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 |
Job Summary per Generation Node
| Generation Node | Total | COMPLETED | FAILED | RUNNING |
|---|
| SOBOL | 20 | 20 | 0 | 0 |
| BOTORCH_MODULAR | 60 | 40 | 1 | 19 |
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 |
| 1 |
60 |
19 |
80 |
Result names and types
| name | min/max |
|---|
| VAL_ACC |
max |
| NORMALIZED_RUNTIME |
min |
Last progressbar status
2025-11-04 13:52:52 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 59 done, running/pending 12/5 = ∑17/20, new result: VAL_ACC: 54.430000, NORMALIZED_RUNTIME: 4.430000
Git-Version
Commit: 5685610daff814b22e25b7680634b540b86a6250 (9042)
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,NORMALIZED_RUNTIME,epochs,lr,batch_size,hidden_size,dropout,num_dense_layers,filter,num_conv_layers
0,1762248936,77,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249013,1762249569,556,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 54 --learning_rate 0.00083459879159927374 --batch_size 578 --hidden_size 812 --dropout 0.01770839281380176544 --num_dense_layers 1 --filter 29 --num_conv_layers 5,0,,c139,1213265,0_0,COMPLETED,SOBOL,54.07999999999999829469743417576,7.610000000000000319744231092045,54,0.000834598791599273741315112574,578,812,0.01770839281380176544189453125,1,29,5
1,1762248936,35,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248971,1762250147,1176,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 99 --learning_rate 0.00037329954272136091 --batch_size 264 --hidden_size 3589 --dropout 0.31410578265786170959 --num_dense_layers 2 --filter 70 --num_conv_layers 7,0,,c143,1213255,1_0,COMPLETED,SOBOL,58.03000000000000113686837721616,16.277000000000001023181539494544,99,0.000373299542721360912665706788,264,3589,0.3141057826578617095947265625,2,70,7
2,1762248936,31,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248967,1762250329,1362,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 134 --learning_rate 0.00059112006882205612 --batch_size 774 --hidden_size 1401 --dropout 0.39918266050517559052 --num_dense_layers 2 --filter 18 --num_conv_layers 6,0,,c151,1213254,2_0,COMPLETED,SOBOL,42.96000000000000085265128291212,18.81500000000000127897692436818,134,0.000591120068822056118733321295,774,1401,0.39918266050517559051513671875,2,18,6
3,1762248936,69,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249005,1762249338,333,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 23 --learning_rate 0.00018081199964508414 --batch_size 71 --hidden_size 2138 --dropout 0.19850734947249293327 --num_dense_layers 1 --filter 52 --num_conv_layers 4,0,,c139,1213263,3_0,COMPLETED,SOBOL,54.869999999999997442046151263639,4.461000000000000298427949019242,23,0.000180811999645084144896708955,71,2138,0.198507349472492933273315429688,1,52,4
4,1762248936,69,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249005,1762249423,418,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 40 --learning_rate 0.00069996667467057711 --batch_size 514 --hidden_size 2782 --dropout 0.13071525515988469124 --num_dense_layers 1 --filter 6 --num_conv_layers 6,0,,c139,1213262,4_0,COMPLETED,SOBOL,32.25,5.727999999999999758415469841566,40,0.000699966674670577109103331015,514,2782,0.130715255159884691238403320312,1,6,6
5,1762248936,57,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248993,1762250517,1524,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 143 --learning_rate 0.00029653103519231081 --batch_size 709 --hidden_size 2052 --dropout 0.45933884475380182266 --num_dense_layers 2 --filter 44 --num_conv_layers 4,0,,c141,1213258,5_0,COMPLETED,SOBOL,49.46999999999999886313162278384,21.08999999999999985789145284798,143,0.000296531035192310814078975323,709,2052,0.459338844753801822662353515625,2,44,4
6,1762248936,17,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248953,1762250269,1316,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 112 --learning_rate 0.00095047403313219554 --batch_size 202 --hidden_size 3217 --dropout 0.2648745877668261528 --num_dense_layers 2 --filter 38 --num_conv_layers 5,0,,c152,1213252,6_0,COMPLETED,SOBOL,59.409999999999996589394868351519,18.198000000000000397903932025656,112,0.000950474033132195535167330291,202,3217,0.264874587766826152801513671875,2,38,5
7,1762248936,57,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248993,1762249779,786,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 75 --learning_rate 0.00048198649678379298 --batch_size 1024 --hidden_size 433 --dropout 0.09056706586852669716 --num_dense_layers 1 --filter 80 --num_conv_layers 7,0,,c140,1213259,7_0,COMPLETED,SOBOL,56.060000000000002273736754432321,10.811999999999999388933247246314,75,0.000481986496783792979243382648,1024,433,0.090567065868526697158813476562,1,80,7
8,1762248936,23,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248959,1762249850,891,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 79 --learning_rate 0.0006384497809223831 --batch_size 154 --hidden_size 1588 --dropout 0.35759810451418161392 --num_dense_layers 1 --filter 10 --num_conv_layers 6,0,,c143,1213253,8_0,COMPLETED,SOBOL,40.659999999999996589394868351519,12.25999999999999978683717927197,79,0.000638449780922383100176253912,154,1588,0.357598104514181613922119140625,1,10,6
9,1762248936,37,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248973,1762250115,1142,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 108 --learning_rate 0.00011547002019360662 --batch_size 945 --hidden_size 2829 --dropout 0.06021462054923176765 --num_dense_layers 2 --filter 50 --num_conv_layers 4,0,,c143,1213257,9_0,COMPLETED,SOBOL,49.689999999999997726263245567679,15.8000000000000007105427357601,108,0.00011547002019360662436898296,945,2829,0.060214620549231767654418945312,2,50,4
10,1762248936,62,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248998,1762250446,1448,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 139 --learning_rate 0.00078347551478073004 --batch_size 435 --hidden_size 130 --dropout 0.22344688186421990395 --num_dense_layers 2 --filter 36 --num_conv_layers 5,0,,c140,1213260,10_0,COMPLETED,SOBOL,42.53999999999999914734871708788,20.01999999999999957367435854394,139,0.000783475514780730035793721022,435,130,0.223446881864219903945922851562,2,36,5
11,1762248936,77,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249013,1762249506,493,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 45 --learning_rate 0.00043453210638836026 --batch_size 661 --hidden_size 3425 --dropout 0.42899164836853742599 --num_dense_layers 1 --filter 72 --num_conv_layers 7,0,,c139,1213264,11_0,COMPLETED,SOBOL,57.6499999999999985789145284798,6.740999999999999658939486835152,45,0.000434532106388360258881348175,661,3425,0.428991648368537425994873046875,1,72,7
12,1762248936,62,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248998,1762249386,388,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 35 --learning_rate 0.00089963065739721067 --batch_size 853 --hidden_size 4052 --dropout 0.49398107454180717468 --num_dense_layers 1 --filter 26 --num_conv_layers 5,0,,c140,1213261,12_0,COMPLETED,SOBOL,52.590000000000003410605131648481,5.336999999999999744204615126364,35,0.000899630657397210674319776302,853,4052,0.4939810745418071746826171875,1,26,5
13,1762248936,77,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249013,1762250651,1638,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 121 --learning_rate 0.00054381475225090978 --batch_size 118 --hidden_size 765 --dropout 0.16632450511679053307 --num_dense_layers 2 --filter 63 --num_conv_layers 7,0,,c138,1213266,13_0,COMPLETED,SOBOL,60.259999999999998010480339871719,22.685999999999999943156581139192,121,0.000543814752250909778409637685,118,765,0.166324505116790533065795898438,2,63,7
14,1762248936,30,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762248966,1762249850,884,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 86 --learning_rate 0.00074757278840988876 --batch_size 626 --hidden_size 2442 --dropout 0.11251587001606822014 --num_dense_layers 2 --filter 18 --num_conv_layers 6,0,,c143,1213256,14_0,COMPLETED,SOBOL,47.549999999999997157829056959599,12.143000000000000682121026329696,86,0.000747572788409888756844257074,626,2442,0.112515870016068220138549804688,2,18,6
15,1762248936,95,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249031,1762249743,712,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 66 --learning_rate 0.00023178136795759199 --batch_size 344 --hidden_size 1193 --dropout 0.28192723356187343597 --num_dense_layers 1 --filter 61 --num_conv_layers 4,0,,c137,1213267,15_0,COMPLETED,SOBOL,54.740000000000001989519660128281,9.79499999999999992894572642399,66,0.000231781367957591989941573685,344,1193,0.28192723356187343597412109375,1,61,4
16,1762248941,85,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249026,1762249688,662,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 61 --learning_rate 0.00073659409536048776 --batch_size 923 --hidden_size 3199 --dropout 0.20497544808313250542 --num_dense_layers 2 --filter 67 --num_conv_layers 6,0,,c137,1213268,16_0,COMPLETED,SOBOL,58.009999999999998010480339871719,9.087999999999999189981281233486,61,0.000736594095360487764560797341,923,3199,0.204975448083132505416870117188,2,67,6
17,1762248946,80,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249026,1762250007,981,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 89 --learning_rate 0.00024191028522327541 --batch_size 172 --hidden_size 352 --dropout 0.37733365781605243683 --num_dense_layers 1 --filter 31 --num_conv_layers 4,0,,c135,1213269,17_0,COMPLETED,SOBOL,46.049999999999997157829056959599,13.542999999999999261035554809496,89,0.000241910285223275410820126252,172,352,0.37733365781605243682861328125,1,31,4
18,1762248951,82,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249033,1762250355,1322,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 124 --learning_rate 0.00093785527823492883 --batch_size 679 --hidden_size 2605 --dropout 0.33888395875692367554 --num_dense_layers 1 --filter 54 --num_conv_layers 5,0,,c135,1213270,18_0,COMPLETED,SOBOL,61.380000000000002557953848736361,18.251000000000001222133505507372,124,0.000937855278234928828665073475,679,2605,0.338883958756923675537109375,1,54,5
19,1762248956,98,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762249054,1762249385,331,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 30 --learning_rate 0.0005046819907613099 --batch_size 413 --hidden_size 1809 --dropout 0.00831106072291731834 --num_dense_layers 2 --filter 15 --num_conv_layers 7,0,,c146,1213271,19_0,COMPLETED,SOBOL,38.71999999999999886313162278384,4.51999999999999957367435854394,30,0.000504681990761309895790476254,413,1809,0.008311060722917318344116210938,2,15,7
20,1762250769,933,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251702,1762252246,544,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 51 --learning_rate 0.00100000000000000002 --batch_size 532 --hidden_size 2484 --dropout 0.44940377002637238446 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c113,1213708,20_0,COMPLETED,BOTORCH_MODULAR,58.450000000000002842170943040401,7.474000000000000198951966012828,51,0.001000000000000000020816681712,532,2484,0.449403770026372384460700004638,1,46,6
21,1762250768,995,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251763,1762252234,471,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 41 --learning_rate 0.00100000000000000002 --batch_size 556 --hidden_size 2426 --dropout 0.38491831739471071838 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c140,1213711,21_0,COMPLETED,BOTORCH_MODULAR,58.96000000000000085265128291212,6.389000000000000234479102800833,41,0.001000000000000000020816681712,556,2426,0.384918317394710718382100367307,1,46,6
22,1762250768,1054,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251822,1762252298,476,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 45 --learning_rate 0.00100000000000000002 --batch_size 491 --hidden_size 2758 --dropout 0.40287713716791434537 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c130,1213719,22_0,COMPLETED,BOTORCH_MODULAR,58.92000000000000170530256582424,6.528000000000000468958205601666,45,0.001000000000000000020816681712,491,2758,0.402877137167914345372565776415,1,46,6
23,1762250768,964,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251732,1762252199,467,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 43 --learning_rate 0.00100000000000000002 --batch_size 498 --hidden_size 2652 --dropout 0.41846094478797818406 --num_dense_layers 1 --filter 47 --num_conv_layers 6,0,,c110,1213710,23_0,COMPLETED,BOTORCH_MODULAR,59.049999999999997157829056959599,6.394000000000000127897692436818,43,0.001000000000000000020816681712,498,2652,0.41846094478797818405624298066,1,47,6
24,1762250768,994,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251762,1762252202,440,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 40 --learning_rate 0.00100000000000000002 --batch_size 301 --hidden_size 3005 --dropout 0.38221541328606506438 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c128,1213713,24_0,COMPLETED,BOTORCH_MODULAR,59.240000000000001989519660128281,6.04499999999999992894572642399,40,0.001000000000000000020816681712,301,3005,0.382215413286065064379926070615,1,46,6
25,1762250768,905,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251673,1762252133,460,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 43 --learning_rate 0.00100000000000000002 --batch_size 468 --hidden_size 2767 --dropout 0.40806849078876183956 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c140,1213702,25_0,COMPLETED,BOTORCH_MODULAR,59.57999999999999829469743417576,6.336000000000000298427949019242,43,0.001000000000000000020816681712,468,2767,0.408068490788761839560550015449,1,46,6
26,1762250768,1005,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251773,1762252243,470,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 43 --learning_rate 0.00100000000000000002 --batch_size 567 --hidden_size 2914 --dropout 0.38674501639115471674 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c71,1213714,26_0,COMPLETED,BOTORCH_MODULAR,58.64000000000000056843418860808,6.354000000000000092370555648813,43,0.001000000000000000020816681712,567,2914,0.386745016391154716739464447528,1,46,6
27,1762250769,903,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251672,1762252125,453,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 41 --learning_rate 0.00100000000000000002 --batch_size 348 --hidden_size 2641 --dropout 0.4086734612096968311 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c133,1213704,27_0,COMPLETED,BOTORCH_MODULAR,59.700000000000002842170943040401,6.211000000000000298427949019242,41,0.001000000000000000020816681712,348,2641,0.40867346120969683109791503739,1,46,6
28,1762250769,993,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251762,1762252231,469,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 500 --hidden_size 2900 --dropout 0.4242180816262464127 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c46,1213715,28_0,COMPLETED,BOTORCH_MODULAR,60.020000000000003126388037344441,6.418999999999999594990640616743,42,0.001000000000000000020816681712,500,2900,0.424218081626246412696445986512,1,46,6
29,1762250769,903,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251672,1762252184,512,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 46 --learning_rate 0.00100000000000000002 --batch_size 362 --hidden_size 2567 --dropout 0.4172354070297223938 --num_dense_layers 1 --filter 47 --num_conv_layers 6,0,,c112,1213706,29_0,COMPLETED,BOTORCH_MODULAR,59.96999999999999886313162278384,7.009000000000000341060513164848,46,0.001000000000000000020816681712,362,2567,0.417235407029722393801307589456,1,47,6
30,1762250768,909,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251677,1762252154,477,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 43 --learning_rate 0.00100000000000000002 --batch_size 407 --hidden_size 2834 --dropout 0.40030312958015917824 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c122,1213705,30_0,COMPLETED,BOTORCH_MODULAR,59,6.467999999999999971578290569596,43,0.001000000000000000020816681712,407,2834,0.400303129580159178235732042594,1,46,6
31,1762250769,1005,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251774,1762252243,469,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 524 --hidden_size 2848 --dropout 0.41959300994727460887 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c139,1213712,31_0,COMPLETED,BOTORCH_MODULAR,59.07999999999999829469743417576,6.314000000000000056843418860808,42,0.001000000000000000020816681712,524,2848,0.419593009947274608872902490475,1,46,6
32,1762250769,1028,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251797,1762252264,467,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 433 --hidden_size 2695 --dropout 0.40297167629785279885 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c89,1213721,32_0,COMPLETED,BOTORCH_MODULAR,59.590000000000003410605131648481,6.29499999999999992894572642399,42,0.001000000000000000020816681712,433,2695,0.402971676297852798853682543267,1,46,6
33,1762250769,994,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251763,1762252210,447,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 498 --hidden_size 2724 --dropout 0.40692026743325371285 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c42,1213717,33_0,COMPLETED,BOTORCH_MODULAR,58.21000000000000085265128291212,6.160999999999999587885213259142,42,0.001000000000000000020816681712,498,2724,0.406920267433253712852092576213,1,46,6
34,1762250769,933,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251702,1762252165,463,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 43 --learning_rate 0.00100000000000000002 --batch_size 509 --hidden_size 2883 --dropout 0.40969530685373772849 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c133,1213707,34_0,COMPLETED,BOTORCH_MODULAR,58.78999999999999914734871708788,6.336999999999999744204615126364,43,0.001000000000000000020816681712,509,2883,0.409695306853737728491893221872,1,46,6
35,1762250769,938,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251707,1762252197,490,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 44 --learning_rate 0.00100000000000000002 --batch_size 453 --hidden_size 2897 --dropout 0.40131155341194346686 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c84,1213709,35_0,COMPLETED,BOTORCH_MODULAR,59.46999999999999886313162278384,6.636000000000000120792265079217,44,0.001000000000000000020816681712,453,2897,0.401311553411943466862510376814,1,46,6
36,1762250771,1021,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251792,1762252260,468,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 44 --learning_rate 0.00100000000000000002 --batch_size 536 --hidden_size 2824 --dropout 0.40509879233204271198 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c131,1213720,36_0,COMPLETED,BOTORCH_MODULAR,58.42999999999999971578290569596,6.427999999999999936051153781591,44,0.001000000000000000020816681712,536,2824,0.405098792332042711983319804858,1,46,6
37,1762250776,1046,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251822,1762252282,460,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 41 --learning_rate 0.00100000000000000002 --batch_size 429 --hidden_size 2649 --dropout 0.42013514016368824766 --num_dense_layers 1 --filter 47 --num_conv_layers 6,0,,c118,1213724,37_0,COMPLETED,BOTORCH_MODULAR,59,6.283000000000000362376795237651,41,0.001000000000000000020816681712,429,2649,0.420135140163688247660900287883,1,47,6
38,1762250776,1021,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251797,1762252271,474,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 352 --hidden_size 2734 --dropout 0.39536479115020667363 --num_dense_layers 1 --filter 46 --num_conv_layers 6,0,,c49,1213722,38_0,COMPLETED,BOTORCH_MODULAR,59.369999999999997442046151263639,6.448999999999999843680598132778,42,0.001000000000000000020816681712,352,2734,0.395364791150206673631117837431,1,46,6
39,1762250776,1051,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762251827,1762252277,450,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 42 --learning_rate 0.00100000000000000002 --batch_size 690 --hidden_size 2612 --dropout 0.38927654465820643592 --num_dense_layers 1 --filter 45 --num_conv_layers 6,0,,c130,1213723,39_0,COMPLETED,BOTORCH_MODULAR,58.700000000000002842170943040401,6.160999999999999587885213259142,42,0.001000000000000000020816681712,690,2612,0.389276544658206435922664923055,1,45,6
40,1762252453,95,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252548,1762253457,909,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 22 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2249 --dropout 0 --num_dense_layers 2 --filter 41 --num_conv_layers 4,0,,c48,1214281,40_0,COMPLETED,BOTORCH_MODULAR,49.07000000000000028421709430404,12.522999999999999687361196265556,22,0.001000000000000000020816681712,8,2249,0,2,41,4
41,1762252453,150,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252603,1762254846,2243,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 73 --learning_rate 0.00100000000000000002 --batch_size 13 --hidden_size 1892 --dropout 0.42768143570428057698 --num_dense_layers 1 --filter 64 --num_conv_layers 7,0,,c127,1214288,41_0,COMPLETED,BOTORCH_MODULAR,65.349999999999994315658113919199,30.987999999999999545252649113536,73,0.001000000000000000020816681712,13,1892,0.427681435704280576981517469903,1,64,7
42,1762252452,66,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252518,1762253532,1014,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 24 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2425 --dropout 0 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c86,1214275,42_0,COMPLETED,BOTORCH_MODULAR,50.57000000000000028421709430404,13.926000000000000156319401867222,24,0.001000000000000000020816681712,8,2425,0,2,40,4
43,1762252452,66,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252518,1762253515,997,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 24 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2331 --dropout 0 --num_dense_layers 2 --filter 41 --num_conv_layers 4,0,,c80,1214276,43_0,COMPLETED,BOTORCH_MODULAR,49.3500000000000014210854715202,13.798000000000000042632564145606,24,0.001000000000000000020816681712,8,2331,0,2,41,4
44,1762252452,97,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252549,1762253684,1135,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 24 --learning_rate 0.00091392507630086352 --batch_size 8 --hidden_size 1768 --dropout 0.0908034923515241843 --num_dense_layers 1 --filter 63 --num_conv_layers 7,0,,c87,1214279,44_0,COMPLETED,BOTORCH_MODULAR,54.729999999999996873611962655559,15.659000000000000696331881044898,24,0.000913925076300863524492168466,8,1768,0.090803492351524184300615161192,1,63,7
45,1762252452,151,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252603,1762253708,1105,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 3131 --dropout 0 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c134,1214277,45_0,COMPLETED,BOTORCH_MODULAR,49.130000000000002557953848736361,15.218999999999999417354956676718,26,0.001000000000000000020816681712,8,3131,0,2,40,4
46,1762252452,97,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252549,1762253524,975,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 23 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2308 --dropout 0.08248372219291268126 --num_dense_layers 2 --filter 41 --num_conv_layers 4,0,,c90,1214278,46_0,COMPLETED,BOTORCH_MODULAR,50.46999999999999886313162278384,13.432000000000000383693077310454,23,0.001000000000000000020816681712,8,2308,0.082483722192912681259180374127,2,41,4
47,1762252452,126,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252578,1762255646,3068,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 70 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2307 --dropout 0.40689864967219541159 --num_dense_layers 1 --filter 64 --num_conv_layers 7,0,,c77,1214285,47_0,COMPLETED,BOTORCH_MODULAR,64.480000000000003979039320256561,42.460999999999998522071109618992,70,0.001000000000000000020816681712,8,2307,0.40689864967219541158627293953,1,64,7
48,1762252453,96,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252549,1762253531,982,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 24 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2368 --dropout 0 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c38,1214284,48_0,COMPLETED,BOTORCH_MODULAR,49.1499999999999985789145284798,13.542999999999999261035554809496,24,0.001000000000000000020816681712,8,2368,0,2,40,4
49,1762252453,96,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252549,1762253413,864,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 21 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2230 --dropout 0 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c39,1214282,49_0,COMPLETED,BOTORCH_MODULAR,48.25,11.894000000000000127897692436818,21,0.001000000000000000020816681712,8,2230,0,2,40,4
50,1762252453,131,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252584,1762253006,422,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 25 --learning_rate 0.00100000000000000002 --batch_size 52 --hidden_size 2577 --dropout 0 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c17,1214286,50_0,COMPLETED,BOTORCH_MODULAR,51.32000000000000028421709430404,5.633000000000000007105427357601,25,0.001000000000000000020816681712,52,2577,0,2,40,4
51,1762252453,96,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252549,1762253444,895,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00089775586831544101 --batch_size 8 --hidden_size 1837 --dropout 0 --num_dense_layers 1 --filter 64 --num_conv_layers 7,0,,c81,1214280,51_0,COMPLETED,BOTORCH_MODULAR,54.310000000000002273736754432321,12.35500000000000042632564145606,20,0.000897755868315441006057009421,8,1837,0,1,64,7
52,1762252453,95,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252548,1762253626,1078,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 23 --learning_rate 0.00092926551894908344 --batch_size 8 --hidden_size 1805 --dropout 0.0632249539169566499 --num_dense_layers 1 --filter 63 --num_conv_layers 7,0,,c39,1214283,52_0,COMPLETED,BOTORCH_MODULAR,53.32999999999999829469743417576,14.906000000000000582645043323282,23,0.000929265518949083440744529661,8,1805,0.063224953916956649901948139814,1,63,7
53,,,,,,,,,,,,53_0,RUNNING,BOTORCH_MODULAR,,,24,0.000933486910415774751026418699,8,2128,0,1,63,7
54,,,,,,,,,,,,54_0,RUNNING,BOTORCH_MODULAR,,,22,0.000944518745520201981162589089,8,2022,0,1,64,7
55,,,,,,,,,,,,55_0,FAILED,BOTORCH_MODULAR,,,26,0.001000000000000000020816681712,8,2439,0,2,41,4
56,,,,,,,,,,,,56_0,RUNNING,BOTORCH_MODULAR,,,21,0.000942117422778393368357208182,8,1764,0.131410642075244721294069449868,1,64,7
57,1762252481,422,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252903,1762253716,813,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2187 --dropout 0 --num_dense_layers 2 --filter 41 --num_conv_layers 4,0,,c114,1214296,57_0,COMPLETED,BOTORCH_MODULAR,48.57999999999999829469743417576,11.204000000000000625277607468888,20,0.001000000000000000020816681712,8,2187,0,2,41,4
58,1762252491,172,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252663,1762253709,1046,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 25 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2530 --dropout 0.0053176047702602966 --num_dense_layers 2 --filter 40 --num_conv_layers 4,0,,c88,1214297,58_0,COMPLETED,BOTORCH_MODULAR,49.82000000000000028421709430404,14.407000000000000028421709430404,25,0.001000000000000000020816681712,8,2530,0.005317604770260296598305416182,2,40,4
59,1762252501,162,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762252663,1762253506,843,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 20 --learning_rate 0.00100000000000000002 --batch_size 8 --hidden_size 2294 --dropout 0.29262628725032963084 --num_dense_layers 2 --filter 41 --num_conv_layers 4,0,,c71,1214298,59_0,COMPLETED,BOTORCH_MODULAR,42.1000000000000014210854715202,11.646000000000000795807864051312,20,0.001000000000000000020816681712,8,2294,0.292626287250329630840184336193,2,41,4
60,1762260358,20,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762260378,1762260717,339,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.00090496355153255352 --batch_size 182 --hidden_size 1829 --dropout 0.42453260285043914468 --num_dense_layers 2 --filter 57 --num_conv_layers 7,0,,c137,1215974,60_0,COMPLETED,BOTORCH_MODULAR,54.729999999999996873611962655559,4.604000000000000092370555648813,26,0.00090496355153255351663754702,182,1829,0.424532602850439144681615744048,2,57,7
61,,,,,,,,,,,,61_0,RUNNING,BOTORCH_MODULAR,,,83,0.000866501100042953620490593369,137,583,0.368351264954234591808557297554,1,65,7
62,1762260358,51,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762260409,1762260748,339,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.0009204219726382315 --batch_size 181 --hidden_size 1832 --dropout 0.43408059424640049739 --num_dense_layers 2 --filter 58 --num_conv_layers 7,0,,c130,1215975,62_0,COMPLETED,BOTORCH_MODULAR,54.619999999999997442046151263639,4.594999999999999751310042483965,26,0.000920421972638231500329908386,181,1832,0.434080594246400497393523210121,2,58,7
63,1762260358,50,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762260408,1762260749,341,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 25 --learning_rate 0.00089550004974726428 --batch_size 159 --hidden_size 1473 --dropout 0.41854183458582089328 --num_dense_layers 2 --filter 65 --num_conv_layers 7,0,,c87,1215976,63_0,COMPLETED,BOTORCH_MODULAR,54.130000000000002557953848736361,4.592999999999999971578290569596,25,0.000895500049747264279323410996,159,1473,0.418541834585820893277485765793,2,65,7
64,,,,,,,,,,,,64_0,RUNNING,BOTORCH_MODULAR,,,83,0.000882240169844533900569971685,8,1516,0.378467584384995170410093123792,1,64,7
65,,,,,,,,,,,,65_0,RUNNING,BOTORCH_MODULAR,,,83,0.000886911361713947090333609591,59,1840,0.381296197294293981450152841717,1,64,7
66,,,,,,,,,,,,66_0,RUNNING,BOTORCH_MODULAR,,,64,0.000872975211855806778812882918,428,1433,0.393507605309196539788985091946,1,64,7
67,1762260359,79,0e3f1d25-4ab3-4dee-bc41-dbc970610c63,1762260438,1762260765,327,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 26 --learning_rate 0.00092318542721830834 --batch_size 203 --hidden_size 2766 --dropout 0.4404532477435971205 --num_dense_layers 2 --filter 55 --num_conv_layers 7,0,,c152,1215977,67_0,COMPLETED,BOTORCH_MODULAR,54.42999999999999971578290569596,4.42999999999999971578290569596,26,0.000923185427218308342182939707,203,2766,0.440453247743597120500425035061,2,55,7
68,,,,,,,,,,,,68_0,RUNNING,BOTORCH_MODULAR,,,26,0.000921513046410864082071123793,181,1833,0.433965615634516965748446182261,2,58,7
69,,,,,,,,,,,,69_0,RUNNING,BOTORCH_MODULAR,,,26,0.000900826250061217579864769611,182,1785,0.42504252531104108614457004478,2,58,7
70,,,,,,,,,,,,70_0,RUNNING,BOTORCH_MODULAR,,,83,0.000882828446545182990087829999,8,1531,0.37307736488069204172646209372,1,63,7
71,,,,,,,,,,,,71_0,RUNNING,BOTORCH_MODULAR,,,26,0.000918275244824327016406051438,182,1807,0.434119977650015975711994542507,2,58,7
72,,,,,,,,,,,,72_0,RUNNING,BOTORCH_MODULAR,,,26,0.000900435795888392619644446935,159,1526,0.42049163703526709623403689875,2,57,7
73,,,,,,,,,,,,73_0,RUNNING,BOTORCH_MODULAR,,,83,0.000870852530691875077739627642,8,699,0.369899723423936011013068991815,1,65,7
74,,,,,,,,,,,,74_0,RUNNING,BOTORCH_MODULAR,,,83,0.000880447639358638645470933604,8,1498,0.377045624324962302953423431973,1,64,7
75,,,,,,,,,,,,75_0,RUNNING,BOTORCH_MODULAR,,,84,0.000878101258712703767496388085,8,1607,0.373496228849191092002968161978,1,65,7
76,,,,,,,,,,,,76_0,RUNNING,BOTORCH_MODULAR,,,27,0.000917944008932930965646845856,246,1924,0.434132000810677931568193343992,2,57,7
77,,,,,,,,,,,,77_0,RUNNING,BOTORCH_MODULAR,,,69,0.000866017733626903759430737395,363,1222,0.385855392998853630182054530451,1,64,7
78,,,,,,,,,,,,78_0,RUNNING,BOTORCH_MODULAR,,,26,0.000903791594989241287190495644,181,1853,0.422601033573470674653549394861,2,57,7
79,,,,,,,,,,,,79_0,RUNNING,BOTORCH_MODULAR,,,83,0.000876736239984471669647869696,11,1505,0.373343067693759800285135952436,1,64,7
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
⚠ Job 1214287 (task: 0) with path /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_0_log.out
----------------------------------------
submitit INFO (2025-11-04 11:36:11,637) - Starting with JobEnvironment(job_id=1214287, hostname=c6, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-11-04 11:36:11,699) - Loading pickle: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_submitted.pkl
Trial-Index: 55
slurmstepd: error: *** JOB 1214287 ON c6 CANCELLED AT 2025-11-04T13:36:19 DUE TO TIME LIMIT ***
To cancel, press CTRL c, then run 'scancel 1213250'
⠋ Importing logging...
⠋ Importing warnings...
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⠋ Importing typing...
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⠋ Importing submitit.LocalExecutor...
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⠋ Importing inspect frame info...
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⠋ Importing cowsay...
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⠋ Importing itertools.combinations...
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⠋ Importing os.path...
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⠋ Importing sixel...
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⠋ Trying to import pyfiglet...
⠙ Importing helpers...
⠋ Importing pareto...
⠋ Parsing arguments...
⠹ Importing torch...
⠋ Importing numpy...
[WARNING 11-04 10:35:03] 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]`.
⠧ Importing ax...
⠋ Importing ax.core.generator_run...
⠋ Importing Cont_X_trans and Y_trans from ax.adapter.registry...
⠋ Importing ax.core.arm...
⠋ Importing ax.core.objective...
⠋ Importing ax.core.Metric...
⠋ Importing ax.exceptions.core...
⠋ Importing ax.exceptions.generation_strategy...
⠋ Importing CORE_DECODER_REGISTRY...
⠋ Trying ax.generation_strategy.generation_node...
⠋ Importing GenerationStep, GenerationStrategy from generation_strategy...
⠋ Importing GenerationNode from generation_node...
⠋ Importing ExternalGenerationNode...
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⠋ Importing Models from ax.generation_strategy.registry...
⠋ Importing get_pending_observation_features...
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⠋ Importing ax logger...
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Run-UUID: e3dd3791-3d02-41b3-8946-f1bb1ea3a533
______________________________________
| OmniOpt2 - Hyperparameters? Nailed it! |
======================================
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_- /--______
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.-XXX( O O )XXXXXXXXXXXXXXX-
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⠋ Writing worker creation log...
omniopt --partition=alpha --experiment_name=mnist_normalized_runtime --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 NORMALIZED_RUNTIME=min --run_program=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdfcmF0ZSAlbHIgLS1iYXRjaF9zaXplICViYXRjaF9zaXplIC0taGlkZGVuX3NpemUgJWhpZGRlbl9zaXplIC0tZHJvcG91dCAlZHJvcG91dCAtLW51bV9kZW5zZV9sYXllcnMgJW51bV9kZW5zZV9sYXllcnMgLS1maWx0ZXIgJShmaWx0ZXIpIC0tbnVtX2NvbnZfbGF5ZXJzICUobnVtX2NvbnZfbGF5ZXJzKQo= --run_program_once=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWluc3RhbGwK --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/1...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
⠋ Writing username state file...
⠋ Writing result names file...
⠋ Writing result min/max file...
Executing command: python3
/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train
--install
Exiting, since the installation should now be done
Setup script completed successfully ✅
⠋ Saving state files...
Run-folder: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1
⠋ Writing live_share file if it is present...
⠋ Writing job_start_time file...
⠸ 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&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 │
│ NORMALIZED_RUNTIME │ min │
└────────────────────┴─────────────┘
⠋ Write files and show overview
BOTORCH_MODULAR, 40 done, running/pending/unknown 9/4/1 = ∑14/20, started new job : 4%|░░░░░░░░░░| 40/1000 [1:00:25<3:44:48, 14.05s/it]sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
handle_failed_job: job is None
BOTORCH_MODULAR, 56 done, timeout 1 = ∑1/20, waiting for 1 job : 6%|▒░░░░░░░░░| 56/1000 [3:07:29<87:10:44, 332.46s/it]
⚠ Job 1214287 (task: 0) with path /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_0_result.pkl
has not produced any output (state: TIMEOUT)
No error stream produced. Look at stdout: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_0_log.out
----------------------------------------
submitit INFO (2025-11-04 11:36:11,637) - Starting with JobEnvironment(job_id=1214287, hostname=c6, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-11-04 11:36:11,699) - Loading pickle: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1214287/1214287_submitted.pkl
Trial-Index: 55
slurmstepd: error: *** JOB 1214287 ON c6 CANCELLED AT 2025-11-04T13:36:19 DUE TO TIME LIMIT ***
BOTORCH_MODULAR, failed: 1, 59 done, running/pending 12/5 = ∑17/20, new result: VAL_ACC: 54.430000, NORMALIZED_RUNTIME: 4.430000: 6%|▒░░░░░░░░░| 60/1000 [3:17:36<151:45:58, 581.23s/it]
2025-11-04 10:35:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, Started OmniOpt2 run...
2025-11-04 10:35:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #1/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #2/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #3/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #4/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #5/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #6/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #7/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #8/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #9/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #10/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #11/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #12/20
2025-11-04 10:35:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #13/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #14/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #15/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #16/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #17/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #18/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #19/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, getting new HP set #20/20
2025-11-04 10:35:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, requested 20 jobs, got 20, 0.17 s/job
2025-11-04 10:35:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #1/20 start
2025-11-04 10:35:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #2/20 start
2025-11-04 10:35:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #3/20 start
2025-11-04 10:35:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #4/20 start
2025-11-04 10:35:22 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #5/20 start
2025-11-04 10:35:24 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #6/20 start
2025-11-04 10:35:24 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #7/20 start
2025-11-04 10:35:24 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #8/20 start
2025-11-04 10:35:25 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #9/20 start
2025-11-04 10:35:28 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #10/20 start
2025-11-04 10:35:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #11/20 start
2025-11-04 10:35:30 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #12/20 start
2025-11-04 10:35:33 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #13/20 start
2025-11-04 10:35:33 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #14/20 start
2025-11-04 10:35:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #15/20 start
2025-11-04 10:35:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #16/20 start
2025-11-04 10:35:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #17/20 start
2025-11-04 10:35:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #18/20 start
2025-11-04 10:35:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #19/20 start
2025-11-04 10:35:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, eval #20/20 start
2025-11-04 10:35:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, starting new job
2025-11-04 10:35:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, unknown 1 = ∑1/20, started new job
2025-11-04 10:35:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, unknown 1 = ∑1/20, starting new job
2025-11-04 10:35:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 10:35:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 10:35:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 2/1 = ∑3/20, started new job
2025-11-04 10:35:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 2/1 = ∑3/20, starting new job
2025-11-04 10:35:56 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 2/1/1 = ∑4/20, started new job
2025-11-04 10:35:56 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 2/1/1 = ∑4/20, starting new job
2025-11-04 10:36:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 2/2/1 = ∑5/20, started new job
2025-11-04 10:36:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 5/1 = ∑6/20, started new job
2025-11-04 10:36:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 6/1 = ∑7/20, started new job
2025-11-04 10:36:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 6/1/1 = ∑8/20, started new job
2025-11-04 10:36:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 6/2/1 = ∑9/20, started new job
2025-11-04 10:36:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 6/3/1 = ∑10/20, started new job
2025-11-04 10:36:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 6/3/2 = ∑11/20, started new job
2025-11-04 10:36:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 11/1 = ∑12/20, started new job
2025-11-04 10:36:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 12/1 = ∑13/20, started new job
2025-11-04 10:36:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 12/1/1 = ∑14/20, started new job
2025-11-04 10:36:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 12/2/1 = ∑15/20, started new job
2025-11-04 10:36:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 15/1 = ∑16/20, started new job
2025-11-04 10:36:56 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 15/1/1 = ∑17/20, started new job
2025-11-04 10:37:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending/unknown 15/2/1 = ∑18/20, started new job
2025-11-04 10:37:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 18/1 = ∑19/20, started new job
2025-11-04 10:37:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 19/1 = ∑20/20, started new job
2025-11-04 10:37:12 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/unknown 19/1 = ∑20/20, waiting for 20 jobs
2025-11-04 10:37:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running/pending 19/1 = ∑20/20, waiting for 20 jobs
2025-11-04 10:37:32 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 10:42:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, running 20 = ∑20/20, new result: VAL_ACC: 54.870000, NORMALIZED_RUNTIME: 4.461000
2025-11-04 10:42:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 1 done, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 10:42:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 1 done, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 10:43:05 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 1 done, running 19 = ∑19/20, new result: VAL_ACC: 38.720000, NORMALIZED_RUNTIME: 4.520000
2025-11-04 10:43:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 2 done, running 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 10:43:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 2 done, running 18 = ∑18/20, waiting for 18 jobs
2025-11-04 10:43:10 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 2 done, running 18 = ∑18/20, new result: VAL_ACC: 52.590000, NORMALIZED_RUNTIME: 5.337000
2025-11-04 10:43:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 3 done, running 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 10:43:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 3 done, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 10:43:43 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 3 done, running 17 = ∑17/20, new result: VAL_ACC: 32.250000, NORMALIZED_RUNTIME: 5.728000
2025-11-04 10:43:48 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 4 done, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 10:43:48 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 4 done, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 10:45:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 4 done, running 16 = ∑16/20, new result: VAL_ACC: 57.650000, NORMALIZED_RUNTIME: 6.741000
2025-11-04 10:45:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 5 done, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 10:45:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 5 done, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 10:46:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 5 done, running 15 = ∑15/20, new result: VAL_ACC: 54.080000, NORMALIZED_RUNTIME: 7.610000
2025-11-04 10:46:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 6 done, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 10:46:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 6 done, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 10:48:08 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 6 done, running 14 = ∑14/20, new result: VAL_ACC: 58.010000, NORMALIZED_RUNTIME: 9.088000
2025-11-04 10:48:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 7 done, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 10:48:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 7 done, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 10:49:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 7 done, running 13 = ∑13/20, new result: VAL_ACC: 54.740000, NORMALIZED_RUNTIME: 9.795000
2025-11-04 10:49:08 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 8 done, running 12 = ∑12/20, waiting for 13 jobs, finished 1 job
2025-11-04 10:49:08 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 8 done, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 10:49:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 8 done, running 12 = ∑12/20, new result: VAL_ACC: 56.060000, NORMALIZED_RUNTIME: 10.812000
2025-11-04 10:49:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 9 done, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 10:49:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 9 done, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 10:50:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 9 done, running 11 = ∑11/20, new result: VAL_ACC: 47.550000, NORMALIZED_RUNTIME: 12.143000
2025-11-04 10:50:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 10 done, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 10:50:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 10 done, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 10:50:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 10 done, running 10 = ∑10/20, new result: VAL_ACC: 40.660000, NORMALIZED_RUNTIME: 12.260000
2025-11-04 10:50:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 11 done, running 9 = ∑9/20, waiting for 10 jobs, finished 1 job
2025-11-04 10:51:00 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 11 done, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 10:53:27 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 11 done, running 9 = ∑9/20, new result: VAL_ACC: 46.050000, NORMALIZED_RUNTIME: 13.543000
2025-11-04 10:53:33 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 12 done, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 10:53:33 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 12 done, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 10:55:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 12 done, running 8 = ∑8/20, new result: VAL_ACC: 49.690000, NORMALIZED_RUNTIME: 15.800000
2025-11-04 10:55:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 13 done, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 10:55:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 13 done, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 10:55:48 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 13 done, running 7 = ∑7/20, new result: VAL_ACC: 58.030000, NORMALIZED_RUNTIME: 16.277000
2025-11-04 10:55:52 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 14 done, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 10:55:52 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 14 done, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 10:57:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 14 done, running 6 = ∑6/20, new result: VAL_ACC: 59.410000, NORMALIZED_RUNTIME: 18.198000
2025-11-04 10:57:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 15 done, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 10:57:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 15 done, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 10:58:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 15 done, running 5 = ∑5/20, new result: VAL_ACC: 42.960000, NORMALIZED_RUNTIME: 18.815000
2025-11-04 10:58:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 16 done, running 4 = ∑4/20, waiting for 5 jobs, finished 1 job
2025-11-04 10:58:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 16 done, running 4 = ∑4/20, waiting for 4 jobs
2025-11-04 10:59:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 16 done, running 4 = ∑4/20, new result: VAL_ACC: 61.380000, NORMALIZED_RUNTIME: 18.251000
2025-11-04 10:59:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 17 done, running 3 = ∑3/20, waiting for 4 jobs, finished 1 job
2025-11-04 10:59:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 17 done, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 11:00:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 17 done, running 3 = ∑3/20, new result: VAL_ACC: 42.540000, NORMALIZED_RUNTIME: 20.020000
2025-11-04 11:00:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 18 done, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 11:00:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 18 done, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 11:01:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 18 done, running 2 = ∑2/20, new result: VAL_ACC: 49.470000, NORMALIZED_RUNTIME: 21.090000
2025-11-04 11:02:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 19 done, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 11:02:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 19 done, running 1 = ∑1/20, waiting for 1 job
2025-11-04 11:04:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 19 done, running 1 = ∑1/20, new result: VAL_ACC: 60.260000, NORMALIZED_RUNTIME: 22.686000
2025-11-04 11:04:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): SOBOL, 20 done, waiting for 1 job, finished 1 job
2025-11-04 11:05:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #1/20
2025-11-04 11:05:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #2/20
2025-11-04 11:05:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #3/20
2025-11-04 11:05:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #4/20
2025-11-04 11:05:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #5/20
2025-11-04 11:05:42 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #6/20
2025-11-04 11:05:42 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #7/20
2025-11-04 11:05:42 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #8/20
2025-11-04 11:05:42 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #9/20
2025-11-04 11:05:43 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #10/20
2025-11-04 11:05:43 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #11/20
2025-11-04 11:05:43 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #12/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #13/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #14/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #15/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #16/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #17/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #18/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #19/20
2025-11-04 11:05:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, getting new HP set #20/20
2025-11-04 11:05:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, requested 20 jobs, got 20, 4.43 s/job
2025-11-04 11:05:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #1/20 start
2025-11-04 11:05:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #2/20 start
2025-11-04 11:05:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #3/20 start
2025-11-04 11:05:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #4/20 start
2025-11-04 11:05:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #5/20 start
2025-11-04 11:05:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #6/20 start
2025-11-04 11:05:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #7/20 start
2025-11-04 11:05:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #8/20 start
2025-11-04 11:05:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #9/20 start
2025-11-04 11:05:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #10/20 start
2025-11-04 11:05:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #11/20 start
2025-11-04 11:05:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #12/20 start
2025-11-04 11:06:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #13/20 start
2025-11-04 11:06:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #14/20 start
2025-11-04 11:06:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #15/20 start
2025-11-04 11:06:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #16/20 start
2025-11-04 11:06:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #17/20 start
2025-11-04 11:06:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #18/20 start
2025-11-04 11:06:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #19/20 start
2025-11-04 11:06:07 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, eval #20/20 start
2025-11-04 11:06:08 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, starting new job
2025-11-04 11:06:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, unknown 1 = ∑1/20, started new job
2025-11-04 11:06:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, unknown 1 = ∑1/20, starting new job
2025-11-04 11:06:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 1/3 = ∑4/20, started new job
2025-11-04 11:06:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 1/3 = ∑4/20, starting new job
2025-11-04 11:06:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 4/2 = ∑6/20, started new job
2025-11-04 11:06:22 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 4/3 = ∑7/20, started new job
2025-11-04 11:06:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 7/2 = ∑9/20, started new job
2025-11-04 11:06:27 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 7/3 = ∑10/20, started new job
2025-11-04 11:06:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 10/3 = ∑13/20, started new job
2025-11-04 11:06:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 13/1 = ∑14/20, started new job
2025-11-04 11:06:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 14/3 = ∑17/20, started new job
2025-11-04 11:06:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 17/2 = ∑19/20, started new job
2025-11-04 11:06:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 17/3 = ∑20/20, started new job
2025-11-04 11:06:48 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending/unknown 17/3 = ∑20/20, waiting for 20 jobs
2025-11-04 11:06:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, pending 20 = ∑20/20, waiting for 20 jobs
2025-11-04 11:23:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, running 20 = ∑20/20, waiting for 20 jobs
2025-11-04 11:28:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 20 done, running 20 = ∑20/20, new result: VAL_ACC: 59.700000, NORMALIZED_RUNTIME: 6.211000
2025-11-04 11:28:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 21 done, running 19 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 11:28:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 21 done, running 19 = ∑19/20, waiting for 19 jobs
2025-11-04 11:28:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 21 done, running 19 = ∑19/20, new result: VAL_ACC: 59.580000, NORMALIZED_RUNTIME: 6.336000
2025-11-04 11:29:00 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 22 done, running 18 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 11:29:00 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 22 done, running 18 = ∑18/20, waiting for 18 jobs
2025-11-04 11:29:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 22 done, running 18 = ∑18/20, new result: VAL_ACC: 59.000000, NORMALIZED_RUNTIME: 6.468000
2025-11-04 11:29:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 23 done, running 17 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 11:29:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 23 done, running 17 = ∑17/20, waiting for 17 jobs
2025-11-04 11:29:25 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 23 done, running 17 = ∑17/20, new result: VAL_ACC: 58.790000, NORMALIZED_RUNTIME: 6.337000
2025-11-04 11:29:32 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 24 done, running 16 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 11:29:32 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 24 done, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 11:29:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 24 done, running 16 = ∑16/20, new result: VAL_ACC: 59.970000, NORMALIZED_RUNTIME: 7.009000
2025-11-04 11:29:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 25 done, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 11:29:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 25 done, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 11:29:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 25 done, running 15 = ∑15/20, new result: VAL_ACC: 59.470000, NORMALIZED_RUNTIME: 6.636000
2025-11-04 11:30:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 26 done, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 11:30:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 26 done, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 11:30:05 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 26 done, running 14 = ∑14/20, new result: VAL_ACC: 59.050000, NORMALIZED_RUNTIME: 6.394000
2025-11-04 11:30:05 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 26 done, running 14 = ∑14/20, new result: VAL_ACC: 59.240000, NORMALIZED_RUNTIME: 6.045000
2025-11-04 11:30:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 28 done, running 12 = ∑12/20, waiting for 14 jobs, finished 2 jobs
2025-11-04 11:30:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 28 done, running 12 = ∑12/20, waiting for 12 jobs
2025-11-04 11:30:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 28 done, running 12 = ∑12/20, new result: VAL_ACC: 58.210000, NORMALIZED_RUNTIME: 6.161000
2025-11-04 11:30:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 29 done, running 11 = ∑11/20, waiting for 12 jobs, finished 1 job
2025-11-04 11:30:20 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 29 done, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 11:30:37 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 29 done, running 11 = ∑11/20, new result: VAL_ACC: 60.020000, NORMALIZED_RUNTIME: 6.419000
2025-11-04 11:30:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 30 done, running 10 = ∑10/20, waiting for 11 jobs, finished 1 job
2025-11-04 11:30:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 30 done, running 10 = ∑10/20, waiting for 10 jobs
2025-11-04 11:30:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 30 done, running 10 = ∑10/20, new result: VAL_ACC: 58.640000, NORMALIZED_RUNTIME: 6.354000
2025-11-04 11:30:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 30 done, running 10 = ∑10/20, new result: VAL_ACC: 59.080000, NORMALIZED_RUNTIME: 6.314000
2025-11-04 11:30:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 30 done, running 10 = ∑10/20, new result: VAL_ACC: 58.960000, NORMALIZED_RUNTIME: 6.389000
2025-11-04 11:31:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 33 done, running 7 = ∑7/20, waiting for 10 jobs, finished 3 jobs
2025-11-04 11:31:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 33 done, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 11:31:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 33 done, running 7 = ∑7/20, new result: VAL_ACC: 58.430000, NORMALIZED_RUNTIME: 6.428000
2025-11-04 11:31:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 33 done, running 7 = ∑7/20, new result: VAL_ACC: 58.450000, NORMALIZED_RUNTIME: 7.474000
2025-11-04 11:31:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 35 done, running 5 = ∑5/20, waiting for 7 jobs, finished 2 jobs
2025-11-04 11:31:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 35 done, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 11:31:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 35 done, running 5 = ∑5/20, new result: VAL_ACC: 59.590000, NORMALIZED_RUNTIME: 6.295000
2025-11-04 11:31:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 35 done, running 5 = ∑5/20, new result: VAL_ACC: 59.370000, NORMALIZED_RUNTIME: 6.449000
2025-11-04 11:31:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 37 done, running 3 = ∑3/20, waiting for 5 jobs, finished 2 jobs
2025-11-04 11:31:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 37 done, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 11:31:27 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 37 done, running 3 = ∑3/20, new result: VAL_ACC: 58.700000, NORMALIZED_RUNTIME: 6.161000
2025-11-04 11:31:27 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 37 done, running 3 = ∑3/20, new result: VAL_ACC: 59.000000, NORMALIZED_RUNTIME: 6.283000
2025-11-04 11:31:39 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 39 done, running 1 = ∑1/20, waiting for 3 jobs, finished 2 jobs
2025-11-04 11:31:39 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 39 done, running 1 = ∑1/20, waiting for 1 job
2025-11-04 11:31:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 39 done, running 1 = ∑1/20, new result: VAL_ACC: 58.920000, NORMALIZED_RUNTIME: 6.528000
2025-11-04 11:31:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, waiting for 1 job, finished 1 job
2025-11-04 11:33:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #1/20
2025-11-04 11:33:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #2/20
2025-11-04 11:33:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #3/20
2025-11-04 11:33:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #4/20
2025-11-04 11:33:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #5/20
2025-11-04 11:33:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #6/20
2025-11-04 11:33:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #7/20
2025-11-04 11:33:37 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #8/20
2025-11-04 11:33:42 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #9/20
2025-11-04 11:33:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #10/20
2025-11-04 11:33:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #11/20
2025-11-04 11:33:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #12/20
2025-11-04 11:33:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #13/20
2025-11-04 11:33:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #14/20
2025-11-04 11:33:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #15/20
2025-11-04 11:33:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #16/20
2025-11-04 11:33:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #17/20
2025-11-04 11:33:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #18/20
2025-11-04 11:33:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #19/20
2025-11-04 11:33:47 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, getting new HP set #20/20
2025-11-04 11:33:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, requested 20 jobs, got 20, 5.80 s/job
2025-11-04 11:33:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #1/20 start
2025-11-04 11:33:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #2/20 start
2025-11-04 11:33:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #3/20 start
2025-11-04 11:33:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #4/20 start
2025-11-04 11:33:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #5/20 start
2025-11-04 11:33:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #6/20 start
2025-11-04 11:33:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #7/20 start
2025-11-04 11:33:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #8/20 start
2025-11-04 11:33:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #9/20 start
2025-11-04 11:33:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #10/20 start
2025-11-04 11:33:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #11/20 start
2025-11-04 11:34:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #12/20 start
2025-11-04 11:34:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #13/20 start
2025-11-04 11:34:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #14/20 start
2025-11-04 11:34:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #15/20 start
2025-11-04 11:34:07 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #16/20 start
2025-11-04 11:34:07 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #17/20 start
2025-11-04 11:34:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #18/20 start
2025-11-04 11:34:10 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #19/20 start
2025-11-04 11:34:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, eval #20/20 start
2025-11-04 11:34:12 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, starting new job
2025-11-04 11:34:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, unknown 1 = ∑1/20, started new job
2025-11-04 11:34:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, unknown 1 = ∑1/20, starting new job
2025-11-04 11:34:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 11:34:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 11:34:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 2/1 = ∑3/20, started new job
2025-11-04 11:34:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 2/1 = ∑3/20, starting new job
2025-11-04 11:35:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 3/2 = ∑5/20, started new job
2025-11-04 11:35:01 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 3/2 = ∑5/20, starting new job
2025-11-04 11:35:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, pending/unknown 5/2 = ∑7/20, started new job
2025-11-04 11:35:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 2/6/1 = ∑9/20, started new job
2025-11-04 11:35:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending 2/7 = ∑9/20, started new job
2025-11-04 11:35:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 2/7/1 = ∑10/20, started new job
2025-11-04 11:35:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 2/8/1 = ∑11/20, started new job
2025-11-04 11:35:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 2/9/1 = ∑12/20, started new job
2025-11-04 11:35:37 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 2/9/2 = ∑13/20, started new job
2025-11-04 11:35:41 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 9/4/1 = ∑14/20, started new job
2025-11-04 11:36:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 12/2/1 = ∑15/20, started new job
2025-11-04 11:36:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 12/3/1 = ∑16/20, started new job
2025-11-04 11:36:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 12/4/1 = ∑17/20, started new job
2025-11-04 11:36:33 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending/unknown 12/4/1 = ∑17/20, waiting for 17 jobs
2025-11-04 11:36:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending 12/5 = ∑17/20, waiting for 17 jobs
2025-11-04 11:36:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending 14/3 = ∑17/20, waiting for 17 jobs
2025-11-04 11:38:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending 16/1 = ∑17/20, waiting for 17 jobs
2025-11-04 11:43:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 40 done, running/pending 16/1 = ∑17/20, new result: VAL_ACC: 51.320000, NORMALIZED_RUNTIME: 5.633000
2025-11-04 11:43:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 41 done, running/pending 15/1 = ∑16/20, waiting for 17 jobs, finished 1 job
2025-11-04 11:43:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 41 done, running/pending 15/1 = ∑16/20, waiting for 16 jobs
2025-11-04 11:44:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 41 done, running 16 = ∑16/20, waiting for 16 jobs
2025-11-04 11:50:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 41 done, running 16 = ∑16/20, new result: VAL_ACC: 48.250000, NORMALIZED_RUNTIME: 11.894000
2025-11-04 11:50:23 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 42 done, running 15 = ∑15/20, waiting for 16 jobs, finished 1 job
2025-11-04 11:50:23 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 42 done, running 15 = ∑15/20, waiting for 15 jobs
2025-11-04 11:50:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 42 done, running 15 = ∑15/20, new result: VAL_ACC: 54.310000, NORMALIZED_RUNTIME: 12.355000
2025-11-04 11:50:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 43 done, running 14 = ∑14/20, waiting for 15 jobs, finished 1 job
2025-11-04 11:50:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 43 done, running 14 = ∑14/20, waiting for 14 jobs
2025-11-04 11:50:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 43 done, running 14 = ∑14/20, new result: VAL_ACC: 49.070000, NORMALIZED_RUNTIME: 12.523000
2025-11-04 11:51:05 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 44 done, running 13 = ∑13/20, waiting for 14 jobs, finished 1 job
2025-11-04 11:51:05 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 44 done, running 13 = ∑13/20, waiting for 13 jobs
2025-11-04 11:51:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 44 done, running 13 = ∑13/20, new result: VAL_ACC: 49.350000, NORMALIZED_RUNTIME: 13.798000
2025-11-04 11:51:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 44 done, running 13 = ∑13/20, new result: VAL_ACC: 42.100000, NORMALIZED_RUNTIME: 11.646000
2025-11-04 11:52:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 46 done, running 11 = ∑11/20, waiting for 13 jobs, finished 2 jobs
2025-11-04 11:52:11 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 46 done, running 11 = ∑11/20, waiting for 11 jobs
2025-11-04 11:52:12 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 46 done, running 11 = ∑11/20, new result: VAL_ACC: 49.150000, NORMALIZED_RUNTIME: 13.543000
2025-11-04 11:52:12 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 46 done, running 11 = ∑11/20, new result: VAL_ACC: 50.470000, NORMALIZED_RUNTIME: 13.432000
2025-11-04 11:52:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 48 done, running 9 = ∑9/20, waiting for 11 jobs, finished 2 jobs
2025-11-04 11:52:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 48 done, running 9 = ∑9/20, waiting for 9 jobs
2025-11-04 11:52:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 48 done, running 9 = ∑9/20, new result: VAL_ACC: 50.570000, NORMALIZED_RUNTIME: 13.926000
2025-11-04 11:52:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 49 done, running 8 = ∑8/20, waiting for 9 jobs, finished 1 job
2025-11-04 11:52:35 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 49 done, running 8 = ∑8/20, waiting for 8 jobs
2025-11-04 11:53:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 49 done, running 8 = ∑8/20, new result: VAL_ACC: 53.330000, NORMALIZED_RUNTIME: 14.906000
2025-11-04 11:53:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 50 done, running 7 = ∑7/20, waiting for 8 jobs, finished 1 job
2025-11-04 11:53:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 50 done, running 7 = ∑7/20, waiting for 7 jobs
2025-11-04 11:54:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 50 done, running 7 = ∑7/20, new result: VAL_ACC: 54.730000, NORMALIZED_RUNTIME: 15.659000
2025-11-04 11:54:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 51 done, running 6 = ∑6/20, waiting for 7 jobs, finished 1 job
2025-11-04 11:54:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 51 done, running 6 = ∑6/20, waiting for 6 jobs
2025-11-04 11:55:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 51 done, running 6 = ∑6/20, new result: VAL_ACC: 49.130000, NORMALIZED_RUNTIME: 15.219000
2025-11-04 11:55:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 52 done, running 5 = ∑5/20, waiting for 6 jobs, finished 1 job
2025-11-04 11:55:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 52 done, running 5 = ∑5/20, waiting for 5 jobs
2025-11-04 11:55:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 52 done, running 5 = ∑5/20, new result: VAL_ACC: 49.820000, NORMALIZED_RUNTIME: 14.407000
2025-11-04 11:55:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 52 done, running 5 = ∑5/20, new result: VAL_ACC: 48.580000, NORMALIZED_RUNTIME: 11.204000
2025-11-04 11:55:32 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 54 done, running 3 = ∑3/20, waiting for 5 jobs, finished 2 jobs
2025-11-04 11:55:32 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 54 done, running 3 = ∑3/20, waiting for 3 jobs
2025-11-04 12:14:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 54 done, running 3 = ∑3/20, new result: VAL_ACC: 65.350000, NORMALIZED_RUNTIME: 30.988000
2025-11-04 12:14:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 55 done, running 2 = ∑2/20, waiting for 3 jobs, finished 1 job
2025-11-04 12:14:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 55 done, running 2 = ∑2/20, waiting for 2 jobs
2025-11-04 12:27:26 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 55 done, running 2 = ∑2/20, new result: VAL_ACC: 64.480000, NORMALIZED_RUNTIME: 42.461000
2025-11-04 12:27:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 56 done, running 1 = ∑1/20, waiting for 2 jobs, finished 1 job
2025-11-04 12:27:36 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 56 done, running 1 = ∑1/20, waiting for 1 job
2025-11-04 13:42:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 56 done, timeout 1 = ∑1/20, waiting for 1 job
2025-11-04 13:43:02 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, 56 done, timeout 1 = ∑1/20, job_failed
2025-11-04 13:43:03 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, waiting for 1 job, finished 1 job
2025-11-04 13:45:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #1/20
2025-11-04 13:45:13 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #2/20
2025-11-04 13:45:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #3/20
2025-11-04 13:45:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #4/20
2025-11-04 13:45:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #5/20
2025-11-04 13:45:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #6/20
2025-11-04 13:45:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #7/20
2025-11-04 13:45:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #8/20
2025-11-04 13:45:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #9/20
2025-11-04 13:45:16 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #10/20
2025-11-04 13:45:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #11/20
2025-11-04 13:45:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #12/20
2025-11-04 13:45:17 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #13/20
2025-11-04 13:45:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #14/20
2025-11-04 13:45:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #15/20
2025-11-04 13:45:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #16/20
2025-11-04 13:45:18 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #17/20
2025-11-04 13:45:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #18/20
2025-11-04 13:45:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #19/20
2025-11-04 13:45:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, getting new HP set #20/20
2025-11-04 13:45:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, requested 20 jobs, got 20, 6.81 s/job
2025-11-04 13:45:21 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #1/20 start
2025-11-04 13:45:22 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #2/20 start
2025-11-04 13:45:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #3/20 start
2025-11-04 13:45:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #4/20 start
2025-11-04 13:45:30 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #5/20 start
2025-11-04 13:45:31 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #6/20 start
2025-11-04 13:45:38 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #7/20 start
2025-11-04 13:45:43 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #8/20 start
2025-11-04 13:45:44 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #9/20 start
2025-11-04 13:45:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #10/20 start
2025-11-04 13:45:46 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #11/20 start
2025-11-04 13:45:48 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #12/20 start
2025-11-04 13:45:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #13/20 start
2025-11-04 13:45:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #14/20 start
2025-11-04 13:45:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #15/20 start
2025-11-04 13:45:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #16/20 start
2025-11-04 13:45:52 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #17/20 start
2025-11-04 13:45:53 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #18/20 start
2025-11-04 13:45:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #19/20 start
2025-11-04 13:45:55 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, eval #20/20 start
2025-11-04 13:45:58 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, starting new job
2025-11-04 13:45:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, unknown 1 = ∑1/20, started new job
2025-11-04 13:45:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, unknown 1 = ∑1/20, starting new job
2025-11-04 13:46:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 13:46:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 13:46:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/unknown 2/2 = ∑4/20, started new job
2025-11-04 13:46:14 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 13:46:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 3/1/1 = ∑5/20, started new job
2025-11-04 13:46:24 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 4/1/1 = ∑6/20, started new job
2025-11-04 13:46:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 5/1/2 = ∑8/20, started new job
2025-11-04 13:46:34 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 7/1/1 = ∑9/20, started new job
2025-11-04 13:46:40 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 8/1/1 = ∑10/20, started new job
2025-11-04 13:46:45 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, pending/running/unknown 9/1/1 = ∑11/20, started new job
2025-11-04 13:46:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/7/1 = ∑12/20, started new job
2025-11-04 13:46:54 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/9/1 = ∑14/20, started new job
2025-11-04 13:46:59 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/10/1 = ∑15/20, started new job
2025-11-04 13:47:04 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/11/1 = ∑16/20, started new job
2025-11-04 13:47:09 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/12/1 = ∑17/20, started new job
2025-11-04 13:47:15 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 4/13/1 = ∑18/20, started new job
2025-11-04 13:47:19 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 6/12/1 = ∑19/20, started new job
2025-11-04 13:47:24 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 6/13/1 = ∑20/20, started new job
2025-11-04 13:47:25 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending/unknown 6/13/1 = ∑20/20, waiting for 20 jobs
2025-11-04 13:47:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending 6/14 = ∑20/20, waiting for 20 jobs
2025-11-04 13:49:06 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending 9/11 = ∑20/20, waiting for 20 jobs
2025-11-04 13:50:49 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending 15/5 = ∑20/20, waiting for 20 jobs
2025-11-04 13:51:57 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 56 done, running/pending 15/5 = ∑20/20, new result: VAL_ACC: 54.730000, NORMALIZED_RUNTIME: 4.604000
2025-11-04 13:52:07 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 57 done, running/pending 14/5 = ∑19/20, waiting for 20 jobs, finished 1 job
2025-11-04 13:52:07 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 57 done, running/pending 14/5 = ∑19/20, waiting for 19 jobs
2025-11-04 13:52:29 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 57 done, running/pending 14/5 = ∑19/20, new result: VAL_ACC: 54.620000, NORMALIZED_RUNTIME: 4.595000
2025-11-04 13:52:39 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 58 done, running/pending 13/5 = ∑18/20, waiting for 19 jobs, finished 1 job
2025-11-04 13:52:39 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 58 done, running/pending 13/5 = ∑18/20, waiting for 18 jobs
2025-11-04 13:52:39 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 58 done, running/pending 13/5 = ∑18/20, new result: VAL_ACC: 54.130000, NORMALIZED_RUNTIME: 4.593000
2025-11-04 13:52:50 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 59 done, running/pending 12/5 = ∑17/20, waiting for 18 jobs, finished 1 job
2025-11-04 13:52:51 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 59 done, running/pending 12/5 = ∑17/20, waiting for 17 jobs
2025-11-04 13:52:52 (0e3f1d25-4ab3-4dee-bc41-dbc970610c63): BOTORCH_MODULAR, failed: 1, 59 done, running/pending 12/5 = ∑17/20, new result: VAL_ACC: 54.430000, NORMALIZED_RUNTIME: 4.430000
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 |
| 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', 'NORMALIZED_RUNTIME=min'] |
| 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 | cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWluc3RhbGwK |
| 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 |
1762248912.8456,20,0,0
1762248936.6978,20,0,0
1762248941.8157,20,1,5
1762248941.9523,20,1,5
1762248946.8116,20,2,10
1762248946.9217,20,2,10
1762248951.822,20,3,15
1762248951.8908,20,3,15
1762248956.8689,20,4,20
1762248956.9494,20,4,20
1762248961.8393,20,5,25
1762248961.9009,20,5,25
1762248966.8484,20,6,30
1762248966.9117,20,6,30
1762248971.8692,20,7,35
1762248971.9358,20,7,35
1762248976.8598,20,8,40
1762248976.9201,20,8,40
1762248981.8635,20,9,45
1762248981.9284,20,9,45
1762248986.8479,20,11,55
1762248986.9199,20,11,55
1762248991.873,20,12,60
1762248991.9427,20,12,60
1762248996.86,20,13,65
1762248996.9225,20,13,65
1762249001.8699,20,14,70
1762249001.9313,20,14,70
1762249006.8717,20,15,75
1762249006.9326,20,15,75
1762249011.8769,20,16,80
1762249011.9365,20,16,80
1762249016.882,20,17,85
1762249016.9452,20,17,85
1762249021.8868,20,18,90
1762249021.9537,20,18,90
1762249026.8928,20,19,95
1762249026.957,20,19,95
1762249031.9004,20,20,100
1762249340.9906,20,20,100
1762249341.039,20,19,95
1762249385.6077,20,19,95
1762249387.6758,20,18,90
1762249390.4211,20,18,90
1762249392.3866,20,17,85
1762249426.0357,20,17,85
1762249428.0204,20,16,80
1762249506.8407,20,16,80
1762249511.0007,20,15,75
1762249573.7848,20,15,75
1762249574.3362,20,14,70
1762249695.8262,20,14,70
1762249696.3843,20,13,65
1762249750.5855,20,13,65
1762249751.1563,20,12,60
1762249780.258,20,12,60
1762249782.3537,20,11,55
1762249850.6507,20,11,55
1762249852.8383,20,10,50
1762249855.8228,20,10,50
1762249857.8853,20,9,45
1762250009.7417,20,9,45
1762250013.1691,20,8,40
1762250117.6462,20,8,40
1762250119.6908,20,7,35
1762250148.4142,20,7,35
1762250150.4574,20,6,30
1762250269.969,20,6,30
1762250272.1708,20,5,25
1762250330.0305,20,5,25
1762250332.0999,20,4,20
1762250361.6707,20,4,20
1762250362.2357,20,3,15
1762250451.9348,20,3,15
1762250453.1223,20,2,10
1762250520.297,20,2,10
1762250522.4006,20,1,5
1762250656.0725,20,1,5
1762250740.6798,20,0,0
1762250769.2889,20,0,0
1762250771.5952,20,1,5
1762250771.7718,20,1,5
1762250776.5701,20,4,20
1762250776.8388,20,4,20
1762250781.573,20,7,35
1762250782.6857,20,7,35
1762250786.5778,20,10,50
1762250787.6937,20,10,50
1762250791.589,20,13,65
1762250791.714,20,13,65
1762250796.5961,20,14,70
1762250796.6681,20,14,70
1762250801.5869,20,17,85
1762250801.7225,20,17,85
1762250806.5904,20,20,100
1762252127.9301,20,20,100
1762252130.6118,20,19,95
1762252134.6218,20,19,95
1762252137.2099,20,18,90
1762252155.9624,20,18,90
1762252158.6116,20,17,85
1762252165.9678,20,17,85
1762252169.6521,20,16,80
1762252185.5246,20,16,80
1762252188.214,20,15,75
1762252197.9682,20,15,75
1762252200.7649,20,14,70
1762252204.619,20,14,70
1762252205.2236,20,13,65
1762252209.8533,20,13,65
1762252214.1605,20,12,60
1762252217.6841,20,12,60
1762252220.345,20,11,55
1762252237.9292,20,11,55
1762252241.05,20,9,45
1762252245.5355,20,9,45
1762252253.8067,20,6,30
1762252263.3755,20,6,30
1762252268.3005,20,4,20
1762252274.9897,20,4,20
1762252280.4925,20,2,10
1762252280.5513,20,2,10
1762252286.2227,20,1,5
1762252301.0138,20,1,5
1762252304.2443,20,0,0
1762252453.3062,20,0,0
1762252471.2404,20,1,5
1762252471.3992,20,1,5
1762252481.2839,20,2,10
1762252481.4946,20,2,10
1762252491.27,20,3,15
1762252491.5065,20,3,15
1762252501.504,20,5,25
1762252501.7153,20,5,25
1762252506.292,20,8,40
1762252506.4354,20,8,40
1762252511.3036,20,9,45
1762252511.34,20,8,40
1762252511.419,20,9,45
1762252516.4685,20,9,45
1762252521.2933,20,10,50
1762252521.3777,20,10,50
1762252531.2992,20,11,55
1762252531.3826,20,11,55
1762252536.3035,20,13,65
1762252537.3422,20,13,65
1762252541.3151,20,14,70
1762252541.4079,20,14,70
1762252571.3528,20,18,90
1762252571.4401,20,18,90
1762252581.354,20,19,95
1762252581.4389,20,19,95
1762252591.7628,20,20,100
1762252665.64,20,20,100
1762252666.2475,20,17,85
1762253006.8944,20,17,85
1762253011.7685,20,16,80
1762253414.3642,20,16,80
1762253419.3467,20,15,75
1762253445.3188,20,15,75
1762253449.2264,20,14,70
1762253457.8848,20,14,70
1762253461.5959,20,13,65
1762253518.3855,20,13,65
1762253524.8098,20,11,55
1762253525.6729,20,11,55
1762253531.3639,20,10,50
1762253532.8177,20,10,50
1762253539.4282,20,8,40
1762253634.6069,20,8,40
1762253635.2115,20,7,35
1762253685.552,20,7,35
1762253691.043,20,6,30
1762253709.436,20,6,30
1762253713.1938,20,4,20
1762253718.3869,20,4,20
1762253725.0737,20,3,15
1762254846.8596,20,3,15
1762254850.8482,20,2,10
1762255646.9877,20,2,10
1762255651.8523,20,1,5
1762259866.6542,20,1,5
1762259868.9237,20,0,0
1762260358.8135,20,0,0
1762260359.0252,20,1,5
1762260359.8558,20,1,5
1762260363.9972,20,2,10
1762260364.3197,20,2,10
1762260374.027,20,4,20
1762260374.5483,20,4,20
1762260379.0439,20,5,25
1762260379.1717,20,5,25
1762260384.0468,20,6,30
1762260384.1828,20,6,30
1762260389.029,20,8,40
1762260389.2504,20,8,40
1762260394.031,20,9,45
1762260394.1622,20,9,45
1762260399.2901,20,10,50
1762260403.9997,20,10,50
1762260405.3162,20,11,55
1762260408.9836,20,11,55
1762260409.0854,20,13,65
1762260413.9881,20,13,65
1762260414.1789,20,14,70
1762260418.993,20,14,70
1762260419.1827,20,15,75
1762260424.0716,20,16,80
1762260424.198,20,16,80
1762260429.1095,20,17,85
1762260429.237,20,17,85
1762260434.7065,20,18,90
1762260434.8767,20,18,90
1762260439.1324,20,19,95
1762260439.2706,20,19,95
1762260444.0953,20,20,100
1762260717.8733,20,20,100
1762260722.7498,20,19,95
1762260749.7572,20,19,95
1762260754.2989,20,17,85
1762260765.0743,20,17,85
1762260769.754,20,16,80
1762260776.3093,20,16,80
1762260783.4883,20,15,75
1762260783.7535,20,15,75
This logs the CPU and RAM usage of the main worker process.
timestamp,ram_usage_mb,cpu_usage_percent
1762248912,806.37890625,11.8
1762249036,850.42578125,9.8
1762249096,850.4375,9
1762249156,850.41015625,8.9
1762249217,850.40234375,9.1
1762249277,856.84375,9.1
1762249337,856.8359375,8.9
1762249397,858.8984375,9.4
1762249458,859.05859375,9
1762249518,859.2265625,9.2
1762249578,859.2265625,9.2
1762249638,859.23828125,8.9
1762249698,859.24609375,10.8
1762249758,859.671875,10.8
1762249818,859.66015625,10.5
1762249878,859.72265625,11.3
1762249938,859.72265625,10.8
1762249998,859.734375,12.2
1762250058,859.8515625,12.2
1762250119,859.92578125,12.2
1762250180,859.88671875,12.2
1762250240,859.890625,12.2
1762250302,859.87890625,12.2
1762250362,860.0390625,12.4
1762250422,860.0390625,12.2
1762250483,860.0390625,12.1
1762250543,860.0390625,12.3
1762250603,860.0390625,12.2
1762250740,880.3046875,13.4
1762250801,891.4921875,14.6
1762250861,883.63671875,12.2
1762250921,883.62890625,12.3
1762250981,883.671875,12.8
1762251041,883.6953125,12.9
1762251101,883.6640625,12.2
1762251161,883.703125,12.1
1762251222,883.71484375,12.3
1762251282,883.66015625,12.2
1762251342,883.6875,12.3
1762251402,883.71875,12.2
1762251462,883.73046875,11.3
1762251522,883.82421875,11.5
1762251582,883.859375,11.5
1762251642,883.875,11.6
1762251702,883.8984375,11.6
1762251762,883.96875,11.9
1762251822,884.03125,11.9
1762251882,884.046875,11.9
1762251942,884.09375,11.3
1762252002,884.16015625,11.4
1762252062,884.20703125,11.9
1762252122,884.2421875,12.8
1762252183,885.7265625,12.1
1762252244,884.94140625,10.4
1762252304,892.65625,50
1762252414,899.35546875,12.3
1762252541,912.34375,12
1762252601,903.32421875,10.6
1762252661,901.72265625,9.5
1762252721,901.7265625,9.8
1762252781,901.90625,9.4
1762252841,901.859375,9.8
1762252901,901.875,9.5
1762252961,901.91796875,9.6
1762253021,903.17578125,10.3
1762253081,903.19140625,10
1762253141,903.1796875,10
1762253201,903.19140625,10
1762253261,903.16015625,10
1762253321,903.7109375,10.5
1762253381,904.1796875,9.5
1762253441,924.484375,10.1
1762253501,954.00390625,10.1
1762253561,966.84765625,10.9
1762253621,967.2890625,10
1762253681,967.8671875,10
1762253743,975.40625,9.9
1762253803,975.40625,11.2
1762253863,975.40625,11.2
1762253923,975.40625,11.2
1762253983,975.40625,11.5
1762254043,975.40625,11.6
1762254103,975.40625,11.6
1762254163,975.40625,11.6
1762254223,975.40625,11.6
1762254287,975.40625,11.5
1762254347,975.40625,11.6
1762254408,975.40625,11.6
1762254468,975.40625,12
1762254528,975.40625,11.6
1762254588,975.40625,11.6
1762254648,975.40625,11.5
1762254708,975.40625,11.5
1762254768,975.40625,11.6
1762254828,975.40625,11.5
1762254888,975.40625,11.5
1762254949,975.40625,11.6
1762255009,975.40625,11.6
1762255069,975.40625,11.5
1762255129,975.40625,11.5
1762255189,975.40625,11.6
1762255249,975.40625,11.5
1762255311,975.40625,11.6
1762255371,975.40625,11.5
1762255431,975.40625,11.6
1762255491,975.40625,11.5
1762255551,975.40625,12
1762255611,975.40625,12
1762255671,975.90625,11.8
1762255731,975.90625,11.6
1762255791,975.90625,11.6
1762255851,975.90625,11.6
1762255911,975.90625,11.6
1762255971,975.90625,11.5
1762256031,975.90625,11.6
1762256091,975.90625,11.5
1762256151,975.90625,11.6
1762256219,975.90625,11.5
1762256280,975.90625,11.5
1762256340,975.90625,11.6
1762256400,975.90625,11.8
1762256460,975.90625,11.8
1762256520,975.90625,11.7
1762256580,975.90625,11.8
1762256641,975.90625,11.8
1762256701,975.90625,12.3
1762256761,975.90625,12
1762256821,975.90625,11.8
1762256881,975.90625,11.7
1762256941,975.90625,11.5
1762257001,975.90625,11.4
1762257061,975.90625,11.4
1762257121,976.40625,12.6
1762257181,976.40625,11.9
1762257241,976.40625,12
1762257301,976.40625,8.9
1762257361,976.40625,11.7
1762257421,976.40625,11.9
1762257481,976.40625,11.9
1762257541,976.40625,11.9
1762257601,976.40625,11.9
1762257661,976.40625,11.9
1762257729,976.40625,11.8
1762257789,976.90625,12.1
1762257849,976.90625,12.4
1762257909,976.90625,11.9
1762257969,976.90625,11.9
1762258029,976.90625,11.8
1762258089,976.90625,12
1762258149,976.90625,12.2
1762258210,976.90625,12.1
1762258270,976.90625,12.2
1762258330,976.90625,12.1
1762258390,976.90625,12.2
1762258454,976.90625,11.7
1762258514,977.40625,10.8
1762258574,977.40625,9.6
1762258634,977.40625,11.9
1762258694,977.40625,11.8
1762258754,977.40625,11.8
1762258814,977.40625,12.2
1762258874,977.40625,11.8
1762258934,977.40625,11.9
1762258995,977.40625,11.9
1762259055,977.40625,11.3
1762259115,977.90625,11.9
1762259175,977.90625,11.8
1762259235,977.90625,11.9
1762259295,977.90625,11.8
1762259355,977.90625,11.8
1762259415,977.90625,11.1
1762259475,977.90625,12
1762259535,977.90625,11.8
1762259595,977.90625,11.6
1762259655,977.90625,11.5
1762259715,978.40625,11.7
1762259775,978.40625,11.8
1762259835,978.40625,11.8
1762259895,978.40625,11.4
1762259955,978.40625,11.9
1762260015,978.40625,10.4
1762260076,978.40625,11.9
1762260136,978.40625,12.6
1762260313,986.14453125,13.1
1762260434,1012.03515625,12.5
1762260494,995.48046875,13.3
1762260555,995.43359375,12.7
1762260615,991.69140625,10.5
1762260676,991.72265625,11
1762260736,993.01171875,13.4
VAL_ACC (goal: maximize)
Best value: 65.35
Achieved at:
- run_time = 2243
- NORMALIZED_RUNTIME = 30.988
- epochs = 73
- lr = 0.001
- batch_size = 13
- hidden_size = 1892
- dropout = 0.42768143570428
- num_dense_layers = 1
- filter = 64
- num_conv_layers = 7
NORMALIZED_RUNTIME (goal: minimize)
Best value: 4.43
Achieved at:
- run_time = 327
- VAL_ACC = 54.43
- epochs = 26
- lr = 0.00092318542721831
- batch_size = 203
- hidden_size = 2766
- dropout = 0.4404532477436
- num_dense_layers = 2
- filter = 55
- num_conv_layers = 7
Parameter statistics
| Parameter | Min | Max | Mean | Std Dev | Count |
|---|
| run_time | 327 | 3068 | 792.0833 | 494.2202 | 60 |
| VAL_ACC | 32.25 | 65.35 | 54.1177 | 6.5455 | 60 |
| NORMALIZED_RUNTIME | 4.43 | 42.461 | 10.8947 | 6.8651 | 60 |
| epochs | 20 | 143 | 51.725 | 31.6172 | 80 |
| lr | 0.0001 | 0.001 | 0.0009 | 0.0002 | 80 |
| batch_size | 8 | 1024 | 287.7 | 270.8245 | 80 |
| hidden_size | 130 | 4052 | 2153.5125 | 759.0877 | 80 |
| dropout | 0 | 0.494 | 0.2828 | 0.1689 | 80 |
| num_dense_layers | 1 | 2 | 1.4 | 0.4899 | 80 |
| filter | 6 | 80 | 49.7375 | 14.3803 | 80 |
| num_conv_layers | 4 | 7 | 5.925 | 1.1486 | 80 |
Show SLURM-Job-ID (if it exists)
submitit INFO (2025-11-04 10:35:51,716) - Starting with JobEnvironment(job_id=1213252, hostname=c152, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-11-04 10:35:51,718) - Loading pickle: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime/1/single_runs/1213252/1213252_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": 112, "lr": 0.0009504740331321955, "batch_size": 202, "hidden_size": 3217, "dropout": 0.26487458776682615, "num_dense_layers": 2, "filter": 38, "num_conv_layers": 5}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 112 --learning_rate 0.00095047403313219554 --batch_size 202 --hidden_size 3217 --dropout 0.2648745877668261528 --num_dense_layers 2 --filter 38 --num_conv_layers 5
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: 1213252
Status-Change-Time: 1762245180.0
Size: 19255 Bytes
Permissions: -rwxr-xr-x
Owner: s3811141
Last access: 1762248903.0
Last modification: 1762241578.0
Hostname: c152
========
DEBUG INFOS END
python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 112 --learning_rate 0.00095047403313219554 --batch_size 202 --hidden_size 3217 --dropout 0.2648745877668261528 --num_dense_layers 2 --filter 38 --num_conv_layers 5
stdout:
Hyperparameters
╭──────────────────┬───────────────────────╮
│ Parameter │ Value │
├──────────────────┼───────────────────────┤
│ Epochs │ 112 │
│ Num Dense Layers │ 2 │
│ Batch size │ 202 │
│ Learning rate │ 0.0009504740331321955 │
│ Hidden size │ 3217 │
│ Dropout │ 0.26487458776682615 │
│ Optimizer │ adam │
│ Momentum │ 0.9 │
│ Weight Decay │ 0.0001 │
│ Activation │ relu │
│ Init Method │ kaiming │
│ Seed │ None │
│ Conv Filters │ 38 │
│ Num Conv Layers │ 5 │
│ Conv Kernel │ 3 │
│ Conv Stride │ 1 │
│ Conv Padding │ 1 │
╰──────────────────┴───────────────────────╯
Model Summary
╭─────────────────┬─────────────────┬──────────╮
│ Layer │ Output Shape │ Param # │
├─────────────────┼─────────────────┼──────────┤
│ conv::conv0 │ [1, 38, 32, 32] │ 1064 │
│ conv::bn0 │ [1, 38, 32, 32] │ 76 │
│ conv::act_conv0 │ [1, 38, 32, 32] │ 0 │
│ conv::conv1 │ [1, 38, 32, 32] │ 13034 │
│ conv::bn1 │ [1, 38, 32, 32] │ 76 │
│ conv::act_conv1 │ [1, 38, 32, 32] │ 0 │
│ conv::pool1 │ [1, 38, 16, 16] │ 0 │
│ conv::conv2 │ [1, 38, 16, 16] │ 13034 │
│ conv::bn2 │ [1, 38, 16, 16] │ 76 │
│ conv::act_conv2 │ [1, 38, 16, 16] │ 0 │
│ conv::conv3 │ [1, 38, 16, 16] │ 13034 │
│ conv::bn3 │ [1, 38, 16, 16] │ 76 │
│ conv::act_conv3 │ [1, 38, 16, 16] │ 0 │
│ conv::pool2 │ [1, 38, 8, 8] │ 0 │
│ conv::conv4 │ [1, 38, 8, 8] │ 13034 │
│ conv::bn4 │ [1, 38, 8, 8] │ 76 │
│ conv::act_conv4 │ [1, 38, 8, 8] │ 0 │
│ dense::fc0 │ [1, 3217] │ 7826961 │
│ dense::act0 │ [1, 3217] │ 0 │
│ dense::dropout0 │ [1, 3217] │ 0 │
│ dense::fc1 │ [1, 3217] │ 10352306 │
│ dense::act1 │ [1, 3217] │ 0 │
│ dense::dropout1 │ [1, 3217] │ 0 │
│ dense::output │ [1, 100] │ 321800 │
│ Total │ - │ 18554647 │
╰─────────────────┴─────────────────┴──────────╯
──────────────────────────── Epoch 1/112 - Training ────────────────────────────
Epoch-Loss: 955.0620
─────────────────────────── Epoch 1/112 - Validation ───────────────────────────
╔══ Epoch 1/112 Summary ══╗
║ Validation Loss: 3.3051 ║
║ Accuracy: 19.91% ║
╚═════════════════════════╝
──────────────────────────── Epoch 2/112 - Training ────────────────────────────
Epoch-Loss: 795.8873
─────────────────────────── Epoch 2/112 - Validation ───────────────────────────
╔══ Epoch 2/112 Summary ══╗
║ Validation Loss: 2.9582 ║
║ Accuracy: 25.99% ║
╚═════════════════════════╝
──────────────────────────── Epoch 3/112 - Training ────────────────────────────
Epoch-Loss: 716.4072
─────────────────────────── Epoch 3/112 - Validation ───────────────────────────
╔══ Epoch 3/112 Summary ══╗
║ Validation Loss: 2.5822 ║
║ Accuracy: 33.20% ║
╚═════════════════════════╝
──────────────────────────── Epoch 4/112 - Training ────────────────────────────
Epoch-Loss: 664.9796
─────────────────────────── Epoch 4/112 - Validation ───────────────────────────
╔══ Epoch 4/112 Summary ══╗
║ Validation Loss: 2.4509 ║
║ Accuracy: 36.72% ║
╚═════════════════════════╝
──────────────────────────── Epoch 5/112 - Training ────────────────────────────
Epoch-Loss: 627.3232
─────────────────────────── Epoch 5/112 - Validation ───────────────────────────
╔══ Epoch 5/112 Summary ══╗
║ Validation Loss: 2.3118 ║
║ Accuracy: 38.92% ║
╚═════════════════════════╝
──────────────────────────── Epoch 6/112 - Training ────────────────────────────
Epoch-Loss: 596.5379
─────────────────────────── Epoch 6/112 - Validation ───────────────────────────
╔══ Epoch 6/112 Summary ══╗
║ Validation Loss: 2.1975 ║
║ Accuracy: 41.88% ║
╚═════════════════════════╝
──────────────────────────── Epoch 7/112 - Training ────────────────────────────
Epoch-Loss: 570.3640
─────────────────────────── Epoch 7/112 - Validation ───────────────────────────
╔══ Epoch 7/112 Summary ══╗
║ Validation Loss: 2.1648 ║
║ Accuracy: 42.61% ║
╚═════════════════════════╝
──────────────────────────── Epoch 8/112 - Training ────────────────────────────
Epoch-Loss: 549.3931
─────────────────────────── Epoch 8/112 - Validation ───────────────────────────
╔══ Epoch 8/112 Summary ══╗
║ Validation Loss: 2.0773 ║
║ Accuracy: 44.73% ║
╚═════════════════════════╝
──────────────────────────── Epoch 9/112 - Training ────────────────────────────
Epoch-Loss: 527.5951
─────────────────────────── Epoch 9/112 - Validation ───────────────────────────
╔══ Epoch 9/112 Summary ══╗
║ Validation Loss: 2.0965 ║
║ Accuracy: 44.57% ║
╚═════════════════════════╝
─────────────────────────── Epoch 10/112 - Training ────────────────────────────
Epoch-Loss: 512.5626
────────────────────────── Epoch 10/112 - Validation ───────────────────────────
╔═ Epoch 10/112 Summary ══╗
║ Validation Loss: 1.9548 ║
║ Accuracy: 47.32% ║
╚═════════════════════════╝
─────────────────────────── Epoch 11/112 - Training ────────────────────────────
Epoch-Loss: 495.8067
────────────────────────── Epoch 11/112 - Validation ───────────────────────────
╔═ Epoch 11/112 Summary ══╗
║ Validation Loss: 1.9587 ║
║ Accuracy: 47.49% ║
╚═════════════════════════╝
─────────────────────────── Epoch 12/112 - Training ────────────────────────────
Epoch-Loss: 483.0820
────────────────────────── Epoch 12/112 - Validation ───────────────────────────
╔═ Epoch 12/112 Summary ══╗
║ Validation Loss: 1.9673 ║
║ Accuracy: 47.68% ║
╚═════════════════════════╝
─────────────────────────── Epoch 13/112 - Training ────────────────────────────
Epoch-Loss: 469.8618
────────────────────────── Epoch 13/112 - Validation ───────────────────────────
╔═ Epoch 13/112 Summary ══╗
║ Validation Loss: 1.8481 ║
║ Accuracy: 50.34% ║
╚═════════════════════════╝
─────────────────────────── Epoch 14/112 - Training ────────────────────────────
Epoch-Loss: 458.0229
────────────────────────── Epoch 14/112 - Validation ───────────────────────────
╔═ Epoch 14/112 Summary ══╗
║ Validation Loss: 1.8222 ║
║ Accuracy: 50.40% ║
╚═════════════════════════╝
─────────────────────────── Epoch 15/112 - Training ────────────────────────────
Epoch-Loss: 448.3076
────────────────────────── Epoch 15/112 - Validation ───────────────────────────
╔═ Epoch 15/112 Summary ══╗
║ Validation Loss: 1.8021 ║
║ Accuracy: 51.33% ║
╚═════════════════════════╝
─────────────────────────── Epoch 16/112 - Training ────────────────────────────
Epoch-Loss: 440.5694
────────────────────────── Epoch 16/112 - Validation ───────────────────────────
╔═ Epoch 16/112 Summary ══╗
║ Validation Loss: 1.8352 ║
║ Accuracy: 50.06% ║
╚═════════════════════════╝
─────────────────────────── Epoch 17/112 - Training ────────────────────────────
Epoch-Loss: 430.5102
────────────────────────── Epoch 17/112 - Validation ───────────────────────────
╔═ Epoch 17/112 Summary ══╗
║ Validation Loss: 1.8278 ║
║ Accuracy: 50.93% ║
╚═════════════════════════╝
─────────────────────────── Epoch 18/112 - Training ────────────────────────────
Epoch-Loss: 420.4505
────────────────────────── Epoch 18/112 - Validation ───────────────────────────
╔═ Epoch 18/112 Summary ══╗
║ Validation Loss: 1.7692 ║
║ Accuracy: 51.87% ║
╚═════════════════════════╝
─────────────────────────── Epoch 19/112 - Training ────────────────────────────
Epoch-Loss: 412.6231
────────────────────────── Epoch 19/112 - Validation ───────────────────────────
╔═ Epoch 19/112 Summary ══╗
║ Validation Loss: 1.8749 ║
║ Accuracy: 50.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 20/112 - Training ────────────────────────────
Epoch-Loss: 405.0877
────────────────────────── Epoch 20/112 - Validation ───────────────────────────
╔═ Epoch 20/112 Summary ══╗
║ Validation Loss: 1.7931 ║
║ Accuracy: 51.97% ║
╚═════════════════════════╝
─────────────────────────── Epoch 21/112 - Training ────────────────────────────
Epoch-Loss: 396.4048
────────────────────────── Epoch 21/112 - Validation ───────────────────────────
╔═ Epoch 21/112 Summary ══╗
║ Validation Loss: 1.7789 ║
║ Accuracy: 52.63% ║
╚═════════════════════════╝
─────────────────────────── Epoch 22/112 - Training ────────────────────────────
Epoch-Loss: 390.3614
────────────────────────── Epoch 22/112 - Validation ───────────────────────────
╔═ Epoch 22/112 Summary ══╗
║ Validation Loss: 1.7309 ║
║ Accuracy: 53.50% ║
╚═════════════════════════╝
─────────────────────────── Epoch 23/112 - Training ────────────────────────────
Epoch-Loss: 384.5438
────────────────────────── Epoch 23/112 - Validation ───────────────────────────
╔═ Epoch 23/112 Summary ══╗
║ Validation Loss: 1.6970 ║
║ Accuracy: 53.97% ║
╚═════════════════════════╝
─────────────────────────── Epoch 24/112 - Training ────────────────────────────
Epoch-Loss: 377.8502
────────────────────────── Epoch 24/112 - Validation ───────────────────────────
╔═ Epoch 24/112 Summary ══╗
║ Validation Loss: 1.7015 ║
║ Accuracy: 53.74% ║
╚═════════════════════════╝
─────────────────────────── Epoch 25/112 - Training ────────────────────────────
Epoch-Loss: 372.2717
────────────────────────── Epoch 25/112 - Validation ───────────────────────────
╔═ Epoch 25/112 Summary ══╗
║ Validation Loss: 1.7145 ║
║ Accuracy: 54.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 26/112 - Training ────────────────────────────
Epoch-Loss: 366.4607
────────────────────────── Epoch 26/112 - Validation ───────────────────────────
╔═ Epoch 26/112 Summary ══╗
║ Validation Loss: 1.7438 ║
║ Accuracy: 53.62% ║
╚═════════════════════════╝
─────────────────────────── Epoch 27/112 - Training ────────────────────────────
Epoch-Loss: 358.5765
────────────────────────── Epoch 27/112 - Validation ───────────────────────────
╔═ Epoch 27/112 Summary ══╗
║ Validation Loss: 1.7222 ║
║ Accuracy: 53.53% ║
╚═════════════════════════╝
─────────────────────────── Epoch 28/112 - Training ────────────────────────────
Epoch-Loss: 353.4684
────────────────────────── Epoch 28/112 - Validation ───────────────────────────
╔═ Epoch 28/112 Summary ══╗
║ Validation Loss: 1.7120 ║
║ Accuracy: 53.78% ║
╚═════════════════════════╝
─────────────────────────── Epoch 29/112 - Training ────────────────────────────
Epoch-Loss: 349.4159
────────────────────────── Epoch 29/112 - Validation ───────────────────────────
╔═ Epoch 29/112 Summary ══╗
║ Validation Loss: 1.7281 ║
║ Accuracy: 53.64% ║
╚═════════════════════════╝
─────────────────────────── Epoch 30/112 - Training ────────────────────────────
Epoch-Loss: 344.3705
────────────────────────── Epoch 30/112 - Validation ───────────────────────────
╔═ Epoch 30/112 Summary ══╗
║ Validation Loss: 1.7114 ║
║ Accuracy: 54.88% ║
╚═════════════════════════╝
─────────────────────────── Epoch 31/112 - Training ────────────────────────────
Epoch-Loss: 297.0058
────────────────────────── Epoch 31/112 - Validation ───────────────────────────
╔═ Epoch 31/112 Summary ══╗
║ Validation Loss: 1.5800 ║
║ Accuracy: 57.71% ║
╚═════════════════════════╝
─────────────────────────── Epoch 32/112 - Training ────────────────────────────
Epoch-Loss: 281.4155
────────────────────────── Epoch 32/112 - Validation ───────────────────────────
╔═ Epoch 32/112 Summary ══╗
║ Validation Loss: 1.5679 ║
║ Accuracy: 57.96% ║
╚═════════════════════════╝
─────────────────────────── Epoch 33/112 - Training ────────────────────────────
Epoch-Loss: 275.1461
────────────────────────── Epoch 33/112 - Validation ───────────────────────────
╔═ Epoch 33/112 Summary ══╗
║ Validation Loss: 1.5676 ║
║ Accuracy: 58.23% ║
╚═════════════════════════╝
─────────────────────────── Epoch 34/112 - Training ────────────────────────────
Epoch-Loss: 272.8964
────────────────────────── Epoch 34/112 - Validation ───────────────────────────
╔═ Epoch 34/112 Summary ══╗
║ Validation Loss: 1.5609 ║
║ Accuracy: 58.04% ║
╚═════════════════════════╝
─────────────────────────── Epoch 35/112 - Training ────────────────────────────
Epoch-Loss: 267.6424
────────────────────────── Epoch 35/112 - Validation ───────────────────────────
╔═ Epoch 35/112 Summary ══╗
║ Validation Loss: 1.5657 ║
║ Accuracy: 58.24% ║
╚═════════════════════════╝
─────────────────────────── Epoch 36/112 - Training ────────────────────────────
Epoch-Loss: 262.5604
────────────────────────── Epoch 36/112 - Validation ───────────────────────────
╔═ Epoch 36/112 Summary ══╗
║ Validation Loss: 1.5662 ║
║ Accuracy: 58.23% ║
╚═════════════════════════╝
─────────────────────────── Epoch 37/112 - Training ────────────────────────────
Epoch-Loss: 261.5757
────────────────────────── Epoch 37/112 - Validation ───────────────────────────
╔═ Epoch 37/112 Summary ══╗
║ Validation Loss: 1.5720 ║
║ Accuracy: 58.22% ║
╚═════════════════════════╝
─────────────────────────── Epoch 38/112 - Training ────────────────────────────
Epoch-Loss: 257.2067
────────────────────────── Epoch 38/112 - Validation ───────────────────────────
╔═ Epoch 38/112 Summary ══╗
║ Validation Loss: 1.5601 ║
║ Accuracy: 58.27% ║
╚═════════════════════════╝
─────────────────────────── Epoch 39/112 - Training ────────────────────────────
Epoch-Loss: 256.0333
────────────────────────── Epoch 39/112 - Validation ───────────────────────────
╔═ Epoch 39/112 Summary ══╗
║ Validation Loss: 1.5614 ║
║ Accuracy: 58.52% ║
╚═════════════════════════╝
─────────────────────────── Epoch 40/112 - Training ────────────────────────────
Epoch-Loss: 250.4659
────────────────────────── Epoch 40/112 - Validation ───────────────────────────
╔═ Epoch 40/112 Summary ══╗
║ Validation Loss: 1.5670 ║
║ Accuracy: 58.58% ║
╚═════════════════════════╝
─────────────────────────── Epoch 41/112 - Training ────────────────────────────
Epoch-Loss: 250.1424
────────────────────────── Epoch 41/112 - Validation ───────────────────────────
╔═ Epoch 41/112 Summary ══╗
║ Validation Loss: 1.5624 ║
║ Accuracy: 58.56% ║
╚═════════════════════════╝
─────────────────────────── Epoch 42/112 - Training ────────────────────────────
Epoch-Loss: 247.8681
────────────────────────── Epoch 42/112 - Validation ───────────────────────────
╔═ Epoch 42/112 Summary ══╗
║ Validation Loss: 1.5681 ║
║ Accuracy: 58.69% ║
╚═════════════════════════╝
─────────────────────────── Epoch 43/112 - Training ────────────────────────────
Epoch-Loss: 244.3929
────────────────────────── Epoch 43/112 - Validation ───────────────────────────
╔═ Epoch 43/112 Summary ══╗
║ Validation Loss: 1.5618 ║
║ Accuracy: 58.82% ║
╚═════════════════════════╝
─────────────────────────── Epoch 44/112 - Training ────────────────────────────
Epoch-Loss: 242.1870
────────────────────────── Epoch 44/112 - Validation ───────────────────────────
╔═ Epoch 44/112 Summary ══╗
║ Validation Loss: 1.5717 ║
║ Accuracy: 58.66% ║
╚═════════════════════════╝
─────────────────────────── Epoch 45/112 - Training ────────────────────────────
Epoch-Loss: 239.7005
────────────────────────── Epoch 45/112 - Validation ───────────────────────────
╔═ Epoch 45/112 Summary ══╗
║ Validation Loss: 1.5666 ║
║ Accuracy: 58.97% ║
╚═════════════════════════╝
─────────────────────────── Epoch 46/112 - Training ────────────────────────────
Epoch-Loss: 239.9914
────────────────────────── Epoch 46/112 - Validation ───────────────────────────
╔═ Epoch 46/112 Summary ══╗
║ Validation Loss: 1.5633 ║
║ Accuracy: 59.00% ║
╚═════════════════════════╝
─────────────────────────── Epoch 47/112 - Training ────────────────────────────
Epoch-Loss: 237.0629
────────────────────────── Epoch 47/112 - Validation ───────────────────────────
╔═ Epoch 47/112 Summary ══╗
║ Validation Loss: 1.5684 ║
║ Accuracy: 58.77% ║
╚═════════════════════════╝
─────────────────────────── Epoch 48/112 - Training ────────────────────────────
Epoch-Loss: 235.9147
────────────────────────── Epoch 48/112 - Validation ───────────────────────────
╔═ Epoch 48/112 Summary ══╗
║ Validation Loss: 1.5705 ║
║ Accuracy: 58.81% ║
╚═════════════════════════╝
─────────────────────────── Epoch 49/112 - Training ────────────────────────────
Epoch-Loss: 233.5921
────────────────────────── Epoch 49/112 - Validation ───────────────────────────
╔═ Epoch 49/112 Summary ══╗
║ Validation Loss: 1.5715 ║
║ Accuracy: 58.59% ║
╚═════════════════════════╝
─────────────────────────── Epoch 50/112 - Training ────────────────────────────
Epoch-Loss: 231.4425
────────────────────────── Epoch 50/112 - Validation ───────────────────────────
╔═ Epoch 50/112 Summary ══╗
║ Validation Loss: 1.5713 ║
║ Accuracy: 58.75% ║
╚═════════════════════════╝
─────────────────────────── Epoch 51/112 - Training ────────────────────────────
Epoch-Loss: 229.4887
────────────────────────── Epoch 51/112 - Validation ───────────────────────────
╔═ Epoch 51/112 Summary ══╗
║ Validation Loss: 1.5699 ║
║ Accuracy: 58.66% ║
╚═════════════════════════╝
─────────────────────────── Epoch 52/112 - Training ────────────────────────────
Epoch-Loss: 228.0993
────────────────────────── Epoch 52/112 - Validation ───────────────────────────
╔═ Epoch 52/112 Summary ══╗
║ Validation Loss: 1.5691 ║
║ Accuracy: 58.86% ║
╚═════════════════════════╝
─────────────────────────── Epoch 53/112 - Training ────────────────────────────
Epoch-Loss: 226.5348
────────────────────────── Epoch 53/112 - Validation ───────────────────────────
╔═ Epoch 53/112 Summary ══╗
║ Validation Loss: 1.5682 ║
║ Accuracy: 59.09% ║
╚═════════════════════════╝
─────────────────────────── Epoch 54/112 - Training ────────────────────────────
Epoch-Loss: 225.4375
────────────────────────── Epoch 54/112 - Validation ───────────────────────────
╔═ Epoch 54/112 Summary ══╗
║ Validation Loss: 1.5633 ║
║ Accuracy: 58.83% ║
╚═════════════════════════╝
─────────────────────────── Epoch 55/112 - Training ────────────────────────────
Epoch-Loss: 223.3518
────────────────────────── Epoch 55/112 - Validation ───────────────────────────
╔═ Epoch 55/112 Summary ══╗
║ Validation Loss: 1.5724 ║
║ Accuracy: 58.90% ║
╚═════════════════════════╝
─────────────────────────── Epoch 56/112 - Training ────────────────────────────
Epoch-Loss: 220.7970
────────────────────────── Epoch 56/112 - Validation ───────────────────────────
╔═ Epoch 56/112 Summary ══╗
║ Validation Loss: 1.5707 ║
║ Accuracy: 58.62% ║
╚═════════════════════════╝
─────────────────────────── Epoch 57/112 - Training ────────────────────────────
Epoch-Loss: 218.5614
────────────────────────── Epoch 57/112 - Validation ───────────────────────────
╔═ Epoch 57/112 Summary ══╗
║ Validation Loss: 1.5786 ║
║ Accuracy: 58.81% ║
╚═════════════════════════╝
─────────────────────────── Epoch 58/112 - Training ────────────────────────────
Epoch-Loss: 219.1053
────────────────────────── Epoch 58/112 - Validation ───────────────────────────
╔═ Epoch 58/112 Summary ══╗
║ Validation Loss: 1.5768 ║
║ Accuracy: 59.23% ║
╚═════════════════════════╝
─────────────────────────── Epoch 59/112 - Training ────────────────────────────
Epoch-Loss: 216.2492
────────────────────────── Epoch 59/112 - Validation ───────────────────────────
╔═ Epoch 59/112 Summary ══╗
║ Validation Loss: 1.5790 ║
║ Accuracy: 59.18% ║
╚═════════════════════════╝
─────────────────────────── Epoch 60/112 - Training ────────────────────────────
Epoch-Loss: 214.3229
────────────────────────── Epoch 60/112 - Validation ───────────────────────────
╔═ Epoch 60/112 Summary ══╗
║ Validation Loss: 1.5749 ║
║ Accuracy: 58.90% ║
╚═════════════════════════╝
─────────────────────────── Epoch 61/112 - Training ────────────────────────────
Epoch-Loss: 210.5554
────────────────────────── Epoch 61/112 - Validation ───────────────────────────
╔═ Epoch 61/112 Summary ══╗
║ Validation Loss: 1.5702 ║
║ Accuracy: 59.15% ║
╚═════════════════════════╝
─────────────────────────── Epoch 62/112 - Training ────────────────────────────
Epoch-Loss: 210.1758
────────────────────────── Epoch 62/112 - Validation ───────────────────────────
╔═ Epoch 62/112 Summary ══╗
║ Validation Loss: 1.5713 ║
║ Accuracy: 59.25% ║
╚═════════════════════════╝
─────────────────────────── Epoch 63/112 - Training ────────────────────────────
Epoch-Loss: 206.4946
────────────────────────── Epoch 63/112 - Validation ───────────────────────────
╔═ Epoch 63/112 Summary ══╗
║ Validation Loss: 1.5718 ║
║ Accuracy: 59.36% ║
╚═════════════════════════╝
─────────────────────────── Epoch 64/112 - Training ────────────────────────────
Epoch-Loss: 208.0394
────────────────────────── Epoch 64/112 - Validation ───────────────────────────
╔═ Epoch 64/112 Summary ══╗
║ Validation Loss: 1.5725 ║
║ Accuracy: 59.12% ║
╚═════════════════════════╝
─────────────────────────── Epoch 65/112 - Training ────────────────────────────
Epoch-Loss: 206.7347
────────────────────────── Epoch 65/112 - Validation ───────────────────────────
╔═ Epoch 65/112 Summary ══╗
║ Validation Loss: 1.5719 ║
║ Accuracy: 59.25% ║
╚═════════════════════════╝
─────────────────────────── Epoch 66/112 - Training ────────────────────────────
Epoch-Loss: 206.5197
────────────────────────── Epoch 66/112 - Validation ───────────────────────────
╔═ Epoch 66/112 Summary ══╗
║ Validation Loss: 1.5743 ║
║ Accuracy: 59.38% ║
╚═════════════════════════╝
─────────────────────────── Epoch 67/112 - Training ────────────────────────────
Epoch-Loss: 205.8980
────────────────────────── Epoch 67/112 - Validation ───────────────────────────
╔═ Epoch 67/112 Summary ══╗
║ Validation Loss: 1.5733 ║
║ Accuracy: 59.43% ║
╚═════════════════════════╝
─────────────────────────── Epoch 68/112 - Training ────────────────────────────
Epoch-Loss: 205.4482
────────────────────────── Epoch 68/112 - Validation ───────────────────────────
╔═ Epoch 68/112 Summary ══╗
║ Validation Loss: 1.5765 ║
║ Accuracy: 59.18% ║
╚═════════════════════════╝
─────────────────────────── Epoch 69/112 - Training ────────────────────────────
Epoch-Loss: 207.0379
────────────────────────── Epoch 69/112 - Validation ───────────────────────────
╔═ Epoch 69/112 Summary ══╗
║ Validation Loss: 1.5757 ║
║ Accuracy: 59.44% ║
╚═════════════════════════╝
─────────────────────────── Epoch 70/112 - Training ────────────────────────────
Epoch-Loss: 204.6876
────────────────────────── Epoch 70/112 - Validation ───────────────────────────
╔═ Epoch 70/112 Summary ══╗
║ Validation Loss: 1.5754 ║
║ Accuracy: 59.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 71/112 - Training ────────────────────────────
Epoch-Loss: 204.0472
────────────────────────── Epoch 71/112 - Validation ───────────────────────────
╔═ Epoch 71/112 Summary ══╗
║ Validation Loss: 1.5737 ║
║ Accuracy: 59.59% ║
╚═════════════════════════╝
─────────────────────────── Epoch 72/112 - Training ────────────────────────────
Epoch-Loss: 204.3597
────────────────────────── Epoch 72/112 - Validation ───────────────────────────
╔═ Epoch 72/112 Summary ══╗
║ Validation Loss: 1.5755 ║
║ Accuracy: 59.50% ║
╚═════════════════════════╝
─────────────────────────── Epoch 73/112 - Training ────────────────────────────
Epoch-Loss: 203.7385
────────────────────────── Epoch 73/112 - Validation ───────────────────────────
╔═ Epoch 73/112 Summary ══╗
║ Validation Loss: 1.5811 ║
║ Accuracy: 59.42% ║
╚═════════════════════════╝
─────────────────────────── Epoch 74/112 - Training ────────────────────────────
Epoch-Loss: 202.9506
────────────────────────── Epoch 74/112 - Validation ───────────────────────────
╔═ Epoch 74/112 Summary ══╗
║ Validation Loss: 1.5773 ║
║ Accuracy: 59.43% ║
╚═════════════════════════╝
─────────────────────────── Epoch 75/112 - Training ────────────────────────────
Epoch-Loss: 203.9671
────────────────────────── Epoch 75/112 - Validation ───────────────────────────
╔═ Epoch 75/112 Summary ══╗
║ Validation Loss: 1.5771 ║
║ Accuracy: 59.37% ║
╚═════════════════════════╝
─────────────────────────── Epoch 76/112 - Training ────────────────────────────
Epoch-Loss: 202.3131
────────────────────────── Epoch 76/112 - Validation ───────────────────────────
╔═ Epoch 76/112 Summary ══╗
║ Validation Loss: 1.5796 ║
║ Accuracy: 59.45% ║
╚═════════════════════════╝
─────────────────────────── Epoch 77/112 - Training ────────────────────────────
Epoch-Loss: 203.4217
────────────────────────── Epoch 77/112 - Validation ───────────────────────────
╔═ Epoch 77/112 Summary ══╗
║ Validation Loss: 1.5801 ║
║ Accuracy: 59.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 78/112 - Training ────────────────────────────
Epoch-Loss: 205.2900
────────────────────────── Epoch 78/112 - Validation ───────────────────────────
╔═ Epoch 78/112 Summary ══╗
║ Validation Loss: 1.5781 ║
║ Accuracy: 59.48% ║
╚═════════════════════════╝
─────────────────────────── Epoch 79/112 - Training ────────────────────────────
Epoch-Loss: 203.8086
────────────────────────── Epoch 79/112 - Validation ───────────────────────────
╔═ Epoch 79/112 Summary ══╗
║ Validation Loss: 1.5784 ║
║ Accuracy: 59.41% ║
╚═════════════════════════╝
─────────────────────────── Epoch 80/112 - Training ────────────────────────────
Epoch-Loss: 202.9850
────────────────────────── Epoch 80/112 - Validation ───────────────────────────
╔═ Epoch 80/112 Summary ══╗
║ Validation Loss: 1.5735 ║
║ Accuracy: 59.60% ║
╚═════════════════════════╝
─────────────────────────── Epoch 81/112 - Training ────────────────────────────
Epoch-Loss: 202.5332
────────────────────────── Epoch 81/112 - Validation ───────────────────────────
╔═ Epoch 81/112 Summary ══╗
║ Validation Loss: 1.5754 ║
║ Accuracy: 59.50% ║
╚═════════════════════════╝
─────────────────────────── Epoch 82/112 - Training ────────────────────────────
Epoch-Loss: 203.6954
────────────────────────── Epoch 82/112 - Validation ───────────────────────────
╔═ Epoch 82/112 Summary ══╗
║ Validation Loss: 1.5756 ║
║ Accuracy: 59.51% ║
╚═════════════════════════╝
─────────────────────────── Epoch 83/112 - Training ────────────────────────────
Epoch-Loss: 201.4668
────────────────────────── Epoch 83/112 - Validation ───────────────────────────
╔═ Epoch 83/112 Summary ══╗
║ Validation Loss: 1.5765 ║
║ Accuracy: 59.41% ║
╚═════════════════════════╝
─────────────────────────── Epoch 84/112 - Training ────────────────────────────
Epoch-Loss: 201.5529
────────────────────────── Epoch 84/112 - Validation ───────────────────────────
╔═ Epoch 84/112 Summary ══╗
║ Validation Loss: 1.5784 ║
║ Accuracy: 59.42% ║
╚═════════════════════════╝
─────────────────────────── Epoch 85/112 - Training ────────────────────────────
Epoch-Loss: 201.3571
────────────────────────── Epoch 85/112 - Validation ───────────────────────────
╔═ Epoch 85/112 Summary ══╗
║ Validation Loss: 1.5768 ║
║ Accuracy: 59.49% ║
╚═════════════════════════╝
─────────────────────────── Epoch 86/112 - Training ────────────────────────────
Epoch-Loss: 202.1545
────────────────────────── Epoch 86/112 - Validation ───────────────────────────
╔═ Epoch 86/112 Summary ══╗
║ Validation Loss: 1.5760 ║
║ Accuracy: 59.47% ║
╚═════════════════════════╝
─────────────────────────── Epoch 87/112 - Training ────────────────────────────
Epoch-Loss: 202.6897
────────────────────────── Epoch 87/112 - Validation ───────────────────────────
╔═ Epoch 87/112 Summary ══╗
║ Validation Loss: 1.5799 ║
║ Accuracy: 59.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 88/112 - Training ────────────────────────────
Epoch-Loss: 201.4446
────────────────────────── Epoch 88/112 - Validation ───────────────────────────
╔═ Epoch 88/112 Summary ══╗
║ Validation Loss: 1.5786 ║
║ Accuracy: 59.33% ║
╚═════════════════════════╝
─────────────────────────── Epoch 89/112 - Training ────────────────────────────
Epoch-Loss: 199.4176
────────────────────────── Epoch 89/112 - Validation ───────────────────────────
╔═ Epoch 89/112 Summary ══╗
║ Validation Loss: 1.5786 ║
║ Accuracy: 59.48% ║
╚═════════════════════════╝
─────────────────────────── Epoch 90/112 - Training ────────────────────────────
Epoch-Loss: 200.7443
────────────────────────── Epoch 90/112 - Validation ───────────────────────────
╔═ Epoch 90/112 Summary ══╗
║ Validation Loss: 1.5774 ║
║ Accuracy: 59.33% ║
╚═════════════════════════╝
─────────────────────────── Epoch 91/112 - Training ────────────────────────────
Epoch-Loss: 200.4885
────────────────────────── Epoch 91/112 - Validation ───────────────────────────
╔═ Epoch 91/112 Summary ══╗
║ Validation Loss: 1.5784 ║
║ Accuracy: 59.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 92/112 - Training ────────────────────────────
Epoch-Loss: 200.9557
────────────────────────── Epoch 92/112 - Validation ───────────────────────────
╔═ Epoch 92/112 Summary ══╗
║ Validation Loss: 1.5792 ║
║ Accuracy: 59.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 93/112 - Training ────────────────────────────
Epoch-Loss: 199.8711
────────────────────────── Epoch 93/112 - Validation ───────────────────────────
╔═ Epoch 93/112 Summary ══╗
║ Validation Loss: 1.5813 ║
║ Accuracy: 59.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 94/112 - Training ────────────────────────────
Epoch-Loss: 199.8850
────────────────────────── Epoch 94/112 - Validation ───────────────────────────
╔═ Epoch 94/112 Summary ══╗
║ Validation Loss: 1.5804 ║
║ Accuracy: 59.44% ║
╚═════════════════════════╝
─────────────────────────── Epoch 95/112 - Training ────────────────────────────
Epoch-Loss: 199.5614
────────────────────────── Epoch 95/112 - Validation ───────────────────────────
╔═ Epoch 95/112 Summary ══╗
║ Validation Loss: 1.5782 ║
║ Accuracy: 59.39% ║
╚═════════════════════════╝
─────────────────────────── Epoch 96/112 - Training ────────────────────────────
Epoch-Loss: 199.9004
────────────────────────── Epoch 96/112 - Validation ───────────────────────────
╔═ Epoch 96/112 Summary ══╗
║ Validation Loss: 1.5797 ║
║ Accuracy: 59.46% ║
╚═════════════════════════╝
─────────────────────────── Epoch 97/112 - Training ────────────────────────────
Epoch-Loss: 200.6092
────────────────────────── Epoch 97/112 - Validation ───────────────────────────
╔═ Epoch 97/112 Summary ══╗
║ Validation Loss: 1.5810 ║
║ Accuracy: 59.40% ║
╚═════════════════════════╝
─────────────────────────── Epoch 98/112 - Training ────────────────────────────
Epoch-Loss: 199.6845
────────────────────────── Epoch 98/112 - Validation ───────────────────────────
╔═ Epoch 98/112 Summary ══╗
║ Validation Loss: 1.5793 ║
║ Accuracy: 59.45% ║
╚═════════════════════════╝
─────────────────────────── Epoch 99/112 - Training ────────────────────────────
Epoch-Loss: 200.0324
────────────────────────── Epoch 99/112 - Validation ───────────────────────────
╔═ Epoch 99/112 Summary ══╗
║ Validation Loss: 1.5763 ║
║ Accuracy: 59.37% ║
╚═════════════════════════╝
─────────────────────────── Epoch 100/112 - Training ───────────────────────────
Epoch-Loss: 200.7459
────────────────────────── Epoch 100/112 - Validation ──────────────────────────
╔═ Epoch 100/112 Summary ═╗
║ Validation Loss: 1.5773 ║
║ Accuracy: 59.43% ║
╚═════════════════════════╝
─────────────────────────── Epoch 101/112 - Training ───────────────────────────
Epoch-Loss: 199.7975
────────────────────────── Epoch 101/112 - Validation ──────────────────────────
╔═ Epoch 101/112 Summary ═╗
║ Validation Loss: 1.5786 ║
║ Accuracy: 59.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 102/112 - Training ───────────────────────────
Epoch-Loss: 201.7962
────────────────────────── Epoch 102/112 - Validation ──────────────────────────
╔═ Epoch 102/112 Summary ═╗
║ Validation Loss: 1.5821 ║
║ Accuracy: 59.30% ║
╚═════════════════════════╝
─────────────────────────── Epoch 103/112 - Training ───────────────────────────
Epoch-Loss: 201.4775
────────────────────────── Epoch 103/112 - Validation ──────────────────────────
╔═ Epoch 103/112 Summary ═╗
║ Validation Loss: 1.5794 ║
║ Accuracy: 59.57% ║
╚═════════════════════════╝
─────────────────────────── Epoch 104/112 - Training ───────────────────────────
Epoch-Loss: 198.0022
────────────────────────── Epoch 104/112 - Validation ──────────────────────────
╔═ Epoch 104/112 Summary ═╗
║ Validation Loss: 1.5795 ║
║ Accuracy: 59.43% ║
╚═════════════════════════╝
─────────────────────────── Epoch 105/112 - Training ───────────────────────────
Epoch-Loss: 199.6490
────────────────────────── Epoch 105/112 - Validation ──────────────────────────
╔═ Epoch 105/112 Summary ═╗
║ Validation Loss: 1.5779 ║
║ Accuracy: 59.59% ║
╚═════════════════════════╝
─────────────────────────── Epoch 106/112 - Training ───────────────────────────
Epoch-Loss: 200.0502
────────────────────────── Epoch 106/112 - Validation ──────────────────────────
╔═ Epoch 106/112 Summary ═╗
║ Validation Loss: 1.5792 ║
║ Accuracy: 59.38% ║
╚═════════════════════════╝
─────────────────────────── Epoch 107/112 - Training ───────────────────────────
Epoch-Loss: 200.1987
────────────────────────── Epoch 107/112 - Validation ──────────────────────────
╔═ Epoch 107/112 Summary ═╗
║ Validation Loss: 1.5802 ║
║ Accuracy: 59.52% ║
╚═════════════════════════╝
─────────────────────────── Epoch 108/112 - Training ───────────────────────────
Epoch-Loss: 199.2626
────────────────────────── Epoch 108/112 - Validation ──────────────────────────
╔═ Epoch 108/112 Summary ═╗
║ Validation Loss: 1.5821 ║
║ Accuracy: 59.31% ║
╚═════════════════════════╝
─────────────────────────── Epoch 109/112 - Training ───────────────────────────
Epoch-Loss: 199.9030
────────────────────────── Epoch 109/112 - Validation ──────────────────────────
╔═ Epoch 109/112 Summary ═╗
║ Validation Loss: 1.5773 ║
║ Accuracy: 59.32% ║
╚═════════════════════════╝
─────────────────────────── Epoch 110/112 - Training ───────────────────────────
Epoch-Loss: 198.7995
────────────────────────── Epoch 110/112 - Validation ──────────────────────────
╔═ Epoch 110/112 Summary ═╗
║ Validation Loss: 1.5774 ║
║ Accuracy: 59.43% ║
╚═════════════════════════╝
─────────────────────────── Epoch 111/112 - Training ───────────────────────────
Epoch-Loss: 199.6145
────────────────────────── Epoch 111/112 - Validation ──────────────────────────
╔═ Epoch 111/112 Summary ═╗
║ Validation Loss: 1.5816 ║
║ Accuracy: 59.40% ║
╚═════════════════════════╝
─────────────────────────── Epoch 112/112 - Training ───────────────────────────
Epoch-Loss: 199.6536
────────────────────────── Epoch 112/112 - Validation ──────────────────────────
╔═ Epoch 112/112 Summary ═╗
║ Validation Loss: 1.5807 ║
║ Accuracy: 59.41% ║
╚═════════════════════════╝
VAL_LOSS: 1.5807276248931885
VAL_ACC: 59.41
RUNTIME: 1310.220
NORMALIZED_RUNTIME: 18.198
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': 59.41, 'NORMALIZED_RUNTIME': 18.198}
Final-results: {'VAL_ACC': 59.41, 'NORMALIZED_RUNTIME': 18.198}
EXIT_CODE: 0
submitit INFO (2025-11-04 10:57:49,412) - Job completed successfully
submitit INFO (2025-11-04 10:57:49,414) - Exiting after successful completion
