OmniOpt2

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	20	9	9	2

Experiment parameters

Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
epochs	range	10	200		int	No
lr	range	1e-05	0.1		float	No
batch_size	range	8	2048		int	No
hidden_size	range	8	2048		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
9	9	2	20

Result names and types

name	min/max
VAL_ACC	max

Last progressbar status

2025-07-31 15:40:07: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 2∑2 (0%/20), waiting for 3 jobs, finished 1 job

Git-Version

Commit: f9547a580b93e0983ebff52a5b7750569294ad57

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
0,1753967672,10,1753967682,1753967944,262,python3 .tests/mnist/train --epochs 127 --learning_rate 0.06689339573502540992 --batch_size 1214 --hidden_size 990 --dropout 0.45562517642974853516 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.59872841835021972656,1,,c143,530836,0_0,FAILED,SOBOL,,127,0.066893395735025409920559980037,1214,990,0.45562517642974853515625,3,0.5987284183502197265625,leaky_relu,normal
1,1753967731,33,1753967764,1753968675,911,python3 .tests/mnist/train --epochs 92 --learning_rate 0.04306331038652919802 --batch_size 658 --hidden_size 1202 --dropout 0.22459426010027527809 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.14708865061402320862,0,,c143,530837,1_0,COMPLETED,SOBOL,11.34999999999999964472863211995,92,0.043063310386529198015015396095,658,1202,0.224594260100275278091430664062,2,0.1470886506140232086181640625,leaky_relu,normal
2,1753967761,23,1753967784,1753967943,159,python3 .tests/mnist/train --epochs 20 --learning_rate 0.07937379036461003623 --batch_size 1636 --hidden_size 513 --dropout 0.03445854969322681427 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.975613446906208992,1,,c143,530838,2_0,FAILED,SOBOL,,20,0.079373790364610036229819911568,1636,513,0.03445854969322681427001953125,1,0.97561344690620899200439453125,leaky_relu,normal
3,1753967794,11,1753967805,1753967947,142,python3 .tests/mnist/train --epochs 199 --learning_rate 0.00556081472378224147 --batch_size 92 --hidden_size 1678 --dropout 0.26975434739142656326 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.27002071961760520935,1,,c141,530839,3_0,FAILED,SOBOL,,199,0.005560814723782241467131548518,92,1678,0.269754347391426563262939453125,4,0.2700207196176052093505859375,leaky_relu,normal
4,1753967842,48,1753967890,1753967939,49,python3 .tests/mnist/train --epochs 169 --learning_rate 0.09104195689666085001 --batch_size 844 --hidden_size 1884 --dropout 0.34263794217258691788 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.78356554172933101654,1,,c141,530842,4_0,FAILED,SOBOL,,169,0.091041956896660850007130250106,844,1884,0.342637942172586917877197265625,3,0.78356554172933101654052734375,leaky_relu,normal
5,1753967873,34,1753967907,1753967938,31,python3 .tests/mnist/train --epochs 38 --learning_rate 0.01723163600472733467 --batch_size 1396 --hidden_size 52 --dropout 0.10320703499019145966 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.45492733269929885864,1,,c137,530845,5_0,FAILED,SOBOL,,38,0.01723163600472733467117159023,1396,52,0.10320703499019145965576171875,2,0.454927332699298858642578125,leaky_relu,normal
6,1753967904,30,1753967934,1753967940,6,python3 .tests/mnist/train --epochs 62 --learning_rate 0.05356406282581389899 --batch_size 422 --hidden_size 1404 --dropout 0.17099775979295372963 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.65649853460490703583,1,,c137,530847,6_0,FAILED,SOBOL,,62,0.053564062825813898993665418402,422,1404,0.170997759792953729629516601562,1,0.65649853460490703582763671875,leaky_relu,normal
7,1753967985,27,1753968012,1753968999,987,python3 .tests/mnist/train --epochs 145 --learning_rate 0.02973663241245784114 --batch_size 1962 --hidden_size 533 --dropout 0.398839601781219244 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.08181141316890716553,0,,c143,530850,7_0,COMPLETED,SOBOL,10.27999999999999936051153781591,145,0.029736632412457841140307479577,1962,533,0.398839601781219244003295898438,4,0.08181141316890716552734375,leaky_relu,normal
8,1753968066,7,1753968073,1753968969,896,python3 .tests/mnist/train --epochs 136 --learning_rate 0.08517047294191085194 --batch_size 332 --hidden_size 226 --dropout 0.21755825495347380638 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.82492708694189786911,0,,c141,530855,8_0,COMPLETED,SOBOL,11.34999999999999964472863211995,136,0.085170472941910851938374094061,332,226,0.217558254953473806381225585938,4,0.824927086941897869110107421875,leaky_relu,normal
9,1753968128,47,1753968175,1753968211,36,python3 .tests/mnist/train --epochs 76 --learning_rate 0.012141729941014201 --batch_size 1908 --hidden_size 1966 --dropout 0.47822938347235321999 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.43676314223557710648,1,,c137,530858,9_0,FAILED,SOBOL,,76,0.012141729941014200999660488378,1908,1966,0.478229383472353219985961914062,1,0.436763142235577106475830078125,leaky_relu,normal
10,1753968178,15,1753968193,1753968229,36,python3 .tests/mnist/train --epochs 52 --learning_rate 0.07266547727916390642 --batch_size 930 --hidden_size 766 --dropout 0.29590566642582416534 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.70181095879524946213,1,,c137,530860,10_0,FAILED,SOBOL,,52,0.072665477279163906421111107647,930,766,0.29590566642582416534423828125,2,0.701810958795249462127685546875,leaky_relu,normal
11,1753968229,45,1753968274,1753968311,37,python3 .tests/mnist/train --epochs 160 --learning_rate 0.04961962326687761188 --batch_size 1454 --hidden_size 1426 --dropout 0.02387435454875230789 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.05969598609954118729,1,,c137,530862,11_0,FAILED,SOBOL,,160,0.04961962326687761187793412887,1454,1426,0.023874354548752307891845703125,3,0.059695986099541187286376953125,leaky_relu,normal
12,,,,,,,,,,,12_0,RUNNING,SOBOL,,179,0.059436874155011039377871639999,1722,1116,0.080644668079912662506103515625,4,0.511717787943780422210693359375,leaky_relu,normal
13,1753968379,33,1753968412,1753968584,172,python3 .tests/mnist/train --epochs 23 --learning_rate 0.03638760253067128969 --batch_size 149 --hidden_size 820 --dropout 0.34951743297278881073 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.24264822248369455338,0,,c139,530869,13_0,COMPLETED,SOBOL,20.12999999999999900524016993586,23,0.036387602530671289691177605619,149,820,0.34951743297278881072998046875,1,0.242648222483694553375244140625,leaky_relu,normal
14,1753968415,18,1753968433,1753968961,528,python3 .tests/mnist/train --epochs 95 --learning_rate 0.09693937056274154473 --batch_size 1124 --hidden_size 1660 --dropout 0.40949616441503167152 --activation leaky_relu --num_dense_layers 2 --init normal --weight_decay 0.88464964833110570908,0,,c139,530875,14_0,COMPLETED,SOBOL,10.27999999999999936051153781591,95,0.096939370562741544734564058672,1124,1660,0.409496164415031671524047851562,2,0.884649648331105709075927734375,leaky_relu,normal
15,1753968460,34,1753968494,1753969096,602,python3 .tests/mnist/train --epochs 107 --learning_rate 0.02390720845982432716 --batch_size 604 --hidden_size 276 --dropout 0.14465939393267035484 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.36953307781368494034,0,,c139,530877,15_0,COMPLETED,SOBOL,9.74000000000000021316282072803,107,0.02390720845982432715692844738,604,276,0.144659393932670354843139648438,3,0.369533077813684940338134765625,leaky_relu,normal
16,1753968495,37,1753968532,1753969170,638,python3 .tests/mnist/train --epochs 112 --learning_rate 0.09531696763547137241 --batch_size 1810 --hidden_size 1307 --dropout 0.25852300785481929779 --activation leaky_relu --num_dense_layers 1 --init normal --weight_decay 0.17023077234625816345,0,,c138,530880,16_0,COMPLETED,SOBOL,10.91000000000000014210854715202,112,0.095316967635471372410904677963,1810,1307,0.25852300785481929779052734375,1,0.1702307723462581634521484375,leaky_relu,normal
17,1753968531,23,1753968554,1753969131,577,python3 .tests/mnist/train --epochs 100 --learning_rate 0.01915742137833498573 --batch_size 301 --hidden_size 629 --dropout 0.06229067686945199966 --activation leaky_relu --num_dense_layers 4 --init normal --weight_decay 0.59164238162338733673,0,,c138,530882,17_0,COMPLETED,SOBOL,9.74000000000000021316282072803,100,0.019157421378334985734293027804,301,629,0.062290676869451999664306640625,4,0.59164238162338733673095703125,leaky_relu,normal
18,1753968567,17,1753968584,1753968768,184,python3 .tests/mnist/train --epochs 28 --learning_rate 0.05783888303508051554 --batch_size 1483 --hidden_size 1852 --dropout 0.24363784631714224815 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.29326960816979408264,0,,c138,530883,18_0,COMPLETED,SOBOL,10.27999999999999936051153781591,28,0.057838883035080515537806888915,1483,1852,0.243637846317142248153686523438,3,0.2932696081697940826416015625,leaky_relu,normal
19,,,,,,,,,,,19_0,RUNNING,SOBOL,,183,0.031662226884029809337306460293,1026,85,0.451230330858379602432250976562,2,0.96854268945753574371337890625,leaky_relu,normal

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Run-UUID: dd6fd54b-612d-4e05-9461-3a0820ca358c
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⠋ Writing worker creation log...
omniopt --partition=alpha --experiment_name=mnist_gpu_noall --mem_gb=10 --time=2880 --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 0.00001 0.1 float false --parameter batch_size range 8 2048 int false --parameter hidden_size range 8 2048 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 aHR0cHM6Ly9pbWFnZXNlZy5zY2Fkcy5kZS9vbW5pYXgvZ3VpP3BhcnRpdGlvbj1hbHBoYSZleHBlcmltZW50X25hbWU9bW5pc3RfZ3B1X25vYWxsJnJlc2VydmF0aW9uPSZhY2NvdW50PSZtZW1fZ2I9MTAmdGltZT0yODgwJndvcmtlcl90aW1lb3V0PTEyMCZtYXhfZXZhbD01MDAmbnVtX3BhcmFsbGVsX2pvYnM9MjAmZ3B1cz0xJm51bV9yYW5kb21fc3RlcHM9MjAmZm9sbG93PTEmbGl2ZV9zaGFyZT0xJnNlbmRfYW5vbnltaXplZF91c2FnZV9zdGF0cz0xJmNvbnN0cmFpbnRzPSZyZXN1bHRfbmFtZXM9VkFMX0FDQyUzRG1heCZydW5fcHJvZ3JhbT1weXRob24zJTIwLnRlc3RzJTJGbW5pc3QlMkZ0cmFpbiUyMC0tZXBvY2hzJTIwJTI1ZXBvY2hzJTIwLS1sZWFybmluZ19yYXRlJTIwJTI1bHIlMjAtLWJhdGNoX3NpemUlMjAlMjViYXRjaF9zaXplJTIwLS1oaWRkZW5fc2l6ZSUyMCUyNWhpZGRlbl9zaXplJTIwLS1kcm9wb3V0JTIwJTI1ZHJvcG91dCUyMC0tYWN0aXZhdGlvbiUyMCUyNWFjdGl2YXRpb24lMjAtLW51bV9kZW5zZV9sYXllcnMlMjAlMjVudW1fZGVuc2VfbGF5ZXJzJTIwLS1pbml0JTIwJTI1aW5pdCUyMC0td2VpZ2h0X2RlY2F5JTIwJTI1d2VpZ2h0X2RlY2F5JmNwdXNfcGVyX3Rhc2s9MSZub2Rlc19wZXJfam9iPTEmc2VlZD0mZHJ5cnVuPTAmZGVidWc9MCZyZXZlcnRfdG9fcmFuZG9tX3doZW5fc2VlbWluZ2x5X2V4aGF1c3RlZD0xJmdyaWRzZWFyY2g9MCZtb2RlbD1CT1RPUkNIX01PRFVMQVImZXh0ZXJuYWxfZ2VuZXJhdG9yPSZuX2VzdGltYXRvcnNfcmFuZG9tZm9yZXN0PTEwMCZpbnN0YWxsYXRpb25fbWV0aG9kPWNsb25lJnJ1bl9tb2RlPWxvY2FsJmRpc2FibGVfdHFkbT0wJnZlcmJvc2VfdHFkbT0wJmZvcmNlX2xvY2FsX2V4ZWN1dGlvbj0wJmF1dG9fZXhjbHVkZV9kZWZlY3RpdmVfaG9zdHM9MCZzaG93X3NpeGVsX2dlbmVyYWw9MCZzaG93X3NpeGVsX3RyaWFsX2luZGV4X3Jlc3VsdD0wJnNob3dfc2l4ZWxfc2NhdHRlcj0wJnNob3dfd29ya2VyX3BlcmNlbnRhZ2VfdGFibGVfYXRfZW5kPTAmb2NjPTAmb2NjX3R5cGU9ZXVjbGlkJm5vX3NsZWVwPTAmc2x1cm1fdXNlX3NydW49MCZ2ZXJib3NlX2JyZWFrX3J1bl9zZWFyY2hfdGFibGU9MCZhYmJyZXZpYXRlX2pvYl9uYW1lcz0wJm1haW5fcHJvY2Vzc19nYj04Jm1heF9ucl9vZl96ZXJvX3Jlc3VsdHM9NTAmc2x1cm1fc2lnbmFsX2RlbGF5X3M9MCZtYXhfZmFpbGVkX2pvYnM9MCZleGNsdWRlPSZ1c2VybmFtZT0mZ2VuZXJhdGlvbl9zdHJhdGVneT0mcm9vdF92ZW52X2Rpcj0md29ya2Rpcj0mZG9udF9qaXRfY29tcGlsZT0wJmZpdF9vdXRfb2ZfZGVzaWduPTAmcmVmaXRfb25fY3Y9MCZzaG93X2dlbmVyYXRlX3RpbWVfdGFibGU9MCZkb250X3dhcm1fc3RhcnRfcmVmaXR0aW5nPTAmbWF4X2F0dGVtcHRzX2Zvcl9nZW5lcmF0aW9uPTIwJm51bV9yZXN0YXJ0cz0yMCZyYXdfc2FtcGxlcz0xMDI0Jm1heF9hYmFuZG9uZWRfcmV0cmlhbD0yMCZtYXhfbnVtX29mX3BhcmFsbGVsX3NydW5zPTE2JmZvcmNlX2Nob2ljZV9mb3JfcmFuZ2VzPTAmbm9fdHJhbnNmb3JtX2lucHV0cz0wJmZpdF9hYmFuZG9uZWQ9MCZub19ub3JtYWxpemVfeT0wJnZlcmJvc2U9MCZnZW5lcmF0ZV9hbGxfam9ic19hdF9vbmNlPTAmZmxhbWVfZ3JhcGg9MCZjaGVja291dF90b19sYXRlc3RfdGVzdGVkX3ZlcnNpb249MCZwYXJhbWV0ZXJfMF9uYW1lPWVwb2NocyZwYXJhbWV0ZXJfMF90eXBlPXJhbmdlJnBhcmFtZXRlcl8wX21pbj0xMCZwYXJhbWV0ZXJfMF9tYXg9MjAwJnBhcmFtZXRlcl8wX251bWJlcl90eXBlPWludCZwYXJhbWV0ZXJfMF9sb2dfc2NhbGU9ZmFsc2UmcGFyYW1ldGVyXzFfbmFtZT1sciZwYXJhbWV0ZXJfMV90eXBlPXJhbmdlJnBhcmFtZXRlcl8xX21pbj0wLjAwMDAxJnBhcmFtZXRlcl8xX21heD0wLjEmcGFyYW1ldGVyXzFfbnVtYmVyX3R5cGU9ZmxvYXQmcGFyYW1ldGVyXzFfbG9nX3NjYWxlPWZhbHNlJnBhcmFtZXRlcl8yX25hbWU9YmF0Y2hfc2l6ZSZwYXJhbWV0ZXJfMl90eXBlPXJhbmdlJnBhcmFtZXRlcl8yX21pbj04JnBhcmFtZXRlcl8yX21heD0yMDQ4JnBhcmFtZXRlcl8yX251bWJlcl90eXBlPWludCZwYXJhbWV0ZXJfMl9sb2dfc2NhbGU9ZmFsc2UmcGFyYW1ldGVyXzNfbmFtZT1oaWRkZW5fc2l6ZSZwYXJhbWV0ZXJfM190eXBlPXJhbmdlJnBhcmFtZXRlcl8zX21pbj04JnBhcmFtZXRlcl8zX21heD0yMDQ4JnBhcmFtZXRlcl8zX251bWJlcl90eXBlPWludCZwYXJhbWV0ZXJfM19sb2dfc2NhbGU9ZmFsc2UmcGFyYW1ldGVyXzRfbmFtZT1kcm9wb3V0JnBhcmFtZXRlcl80X3R5cGU9cmFuZ2UmcGFyYW1ldGVyXzRfbWluPTAmcGFyYW1ldGVyXzRfbWF4PTAuNSZwYXJhbWV0ZXJfNF9udW1iZXJfdHlwZT1mbG9hdCZwYXJhbWV0ZXJfNF9sb2dfc2NhbGU9ZmFsc2UmcGFyYW1ldGVyXzVfbmFtZT1hY3RpdmF0aW9uJnBhcmFtZXRlcl81X3R5cGU9Zml4ZWQmcGFyYW1ldGVyXzVfdmFsdWU9bGVha3lfcmVsdSZwYXJhbWV0ZXJfNl9uYW1lPW51bV9kZW5zZV9sYXllcnMmcGFyYW1ldGVyXzZfdHlwZT1yYW5nZSZwYXJhbWV0ZXJfNl9taW49MSZwYXJhbWV0ZXJfNl9tYXg9NCZwYXJhbWV0ZXJfNl9udW1iZXJfdHlwZT1pbnQmcGFyYW1ldGVyXzZfbG9nX3NjYWxlPWZhbHNlJnBhcmFtZXRlcl83X25hbWU9aW5pdCZwYXJhbWV0ZXJfN190eXBlPWZpeGVkJnBhcmFtZXRlcl83X3ZhbHVlPW5vcm1hbCZwYXJhbWV0ZXJfOF9uYW1lPXdlaWdodF9kZWNheSZwYXJhbWV0ZXJfOF90eXBlPXJhbmdlJnBhcmFtZXRlcl84X21pbj0wJnBhcmFtZXRlcl84X21heD0xJnBhcmFtZXRlcl84X251bWJlcl90eXBlPWZsb2F0JnBhcmFtZXRlcl84X2xvZ19zY2FsZT1mYWxzZSZwYXJ0aXRpb249YWxwaGEmbnVtX3BhcmFtZXRlcnM9OQ==
⠋ Disabling logging...
⠋ Setting run folder...
⠋ Creating folder /data/cat/ws/pwinkler-mnist_tst/omniopt_1/runs/mnist_gpu_noall/0...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
⠋ Writing username state file...
⠋ Writing result names file...
⠋ Writing result min/max file...
⠋ Saving state files...
Run-folder: /data/cat/ws/pwinkler-mnist_tst/omniopt_1/runs/mnist_gpu_noall/0
⠋ Printing run info...
⠋ Initializing NVIDIA-Logs...
⠋ Writing ui_url file if it is present...
⠋ Writing live_share file if it is present...
⠋ Writing job_start_time file...
⠙ Writing git info file...
⠋ Checking max_eval...
⠋ Calculating number of steps...
⠋ Adding excluded nodes...
⠋ Handling random steps...
⠋ Initializing ax_client...
[WARNING 07-31 15:13:59] 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 │ 1e-05       │ 0.1         │            │ float │ No         │
│ batch_size       │ range │ 8           │ 2048        │            │ int   │ No         │
│ hidden_size      │ range │ 8           │ 2048        │            │ 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=6 for live-results.

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Sobol, failed: 9 ('VAL_ACC: ' not found), best VAL_ACC: 20.13, running 2∑2 (0%/20), waiting for 3 jobs, finished 1 job        :   2%|░░░░░░░░░░| 9/500 [26:03<10:47:30, 79.13s/it]

2025-07-31 15:14:04: SOBOL, Started OmniOpt2 run...
2025-07-31 15:14:12: Sobol, getting new HP set     
2025-07-31 15:14:20: Sobol, requested 1 jobs, got 1, 8.22 s/job
2025-07-31 15:14:24: Sobol, eval #1/1 start                    
2025-07-31 15:14:28: Sobol, starting new job                   
2025-07-31 15:14:33: Sobol, unknown 1∑1 (5%/20), started new job
2025-07-31 15:14:38: Sobol, running 1∑1 (5%/20), getting new HP set
2025-07-31 15:14:47: Sobol, running 1∑1 (5%/20), requested 1 jobs, got 1, 9.62 s/job
2025-07-31 15:15:06: Sobol, running 1∑1 (5%/20), eval #1/1 start                    
2025-07-31 15:15:26: Sobol, running 1∑1 (5%/20), starting new job                   
2025-07-31 15:15:32: Sobol, running/unknown 1/1∑2 (10%/20), started new job         
2025-07-31 15:15:38: Sobol, running 2∑2 (10%/20), getting new HP set                
2025-07-31 15:15:47: Sobol, running 2∑2 (10%/20), requested 1 jobs, got 1, 10.16 s/job
2025-07-31 15:15:52: Sobol, running 2∑2 (10%/20), eval #1/1 start                     
2025-07-31 15:15:57: Sobol, running 2∑2 (10%/20), starting new job                    
2025-07-31 15:16:03: Sobol, running/unknown 2/1∑3 (15%/20), started new job           
2025-07-31 15:16:09: Sobol, running 3∑3 (15%/20), getting new HP set                  
2025-07-31 15:16:19: Sobol, running 3∑3 (15%/20), requested 1 jobs, got 1, 9.96 s/job 
2025-07-31 15:16:24: Sobol, running 3∑3 (15%/20), eval #1/1 start                     
2025-07-31 15:16:29: Sobol, running 3∑3 (15%/20), starting new job                    
2025-07-31 15:16:35: Sobol, running/unknown 3/1∑4 (20%/20), started new job           
2025-07-31 15:16:54: Sobol, running 4∑4 (20%/20), getting new HP set                  
2025-07-31 15:17:08: Sobol, running 4∑4 (20%/20), requested 1 jobs, got 1, 14.02 s/job
2025-07-31 15:17:12: Sobol, running 4∑4 (20%/20), eval #1/1 start                     
2025-07-31 15:17:18: Sobol, running 4∑4 (20%/20), starting new job                    
2025-07-31 15:17:23: Sobol, running/unknown 4/1∑5 (25%/20), started new job           
2025-07-31 15:17:29: Sobol, running/pending 4/1∑5 (25%/20), getting new HP set        
2025-07-31 15:17:39: Sobol, running 5∑5 (25%/20), requested 1 jobs, got 1, 9.82 s/job 
2025-07-31 15:17:43: Sobol, running 5∑5 (25%/20), eval #1/1 start                     
2025-07-31 15:17:48: Sobol, running 5∑5 (25%/20), starting new job                    
2025-07-31 15:17:54: Sobol, running/unknown 5/1∑6 (30%/20), started new job           
2025-07-31 15:18:00: Sobol, running/pending 5/1∑6 (30%/20), getting new HP set        
2025-07-31 15:18:10: Sobol, running 6∑6 (30%/20), requested 1 jobs, got 1, 10.28 s/job
2025-07-31 15:18:15: Sobol, running 6∑6 (30%/20), eval #1/1 start                     
2025-07-31 15:18:20: Sobol, running 6∑6 (30%/20), starting new job                    
2025-07-31 15:18:25: Sobol, running/unknown 6/1∑7 (35%/20), started new job           
2025-07-31 15:18:44: Sobol, running 7∑7 (35%/20), getting new HP set                  
2025-07-31 15:18:55: Sobol, running 7∑7 (35%/20), requested 1 jobs, got 1, 11.06 s/job
2025-07-31 15:18:59: Sobol, running 7∑7 (30%/20), eval #1/1 start                     
2025-07-31 15:19:36: Sobol, completed/running 6/1∑7 (5%/20), starting new job         
2025-07-31 15:19:46: Sobol, completed/running/unknown 6/1/1∑8 (10%/20), started new job
2025-07-31 15:20:02: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:02: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:02: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:03: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:03: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:07: Sobol, completed/running/pending 6/1/1∑8 (10%/20), job_failed     
2025-07-31 15:20:27: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 2∑2 (10%/20), finishing jobs (_get_next_trials), finished 6 jobs
2025-07-31 15:20:33: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 2∑2 (10%/20), getting new HP set                                
2025-07-31 15:20:44: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 2∑2 (10%/20), requested 1 jobs, got 1, 10.25 s/job              
2025-07-31 15:20:49: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 2∑2 (10%/20), eval #1/1 start                                   
2025-07-31 15:20:55: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 2∑2 (10%/20), starting new job                                  
2025-07-31 15:21:07: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/unknown 2/1∑3 (15%/20), started new job                         
2025-07-31 15:21:13: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), getting new HP set                                
2025-07-31 15:21:24: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), requested 1 jobs, got 1, 10.21 s/job              
2025-07-31 15:21:30: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), eval #1/1 start                                   
2025-07-31 15:21:53: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 3∑3 (15%/20), starting new job                                  
2025-07-31 15:22:09: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/unknown 3/1∑4 (20%/20), started new job                         
2025-07-31 15:22:27: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 3/1∑4 (20%/20), getting new HP set                      
2025-07-31 15:22:42: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 3/1∑4 (20%/20), requested 1 jobs, got 1, 15.23 s/job    
2025-07-31 15:22:48: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), eval #1/1 start                                   
2025-07-31 15:22:53: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), starting new job                                  
2025-07-31 15:23:01: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/unknown 4/1∑5 (25%/20), started new job                         
2025-07-31 15:23:07: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 4/1∑5 (25%/20), getting new HP set                      
2025-07-31 15:23:17: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 5∑5 (25%/20), requested 1 jobs, got 1, 10.40 s/job              
2025-07-31 15:23:21: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running 5∑5 (25%/20), eval #1/1 start                                   
2025-07-31 15:23:44: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed 4/1∑5 (20%/20), starting new job                      
2025-07-31 15:23:50: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed/unknown 4/1/1∑6 (20%/20), started new job             
2025-07-31 15:24:02: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 3/2/1∑6 (20%/20), job_failed                  
2025-07-31 15:24:03: Sobol, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/completed/pending 3/2/1∑6 (20%/20), job_failed                  
2025-07-31 15:24:30: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), finishing jobs (_get_next_trials), finished 2 jobs
2025-07-31 15:24:35: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), getting new HP set                                
2025-07-31 15:24:46: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), requested 1 jobs, got 1, 11.21 s/job              
2025-07-31 15:24:52: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), eval #1/1 start                                   
2025-07-31 15:24:57: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), starting new job                                  
2025-07-31 15:25:04: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running/unknown 4/1∑5 (25%/20), started new job                         
2025-07-31 15:25:24: Sobol, failed: 8 ('VAL_ACC: <FLOAT>' not found), running/completed 4/1∑5 (20%/20), job_failed                            
2025-07-31 15:25:34: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), finishing jobs (_get_next_trials), finished 1 job 
2025-07-31 15:25:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), getting new HP set                                
2025-07-31 15:26:01: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), requested 1 jobs, got 1, 21.98 s/job              
2025-07-31 15:26:08: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), eval #1/1 start                                   
2025-07-31 15:26:13: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 4∑4 (20%/20), starting new job                                  
2025-07-31 15:26:20: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 4/1∑5 (25%/20), started new job                         
2025-07-31 15:26:26: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 4/1∑5 (25%/20), getting new HP set                      
2025-07-31 15:26:37: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 5∑5 (25%/20), requested 1 jobs, got 1, 11.56 s/job              
2025-07-31 15:26:43: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 5∑5 (25%/20), eval #1/1 start                                   
2025-07-31 15:26:50: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 5∑5 (25%/20), starting new job                                  
2025-07-31 15:26:58: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 5/1∑6 (30%/20), started new job                         
2025-07-31 15:27:05: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 5/1∑6 (30%/20), getting new HP set                      
2025-07-31 15:27:18: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 6∑6 (30%/20), requested 1 jobs, got 1, 13.26 s/job              
2025-07-31 15:27:24: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 6∑6 (30%/20), eval #1/1 start                                   
2025-07-31 15:27:35: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 6∑6 (30%/20), starting new job                                  
2025-07-31 15:27:41: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 6/1∑7 (35%/20), started new job                         
2025-07-31 15:27:47: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 6/1∑7 (35%/20), getting new HP set                      
2025-07-31 15:27:58: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 6/1∑7 (35%/20), requested 1 jobs, got 1, 11.33 s/job    
2025-07-31 15:28:05: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 6/1∑7 (35%/20), eval #1/1 start                         
2025-07-31 15:28:10: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 6/1∑7 (35%/20), starting new job                        
2025-07-31 15:28:17: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 7/1∑8 (40%/20), started new job                         
2025-07-31 15:28:23: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 8∑8 (40%/20), getting new HP set                                
2025-07-31 15:28:34: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 8∑8 (40%/20), requested 1 jobs, got 1, 11.30 s/job              
2025-07-31 15:28:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 8∑8 (40%/20), eval #1/1 start                                   
2025-07-31 15:28:46: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 8∑8 (40%/20), starting new job                                  
2025-07-31 15:28:52: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 8/1∑9 (45%/20), started new job                         
2025-07-31 15:28:58: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 8/1∑9 (45%/20), getting new HP set                      
2025-07-31 15:29:09: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 9∑9 (45%/20), requested 1 jobs, got 1, 11.46 s/job              
2025-07-31 15:29:15: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 9∑9 (45%/20), eval #1/1 start                                   
2025-07-31 15:29:21: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running 9∑9 (45%/20), starting new job                                  
2025-07-31 15:29:28: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/unknown 9/1∑10 (50%/20), started new job                        
2025-07-31 15:29:35: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 9/1∑10 (50%/20), getting new HP set                     
2025-07-31 15:29:46: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/completed 9/1∑10 (45%/20), requested 1 jobs, got 1, 11.90 s/job 
2025-07-31 15:29:53: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/completed 9/1∑10 (45%/20), eval #1/1 start                      
2025-07-31 15:30:04: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/completed 9/1∑10 (45%/20), starting new job
2025-07-31 15:30:12: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/completed/unknown 9/1/1∑11 (50%/20), started new job
2025-07-31 15:30:18: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/completed/pending 9/1/1∑11 (50%/20), new result: 20.13
2025-07-31 15:30:35: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/pending 9/1∑10 (50%/20), finishing jobs, finished 1 job
2025-07-31 15:30:42: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/pending 9/1∑10 (50%/20), waiting for 10 jobs           
2025-07-31 15:30:55: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running/pending 9/1∑10 (50%/20), waiting for 10 jobs           
2025-07-31 15:31:06: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 10∑10 (50%/20), waiting for 10 jobs                    
2025-07-31 15:31:17: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 10∑10 (50%/20), new result: 11.35                      
2025-07-31 15:31:34: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 10 jobs, finished 1 job      
2025-07-31 15:31:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:31:51: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:32:03: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:32:14: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:32:29: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:32:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (45%/20), waiting for 9 jobs                       
2025-07-31 15:32:51: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 9∑9 (40%/20), new result: 10.28                        
2025-07-31 15:33:10: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 9 jobs, finished 1 job       
2025-07-31 15:33:15: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:33:27: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:33:42: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:33:53: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:34:05: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:34:16: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:34:27: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:34:39: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:34:50: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:35:01: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:35:15: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:35:32: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:35:43: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (40%/20), waiting for 8 jobs                       
2025-07-31 15:36:03: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 8∑8 (35%/20), new result: 10.28                        
2025-07-31 15:36:22: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 7∑7 (30%/20), waiting for 8 jobs, finished 1 job       
2025-07-31 15:36:29: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 7∑7 (30%/20), waiting for 7 jobs                       
2025-07-31 15:36:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 7∑7 (25%/20), new result: 10.28                        
2025-07-31 15:36:40: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 7∑7 (25%/20), new result: 11.35                        
2025-07-31 15:37:05: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 7 jobs, finished 2 jobs      
2025-07-31 15:37:12: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 5 jobs                       
2025-07-31 15:37:24: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 5 jobs                       
2025-07-31 15:37:35: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 5 jobs                       
2025-07-31 15:38:04: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 5 jobs                       
2025-07-31 15:38:15: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (25%/20), waiting for 5 jobs                       
2025-07-31 15:38:26: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 5∑5 (20%/20), new result: 9.74                         
2025-07-31 15:38:45: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 4∑4 (20%/20), waiting for 5 jobs, finished 1 job       
2025-07-31 15:38:52: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 4∑4 (20%/20), waiting for 4 jobs                       
2025-07-31 15:39:04: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 4∑4 (15%/20), new result: 9.74                         
2025-07-31 15:39:23: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 3∑3 (15%/20), waiting for 4 jobs, finished 1 job       
2025-07-31 15:39:29: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 3∑3 (15%/20), waiting for 3 jobs                       
2025-07-31 15:39:52: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 3∑3 (10%/20), new result: 10.91                        
2025-07-31 15:40:07: Sobol, failed: 9 ('VAL_ACC: <FLOAT>' not found), best VAL_ACC: 20.13, running 2∑2 (0%/20), waiting for 3 jobs, 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	[["'cHl0aG9uMyAudGVzdHMvbW5pc3QvdHJhaW4gLS1lcG9jaHMgJWVwb2NocyAtLWxlYXJuaW5nX3JhdGUgJWxyIC0tYmF0Y2hfc2l6ZSAlYmF0Y2hfc2l6ZSAtLWhpZGRlbl9zaXplICVoaWRkZW5…
experiment_name	mnist_gpu_noall
mem_gb	10
parameter	[['epochs', 'range', '10', '200', 'int', 'false'], ['lr', 'range', '0.00001', '0.1', 'float', 'false'], ['batch_size', 'range', '8', '2048', 'int',
	'false'], ['hidden_size', 'range', '8', '2048', '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	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
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

1753967644.2211282,20,0,0
1753967647.7410944,20,0,0
1753967647.8602407,20,0,0
1753967652.7192628,20,0,0
1753967660.720339,20,0,0
1753967664.703013,20,0,0
1753967668.7666395,20,0,0
1753967673.8767617,20,1,5
1753967678.7143652,20,1,5
1753967687.7085037,20,1,5
1753967706.707943,20,1,5
1753967726.1001718,20,1,5
1753967732.893962,20,2,10
1753967737.9840584,20,2,10
1753967747.8443713,20,2,10
1753967752.7789688,20,2,10
1753967757.2792275,20,2,10
1753967763.0882115,20,3,15
1753967769.3448913,20,3,15
1753967778.980912,20,3,15
1753967783.7601418,20,3,15
1753967789.3165607,20,3,15
1753967795.837868,20,4,20
1753967814.7107744,20,4,20
1753967828.1210747,20,4,20
1753967832.7407355,20,4,20
1753967837.8966107,20,4,20
1753967843.8925586,20,5,25
1753967849.712782,20,5,25
1753967858.9250119,20,5,25
1753967863.7964315,20,5,25
1753967868.7311363,20,5,25
1753967874.8378797,20,6,30
1753967880.2820826,20,6,30
1753967890.2467442,20,6,30
1753967895.7160554,20,6,30
1753967900.1189172,20,6,30
1753967905.8346262,20,7,35
1753967924.723261,20,7,35
1753967935.2660596,20,7,35
1753967939.700056,20,6,30
1753967976.2403433,20,1,5
1753967986.4525096,20,2,10
1753968002.7099497,20,2,10
1753968002.7142658,20,2,10
1753968002.727753,20,2,10
1753968003.7215846,20,2,10
1753968003.7475672,20,2,10
1753968007.6981468,20,2,10
1753968027.7284634,20,2,10
1753968033.717795,20,2,10
1753968044.7085254,20,2,10
1753968049.7150424,20,2,10
1753968054.952823,20,2,10
1753968067.7146788,20,3,15
1753968073.7787275,20,3,15
1753968083.2897604,20,3,15
1753968090.7145321,20,3,15
1753968112.9694378,20,3,15
1753968129.9093769,20,4,20
1753968147.714594,20,4,20
1753968162.7009466,20,4,20
1753968168.707309,20,4,20
1753968173.8950186,20,4,20
1753968179.9911752,20,5,25
1753968186.9719458,20,5,25
1753968196.9269242,20,5,25
1753968201.884365,20,5,25
1753968224.723713,20,4,20
1753968230.8836393,20,4,20
1753968242.071311,20,4,20
1753968242.0769813,20,4,20
1753968270.04652,20,4,20
1753968275.715433,20,4,20
1753968286.711705,20,4,20
1753968292.0553877,20,4,20
1753968297.7525384,20,4,20
1753968304.1400354,20,5,25
1753968324.714325,20,4,20
1753968334.9720662,20,4,20
1753968340.7210023,20,4,20
1753968361.8702526,20,4,20
1753968367.982832,20,4,20
1753968373.7136464,20,4,20
1753968380.8855803,20,5,25
1753968386.7296448,20,5,25
1753968397.8846774,20,5,25
1753968403.80329,20,5,25
1753968410.7750459,20,5,25
1753968418.210284,20,6,30
1753968424.72158,20,6,30
1753968437.7180557,20,6,30
1753968444.046268,20,6,30
1753968455.0922658,20,6,30
1753968461.8910725,20,7,35
1753968467.8007088,20,7,35
1753968478.7298079,20,7,35
1753968485.0943537,20,7,35
1753968490.8627732,20,7,35
1753968497.2021344,20,8,40
1753968503.7109485,20,8,40
1753968514.7201848,20,8,40
1753968520.7065802,20,8,40
1753968526.172456,20,8,40
1753968532.7115388,20,9,45
1753968538.770606,20,9,45
1753968549.8357947,20,9,45
1753968555.8002386,20,9,45
1753968561.7138531,20,9,45
1753968568.876604,20,10,50
1753968575.0486646,20,10,50
1753968586.719112,20,9,45
1753968593.1696148,20,9,45
1753968604.1472635,20,9,45
1753968611.9927146,20,10,50
1753968618.0302699,20,10,50
1753968623.7127616,20,10,50
1753968635.7130744,20,10,50
1753968642.707166,20,10,50
1753968642.8162982,20,10,50
1753968642.957332,20,10,50
1753968655.0733826,20,10,50
1753968666.0896714,20,10,50
1753968676.9900937,20,10,50
1753968682.7156262,20,9,45
1753968694.7011416,20,9,45
1753968700.037881,20,9,45
1753968711.2095308,20,9,45
1753968723.1226318,20,9,45
1753968734.3148696,20,9,45
1753968749.0047433,20,9,45
1753968760.7014616,20,9,45
1753968771.7839885,20,8,40
1753968777.1465638,20,8,40
1753968790.053755,20,8,40
1753968795.7107291,20,8,40
1753968806.9521673,20,8,40
1753968822.8110416,20,8,40
1753968833.7146723,20,8,40
1753968845.0940056,20,8,40
1753968856.1754816,20,8,40
1753968866.8530843,20,8,40
1753968878.977937,20,8,40
1753968890.753377,20,8,40
1753968901.8464968,20,8,40
1753968915.0369787,20,8,40
1753968932.9107597,20,8,40
1753968943.7176561,20,8,40
1753968963.2221885,20,7,35
1753968970.2484486,20,6,30
1753968982.8930237,20,6,30
1753968989.712592,20,6,30
1753969000.7142212,20,5,25
1753969000.7258966,20,5,25
1753969010.1100936,20,5,25
1753969010.2369215,20,5,25
1753969025.7639604,20,5,25
1753969032.7335215,20,5,25
1753969044.9624035,20,5,25
1753969055.89407,20,5,25
1753969083.072827,20,5,25
1753969095.0801666,20,5,25
1753969106.7168186,20,4,20
1753969112.7178988,20,4,20
1753969125.3183563,20,4,20
1753969132.1165552,20,4,20
1753969144.0633905,20,3,15
1753969150.7109811,20,3,15
1753969163.7573593,20,3,15
1753969169.3089476,20,3,15
1753969192.2432013,20,2,10
1753969197.7105398,20,1,5

timestamp,ram_usage_mb,cpu_usage_percent
1753967639,711.3984375,5.8
1753967644,711.8984375,5.8
1753967647,711.8984375,5.8
1753967647,711.8984375,12.5
1753967647,711.8984375,7.2
1753967647,711.8984375,7.1
1753967647,711.8984375,9.1
1753968623,737.72265625,10.0
1753968642,735.9921875,6.1
1753968642,735.9921875,15.4
1753968642,735.9921875,7.7
1753968642,735.9921875,9.1
1753968682,738.078125,10.0
1753968777,738.12109375,18.2
1753968970,736.20703125,10.0
1753969010,736.20703125,6.3
1753969010,736.20703125,9.1
1753969010,736.20703125,12.5
1753969112,736.70703125,6.0
1753969112,736.70703125,8.3
1753969150,736.70703125,10.0

Parameter statistics

Parameter	Min	Max	Mean	Std Dev	Count
run_time	6	987	347.3889	333.3944	18
VAL_ACC	9.74	20.13	11.5622	3.0821	9
epochs	20	199	105.15	55.1247	20
lr	0.0056	0.0969	0.0513	0.0283	20
batch_size	92	1962	1053.35	593.8654	20
hidden_size	52	1966	1019.25	606.6682	20
dropout	0.0239	0.4782	0.2508	0.1424	20
num_dense_layers	1	4	2.5	1.118	20
weight_decay	0.0597	0.9756	0.5012	0.2872	20
activation	No numerical statistics available
init	No numerical statistics available

submitit INFO (2025-07-31 15:14:38,335) - Starting with JobEnvironment(job_id=530836, hostname=c143, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-07-31 15:14:38,336) - Loading pickle: /data/cat/ws/pwinkler-mnist_tst/omniopt_1/runs/mnist_gpu_noall/0/single_runs/530836/530836_submitted.pkl
ERROR: Could not install packages due to an OSError: [Errno 2] Datei oder Verzeichnis nicht gefunden: '/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/.torch_venv_1bdd5e1e8b/lib/python3.11/site-packages/torch/include/ATen/ops/_grid_sampler_2d_cpu_fallback_backward_ops.h'

Traceback (most recent call last):
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 46, in ensure_venv_and_rich
    import torchvision
ModuleNotFoundError: No module named 'torchvision'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 60, in <module>
    ensure_venv_and_rich()
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 49, in ensure_venv_and_rich
    create_and_setup_venv()
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 28, in create_and_setup_venv
    subprocess.check_call([str(PYTHON_BIN), "-m", "pip", "install", "rich", "torch", "torchvision"])
  File "/software/genoa/r24.04/Python/3.11.3-GCCcore-12.3.0/lib/python3.11/subprocess.py", line 413, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['.tests/mnist/.torch_venv_1bdd5e1e8b/bin/python', '-m', 'pip', 'install', 'rich', 'torch', 'torchvision']' returned non-zero exit status 1.

Parameters: {"epochs": 127, "lr": 0.06689339573502541, "batch_size": 1214, "hidden_size": 990, "dropout": 0.45562517642974854, "num_dense_layers": 3, "weight_decay": 0.5987284183502197, "activation": "leaky_relu", "init": "normal"}
Debug-Infos: 
========
DEBUG INFOS START:
Program-Code: python3 .tests/mnist/train --epochs 127 --learning_rate 0.06689339573502540992 --batch_size 1214 --hidden_size 990 --dropout 0.45562517642974853516 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.59872841835021972656
pwd: /data/cat/ws/pwinkler-mnist_tst/omniopt_1
File: .tests/mnist/train
UID: 2054851
GID: 200270
SLURM_JOB_ID: 530836
Status-Change-Time: 1753967537.933081
Size: 12760 Bytes
Permissions: -rwxr-xr-x
Owner: pwinkler
Last access: 1753967682.8331795
Last modification: 1753967537.933081
Hostname: c143
========
DEBUG INFOS END

python3 .tests/mnist/train --epochs 127 --learning_rate 0.06689339573502540992 --batch_size 1214 --hidden_size 990 --dropout 0.45562517642974853516 --activation leaky_relu --num_dense_layers 3 --init normal --weight_decay 0.59872841835021972656
stdout:
 Requirement already satisfied: pip in ./.tests/mnist/.torch_venv_1bdd5e1e8b/lib/python3.11/site-packages (22.3.1)
Collecting pip
  Downloading pip-25.2-py3-none-any.whl (1.8 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 9.4 MB/s eta 0:00:00
Installing collected packages: pip
  Attempting uninstall: pip
    Found existing installation: pip 22.3.1
    Uninstalling pip-22.3.1:
      Successfully uninstalled pip-22.3.1
Successfully installed pip-25.2
Collecting rich
  Using cached rich-14.1.0-py3-none-any.whl.metadata (18 kB)
Collecting torch
  Using cached torch-2.7.1-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (29 kB)
Collecting torchvision
  Using cached torchvision-0.22.1-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (6.1 kB)
Collecting markdown-it-py>=2.2.0 (from rich)
  Using cached markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)
Collecting pygments<3.0.0,>=2.13.0 (from rich)
  Using cached pygments-2.19.2-py3-none-any.whl.metadata (2.5 kB)
Collecting filelock (from torch)
  Using cached filelock-3.18.0-py3-none-any.whl.metadata (2.9 kB)
Collecting typing-extensions>=4.10.0 (from torch)
  Using cached typing_extensions-4.14.1-py3-none-any.whl.metadata (3.0 kB)
Collecting sympy>=1.13.3 (from torch)
  Using cached sympy-1.14.0-py3-none-any.whl.metadata (12 kB)
Collecting networkx (from torch)
  Using cached networkx-3.5-py3-none-any.whl.metadata (6.3 kB)
Collecting jinja2 (from torch)
  Using cached jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB)
Collecting fsspec (from torch)
  Using cached fsspec-2025.7.0-py3-none-any.whl.metadata (12 kB)
Collecting nvidia-cuda-nvrtc-cu12==12.6.77 (from torch)
  Using cached nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-runtime-cu12==12.6.77 (from torch)
  Using cached nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cuda-cupti-cu12==12.6.80 (from torch)
  Using cached nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cudnn-cu12==9.5.1.17 (from torch)
  Using cached nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cublas-cu12==12.6.4.1 (from torch)
  Using cached nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cufft-cu12==11.3.0.4 (from torch)
  Using cached nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-curand-cu12==10.3.7.77 (from torch)
  Using cached nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cusolver-cu12==11.7.1.2 (from torch)
  Using cached nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparse-cu12==12.5.4.2 (from torch)
  Using cached nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-cusparselt-cu12==0.6.3 (from torch)
  Using cached nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl.metadata (6.8 kB)
Collecting nvidia-nccl-cu12==2.26.2 (from torch)
  Using cached nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB)
Collecting nvidia-nvtx-cu12==12.6.77 (from torch)
  Using cached nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.6 kB)
Collecting nvidia-nvjitlink-cu12==12.6.85 (from torch)
  Using cached nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.5 kB)
Collecting nvidia-cufile-cu12==1.11.1.6 (from torch)
  Using cached nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.5 kB)
Collecting triton==3.3.1 (from torch)
  Using cached triton-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.5 kB)
Requirement already satisfied: setuptools>=40.8.0 in ./.tests/mnist/.torch_venv_1bdd5e1e8b/lib/python3.11/site-packages (from triton==3.3.1->torch) (65.5.0)
Collecting numpy (from torchvision)
  Using cached numpy-2.3.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)
Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)
  Using cached pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (9.0 kB)
Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich)
  Using cached mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)
Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch)
  Using cached mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch)
  Using cached MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB)
Using cached rich-14.1.0-py3-none-any.whl (243 kB)
Using cached pygments-2.19.2-py3-none-any.whl (1.2 MB)
Using cached torch-2.7.1-cp311-cp311-manylinux_2_28_x86_64.whl (821.2 MB)
Using cached nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (393.1 MB)
Using cached nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (8.9 MB)
Using cached nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (23.7 MB)
Using cached nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (897 kB)
Using cached nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl (571.0 MB)
Using cached nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (200.2 MB)
Using cached nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.1 MB)
Using cached nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (56.3 MB)
Using cached nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (158.2 MB)
Using cached nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (216.6 MB)
Using cached nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl (156.8 MB)
Using cached nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (201.3 MB)
Using cached nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl (19.7 MB)
Using cached nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (89 kB)
Using cached triton-3.3.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (155.7 MB)
Using cached torchvision-0.22.1-cp311-cp311-manylinux_2_28_x86_64.whl (7.5 MB)
Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)
Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)
Using cached pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (6.6 MB)
Using cached sympy-1.14.0-py3-none-any.whl (6.3 MB)
Using cached mpmath-1.3.0-py3-none-any.whl (536 kB)
Using cached typing_extensions-4.14.1-py3-none-any.whl (43 kB)
Using cached filelock-3.18.0-py3-none-any.whl (16 kB)
Using cached fsspec-2025.7.0-py3-none-any.whl (199 kB)
Using cached jinja2-3.1.6-py3-none-any.whl (134 kB)
Using cached MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23 kB)
Using cached networkx-3.5-py3-none-any.whl (2.0 MB)
Using cached numpy-2.3.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.9 MB)
Installing collected packages: nvidia-cusparselt-cu12, mpmath, typing-extensions, triton, sympy, pygments, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, mdurl, MarkupSafe, fsspec, filelock, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, markdown-it-py, jinja2, rich, nvidia-cusolver-cu12, torch, torchvision


stderr:
 ERROR: Could not install packages due to an OSError: [Errno 2] Datei oder Verzeichnis nicht gefunden: '/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/.torch_venv_1bdd5e1e8b/lib/python3.11/site-packages/torch/include/ATen/ops/_grid_sampler_2d_cpu_fallback_backward_ops.h'

Traceback (most recent call last):
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 46, in ensure_venv_and_rich
    import torchvision
ModuleNotFoundError: No module named 'torchvision'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 60, in <module>
    ensure_venv_and_rich()
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 49, in ensure_venv_and_rich
    create_and_setup_venv()
  File "/data/cat/ws/pwinkler-mnist_tst/omniopt_1/.tests/mnist/train", line 28, in create_and_setup_venv
    subprocess.check_call([str(PYTHON_BIN), "-m", "pip", "install", "rich", "torch", "torchvision"])
  File "/software/genoa/r24.04/Python/3.11.3-GCCcore-12.3.0/lib/python3.11/subprocess.py", line 413, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['.tests/mnist/.torch_venv_1bdd5e1e8b/bin/python', '-m', 'pip', 'install', 'rich', 'torch', 'torchvision']' returned non-zero exit status 1.

Result: {'VAL_ACC': None}
Final-results: {'VAL_ACC': None}
EXIT_CODE: 1
submitit INFO (2025-07-31 15:19:04,441) - Job completed successfully
submitit INFO (2025-07-31 15:19:04,443) - Exiting after successful completion