diff --git a/ppcls/static/program.py b/ppcls/static/program.py
index 71f630f7b7913988101510c1766e82da36a29932..e6022bbde4529b353db6102e5ac93f798a1cd196 100644
--- a/ppcls/static/program.py
+++ b/ppcls/static/program.py
@@ -38,7 +38,7 @@ from ppcls.optimizer import build_optimizer
 from ppcls.optimizer import build_lr_scheduler
 
 from ppcls.utils.misc import AverageMeter
-from ppcls.utils import logger
+from ppcls.utils import logger, profiler
 
 
 def create_feeds(image_shape, use_mix=None, dtype="float32"):
@@ -326,7 +326,8 @@ def run(dataloader,
         mode='train',
         config=None,
         vdl_writer=None,
-        lr_scheduler=None):
+        lr_scheduler=None,
+        profiler_options=None):
     """
     Feed data to the model and fetch the measures and loss
 
@@ -382,6 +383,8 @@ def run(dataloader,
 
         metric_dict['reader_time'].update(time.time() - tic)
 
+        profiler.add_profiler_step(profiler_options)
+
         if use_dali:
             batch_size = batch[0]["data"].shape()[0]
             feed_dict = batch[0]
diff --git a/ppcls/static/train.py b/ppcls/static/train.py
index d894ce8ca2eb4853db30441c4304964751e91e71..a3aa0b591ce2db7d1066f1fada521e3a91cfd239 100644
--- a/ppcls/static/train.py
+++ b/ppcls/static/train.py
@@ -43,6 +43,13 @@ def parse_args():
         type=str,
         default='configs/ResNet/ResNet50.yaml',
         help='config file path')
+    parser.add_argument(
+        '-p',
+        '--profiler_options',
+        type=str,
+        default=None,
+        help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
+    )
     parser.add_argument(
         '-o',
         '--override',
@@ -166,7 +173,7 @@ def main(args):
         # 1. train with train dataset
         program.run(train_dataloader, exe, compiled_train_prog, train_feeds,
                     train_fetchs, epoch_id, 'train', config, vdl_writer,
-                    lr_scheduler)
+                    lr_scheduler, args.profiler_options)
         # 2. evaate with eval dataset
         if global_config["eval_during_train"] and epoch_id % global_config[
                 "eval_interval"] == 0: