diff --git a/research/cv/PDarts/src/call_backs.py b/research/cv/PDarts/src/call_backs.py
index 4cac04de37decff05c4eeefa3f1b183c64922e32..93d4df358b3ea788a457e461ac5f3850149142b7 100644
--- a/research/cv/PDarts/src/call_backs.py
+++ b/research/cv/PDarts/src/call_backs.py
@@ -13,12 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """train callbacks"""
-try:
-    from moxing.framework import file
-    print("import moxing success")
-except ModuleNotFoundError as e:
-    print(f'not modelarts env, error={e}')
-
 import os
 import time
 
@@ -132,6 +126,7 @@ class Val_Callback(Callback):
             ckpt_file = os.path.join(ckpt_path, 'model_checkpoint.ckpt')
             save_checkpoint(cb_params.train_network, ckpt_file)
             if self.checkpoint_path.startswith('s3://') or self.checkpoint_path.startswith('obs://'):
+                from moxing.framework import file
                 file.copy_parallel(save_path, os.path.join(
                     self.checkpoint_path, model_info))
             print('==============save checkpoint finished===================')
diff --git a/research/cv/PDarts/train.py b/research/cv/PDarts/train.py
index 5b101c5522cf5702250f658fdbdb628f1fe6cd12..d59beceb979ae24d714e763cfdb35f33c4fe3fd2 100644
--- a/research/cv/PDarts/train.py
+++ b/research/cv/PDarts/train.py
@@ -13,12 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """train the PDarts model"""
-try:
-    from moxing.framework import file
-    print("import moxing success")
-except ModuleNotFoundError as e:
-    print(f'not modelarts env, error={e}')
-
 import os
 import time
 import logging
@@ -136,6 +130,7 @@ def main():
         load_param_into_net(network, param_dict)
 
     if args.data_url.startswith('s3://') or args.data_url.startswith('obs://'):
+        from moxing.framework import file
         data_url_cache = os.path.join(args.local_data_root, 'data')
         file.copy_parallel(args.data_url, data_url_cache)
         args.data_url = data_url_cache
diff --git a/research/cv/yolov3_tiny/scripts/run_standalone_train.sh b/research/cv/yolov3_tiny/scripts/run_standalone_train.sh
index 29adf4b1e9ece4cffdccaea59a118c1b64067e5c..7c7fc6facb7cc02acb2dda73934fe1c825f16caa 100644
--- a/research/cv/yolov3_tiny/scripts/run_standalone_train.sh
+++ b/research/cv/yolov3_tiny/scripts/run_standalone_train.sh
@@ -65,7 +65,6 @@ python train.py \
     --max_epoch=300 \
     --warmup_epochs=4 \
     --training_shape=640 \
-    --per_batch_size=32 \
     --weight_decay=0.016 \
     --lr_scheduler=cosine_annealing > log.txt 2>&1 &
 cd ..
diff --git a/research/cv/yolov3_tiny/src/yolo_dataset.py b/research/cv/yolov3_tiny/src/yolo_dataset.py
index 1678f9d7e317692281b91599bbcf9f1b1f10db2d..78c18779ec73d0ff4e5bb89253d4ea1974c17662 100644
--- a/research/cv/yolov3_tiny/src/yolo_dataset.py
+++ b/research/cv/yolov3_tiny/src/yolo_dataset.py
@@ -298,7 +298,7 @@ def create_yolo_dataset(
             input_columns=dataset_column_names,
             output_columns=map1_out_column_names,
             column_order=map1_out_column_names,
-            num_parallel_workers=num_parallel_workers,
+            num_parallel_workers=16,
             python_multiprocessing=True
         )
         ds = ds.map(
@@ -306,7 +306,7 @@ def create_yolo_dataset(
             input_columns=map2_in_column_names,
             output_columns=map2_out_column_names,
             column_order=output_column_names,
-            num_parallel_workers=num_parallel_workers,
+            num_parallel_workers=2,
             python_multiprocessing=False
         )
         mean = [m * 255 for m in [0.485, 0.456, 0.406]]
@@ -316,9 +316,9 @@ def create_yolo_dataset(
                 CV.Normalize(mean, std),
                 hwc_to_chw
             ],
-            num_parallel_workers=num_parallel_workers
+            num_parallel_workers=2
         )
-        ds = ds.batch(batch_size, num_parallel_workers=num_parallel_workers, drop_remainder=True)
+        ds = ds.batch(batch_size, num_parallel_workers=1, drop_remainder=True)
     else:
         ds = de.GeneratorDataset(
             yolo_dataset,