GUI page with all the settings of this job Experiment overview Setting Value Model for non-random steps BOTORCH_MODULAR Max. nr. evaluations 500 Number random steps 20 Nr. of workers (parameter) 20 Main process memory (GB) 8 Worker memory (GB) 10
Job Summary per Generation Node
Generation Node Total COMPLETED FAILED RUNNING
SOBOL 14 1 11 2
Experiment parameters Name Type Lower bound Upper bound Values Type Log Scale? epochs range 10 200 int No lr range 0.0001 0.1 float No batch_size range 8 4096 int No hidden_size range 8 8192 int No dropout range 0 0.5 float No activation fixed leaky_relu num_dense_layers range 1 4 int No init fixed normal weight_decay range 0 1 float No
Number of evaluations
Failed
Succeeded
Running
Total
11
1
2
14
Result names and types
Last progressbar status
2025-07-28 12:57:30: Sobol, failed: 11 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), finishing jobs (_get_next_trials), finished 1 job Git-Version
Commit: 763ea96fbc9b7fc932e55190ab5e45707b9113f6 (7747)
trial_index,submit_time,queue_time,start_time,end_time,run_time,program_string,exit_code,signal,hostname,OO_Info_SLURM_JOB_ID,arm_name,trial_status,generation_node,VAL_ACC,epochs,lr,batch_size,hidden_size,dropout,num_dense_layers,weight_decay,activation,init
0,1753699570,15,1753699585,1753699725,140,python3 .tests/mnist/train --epochs 67 --learning_rate 0.08675298514962197227 --batch_size 257 --hidden_size 1100 --dropout 0.00769690331071615219 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.68262010812759399414,1,,c137,525705,0_0,FAILED,SOBOL,,67,0.086752985149621972271738457039,257,1100,0.007696903310716152191162109375,4,0.682620108127593994140625,leaky_relu,normal
1,1753699606,29,1753699635,1753699726,91,python3 .tests/mnist/train --epochs 161 --learning_rate 0.0482066746130585741 --batch_size 3998 --hidden_size 6392 --dropout 0.48955024313181638718 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.42407821957021951675,1,,c137,525708,1_0,FAILED,SOBOL,,161,0.048206674613058574097035346995,3998,6392,0.489550243131816387176513671875,1,0.424078219570219516754150390625,leaky_relu,normal
2,,,,,,,,,,,2_0,RUNNING,SOBOL,,139,0.068563694845791917087396427632,1185,2753,0.268582780845463275909423828125,2,0.9848288409411907196044921875,leaky_relu,normal
3,1753699680,31,1753699711,1753700006,295,python3 .tests/mnist/train --epochs 41 --learning_rate 0.00409117825105786374 --batch_size 3073 --hidden_size 5646 --dropout 0.22037612739950418472 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.24337655678391456604,0,,c132,525710,3_0,COMPLETED,SOBOL,11.34999999999999964472863211995,41,0.004091178251057863736461772675,3073,5646,0.220376127399504184722900390625,3,0.2433765567839145660400390625,leaky_relu,normal
4,1753699714,30,1753699744,1753699762,18,python3 .tests/mnist/train --epochs 27 --learning_rate 0.05066432487647980903 --batch_size 3565 --hidden_size 4434 --dropout 0.16181570477783679962 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.86962887551635503769,1,,c131,525711,4_0,FAILED,SOBOL,,27,0.050664324876479809034446333271,3565,4434,0.16181570477783679962158203125,4,0.869628875516355037689208984375,leaky_relu,normal
5,1753699750,5,1753699755,1753699767,12,python3 .tests/mnist/train --epochs 118 --learning_rate 0.01447770324321463742 --batch_size 662 --hidden_size 3587 --dropout 0.33507583849132061005 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.11254384275525808334,1,,c137,525713,5_0,FAILED,SOBOL,,118,0.014477703243214637418567747318,662,3587,0.33507583849132061004638671875,1,0.112543842755258083343505859375,leaky_relu,normal
6,1753699802,13,1753699815,1753699827,12,python3 .tests/mnist/train --epochs 194 --learning_rate 0.09412205248679966774 --batch_size 2445 --hidden_size 8113 --dropout 0.4231793135404586792 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.54788884706795215607,1,,c137,525714,6_0,FAILED,SOBOL,,194,0.094122052486799667736505625726,2445,8113,0.42317931354045867919921875,2,0.54788884706795215606689453125,leaky_relu,normal
7,1753699851,24,1753699875,1753699887,12,python3 .tests/mnist/train --epochs 94 --learning_rate 0.03352140318797901947 --batch_size 1778 --hidden_size 776 --dropout 0.06637933477759361267 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.30496422387659549713,1,,c137,525716,7_0,FAILED,SOBOL,,94,0.033521403187979019466791186233,1778,776,0.0663793347775936126708984375,3,0.30496422387659549713134765625,leaky_relu,normal
8,1753699904,36,1753699940,1753699952,12,python3 .tests/mnist/train --epochs 86 --learning_rate 0.07425303167663514781 --batch_size 2305 --hidden_size 3976 --dropout 0.45886279270052909851 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.78395914658904075623,1,,c137,525722,8_0,FAILED,SOBOL,,86,0.074253031676635147806386783031,2305,3976,0.4588627927005290985107421875,3,0.7839591465890407562255859375,leaky_relu,normal
9,1753699968,27,1753699995,1753700008,13,python3 .tests/mnist/train --epochs 179 --learning_rate 0.01075610120547935283 --batch_size 1950 --hidden_size 4920 --dropout 0.03838053345680236816 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.04250865057110786438,1,,c137,525729,9_0,FAILED,SOBOL,,179,0.010756101205479352833638273523,1950,4920,0.0383805334568023681640625,2,0.0425086505711078643798828125,leaky_relu,normal
10,1753700028,27,1753700055,1753700067,12,python3 .tests/mnist/train --epochs 109 --learning_rate 0.08106359761245549023 --batch_size 3233 --hidden_size 386 --dropout 0.18850254826247692108 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.60321074817329645157,1,,c137,525732,10_0,FAILED,SOBOL,,109,0.081063597612455490226679444277,3233,386,0.18850254826247692108154296875,1,0.603210748173296451568603515625,leaky_relu,normal
11,1753700092,23,1753700115,1753700127,12,python3 .tests/mnist/train --epochs 12 --learning_rate 0.04154170197574422185 --batch_size 1025 --hidden_size 7628 --dropout 0.30046017654240131378 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.34467066172510385513,1,,c137,525733,11_0,FAILED,SOBOL,,12,0.04154170197574422185304854338,1025,7628,0.30046017654240131378173828125,4,0.344670661725103855133056640625,leaky_relu,normal
12,1753700162,13,1753700175,1753700187,12,python3 .tests/mnist/train --epochs 56 --learning_rate 0.08813927546525374135 --batch_size 1524 --hidden_size 6802 --dropout 0.36689219716936349869 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.72238566167652606964,1,,c137,525735,12_0,FAILED,SOBOL,,56,0.088139275465253741348448102144,1524,6802,0.366892197169363498687744140625,3,0.72238566167652606964111328125,leaky_relu,normal
13,1753700229,6,1753700235,1753700248,13,python3 .tests/mnist/train --epochs 147 --learning_rate 0.02695327441785484723 --batch_size 2703 --hidden_size 1606 --dropout 0.13000315893441438675 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.47946306504309177399,1,,c137,525736,13_0,RUNNING,SOBOL,,147,0.026953274417854847233577686438,2703,1606,0.130003158934414386749267578125,2,0.47946306504309177398681640625,leaky_relu,normal
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Run-UUID: 46baab75-b661-4a30-8bab-0a4a62e27684
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| OmniOpt2 - Taking parameters to the next level. |
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omniopt --partition=alpha --experiment_name=mnist_gpu_noall --mem_gb=10 --time=1440 --worker_timeout=120 --max_eval=500 --num_parallel_jobs=20 --gpus=1 --num_random_steps=20 --follow --live_share --send_anonymized_usage_stats --result_names VAL_ACC=max --run_program=cHl0aG9uMyAudGVzdHMvbW5pc3QvdHJhaW4gLS1lcG9jaHMgJWVwb2NocyAtLWxlYXJuaW5nX3JhdGUgJWxyIC0tYmF0Y2hfc2l6ZSAlYmF0Y2hfc2l6ZSAtLWhpZGRlbl9zaXplICVoaWRkZW5fc2l6ZSAtLWRyb3BvdXQgJWRyb3BvdXQgLS1hY3RpdmF0aW9uICVhY3RpdmF0aW9uIC0tbnVtX2RlbnNlX2xheWVycyAlbnVtX2RlbnNlX2xheWVycyAtLWluaXQgJWluaXQgLS13ZWlnaHRfZGVjYXkgJXdlaWdodF9kZWNheQ== --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=8 --max_nr_of_zero_results=50 --slurm_signal_delay_s=0 --max_failed_jobs=0 --max_attempts_for_generation=20 --num_restarts=20 --raw_samples=1024 --max_abandoned_retrial=20 --max_num_of_parallel_sruns=16 --parameter epochs range 10 200 int false --parameter lr range 0.0001 0.1 float false --parameter batch_size range 8 4096 int false --parameter hidden_size range 8 8192 int false --parameter dropout range 0 0.5 float false --parameter activation fixed leaky_relu --parameter num_dense_layers range 1 4 int false --parameter init fixed normal --parameter weight_decay range 0 1 float false --ui_url 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⠋ Disabling logging...
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⠋ Creating folder /data/horse/ws/pwinkler-mnist_tst/omniopt/runs/mnist_gpu_noall/0...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
⠋ Writing username state file...
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Run-folder : /data/horse/ws/pwinkler-mnist_tst/omniopt/runs/mnist_gpu_noall/ 0
⠋ Printing run info...
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⠋ Writing ui_url file if it is present...
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⠋ Checking max_eval...
⠋ Calculating number of steps...
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⠋ Handling random steps...
⠋ Initializing ax_client...
[WARNING 07-28 12:44:13] ax.service.ax_client: Selecting a GenerationStrategy when using BatchTrials is in beta. Double check the recommended strategy matches your expectations.
⠋ Setting orchestrator...
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 480 steps.
Run-Program: python3 .tests/mnist/train --epochs %epochs --learning_rate %lr --batch_size %batch_size --hidden_size %hidden_size --dropout %dropout --activation %activation --num_dense_layers %num_dense_layers --init %init --weight_decay %weight_decay
Experiment parameters
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Name ┃ Type ┃ Lower bound ┃ Upper bound ┃ Values ┃ Type ┃ Log Scale? ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ epochs │ range │ 10 │ 200 │ │ int │ No │
│ lr │ range │ 0.0001 │ 0.1 │ │ float │ No │
│ batch_size │ range │ 8 │ 4096 │ │ int │ No │
│ hidden_size │ range │ 8 │ 8192 │ │ int │ No │
│ dropout │ range │ 0 │ 0.5 │ │ float │ No │
│ activation │ fixed │ │ │ leaky_relu │ │ │
│ num_dense_layers │ range │ 1 │ 4 │ │ int │ No │
│ init │ fixed │ │ │ normal │ │ │
│ weight_decay │ range │ 0 │ 1 │ │ float │ No │
└──────────────────┴───────┴─────────────┴─────────────┴────────────┴───────┴────────────┘
Result-Names
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Result-Name ┃ Min or max? ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ VAL_ACC │ max │
└─────────────┴─────────────┘
See https://imageseg.scads.de/omniax/share?user_id=pwinkler&experiment_name=mnist_gpu_noall&run_nr=0 for live-results.
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Sobol, failed: 11 ('VAL_ACC: ' not found) , best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), finishing jobs (_get_next_trials), finished 1 job: 0%|░░░░░░░░░░| 1/500 [12:44<74:17:55, 536.02s/it]
Runtime (end): 25 minutes and 53 seconds, PID: 1589565
2025-07-28 12:44:46: SOBOL, Started OmniOpt2 run...
2025-07-28 12:45:23: Sobol, getting new HP set
2025-07-28 12:45:51: Sobol, requested 1 jobs, got 1, 28.37 s/job
2025-07-28 12:45:59: Sobol, eval #1/1 start
2025-07-28 12:46:05: Sobol, starting new job
2025-07-28 12:46:11: Sobol, unknown 1∑1 (5%/20), started new job
2025-07-28 12:46:17: Sobol, running 1∑1 (5%/20), getting new HP set
2025-07-28 12:46:29: Sobol, running 1∑1 (5%/20), requested 1 jobs, got 1, 10.70 s/job
2025-07-28 12:46:34: Sobol, running 1∑1 (5%/20), eval #1/1 start
2025-07-28 12:46:40: Sobol, running 1∑1 (5%/20), starting new job
2025-07-28 12:46:47: Sobol, running/unknown 1/1∑2 (10%/20), started new job
2025-07-28 12:46:54: Sobol, running/pending 1/1∑2 (10%/20), getting new HP set
2025-07-28 12:47:06: Sobol, running/pending 1/1∑2 (10%/20), requested 1 jobs, got 1, 12.67 s/job
2025-07-28 12:47:12: Sobol, running/pending 1/1∑2 (10%/20), eval #1/1 start
2025-07-28 12:47:20: Sobol, running/pending 1/1∑2 (10%/20), starting new job
2025-07-28 12:47:29: Sobol, running/unknown 2/1∑3 (15%/20), started new job
2025-07-28 12:47:35: Sobol, running/pending 2/1∑3 (15%/20), getting new HP set
2025-07-28 12:47:46: Sobol, running 3∑3 (15%/20), requested 1 jobs, got 1, 11.33 s/job
2025-07-28 12:47:51: Sobol, running 3∑3 (15%/20), eval #1/1 start
2025-07-28 12:47:56: Sobol, running 3∑3 (15%/20), starting new job
2025-07-28 12:48:02: Sobol, running/unknown 3/1∑4 (20%/20), started new job
2025-07-28 12:48:08: Sobol, running/pending 3/1∑4 (20%/20), getting new HP set
2025-07-28 12:48:18: Sobol, running 4∑4 (20%/20), requested 1 jobs, got 1, 10.06 s/job
2025-07-28 12:48:23: Sobol, running 4∑4 (20%/20), eval #1/1 start
2025-07-28 12:48:30: Sobol, running 4∑4 (20%/20), starting new job
2025-07-28 12:48:35: Sobol, running/unknown 4/1∑5 (25%/20), started new job
2025-07-28 12:48:41: Sobol, running/pending 4/1∑5 (25%/20), getting new HP set
2025-07-28 12:48:51: Sobol, completed/running 2/3∑5 (15%/20), requested 1 jobs, got 1, 10.05 s/job
2025-07-28 12:48:55: Sobol, completed/running 2/3∑5 (15%/20), eval #1/1 start
2025-07-28 12:49:06: Sobol, completed/running 2/3∑5 (15%/20), starting new job
2025-07-28 12:49:15: Sobol, completed/running 2/4∑6 (20%/20), started new job
2025-07-28 12:49:21: Sobol, completed/running 2/4∑6 (20%/20), job_failed
2025-07-28 12:49:21: Sobol, completed/running 2/4∑6 (20%/20), job_failed
2025-07-28 12:49:33: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed 2/2∑4 (10%/20), finishing jobs (_get_next_trials), finished 2 jobs
2025-07-28 12:49:38: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed 2/2∑4 (10%/20), getting new HP set
2025-07-28 12:49:48: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed 2/2∑4 (10%/20), requested 1 jobs, got 1, 9.98 s/job
2025-07-28 12:49:53: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed 2/2∑4 (10%/20), eval #1/1 start
2025-07-28 12:49:57: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed 2/2∑4 (10%/20), starting new job
2025-07-28 12:50:03: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed/unknown 2/2/1∑5 (15%/20), started new job
2025-07-28 12:50:09: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 2/2/1∑5 (15%/20), job_failed
2025-07-28 12:50:09: Sobol, failed: 2 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 2/2/1∑5 (15%/20), job_failed
2025-07-28 12:50:21: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), finishing jobs (_get_next_trials), finished 2 jobs
2025-07-28 12:50:26: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), getting new HP set
2025-07-28 12:50:36: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/completed 2/1∑3 (10%/20), requested 1 jobs, got 1, 9.80 s/job
2025-07-28 12:50:40: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/completed 2/1∑3 (10%/20), eval #1/1 start
2025-07-28 12:50:47: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/completed 2/1∑3 (10%/20), starting new job
2025-07-28 12:50:53: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/completed/unknown 2/1/1∑4 (15%/20), started new job
2025-07-28 12:50:58: Sobol, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 2/1/1∑4 (15%/20), job_failed
2025-07-28 12:51:10: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), finishing jobs (_get_next_trials), finished 1 job
2025-07-28 12:51:15: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), getting new HP set
2025-07-28 12:51:25: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), requested 1 jobs, got 1, 10.07 s/job
2025-07-28 12:51:30: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/completed 2/1∑3 (10%/20), eval #1/1 start
2025-07-28 12:51:39: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/completed 2/1∑3 (10%/20), starting new job
2025-07-28 12:51:46: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/completed/unknown 2/1/1∑4 (15%/20), started new job
2025-07-28 12:51:52: Sobol, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 2/1/1∑4 (15%/20), job_failed
2025-07-28 12:52:02: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), finishing jobs (_get_next_trials), finished 1 job
2025-07-28 12:52:11: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), getting new HP set
2025-07-28 12:52:25: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), requested 1 jobs, got 1, 13.88 s/job
2025-07-28 12:52:31: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), eval #1/1 start
2025-07-28 12:52:44: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (10%/20), starting new job
2025-07-28 12:52:51: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed/unknown 2/1/1∑4 (15%/20), started new job
2025-07-28 12:52:57: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 2/1/1∑4 (15%/20), job_failed
2025-07-28 12:53:10: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), finishing jobs (_get_next_trials), finished 1 job
2025-07-28 12:53:15: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), getting new HP set
2025-07-28 12:53:26: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 2/1∑3 (15%/20), requested 1 jobs, got 1, 10.42 s/job
2025-07-28 12:53:32: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/completed 1/2∑3 (5%/20), eval #1/1 start
2025-07-28 12:53:42: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/2∑3 (5%/20), starting new job
2025-07-28 12:53:51: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/unknown 1/2/1∑4 (10%/20), started new job
2025-07-28 12:53:57: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/pending 1/2/1∑4 (10%/20), job_failed
2025-07-28 12:53:57: Sobol, failed: 7 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/pending 1/2/1∑4 (10%/20), new result: 11.35
2025-07-28 12:54:18: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (10%/20), finishing jobs (_get_next_trials), finished 2 jobs
2025-07-28 12:54:24: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (10%/20), getting new HP set
2025-07-28 12:54:36: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (5%/20), requested 1 jobs, got 1, 12.56 s/job
2025-07-28 12:54:41: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (5%/20), eval #1/1 start
2025-07-28 12:54:47: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (5%/20), starting new job
2025-07-28 12:54:54: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/unknown 1/1/1∑3 (10%/20), started new job
2025-07-28 12:55:05: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/pending 1/1/1∑3 (10%/20), job_failed
2025-07-28 12:55:16: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/pending 1/1∑2 (10%/20), finishing jobs (_get_next_trials), finished 1 job
2025-07-28 12:55:22: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/pending 1/1∑2 (10%/20), getting new HP set
2025-07-28 12:55:33: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), requested 1 jobs, got 1, 11.62 s/job
2025-07-28 12:55:44: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), eval #1/1 start
2025-07-28 12:55:55: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), starting new job
2025-07-28 12:56:05: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/unknown 1/1/1∑3 (10%/20), started new job
2025-07-28 12:56:13: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 2/1∑3 (10%/20), job_failed
2025-07-28 12:56:26: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (10%/20), finishing jobs (_get_next_trials), finished 1 job
2025-07-28 12:56:38: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (5%/20), getting new HP set
2025-07-28 12:56:50: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running 2∑2 (5%/20), requested 1 jobs, got 1, 18.22 s/job
2025-07-28 12:56:56: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), eval #1/1 start
2025-07-28 12:57:03: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), starting new job
2025-07-28 12:57:11: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed/unknown 1/1/1∑3 (10%/20), started new job
2025-07-28 12:57:17: Sobol, failed: 10 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 2/1∑3 (10%/20), job_failed
2025-07-28 12:57:30: Sobol, failed: 11 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 11.35, running/completed 1/1∑2 (5%/20), finishing jobs (_get_next_trials), finished 1 job
Arguments Overview Key Value config_yaml None config_toml None config_json None num_random_steps 20 max_eval 500 run_program [['cHl0aG9uMyAudGVzdHMvbW5pc3QvdHJhaW4gLS1lcG9jaHMgJWVwb2NocyAtLWxlYXJuaW5nX3JhdGUgJWxyIC0tYmF0Y2hfc2l6ZSAlYmF0Y2hfc2l6ZSAtLWhpZGRlbl9zaXplICVoaWRkZW5f… experiment_name mnist_gpu_noall mem_gb 10 parameter [['epochs', 'range', '10', '200', 'int', 'false'], ['lr', 'range', '0.0001', '0.1', 'float', 'false'], ['batch_size', 'range', '8', '4096', 'int', 'false'], ['hidden_size', 'range', '8', '8192', 'int', 'false'], ['dropout', 'range', '0', '0.5', 'float', 'false'], ['activation', 'fixed', 'leaky_relu'], ['num_dense_layers', 'range', '1', '4', 'int', 'false'], ['init', 'fixed', 'normal'], ['weight_decay', 'range', '0', '1', 'float', '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 aHR0cHM6Ly9pbWFnZXNlZy5zY2Fkcy5kZS9vbW5pYXgvZ3VpP3BhcnRpdGlvbj1hbHBoYSZleHBlcmltZW50X25hbWU9bW5pc3RfZ3B1X25vYWxsJnJlc2VydmF0aW9uPSZhY2NvdW50PSZtZW1fZ2I… root_venv_dir /home/pwinkler exclude None main_process_gb 8 max_nr_of_zero_results 50 abbreviate_job_names False orchestrator_file None checkout_to_latest_tested_version False live_share True disable_tqdm False disable_previous_job_constraint False workdir occ_type euclid result_names ['VAL_ACC=max'] minkowski_p 2 signed_weighted_euclidean_weights generation_strategy None generate_all_jobs_at_once False 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 None max_failed_jobs 0 num_cpus_main_job None calculate_pareto_front_of_job [] show_generate_time_table False force_choice_for_ranges False max_abandoned_retrial 20 share_password None dryrun False db_url None run_program_once None 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 [] num_parallel_jobs 20 worker_timeout 120 slurm_use_srun False time 1440 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 run_mode local verbose False verbose_break_run_search_table False debug False flame_graph 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
1753699486.7406566,20,0,0
1753699509.778334,20,0,0
1753699509.8905413,20,0,0
1753699523.998447,20,0,0
1753699551.7192903,20,0,0
1753699559.731185,20,0,0
1753699565.7324126,20,0,0
1753699571.9373379,20,1,5
1753699577.7305799,20,1,5
1753699587.9069784,20,1,5
1753699594.7249389,20,1,5
1753699600.1948514,20,1,5
1753699607.316716,20,2,10
1753699614.3198211,20,2,10
1753699626.7305703,20,2,10
1753699632.7261198,20,2,10
1753699640.823155,20,2,10
1753699648.7200491,20,3,15
1753699655.7410574,20,3,15
1753699666.72506,20,3,15
1753699671.169428,20,3,15
1753699676.2432048,20,3,15
1753699682.0612767,20,4,20
1753699688.224566,20,4,20
1753699697.9748986,20,4,20
1753699703.7259638,20,4,20
1753699710.1851532,20,4,20
1753699715.8889482,20,5,25
1753699721.2446227,20,5,25
1753699731.1046622,20,3,15
1753699735.941302,20,3,15
1753699746.1545424,20,3,15
1753699752.950353,20,4,20
1753699761.0200992,20,4,20
1753699761.0291533,20,4,20
1753699773.0235035,20,2,10
1753699778.30268,20,2,10
1753699788.0282998,20,2,10
1753699793.0120904,20,2,10
1753699797.817368,20,2,10
1753699803.9686053,20,3,15
1753699809.2320952,20,3,15
1753699809.238927,20,3,15
1753699821.7329917,20,3,15
1753699826.7432747,20,3,15
1753699836.0439084,20,2,10
1753699840.7315016,20,2,10
1753699847.1261477,20,2,10
1753699853.117294,20,3,15
1753699858.9061728,20,3,15
1753699870.0756757,20,3,15
1753699875.7044244,20,3,15
1753699885.728838,20,3,15
1753699890.7241068,20,2,10
1753699899.865101,20,2,10
1753699906.0906312,20,3,15
1753699911.083229,20,3,15
1753699921.9819987,20,3,15
1753699931.7379243,20,3,15
1753699945.22693,20,3,15
1753699951.787929,20,3,15
1753699964.1043887,20,2,10
1753699971.2375917,20,3,15
1753699977.19694,20,3,15
1753699988.0908601,20,3,15
1753699995.8087916,20,3,15
1753700006.9652517,20,3,15
1753700011.9459836,20,1,5
1753700022.7931676,20,1,5
1753700030.7576435,20,2,10
1753700037.71717,20,2,10
1753700037.7214563,20,2,10
1753700045.8210657,20,2,10
timestamp,ram_usage_mb,cpu_usage_percent
1753699455,709.10546875,19.2
1753699486,709.10546875,19.2
1753699509,709.60546875,11.6
1753699509,709.60546875,16.7
1753699509,709.60546875,10.4
1753699509,709.60546875,11.4
1753699509,709.60546875,16.7
1753700045,736.55859375,13.0
Parameter statistics Parameter Min Max Mean Std Dev Count run_time 12 295 50.3077 80.2279 13 VAL_ACC 11.35 11.35 11.35 0 1 epochs 12 194 102.1429 55.2137 14 lr 0.0041 0.0941 0.0517 0.0298 14 batch_size 257 3998 2121.6429 1082.7363 14 hidden_size 386 8113 4151.3571 2484.2062 14 dropout 0.0077 0.4896 0.2468 0.1509 14 num_dense_layers 1 4 2.5 1.0522 14 weight_decay 0.0425 0.9848 0.5104 0.2726 14 activation No numerical statistics available init No numerical statistics available
525705 (2m:32s, exit-code: 1) ❌
525708 (1m:34s, exit-code: 1) ❌
525709
525710 (5m:8s, VAL_ACC: 11.35) ✅
525711 (38s, exit-code: 1) ❌
525713 (15s, exit-code: 1) ❌
525714 (15s, exit-code: 1) ❌
525716 (15s, exit-code: 1) ❌
525722 (18s, exit-code: 1) ❌
525729 (15s, exit-code: 1) ❌
525732 (15s, exit-code: 1) ❌
525733 (15s, exit-code: 1) ❌
525735 (15s, exit-code: 1) ❌
525736 (16s, exit-code: 1) ❌
submitit INFO (2025-07-28 12:46:13,037 ) - Starting with JobEnvironment(job_id=525705, hostname=c137, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-07-28 12:46:13,039 ) - Loading pickle: /data/horse/ws/pwinkler-mnist_tst/omniopt/runs/mnist_gpu_noall/0/single_runs/525705/525705_submitted.pkl
/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/networkx/utils/backends.py:135: RuntimeWarning: networkx backend defined more than once: nx-loopback
backends.update(_get_backends("networkx.backends"))
Traceback (most recent call last):
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/train", line 285, in main
model = SimpleMLP(
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1355, in to
return self._apply(convert)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 915, in _apply
module._apply(fn)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 915, in _apply
module._apply(fn)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 942, in _apply
param_applied = fn(param)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1341, in convert
return t.to(
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Parameters: {"epochs": 67, "lr": 0.08675298514962197, "batch_size": 257, "hidden_size": 1100, "dropout": 0.007696903310716152, "num_dense_layers": 4, "weight_decay": 0.682620108127594, "activation": "leaky_relu", "init": "normal"}
Debug-Infos:
========
DEBUG INFOS START:
Program-Code: python3 .tests/mnist/train --epochs 67 --learning_rate 0.08675298514962197227 --batch_size 257 --hidden_size 1100 --dropout 0.00769690331071615219 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.68262010812759399414
pwd: /data/horse/ws/pwinkler-mnist_tst/omniopt
File: .tests/mnist/train
UID: 2054851
GID: 200270
SLURM_JOB_ID: 525705
Status-Change-Time: 1753273859.0
Size: 12359 Bytes
Permissions: -rwxr-xr-x
Owner: pwinkler
Last access: 1753699585.0
Last modification: 1753270258.0
Hostname: c137
========
DEBUG INFOS END
python3 .tests/mnist/train --epochs 67 --learning_rate 0.08675298514962197227 --batch_size 257 --hidden_size 1100 --dropout 0.00769690331071615219 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.68262010812759399414
stdout:
Hyperparameters
╭──────────────────┬──────────────────────╮
│ Parameter │ Value │
├──────────────────┼──────────────────────┤
│ Device │ cuda │
│ Epochs │ 67 │
│ Num Dense Layers │ 4 │
│ Batch size │ 257 │
│ Learning rate │ 0.08675298514962197 │
│ Hidden size │ 1100 │
│ Dropout │ 0.007696903310716152 │
│ Optimizer │ adam │
│ Momentum │ 0.9 │
│ Weight Decay │ 0.682620108127594 │
│ Activation │ leaky_relu │
│ Init Method │ normal │
│ Seed │ None │
╰──────────────────┴──────────────────────╯
Using device: cuda
An error occurred: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so
the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
stderr:
/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/networkx/utils/backends.py:135: RuntimeWarning: networkx backend defined more than once: nx-loopback
backends.update(_get_backends("networkx.backends"))
Traceback (most recent call last):
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/train", line 285, in main
model = SimpleMLP(
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1355, in to
return self._apply(convert)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 915, in _apply
module._apply(fn)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 915, in _apply
module._apply(fn)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 942, in _apply
param_applied = fn(param)
File "/data/horse/ws/pwinkler-mnist_tst/omniopt/.tests/mnist/.torch_venv/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1341, in convert
return t.to(
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Result: {'VAL_ACC': None}
Final-results: {'VAL_ACC': None}
EXIT_CODE: 1
submitit INFO (2025-07-28 12:48:45,150 ) - Job completed successfully
submitit INFO (2025-07-28 12:48:45,152 ) - Exiting after successful completion
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