diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index 6e339e8652cd..1a8cac0722e8 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -1373,6 +1373,7 @@ def train(
                     # AT THE VERY END!
                     _ = list(train_dataloader.sampler)
 
+        start_train_stable_time = 0
         for epoch in range(epochs_trained, num_train_epochs):
             if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                 train_dataloader.sampler.set_epoch(epoch)
@@ -1402,6 +1403,9 @@ def train(
             step = -1
             for step, inputs in enumerate(epoch_iterator):
 
+                if (self.state.global_step == 10):
+                    start_train_stable_time = time.time()
+
                 # Skip past any already trained steps if resuming training
                 if steps_trained_in_current_epoch > 0:
                     steps_trained_in_current_epoch -= 1
@@ -1549,6 +1553,11 @@ def train(
         train_loss = self._total_loss_scalar / self.state.global_step
 
         metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
+
+        total_samples = args.max_steps*total_train_batch_size if args.max_steps > 0  else num_examples*num_train_epochs
+        perf_samples = total_samples - 10*total_train_batch_size
+        stable_train_metrics = speed_metrics("stable_train", start_train_stable_time, perf_samples)
+
         self.store_flos()
         metrics["total_flos"] = self.state.total_flos
         metrics["train_loss"] = train_loss
@@ -1559,6 +1568,8 @@ def train(
 
         self.log(metrics)
 
+        self.log(stable_train_metrics)
+
         self.control = self.callback_handler.on_train_end(args, self.state, self.control)
 
         return TrainOutput(self.state.global_step, train_loss, metrics)