Skip to content

Cleaning Up Failed Trials#

The NePS clean command provides a utility to remove failed, crashed, or corrupted trials from your optimization working directory. This is useful for managing your optimization state and preventing problematic trials from interfering with future optimization runs.

Command Line Usage#

Basic Usage#

To clean all non-success stated trials from your optimization directory:

python -m neps.clean <root_directory>

Dry Run#

Preview what will be deleted without making any changes:

python -m neps.clean <root_directory> --dry_run

Removing Specific Trial IDs#

Remove only specific trials by their IDs (regardless of state):

python -m neps.clean <root_directory> --trial_ids <trial_id_1> <trial_id_2> <trial_id_3>

Trial IDs are reported in metadata.json within each config directory.

Python API#

You can also use the clean functionality programmatically in Python:

Clean Failed/Crashed/Corrupted Trials#

from pathlib import Path
from neps.clean.clean import clean_failed_trials
from neps.state.trial import Trial

root_dir = Path("<root_directory>")
stats = clean_failed_trials(
    root_directory=root_dir,
    desired_states=[
        Trial.State.FAILED,
        Trial.State.CRASHED,
        Trial.State.CORRUPTED,
    ],
    dry_run=False,
)

print(f"Removed {stats['total_removed']} trials")

Clean Specific Trial IDs#

from pathlib import Path
from neps.clean.clean import clean_failed_trials

root_dir = Path("<root_directory>")
stats = clean_failed_trials(
    root_directory=root_dir,
    trial_ids=["1", "2"],
    dry_run=False,
)

print(f"Removed {stats['removed']} trials")
print(f"Not found: {stats['not_found']} trials")