Comments (4)
Do we have the version of ert that was used and the error message?
from ert.
Make sure this is logged so we can evaluate how often this happens and follow up!
from ert.
How did they notice? Did they get an error message?
from ert.
How did they notice? Did they get an error message?
They noticed as no ensembles showed up, but there were no errors, because of this:
def _load_ensembles(self) -> Dict[UUID, LocalEnsemble]:
if not (self.path / "ensembles").exists():
return {}
ensembles: List[LocalEnsemble] = []
for ensemble_path in (self.path / "ensembles").iterdir():
try:
ensemble = LocalEnsemble(self, ensemble_path, self.mode)
ensembles.append(ensemble)
except FileNotFoundError:
continue
# Make sure that the ensembles are sorted by name in reverse. Given
# multiple ensembles with a common name, iterating over the ensemble
# dictionary will yield the newest ensemble first.
return {
x.id: x for x in sorted(ensembles, key=lambda x: x.started_at, reverse=True)
}
we added the exception handling to show them, but no logging, suggest adding logger.exception in the except FileNotFoundError
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Related Issues (20)
- Refactor the gui models and how data is consumed from snapshot events
- New LSF driver makes it difficult to see which komodo release is used with bhist
- Implement NUM_CPU config in the LSFDriver
- Investigate use of bmod for memory managment
- Improve scheduler tests towards NUM_CPU config key
- Fix integration_tests/scheduler/test_lsf_driver.py::test_submit_with_num_cpu
- Presumably finished realization displayed as pending in joblist view HOT 3
- Success or failure of realization is logged with debug log level HOT 2
- Jobs not submitted to lsf HOT 1
- Validate that MAX_SUBMIT is larger than 0
- Provide correct realization state when resubmitting the forward model HOT 2
- Clicking observations in Experiment manager crashes ert HOT 1
- Drivers should pick NUM_CPU from the Realization class HOT 1
- Deprecate NUM_NODES and NUM_CPUS_PER_NODE in OpenPBS driver HOT 1
- Acquiring LSF jobid is flaky when submitting the job HOT 2
- Task running the job was not killed properly
- Investigate frequent bjobs failure HOT 2
- Python LSF driver does not pick the correct NUM_CPU
- Terminating jobs fails using jobqueue HOT 3
- Wrong state of realizations in Scheduler after terminate experiment
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