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fpaissan
requested changes
Jul 5, 2024
| "lr": 0.001, # this is ignored if you are overriding the configure_optimizers | ||
| "debug": False, | ||
| "log_wandb": False, | ||
| "wandb_resume": "auto", # ["allow", "must", "never", "auto" or None] |
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I'm not sure what is the best way to do this. But we need to define better the effect of each field of default_cfg.
Maybe a long comment ("""xx""") after the closing bracket?
| name=self.hparams.experiment_name, | ||
| resume=self.hparams.wandb_resume, | ||
| id=self.hparams.experiment_name, | ||
| config=self.hparams, |
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here we can check if the configuration provides any extra arguments to be passed to the init. eventually, we load them (check the usage of ** operator).
| else: | ||
| val_metrics = train_metrics.update({"val_loss": loss_epoch / (idx + 1)}) | ||
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| if self.hparams.log_wandb: # wandb log |
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I might be wrong, but it would be nicer if we divide the train and valid panes inside the wandb logging. this would look something like val/{metric_name} will be logged instead of val_{metric_name}. ofc this change only applies to the .log call and should not be propagated to the entire core.
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Add wandb log, from default is not active.
To start log just set hyperparameter 'log_wandb' = True
During train loop it will automatically log train, validation metrics and lr if there is a lr_scheduler.
It start a wandb project named by default 'micromind' and each run are the name of the experiment. It automatically resume the log if the experiment restart after a crash.