also log epoch

modelforge/train/training.py

@@ -1157,7 +1157,9 @@ def _log_figures_for_each_phase(
             # Log outlier error counts for non-training phases
             if phase != "train":
                 self._identify__and_log_top_k_errors(errors, gathered_indices, phase)
-                self.log_dict(self.outlier_errors_over_epochs, on_epoch=True)
+                self.log_dict(
+                    self.outlier_errors_over_epochs, on_epoch=True, rank_zero_only=True
+                )
 
     def _identify__and_log_top_k_errors(
         self,
@@ -1197,7 +1199,9 @@ def _identify__and_log_top_k_errors(
             if key not in self.outlier_errors_over_epochs:
                 self.outlier_errors_over_epochs[key] = 0
             self.outlier_errors_over_epochs[key] += 1
-            log.info(f"{phase} : Outlier error {error} at index {idx}.")
+            log.info(
+                f"{self.current_epoch}: {phase} : Outlier error {error} at index {idx}."
+            )
 
     def _clear_error_tracking(self, preds, targets, incides):
         """
@@ -1293,24 +1297,28 @@ def on_train_epoch_end(self):
         self._log_time()
         self._log_histograms()
         # log the weights of the different loss components
-        for key, weight in self.loss.weights_scheduling.items():
-            self.log(
-                f"loss/{key}/weight",
-                weight[self.current_epoch],
-            )
+        if self.trainer.is_global_zero:
+            for key, weight in self.loss.weights_scheduling.items():
+                self.log(
+                    f"loss/{key}/weight",
+                    weight[self.current_epoch],
+                    rank_zero_only=True,
+                )
 
     def _log_learning_rate(self):
         """Logs the current learning rate."""
         sch = self.lr_schedulers()
-        try:
-            self.log(
-                "lr",
-                sch.get_last_lr()[0],
-                on_epoch=True,
-                prog_bar=True,
-            )
-        except AttributeError:
-            pass
+        if self.trainer.is_global_zero:
+            try:
+                self.log(
+                    "lr",
+                    sch.get_last_lr()[0],
+                    on_epoch=True,
+                    prog_bar=True,
+                    rank_zero_only=True,
+                )
+            except AttributeError:
+                pass
 
     def _log_metrics(self, metrics: ModuleDict, phase: str):
         """