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 |
Experiment parameters
| Name | Type | Lower bound | Upper bound | Type | Log Scale? |
|---|
| epochs | range | 20 | 150 | int | No |
| lr | range | 0.0001 | 0.001 | float | No |
| batch_size | range | 8 | 1024 | int | No |
| hidden_size | range | 8 | 4096 | int | No |
| dropout | range | 0 | 0.5 | float | No |
| num_dense_layers | range | 1 | 2 | int | No |
| filter | range | 4 | 80 | int | No |
| num_conv_layers | range | 4 | 7 | int | No |
Result names and types
Last progressbar status
2025-11-04 16:23:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending 9 = ∑9/20, waiting for 9 jobs
Git-Version
Commit: 86ec80a2d91b6b5a1d9c417f907f93a644d0ab96 (9060)
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
To cancel, press CTRL c, then run 'scancel 1216265'
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[WARNING 11-04 16:20:48] 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]`.
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Run-UUID: dd580c09-c757-4b09-b23e-a638fad1be6e
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⠋ Writing worker creation log...
omniopt --partition=alpha --experiment_name=mnist_normalized_runtime_mono --mem_gb=40 --time=2880 --worker_timeout=120 --max_eval=1000 --num_parallel_jobs=20 --gpus=1 --num_random_steps=20 --follow --live_share --send_anonymized_usage_stats --result_names VAL_ACC=max --run_program=cHl0aG9uMyAvZGF0YS9ob3JzZS93cy9zMzgxMTE0MS1vbW5pb3B0X21uaXN0X3Rlc3RfY2FsbC9vbW5pb3B0Ly50ZXN0cy9tbmlzdC90cmFpbiAtLWVwb2NocyAlZXBvY2hzIC0tbGVhcm5pbmdfcmF0ZSAlbHIgLS1iYXRjaF9zaXplICViYXRjaF9zaXplIC0taGlkZGVuX3NpemUgJWhpZGRlbl9zaXplIC0tZHJvcG91dCAlZHJvcG91dCAtLW51bV9kZW5zZV9sYXllcnMgJW51bV9kZW5zZV9sYXllcnMgLS1maWx0ZXIgJShmaWx0ZXIpIC0tbnVtX2NvbnZfbGF5ZXJzICUobnVtX2NvbnZfbGF5ZXJzKQo= --cpus_per_task=1 --nodes_per_job=1 --revert_to_random_when_seemingly_exhausted --model=BOTORCH_MODULAR --n_estimators_randomforest=100 --run_mode=local --occ_type=euclid --main_process_gb=20 --max_nr_of_zero_results=50 --slurm_signal_delay_s=0 --max_failed_jobs=0 --username=conv_test_non_normalized --max_attempts_for_generation=20 --num_restarts=20 --raw_samples=1024 --max_abandoned_retrial=20 --max_num_of_parallel_sruns=16 --number_of_generators=1 --generate_all_jobs_at_once --parameter epochs range 20 150 int false --parameter lr range 0.0001 0.001 float false --parameter batch_size range 8 1024 int false --parameter hidden_size range 8 4096 int false --parameter dropout range 0 0.5 float false --parameter num_dense_layers range 1 2 int false --parameter filter range 4 80 int false --parameter num_conv_layers range 4 7 int false
⠋ Disabling logging...
⠋ Setting run folder...
⠋ Creating folder /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime_mono/1...
⠋ Writing revert_to_random_when_seemingly_exhausted file ...
⠋ Writing username state file...
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⠋ Writing result min/max file...
⠋ Saving state files...
Run-folder: /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/runs/mnist_normalized_runtime_mono/1
⠋ Writing live_share file if it is present...
⠋ Writing job_start_time file...
⠼ Writing git information
⠋ Checking max_eval...
⠋ Calculating number of steps...
⠋ Adding excluded nodes...
⠋ Initializing ax_client...
⠋ Setting orchestrator...
See https://imageseg.scads.de/omniax/share?user_id=conv_test_non_normalized&experiment_name=mnist_normalized_runtime_mono&run_nr=0 for live-results.
You have 1 CPUs available for the main process. Using CUDA device NVIDIA H100.
Generation strategy: SOBOL for 20 steps and then BOTORCH_MODULAR for 980 steps.
Run-Program: python3 /data/horse/ws/s3811141-omniopt_mnist_test_call/omniopt/.tests/mnist/train --epochs %epochs --learning_rate %lr --batch_size %batch_size --hidden_size %hidden_size --dropout %dropout --num_dense_layers %num_dense_layers --filter %(filter) --num_conv_layers %(num_conv_layers)
Experiment parameters
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Name ┃ Type ┃ Lower bound ┃ Upper bound ┃ Type ┃ Log Scale? ┃
┡━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ epochs │ range │ 20 │ 150 │ int │ No │
│ lr │ range │ 0.0001 │ 0.001 │ float │ No │
│ batch_size │ range │ 8 │ 1024 │ int │ No │
│ hidden_size │ range │ 8 │ 4096 │ int │ No │
│ dropout │ range │ 0 │ 0.5 │ float │ No │
│ num_dense_layers │ range │ 1 │ 2 │ int │ No │
│ filter │ range │ 4 │ 80 │ int │ No │
│ num_conv_layers │ range │ 4 │ 7 │ int │ No │
└──────────────────┴───────┴─────────────┴─────────────┴───────┴────────────┘
Result-Names
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Result-Name ┃ Min or max? ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ VAL_ACC │ max │
└─────────────┴─────────────┘
⠋ Write files and show overview
SOBOL, pending/unknown 8/1 = ∑9/20, started new job : 0%|░░░░░░░░░░| 0/1000 [01:51
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
sbatch: error: Batch job submission failed: Unexpected message received
⚠ FAILED: sbatch: error: Batch job submission failed: Unexpected message received
SOBOL, pending 9 = ∑9/20, waiting for 9 jobs : 0%|░░░░░░░░░░| 0/1000 [02:58
2025-11-04 16:20:59 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, Started OmniOpt2 run...
2025-11-04 16:21:00 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #1/20
2025-11-04 16:21:10 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #2/20
2025-11-04 16:21:11 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #3/20
2025-11-04 16:21:12 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #4/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #5/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #6/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #7/20
2025-11-04 16:21:13 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #8/20
2025-11-04 16:21:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #9/20
2025-11-04 16:21:14 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #10/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #11/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #12/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #13/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #14/20
2025-11-04 16:21:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #15/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #16/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #17/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #18/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #19/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, getting new HP set #20/20
2025-11-04 16:21:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, requested 20 jobs, got 20, 0.85 s/job
2025-11-04 16:21:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #1/20 start
2025-11-04 16:21:17 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #2/20 start
2025-11-04 16:21:18 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #3/20 start
2025-11-04 16:21:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #4/20 start
2025-11-04 16:21:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #5/20 start
2025-11-04 16:21:21 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #6/20 start
2025-11-04 16:21:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #7/20 start
2025-11-04 16:21:23 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #8/20 start
2025-11-04 16:21:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #9/20 start
2025-11-04 16:21:28 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #10/20 start
2025-11-04 16:21:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #11/20 start
2025-11-04 16:21:29 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #12/20 start
2025-11-04 16:21:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #13/20 start
2025-11-04 16:21:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #14/20 start
2025-11-04 16:21:31 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #15/20 start
2025-11-04 16:21:32 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #16/20 start
2025-11-04 16:21:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #17/20 start
2025-11-04 16:21:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #18/20 start
2025-11-04 16:21:36 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #19/20 start
2025-11-04 16:21:39 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, eval #20/20 start
2025-11-04 16:21:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, starting new job
2025-11-04 16:22:15 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, unknown 1 = ∑1/20, started new job
2025-11-04 16:22:16 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, unknown 1 = ∑1/20, starting new job
2025-11-04 16:22:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 1/1 = ∑2/20, started new job
2025-11-04 16:22:20 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 1/1 = ∑2/20, starting new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/1 = ∑3/20, started new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/1 = ∑3/20, starting new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/2 = ∑4/20, started new job
2025-11-04 16:22:25 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 2/2 = ∑4/20, starting new job
2025-11-04 16:22:30 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 4/1 = ∑5/20, started new job
2025-11-04 16:22:35 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 5/1 = ∑6/20, started new job
2025-11-04 16:22:40 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 6/1 = ∑7/20, started new job
2025-11-04 16:22:46 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 7/1 = ∑8/20, started new job
2025-11-04 16:22:50 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending/unknown 8/1 = ∑9/20, started new job
2025-11-04 16:23:57 (e4437c24-1ec3-4f6d-bb03-ab4f8e2bb9a3): SOBOL, pending 9 = ∑9/20, waiting for 9 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_normalized_runtime_mono |
| mem_gb | 40 |
| parameter | [['epochs', 'range', '20', '150', 'int', 'false'], ['lr', 'range', '0.0001', '0.001', 'float', 'false'], ['batch_size', 'range', '8', '1024', 'int', |
| 'false'], ['hidden_size', 'range', '8', '4096', 'int', 'false'], ['dropout', 'range', '0', '0.5', 'float', 'false'], ['num_dense_layers', 'range', '1', |
| '2', 'int', 'false'], ['filter', 'range', '4', '80', 'int', 'false'], ['num_conv_layers', 'range', '4', '7', 'int', 'false']] |
| continue_previous_job | None |
| experiment_constraints | None |
| run_dir | runs |
| seed | None |
| verbose_tqdm | False |
| model | BOTORCH_MODULAR |
| gridsearch | False |
| occ | False |
| show_sixel_scatter | False |
| show_sixel_general | False |
| show_sixel_trial_index_result | False |
| follow | True |
| send_anonymized_usage_stats | True |
| ui_url | None |
| root_venv_dir | /home/s3811141 |
| exclude | None |
| main_process_gb | 20 |
| max_nr_of_zero_results | 50 |
| abbreviate_job_names | False |
| orchestrator_file | None |
| checkout_to_latest_tested_version | False |
| live_share | True |
| disable_tqdm | False |
| disable_previous_job_constraint | False |
| workdir | |
| occ_type | euclid |
| result_names | ['VAL_ACC=max'] |
| minkowski_p | 2 |
| signed_weighted_euclidean_weights | |
| generation_strategy | None |
| generate_all_jobs_at_once | True |
| revert_to_random_when_seemingly_exhausted | True |
| load_data_from_existing_jobs | [] |
| n_estimators_randomforest | 100 |
| max_attempts_for_generation | 20 |
| external_generator | None |
| username | conv_test_non_normalized |
| max_failed_jobs | 0 |
| num_cpus_main_job | None |
| calculate_pareto_front_of_job | [] |
| show_generate_time_table | False |
| force_choice_for_ranges | False |
| max_abandoned_retrial | 20 |
| share_password | None |
| dryrun | False |
| db_url | None |
| run_program_once | None |
| worker_generator_path | None |
| save_to_database | False |
| range_max_difference | 1000000 |
| skip_search | False |
| dont_warm_start_refitting | False |
| refit_on_cv | False |
| fit_out_of_design | False |
| fit_abandoned | False |
| dont_jit_compile | False |
| num_restarts | 20 |
| raw_samples | 1024 |
| max_num_of_parallel_sruns | 16 |
| no_transform_inputs | False |
| no_normalize_y | False |
| transforms | [] |
| number_of_generators | 1 |
| num_parallel_jobs | 20 |
| worker_timeout | 120 |
| slurm_use_srun | False |
| time | 2880 |
| partition | alpha |
| reservation | None |
| force_local_execution | False |
| slurm_signal_delay_s | 0 |
| nodes_per_job | 1 |
| cpus_per_task | 1 |
| account | None |
| gpus | 1 |
| dependency | None |
| run_mode | local |
| verbose | False |
| verbose_break_run_search_table | False |
| debug | False |
| flame_graph | False |
| memray | False |
| no_sleep | False |
| tests | False |
| show_worker_percentage_table_at_end | False |
| auto_exclude_defective_hosts | False |
| run_tests_that_fail_on_taurus | False |
| raise_in_eval | False |
| show_ram_every_n_seconds | 0 |
| show_generation_and_submission_sixel | False |
| just_return_defaults | False |
| prettyprint | False |
| runtime_debug | False |
| debug_stack_regex | |
| debug_stack_trace_regex | None |
| show_func_name | False |
| beartype | False |
1762269654.1781,20,0,0
1762269701.306,20,0,0
1762269735.8575,20,1,5
1762269736.0158,20,1,5
1762269740.8565,20,2,10
1762269740.9738,20,2,10
1762269745.8583,20,4,20
1762269745.9864,20,4,20
1762269750.8763,20,5,25
1762269750.9432,20,5,25
1762269755.8807,20,6,30
1762269755.9397,20,6,30
1762269760.8754,20,7,35
1762269760.9485,20,7,35
1762269765.9532,20,8,40
1762269766.0392,20,8,40
1762269770.8978,20,9,45
1762269840.6128,20,9,45
1762269842.2266,20,20,100
1762270245.3947,20,20,100
This logs the CPU and RAM usage of the main worker process.
timestamp,ram_usage_mb,cpu_usage_percent
1762269654,808.10546875,12.7
1762269836,850.765625,12.9
1762269896,850.49609375,13
1762269956,850.47265625,13
1762270016,850.484375,12.8
1762270076,850.48046875,12.9
1762270137,850.484375,12.9
1762270197,850.484375,13
