diff --git a/examples/mlperf/model_train.py b/examples/mlperf/model_train.py
index c581eb0324..ab90580fad 100644
--- a/examples/mlperf/model_train.py
+++ b/examples/mlperf/model_train.py
@@ -181,13 +181,15 @@ def train_resnet():
         median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
         estimated_total_minutes = int(median_step_time * steps_in_train_epoch * epochs / 60)
         print(f"Estimated training time: {estimated_total_minutes // 60}h{estimated_total_minutes % 60}m")
+        print(f"epoch global_ops: {steps_in_train_epoch * GlobalCounters.global_ops:_}, "
+              f"epoch global_mem: {steps_in_train_epoch * GlobalCounters.global_mem:_}")
         # if we are doing beam search, run the first eval too
         if (TRAIN_BEAM or EVAL_BEAM) and e == start_epoch: break
         return
 
     # ** eval loop **
     if (e + 1 - eval_start_epoch) % eval_epochs == 0 and steps_in_val_epoch > 0:
-      train_step.reset()  # free the train step memory :(
+      if getenv("RESET_STEP", 1): train_step.reset()  # free the train step memory :(
       eval_loss = []
       eval_times = []
       eval_top_1_acc = []
@@ -214,7 +216,7 @@ def train_resnet():
         et = time.time()
         eval_times.append(et - st)
 
-      eval_step.reset()
+      if getenv("RESET_STEP", 1): eval_step.reset()
       total_loss = sum(eval_loss) / len(eval_loss)
       total_top_1 = sum(eval_top_1_acc) / len(eval_top_1_acc)
       total_fw_time = sum(eval_times) / len(eval_times)