Prevent MLflow exception from disrupting training (#28779)

Modified MLflow logging metrics from synchronous to asynchronous
Co-authored-by: codiceSpaghetti <alessio.ser@hotmail.it>

src/transformers/integrations/integration_utils.py

@@ -1036,7 +1036,7 @@ class MLflowCallback(TrainerCallback):
                         f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. '
                         "MLflow's log_metric() only accepts float and int types so we dropped this attribute."
                     )
-            self._ml_flow.log_metrics(metrics=metrics, step=state.global_step)
+            self._ml_flow.log_metrics(metrics=metrics, step=state.global_step, synchronous=False)
 
     def on_train_end(self, args, state, control, **kwargs):
         if self._initialized and state.is_world_process_zero: