Last bits of the equation

README.md

@@ -97,15 +97,16 @@ information.
 
 ### Validation
 
-Validation is run at intervals requested by the user using the lightning interface.
-See `--val_check_interval` and `--check_val_every_n_epoch`
+Validation is run at intervals requested by the user. See
+`--val_check_interval` and `--check_val_every_n_epoch`
 [here](https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api).
-Particular evaluation metrics can also be requested with `--eval_metric`. For example
+Additional evaluation metrics can also be requested with `--eval_metric`. For
+example
 
-    yoyodyne-train --eval_metric accuracy --eval_metric ser ...
+    yoyodyne-train --eval_metric ser ...
 
-will compute both accuracy and symbol error rate (SER) each time validation is
-requested. Additional metrics can be added in
+will additionally compute symbol error rate (SER) each time validation is
+performed. Additional metrics can be added to
 [`evaluators.py`](yoyodyne/evaluators.py).
 
 ### Prediction

yoyodyne/__init__.py

@@ -5,5 +5,6 @@
     "ignore", ".*does not have many workers which may be a bottleneck.*"
 )
 warnings.filterwarnings(
-    "ignore", ".*option adds dropout after all but last recurrent layer*."
+    "ignore", ".*option adds dropout after all but last recurrent layer.*"
 )
+warnings.filterwarnings("ignore", ".*is a wandb run already in progress.*")

yoyodyne/train.py

@@ -14,7 +14,7 @@ class Error(Exception):
     pass
 
 
-def _get_logger(experiment: str, model_dir: str, log_wandb: bool) -> List:
+def _get_loggers(experiment: str, model_dir: str, log_wandb: bool) -> List:
     """Creates the logger(s).
 
     Args:
@@ -25,12 +25,12 @@ def _get_logger(experiment: str, model_dir: str, log_wandb: bool) -> List:
     Returns:
         List: logger.
     """
-    trainer_logger = [loggers.CSVLogger(model_dir, name=experiment)]
+    trainer_loggers = [loggers.CSVLogger(model_dir, name=experiment)]
     if log_wandb:
-        trainer_logger.append(loggers.WandbLogger(project=experiment))
+        trainer_loggers.append(loggers.WandbLogger(project=experiment))
         # Logs the path to local artifacts made by PTL.
-        wandb.config["local_run_dir"] = trainer_logger[0].log_dir
-    return trainer_logger
+        wandb.config["local_run_dir"] = trainer_loggers[0].log_dir
+    return trainer_loggers
 
 
 def _get_callbacks(
@@ -72,7 +72,6 @@ def _get_callbacks(
                 mode=metric.mode,
                 monitor=metric.monitor,
                 patience=patience,
-                verbose=True,
             )
         )
     # Checkpointing callback. Ensure that this is the last checkpoint,
@@ -114,7 +113,7 @@ def get_trainer_from_argparse_args(
         ),
         default_root_dir=args.model_dir,
         enable_checkpointing=True,
-        logger=_get_logger(args.experiment, args.model_dir, args.log_wandb),
+        logger=_get_loggers(args.experiment, args.model_dir, args.log_wandb),
     )
 
 
@@ -379,6 +378,8 @@ def main() -> None:
     add_argparse_args(parser)
     args = parser.parse_args()
     util.log_arguments(args)
+    if args.log_wandb:
+        wandb.init()
     pl.seed_everything(args.seed)
     trainer = get_trainer_from_argparse_args(args)
     datamodule = get_datamodule_from_argparse_args(args)