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MLFlow generates random run names using the random module. Since random.seed is set before instantiation of the mlflow logger, the same run name is always generated.
My current fix is to instantiate the loggers before the line: L.seed_everything(cfg.seed, workers=True)
and forcing the setup of the logger during instatiation by logging some dummy variable:
...
for _, lg_conf in logger_cfg.items():
if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
log.info(f"Instantiating logger <{lg_conf._target_}>")
_logger = hydra.utils.instantiate(lg_conf)
logger.append(_logger)
_logger.log_metrics({"dummy": 0})
return logger
If the above is not added, the setup of the logger (which includes generating a random name), only happens at log_hyperparameters, and thus the seed would already be set and the run name would again be identical.
This feels like a hacky solution. Do you have any suggestions on a propper way to address the problem?
The text was updated successfully, but these errors were encountered:
MLFlow generates random run names using the random module. Since random.seed is set before instantiation of the mlflow logger, the same run name is always generated.
https://stackoverflow.com/questions/76053273/how-can-i-set-the-seed-that-mlflow-uses-to-randomly-generate-run-names
My current fix is to instantiate the loggers before the line:
L.seed_everything(cfg.seed, workers=True)
and forcing the setup of the logger during instatiation by logging some dummy variable:
If the above is not added, the setup of the logger (which includes generating a random name), only happens at
log_hyperparameters
, and thus the seed would already be set and the run name would again be identical.This feels like a hacky solution. Do you have any suggestions on a propper way to address the problem?
The text was updated successfully, but these errors were encountered: