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Current git-hash: cb80b776bcc7f09c64c9df008280d045d3d61256 (last fully tested stable version 5 commits ago [208aac92c9d373d0a7ca51bdae9ece179ef45cd2, 8673])
Using coverage run -p because $RUN_WITH_COVERAGE is set
⠋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...⠋Parsing arguments...⠸Importing torch...⠋Importing numpy...
[WARNING 10-17 10:58:58] 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...Using old run's --time: 60Using old run's --gpus: 0
Run-UUID: 9593d0d6-a9e6-4232-9ad4-b7e05a45330d
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/ \
| OmniOpt2 - Adjusting parameters with the precisio |
| n of a laser-guided squirrel. |
\ /
=================================================
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.--.
|o_o |
|:_/ |
// \ \
(| | )
/'\_ _/`\
\___)=(___/
⠋Writing worker creation log...
omniopt --continue runs/__main__tests__BOTORCH_MODULAR___nogridsearch/0 --num_parallel_jobs 1 --worker_timeout 10 --follow --gpus=0 --mem_gb=4 --num_random_steps=1 --max_eval=2 --send_anonymized_usage_stats --generate_all_jobs_at_once --live_share
⠋Disabling logging...⠋Setting run folder...⠋Creating folder /var/opt/omniopt/runs/__main__tests__BOTORCH_MODULAR___nogridsearch/1...⠋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: /var/opt/omniopt/runs/__main__tests__BOTORCH_MODULAR___nogridsearch/1
Continuation from runs/__main__tests__BOTORCH_MODULAR___nogridsearch/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...⠋Setting global generation strategy⠋Initializing ax_client...⠋Setting orchestrator...Warning: The disk space is almost full. This may lead to error messages and you not being able to push jobs. If you want to see results anyway, check https://imageseg.scads.de/omniax/tutorials?tutorial=oo_share#run-locally-in-docker on how to install it locally (with docker). If you run on HPC, you may want to install this into a Research Cloud at the TU Dresden.
See https://imageseg.scads.de/omniax/share?user_id=defaultuser&experiment_name=__main__tests__BOTORCH_MODULAR___nogridsearch&run_nr=336 for live-results.You have 4 CPUs available for the main process.No CUDA devices found.Generation strategy: BOTORCH_MODULAR for 5 steps.
Run-Program: ./.tests/optimization_example --int_param='%(int_param)' --float_param='%(float_param)' --choice_param='%(choice_param)' --int_param_two='%(int_param_two)' --nr_results=1
Experiment parameters
┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃Name ┃Type ┃Lower bound┃Upper bound┃Values ┃Type ┃Log Scale?┃
┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│int_param │rangeparameter │-100 │10 ││int │No │
│float_param │rangeparameter │-100 │10 ││float│No │
│choice_param │choiceparameter│││1, 2, 4, 8, 16, hallo│││
│int_param_two│rangeparameter │-100 │10 ││int │No │
└───────────────┴─────────────────┴─────────────┴─────────────┴───────────────────────┴───────┴────────────┘
Result-Names
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃Result-Name┃Min or max?┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│RESULT │ min│
└─────────────┴─────────────┘
⠋Write files and show overview
BOTORCH_MODULAR, best RESULT: -236012.565755265, finishing jobs, finished 1 job : 100%|██████████| 4/4 [00:25<00:00, 6.49s/it]
Best RESULT, min (total: 2 + inserted jobs: 2)
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃OO_Info_int_param┃OO_Info_int_param_two┃OO_Info_float_param┃OO_Info_choice_param┃int_param┃float_param ┃int_param_two┃choice_param┃RESULT ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│-100.0 │-100.0 │-35.03953765375506 │8.0 │-100 │-35.03953765375506│-100 │8 │-236012.565755265│
└───────────────────┴───────────────────────┴─────────────────────┴──────────────────────┴───────────┴────────────────────┴───────────────┴──────────────┴───────────────────┘
Runtime Infos
┏━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃Number of evaluations┃Min time ┃Max time ┃Average time┃Median time┃
┡━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ 2 │ 6.00 sec │ 6.00 sec │ 6.00 sec │ 6.00 sec │
└───────────────────────┴───────────┴───────────┴──────────────┴─────────────┘
2025-10-17 10:59:00 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, Started OmniOpt2 run...
2025-10-17 10:59:02 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, getting new HP set (no sbatch)
2025-10-17 10:59:02 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, requested 1 jobs, got 1, 1.44 s/job
2025-10-17 10:59:02 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, eval #1/1 start
2025-10-17 10:59:02 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, starting new job
2025-10-17 10:59:02 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, started new job
2025-10-17 10:59:11 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, new result: -145886.92
2025-10-17 10:59:13 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, finishing jobs, finished 1 job
2025-10-17 10:59:15 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, getting new HP set (no sbatch)
2025-10-17 10:59:15 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, requested 1 jobs, got 1, 1.13 s/job
2025-10-17 10:59:15 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, eval #1/1 start
2025-10-17 10:59:15 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, starting new job
2025-10-17 10:59:15 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, started new job
2025-10-17 10:59:24 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -194842.556970697, new result: -236012.565755265
2025-10-17 10:59:26 (d6f293b8-4a86-4f32-99f3-e7a9e60d0931): BOTORCH_MODULAR, best RESULT: -236012.565755265, finishing jobs, finished 1 job
Job submission durations
┏━━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━┓
┃ Batch ┃ Seconds ┃ Jobs ┃ Time per job ┃
┡━━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━┩
│ 1 │ 0.170 │ 1 │ 0.170 │
│ 2 │ 0.100 │ 1 │ 0.100 │
├─────────┼─────────┼──────┼──────────────┤
│ Average │ 0.135 │ │ │
│ Median │ 0.135 │ │ │
│ Total │ 0.270 │ │ │
│ Max │ 0.170 │ │ │
│ Min │ 0.100 │ │ │
└─────────┴─────────┴──────┴──────────────┘
Model generation times
┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━┓
┃ Iteration ┃ Seconds ┃ Jobs ┃ Time per job ┃
┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━┩
│ 1 │ 1.436 │ 1 │ 1.436 │
│ 2 │ 1.129 │ 1 │ 1.129 │
├───────────┼─────────┼──────┼──────────────┤
│ Average │ 1.283 │ │ │
│ Median │ 1.283 │ │ │
│ Total │ 2.566 │ │ │
│ Max │ 1.436 │ │ │
│ Min │ 1.129 │ │ │
└───────────┴─────────┴──────┴──────────────┘