-04 17:24:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 14/3 = ∑17/20, started new job
2025-11-04 17:24:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 17/3 = ∑20/20, started new job
2025-11-04 17:24:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending/unknown 17/3 = ∑20/20, waiting for 20 jobs
2025-11-04 17:24:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 20 = ∑20/20, waiting for 20 jobs
2025-11-04 17:44:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 20 = ∑20/20, new result: VAL_ACC: 53.110000
2025-11-04 17:44:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 17:44:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 19 = ∑19/20, waiting for 19 jobs
2025-11-04 17:45:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 19 = ∑19/20, new result: VAL_ACC: 49.050000
2025-11-04 17:45:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 17:45:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 18 = ∑18/20, waiting for 18 jobs
2025-11-04 17:47:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 18 = ∑18/20, new result: VAL_ACC: 34.190000
2025-11-04 17:47:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 17:47:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, pending 17 = ∑17/20, waiting for 17 jobs
2025-11-04 17:48:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 17:48:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 17 = ∑17/20, new result: VAL_ACC: 56.070000
2025-11-04 17:49:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 17:49:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 17:49:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 16 = ∑16/20, new result: VAL_ACC: 45.690000
2025-11-04 17:49:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 17:49:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 17:51:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 15 = ∑15/20, new result: VAL_ACC: 49.500000
2025-11-04 17:51:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 17:51:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 17:52:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 14 = ∑14/20, new result: VAL_ACC: 59.880000
2025-11-04 17:52:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 17:52:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 17:53:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 13 = ∑13/20, new result: VAL_ACC: 55.790000
2025-11-04 17:53:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 17:53:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 17:57:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 12 = ∑12/20, new result: VAL_ACC: 53.070000
2025-11-04 17:57:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 17:57:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 17:57:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 11 = ∑11/20, new result: VAL_ACC: 49.770000
2025-11-04 17:57:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 17:57:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 17:57:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 10 = ∑10/20, new result: VAL_ACC: 53.700000
2025-11-04 17:57:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 9 = ∑9/20, waiting for 10 jobs, finished 1 job
2025-11-04 17:57:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 17:59:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 9 = ∑9/20, new result: VAL_ACC: 56.000000
2025-11-04 17:59:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 17:59:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 17:59:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 8 = ∑8/20, new result: VAL_ACC: 58.640000
2025-11-04 17:59:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 17:59:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 17:59:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 7 = ∑7/20, new result: VAL_ACC: 39.280000
2025-11-04 17:59:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 17:59:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 18:02:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 6 = ∑6/20, new result: VAL_ACC: 45.860000
2025-11-04 18:02:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 18:02:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 18:02:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 5 = ∑5/20, new result: VAL_ACC: 52.450000
2025-11-04 18:02:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 18:02:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 18:03:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 4 = ∑4/20, new result: VAL_ACC: 55.750000
2025-11-04 18:03:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 18:03:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 18:04:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 3 = ∑3/20, new result: VAL_ACC: 43.280000
2025-11-04 18:04:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 18:04:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 18:08:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 2 = ∑2/20, new result: VAL_ACC: 56.370000
2025-11-04 18:08:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 18:08:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, waiting for 1 job
2025-11-04 18:11:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, running 1 = ∑1/20, new result: VAL_ACC: 57.090000
2025-11-04 18:11:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, best VAL_ACC: 60.67, waiting for 1 job, finished 1 job
2025-11-04 18:12:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #1/20
2025-11-04 18:12:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #2/20
2025-11-04 18:12:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #3/20
2025-11-04 18:12:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #4/20
2025-11-04 18:12:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #5/20
2025-11-04 18:12:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #6/20
2025-11-04 18:12:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #7/20
2025-11-04 18:12:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #8/20
2025-11-04 18:12:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #9/20
2025-11-04 18:12:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #10/20
2025-11-04 18:12:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #11/20
2025-11-04 18:12:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #12/20
2025-11-04 18:12:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #13/20
2025-11-04 18:12:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #14/20
2025-11-04 18:12:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #15/20
2025-11-04 18:12:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #16/20
2025-11-04 18:12:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #17/20
2025-11-04 18:12:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #18/20
2025-11-04 18:12:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #19/20
2025-11-04 18:12:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, getting new HP set #20/20
2025-11-04 18:12:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, requested 20 jobs, got 20, 2.81 s/job
2025-11-04 18:12:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #1/20 start
2025-11-04 18:12:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #2/20 start
2025-11-04 18:12:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #3/20 start
2025-11-04 18:12:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #4/20 start
2025-11-04 18:12:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #5/20 start
2025-11-04 18:12:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #6/20 start
2025-11-04 18:12:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #7/20 start
2025-11-04 18:12:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #8/20 start
2025-11-04 18:12:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #9/20 start
2025-11-04 18:12:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #10/20 start
2025-11-04 18:12:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #11/20 start
2025-11-04 18:12:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #12/20 start
2025-11-04 18:12:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #13/20 start
2025-11-04 18:12:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #14/20 start
2025-11-04 18:12:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #15/20 start
2025-11-04 18:12:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #16/20 start
2025-11-04 18:12:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #17/20 start
2025-11-04 18:12:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #18/20 start
2025-11-04 18:12:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #19/20 start
2025-11-04 18:12:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, eval #20/20 start
2025-11-04 18:12:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, starting new job
2025-11-04 18:12:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, unknown 2 = ∑2/20, started new job
2025-11-04 18:12:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, unknown 3 = ∑3/20, started new job
2025-11-04 18:12:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, unknown 3 = ∑3/20, starting new job
2025-11-04 18:13:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending 3 = ∑3/20, starting new job
2025-11-04 18:13:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending/unknown 3/1 = ∑4/20, started new job
2025-11-04 18:13:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending/unknown 3/1 = ∑4/20, starting new job
2025-11-04 18:13:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending/unknown 4/1 = ∑5/20, started new job
2025-11-04 18:13:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending/unknown 5/2 = ∑7/20, started new job
2025-11-04 18:13:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, pending/unknown 5/3 = ∑8/20, started new job
2025-11-04 18:13:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/6/1 = ∑9/20, started new job
2025-11-04 18:13:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/7/1 = ∑10/20, started new job
2025-11-04 18:13:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/8/1 = ∑11/20, started new job
2025-11-04 18:13:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/9/2 = ∑13/20, started new job
2025-11-04 18:13:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/11/2 = ∑15/20, started new job
2025-11-04 18:13:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/11/3 = ∑16/20, started new job
2025-11-04 18:13:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 2/14/3 = ∑19/20, started new job
2025-11-04 18:13:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 3/16/1 = ∑20/20, started new job
2025-11-04 18:13:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending/unknown 3/16/1 = ∑20/20, waiting for 20 jobs
2025-11-04 18:13:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending 3/17 = ∑20/20, waiting for 20 jobs
2025-11-04 18:15:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending 4/16 = ∑20/20, waiting for 20 jobs
2025-11-04 18:17:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending 8/12 = ∑20/20, waiting for 20 jobs
2025-11-04 18:20:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running/pending 12/8 = ∑20/20, waiting for 20 jobs
2025-11-04 18:27:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 18:30:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 60.67, running 20 = ∑20/20, new result: VAL_ACC: 62.920000
2025-11-04 18:30:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 18:30:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 18:32:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 19 = ∑19/20, new result: VAL_ACC: 59.360000
2025-11-04 18:32:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 18:32:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 18 = ∑18/20, waiting for 18 jobs
2025-11-04 18:34:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 18 = ∑18/20, new result: VAL_ACC: 62.210000
2025-11-04 18:34:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 18:34:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 18:35:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 17 = ∑17/20, new result: VAL_ACC: 62.730000
2025-11-04 18:35:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 18:35:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 18:37:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 16 = ∑16/20, new result: VAL_ACC: 59.440000
2025-11-04 18:37:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 18:37:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 18:39:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 15 = ∑15/20, new result: VAL_ACC: 59.580000
2025-11-04 18:39:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 18:39:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 18:40:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 14 = ∑14/20, new result: VAL_ACC: 62.730000
2025-11-04 18:40:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 18:40:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 18:40:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 13 = ∑13/20, new result: VAL_ACC: 59.470000
2025-11-04 18:40:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 18:40:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 18:40:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 12 = ∑12/20, new result: VAL_ACC: 59.690000
2025-11-04 18:40:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 18:40:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 18:41:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 11 = ∑11/20, new result: VAL_ACC: 62.910000
2025-11-04 18:41:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 18:41:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 18:42:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 10 = ∑10/20, new result: VAL_ACC: 62.140000
2025-11-04 18:42:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 9 = ∑9/20, waiting for 10 jobs, finished 1 job
2025-11-04 18:42:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 18:42:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 9 = ∑9/20, new result: VAL_ACC: 61.970000
2025-11-04 18:42:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 18:42:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 18:48:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 62.92, running 8 = ∑8/20, new result: VAL_ACC: 64.140000
2025-11-04 18:48:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 18:48:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 18:49:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, new result: VAL_ACC: 64.090000
2025-11-04 18:49:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 18:49:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 19:03:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, new result: VAL_ACC: 64.020000
2025-11-04 19:03:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 19:03:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 19:23:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, new result: VAL_ACC: 63.420000
2025-11-04 19:23:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 19:23:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 19:25:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, new result: VAL_ACC: 63.770000
2025-11-04 19:25:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 19:25:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 19:31:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, new result: VAL_ACC: 63.690000
2025-11-04 19:31:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 19:31:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 19:31:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, new result: VAL_ACC: 63.340000
2025-11-04 19:31:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 19:31:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, waiting for 1 job
2025-11-04 19:34:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, new result: VAL_ACC: 63.770000
2025-11-04 19:34:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, waiting for 1 job, finished 1 job
2025-11-04 19:36:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #1/20
2025-11-04 19:36:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #2/20
2025-11-04 19:36:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #3/20
2025-11-04 19:36:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #4/20
2025-11-04 19:36:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #5/20
2025-11-04 19:36:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #6/20
2025-11-04 19:36:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #7/20
2025-11-04 19:36:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #8/20
2025-11-04 19:36:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #9/20
2025-11-04 19:36:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #10/20
2025-11-04 19:36:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #11/20
2025-11-04 19:36:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #12/20
2025-11-04 19:36:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #13/20
2025-11-04 19:36:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #14/20
2025-11-04 19:36:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #15/20
2025-11-04 19:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #16/20
2025-11-04 19:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #17/20
2025-11-04 19:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #18/20
2025-11-04 19:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #19/20
2025-11-04 19:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #20/20
2025-11-04 19:36:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, requested 20 jobs, got 20, 5.12 s/job
2025-11-04 19:36:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #1/20 start
2025-11-04 19:36:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #2/20 start
2025-11-04 19:36:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #3/20 start
2025-11-04 19:36:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #4/20 start
2025-11-04 19:36:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #5/20 start
2025-11-04 19:36:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #6/20 start
2025-11-04 19:36:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #7/20 start
2025-11-04 19:36:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #8/20 start
2025-11-04 19:36:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #9/20 start
2025-11-04 19:36:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #10/20 start
2025-11-04 19:36:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #11/20 start
2025-11-04 19:36:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #12/20 start
2025-11-04 19:36:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #13/20 start
2025-11-04 19:36:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #14/20 start
2025-11-04 19:36:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #15/20 start
2025-11-04 19:36:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #16/20 start
2025-11-04 19:36:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #17/20 start
2025-11-04 19:36:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #18/20 start
2025-11-04 19:36:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #19/20 start
2025-11-04 19:36:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #20/20 start
2025-11-04 19:36:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, starting new job
2025-11-04 19:36:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, unknown 3 = ∑3/20, started new job
2025-11-04 19:36:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, unknown 3 = ∑3/20, starting new job
2025-11-04 19:37:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/unknown 3/1 = ∑4/20, started new job
2025-11-04 19:37:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/unknown 3/1 = ∑4/20, starting new job
2025-11-04 19:37:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 3/1/1 = ∑5/20, started new job
2025-11-04 19:37:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 3/2/1 = ∑6/20, started new job
2025-11-04 19:37:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 3/3/1 = ∑7/20, started new job
2025-11-04 19:37:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 3/4/1 = ∑8/20, started new job
2025-11-04 19:37:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/unknown 8/1 = ∑9/20, started new job
2025-11-04 19:37:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 8/1/1 = ∑10/20, started new job
2025-11-04 19:37:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 8/2/1 = ∑11/20, started new job
2025-11-04 19:37:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 8/3/1 = ∑12/20, started new job
2025-11-04 19:37:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 8/4/1 = ∑13/20, started new job
2025-11-04 19:38:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/2/1 = ∑14/20, started new job
2025-11-04 19:38:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/3/1 = ∑15/20, started new job
2025-11-04 19:38:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/4/1 = ∑16/20, started new job
2025-11-04 19:38:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/5/1 = ∑17/20, started new job
2025-11-04 19:38:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/6/2 = ∑19/20, started new job
2025-11-04 19:38:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 14/5/1 = ∑20/20, started new job
2025-11-04 19:38:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 14/5/1 = ∑20/20, waiting for 20 jobs
2025-11-04 19:38:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending 14/6 = ∑20/20, waiting for 20 jobs
2025-11-04 19:40:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 20:02:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 20 = ∑20/20, new result: VAL_ACC: 58.240000
2025-11-04 20:03:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 20:03:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 20:03:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, new result: VAL_ACC: 57.670000
2025-11-04 20:03:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, new result: VAL_ACC: 57.180000
2025-11-04 20:03:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, waiting for 19 jobs, finished 2 jobs
2025-11-04 20:03:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 20:03:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, new result: VAL_ACC: 58.010000
2025-11-04 20:03:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 20:03:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 20:03:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, new result: VAL_ACC: 57.480000
2025-11-04 20:03:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 20:03:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 20:03:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 15 = ∑15/20, new result: VAL_ACC: 58.250000
2025-11-04 20:04:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 20:04:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 20:04:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 14 = ∑14/20, new result: VAL_ACC: 57.300000
2025-11-04 20:04:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 20:04:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 20:04:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 13 = ∑13/20, new result: VAL_ACC: 58.020000
2025-11-04 20:04:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 20:04:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 20:04:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 12 = ∑12/20, new result: VAL_ACC: 58.160000
2025-11-04 20:04:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 20:04:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 20:04:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 11 = ∑11/20, new result: VAL_ACC: 56.630000
2025-11-04 20:04:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 20:04:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 20:04:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 10 = ∑10/20, new result: VAL_ACC: 58.050000
2025-11-04 20:04:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 10 = ∑10/20, new result: VAL_ACC: 57.710000
2025-11-04 20:04:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 8 = ∑8/20, waiting for 10 jobs, finished 2 jobs
2025-11-04 20:04:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 20:04:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 8 = ∑8/20, new result: VAL_ACC: 59.120000
2025-11-04 20:04:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 20:04:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 20:04:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 7 = ∑7/20, new result: VAL_ACC: 57.570000
2025-11-04 20:05:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 20:05:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 20:05:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 6 = ∑6/20, new result: VAL_ACC: 57.330000
2025-11-04 20:05:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 20:05:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 20:05:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 5 = ∑5/20, new result: VAL_ACC: 57.550000
2025-11-04 20:05:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 20:05:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 20:05:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 4 = ∑4/20, new result: VAL_ACC: 57.520000
2025-11-04 20:06:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 20:06:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 20:06:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 3 = ∑3/20, new result: VAL_ACC: 57.200000
2025-11-04 20:06:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 20:06:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 20:06:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 2 = ∑2/20, new result: VAL_ACC: 58.220000
2025-11-04 20:06:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 20:06:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, waiting for 1 job
2025-11-04 20:06:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 1 = ∑1/20, new result: VAL_ACC: 57.580000
2025-11-04 20:06:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, waiting for 1 job, finished 1 job
2025-11-04 20:07:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #1/20
2025-11-04 20:07:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #2/20
2025-11-04 20:07:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #3/20
2025-11-04 20:07:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #4/20
2025-11-04 20:07:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #5/20
2025-11-04 20:07:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #6/20
2025-11-04 20:07:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #7/20
2025-11-04 20:07:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #8/20
2025-11-04 20:07:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #9/20
2025-11-04 20:07:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #10/20
2025-11-04 20:07:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #11/20
2025-11-04 20:07:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #12/20
2025-11-04 20:07:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #13/20
2025-11-04 20:07:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #14/20
2025-11-04 20:07:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #15/20
2025-11-04 20:07:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #16/20
2025-11-04 20:07:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #17/20
2025-11-04 20:07:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #18/20
2025-11-04 20:07:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #19/20
2025-11-04 20:07:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, getting new HP set #20/20
2025-11-04 20:07:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, requested 20 jobs, got 20, 2.91 s/job
2025-11-04 20:07:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #1/20 start
2025-11-04 20:08:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #2/20 start
2025-11-04 20:08:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #3/20 start
2025-11-04 20:08:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #4/20 start
2025-11-04 20:08:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #5/20 start
2025-11-04 20:08:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #6/20 start
2025-11-04 20:08:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #7/20 start
2025-11-04 20:08:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #8/20 start
2025-11-04 20:08:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #9/20 start
2025-11-04 20:08:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #10/20 start
2025-11-04 20:08:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #11/20 start
2025-11-04 20:08:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #12/20 start
2025-11-04 20:08:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #13/20 start
2025-11-04 20:08:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #14/20 start
2025-11-04 20:08:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #15/20 start
2025-11-04 20:08:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #16/20 start
2025-11-04 20:08:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #17/20 start
2025-11-04 20:08:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #18/20 start
2025-11-04 20:08:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #19/20 start
2025-11-04 20:08:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, eval #20/20 start
2025-11-04 20:08:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, starting new job
2025-11-04 20:08:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, unknown 2 = ∑2/20, started new job
2025-11-04 20:08:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, unknown 2 = ∑2/20, starting new job
2025-11-04 20:08:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, pending/unknown 2/2 = ∑4/20, started new job
2025-11-04 20:08:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 20:08:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, pending/unknown 4/1 = ∑5/20, started new job
2025-11-04 20:08:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/unknown 5/2 = ∑7/20, started new job
2025-11-04 20:08:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/2/3 = ∑10/20, started new job
2025-11-04 20:08:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/5/2 = ∑12/20, started new job
2025-11-04 20:08:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/7/1 = ∑13/20, started new job
2025-11-04 20:08:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/7/2 = ∑14/20, started new job
2025-11-04 20:09:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/9/2 = ∑16/20, started new job
2025-11-04 20:09:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/9/3 = ∑17/20, started new job
2025-11-04 20:09:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 5/12/1 = ∑18/20, started new job
2025-11-04 20:09:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/7/1 = ∑19/20, started new job
2025-11-04 20:09:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/8/1 = ∑20/20, started new job
2025-11-04 20:09:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending/unknown 11/8/1 = ∑20/20, waiting for 20 jobs
2025-11-04 20:09:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending 11/9 = ∑20/20, waiting for 20 jobs
2025-11-04 20:09:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending 17/3 = ∑20/20, waiting for 20 jobs
2025-11-04 20:10:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running/pending 19/1 = ∑20/20, waiting for 20 jobs
2025-11-04 20:11:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 20:12:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 20 = ∑20/20, new result: VAL_ACC: 2.720000
2025-11-04 20:12:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 20:12:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 20:22:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 19 = ∑19/20, new result: VAL_ACC: 54.110000
2025-11-04 20:22:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 20:22:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 18 = ∑18/20, waiting for 18 jobs
2025-11-04 20:25:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 18 = ∑18/20, new result: VAL_ACC: 56.040000
2025-11-04 20:25:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 20:25:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 20:32:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 17 = ∑17/20, new result: VAL_ACC: 62.080000
2025-11-04 20:32:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 20:32:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 20:34:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.14, running 16 = ∑16/20, new result: VAL_ACC: 64.440000
2025-11-04 20:34:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 20:34:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 20:35:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 15 = ∑15/20, new result: VAL_ACC: 57.090000
2025-11-04 20:36:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 20:36:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 20:40:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.44, running 14 = ∑14/20, new result: VAL_ACC: 64.730000
2025-11-04 20:41:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 20:41:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 20:47:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, new result: VAL_ACC: 58.770000
2025-11-04 20:47:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 20:47:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 20:50:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, new result: VAL_ACC: 62.630000
2025-11-04 20:50:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 20:50:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 21:09:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, new result: VAL_ACC: 61.760000
2025-11-04 21:09:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 21:09:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 21:13:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, new result: VAL_ACC: 61.720000
2025-11-04 21:13:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, waiting for 10 jobs, finished 1 job
2025-11-04 21:13:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 21:14:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, new result: VAL_ACC: 61.910000
2025-11-04 21:14:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 21:14:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 21:18:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, new result: VAL_ACC: 62.680000
2025-11-04 21:18:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 21:18:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 21:19:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, new result: VAL_ACC: 61.690000
2025-11-04 21:19:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 21:19:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 21:21:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, new result: VAL_ACC: 61.990000
2025-11-04 21:21:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 21:21:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 21:22:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, new result: VAL_ACC: 62.090000
2025-11-04 21:22:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 21:22:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 21:22:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, new result: VAL_ACC: 62.360000
2025-11-04 21:22:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 21:22:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 21:23:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, new result: VAL_ACC: 61.510000
2025-11-04 21:23:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 21:23:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 21:27:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, new result: VAL_ACC: 61.310000
2025-11-04 21:27:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 21:27:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, waiting for 1 job
2025-11-04 21:28:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, new result: VAL_ACC: 61.610000
2025-11-04 21:28:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, waiting for 1 job, finished 1 job
2025-11-04 21:29:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #1/20
2025-11-04 21:29:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #2/20
2025-11-04 21:29:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #3/20
2025-11-04 21:29:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #4/20
2025-11-04 21:29:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #5/20
2025-11-04 21:29:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #6/20
2025-11-04 21:29:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #7/20
2025-11-04 21:29:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #8/20
2025-11-04 21:29:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #9/20
2025-11-04 21:29:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #10/20
2025-11-04 21:29:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #11/20
2025-11-04 21:29:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #12/20
2025-11-04 21:29:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #13/20
2025-11-04 21:29:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #14/20
2025-11-04 21:29:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #15/20
2025-11-04 21:29:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #16/20
2025-11-04 21:29:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #17/20
2025-11-04 21:29:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #18/20
2025-11-04 21:29:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #19/20
2025-11-04 21:29:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #20/20
2025-11-04 21:29:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, requested 20 jobs, got 20, 2.77 s/job
2025-11-04 21:29:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #1/20 start
2025-11-04 21:29:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #2/20 start
2025-11-04 21:29:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #3/20 start
2025-11-04 21:29:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #4/20 start
2025-11-04 21:29:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #5/20 start
2025-11-04 21:29:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #6/20 start
2025-11-04 21:29:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #7/20 start
2025-11-04 21:29:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #8/20 start
2025-11-04 21:29:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #9/20 start
2025-11-04 21:29:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #10/20 start
2025-11-04 21:29:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #11/20 start
2025-11-04 21:29:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #12/20 start
2025-11-04 21:29:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #13/20 start
2025-11-04 21:29:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #14/20 start
2025-11-04 21:29:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #15/20 start
2025-11-04 21:29:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #16/20 start
2025-11-04 21:29:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #17/20 start
2025-11-04 21:29:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #18/20 start
2025-11-04 21:29:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #19/20 start
2025-11-04 21:29:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #20/20 start
2025-11-04 21:29:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, starting new job
2025-11-04 21:29:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, unknown 2 = ∑2/20, started new job
2025-11-04 21:29:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, unknown 2 = ∑2/20, starting new job
2025-11-04 21:29:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, unknown 3 = ∑3/20, started new job
2025-11-04 21:29:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, unknown 3 = ∑3/20, starting new job
2025-11-04 21:29:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending/unknown 3/1 = ∑4/20, started new job
2025-11-04 21:29:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending/unknown 3/1 = ∑4/20, starting new job
2025-11-04 21:29:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 3/1/1 = ∑5/20, started new job
2025-11-04 21:30:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 3/2/1 = ∑6/20, started new job
2025-11-04 21:30:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 3/3/2 = ∑8/20, started new job
2025-11-04 21:30:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 3/5/2 = ∑10/20, started new job
2025-11-04 21:30:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 3/5/3 = ∑11/20, started new job
2025-11-04 21:30:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 4/7/1 = ∑12/20, started new job
2025-11-04 21:30:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 4/8/1 = ∑13/20, started new job
2025-11-04 21:30:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 4/9/2 = ∑15/20, started new job
2025-11-04 21:30:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 4/11/1 = ∑16/20, started new job
2025-11-04 21:30:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 4/12/1 = ∑17/20, started new job
2025-11-04 21:30:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending/unknown 9/8/1 = ∑18/20, started new job
2025-11-04 21:31:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 9/10 = ∑19/20, started new job
2025-11-04 21:31:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 9/11 = ∑20/20, started new job
2025-11-04 21:31:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 9/11 = ∑20/20, waiting for 20 jobs
2025-11-04 21:31:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 11/9 = ∑20/20, waiting for 20 jobs
2025-11-04 21:32:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 12/8 = ∑20/20, waiting for 20 jobs
2025-11-04 21:33:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 17/3 = ∑20/20, waiting for 20 jobs
2025-11-04 21:35:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 21:42:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 20 = ∑20/20, new result: VAL_ACC: 47.720000
2025-11-04 21:42:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 21:42:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 21:42:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 19 = ∑19/20, new result: VAL_ACC: 61.640000
2025-11-04 21:43:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 21:43:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 18 = ∑18/20, waiting for 18 jobs
2025-11-04 21:43:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 18 = ∑18/20, new result: VAL_ACC: 59.420000
2025-11-04 21:43:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 21:43:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 21:44:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 17 = ∑17/20, new result: VAL_ACC: 60.390000
2025-11-04 21:44:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 21:44:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 21:49:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 16 = ∑16/20, new result: VAL_ACC: 63.320000
2025-11-04 21:49:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 21:49:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 22:07:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 15 = ∑15/20, new result: VAL_ACC: 1.000000
2025-11-04 22:08:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 22:08:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 22:08:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 14 = ∑14/20, new result: VAL_ACC: 1.000000
2025-11-04 22:08:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 22:08:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 22:08:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 13 = ∑13/20, new result: VAL_ACC: 1.000000
2025-11-04 22:08:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 22:08:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 22:08:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 12 = ∑12/20, new result: VAL_ACC: 1.000000
2025-11-04 22:08:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 22:08:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 22:09:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 11 = ∑11/20, new result: VAL_ACC: 1.000000
2025-11-04 22:09:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 22:09:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 22:10:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 10 = ∑10/20, new result: VAL_ACC: 63.690000
2025-11-04 22:10:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, waiting for 10 jobs, finished 1 job
2025-11-04 22:10:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 22:10:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 9 = ∑9/20, new result: VAL_ACC: 20.490000
2025-11-04 22:10:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 22:10:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 22:10:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 8 = ∑8/20, new result: VAL_ACC: 1.000000
2025-11-04 22:10:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 22:10:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 22:10:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 7 = ∑7/20, new result: VAL_ACC: 63.830000
2025-11-04 22:10:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 22:10:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 22:11:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 6 = ∑6/20, new result: VAL_ACC: 1.000000
2025-11-04 22:11:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 22:11:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 22:11:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, new result: VAL_ACC: 1.000000
2025-11-04 22:11:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 22:11:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 22:12:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, new result: VAL_ACC: 1.000000
2025-11-04 22:12:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 22:12:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 22:12:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 3 = ∑3/20, new result: VAL_ACC: 22.140000
2025-11-04 22:12:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 22:12:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 22:13:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 2 = ∑2/20, new result: VAL_ACC: 1.000000
2025-11-04 22:13:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 22:13:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, waiting for 1 job
2025-11-04 22:14:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 1 = ∑1/20, new result: VAL_ACC: 18.480000
2025-11-04 22:14:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, waiting for 1 job, finished 1 job
2025-11-04 22:15:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #1/20
2025-11-04 22:15:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #2/20
2025-11-04 22:15:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #3/20
2025-11-04 22:15:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #4/20
2025-11-04 22:15:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #5/20
2025-11-04 22:16:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, getting new HP set #6/20
2025-11-04 22:16:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, requested 20 jobs, got 5, 33.37 s/job
2025-11-04 22:17:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #1/5 start
2025-11-04 22:17:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #2/5 start
2025-11-04 22:17:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #3/5 start
2025-11-04 22:17:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #4/5 start
2025-11-04 22:17:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, eval #5/5 start
2025-11-04 22:17:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, starting new job
2025-11-04 22:17:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, unknown 1 = ∑1/20, started new job
2025-11-04 22:17:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending 4 = ∑4/20, started new job
2025-11-04 22:17:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending/unknown 4/1 = ∑5/20, started new job
2025-11-04 22:17:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending/unknown 4/1 = ∑5/20, waiting for 5 jobs
2025-11-04 22:17:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, pending 5 = ∑5/20, waiting for 5 jobs
2025-11-04 22:18:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 1/4 = ∑5/20, waiting for 5 jobs
2025-11-04 22:18:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running/pending 3/2 = ∑5/20, waiting for 5 jobs
2025-11-04 22:20:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 23:53:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 5 = ∑5/20, new result: VAL_ACC: 64.490000
2025-11-04 23:53:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 23:53:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 23:54:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 64.73, running 4 = ∑4/20, new result: VAL_ACC: 65.670000
2025-11-04 23:54:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 23:54:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 23:54:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, new result: VAL_ACC: 64.460000
2025-11-04 23:54:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 23:54:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 23:55:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, new result: VAL_ACC: 62.920000
2025-11-04 23:55:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 23:55:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 1 = ∑1/20, waiting for 1 job
2025-11-05 00:11:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 1 = ∑1/20, new result: VAL_ACC: 64.640000
2025-11-05 00:11:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, waiting for 1 job, finished 1 job
2025-11-05 00:12:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #1/20
2025-11-05 00:12:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #2/20
2025-11-05 00:12:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #3/20
2025-11-05 00:12:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #4/20
2025-11-05 00:12:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #5/20
2025-11-05 00:12:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #6/20
2025-11-05 00:12:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #7/20
2025-11-05 00:12:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #8/20
2025-11-05 00:12:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #9/20
2025-11-05 00:12:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #10/20
2025-11-05 00:12:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #11/20
2025-11-05 00:12:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #12/20
2025-11-05 00:12:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #13/20
2025-11-05 00:12:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #14/20
2025-11-05 00:12:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #15/20
2025-11-05 00:12:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, getting new HP set #16/20
2025-11-05 00:14:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, requested 20 jobs, got 15, 11.51 s/job
2025-11-05 00:14:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #1/15 start
2025-11-05 00:14:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #2/15 start
2025-11-05 00:14:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #3/15 start
2025-11-05 00:14:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #4/15 start
2025-11-05 00:14:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #5/15 start
2025-11-05 00:14:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #6/15 start
2025-11-05 00:14:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #7/15 start
2025-11-05 00:14:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #8/15 start
2025-11-05 00:14:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #9/15 start
2025-11-05 00:14:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #10/15 start
2025-11-05 00:14:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #11/15 start
2025-11-05 00:14:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #12/15 start
2025-11-05 00:14:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #13/15 start
2025-11-05 00:14:34 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #14/15 start
2025-11-05 00:14:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, eval #15/15 start
2025-11-05 00:14:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, starting new job
2025-11-05 00:16:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, unknown 3 = ∑3/20, started new job
2025-11-05 00:16:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, unknown 5 = ∑5/20, started new job
2025-11-05 00:16:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, unknown 5 = ∑5/20, waiting for 5 jobs
2025-11-05 00:16:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, pending 5 = ∑5/20, waiting for 5 jobs
2025-11-05 00:21:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 5 = ∑5/20, waiting for 5 jobs
2025-11-05 02:13:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 5 = ∑5/20, new result: VAL_ACC: 65.010000
2025-11-05 02:13:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-05 02:13:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 4 = ∑4/20, waiting for 4 jobs
2025-11-05 02:13:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 4 = ∑4/20, new result: VAL_ACC: 65.190000
2025-11-05 02:14:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-05 02:14:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, waiting for 3 jobs
2025-11-05 02:14:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 3 = ∑3/20, new result: VAL_ACC: 65.150000
2025-11-05 02:14:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-05 02:14:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, waiting for 2 jobs
2025-11-05 02:15:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.67, running 2 = ∑2/20, new result: VAL_ACC: 65.700000
2025-11-05 02:15:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-05 02:15:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 1 job
2025-11-05 02:19:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, new result: VAL_ACC: 64.640000
2025-11-05 02:19:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, waiting for 1 job, finished 1 job
2025-11-05 02:21:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 02:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #2/20
2025-11-05 02:21:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #3/20
2025-11-05 02:21:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #4/20
2025-11-05 02:21:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #5/20
2025-11-05 02:21:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #6/20
2025-11-05 02:21:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #7/20
2025-11-05 02:21:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #8/20
2025-11-05 02:21:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #9/20
2025-11-05 02:22:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #10/20
2025-11-05 02:22:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #11/20
2025-11-05 02:22:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 10, 14.78 s/job
2025-11-05 02:22:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #1/10 start
2025-11-05 02:22:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #2/10 start
2025-11-05 02:22:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #3/10 start
2025-11-05 02:22:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #4/10 start
2025-11-05 02:22:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #5/10 start
2025-11-05 02:22:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #6/10 start
2025-11-05 02:22:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #7/10 start
2025-11-05 02:22:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #8/10 start
2025-11-05 02:22:22 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #9/10 start
2025-11-05 02:22:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #10/10 start
2025-11-05 02:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, starting new job
2025-11-05 02:22:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 1 = ∑1/20, started new job
2025-11-05 02:22:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 2 = ∑2/20, started new job
2025-11-05 02:22:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 2 = ∑2/20, waiting for 2 jobs
2025-11-05 02:22:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, pending 2 = ∑2/20, waiting for 2 jobs
2025-11-05 02:22:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 2 = ∑2/20, waiting for 2 jobs
2025-11-05 04:07:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 2 = ∑2/20, new result: VAL_ACC: 63.690000
2025-11-05 04:07:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-05 04:07:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 1 job
2025-11-05 04:20:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, new result: VAL_ACC: 64.260000
2025-11-05 04:20:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, waiting for 1 job, finished 1 job
2025-11-05 04:21:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 04:21:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #2/20
2025-11-05 04:21:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #3/20
2025-11-05 04:21:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #4/20
2025-11-05 04:21:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #5/20
2025-11-05 04:21:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #6/20
2025-11-05 04:21:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #7/20
2025-11-05 04:21:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #8/20
2025-11-05 04:21:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #9/20
2025-11-05 04:22:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 9, 17.61 s/job
2025-11-05 04:22:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #1/9 start
2025-11-05 04:22:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #2/9 start
2025-11-05 04:22:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #3/9 start
2025-11-05 04:22:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #4/9 start
2025-11-05 04:22:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #5/9 start
2025-11-05 04:22:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #6/9 start
2025-11-05 04:23:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #7/9 start
2025-11-05 04:23:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #8/9 start
2025-11-05 04:23:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #9/9 start
2025-11-05 04:23:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, starting new job
2025-11-05 04:23:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 1 = ∑1/20, started new job
2025-11-05 04:23:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 1 = ∑1/20, starting new job
2025-11-05 04:23:08 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 2 = ∑2/20, started new job
2025-11-05 04:23:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 3 = ∑3/20, started new job
2025-11-05 04:23:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 3 = ∑3/20, waiting for 3 jobs
2025-11-05 04:23:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, pending 3 = ∑3/20, waiting for 3 jobs
2025-11-05 04:23:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 3 = ∑3/20, waiting for 3 jobs
2025-11-05 04:37:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 3 = ∑3/20, new result: VAL_ACC: 53.240000
2025-11-05 04:37:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-05 04:37:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 2 = ∑2/20, waiting for 2 jobs
2025-11-05 04:38:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 2 = ∑2/20, new result: VAL_ACC: 54.980000
2025-11-05 04:38:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-05 04:38:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 1 job
2025-11-05 04:49:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, new result: VAL_ACC: 42.310000
2025-11-05 04:49:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, waiting for 1 job, finished 1 job
2025-11-05 04:51:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 04:51:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #2/20
2025-11-05 04:51:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #3/20
2025-11-05 04:51:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #4/20
2025-11-05 04:51:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #5/20
2025-11-05 04:51:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #6/20
2025-11-05 04:51:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #7/20
2025-11-05 04:51:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #8/20
2025-11-05 04:51:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #9/20
2025-11-05 04:51:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #10/20
2025-11-05 04:51:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #11/20
2025-11-05 04:51:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #12/20
2025-11-05 04:51:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #13/20
2025-11-05 04:51:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #14/20
2025-11-05 04:51:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #15/20
2025-11-05 04:52:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 14, 13.84 s/job
2025-11-05 04:52:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #1/14 start
2025-11-05 04:52:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #2/14 start
2025-11-05 04:52:47 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #3/14 start
2025-11-05 04:52:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #4/14 start
2025-11-05 04:52:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #5/14 start
2025-11-05 04:52:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #6/14 start
2025-11-05 04:52:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #7/14 start
2025-11-05 04:52:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #8/14 start
2025-11-05 04:52:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #9/14 start
2025-11-05 04:52:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #10/14 start
2025-11-05 04:52:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #11/14 start
2025-11-05 04:52:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #12/14 start
2025-11-05 04:52:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #13/14 start
2025-11-05 04:52:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #14/14 start
2025-11-05 04:53:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, starting new job
2025-11-05 04:53:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, unknown 1 = ∑1/20, started new job
2025-11-05 04:53:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, waiting for 1 job
2025-11-05 05:27:53 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, running 1 = ∑1/20, new result: VAL_ACC: 65.170000
2025-11-05 05:28:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, waiting for 1 job, finished 1 job
2025-11-05 05:29:38 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:29:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #2/20
2025-11-05 05:29:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #3/20
2025-11-05 05:29:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #4/20
2025-11-05 05:29:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #5/20
2025-11-05 05:29:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #6/20
2025-11-05 05:30:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #7/20
2025-11-05 05:31:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 6, 29.03 s/job
2025-11-05 05:31:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #1/6 start
2025-11-05 05:31:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #2/6 start
2025-11-05 05:31:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #3/6 start
2025-11-05 05:31:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #4/6 start
2025-11-05 05:31:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #5/6 start
2025-11-05 05:31:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, eval #6/6 start
2025-11-05 05:33:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:33:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:33:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 1 zero-jobs (max: 50)
2025-11-05 05:34:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:34:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:34:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 2 zero-jobs (max: 50)
2025-11-05 05:36:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:36:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:36:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 3 zero-jobs (max: 50)
2025-11-05 05:38:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:38:09 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:38:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 4 zero-jobs (max: 50)
2025-11-05 05:39:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:39:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:39:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 5 zero-jobs (max: 50)
2025-11-05 05:41:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:41:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:41:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 6 zero-jobs (max: 50)
2025-11-05 05:43:06 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:43:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:43:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 7 zero-jobs (max: 50)
2025-11-05 05:44:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:44:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:45:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 8 zero-jobs (max: 50)
2025-11-05 05:46:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:47:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:47:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 9 zero-jobs (max: 50)
2025-11-05 05:49:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:49:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:49:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 10 zero-jobs (max: 50)
2025-11-05 05:51:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:51:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:51:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 11 zero-jobs (max: 50)
2025-11-05 05:53:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:53:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:53:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 12 zero-jobs (max: 50)
2025-11-05 05:54:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:54:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:54:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 13 zero-jobs (max: 50)
2025-11-05 05:56:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:56:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:56:37 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 14 zero-jobs (max: 50)
2025-11-05 05:58:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 05:58:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 05:58:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 15 zero-jobs (max: 50)
2025-11-05 05:59:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:00:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:00:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 16 zero-jobs (max: 50)
2025-11-05 06:01:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:01:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:01:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 17 zero-jobs (max: 50)
2025-11-05 06:03:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:03:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:03:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 18 zero-jobs (max: 50)
2025-11-05 06:04:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:05:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:05:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 19 zero-jobs (max: 50)
2025-11-05 06:06:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:06:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:06:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 20 zero-jobs (max: 50)
2025-11-05 06:08:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:08:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:08:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 21 zero-jobs (max: 50)
2025-11-05 06:10:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:10:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:10:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 22 zero-jobs (max: 50)
2025-11-05 06:12:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:12:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:12:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 23 zero-jobs (max: 50)
2025-11-05 06:13:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:13:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:14:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 24 zero-jobs (max: 50)
2025-11-05 06:15:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:15:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:15:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 25 zero-jobs (max: 50)
2025-11-05 06:17:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:17:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:17:26 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 26 zero-jobs (max: 50)
2025-11-05 06:19:03 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:19:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:19:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 27 zero-jobs (max: 50)
2025-11-05 06:20:48 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:20:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:20:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 28 zero-jobs (max: 50)
2025-11-05 06:22:27 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:22:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:22:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 29 zero-jobs (max: 50)
2025-11-05 06:24:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:24:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:24:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 30 zero-jobs (max: 50)
2025-11-05 06:26:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:26:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:26:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 31 zero-jobs (max: 50)
2025-11-05 06:27:54 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:28:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:28:02 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 32 zero-jobs (max: 50)
2025-11-05 06:29:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:29:42 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:29:44 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 33 zero-jobs (max: 50)
2025-11-05 06:31:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:31:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:31:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 34 zero-jobs (max: 50)
2025-11-05 06:33:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:33:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:33:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 35 zero-jobs (max: 50)
2025-11-05 06:34:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:34:55 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:35:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 36 zero-jobs (max: 50)
2025-11-05 06:37:05 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:37:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:37:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 37 zero-jobs (max: 50)
2025-11-05 06:38:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:39:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:39:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 38 zero-jobs (max: 50)
2025-11-05 06:40:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:40:43 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:40:45 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 39 zero-jobs (max: 50)
2025-11-05 06:42:19 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:42:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:42:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 40 zero-jobs (max: 50)
2025-11-05 06:44:04 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:44:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:44:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 41 zero-jobs (max: 50)
2025-11-05 06:45:51 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:45:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:46:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 42 zero-jobs (max: 50)
2025-11-05 06:47:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:47:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:47:41 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 43 zero-jobs (max: 50)
2025-11-05 06:49:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:49:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:49:24 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 44 zero-jobs (max: 50)
2025-11-05 06:51:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:51:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:51:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 45 zero-jobs (max: 50)
2025-11-05 06:52:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:52:56 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:52:58 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 46 zero-jobs (max: 50)
2025-11-05 06:54:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:54:49 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:54:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 47 zero-jobs (max: 50)
2025-11-05 06:56:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:56:33 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:56:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 48 zero-jobs (max: 50)
2025-11-05 06:58:07 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:58:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 06:58:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 49 zero-jobs (max: 50)
2025-11-05 06:59:52 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, getting new HP set #1/20
2025-11-05 06:59:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, requested 20 jobs, got 0, n/a
2025-11-05 07:00:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, found 50 zero-jobs (max: 50)
2025-11-05 07:00:01 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): BOTORCH_MODULAR, best VAL_ACC: 65.7, 50 empty jobs (>= 50)
Job submission durations
┏━━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━┓
┃ Batch ┃ Seconds ┃ Jobs ┃ Time per job ┃
┡━━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━┩
│ 1 │ 136.903 │ 20 │ 6.845 │
│ 2 │ 61.130 │ 20 │ 3.056 │
│ 3 │ 57.979 │ 20 │ 2.899 │
│ 4 │ 99.191 │ 20 │ 4.960 │
│ 5 │ 59.508 │ 20 │ 2.975 │
│ 6 │ 100.224 │ 20 │ 5.011 │
│ 7 │ 21.947 │ 5 │ 4.389 │
│ 8 │ 95.690 │ 15 │ 6.379 │
│ 9 │ 4.419 │ 10 │ 0.442 │
│ 10 │ 5.829 │ 9 │ 0.648 │
│ 11 │ 5.434 │ 14 │ 0.388 │
│ 12 │ 3.071 │ 6 │ 0.512 │
├─────────┼─────────┼──────┼──────────────┤
│ Average │ 54.277 │ │ │
│ Median │ 58.744 │ │ │
│ Total │ 651.323 │ │ │
│ Max │ 136.903 │ │ │
│ Min │ 3.071 │ │ │
└─────────┴─────────┴──────┴──────────────┘
Model generation times
┏━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━┓
┃ Iteration ┃ Seconds ┃ Jobs ┃ Time per job ┃
┡━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━┩
│ 1 │ 16.952 │ 20 │ 0.848 │
│ 2 │ 9.111 │ 20 │ 0.456 │
│ 3 │ 56.196 │ 20 │ 2.810 │
│ 4 │ 102.312 │ 20 │ 5.116 │
│ 5 │ 58.228 │ 20 │ 2.911 │
│ 6 │ 55.419 │ 20 │ 2.771 │
│ 7 │ 166.854 │ 5 │ 33.371 │
│ 8 │ 172.596 │ 15 │ 11.506 │
│ 9 │ 147.764 │ 10 │ 14.776 │
│ 10 │ 158.457 │ 9 │ 17.606 │
│ 11 │ 193.814 │ 14 │ 13.844 │
│ 12 │ 174.206 │ 6 │ 29.034 │
│ 13 │ 104.163 │ 0 │ 0.000 │
│ 14 │ 94.144 │ 0 │ 0.000 │
│ 15 │ 96.917 │ 0 │ 0.000 │
│ 16 │ 95.640 │ 0 │ 0.000 │
│ 17 │ 99.405 │ 0 │ 0.000 │
│ 18 │ 100.821 │ 0 │ 0.000 │
│ 19 │ 97.975 │ 0 │ 0.000 │
│ 20 │ 97.244 │ 0 │ 0.000 │
│ 21 │ 160.068 │ 0 │ 0.000 │
│ 22 │ 111.459 │ 0 │ 0.000 │
│ 23 │ 108.578 │ 0 │ 0.000 │
│ 24 │ 98.861 │ 0 │ 0.000 │
│ 25 │ 99.540 │ 0 │ 0.000 │
│ 26 │ 100.144 │ 0 │ 0.000 │
│ 27 │ 95.262 │ 0 │ 0.000 │
│ 28 │ 102.870 │ 0 │ 0.000 │
│ 29 │ 98.693 │ 0 │ 0.000 │
│ 30 │ 103.657 │ 0 │ 0.000 │
│ 31 │ 90.730 │ 0 │ 0.000 │
│ 32 │ 103.455 │ 0 │ 0.000 │
│ 33 │ 107.310 │ 0 │ 0.000 │
│ 34 │ 99.047 │ 0 │ 0.000 │
│ 35 │ 107.429 │ 0 │ 0.000 │
│ 36 │ 101.385 │ 0 │ 0.000 │
│ 37 │ 98.961 │ 0 │ 0.000 │
│ 38 │ 100.241 │ 0 │ 0.000 │
│ 39 │ 103.149 │ 0 │ 0.000 │
│ 40 │ 101.620 │ 0 │ 0.000 │
│ 41 │ 103.115 │ 0 │ 0.000 │
│ 42 │ 100.065 │ 0 │ 0.000 │
│ 43 │ 103.106 │ 0 │ 0.000 │
│ 44 │ 98.388 │ 0 │ 0.000 │
│ 45 │ 99.380 │ 0 │ 0.000 │
│ 46 │ 104.083 │ 0 │ 0.000 │
│ 47 │ 97.896 │ 0 │ 0.000 │
│ 48 │ 100.555 │ 0 │ 0.000 │
│ 49 │ 103.387 │ 0 │ 0.000 │
│ 50 │ 102.422 │ 0 │ 0.000 │
│ 51 │ 100.568 │ 0 │ 0.000 │
│ 52 │ 101.923 │ 0 │ 0.000 │
│ 53 │ 102.863 │ 0 │ 0.000 │
│ 54 │ 101.335 │ 0 │ 0.000 │
│ 55 │ 97.070 │ 0 │ 0.000 │
│ 56 │ 98.456 │ 0 │ 0.000 │
│ 57 │ 103.704 │ 0 │ 0.000 │
│ 58 │ 103.427 │ 0 │ 0.000 │
│ 59 │ 109.860 │ 0 │ 0.000 │
│ 60 │ 102.030 │ 0 │ 0.000 │
│ 61 │ 99.146 │ 0 │ 0.000 │
│ 62 │ 100.751 │ 0 │ 0.000 │
├───────────┼──────────┼──────┼──────────────┤
│ Average │ 103.616 │ │ │
│ Median │ 101.078 │ │ │
│ Total │ 6424.210 │ │ │
│ Max │ 193.814 │ │ │
│ Min │ 9.111 │ │ │
└───────────┴──────────┴──────┴──────────────┘
Arguments Overview
| Key | Value |
|---|
| config_yaml | None |
| config_toml | None |
| config_json | None |
| num_random_steps | 20 |
| max_eval | 1000 |
| run_program | [['cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdf… |
| experiment_name | mnist_normalized_runtime_mono |
| mem_gb | 40 |
| parameter | [['epochs', 'range', '20', '150', 'int', 'false'], ['lr', 'range', '0.0001', '0.001', 'float', 'false'], ['batch_size', 'range', '8', '1024', 'int', |
| 'false'], ['hidden_size', 'range', '8', '4096', 'int', 'false'], ['dropout', 'range', '0', '0.5', 'float', 'false'], ['num_dense_layers', 'range', '1', |
| '2', 'int', 'false'], ['filter', 'range', '4', '80', 'int', 'false'], ['num_conv_layers', 'range', '4', '7', 'int', 'false']] |
| continue_previous_job | None |
| experiment_constraints | None |
| run_dir | runs |
| seed | None |
| verbose_tqdm | False |
| model | BOTORCH_MODULAR |
| gridsearch | False |
| occ | False |
| show_sixel_scatter | False |
| show_sixel_general | False |
| show_sixel_trial_index_result | False |
| follow | True |
| send_anonymized_usage_stats | True |
| ui_url | None |
| root_venv_dir | /home/s3811141 |
| exclude | None |
| main_process_gb | 20 |
| max_nr_of_zero_results | 50 |
| abbreviate_job_names | False |
| orchestrator_file | None |
| checkout_to_latest_tested_version | False |
| live_share | True |
| disable_tqdm | False |
| disable_previous_job_constraint | False |
| workdir | |
| occ_type | euclid |
| result_names | ['VAL_ACC=max'] |
| minkowski_p | 2 |
| signed_weighted_euclidean_weights | |
| generation_strategy | None |
| generate_all_jobs_at_once | True |
| revert_to_random_when_seemingly_exhausted | True |
| load_data_from_existing_jobs | [] |
| n_estimators_randomforest | 100 |
| max_attempts_for_generation | 20 |
| external_generator | None |
| username | conv_test_non_normalized |
| max_failed_jobs | 0 |
| num_cpus_main_job | None |
| calculate_pareto_front_of_job | [] |
| show_generate_time_table | False |
| force_choice_for_ranges | False |
| max_abandoned_retrial | 20 |
| share_password | None |
| dryrun | False |
| db_url | None |
| run_program_once | None |
| worker_generator_path | None |
| save_to_database | False |
| range_max_difference | 1000000 |
| skip_search | False |
| dont_warm_start_refitting | False |
| refit_on_cv | False |
| fit_out_of_design | False |
| fit_abandoned | False |
| dont_jit_compile | False |
| num_restarts | 20 |
| raw_samples | 1024 |
| max_num_of_parallel_sruns | 16 |
| no_transform_inputs | False |
| no_normalize_y | False |
| transforms | [] |
| number_of_generators | 1 |
| num_parallel_jobs | 20 |
| worker_timeout | 120 |
| slurm_use_srun | False |
| time | 2880 |
| partition | alpha |
| reservation | None |
| force_local_execution | False |
| slurm_signal_delay_s | 0 |
| nodes_per_job | 1 |
| cpus_per_task | 1 |
| account | None |
| gpus | 1 |
| dependency | None |
| run_mode | local |
| verbose | False |
| verbose_break_run_search_table | False |
| debug | False |
| flame_graph | False |
| memray | False |
| no_sleep | False |
| tests | False |
| show_worker_percentage_table_at_end | False |
| auto_exclude_defective_hosts | False |
| run_tests_that_fail_on_taurus | False |
| raise_in_eval | False |
| show_ram_every_n_seconds | 0 |
| show_generation_and_submission_sixel | False |
| just_return_defaults | False |
| prettyprint | False |
| runtime_debug | False |
| debug_stack_regex | |
| debug_stack_trace_regex | None |
| show_func_name | False |
| beartype | False |
1762269654.1781,20,0,0
1762269701.306,20,0,0
1762269735.8575,20,1,5
1762269736.0158,20,1,5
1762269740.8565,20,2,10
1762269740.9738,20,2,10
1762269745.8583,20,4,20
1762269745.9864,20,4,20
1762269750.8763,20,5,25
1762269750.9432,20,5,25
1762269755.8807,20,6,30
1762269755.9397,20,6,30
1762269760.8754,20,7,35
1762269760.9485,20,7,35
1762269765.9532,20,8,40
1762269766.0392,20,8,40
1762269770.8978,20,9,45
1762269840.6128,20,9,45
1762269842.2266,20,20,100
1762270536.5591,20,20,100
1762270537.1354,20,19,95
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1762270562.5573,20,18,90
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1762270627.5307,20,17,85
1762270633.1735,20,15,75
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1762270643.1272,20,13,65
1762270687.5578,20,13,65
1762270688.1709,20,12,60
1762270702.7575,20,12,60
1762270703.337,20,10,50
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1762270964.4001,20,8,40
1762271198.1912,20,8,40
1762271200.5022,20,7,35
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1762271295.5347,20,5,25
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1762271335.3036,20,4,20
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1762271432.1498,20,3,15
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1762271792.5828,20,1,5
1762273399.9052,20,1,5
1762273402.0205,20,0,0
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1762273454.2137,20,2,10
1762273454.4652,20,2,10
1762273459.2052,20,4,20
1762273459.4453,20,4,20
1762273469.2091,20,7,35
1762273469.3787,20,7,35
1762273474.2222,20,10,50
1762273475.2588,20,10,50
1762273479.2005,20,11,55
1762273479.2846,20,11,55
1762273484.2157,20,14,70
1762273484.3947,20,14,70
1762273489.233,20,17,85
1762273489.5543,20,17,85
1762273494.2262,20,20,100
1762274688.2179,20,20,100
1762274692.3116,20,19,95
1762274733.4889,20,19,95
1762274738.7084,20,18,90
1762274838.4111,20,18,90
1762274841.2114,20,17,85
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1762274943.185,20,16,80
1762274981.1958,20,16,80
1762274984.8369,20,15,75
1762275111.7224,20,15,75
1762275118.3813,20,14,70
1762275151.1599,20,14,70
1762275157.4587,20,13,65
1762275228.4193,20,13,65
1762275231.2294,20,12,60
1762275429.6812,20,12,60
1762275434.5123,20,11,55
1762275458.4731,20,11,55
1762275459.1657,20,10,50
1762275473.3069,20,10,50
1762275477.402,20,9,45
1762275560.5716,20,9,45
1762275563.3462,20,8,40
1762275581.2781,20,8,40
1762275585.0058,20,7,35
1762275592.1322,20,7,35
1762275594.8427,20,6,30
1762275763.5902,20,6,30
1762275769.8447,20,5,25
1762275771.095,20,5,25
1762275775.0917,20,4,20
1762275833.9442,20,4,20
1762275838.0162,20,3,15
1762275863.1191,20,3,15
1762275868.407,20,2,10
1762276101.7884,20,2,10
1762276102.4224,20,1,5
1762276281.7032,20,1,5
1762276326.0042,20,0,0
1762276375.9303,20,0,0
1762276376.0441,20,1,5
1762276376.1131,20,1,5
1762276376.4049,20,2,10
1762276376.8298,20,2,10
1762276376.8376,20,0,0
1762276377.0687,20,3,15
1762276381.6786,20,3,15
1762276388.1999,20,5,25
1762276388.5026,20,5,25
1762276396.6007,20,8,40
1762276397.8015,20,8,40
1762276401.6068,20,9,45
1762276401.7082,20,9,45
1762276406.5724,20,10,50
1762276406.6731,20,10,50
1762276411.5823,20,11,55
1762276411.6838,20,11,55
1762276416.5588,20,13,65
1762276416.7273,20,13,65
1762276421.5955,20,16,80
1762276422.7,20,16,80
1762276426.5917,20,19,95
1762276426.8024,20,19,95
1762276431.587,20,20,100
1762277402.2422,20,20,100
1762277405.8461,20,19,95
1762277567.1754,20,19,95
1762277570.6961,20,18,90
1762277652.7262,20,18,90
1762277656.7939,20,17,85
1762277721.7157,20,17,85
1762277722.3342,20,16,80
1762277871.5775,20,16,80
1762277875.8369,20,15,75
1762277953.6563,20,15,75
1762277966.0335,20,14,70
1762278036.0245,20,14,70
1762278040.846,20,12,60
1762278047.7748,20,12,60
1762278051.458,20,11,55
1762278097.5627,20,11,55
1762278105.003,20,10,50
1762278144.6285,20,10,50
1762278147.9186,20,9,45
1762278150.2152,20,9,45
1762278154.0201,20,8,40
1762278514.4103,20,8,40
1762278519.4979,20,7,35
1762278592.8751,20,7,35
1762278596.4272,20,6,30
1762279407.2657,20,6,30
1762279411.1368,20,5,25
1762280593.3962,20,5,25
1762280597.6155,20,4,20
1762280737.1428,20,4,20
1762280741.4585,20,3,15
1762281076.881,20,3,15
1762281077.5159,20,2,10
1762281091.3437,20,2,10
1762281094.8151,20,1,5
1762281284.1821,20,1,5
1762281290.7274,20,0,0
1762281417.81,20,0,0
1762281418.5879,20,1,5
1762281418.6005,20,2,10
1762281419.6326,20,2,10
1762281424.0084,20,4,20
1762281424.286,20,4,20
1762281429.0559,20,5,25
1762281429.1867,20,5,25
1762281439.0459,20,6,30
1762281439.1758,20,6,30
1762281444.0589,20,7,35
1762281444.19,20,7,35
1762281449.0444,20,8,40
1762281449.1755,20,8,40
1762281454.0744,20,9,45
1762281454.2118,20,9,45
1762281459.038,20,10,50
1762281459.1657,20,10,50
1762281464.0511,20,11,55
1762281464.1876,20,11,55
1762281469.046,20,12,60
1762281469.1747,20,12,60
1762281474.0547,20,13,65
1762281474.1858,20,13,65
1762281484.1104,20,14,70
1762281484.2401,20,14,70
1762281489.0756,20,15,75
1762281489.2107,20,15,75
1762281494.0761,20,16,80
1762281494.2073,20,16,80
1762281499.0812,20,17,85
1762281499.2119,20,17,85
1762281510.1383,20,19,95
1762281510.9929,20,19,95
1762281514.0962,20,20,100
1762282976.5683,20,20,100
1762282981.9898,20,19,95
1762283015.7204,20,19,95
1762283022.3088,20,17,85
1762283023.7983,20,17,85
1762283028.0917,20,16,80
1762283029.5727,20,16,80
1762283033.6964,20,14,70
1762283042.1304,20,14,70
1762283046.3735,20,13,65
1762283047.8873,20,13,65
1762283051.9098,20,12,60
1762283058.9194,20,12,60
1762283063.1262,20,11,55
1762283071.9166,20,11,55
1762283077.872,20,10,50
1762283079.8993,20,10,50
1762283086.9737,20,7,35
1762283096.7914,20,7,35
1762283100.9359,20,6,30
1762283144.7883,20,6,30
1762283149.0451,20,4,20
1762283156.9635,20,4,20
1762283161.1275,20,3,15
1762283165.0211,20,3,15
1762283169.4215,20,2,10
1762283177.5682,20,2,10
1762283181.8058,20,1,5
1762283213.6261,20,1,5
1762283218.6654,20,0,0
1762283301.6912,20,0,0
1762283301.9477,20,1,5
1762283302.1157,20,1,5
1762283302.6296,20,2,10
1762283303.0623,20,2,10
1762283307.4201,20,4,20
1762283308.0426,20,4,20
1762283317.414,20,5,25
1762283317.5566,20,5,25
1762283322.4164,20,7,35
1762283322.6814,20,7,35
1762283327.4396,20,10,50
1762283327.8327,20,10,50
1762283332.4342,20,12,60
1762283332.7144,20,12,60
1762283337.4645,20,14,70
1762283338.6108,20,14,70
1762283342.4775,20,17,85
1762283343.6028,20,17,85
1762283347.4646,20,18,90
1762283347.6086,20,18,90
1762283352.4581,20,19,95
1762283352.6115,20,19,95
1762283357.4605,20,20,100
1762283552.6609,20,20,100
1762283557.8338,20,19,95
1762284155.4816,20,19,95
1762284160.3618,20,18,90
1762284337.1207,20,18,90
1762284344.7781,20,17,85
1762284731.6813,20,17,85
1762284737.3376,20,16,80
1762284876.8597,20,16,80
1762284881.8895,20,15,75
1762284957.1268,20,15,75
1762284962.1762,20,14,70
1762285255.8666,20,14,70
1762285260.7112,20,13,65
1762285644.3763,20,13,65
1762285649.4336,20,12,60
1762285820.6913,20,12,60
1762285825.8353,20,11,55
1762286981.7832,20,11,55
1762286986.7969,20,10,50
1762287204.0119,20,10,50
1762287209.064,20,9,45
1762287277.603,20,9,45
1762287282.4956,20,8,40
1762287523.4418,20,8,40
1762287528.3699,20,7,35
1762287553.3857,20,7,35
1762287558.1132,20,6,30
1762287695.8532,20,6,30
1762287700.6551,20,5,25
1762287743.8683,20,5,25
1762287748.4983,20,4,20
1762287750.2098,20,4,20
1762287754.9324,20,3,15
1762287816.9783,20,3,15
1762287822.7659,20,2,10
1762288022.7019,20,2,10
1762288027.7199,20,1,5
1762288084.9528,20,1,5
1762288091.2091,20,0,0
1762288169.874,20,0,0
1762288180.7696,20,3,15
1762288182.2302,20,3,15
1762288185.7683,20,4,20
1762288187.233,20,4,20
1762288190.7826,20,5,25
1762288190.9542,20,5,25
1762288200.781,20,6,30
1762288200.9619,20,6,30
1762288205.7864,20,8,40
1762288206.087,20,8,40
1762288215.7944,20,11,55
1762288217.2578,20,11,55
1762288220.8063,20,12,60
1762288220.979,20,12,60
1762288225.8455,20,13,65
1762288226.0172,20,13,65
1762288235.8122,20,16,80
1762288236.2739,20,16,80
1762288240.8561,20,17,85
1762288241.2305,20,17,85
1762288250.8656,20,18,90
1762288251.0568,20,18,90
1762288260.846,20,19,95
1762288261.0197,20,19,95
1762288265.8599,20,20,100
1762288951.4855,20,20,100
1762288958.7481,20,19,95
1762288976.7399,20,19,95
1762288986.186,20,18,90
1762289022.65,20,18,90
1762289028.0124,20,17,85
1762289081.2394,20,17,85
1762289086.7035,20,16,80
1762289373.713,20,16,80
1762289379.2624,20,15,75
1762290479.2437,20,15,75
1762290484.758,20,14,70
1762290500.3792,20,14,70
1762290506.0239,20,13,65
1762290516.6236,20,13,65
1762290522.2938,20,12,60
1762290525.5493,20,12,60
1762290531.2848,20,11,55
1762290591.7014,20,11,55
1762290597.4099,20,10,50
1762290601.9375,20,10,50
1762290607.7608,20,9,45
1762290610.3477,20,9,45
1762290616.0184,20,8,40
1762290628.8113,20,8,40
1762290634.3,20,7,35
1762290638.193,20,7,35
1762290644.0198,20,6,30
1762290670.2596,20,6,30
1762290676.025,20,5,25
1762290701.5403,20,5,25
1762290707.2204,20,4,20
1762290737.1762,20,4,20
1762290743.9432,20,3,15
1762290745.8965,20,3,15
1762290751.7556,20,2,10
1762290817.417,20,2,10
1762290823.2167,20,1,5
1762290841.3639,20,1,5
1762290847.1848,20,0,0
1762291025.5802,20,0,0
1762291035.3937,20,1,5
1762291035.5827,20,1,5
1762291040.3908,20,4,20
1762291040.8799,20,4,20
1762291045.3992,20,5,25
1762296825.3782,20,5,25
1762296834.2035,20,4,20
1762296848.2254,20,4,20
1762296854.8777,20,3,15
1762296869.4873,20,3,15
1762296875.5935,20,2,10
1762296943.0579,20,2,10
1762296949.2069,20,1,5
1762297871.1667,20,1,5
1762297877.6376,20,0,0
1762298078.9994,20,0,0
1762298169.8019,20,5,25
1762305219.2544,20,5,25
1762305232.735,20,4,20
1762305235.3337,20,4,20
1762305241.914,20,3,15
1762305277.142,20,3,15
1762305284.44,20,2,10
1762305330.8283,20,2,10
1762305338.8028,20,1,5
1762305572.7195,20,1,5
1762305579.2727,20,0,0
1762305745.3539,20,0,0
1762305746.183,20,1,5
1762305750.935,20,1,5
1762305751.6885,20,2,10
1762312042.9029,20,2,10
1762312051.0773,20,1,5
1762312803.6315,20,1,5
1762312810.5005,20,0,0
1762312986.047,20,0,0
1762312987.0526,20,1,5
1762312989.8262,20,1,5
1762312991.1521,20,3,15
1762313824.2951,20,3,15
1762313832.1961,20,2,10
1762313911.7938,20,2,10
1762313918.4393,20,1,5
1762314561.3396,20,1,5
1762314568.077,20,0,0
1762314781.8638,20,0,0
1762314782.6594,20,1,5
1762316874.143,20,1,5
1762316885.3172,20,0,0
1762322401.2284,20,0,0
This logs the CPU and RAM usage of the main worker process.
timestamp,ram_usage_mb,cpu_usage_percent
1762269654,808.10546875,12.7
1762269836,850.765625,12.9
1762269896,850.49609375,13
1762269956,850.47265625,13
1762270016,850.484375,12.8
1762270076,850.48046875,12.9
1762270137,850.484375,12.9
1762270197,850.484375,13
1762270257,850.484375,12.6
1762270317,857.828125,12.9
1762270377,857.84375,12.9
1762270437,857.87109375,12.7
1762270502,857.875,11.5
1762270562,857.890625,11.8
1762270622,857.90625,11.6
1762270682,857.92578125,11.7
1762270742,857.953125,11.6
1762270802,857.9609375,11.8
1762270862,857.9765625,11.3
1762270922,857.98828125,11.2
1762270982,860.1328125,10.7
1762271042,860.1328125,11.2
1762271102,858.6328125,11.1
1762271162,858.6328125,11.5
1762271222,858.83984375,10.7
1762271282,858.87890625,11.6
1762271342,859.4921875,11.4
1762271402,859.4921875,11.5
1762271462,859.4921875,11.3
1762271522,859.4921875,11.2
1762271582,859.4921875,11.3
1762271642,859.4921875,11.6
1762271703,859.4921875,11.7
1762271763,859.4921875,12.1
1762271823,859.9921875,11.4
1762271883,859.9921875,12.5
1762271943,859.9921875,12.6
1762272003,859.9921875,12.7
1762272063,859.9921875,12.7
1762272123,859.9921875,12.8
1762272183,859.9921875,13
1762272243,859.9921875,12.7
1762272307,859.9921875,14.5
1762272368,859.9921875,14.5
1762272428,859.9921875,14.4
1762272488,859.9921875,14.5
1762272548,859.9921875,14.6
1762272608,859.9921875,14.5
1762272668,859.9921875,14.3
1762272728,859.9921875,14.5
1762272788,859.9921875,14.6
1762272848,860.4921875,14.5
1762272908,860.4921875,14.2
1762272968,860.4921875,12.4
1762273035,860.4921875,10.1
1762273095,860.4921875,15.2
1762273155,860.4921875,10.6
1762273216,860.4921875,11.9
1762273276,860.4921875,16.7
1762273336,860.4921875,13.8
1762273398,860.9921875,10.9
1762273459,870.98046875,12.6
1762273519,882.33203125,14.4
1762273596,894.87109375,11.8
1762273657,895.3671875,12.7
1762273717,896.390625,11.7
1762273778,896.48828125,11.8
1762273838,896.44140625,14.2
1762273899,896.45703125,11.5
1762273960,896.55078125,13.5
1762274021,896.55078125,14.5
1762274081,896.6015625,11.9
1762274142,896.61328125,13.2
1762274202,896.640625,12.1
1762274262,896.6875,10.5
1762274322,896.73046875,11.1
1762274382,896.7578125,12.1
1762274442,896.7734375,12.7
1762274502,896.8359375,9.2
1762274562,896.83984375,13.5
1762274622,896.91015625,13.8
1762274682,896.921875,14.2
1762274742,898.76171875,14.1
1762274806,898.8046875,13.8
1762274867,899.91015625,12.7
1762274943,900.00390625,12.3
1762275004,900.01953125,10.5
1762275065,900.0078125,10.2
1762275125,900.1875,17.1
1762275187,900.25,14.1
1762275247,900.32421875,13.5
1762275307,900.33984375,11.8
1762275368,900.37109375,10.3
1762275428,900.33984375,14.3
1762275488,902.9375,13.7
1762275548,902.9765625,11.7
1762275609,903.3828125,13.8
1762275670,903.3828125,10.5
1762275730,903.3828125,12.6
1762275791,903.64453125,9.9
1762275852,903.64453125,9.3
1762275913,903.64453125,10.3
1762275973,903.64453125,11
1762276033,903.64453125,12.9
1762276093,903.64453125,14.9
1762276153,904.14453125,15
1762276213,904.14453125,15.7
1762276273,904.14453125,16.9
1762276356,922.46875,14.9
1762276451,921.6875,13.9
1762276511,921.703125,14.2
1762276571,921.66015625,14.3
1762276631,921.6640625,14.2
1762276691,921.65625,14.1
1762276751,921.6796875,14.2
1762276811,921.69921875,14.2
1762276871,921.67578125,14.1
1762276931,921.73828125,14.3
1762276991,921.76171875,15.6
1762277051,920.40234375,14.1
1762277111,920.42578125,16.6
1762277171,920.43359375,14.3
1762277231,920.484375,14.3
1762277291,920.46875,14.4
1762277351,920.51953125,14.3
1762277411,922.390625,14.4
1762277471,922.36328125,13.7
1762277531,922.359375,12.8
1762277592,924.72265625,14
1762277682,924.90234375,14.3
1762277742,926.890625,12.8
1762277802,926.90625,14.7
1762277862,926.890625,11.6
1762277922,927.140625,11.9
1762277982,929.41015625,15.6
1762278072,929.6015625,13.4
1762278135,931.8828125,14
1762278195,931.98046875,13.9
1762278255,931.98046875,13.4
1762278315,931.98046875,13.1
1762278375,931.98046875,11.7
1762278435,931.984375,12
1762278495,931.984375,13.6
1762278555,935.42578125,13.5
1762278615,935.53515625,13.6
1762278675,935.53515625,13.5
1762278736,935.53515625,13.1
1762278796,935.53515625,14
1762278857,935.53515625,13
1762278917,935.53515625,13.1
1762278977,935.53515625,13.3
1762279037,935.53515625,12.6
1762279097,935.53515625,13.1
1762279157,935.53515625,13.3
1762279217,935.53515625,12.5
1762279277,935.53515625,12.9
1762279341,935.53515625,13.1
1762279401,935.53515625,13.2
1762279461,938.0546875,13.1
1762279521,938.0546875,13.2
1762279581,938.18359375,13.1
1762279641,938.18359375,13.1
1762279701,938.18359375,13.2
1762279761,938.18359375,13.2
1762279821,938.18359375,13.1
1762279881,938.18359375,13
1762279941,938.18359375,13.1
1762280001,938.18359375,13.1
1762280061,938.18359375,13.1
1762280122,938.18359375,13.1
1762280182,938.18359375,13.2
1762280242,938.18359375,11.2
1762280302,938.18359375,13.2
1762280362,938.18359375,13.1
1762280422,938.17578125,13.2
1762280482,938.17578125,13.2
1762280542,938.17578125,12.2
1762280602,938.67578125,14.5
1762280662,938.67578125,13.3
1762280727,938.67578125,11.9
1762280787,941.140625,13.6
1762280847,941.140625,13.5
1762280907,941.140625,13.3
1762280968,941.140625,13.4
1762281028,941.140625,13.1
1762281088,943.2578125,12.7
1762281148,943.2578125,12.5
1762281208,943.2578125,14.2
1762281268,943.2578125,13.3
1762281384,945.2578125,16
1762281474,963.62109375,13.5
1762281540,962.078125,14.4
1762281600,962.08984375,14.6
1762281660,962.08203125,14
1762281720,962.08203125,15
1762281780,962.125,14.8
1762281840,962.0703125,11.9
1762281900,962.125,13.8
1762281960,962.0859375,16.6
1762282020,962.0859375,16.9
1762282080,962.12890625,16.9
1762282140,962.09765625,16.9
1762282200,962.14453125,16.8
1762282260,962.109375,16.6
1762282320,962.15234375,16.5
1762282380,962.109375,16.3
1762282440,962.12890625,18
1762282500,962.10546875,17.6
1762282563,962.17578125,17.7
1762282623,962.125,19
1762282683,962.1328125,18
1762282743,962.109375,18.5
1762282803,962.12109375,18.8
1762282863,966.6796875,18.5
1762282923,966.66015625,18.5
1762282983,967.109375,18.8
1762283046,969.41015625,19.3
1762283107,976.01953125,19.4
1762283169,976.01171875,19.2
1762283273,981.01171875,19.5
1762283368,999.0625,18.7
1762283428,999.109375,18.6
1762283488,999.125,18.3
1762283548,999.0625,16.6
1762283608,999.9765625,18.6
1762283668,999.94921875,18.4
1762283728,999.921875,18.3
1762283789,999.93359375,18.1
1762283849,999.91015625,18.3
1762283909,999.953125,18.6
1762283969,999.921875,18
1762284029,999.984375,18.3
1762284089,999.953125,18.4
1762284149,999.93359375,18.3
1762284209,1000.15625,18.3
1762284269,1000.16015625,18.5
1762284329,1000.171875,18.6
1762284389,1000.171875,18.4
1762284449,1000.16796875,18.3
1762284509,1000.1953125,18
1762284569,1000.18359375,18.2
1762284629,1000.171875,18.2
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1762314527,1050.55078125,11.4
1762314661,1059.71875,12.4
1762314731,1059.71875,9.1
1762314791,1062.8515625,8.8
1762314851,1062.8515625,8.1
1762314911,1062.8515625,8.2
1762314971,1062.8515625,6.7
1762315031,1062.8515625,6.6
1762315091,1062.8515625,6.8
1762315151,1062.8515625,7.2
1762315211,1062.8515625,7.1
1762315271,1062.8515625,7.2
1762315331,1062.8515625,7.2
1762315391,1062.8515625,7.2
1762315451,1062.8515625,7.2
1762315511,1062.8515625,7.5
1762315571,1062.8515625,7.3
1762315631,1062.8515625,7.8
1762315691,1062.8515625,7.4
1762315751,1062.8515625,7.5
1762315811,1062.8515625,7.4
1762315871,1062.8515625,7.5
1762315931,1062.8515625,7.3
1762315991,1062.8515625,7.4
1762316051,1062.8515625,7.3
1762316111,1071.3515625,7.4
1762316171,1075.3515625,7.4
1762316232,1081.8515625,7.4
1762316292,1088.3515625,7.3
1762316352,1088.3515625,7.4
1762316412,1088.3515625,7.3
1762316472,1088.3515625,7.3
1762316532,1088.3515625,7.3
1762316592,1088.3515625,7.3
1762316652,1088.3515625,7.3
1762316712,1088.3515625,7.3
1762316772,1088.3515625,7.3
1762316832,1088.3515625,7.3
1762316978,1091.6796875,8.1
1762317055,1092.1796875,8.2
1762317182,1085.54296875,8.3
1762317281,1087.7734375,8.3
1762317381,1091.2734375,8.3
1762317480,1096.78125,8.3
1762317583,1096.78125,8.3
1762317686,1096.78125,8.3
1762317786,1096.78125,8.3
1762317889,1097.28125,8.2
1762317989,1097.28125,8.3
1762318059,1097.28125,6.9
1762318162,1097.28125,8.3
1762318277,1097.28125,8.2
1762318382,1097.78125,8.2
1762318486,1097.78125,8.2
1762318586,1097.78125,8.4
1762318685,1097.78125,8.3
1762318790,1097.78125,8.3
1762318890,1097.78125,8.3
1762318997,1098.28125,8.2
1762319093,1098.28125,8.3
1762319199,1098.78125,8.6
1762319305,1098.78125,8.5
1762319417,1098.78125,8.4
1762319523,1099.28125,8.5
1762319628,1099.28125,8.4
1762319732,1099.28125,8.4
1762319835,1099.28125,8.4
1762319943,1099.28125,8.5
1762320048,1099.28125,8.5
1762320147,1099.78125,8.5
1762320257,1099.78125,8.5
1762320370,1099.78125,8.2
1762320474,1099.78125,8
1762320576,1099.78125,7.9
1762320683,1099.78125,7.8
1762320781,1100.28125,7.5
1762320886,1100.28125,7.2
1762321025,1098.90625,7.2
1762321131,1098.90625,7.3
1762321233,1099.40625,7.4
1762321339,1099.40625,7.4
1762321444,1099.40625,7.4
1762321551,1099.90625,7.5
1762321650,1099.90625,7.5
1762321751,1100.90625,7.4
1762321860,1100.90625,7.4
1762321966,1100.90625,7.4
1762322079,1100.90625,7.4
1762322183,1100.90625,7.4
1762322286,1101.40625,7.4
1762322392,1101.40625,7.4
VAL_ACC (goal: maximize)
Best value: 65.7
Achieved at:
- run_time = 6947
- epochs = 150
- lr = 0.001
- batch_size = 8
- hidden_size = 1853
- dropout = 0.5
- num_dense_layers = 1
- filter = 80
- num_conv_layers = 7
Parameter statistics
| Parameter | Min | Max | Mean | Std Dev | Count |
|---|
| run_time | 231 | 7218 | 2283.12 | 1744.4215 | 125 |
| VAL_ACC | 1 | 65.7 | 52.0594 | 18.0849 | 125 |
| epochs | 20 | 150 | 108.6706 | 41.2769 | 170 |
| lr | 0.0001 | 0.001 | 0.0008 | 0.0003 | 170 |
| batch_size | 8 | 1024 | 284.4471 | 343.5079 | 170 |
| hidden_size | 8 | 4096 | 1923.9765 | 1322.6697 | 170 |
| dropout | 0 | 0.5 | 0.25 | 0.1842 | 170 |
| num_dense_layers | 1 | 2 | 1.3529 | 0.4779 | 170 |
| filter | 4 | 80 | 56.1588 | 18.4223 | 170 |
| num_conv_layers | 4 | 7 | 4.9471 | 1.1944 | 170 |
Show SLURM-Job-ID (if it exists)
submitit INFO (2025-11-04 16:32:36,589) - Starting with JobEnvironment(job_id=1216290, hostname=c143, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-11-04 16:32:36,591) - Loading pickle: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime_mono/1/single_runs/1216290/1216290_submitted.pkl
Trial-Index: 6
/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/utils/data/dataloader.py:627: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(
Parameters: {"epochs": 141, "lr": 0.00010308470716699958, "batch_size": 785, "hidden_size": 2475, "dropout": 0.09324374934658408, "num_dense_layers": 1, "filter": 32, "num_conv_layers": 4}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 141 --learning_rate 0.00010308470716699958 --batch_size 785 --hidden_size 2475 --dropout 0.09324374934658408165 --num_dense_layers 1 --filter 32 --num_conv_layers 4
pwd: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt
File: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train
UID: 2105408
GID: 200270
SLURM_JOB_ID: 1216290
Status-Change-Time: 1762264114.0
Size: 20257 Bytes
Permissions: -rwxr-xr-x
Owner: s3811141
Last access: 1762270319.0
Last modification: 1762260506.0
Hostname: c143
========
DEBUG INFOS END
python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 141 --learning_rate 0.00010308470716699958 --batch_size 785 --hidden_size 2475 --dropout 0.09324374934658408165 --num_dense_layers 1 --filter 32 --num_conv_layers 4
stdout:
Hyperparameters
╭──────────────────┬────────────────────────╮
│ Parameter │ Value │
├──────────────────┼────────────────────────┤
│ Epochs │ 141 │
│ Num Dense Layers │ 1 │
│ Batch size │ 785 │
│ Learning rate │ 0.00010308470716699958 │
│ Hidden size │ 2475 │
│ Dropout │ 0.09324374934658408 │
│ Optimizer │ adam │
│ Momentum │ 0.9 │
│ Weight Decay │ 0.0001 │
│ Activation │ relu │
│ Dense-Activation │ relu │
│ Init Method │ kaiming │
│ Seed │ None │
│ Conv Filters │ 32 │
│ Num Conv Layers │ 4 │
│ Conv Kernel │ 3 │
│ Conv Stride │ 1 │
│ Conv Padding │ 1 │
│ Pool-Stride │ 2 │
│ Pool-Kernel │ 2 │
╰──────────────────┴────────────────────────╯
Model Summary
╭─────────────────┬─────────────────┬─────────╮
│ Layer │ Output Shape │ Param # │
├─────────────────┼─────────────────┼─────────┤
│ conv::conv0 │ [1, 32, 32, 32] │ 896 │
│ conv::bn0 │ [1, 32, 32, 32] │ 64 │
│ conv::act_conv0 │ [1, 32, 32, 32] │ 0 │
│ conv::conv1 │ [1, 32, 32, 32] │ 9248 │
│ conv::bn1 │ [1, 32, 32, 32] │ 64 │
│ conv::act_conv1 │ [1, 32, 32, 32] │ 0 │
│ conv::pool1 │ [1, 32, 16, 16] │ 0 │
│ conv::conv2 │ [1, 32, 16, 16] │ 9248 │
│ conv::bn2 │ [1, 32, 16, 16] │ 64 │
│ conv::act_conv2 │ [1, 32, 16, 16] │ 0 │
│ conv::conv3 │ [1, 32, 16, 16] │ 9248 │
│ conv::bn3 │ [1, 32, 16, 16] │ 64 │
│ conv::act_conv3 │ [1, 32, 16, 16] │ 0 │
│ conv::pool2 │ [1, 32, 8, 8] │ 0 │
│ dense::fc0 │ [1, 2475] │ 5071275 │
│ dense::act0 │ [1, 2475] │ 0 │
│ dense::dropout0 │ [1, 2475] │ 0 │
│ dense::output │ [1, 100] │ 247600 │
│ Total │ - │ 5347771 │
╰─────────────────┴─────────────────┴─────────╯
──────────────────────────── Epoch 1/141 - Training ────────────────────────────
Epoch-Loss: 281.0326
─────────────────────────── Epoch 1/141 - Validation ───────────────────────────
╔══ Epoch 1/141 Summary ══╗
║ Validation Loss: 3.8690 ║
║ Accuracy: 12.37% ║
╚═════════════════════════╝
──────────────────────────── Epoch 2/141 - Training ────────────────────────────
Epoch-Loss: 244.9271
─────────────────────────── Epoch 2/141 - Validation ───────────────────────────
╔══ Epoch 2/141 Summary ══╗
║ Validation Loss: 3.5799 ║
║ Accuracy: 17.05% ║
╚═════════════════════════╝
──────────────────────────── Epoch 3/141 - Training ────────────────────────────
Epoch-Loss: 230.9683
─────────────────────────── Epoch 3/141 - Validation ───────────────────────────
╔══ Epoch 3/141 Summary ══╗
║ Validation Loss: 3.3958 ║
║ Accuracy: 20.65% ║
╚═════════════════════════╝
──────────────────────────── Epoch 4/141 - Training ────────────────────────────
Epoch-Loss: 221.1593
─────────────────────────── Epoch 4/141 - Validation ───────────────────────────
╔══ Epoch 4/141 Summary ══╗
║ Validation Loss: 3.2688 ║
║ Accuracy: 22.45% ║
╚═════════════════════════╝
──────────────────────────── Epoch 5/141 - Training ────────────────────────────
Epoch-Loss: 214.0623
─────────────────────────── Epoch 5/141 - Validation ───────────────────────────
╔══ Epoch 5/141 Summary ══╗
║ Validation Loss: 3.1791 ║
║ Accuracy: 24.00% ║
╚═════════════════════════╝
──────────────────────────── Epoch 6/141 - Training ────────────────────────────
Epoch-Loss: 208.5748
─────────────────────────── Epoch 6/141 - Validation ───────────────────────────
╔══ Epoch 6/141 Summary ══╗
║ Validation Loss: 3.0825 ║
║ Accuracy: 25.84% ║
╚═════════════════════════╝
──────────────────────────── Epoch 7/141 - Training ────────────────────────────
Epoch-Loss: 203.5849
─────────────────────────── Epoch 7/141 - Validation ───────────────────────────
╔══ Epoch 7/141 Summary ══╗
║ Validation Loss: 3.0113 ║
║ Accuracy: 27.00% ║
╚═════════════════════════╝
──────────────────────────── Epoch 8/141 - Training ────────────────────────────
Epoch-Loss: 199.2895
─────────────────────────── Epoch 8/141 - Validation ───────────────────────────
╔══ Epoch 8/141 Summary ══╗
║ Validation Loss: 2.9474 ║
║ Accuracy: 28.51% ║
╚═════════════════════════╝
──────────────────────────── Epoch 9/141 - Training ────────────────────────────
Epoch-Loss: 194.7300
─────────────────────────── Epoch 9/141 - Validation ───────────────────────────
╔══ Epoch 9/141 Summary ══╗
║ Validation Loss: 2.8867 ║
║ Accuracy: 29.21% ║
╚═════════════════════════╝
─────────────────────────── Epoch 10/141 - Training ────────────────────────────
Epoch-Loss: 190.7255
────────────────────────── Epoch 10/141 - Validation ───────────────────────────
╔═ Epoch 10/141 Summary ══╗
║ Validation Loss: 2.8269 ║
║ Accuracy: 30.51% ║
╚═════════════════════════╝
─────────────────────────── Epoch 11/141 - Training ────────────────────────────
Epoch-Loss: 187.1069
────────────────────────── Epoch 11/141 - Validation ───────────────────────────
╔═ Epoch 11/141 Summary ══╗
║ Validation Loss: 2.7904 ║
║ Accuracy: 31.23% ║
╚═════════════════════════╝
─────────────────────────── Epoch 12/141 - Training ────────────────────────────
Epoch-Loss: 183.8687
────────────────────────── Epoch 12/141 - Validation ───────────────────────────
╔═ Epoch 12/141 Summary ══╗
║ Validation Loss: 2.7447 ║
║ Accuracy: 31.92% ║
╚═════════════════════════╝
─────────────────────────── Epoch 13/141 - Training ────────────────────────────
Epoch-Loss: 180.5614
────────────────────────── Epoch 13/141 - Validation ───────────────────────────
╔═ Epoch 13/141 Summary ══╗
║ Validation Loss: 2.7101 ║
║ Accuracy: 32.76% ║
╚═════════════════════════╝
─────────────────────────── Epoch 14/141 - Training ────────────────────────────
Epoch-Loss: 178.2014
────────────────────────── Epoch 14/141 - Validation ───────────────────────────
╔═ Epoch 14/141 Summary ══╗
║ Validation Loss: 2.6679 ║
║ Accuracy: 33.57% ║
╚═════════════════════════╝
─────────────────────────── Epoch 15/141 - Training ────────────────────────────
Epoch-Loss: 175.5195
────────────────────────── Epoch 15/141 - Validation ───────────────────────────
╔═ Epoch 15/141 Summary ══╗
║ Validation Loss: 2.6319 ║
║ Accuracy: 34.37% ║
╚═════════════════════════╝
─────────────────────────── Epoch 16/141 - Training ────────────────────────────
Epoch-Loss: 172.9396
────────────────────────── Epoch 16/141 - Validation ───────────────────────────
╔═ Epoch 16/141 Summary ══╗
║ Validation Loss: 2.6018 ║
║ Accuracy: 35.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 17/141 - Training ────────────────────────────
Epoch-Loss: 170.4794
────────────────────────── Epoch 17/141 - Validation ───────────────────────────
╔═ Epoch 17/141 Summary ══╗
║ Validation Loss: 2.5706 ║
║ Accuracy: 35.45% ║
╚═════════════════════════╝
─────────────────────────── Epoch 18/141 - Training ────────────────────────────
Epoch-Loss: 167.8748
────────────────────────── Epoch 18/141 - Validation ───────────────────────────
╔═ Epoch 18/141 Summary ══╗
║ Validation Loss: 2.5419 ║
║ Accuracy: 36.25% ║
╚═════════════════════════╝
─────────────────────────── Epoch 19/141 - Training ────────────────────────────
Epoch-Loss: 165.6578
────────────────────────── Epoch 19/141 - Validation ───────────────────────────
╔═ Epoch 19/141 Summary ══╗
║ Validation Loss: 2.5082 ║
║ Accuracy: 37.01% ║
╚═════════════════════════╝
─────────────────────────── Epoch 20/141 - Training ────────────────────────────
Epoch-Loss: 163.3709
────────────────────────── Epoch 20/141 - Validation ───────────────────────────
╔═ Epoch 20/141 Summary ══╗
║ Validation Loss: 2.4881 ║
║ Accuracy: 37.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 21/141 - Training ────────────────────────────
Epoch-Loss: 161.5093
────────────────────────── Epoch 21/141 - Validation ───────────────────────────
╔═ Epoch 21/141 Summary ══╗
║ Validation Loss: 2.4678 ║
║ Accuracy: 37.99% ║
╚═════════════════════════╝
─────────────────────────── Epoch 22/141 - Training ────────────────────────────
Epoch-Loss: 159.3756
────────────────────────── Epoch 22/141 - Validation ───────────────────────────
╔═ Epoch 22/141 Summary ══╗
║ Validation Loss: 2.4456 ║
║ Accuracy: 38.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 23/141 - Training ────────────────────────────
Epoch-Loss: 157.0457
────────────────────────── Epoch 23/141 - Validation ───────────────────────────
╔═ Epoch 23/141 Summary ══╗
║ Validation Loss: 2.4131 ║
║ Accuracy: 39.02% ║
╚═════════════════════════╝
─────────────────────────── Epoch 24/141 - Training ────────────────────────────
Epoch-Loss: 155.3205
────────────────────────── Epoch 24/141 - Validation ───────────────────────────
╔═ Epoch 24/141 Summary ══╗
║ Validation Loss: 2.4008 ║
║ Accuracy: 39.33% ║
╚═════════════════════════╝
─────────────────────────── Epoch 25/141 - Training ────────────────────────────
Epoch-Loss: 153.5701
────────────────────────── Epoch 25/141 - Validation ───────────────────────────
╔═ Epoch 25/141 Summary ══╗
║ Validation Loss: 2.3764 ║
║ Accuracy: 39.86% ║
╚═════════════════════════╝
─────────────────────────── Epoch 26/141 - Training ────────────────────────────
Epoch-Loss: 151.9173
────────────────────────── Epoch 26/141 - Validation ───────────────────────────
╔═ Epoch 26/141 Summary ══╗
║ Validation Loss: 2.3531 ║
║ Accuracy: 40.79% ║
╚═════════════════════════╝
─────────────────────────── Epoch 27/141 - Training ────────────────────────────
Epoch-Loss: 150.5633
────────────────────────── Epoch 27/141 - Validation ───────────────────────────
╔═ Epoch 27/141 Summary ══╗
║ Validation Loss: 2.3387 ║
║ Accuracy: 40.47% ║
╚═════════════════════════╝
─────────────────────────── Epoch 28/141 - Training ────────────────────────────
Epoch-Loss: 149.0119
────────────────────────── Epoch 28/141 - Validation ───────────────────────────
╔═ Epoch 28/141 Summary ══╗
║ Validation Loss: 2.3244 ║
║ Accuracy: 41.24% ║
╚═════════════════════════╝
─────────────────────────── Epoch 29/141 - Training ────────────────────────────
Epoch-Loss: 146.7817
────────────────────────── Epoch 29/141 - Validation ───────────────────────────
╔═ Epoch 29/141 Summary ══╗
║ Validation Loss: 2.3012 ║
║ Accuracy: 41.34% ║
╚═════════════════════════╝
─────────────────────────── Epoch 30/141 - Training ────────────────────────────
Epoch-Loss: 145.1031
────────────────────────── Epoch 30/141 - Validation ───────────────────────────
╔═ Epoch 30/141 Summary ══╗
║ Validation Loss: 2.2887 ║
║ Accuracy: 41.77% ║
╚═════════════════════════╝
─────────────────────────── Epoch 31/141 - Training ────────────────────────────
Epoch-Loss: 141.6777
────────────────────────── Epoch 31/141 - Validation ───────────────────────────
╔═ Epoch 31/141 Summary ══╗
║ Validation Loss: 2.2573 ║
║ Accuracy: 42.62% ║
╚═════════════════════════╝
─────────────────────────── Epoch 32/141 - Training ────────────────────────────
Epoch-Loss: 141.2472
────────────────────────── Epoch 32/141 - Validation ───────────────────────────
╔═ Epoch 32/141 Summary ══╗
║ Validation Loss: 2.2534 ║
║ Accuracy: 42.68% ║
╚═════════════════════════╝
─────────────────────────── Epoch 33/141 - Training ────────────────────────────
Epoch-Loss: 140.6238
────────────────────────── Epoch 33/141 - Validation ───────────────────────────
╔═ Epoch 33/141 Summary ══╗
║ Validation Loss: 2.2515 ║
║ Accuracy: 42.89% ║
╚═════════════════════════╝
─────────────────────────── Epoch 34/141 - Training ────────────────────────────
Epoch-Loss: 140.5637
────────────────────────── Epoch 34/141 - Validation ───────────────────────────
╔═ Epoch 34/141 Summary ══╗
║ Validation Loss: 2.2487 ║
║ Accuracy: 42.62% ║
╚═════════════════════════╝
─────────────────────────── Epoch 35/141 - Training ────────────────────────────
Epoch-Loss: 140.6202
────────────────────────── Epoch 35/141 - Validation ───────────────────────────
╔═ Epoch 35/141 Summary ══╗
║ Validation Loss: 2.2452 ║
║ Accuracy: 42.84% ║
╚═════════════════════════╝
─────────────────────────── Epoch 36/141 - Training ────────────────────────────
Epoch-Loss: 139.9363
────────────────────────── Epoch 36/141 - Validation ───────────────────────────
╔═ Epoch 36/141 Summary ══╗
║ Validation Loss: 2.2427 ║
║ Accuracy: 42.86% ║
╚═════════════════════════╝
─────────────────────────── Epoch 37/141 - Training ────────────────────────────
Epoch-Loss: 139.8579
────────────────────────── Epoch 37/141 - Validation ───────────────────────────
╔═ Epoch 37/141 Summary ══╗
║ Validation Loss: 2.2405 ║
║ Accuracy: 42.76% ║
╚═════════════════════════╝
─────────────────────────── Epoch 38/141 - Training ────────────────────────────
Epoch-Loss: 139.6221
────────────────────────── Epoch 38/141 - Validation ───────────────────────────
╔═ Epoch 38/141 Summary ══╗
║ Validation Loss: 2.2372 ║
║ Accuracy: 43.03% ║
╚═════════════════════════╝
─────────────────────────── Epoch 39/141 - Training ────────────────────────────
Epoch-Loss: 139.1610
────────────────────────── Epoch 39/141 - Validation ───────────────────────────
╔═ Epoch 39/141 Summary ══╗
║ Validation Loss: 2.2364 ║
║ Accuracy: 42.91% ║
╚═════════════════════════╝
─────────────────────────── Epoch 40/141 - Training ────────────────────────────
Epoch-Loss: 139.2054
────────────────────────── Epoch 40/141 - Validation ───────────────────────────
╔═ Epoch 40/141 Summary ══╗
║ Validation Loss: 2.2371 ║
║ Accuracy: 43.01% ║
╚═════════════════════════╝
─────────────────────────── Epoch 41/141 - Training ────────────────────────────
Epoch-Loss: 139.4482
────────────────────────── Epoch 41/141 - Validation ───────────────────────────
╔═ Epoch 41/141 Summary ══╗
║ Validation Loss: 2.2347 ║
║ Accuracy: 43.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 42/141 - Training ────────────────────────────
Epoch-Loss: 138.9242
────────────────────────── Epoch 42/141 - Validation ───────────────────────────
╔═ Epoch 42/141 Summary ══╗
║ Validation Loss: 2.2340 ║
║ Accuracy: 43.01% ║
╚═════════════════════════╝
─────────────────────────── Epoch 43/141 - Training ────────────────────────────
Epoch-Loss: 138.8625
────────────────────────── Epoch 43/141 - Validation ───────────────────────────
╔═ Epoch 43/141 Summary ══╗
║ Validation Loss: 2.2303 ║
║ Accuracy: 43.00% ║
╚═════════════════════════╝
─────────────────────────── Epoch 44/141 - Training ────────────────────────────
Epoch-Loss: 138.2975
────────────────────────── Epoch 44/141 - Validation ───────────────────────────
╔═ Epoch 44/141 Summary ══╗
║ Validation Loss: 2.2282 ║
║ Accuracy: 43.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 45/141 - Training ────────────────────────────
Epoch-Loss: 138.2269
────────────────────────── Epoch 45/141 - Validation ───────────────────────────
╔═ Epoch 45/141 Summary ══╗
║ Validation Loss: 2.2274 ║
║ Accuracy: 43.35% ║
╚═════════════════════════╝
─────────────────────────── Epoch 46/141 - Training ────────────────────────────
Epoch-Loss: 138.4784
────────────────────────── Epoch 46/141 - Validation ───────────────────────────
╔═ Epoch 46/141 Summary ══╗
║ Validation Loss: 2.2249 ║
║ Accuracy: 43.26% ║
╚═════════════════════════╝
─────────────────────────── Epoch 47/141 - Training ────────────────────────────
Epoch-Loss: 138.1074
────────────────────────── Epoch 47/141 - Validation ───────────────────────────
╔═ Epoch 47/141 Summary ══╗
║ Validation Loss: 2.2228 ║
║ Accuracy: 43.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 48/141 - Training ────────────────────────────
Epoch-Loss: 138.3265
────────────────────────── Epoch 48/141 - Validation ───────────────────────────
╔═ Epoch 48/141 Summary ══╗
║ Validation Loss: 2.2213 ║
║ Accuracy: 43.32% ║
╚═════════════════════════╝
─────────────────────────── Epoch 49/141 - Training ────────────────────────────
Epoch-Loss: 138.0333
────────────────────────── Epoch 49/141 - Validation ───────────────────────────
╔═ Epoch 49/141 Summary ══╗
║ Validation Loss: 2.2212 ║
║ Accuracy: 43.41% ║
╚═════════════════════════╝
─────────────────────────── Epoch 50/141 - Training ────────────────────────────
Epoch-Loss: 137.3699
────────────────────────── Epoch 50/141 - Validation ───────────────────────────
╔═ Epoch 50/141 Summary ══╗
║ Validation Loss: 2.2164 ║
║ Accuracy: 43.64% ║
╚═════════════════════════╝
─────────────────────────── Epoch 51/141 - Training ────────────────────────────
Epoch-Loss: 137.8775
────────────────────────── Epoch 51/141 - Validation ───────────────────────────
╔═ Epoch 51/141 Summary ══╗
║ Validation Loss: 2.2141 ║
║ Accuracy: 43.55% ║
╚═════════════════════════╝
─────────────────────────── Epoch 52/141 - Training ────────────────────────────
Epoch-Loss: 137.0785
────────────────────────── Epoch 52/141 - Validation ───────────────────────────
╔═ Epoch 52/141 Summary ══╗
║ Validation Loss: 2.2149 ║
║ Accuracy: 43.61% ║
╚═════════════════════════╝
─────────────────────────── Epoch 53/141 - Training ────────────────────────────
Epoch-Loss: 137.2457
────────────────────────── Epoch 53/141 - Validation ───────────────────────────
╔═ Epoch 53/141 Summary ══╗
║ Validation Loss: 2.2129 ║
║ Accuracy: 43.86% ║
╚═════════════════════════╝
─────────────────────────── Epoch 54/141 - Training ────────────────────────────
Epoch-Loss: 137.1478
────────────────────────── Epoch 54/141 - Validation ───────────────────────────
╔═ Epoch 54/141 Summary ══╗
║ Validation Loss: 2.2125 ║
║ Accuracy: 43.54% ║
╚═════════════════════════╝
─────────────────────────── Epoch 55/141 - Training ────────────────────────────
Epoch-Loss: 137.0322
────────────────────────── Epoch 55/141 - Validation ───────────────────────────
╔═ Epoch 55/141 Summary ══╗
║ Validation Loss: 2.2098 ║
║ Accuracy: 43.85% ║
╚═════════════════════════╝
─────────────────────────── Epoch 56/141 - Training ────────────────────────────
Epoch-Loss: 136.5377
────────────────────────── Epoch 56/141 - Validation ───────────────────────────
╔═ Epoch 56/141 Summary ══╗
║ Validation Loss: 2.2060 ║
║ Accuracy: 43.82% ║
╚═════════════════════════╝
─────────────────────────── Epoch 57/141 - Training ────────────────────────────
Epoch-Loss: 136.2921
────────────────────────── Epoch 57/141 - Validation ───────────────────────────
╔═ Epoch 57/141 Summary ══╗
║ Validation Loss: 2.2046 ║
║ Accuracy: 43.76% ║
╚═════════════════════════╝
─────────────────────────── Epoch 58/141 - Training ────────────────────────────
Epoch-Loss: 136.1371
────────────────────────── Epoch 58/141 - Validation ───────────────────────────
╔═ Epoch 58/141 Summary ══╗
║ Validation Loss: 2.2042 ║
║ Accuracy: 43.82% ║
╚═════════════════════════╝
─────────────────────────── Epoch 59/141 - Training ────────────────────────────
Epoch-Loss: 135.9464
────────────────────────── Epoch 59/141 - Validation ───────────────────────────
╔═ Epoch 59/141 Summary ══╗
║ Validation Loss: 2.2049 ║
║ Accuracy: 43.82% ║
╚═════════════════════════╝
─────────────────────────── Epoch 60/141 - Training ────────────────────────────
Epoch-Loss: 135.8138
────────────────────────── Epoch 60/141 - Validation ───────────────────────────
╔═ Epoch 60/141 Summary ══╗
║ Validation Loss: 2.2018 ║
║ Accuracy: 43.97% ║
╚═════════════════════════╝
─────────────────────────── Epoch 61/141 - Training ────────────────────────────
Epoch-Loss: 135.7658
────────────────────────── Epoch 61/141 - Validation ───────────────────────────
╔═ Epoch 61/141 Summary ══╗
║ Validation Loss: 2.2003 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 62/141 - Training ────────────────────────────
Epoch-Loss: 135.3649
────────────────────────── Epoch 62/141 - Validation ───────────────────────────
╔═ Epoch 62/141 Summary ══╗
║ Validation Loss: 2.2015 ║
║ Accuracy: 43.99% ║
╚═════════════════════════╝
─────────────────────────── Epoch 63/141 - Training ────────────────────────────
Epoch-Loss: 135.4348
────────────────────────── Epoch 63/141 - Validation ───────────────────────────
╔═ Epoch 63/141 Summary ══╗
║ Validation Loss: 2.1995 ║
║ Accuracy: 44.00% ║
╚═════════════════════════╝
─────────────────────────── Epoch 64/141 - Training ────────────────────────────
Epoch-Loss: 135.4150
────────────────────────── Epoch 64/141 - Validation ───────────────────────────
╔═ Epoch 64/141 Summary ══╗
║ Validation Loss: 2.1994 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 65/141 - Training ────────────────────────────
Epoch-Loss: 135.1891
────────────────────────── Epoch 65/141 - Validation ───────────────────────────
╔═ Epoch 65/141 Summary ══╗
║ Validation Loss: 2.1991 ║
║ Accuracy: 44.01% ║
╚═════════════════════════╝
─────────────────────────── Epoch 66/141 - Training ────────────────────────────
Epoch-Loss: 135.8634
────────────────────────── Epoch 66/141 - Validation ───────────────────────────
╔═ Epoch 66/141 Summary ══╗
║ Validation Loss: 2.1994 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 67/141 - Training ────────────────────────────
Epoch-Loss: 135.3192
────────────────────────── Epoch 67/141 - Validation ───────────────────────────
╔═ Epoch 67/141 Summary ══╗
║ Validation Loss: 2.1996 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 68/141 - Training ────────────────────────────
Epoch-Loss: 135.2925
────────────────────────── Epoch 68/141 - Validation ───────────────────────────
╔═ Epoch 68/141 Summary ══╗
║ Validation Loss: 2.1990 ║
║ Accuracy: 44.16% ║
╚═════════════════════════╝
─────────────────────────── Epoch 69/141 - Training ────────────────────────────
Epoch-Loss: 135.3174
────────────────────────── Epoch 69/141 - Validation ───────────────────────────
╔═ Epoch 69/141 Summary ══╗
║ Validation Loss: 2.1982 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 70/141 - Training ────────────────────────────
Epoch-Loss: 134.9860
────────────────────────── Epoch 70/141 - Validation ───────────────────────────
╔═ Epoch 70/141 Summary ══╗
║ Validation Loss: 2.1983 ║
║ Accuracy: 44.12% ║
╚═════════════════════════╝
─────────────────────────── Epoch 71/141 - Training ────────────────────────────
Epoch-Loss: 135.1212
────────────────────────── Epoch 71/141 - Validation ───────────────────────────
╔═ Epoch 71/141 Summary ══╗
║ Validation Loss: 2.1990 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 72/141 - Training ────────────────────────────
Epoch-Loss: 135.2837
────────────────────────── Epoch 72/141 - Validation ───────────────────────────
╔═ Epoch 72/141 Summary ══╗
║ Validation Loss: 2.1986 ║
║ Accuracy: 44.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 73/141 - Training ────────────────────────────
Epoch-Loss: 135.5827
────────────────────────── Epoch 73/141 - Validation ───────────────────────────
╔═ Epoch 73/141 Summary ══╗
║ Validation Loss: 2.1987 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 74/141 - Training ────────────────────────────
Epoch-Loss: 135.4396
────────────────────────── Epoch 74/141 - Validation ───────────────────────────
╔═ Epoch 74/141 Summary ══╗
║ Validation Loss: 2.1982 ║
║ Accuracy: 44.12% ║
╚═════════════════════════╝
─────────────────────────── Epoch 75/141 - Training ────────────────────────────
Epoch-Loss: 135.1325
────────────────────────── Epoch 75/141 - Validation ───────────────────────────
╔═ Epoch 75/141 Summary ══╗
║ Validation Loss: 2.1975 ║
║ Accuracy: 44.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 76/141 - Training ────────────────────────────
Epoch-Loss: 135.3259
────────────────────────── Epoch 76/141 - Validation ───────────────────────────
╔═ Epoch 76/141 Summary ══╗
║ Validation Loss: 2.1975 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 77/141 - Training ────────────────────────────
Epoch-Loss: 134.9572
────────────────────────── Epoch 77/141 - Validation ───────────────────────────
╔═ Epoch 77/141 Summary ══╗
║ Validation Loss: 2.1976 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 78/141 - Training ────────────────────────────
Epoch-Loss: 135.3170
────────────────────────── Epoch 78/141 - Validation ───────────────────────────
╔═ Epoch 78/141 Summary ══╗
║ Validation Loss: 2.1975 ║
║ Accuracy: 44.18% ║
╚═════════════════════════╝
─────────────────────────── Epoch 79/141 - Training ────────────────────────────
Epoch-Loss: 135.2385
────────────────────────── Epoch 79/141 - Validation ───────────────────────────
╔═ Epoch 79/141 Summary ══╗
║ Validation Loss: 2.1965 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 80/141 - Training ────────────────────────────
Epoch-Loss: 135.0382
────────────────────────── Epoch 80/141 - Validation ───────────────────────────
╔═ Epoch 80/141 Summary ══╗
║ Validation Loss: 2.1970 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 81/141 - Training ────────────────────────────
Epoch-Loss: 135.3847
────────────────────────── Epoch 81/141 - Validation ───────────────────────────
╔═ Epoch 81/141 Summary ══╗
║ Validation Loss: 2.1969 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 82/141 - Training ────────────────────────────
Epoch-Loss: 135.2499
────────────────────────── Epoch 82/141 - Validation ───────────────────────────
╔═ Epoch 82/141 Summary ══╗
║ Validation Loss: 2.1970 ║
║ Accuracy: 44.03% ║
╚═════════════════════════╝
─────────────────────────── Epoch 83/141 - Training ────────────────────────────
Epoch-Loss: 135.1756
────────────────────────── Epoch 83/141 - Validation ───────────────────────────
╔═ Epoch 83/141 Summary ══╗
║ Validation Loss: 2.1967 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 84/141 - Training ────────────────────────────
Epoch-Loss: 135.2532
────────────────────────── Epoch 84/141 - Validation ───────────────────────────
╔═ Epoch 84/141 Summary ══╗
║ Validation Loss: 2.1961 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 85/141 - Training ────────────────────────────
Epoch-Loss: 135.1212
────────────────────────── Epoch 85/141 - Validation ───────────────────────────
╔═ Epoch 85/141 Summary ══╗
║ Validation Loss: 2.1963 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 86/141 - Training ────────────────────────────
Epoch-Loss: 135.5477
────────────────────────── Epoch 86/141 - Validation ───────────────────────────
╔═ Epoch 86/141 Summary ══╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.14% ║
╚═════════════════════════╝
─────────────────────────── Epoch 87/141 - Training ────────────────────────────
Epoch-Loss: 135.1269
────────────────────────── Epoch 87/141 - Validation ───────────────────────────
╔═ Epoch 87/141 Summary ══╗
║ Validation Loss: 2.1962 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 88/141 - Training ────────────────────────────
Epoch-Loss: 135.3482
────────────────────────── Epoch 88/141 - Validation ───────────────────────────
╔═ Epoch 88/141 Summary ══╗
║ Validation Loss: 2.1961 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 89/141 - Training ────────────────────────────
Epoch-Loss: 135.2440
────────────────────────── Epoch 89/141 - Validation ───────────────────────────
╔═ Epoch 89/141 Summary ══╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 90/141 - Training ────────────────────────────
Epoch-Loss: 135.3016
────────────────────────── Epoch 90/141 - Validation ───────────────────────────
╔═ Epoch 90/141 Summary ══╗
║ Validation Loss: 2.1962 ║
║ Accuracy: 44.03% ║
╚═════════════════════════╝
─────────────────────────── Epoch 91/141 - Training ────────────────────────────
Epoch-Loss: 134.9473
────────────────────────── Epoch 91/141 - Validation ───────────────────────────
╔═ Epoch 91/141 Summary ══╗
║ Validation Loss: 2.1963 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 92/141 - Training ────────────────────────────
Epoch-Loss: 135.1455
────────────────────────── Epoch 92/141 - Validation ───────────────────────────
╔═ Epoch 92/141 Summary ══╗
║ Validation Loss: 2.1958 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 93/141 - Training ────────────────────────────
Epoch-Loss: 134.7862
────────────────────────── Epoch 93/141 - Validation ───────────────────────────
╔═ Epoch 93/141 Summary ══╗
║ Validation Loss: 2.1962 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 94/141 - Training ────────────────────────────
Epoch-Loss: 135.1959
────────────────────────── Epoch 94/141 - Validation ───────────────────────────
╔═ Epoch 94/141 Summary ══╗
║ Validation Loss: 2.1956 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 95/141 - Training ────────────────────────────
Epoch-Loss: 134.6833
────────────────────────── Epoch 95/141 - Validation ───────────────────────────
╔═ Epoch 95/141 Summary ══╗
║ Validation Loss: 2.1960 ║
║ Accuracy: 44.04% ║
╚═════════════════════════╝
─────────────────────────── Epoch 96/141 - Training ────────────────────────────
Epoch-Loss: 134.5079
────────────────────────── Epoch 96/141 - Validation ───────────────────────────
╔═ Epoch 96/141 Summary ══╗
║ Validation Loss: 2.1954 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 97/141 - Training ────────────────────────────
Epoch-Loss: 135.3642
────────────────────────── Epoch 97/141 - Validation ───────────────────────────
╔═ Epoch 97/141 Summary ══╗
║ Validation Loss: 2.1958 ║
║ Accuracy: 44.13% ║
╚═════════════════════════╝
─────────────────────────── Epoch 98/141 - Training ────────────────────────────
Epoch-Loss: 134.9442
────────────────────────── Epoch 98/141 - Validation ───────────────────────────
╔═ Epoch 98/141 Summary ══╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.17% ║
╚═════════════════════════╝
─────────────────────────── Epoch 99/141 - Training ────────────────────────────
Epoch-Loss: 135.2755
────────────────────────── Epoch 99/141 - Validation ───────────────────────────
╔═ Epoch 99/141 Summary ══╗
║ Validation Loss: 2.1955 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 100/141 - Training ───────────────────────────
Epoch-Loss: 135.1131
────────────────────────── Epoch 100/141 - Validation ──────────────────────────
╔═ Epoch 100/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.03% ║
╚═════════════════════════╝
─────────────────────────── Epoch 101/141 - Training ───────────────────────────
Epoch-Loss: 135.0650
────────────────────────── Epoch 101/141 - Validation ──────────────────────────
╔═ Epoch 101/141 Summary ═╗
║ Validation Loss: 2.1953 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 102/141 - Training ───────────────────────────
Epoch-Loss: 135.1945
────────────────────────── Epoch 102/141 - Validation ──────────────────────────
╔═ Epoch 102/141 Summary ═╗
║ Validation Loss: 2.1956 ║
║ Accuracy: 44.02% ║
╚═════════════════════════╝
─────────────────────────── Epoch 103/141 - Training ───────────────────────────
Epoch-Loss: 135.0194
────────────────────────── Epoch 103/141 - Validation ──────────────────────────
╔═ Epoch 103/141 Summary ═╗
║ Validation Loss: 2.1949 ║
║ Accuracy: 44.03% ║
╚═════════════════════════╝
─────────────────────────── Epoch 104/141 - Training ───────────────────────────
Epoch-Loss: 134.7878
────────────────────────── Epoch 104/141 - Validation ──────────────────────────
╔═ Epoch 104/141 Summary ═╗
║ Validation Loss: 2.1954 ║
║ Accuracy: 44.12% ║
╚═════════════════════════╝
─────────────────────────── Epoch 105/141 - Training ───────────────────────────
Epoch-Loss: 134.7098
────────────────────────── Epoch 105/141 - Validation ──────────────────────────
╔═ Epoch 105/141 Summary ═╗
║ Validation Loss: 2.1959 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 106/141 - Training ───────────────────────────
Epoch-Loss: 134.9597
────────────────────────── Epoch 106/141 - Validation ──────────────────────────
╔═ Epoch 106/141 Summary ═╗
║ Validation Loss: 2.1946 ║
║ Accuracy: 44.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 107/141 - Training ───────────────────────────
Epoch-Loss: 134.6732
────────────────────────── Epoch 107/141 - Validation ──────────────────────────
╔═ Epoch 107/141 Summary ═╗
║ Validation Loss: 2.1954 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 108/141 - Training ───────────────────────────
Epoch-Loss: 134.9245
────────────────────────── Epoch 108/141 - Validation ──────────────────────────
╔═ Epoch 108/141 Summary ═╗
║ Validation Loss: 2.1951 ║
║ Accuracy: 44.21% ║
╚═════════════════════════╝
─────────────────────────── Epoch 109/141 - Training ───────────────────────────
Epoch-Loss: 135.0768
────────────────────────── Epoch 109/141 - Validation ──────────────────────────
╔═ Epoch 109/141 Summary ═╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.12% ║
╚═════════════════════════╝
─────────────────────────── Epoch 110/141 - Training ───────────────────────────
Epoch-Loss: 134.9803
────────────────────────── Epoch 110/141 - Validation ──────────────────────────
╔═ Epoch 110/141 Summary ═╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.04% ║
╚═════════════════════════╝
─────────────────────────── Epoch 111/141 - Training ───────────────────────────
Epoch-Loss: 135.0155
────────────────────────── Epoch 111/141 - Validation ──────────────────────────
╔═ Epoch 111/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 112/141 - Training ───────────────────────────
Epoch-Loss: 135.0271
────────────────────────── Epoch 112/141 - Validation ──────────────────────────
╔═ Epoch 112/141 Summary ═╗
║ Validation Loss: 2.1953 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 113/141 - Training ───────────────────────────
Epoch-Loss: 135.2588
────────────────────────── Epoch 113/141 - Validation ──────────────────────────
╔═ Epoch 113/141 Summary ═╗
║ Validation Loss: 2.1957 ║
║ Accuracy: 44.04% ║
╚═════════════════════════╝
─────────────────────────── Epoch 114/141 - Training ───────────────────────────
Epoch-Loss: 135.1546
────────────────────────── Epoch 114/141 - Validation ──────────────────────────
╔═ Epoch 114/141 Summary ═╗
║ Validation Loss: 2.1954 ║
║ Accuracy: 44.05% ║
╚═════════════════════════╝
─────────────────────────── Epoch 115/141 - Training ───────────────────────────
Epoch-Loss: 135.3270
────────────────────────── Epoch 115/141 - Validation ──────────────────────────
╔═ Epoch 115/141 Summary ═╗
║ Validation Loss: 2.1954 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 116/141 - Training ───────────────────────────
Epoch-Loss: 135.0058
────────────────────────── Epoch 116/141 - Validation ──────────────────────────
╔═ Epoch 116/141 Summary ═╗
║ Validation Loss: 2.1950 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 117/141 - Training ───────────────────────────
Epoch-Loss: 135.0787
────────────────────────── Epoch 117/141 - Validation ──────────────────────────
╔═ Epoch 117/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 118/141 - Training ───────────────────────────
Epoch-Loss: 134.9599
────────────────────────── Epoch 118/141 - Validation ──────────────────────────
╔═ Epoch 118/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 119/141 - Training ───────────────────────────
Epoch-Loss: 135.3804
────────────────────────── Epoch 119/141 - Validation ──────────────────────────
╔═ Epoch 119/141 Summary ═╗
║ Validation Loss: 2.1946 ║
║ Accuracy: 44.13% ║
╚═════════════════════════╝
─────────────────────────── Epoch 120/141 - Training ───────────────────────────
Epoch-Loss: 135.1164
────────────────────────── Epoch 120/141 - Validation ──────────────────────────
╔═ Epoch 120/141 Summary ═╗
║ Validation Loss: 2.1949 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 121/141 - Training ───────────────────────────
Epoch-Loss: 134.7538
────────────────────────── Epoch 121/141 - Validation ──────────────────────────
╔═ Epoch 121/141 Summary ═╗
║ Validation Loss: 2.1955 ║
║ Accuracy: 44.07% ║
╚═════════════════════════╝
─────────────────────────── Epoch 122/141 - Training ───────────────────────────
Epoch-Loss: 135.4653
────────────────────────── Epoch 122/141 - Validation ──────────────────────────
╔═ Epoch 122/141 Summary ═╗
║ Validation Loss: 2.1947 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 123/141 - Training ───────────────────────────
Epoch-Loss: 134.9825
────────────────────────── Epoch 123/141 - Validation ──────────────────────────
╔═ Epoch 123/141 Summary ═╗
║ Validation Loss: 2.1951 ║
║ Accuracy: 44.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 124/141 - Training ───────────────────────────
Epoch-Loss: 134.9764
────────────────────────── Epoch 124/141 - Validation ──────────────────────────
╔═ Epoch 124/141 Summary ═╗
║ Validation Loss: 2.1953 ║
║ Accuracy: 44.01% ║
╚═════════════════════════╝
─────────────────────────── Epoch 125/141 - Training ───────────────────────────
Epoch-Loss: 135.1138
────────────────────────── Epoch 125/141 - Validation ──────────────────────────
╔═ Epoch 125/141 Summary ═╗
║ Validation Loss: 2.1956 ║
║ Accuracy: 44.13% ║
╚═════════════════════════╝
─────────────────────────── Epoch 126/141 - Training ───────────────────────────
Epoch-Loss: 135.2369
────────────────────────── Epoch 126/141 - Validation ──────────────────────────
╔═ Epoch 126/141 Summary ═╗
║ Validation Loss: 2.1951 ║
║ Accuracy: 44.04% ║
╚═════════════════════════╝
─────────────────────────── Epoch 127/141 - Training ───────────────────────────
Epoch-Loss: 134.6862
────────────────────────── Epoch 127/141 - Validation ──────────────────────────
╔═ Epoch 127/141 Summary ═╗
║ Validation Loss: 2.1958 ║
║ Accuracy: 43.99% ║
╚═════════════════════════╝
─────────────────────────── Epoch 128/141 - Training ───────────────────────────
Epoch-Loss: 135.1172
────────────────────────── Epoch 128/141 - Validation ──────────────────────────
╔═ Epoch 128/141 Summary ═╗
║ Validation Loss: 2.1950 ║
║ Accuracy: 44.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 129/141 - Training ───────────────────────────
Epoch-Loss: 134.9021
────────────────────────── Epoch 129/141 - Validation ──────────────────────────
╔═ Epoch 129/141 Summary ═╗
║ Validation Loss: 2.1949 ║
║ Accuracy: 44.11% ║
╚═════════════════════════╝
─────────────────────────── Epoch 130/141 - Training ───────────────────────────
Epoch-Loss: 135.0658
────────────────────────── Epoch 130/141 - Validation ──────────────────────────
╔═ Epoch 130/141 Summary ═╗
║ Validation Loss: 2.1944 ║
║ Accuracy: 44.21% ║
╚═════════════════════════╝
─────────────────────────── Epoch 131/141 - Training ───────────────────────────
Epoch-Loss: 134.7515
────────────────────────── Epoch 131/141 - Validation ──────────────────────────
╔═ Epoch 131/141 Summary ═╗
║ Validation Loss: 2.1950 ║
║ Accuracy: 44.00% ║
╚═════════════════════════╝
─────────────────────────── Epoch 132/141 - Training ───────────────────────────
Epoch-Loss: 135.5540
────────────────────────── Epoch 132/141 - Validation ──────────────────────────
╔═ Epoch 132/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.18% ║
╚═════════════════════════╝
─────────────────────────── Epoch 133/141 - Training ───────────────────────────
Epoch-Loss: 134.8060
────────────────────────── Epoch 133/141 - Validation ──────────────────────────
╔═ Epoch 133/141 Summary ═╗
║ Validation Loss: 2.1951 ║
║ Accuracy: 44.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 134/141 - Training ───────────────────────────
Epoch-Loss: 134.8806
────────────────────────── Epoch 134/141 - Validation ──────────────────────────
╔═ Epoch 134/141 Summary ═╗
║ Validation Loss: 2.1951 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 135/141 - Training ───────────────────────────
Epoch-Loss: 135.1187
────────────────────────── Epoch 135/141 - Validation ──────────────────────────
╔═ Epoch 135/141 Summary ═╗
║ Validation Loss: 2.1949 ║
║ Accuracy: 44.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 136/141 - Training ───────────────────────────
Epoch-Loss: 135.0254
────────────────────────── Epoch 136/141 - Validation ──────────────────────────
╔═ Epoch 136/141 Summary ═╗
║ Validation Loss: 2.1950 ║
║ Accuracy: 44.17% ║
╚═════════════════════════╝
─────────────────────────── Epoch 137/141 - Training ───────────────────────────
Epoch-Loss: 135.1444
────────────────────────── Epoch 137/141 - Validation ──────────────────────────
╔═ Epoch 137/141 Summary ═╗
║ Validation Loss: 2.1950 ║
║ Accuracy: 44.10% ║
╚═════════════════════════╝
─────────────────────────── Epoch 138/141 - Training ───────────────────────────
Epoch-Loss: 135.6434
────────────────────────── Epoch 138/141 - Validation ──────────────────────────
╔═ Epoch 138/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.16% ║
╚═════════════════════════╝
─────────────────────────── Epoch 139/141 - Training ───────────────────────────
Epoch-Loss: 135.3379
────────────────────────── Epoch 139/141 - Validation ──────────────────────────
╔═ Epoch 139/141 Summary ═╗
║ Validation Loss: 2.1947 ║
║ Accuracy: 44.13% ║
╚═════════════════════════╝
─────────────────────────── Epoch 140/141 - Training ───────────────────────────
Epoch-Loss: 134.6690
────────────────────────── Epoch 140/141 - Validation ──────────────────────────
╔═ Epoch 140/141 Summary ═╗
║ Validation Loss: 2.1947 ║
║ Accuracy: 44.08% ║
╚═════════════════════════╝
─────────────────────────── Epoch 141/141 - Training ───────────────────────────
Epoch-Loss: 135.2012
────────────────────────── Epoch 141/141 - Validation ──────────────────────────
╔═ Epoch 141/141 Summary ═╗
║ Validation Loss: 2.1952 ║
║ Accuracy: 44.04% ║
╚═════════════════════════╝
VAL_LOSS: 2.195161507679866
VAL_ACC: 44.04
RUNTIME: 1423.050
NORMALIZED_RUNTIME: 19.765
stderr:
/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/utils/data/dataloader.py:627: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
warnings.warn(
Result: {'VAL_ACC': 44.04}
Final-results: {'VAL_ACC': 44.04}
EXIT_CODE: 0
submitit INFO (2025-11-04 16:56:27,144) - Job completed successfully
submitit INFO (2025-11-04 16:56:27,145) - Exiting after successful completion
