Small fixes to NotebookProgressCallback (#7813)

src/transformers/file_utils.py

@@ -153,7 +153,7 @@ try:
     import IPython  # noqa: F401
 
     _in_notebook = True
-except:  # noqa: E722
+except (AttributeError, ImportError, KeyError):
     _in_notebook = False
 
 

src/transformers/utils/notebook.py

@@ -19,6 +19,7 @@ from typing import Optional
 import IPython.display as disp
 
 from ..trainer_callback import TrainerCallback
+from ..trainer_utils import EvaluationStrategy
 
 
 def format_time(t):
@@ -146,7 +147,7 @@ class NotebookProgressBar:
             self.first_calls = self.warmup
             self.wait_for = 1
             self.update_bar(value)
-        elif value <= self.last_value:
+        elif value <= self.last_value and not force_update:
             return
         elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total):
             if self.first_calls > 0:
@@ -272,17 +273,25 @@ class NotebookProgressCallback(TrainerCallback):
     def __init__(self):
         self.training_tracker = None
         self.prediction_bar = None
+        self._force_next_update = False
 
     def on_train_begin(self, args, state, control, **kwargs):
-        self.first_column = "Epoch" if args.max_steps <= 0 else "Step"
+        self.first_column = "Epoch" if args.evaluation_strategy == EvaluationStrategy.EPOCH else "Step"
         self.training_loss = 0
         self.last_log = 0
-        column_names = [self.first_column] + ["Training Loss", "Validation Loss"]
+        column_names = [self.first_column] + ["Training Loss"]
+        if args.evaluation_strategy != EvaluationStrategy.NO:
+            column_names.append("Validation Loss")
         self.training_tracker = NotebookTrainingTracker(state.max_steps, column_names)
 
     def on_step_end(self, args, state, control, **kwargs):
         epoch = int(state.epoch) if int(state.epoch) == state.epoch else f"{state.epoch:.2f}"
-        self.training_tracker.update(state.global_step + 1, comment=f"Epoch {epoch}/{state.num_train_epochs}")
+        self.training_tracker.update(
+            state.global_step + 1,
+            comment=f"Epoch {epoch}/{state.num_train_epochs}",
+            force_update=self._force_next_update,
+        )
+        self._force_next_update = False
 
     def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs):
         if self.prediction_bar is None:
@@ -294,6 +303,14 @@ class NotebookProgressCallback(TrainerCallback):
         else:
             self.prediction_bar.update(self.prediction_bar.value + 1)
 
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        # Only for when there is no evaluation
+        if args.evaluation_strategy == EvaluationStrategy.NO and "loss" in logs:
+            values = {"Training Loss": logs["loss"]}
+            # First column is necessarily Step sine we're not in epoch eval strategy
+            values["Step"] = state.global_step
+            self.training_tracker.write_line(values)
+
     def on_evaluate(self, args, state, control, metrics=None, **kwargs):
         if self.training_tracker is not None:
             values = {"Training Loss": "No log"}
@@ -319,6 +336,8 @@ class NotebookProgressCallback(TrainerCallback):
             self.training_tracker.write_line(values)
             self.training_tracker.remove_child()
             self.prediction_bar = None
+            # Evaluation takes a long time so we should force the next update.
+            self._force_next_update = True
 
     def on_train_end(self, args, state, control, **kwargs):
         self.training_tracker.update(