diff --git a/examples/mlperf/model_train.py b/examples/mlperf/model_train.py
index 7c3c37e6df..5431d18842 100644
--- a/examples/mlperf/model_train.py
+++ b/examples/mlperf/model_train.py
@@ -359,6 +359,7 @@ def train_retinanet():
 
   NUM_CLASSES = len(MLPERF_CLASSES)
   BASE_DIR = getenv("BASE_DIR", BASEDIR)
+  BENCHMARK = getenv("BENCHMARK")
   config["gpus"] = GPUS = [f"{Device.DEFAULT}:{i}" for i in range(getenv("GPUS", 1))]
 
   for x in GPUS: Device[x]
@@ -438,11 +439,12 @@ def train_retinanet():
   val_dataset = COCO(download_dataset(BASE_DIR, "validation"))
 
   config["steps_in_train_epoch"] = steps_in_train_epoch = round_up(len(train_dataset.imgs.keys()), bs) // bs
+  step_times = []
 
   # ** training loop **
   for e in range(1, num_epochs + 1):
     train_dataloader = batch_load_retinanet(train_dataset, False, Path(BASE_DIR), batch_size=bs, seed=seed)
-    it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e}"))
+    it = iter(tqdm(train_dataloader, total=steps_in_train_epoch, desc=f"epoch {e}", disable=BENCHMARK))
     i, proc = 0, _data_get(it)
 
     # if e < LR_WARMUP_EPOCHS:
@@ -474,6 +476,7 @@ def train_retinanet():
       loss = loss.item()
 
       cl = time.perf_counter()
+      if BENCHMARK: step_times.append(cl - st)
 
       tqdm.write(
         f"{i:5} {((cl - st)) * 1000.0:7.2f} ms run, {(pt - st) * 1000.0:7.2f} ms python, {(dt - pt) * 1000.0:6.2f} ms fetch data, "
@@ -491,6 +494,15 @@ def train_retinanet():
       proc, next_proc = next_proc, None  # return old cookie
       i += 1
 
+      if i == BENCHMARK:
+        assert not math.isnan(loss)
+        median_step_time = sorted(step_times)[(BENCHMARK + 1) // 2]  # in seconds
+        estimated_total_minutes = int(median_step_time * steps_in_train_epoch * e / 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:_}")
+        return
+
 def train_unet3d():
   """
   Trains the UNet3D model.