OmniOpt2

Experiment overview

Setting	Value
Model for non-random steps	BOTORCH_MODULAR
Max. nr. evaluations	1000
Number random steps	20
Nr. of workers (parameter)	20
Main process memory (GB)	20
Worker memory (GB)	40

Job Summary per Generation Node

Generation Node	Total	FAILED	RUNNING
SOBOL	20	10	10

Experiment parameters

Name	Type	Lower bound	Upper bound	Values	Type	Log Scale?
epochs	range	50	250		int	No
lr	range	0.0001	0.003		float	No
batch_size	range	8	256		int	No
hidden_size	range	500	8000		int	No
dropout	range	0	0.6		float	No
num_dense_layers	fixed			1
filter	range	20	250		int	No
num_conv_layers	range	4	6		int	No
pool_kernel	range	1	4		int	No
conv_activation	choice			relu, tanh, leaky_relu, sigmoid, swiglu

Number of evaluations

Failed	Succeeded	Running	Total
10	0	10	20

Result names and types

name	min/max
VAL_ACC	max

Last progressbar status

2025-11-04 13:36:09 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 10 ('VAL_ACC: <FLOAT>' not found), running 10 = ∑10/20, waiting for 10 jobs

Git-Version

Commit: d0b10ca27ad8d8b9afc9b2b1789728d0116370f8 (9050-4-gd0b10ca27)

trial_index,submit_time,queue_time,worker_generator_uuid,start_time,end_time,run_time,program_string,VAL_ACC,exit_code,signal,hostname,OO_Info_SLURM_JOB_ID,arm_name,trial_status,generation_node,epochs,lr,batch_size,hidden_size,dropout,filter,num_conv_layers,pool_kernel,conv_activation,num_dense_layers
0,,,,,,,,,,,,,0_0,RUNNING,SOBOL,214,0.001150999224185943669782061782,139,6549,0.497870492935180652960269753748,226,5,2,sigmoid,1
1,1762259512,25,00139b19-a768-4a4b-9917-141677a00b80,1762259537,1762259563,26,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 103 --learning_rate 0.00243070781547576184 --batch_size 107 --hidden_size 1143 --dropout 0.0677461473271250697 --filter 81 --num_conv_layers 5 --activation swiglu --pool_kernel 4 --num_dense_layers 1,,1,,c106,1215924,1_0,FAILED,SOBOL,103,0.002430707815475761836981893893,107,1143,0.067746147327125069703690485312,81,5,4,swiglu,1
2,1762259512,25,00139b19-a768-4a4b-9917-141677a00b80,1762259537,1762259563,26,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 68 --learning_rate 0.00051047475403174776 --batch_size 241 --hidden_size 4399 --dropout 0.22597232591360805198 --filter 25 --num_conv_layers 6 --activation tanh --pool_kernel 3 --num_dense_layers 1,,1,,c84,1215925,2_0,FAILED,SOBOL,68,0.000510474754031747758251102987,241,4399,0.225972325913608051983771929372,25,6,3,tanh,1
3,,,,,,,,,,,,,3_0,RUNNING,SOBOL,154,0.002153477755561471022882624737,26,3271,0.395811362564563729016242632497,170,4,1,leaky_relu,1
4,1762259512,146,00139b19-a768-4a4b-9917-141677a00b80,1762259658,1762259671,13,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 193 --learning_rate 0.00039572207536548375 --batch_size 83 --hidden_size 4242 --dropout 0.36918648350983856998 --filter 200 --num_conv_layers 6 --activation sigmoid --pool_kernel 3 --num_dense_layers 1,,1,,c129,1215938,4_0,FAILED,SOBOL,193,0.000395722075365483754762208068,83,4242,0.369186483509838569982974831873,200,6,3,sigmoid,1
5,,,,,,,,,,,,,5_0,RUNNING,SOBOL,78,0.001678990716207772772305961695,178,5312,0.199347553029656399115054909998,115,4,1,relu,1
6,,,,,,,,,,,,,6_0,RUNNING,SOBOL,138,0.001220151804387569621779263684,65,2048,0.097298033908009520787096846561,59,6,2,tanh,1
7,1762259512,85,00139b19-a768-4a4b-9917-141677a00b80,1762259597,1762259609,12,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 227 --learning_rate 0.00285946198059245944 --batch_size 218 --hidden_size 7513 --dropout 0.52742249686270947784 --filter 144 --num_conv_layers 4 --activation swiglu --pool_kernel 4 --num_dense_layers 1,,1,,c111,1215932,7_0,FAILED,SOBOL,227,0.0028594619805924594360679869,218,7513,0.527422496862709477838393468119,144,4,4,swiglu,1
8,1762259512,25,00139b19-a768-4a4b-9917-141677a00b80,1762259537,1762259563,26,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 240 --learning_rate 0.00072233800562098631 --batch_size 52 --hidden_size 5956 --dropout 0.01199969016015529563 --filter 182 --num_conv_layers 6 --activation sigmoid --pool_kernel 3 --num_dense_layers 1,,1,,c140,1215923,8_0,FAILED,SOBOL,240,0.000722338005620986310130471342,52,5956,0.011999690160155295631794203359,182,6,3,sigmoid,1
9,,,,,,,,,,,,,9_0,RUNNING,SOBOL,126,0.001998437402956187991520664582,209,3362,0.481839626468718029705939898122,39,4,1,relu,1
10,,,,,,,,,,,,,10_0,RUNNING,SOBOL,91,0.000984520414751023035387911264,96,7571,0.415064902044832706451416015625,98,5,2,leaky_relu,1
11,1762259512,85,00139b19-a768-4a4b-9917-141677a00b80,1762259597,1762259610,13,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 180 --learning_rate 0.00263099982365965846 --batch_size 187 --hidden_size 1754 --dropout 0.28494143597781657062 --filter 242 --num_conv_layers 5 --activation sigmoid --pool_kernel 4 --num_dense_layers 1,,1,,c140,1215931,11_0,FAILED,SOBOL,180,0.002630999823659658457680743382,187,1754,0.284941435977816570623843972498,242,5,4,sigmoid,1
12,,,,,,,,,,,,,12_0,RUNNING,SOBOL,167,0.001370970826782286298289870174,233,851,0.180386983416974538974031361249,158,5,2,swiglu,1
13,1762259512,55,00139b19-a768-4a4b-9917-141677a00b80,1762259567,1762259587,20,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 56 --learning_rate 0.00265218460718169829 --batch_size 13 --hidden_size 6608 --dropout 0.31051034107804298401 --filter 70 --num_conv_layers 5 --activation tanh --pool_kernel 4 --num_dense_layers 1,,1,,c111,1215928,13_0,FAILED,SOBOL,56,0.002652184607181698287881044251,13,6608,0.3105103410780429840087890625,70,5,4,tanh,1
14,1762259513,85,00139b19-a768-4a4b-9917-141677a00b80,1762259598,1762259616,18,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 115 --learning_rate 0.00019952970854938032 --batch_size 148 --hidden_size 2393 --dropout 0.58346192166209220886 --filter 130 --num_conv_layers 6 --activation swiglu --pool_kernel 3 --num_dense_layers 1,,1,,c42,1215935,14_0,FAILED,SOBOL,115,0.000199529708549380321467789368,148,2393,0.5834619216620922088623046875,130,6,3,swiglu,1
15,,,,,,,,,,,,,15_0,RUNNING,SOBOL,201,0.001841027496661991119317192123,119,5044,0.113621879182755944337479547812,217,4,1,sigmoid,1
16,1762259512,146,00139b19-a768-4a4b-9917-141677a00b80,1762259658,1762259683,25,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 210 --learning_rate 0.0001693308056332171 --batch_size 202 --hidden_size 2236 --dropout 0.37855422869324684143 --filter 65 --num_conv_layers 5 --activation relu --pool_kernel 3 --num_dense_layers 1,,1,,c81,1215939,16_0,FAILED,SOBOL,210,0.000169330805633217100101758912,202,2236,0.3785542286932468414306640625,65,5,3,relu,1
17,,,,,,,,,,,,,17_0,RUNNING,SOBOL,124,0.001815112588740885489133058961,44,7085,0.246232389472424983978271484375,153,5,1,sigmoid,1
18,,,,,,,,,,,,,18_0,RUNNING,SOBOL,58,0.001537512403633445670636525904,179,3786,0.050415766797959803147133328594,209,4,2,swiglu,1
19,1762259528,209,00139b19-a768-4a4b-9917-141677a00b80,1762259737,1762259762,25,python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 169 --learning_rate 0.0028143315520137548 --batch_size 87 --hidden_size 5529 --dropout 0.51805732771754264832 --filter 121 --num_conv_layers 6 --activation tanh --pool_kernel 4 --num_dense_layers 1,,1,,c112,1215942,19_0,FAILED,SOBOL,169,0.002814331552013754796093270016,87,5529,0.5180573277175426483154296875,121,6,4,tanh,1

⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}

To cancel, press CTRL c, then run 'scancel 1215920'
⠋ Importing logging...
⠋ Importing warnings...
⠋ Importing argparse...
⠋ Importing datetime...
⠋ Importing dataclass...
⠋ Importing socket...
⠋ Importing stat...
⠋ Importing pwd...
⠋ Importing base64...
⠋ Importing json...
⠋ Importing yaml...
⠋ Importing toml...
⠋ Importing csv...
⠋ Importing ast...
⠋ Importing rich.table...
⠋ Importing rich print...
⠋ Importing rich.pretty...
⠋ Importing pformat...
⠋ Importing rich.prompt...
⠋ Importing types.FunctionType...
⠋ Importing typing...
⠋ Importing ThreadPoolExecutor...
⠋ Importing submitit.LocalExecutor...
⠋ Importing submitit.Job...
⠋ Importing importlib.util...
⠋ Importing platform...
⠋ Importing inspect frame info...
⠋ Importing pathlib.Path...
⠋ Importing uuid...
⠋ Importing cowsay...
⠋ Importing shutil...
⠋ Importing itertools.combinations...
⠋ Importing os.listdir...
⠋ Importing os.path...
⠋ Importing PIL.Image...
⠋ Importing sixel...
⠋ Importing subprocess...
⠋ Importing tqdm...
⠋ Importing beartype...
⠋ Importing statistics...
⠋ Trying to import pyfiglet...
⠙ Importing helpers...
⠋ Importing pareto...
⠋ Parsing arguments...
⠸ Importing torch...
⠋ Importing numpy...
[WARNING 11-04 13:31:02] ax.service.utils.with_db_settings_base: Ax currently requires a sqlalchemy version below 2.0. This will be addressed in a future release. Disabling SQL storage in Ax for now, if you would like to use SQL storage please install Ax with mysql extras via `pip install ax-platform[mysql]`.
⠇ Importing ax...
⠋ Importing ax.core.generator_run...
⠋ Importing Cont_X_trans and Y_trans from ax.adapter.registry...
⠋ Importing ax.core.arm...
⠋ Importing ax.core.objective...
⠋ Importing ax.core.Metric...
⠋ Importing ax.exceptions.core...
⠋ Importing ax.exceptions.generation_strategy...
⠋ Importing CORE_DECODER_REGISTRY...
⠋ Trying ax.generation_strategy.generation_node...
⠋ Importing GenerationStep, GenerationStrategy from generation_strategy...
⠋ Importing GenerationNode from generation_node...
⠋ Importing ExternalGenerationNode...
⠋ Importing MaxTrials...
⠋ Importing GeneratorSpec...
⠋ Importing Models from ax.generation_strategy.registry...
⠋ Importing get_pending_observation_features...
⠋ Importing load_experiment...
⠋ Importing save_experiment...
⠋ Importing save_experiment_to_db...
⠋ Importing TrialStatus...
⠋ Importing Data...
⠋ Importing Experiment...
⠋ Importing parameter types...
⠋ Importing TParameterization...
⠋ Importing pandas...
⠋ Importing AxClient and ObjectiveProperties...
⠋ Importing RandomForestRegressor...
⠋ Importing botorch...
⠋ Importing submitit...
⠋ Importing ax logger...
⠋ Importing SQL-Storage-Stuff...
Run-UUID: ec6f0cf6-20a4-4fe3-b5bc-4f75abc7d3a4
  __                         __           __ 
 /\_\/                   o  /\_\/        /  )
|    | _  _  _    _  _     |    |  _ _|_   / 
|    |/ |/ |/ |  / |/ |  | |    ||/ \_|   /  
 \__/   |  |  |_/  |  |_/|_/\__/ |__/ |_//___
                                /|           
                                \|           

⠋ Writing worker creation log...
omniopt --partition=alpha --experiment_name=mnist_mono --mem_gb=40 --time=1440 --worker_timeout=120 --max_eval=1000 --num_parallel_jobs=20 --gpus=1 --num_random_steps=20 --follow --live_share --send_anonymized_usage_stats --result_names VAL_ACC=max --run_program=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdfcmF0ZSAlbHIgLS1iYXRjaF9zaXplICViYXRjaF9zaXplIC0taGlkZGVuX3NpemUgJWhpZGRlbl9zaXplIC0tZHJvcG91dCAlZHJvcG91dCAtLWZpbHRlciAlKGZpbHRlcikgLS1udW1fY29udl9sYXllcnMgJShudW1fY29udl9sYXllcnMpIC0tYWN0aXZhdGlvbiAlY29udl9hY3RpdmF0aW9uIC0tcG9vbF9rZXJuZWwgJXBvb2xfa2VybmVsIC0tbnVtX2RlbnNlX2xheWVycyAlbnVtX2RlbnNlX2xheWVycwo= --run_program_once=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWluc3RhbGw= --cpus_per_task=1 --nodes_per_job=1 --revert_to_random_when_seemingly_exhausted --model=BOTORCH_MODULAR --n_estimators_randomforest=100 --run_mode=local --occ_type=euclid --main_process_gb=20 --max_nr_of_zero_results=50 --slurm_signal_delay_s=0 --max_failed_jobs=0 --max_attempts_for_generation=20 --num_restarts=20 --raw_samples=1024 --max_abandoned_retrial=20 --max_num_of_parallel_sruns=16 --number_of_generators=1 --generate_all_jobs_at_once --parameter epochs range 50 250 int false --parameter lr range 0.0001 0.003 float false --parameter batch_size range 8 256 int false --parameter hidden_size range 500 8000 int false --parameter dropout range 0 0.6 float false --parameter num_dense_layers fixed 1 --parameter filter range 20 250 int false --parameter num_conv_layers range 4 6 int false --parameter pool_kernel range 1 4 int false --parameter conv_activation choice relu,tanh,leaky_relu,sigmoid,swiglu
⠋ Disabling logging...
⠋ Setting run folder...
⠋ Creating folder /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_mono/0...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
⠋ Writing username state file...
⠋ Writing result names file...
⠋ Writing result min/max file...
Executing command: python3 
/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train 
--install
Exiting, since the installation should now be done
Setup script completed successfully ✅
⠋ Saving state files...
Run-folder: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_mono/0
⠋ Writing live_share file if it is present...
⠋ Writing job_start_time file...
⠼ Writing git information
⠋ Checking max_eval...
⠋ Calculating number of steps...
⠋ Adding excluded nodes...
⠋ Initializing ax_client...
⠋ Setting orchestrator...
See https://imageseg.scads.de/omniax/share?user_id=s3811141&experiment_name=mnist_mono&run_nr=6 for live-results.

You have 1 CPUs available for the main process. Using CUDA device NVIDIA H100.
Generation strategy: SOBOL for 20 steps and then BOTORCH_MODULAR for 980 steps.
Run-Program: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs %epochs --learning_rate %lr --batch_size %batch_size --hidden_size %hidden_size --dropout %dropout --filter %(filter) --num_conv_layers %(num_conv_layers) --activation %conv_activation --pool_kernel %pool_kernel --num_dense_layers %num_dense_layers

                                                 Experiment parameters                                                  
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Name             ┃ Type   ┃ Lower bound ┃ Upper bound ┃ Values                                  ┃ Type  ┃ Log Scale? ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ epochs           │ range  │ 50          │ 250         │                                         │ int   │ No         │
│ lr               │ range  │ 0.0001      │ 0.003       │                                         │ float │ No         │
│ batch_size       │ range  │ 8           │ 256         │                                         │ int   │ No         │
│ hidden_size      │ range  │ 500         │ 8000        │                                         │ int   │ No         │
│ dropout          │ range  │ 0           │ 0.6         │                                         │ float │ No         │
│ num_dense_layers │ fixed  │             │             │ 1                                       │       │            │
│ filter           │ range  │ 20          │ 250         │                                         │ int   │ No         │
│ num_conv_layers  │ range  │ 4           │ 6           │                                         │ int   │ No         │
│ pool_kernel      │ range  │ 1           │ 4           │                                         │ int   │ No         │
│ conv_activation  │ choice │             │             │ relu, tanh, leaky_relu, sigmoid, swiglu │       │            │
└──────────────────┴────────┴─────────────┴─────────────┴─────────────────────────────────────────┴───────┴────────────┘
        Result-Names         
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Result-Name ┃ Min or max? ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ VAL_ACC     │         max │
└─────────────┴─────────────┘
⠋ Write files and show overview
SOBOL, completed/running/pending/unknown 4/4/11/1 = ∑20/20, started new job:   0%|░░░░░░░░░░| 0/1000 [01:41
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}

⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}

⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}

⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 4 ('VAL_ACC: ' not found), running/pending 10/6 = ∑16/20, waiting for 16 jobs                      :   0%|░░░░░░░░░░| 0/1000 [01:44
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 4 ('VAL_ACC: ' not found), running/pending 10/6 = ∑16/20, job_failed                               :   0%|░░░░░░░░░░| 0/1000 [01:57
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 6 ('VAL_ACC: ' not found), running/pending 8/6 = ∑14/20, waiting for 14 jobs                       :   0%|░░░░░░░░░░| 0/1000 [01:58
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 7 ('VAL_ACC: ' not found), running/pending 11/2 = ∑13/20, waiting for 13 jobs                      :   0%|░░░░░░░░░░| 0/1000 [02:47
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 8 ('VAL_ACC: ' not found), running/pending 10/2 = ∑12/20, waiting for 12 jobs                      :   0%|░░░░░░░░░░| 0/1000 [03:00
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 9 ('VAL_ACC: ' not found), running/pending 10/1 = ∑11/20, waiting for 11 jobs                      :   0%|░░░░░░░░░░| 0/1000 [03:54
⚠ It seems like the program that was about to be run didn't have 'RESULT: ' in it's output string.
Error: Raw data for an arm is expected to either be a tuple of numerical mean and SEM or just a numerical mean. Got: None for metric 'VAL_ACC'.
Job-result: {'VAL_ACC': None}
SOBOL, failed: 10 ('VAL_ACC: ' not found), running 10 = ∑10/20, waiting for 10 jobs                               :   0%|░░░░░░░░░░| 0/1000 [04:38

2025-11-04 13:31:32 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, Started OmniOpt2 run...
2025-11-04 13:31:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #1/20
2025-11-04 13:31:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #2/20
2025-11-04 13:31:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #3/20
2025-11-04 13:31:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #4/20
2025-11-04 13:31:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #5/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #6/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #7/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #8/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #9/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #10/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #11/20
2025-11-04 13:31:34 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #12/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #13/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #14/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #15/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #16/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #17/20
2025-11-04 13:31:35 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #18/20
2025-11-04 13:31:36 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #19/20
2025-11-04 13:31:36 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, getting new HP set #20/20
2025-11-04 13:31:37 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, requested 20 jobs, got 20, 0.15 s/job
2025-11-04 13:31:38 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #1/20 start                     
2025-11-04 13:31:38 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #2/20 start                     
2025-11-04 13:31:39 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #3/20 start                     
2025-11-04 13:31:39 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #4/20 start                     
2025-11-04 13:31:40 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #5/20 start                     
2025-11-04 13:31:40 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #6/20 start                     
2025-11-04 13:31:40 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #7/20 start                     
2025-11-04 13:31:41 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #8/20 start                     
2025-11-04 13:31:41 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #9/20 start                     
2025-11-04 13:31:42 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #10/20 start                    
2025-11-04 13:31:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #11/20 start                    
2025-11-04 13:31:44 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #12/20 start                    
2025-11-04 13:31:45 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #13/20 start                    
2025-11-04 13:31:47 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #14/20 start                    
2025-11-04 13:31:47 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #15/20 start                    
2025-11-04 13:31:48 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #16/20 start                    
2025-11-04 13:31:48 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #17/20 start                    
2025-11-04 13:31:50 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #18/20 start                    
2025-11-04 13:31:50 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #19/20 start                    
2025-11-04 13:31:51 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, eval #20/20 start                    
2025-11-04 13:31:52 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, starting new job                     
2025-11-04 13:31:52 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, unknown 1 = ∑1/20, starting new job  
2025-11-04 13:31:52 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, unknown 1 = ∑1/20, started new job   
2025-11-04 13:31:52 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, unknown 1 = ∑1/20, starting new job  
2025-11-04 13:31:58 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 13:31:58 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 13:32:08 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, pending/unknown 2/2 = ∑4/20, started new job 
2025-11-04 13:32:08 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 13:32:13 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, pending/unknown 4/1 = ∑5/20, started new job 
2025-11-04 13:32:17 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/unknown 5/1 = ∑6/20, started new job 
2025-11-04 13:32:23 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/1/2 = ∑8/20, started new job
2025-11-04 13:32:27 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/3/1 = ∑9/20, started new job
2025-11-04 13:32:33 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/4/1 = ∑10/20, started new job
2025-11-04 13:32:38 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/5/1 = ∑11/20, started new job
2025-11-04 13:32:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/6/1 = ∑12/20, started new job
2025-11-04 13:32:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, running/pending/unknown 5/6/2 = ∑13/20, started new job
2025-11-04 13:32:48 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 3/5/5/1 = ∑14/20, started new job
2025-11-04 13:32:53 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 3/5/6/1 = ∑15/20, started new job
2025-11-04 13:32:53 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 3/5/6/2 = ∑16/20, started new job
2025-11-04 13:32:58 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 3/5/8/1 = ∑17/20, started new job
2025-11-04 13:33:03 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 3/5/9/1 = ∑18/20, started new job
2025-11-04 13:33:08 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 4/4/10/1 = ∑19/20, started new job
2025-11-04 13:33:14 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 4/4/11/1 = ∑20/20, started new job
2025-11-04 13:33:14 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, completed/running/pending/unknown 4/4/11/1 = ∑20/20, job_failed     
2025-11-04 13:33:14 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/pending/unknown 4/11/1 = ∑16/20, finishing jobs, finished 4 jobs
2025-11-04 13:33:15 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/pending/unknown 4/11/1 = ∑16/20, waiting for 16 jobs            
2025-11-04 13:33:16 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/pending 10/6 = ∑16/20, waiting for 16 jobs                      
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 4 ('VAL_ACC: <FLOAT>' not found), running/pending 10/6 = ∑16/20, job_failed                               
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 9/6 = ∑15/20, waiting for 16 jobs, finished 1 job       
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 9/6 = ∑15/20, waiting for 15 jobs                       
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 5 ('VAL_ACC: <FLOAT>' not found), running/pending 9/6 = ∑15/20, job_failed                                
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 8/6 = ∑14/20, waiting for 15 jobs, finished 1 job       
2025-11-04 13:33:30 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 8/6 = ∑14/20, waiting for 14 jobs                       
2025-11-04 13:33:36 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 6 ('VAL_ACC: <FLOAT>' not found), running/pending 8/6 = ∑14/20, job_failed                                
2025-11-04 13:33:36 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 7/6 = ∑13/20, waiting for 14 jobs, finished 1 job       
2025-11-04 13:33:36 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 7/6 = ∑13/20, waiting for 13 jobs                       
2025-11-04 13:34:20 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 11/2 = ∑13/20, waiting for 13 jobs                      
2025-11-04 13:34:31 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 7 ('VAL_ACC: <FLOAT>' not found), running/pending 11/2 = ∑13/20, job_failed                               
2025-11-04 13:34:31 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 8 ('VAL_ACC: <FLOAT>' not found), running/pending 10/2 = ∑12/20, waiting for 13 jobs, finished 1 job      
2025-11-04 13:34:31 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 8 ('VAL_ACC: <FLOAT>' not found), running/pending 10/2 = ∑12/20, waiting for 12 jobs                      
2025-11-04 13:34:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 8 ('VAL_ACC: <FLOAT>' not found), running/pending 10/2 = ∑12/20, job_failed                               
2025-11-04 13:34:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 9/2 = ∑11/20, waiting for 12 jobs, finished 1 job       
2025-11-04 13:34:43 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 9/2 = ∑11/20, waiting for 11 jobs                       
2025-11-04 13:35:27 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 10/1 = ∑11/20, waiting for 11 jobs                      
2025-11-04 13:36:03 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 9 ('VAL_ACC: <FLOAT>' not found), running/pending 10/1 = ∑11/20, job_failed                               
2025-11-04 13:36:09 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 10 ('VAL_ACC: <FLOAT>' not found), running 10 = ∑10/20, waiting for 11 jobs, finished 1 job               
2025-11-04 13:36:09 (00139b19-a768-4a4b-9917-141677a00b80): SOBOL, failed: 10 ('VAL_ACC: <FLOAT>' not found), running 10 = ∑10/20, waiting for 10 jobs

Arguments Overview

Key	Value
config_yaml	None
config_toml	None
config_json	None
num_random_steps	20
max_eval	1000
run_program	[['cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdf…
experiment_name	mnist_mono
mem_gb	40
parameter	[['epochs', 'range', '50', '250', 'int', 'false'], ['lr', 'range', '0.0001', '0.003', 'float', 'false'], ['batch_size', 'range', '8', '256', 'int',
	'false'], ['hidden_size', 'range', '500', '8000', 'int', 'false'], ['dropout', 'range', '0', '0.6', 'float', 'false'], ['num_dense_layers', 'fixed',
	'1'], ['filter', 'range', '20', '250', 'int', 'false'], ['num_conv_layers', 'range', '4', '6', 'int', 'false'], ['pool_kernel', 'range', '1', '4',
	'int', 'false'], ['conv_activation', 'choice', 'relu,tanh,leaky_relu,sigmoid,swiglu']]
continue_previous_job	None
experiment_constraints	None
run_dir	runs
seed	None
verbose_tqdm	False
model	BOTORCH_MODULAR
gridsearch	False
occ	False
show_sixel_scatter	False
show_sixel_general	False
show_sixel_trial_index_result	False
follow	True
send_anonymized_usage_stats	True
ui_url	None
root_venv_dir	/home/s3811141
exclude	None
main_process_gb	20
max_nr_of_zero_results	50
abbreviate_job_names	False
orchestrator_file	None
checkout_to_latest_tested_version	False
live_share	True
disable_tqdm	False
disable_previous_job_constraint	False
workdir
occ_type	euclid
result_names	['VAL_ACC=max']
minkowski_p	2
signed_weighted_euclidean_weights
generation_strategy	None
generate_all_jobs_at_once	True
revert_to_random_when_seemingly_exhausted	True
load_data_from_existing_jobs	[]
n_estimators_randomforest	100
max_attempts_for_generation	20
external_generator	None
username	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	cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWluc3RhbGw=
worker_generator_path	None
save_to_database	False
range_max_difference	1000000
skip_search	False
dont_warm_start_refitting	False
refit_on_cv	False
fit_out_of_design	False
fit_abandoned	False
dont_jit_compile	False
num_restarts	20
raw_samples	1024
max_num_of_parallel_sruns	16
no_transform_inputs	False
no_normalize_y	False
transforms	[]
number_of_generators	1
num_parallel_jobs	20
worker_timeout	120
slurm_use_srun	False
time	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
dependency	None
run_mode	local
verbose	False
verbose_break_run_search_table	False
debug	False
flame_graph	False
memray	False
no_sleep	False
tests	False
show_worker_percentage_table_at_end	False
auto_exclude_defective_hosts	False
run_tests_that_fail_on_taurus	False
raise_in_eval	False
show_ram_every_n_seconds	0
show_generation_and_submission_sixel	False
just_return_defaults	False
prettyprint	False
runtime_debug	False
debug_stack_regex
debug_stack_trace_regex	None
show_func_name	False
beartype	False

1762259485.806,20,0,0
1762259512.6218,20,0,0
1762259512.7378,20,1,5
1762259512.7514,20,1,5
1762259512.8168,20,0,0
1762259512.9517,20,1,5
1762259513.8394,20,1,5
1762259517.9591,20,2,10
1762259518.0788,20,2,10
1762259527.9683,20,4,20
1762259528.0919,20,4,20
1762259533.1566,20,5,25
1762259537.9461,20,5,25
1762259543.5951,20,8,40
1762259547.9583,20,8,40
1762259552.9826,20,10,50
1762259553.0454,20,10,50
1762259557.9781,20,11,55
1762259558.0477,20,11,55
1762259562.9859,20,13,65
1762259563.0627,20,13,65
1762259567.9894,20,11,55
1762259568.0542,20,11,55
1762259573.0065,20,13,65
1762259573.0884,20,13,65
1762259578.0717,20,14,70
1762259578.1379,20,14,70
1762259583.0894,20,15,75
1762259583.158,20,15,75
1762259588.0322,20,16,80
1762259616.8062,20,16,80
1762259617.4209,20,14,70
1762259621.5706,20,14,70
1762259622.1842,20,13,65
1762259676.8843,20,13,65
1762259678.5071,20,12,60
1762259686.5814,20,12,60
1762259687.1674,20,11,55
1762259763.1289,20,11,55
1762259769.2853,20,10,50
1762259828.6231,20,10,50

This logs the CPU and RAM usage of the main worker process.

timestamp,ram_usage_mb,cpu_usage_percent
1762259485,807.984375,13.9
1762259609,851.15234375,13.1
1762259669,851.19140625,12.6
1762259729,851.23046875,12.8
1762259789,851.296875,12.8

submitit INFO (2025-11-04 13:32:15,852) - Starting with JobEnvironment(job_id=1215923, hostname=c140, local_rank=0(1), node=0(1), global_rank=0(1))
submitit INFO (2025-11-04 13:32:15,854) - Loading pickle: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_mono/0/single_runs/1215923/1215923_submitted.pkl
Trial-Index: 8
/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/utils/data/dataloader.py:627: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
  warnings.warn(
Traceback (most recent call last):
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train", line 457, in main
    print_model_summary(model, input_size=(3, 32, 32))
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train", line 298, in print_model_summary
    x = layer(x)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/linear.py", line 125, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x1638 and 2912x5956)

Parameters: {"epochs": 240, "lr": 0.0007223380056209863, "batch_size": 52, "hidden_size": 5956, "dropout": 0.011999690160155296, "filter": 182, "num_conv_layers": 6, "pool_kernel": 3, "conv_activation": "sigmoid", "num_dense_layers": 1}
Debug-Infos: 
========
DEBUG INFOS START:
Program-Code: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 240 --learning_rate 0.00072233800562098631 --batch_size 52 --hidden_size 5956 --dropout 0.01199969016015529563 --filter 182 --num_conv_layers 6 --activation sigmoid --pool_kernel 3 --num_dense_layers 1 
pwd: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt
File: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train
UID: 2105408
GID: 200270
SLURM_JOB_ID: 1215923
Status-Change-Time: 1762258624.0
Size: 20197 Bytes
Permissions: -rwxr-xr-x
Owner: s3811141
Last access: 1762259548.0
Last modification: 1762258624.0
Hostname: c140
========
DEBUG INFOS END

python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs 240 --learning_rate 0.00072233800562098631 --batch_size 52 --hidden_size 5956 --dropout 0.01199969016015529563 --filter 182 --num_conv_layers 6 --activation sigmoid --pool_kernel 3 --num_dense_layers 1 
stdout:
               Hyperparameters               
╭──────────────────┬───────────────────────╮
│ Parameter        │ Value                 │
├──────────────────┼───────────────────────┤
│ Epochs           │ 240                   │
│ Num Dense Layers │ 1                     │
│ Batch size       │ 52                    │
│ Learning rate    │ 0.0007223380056209863 │
│ Hidden size      │ 5956                  │
│ Dropout          │ 0.011999690160155296  │
│ Optimizer        │ adam                  │
│ Momentum         │ 0.9                   │
│ Weight Decay     │ 0.0001                │
│ Activation       │ sigmoid               │
│ Dense-Activation │ relu                  │
│ Init Method      │ kaiming               │
│ Seed             │ None                  │
│ Conv Filters     │ 182                   │
│ Num Conv Layers  │ 6                     │
│ Conv Kernel      │ 3                     │
│ Conv Stride      │ 1                     │
│ Conv Padding     │ 1                     │
│ Pool-Stride      │ 2                     │
│ Pool-Kernel      │ 3                     │
╰──────────────────┴───────────────────────╯
An error occurred: mat1 and mat2 shapes cannot be multiplied (1x1638 and 
2912x5956)

stderr:
 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/utils/data/dataloader.py:627: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
  warnings.warn(
Traceback (most recent call last):
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train", line 457, in main
    print_model_summary(model, input_size=(3, 32, 32))
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train", line 298, in print_model_summary
    x = layer(x)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
    return forward_call(*args, **kwargs)
  File "/data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/.torch_venv_1bdd5e1e8b/lib64/python3.9/site-packages/torch/nn/modules/linear.py", line 125, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x1638 and 2912x5956)

Result: {'VAL_ACC': None}
Final-results: {'VAL_ACC': None}
EXIT_CODE: 1
submitit INFO (2025-11-04 13:32:43,170) - Job completed successfully
submitit INFO (2025-11-04 13:32:43,172) - Exiting after successful completion