diff --git a/model_zoo/official/cv/yolov3_darknet53/README.md b/model_zoo/official/cv/yolov3_darknet53/README.md
index c44a8b16950..f29505009d5 100644
--- a/model_zoo/official/cv/yolov3_darknet53/README.md
+++ b/model_zoo/official/cv/yolov3_darknet53/README.md
@@ -101,41 +101,93 @@ Dataset used: [COCO2014](https://cocodataset.org/#download)
       python hccl_tools.py --device_num "[0,8)"
       ```
 
-```network
-# The parameter of training_shape define image shape for network, default is "".
-# It means use 10 kinds of shape as input shape, or it can be set some kind of shape.
-# run training example(1p) by python command.
-python train.py \
-    --data_dir=./dataset/coco2014 \
-    --pretrained_backbone=darknet53_backbone.ckpt \
-    --is_distributed=0 \
-    --lr=0.001 \
-    --loss_scale=1024 \
-    --weight_decay=0.016 \
-    --T_max=320 \
-    --max_epoch=320 \
-    --warmup_epochs=4 \
-    --training_shape=416 \
-    --lr_scheduler=cosine_annealing > log.txt 2>&1 &
+- Train on local
 
-# standalone training example(1p) by shell script
-bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
+  ```network
+  # The parameter of training_shape define image shape for network, default is "".
+  # It means use 10 kinds of shape as input shape, or it can be set some kind of shape.
+  # run training example(1p) by python command.
+  python train.py \
+      --data_dir=./dataset/coco2014 \
+      --pretrained_backbone=darknet53_backbone.ckpt \
+      --is_distributed=0 \
+      --lr=0.001 \
+      --loss_scale=1024 \
+      --weight_decay=0.016 \
+      --T_max=320 \
+      --max_epoch=320 \
+      --warmup_epochs=4 \
+      --training_shape=416 \
+      --lr_scheduler=cosine_annealing > log.txt 2>&1 &
 
-# For Ascend device, distributed training example(8p) by shell script
-bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
+  # standalone training example(1p) by shell script
+  bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
 
-# For GPU device, distributed training example(8p) by shell script
-bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
+  # For Ascend device, distributed training example(8p) by shell script
+  bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 
-# run evaluation by python command
-python eval.py \
-    --data_dir=./dataset/coco2014 \
-    --pretrained=yolov3.ckpt \
-    --testing_shape=416 > log.txt 2>&1 &
+  # For GPU device, distributed training example(8p) by shell script
+  bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 
-# run evaluation by shell script
-bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
-```
+  # run evaluation by python command
+  python eval.py \
+      --data_dir=./dataset/coco2014 \
+      --pretrained=yolov3.ckpt \
+      --testing_shape=416 > log.txt 2>&1 &
+
+  # run evaluation by shell script
+  bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+  ```
+
+- Train on [ModelArts](https://support.huaweicloud.com/modelarts/)
+
+  ```python
+  # Train 8p with Ascend
+  # (1) Perform a or b.
+  #       a. Set "enable_modelarts=True" on base_config.yaml file.
+  #          Set "data_dir='/cache/data/coco2014/'" on base_config.yaml file.
+  #          Set "checkpoint_url='s3://dir_to_your_pretrain/'" on base_config.yaml file.
+  #          Set "pretrained_backbone='/cache/checkpoint_path/0-148_92000.ckpt'" on base_config.yaml file.
+  #          Set "weight_decay=0.016" on base_config.yaml file.
+  #          Set "warmup_epochs=4" on base_config.yaml file.
+  #          Set "lr_scheduler='cosine_annealing'" on base_config.yaml file.
+  #          Set other parameters on base_config.yaml file you need.
+  #       b. Add "enable_modelarts=True" on the website UI interface.
+  #          Add "data_dir=/cache/data/coco2014/" on the website UI interface.
+  #          Add "checkpoint_url=s3://dir_to_your_pretrain/" on the website UI interface.
+  #          Add "pretrained_backbone=/cache/checkpoint_path/0-148_92000.ckpt" on the website UI interface.
+  #          Add "weight_decay=0.016" on the website UI interface.
+  #          Add "warmup_epochs=4" on the website UI interface.
+  #          Add "lr_scheduler=cosine_annealing" on the website UI interface.
+  #          Add other parameters on the website UI interface.
+  # (3) Upload or copy your pretrained model to S3 bucket.
+  # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+  # (5) Set the code directory to "/path/yolov3_darknet53" on the website UI interface.
+  # (6) Set the startup file to "train.py" on the website UI interface.
+  # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+  # (8) Create your job.
+  #
+  # Eval with Ascend
+  # (1) Perform a or b.
+  #       a. Set "enable_modelarts=True" on base_config.yaml file.
+  #          Set "data_dir='/cache/data/coco2014/'" on base_config.yaml file.
+  #          Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on base_config.yaml file.
+  #          Set "pretrained='/cache/checkpoint_path/0-320_102400.ckpt'" on base_config.yaml file.
+  #          Set "testing_shape=416" on base_config.yaml file.
+  #          Set other parameters on base_config.yaml file you need.
+  #       b. Add "enable_modelarts=True" on the website UI interface.
+  #          Add "data_dir=/cache/data/coco2014/" on the website UI interface.
+  #          Add "checkpoint_url=s3://dir_to_your_trained_ckpt/" on the website UI interface.
+  #          Add "pretrained=/cache/checkpoint_path/0-320_102400.ckpt" on the website UI interface.
+  #          Add "testing_shape=416" on the website UI interface.
+  #          Add other parameters on the website UI interface.
+  # (3) Upload or copy your trained model to S3 bucket.
+  # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+  # (5) Set the code directory to "/path/yolov3_darknet53" on the website UI interface.
+  # (6) Set the startup file to "eval.py" on the website UI interface.
+  # (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+  # (8) Create your job.
+  ```
 
 ## [Script Description](#contents)
 
diff --git a/model_zoo/official/cv/yolov3_darknet53/README_CN.md b/model_zoo/official/cv/yolov3_darknet53/README_CN.md
index 5fab0064a71..85745876098 100644
--- a/model_zoo/official/cv/yolov3_darknet53/README_CN.md
+++ b/model_zoo/official/cv/yolov3_darknet53/README_CN.md
@@ -104,49 +104,91 @@ YOLOv3使用DarkNet53执行特征提取，这是YOLOv2中的Darknet-19和残差
       python hccl_tools.py --device_num "[0,8)"
       ```
 
-```python
-# training_shape参数定义网络图像形状，默认为""。
-# 意思是使用10种形状作为输入形状，或者可以设置某种形状。
-# 通过python命令执行训练示例(1卡)。
-python train.py \
-    --data_dir=./dataset/coco2014 \
-    --pretrained_backbone=darknet53_backbone.ckpt \
-    --is_distributed=0 \
-    --lr=0.1 \
-    --T_max=320 \
-    --max_epoch=320 \
-    --warmup_epochs=4 \
-    --training_shape=416 \
-    --lr_scheduler=cosine_annealing > log.txt 2>&1 &
-```
+- 在本地进行训练
 
-```shell script
-# shell脚本单机训练示例(1卡)
-bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
-```
+  ```constet
+  # training_shape参数定义网络图像形状，默认为""。
+  # 意思是使用10种形状作为输入形状，或者可以设置某种形状。
+  # 通过python命令执行训练示例(1卡)。
+  python train.py \
+      --data_dir=./dataset/coco2014 \
+      --pretrained_backbone=darknet53_backbone.ckpt \
+      --is_distributed=0 \
+      --lr=0.1 \
+      --T_max=320 \
+      --max_epoch=320 \
+      --warmup_epochs=4 \
+      --training_shape=416 \
+      --lr_scheduler=cosine_annealing > log.txt 2>&1 &
 
-```shell script
-# 对于Ascend设备，使用shell脚本分布式训练示例(8卡)
-bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
-```
+  # shell脚本单机训练示例(1卡)
+  bash run_standalone_train.sh dataset/coco2014 darknet53_backbone.ckpt
 
-```shell script
-# 对于GPU设备，使用shell脚本分布式训练示例(8卡)
-bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
-```
+  # 对于Ascend设备，使用shell脚本分布式训练示例(8卡)
+  bash run_distribute_train.sh dataset/coco2014 darknet53_backbone.ckpt rank_table_8p.json
 
-```python
-# 使用python命令评估
-python eval.py \
-    --data_dir=./dataset/coco2014 \
-    --pretrained=yolov3.ckpt \
-    --testing_shape=416 > log.txt 2>&1 &
-```
+  # 对于GPU设备，使用shell脚本分布式训练示例(8卡)
+  bash run_distribute_train_gpu.sh dataset/coco2014 darknet53_backbone.ckpt
 
-```shell script
-# 通过shell脚本运行评估
-bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
-```
+  # 使用python命令评估
+  python eval.py \
+      --data_dir=./dataset/coco2014 \
+      --pretrained=yolov3.ckpt \
+      --testing_shape=416 > log.txt 2>&1 &
+
+  # 通过shell脚本运行评估
+  bash run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
+  ```
+
+- 在 [ModelArts](https://support.huaweicloud.com/modelarts/) 上训练
+
+  ```python
+  # 在modelarts上进行8卡训练（Ascend）
+  # (1) 执行a或者b
+  #       a. 在 base_config.yaml 文件中配置 "enable_modelarts=True"
+  #          在 base_config.yaml 文件中配置 "data_dir='/cache/data/coco2014/'"
+  #          在 base_config.yaml 文件中配置 "checkpoint_url='s3://dir_to_your_pretrain/'"
+  #          在 base_config.yaml 文件中配置 "pretrained_backbone='/cache/checkpoint_path/0-148_92000.ckpt'"
+  #          在 base_config.yaml 文件中配置 "weight_decay=0.016"
+  #          在 base_config.yaml 文件中配置 "warmup_epochs=4"
+  #          在 base_config.yaml 文件中配置 "lr_scheduler='cosine_annealing'"
+  #          在 base_config.yaml 文件中配置 其他参数
+  #       b. 在网页上设置 "enable_modelarts=True"
+  #          在网页上设置 "data_dir=/cache/data/coco2014/"
+  #          在网页上设置 "checkpoint_url=s3://dir_to_your_pretrain/"
+  #          在网页上设置 "pretrained_backbone=/cache/checkpoint_path/0-148_92000.ckpt"
+  #          在网页上设置 "weight_decay=0.016"
+  #          在网页上设置 "warmup_epochs=4"
+  #          在网页上设置 "lr_scheduler=cosine_annealing"
+  #          在网页上设置 其他参数
+  # (2) 上传你的预训练模型到 S3 桶上
+  # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+  # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+  # (5) 在网页上设置启动文件为 "train.py"
+  # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+  # (7) 创建训练作业
+  #
+  # 在modelarts上进行验证（Ascend）
+  # (1) 执行a或者b
+  #       a. 在 base_config.yaml 文件中配置 "enable_modelarts=True"
+  #          在 base_config.yaml 文件中配置 "data_dir='/cache/data/coco2014/'"
+  #          在 base_config.yaml 文件中配置 "checkpoint_url='s3://dir_to_your_trained_ckpt/'"
+  #          在 base_config.yaml 文件中配置 "pretrained='/cache/checkpoint_path/0-320_102400.ckpt'"
+  #          在 base_config.yaml 文件中配置 "testing_shape=416"
+  #          在 base_config.yaml 文件中配置 其他参数
+  #       b. 在网页上设置 "enable_modelarts=True"
+  #          在网页上设置 "data_dir=/cache/data/coco2014/"
+  #          在网页上设置 "checkpoint_url=s3://dir_to_your_trained_ckpt/"
+  #          在网页上设置 "pretrained=/cache/checkpoint_path/0-320_102400.ckpt"
+  #          在网页上设置 "testing_shape=416"
+  #          在网页上设置 其他参数
+  # (2) 上传你的预训练模型到 S3 桶上
+  # (3) 上传你的压缩数据集到 S3 桶上 (你也可以上传原始的数据集，但那可能会很慢。)
+  # (4) 在网页上设置你的代码路径为 "/path/deeplabv3"
+  # (5) 在网页上设置启动文件为 "train.py"
+  # (6) 在网页上设置"训练数据集"、"训练输出文件路径"、"作业日志路径"等
+  # (7) 创建训练作业
+  ```
 
 # 脚本说明
 
diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc
index f737745cef7..bbe1a8ac38e 100644
--- a/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc
+++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_infer/src/main.cc
@@ -28,9 +28,9 @@
 #include "include/api/context.h"
 #include "include/api/types.h"
 #include "include/api/serialization.h"
-#include "include/dataset/vision_ascend.h"
-#include "include/dataset/execute.h"
-#include "include/dataset/vision.h"
+#include "include/minddata/dataset/include/vision_ascend.h"
+#include "include/minddata/dataset/include/execute.h"
+#include "include/minddata/dataset/include/vision.h"
 #include "inc/utils.h"
 
 using mindspore::Context;
diff --git a/model_zoo/official/cv/yolov3_darknet53/default_config.yaml b/model_zoo/official/cv/yolov3_darknet53/default_config.yaml
new file mode 100644
index 00000000000..b6d55cae2c4
--- /dev/null
+++ b/model_zoo/official/cv/yolov3_darknet53/default_config.yaml
@@ -0,0 +1,168 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+# Url for modelarts
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+# Path for local
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: "Ascend" # ['Ascend', 'GPU']
+need_modelarts_dataset_unzip: True
+modelarts_dataset_unzip_name: "coco2014"
+
+# ==============================================================================
+# Training options
+
+# dataset related
+data_dir: "/cache/data/coco2014/"
+per_batch_size: 32
+
+# network related
+pretrained_backbone: "/cache/checkpoint_path/0-148_92000.ckpt"
+resume_yolov3: ""
+
+# optimizer and lr related
+lr_scheduler: "exponential"
+lr: 0.001
+lr_epochs: "220,250"
+lr_gamma: 0.1
+eta_min: 0.0
+T_max: 320
+max_epoch: 320
+warmup_epochs: 0
+weight_decay: 0.0005
+momentum: 0.9
+
+# loss related
+loss_scale: 1024
+label_smooth: 0
+label_smooth_factor: 0.1
+
+# logging related
+log_interval: 100
+ckpt_path: "outputs/"
+ckpt_interval: -1
+is_save_on_master: 1
+
+# distributed related
+is_distributed: 1
+rank: 0
+group_size: 1
+
+# profiler init
+need_profiler: 0
+
+# reset default config
+training_shape: ""
+
+
+# Eval option
+pretrained: ""
+log_path: "outputs/"
+nms_thresh: 0.5
+annFile: ""
+testing_shape: ""
+eval_ignore_threshold: 0.001
+
+
+# Export option
+device_id: 0
+batch_size: 1
+ckpt_file: ""
+file_name: "yolov3_darknet53"
+file_format: "AIR" # ["AIR", "ONNX", "MINDIR"]
+
+
+# Other default config
+hue: 0.1
+saturation: 1.5
+value: 1.5
+jitter: 0.3
+
+resize_rate: 1
+multi_scale: [[320, 320],
+              [352, 352],
+              [384, 384],
+              [416, 416],
+              [448, 448],
+              [480, 480],
+              [512, 512],
+              [544, 544],
+              [576, 576],
+              [608, 608]
+              ]
+
+num_classes: 80
+out_channel: 255 #3 * (num_classes + 5)
+max_box: 50
+
+backbone_input_shape: [32, 64, 128, 256, 512]
+backbone_shape: [64, 128, 256, 512, 1024]
+backbone_layers: [1, 2, 8, 8, 4]
+
+# confidence under ignore_threshold means no object when training
+ignore_threshold: 0.7
+
+# h->w
+anchor_scales: [[10, 13],
+                [16, 30],
+                [33, 23],
+                [30, 61],
+                [62, 45],
+                [59, 119],
+                [116, 90],
+                [156, 198],
+                [373, 326]]
+
+# test_param
+test_img_shape: [416, 416]
+
+---
+
+# Help description for each configuration
+data_dir: "Train dataset directory."
+per_batch_size: "Batch size for Training."
+pretrained_backbone: "The ckpt file of DarkNet53."
+resume_yolov3: "The ckpt file of YOLOv3, which used to fine tune."
+
+lr_scheduler: "Learning rate scheduler, options: exponential, cosine_annealing."
+lr: "Learning rate."
+lr_epochs: "Epoch of changing of lr changing, split with ',' ."
+lr_gamma: "Decrease lr by a factor of exponential lr_scheduler."
+eta_min: "Eta_min in cosine_annealing scheduler."
+T_max: "T-max in cosine_annealing scheduler."
+max_epoch: "Max epoch num to train the model."
+warmup_epochs: "Warmup epochs."
+weight_decay: "Weight decay factor."
+momentum: "Momentum."
+loss_scale: "Static loss scale."
+label_smooth: "Whether to use label smooth in CE."
+label_smooth_factor: "Smooth strength of original one-hot."
+log_interval: "Logging interval steps."
+ckpt_path: "Checkpoint save location."
+ckpt_interval: "Save checkpoint interval."
+is_save_on_master: "Save ckpt on master or all rank, 1 for master, 0 for all ranks."
+is_distributed: "Distribute train or not, 1 for yes, 0 for no."
+rank: "Local rank of distributed."
+group_size: "World size of device."
+need_profiler: "Whether use profiler. 0 for no, 1 for yes."
+training_shape: "Fix training shape."
+resize_rate: "Resize rate for multi-scale training."
+
+# eval option
+pretrained: "model_path, local pretrained model to load."
+log_path: "checkpoint save location."
+nms_thresh: "threshold for NMS."
+annFile: "path to annotation."
+testing_shape: "shape for test."
+eval_ignore_threshold: "threshold to throw low quality boxes for eval."
+
+# export option
+device_id: "Device id"
+batch_size: "batch size"
+ckpt_file: "Checkpoint file path."
+file_name: "output file name."
+file_format: "file format choices in ['AIR', 'ONNX', 'MINDIR']"
+device_target: "device target. choices in ['Ascend', 'GPU'] for train. choices in ['Ascend', 'GPU', 'CPU'] for export."
\ No newline at end of file
diff --git a/model_zoo/official/cv/yolov3_darknet53/eval.py b/model_zoo/official/cv/yolov3_darknet53/eval.py
index afa4811f0be..46751724bbd 100644
--- a/model_zoo/official/cv/yolov3_darknet53/eval.py
+++ b/model_zoo/official/cv/yolov3_darknet53/eval.py
@@ -14,7 +14,6 @@
 # ============================================================================
 """YoloV3 eval."""
 import os
-import argparse
 import datetime
 import time
 import sys
@@ -31,7 +30,10 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.yolo import YOLOV3DarkNet53
 from src.logger import get_logger
 from src.yolo_dataset import create_yolo_dataset
-from src.config import ConfigYOLOV3DarkNet53
+
+from model_utils.config import config
+from model_utils.moxing_adapter import moxing_wrapper
+from model_utils.device_adapter import get_device_id, get_device_num
 
 
 class Redirct:
@@ -48,7 +50,7 @@ class Redirct:
 class DetectionEngine:
     """Detection engine."""
     def __init__(self, args):
-        self.ignore_threshold = args.ignore_threshold
+        self.eval_ignore_threshold = args.eval_ignore_threshold
         self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
                        'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
                        'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
@@ -186,7 +188,7 @@ class DetectionEngine:
                     flag[i, c] = True
                 confidence = cls_emb[flag] * conf
                 for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence, cls_argmax):
-                    if confi < self.ignore_threshold:
+                    if confi < self.eval_ignore_threshold:
                         continue
                     if img_id not in self.results:
                         self.results[img_id] = defaultdict(list)
@@ -199,68 +201,90 @@ class DetectionEngine:
                     self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
 
 
-def parse_args():
-    """Parse arguments."""
-    parser = argparse.ArgumentParser('mindspore coco testing')
-
-    # device related
-    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
-                        help='device where the code will be implemented. (Default: Ascend)')
-
-    # dataset related
-    parser.add_argument('--data_dir', type=str, default='', help='train data dir')
-    parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
-
-    # network related
-    parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
-
-    # logging related
-    parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
-
-    # detect_related
-    parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
-    parser.add_argument('--annFile', type=str, default='', help='path to annotation')
-    parser.add_argument('--testing_shape', type=str, default='', help='shape for test ')
-    parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
-
-    args, _ = parser.parse_known_args()
-
-    args.data_root = os.path.join(args.data_dir, 'val2014')
-    args.annFile = os.path.join(args.data_dir, 'annotations/instances_val2014.json')
-
-    return args
-
-
 def conver_testing_shape(args):
     """Convert testing shape to list."""
     testing_shape = [int(args.testing_shape), int(args.testing_shape)]
     return testing_shape
 
 
-def test():
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    def unzip(zip_file, save_dir):
+        import zipfile
+        s_time = time.time()
+        if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
+            zip_isexist = zipfile.is_zipfile(zip_file)
+            if zip_isexist:
+                fz = zipfile.ZipFile(zip_file, 'r')
+                data_num = len(fz.namelist())
+                print("Extract Start...")
+                print("unzip file num: {}".format(data_num))
+                data_print = int(data_num / 100) if data_num > 100 else 1
+                i = 0
+                for file in fz.namelist():
+                    if i % data_print == 0:
+                        print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
+                    i += 1
+                    fz.extract(file, save_dir)
+                print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
+                                                     int(int(time.time() - s_time) % 60)))
+                print("Extract Done.")
+            else:
+                print("This is not zip.")
+        else:
+            print("Zip has been extracted.")
+
+    if config.need_modelarts_dataset_unzip:
+        zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
+        save_dir_1 = os.path.join(config.data_path)
+
+        sync_lock = "/tmp/unzip_sync.lock"
+
+        # Each server contains 8 devices as most.
+        if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+            print("Zip file path: ", zip_file_1)
+            print("Unzip file save dir: ", save_dir_1)
+            unzip(zip_file_1, save_dir_1)
+            print("===Finish extract data synchronization===")
+            try:
+                os.mknod(sync_lock)
+            except IOError:
+                pass
+
+        while True:
+            if os.path.exists(sync_lock):
+                break
+            time.sleep(1)
+
+        print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_test():
     """The function of eval."""
     start_time = time.time()
-    args = parse_args()
+    config.data_root = os.path.join(config.data_dir, 'val2014')
+    config.annFile = os.path.join(config.data_dir, 'annotations/instances_val2014.json')
 
     devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, save_graphs=False, device_id=devid)
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, save_graphs=False, device_id=devid)
 
     # logger
-    args.outputs_dir = os.path.join(args.log_path,
-                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
+    config.outputs_dir = os.path.join(config.log_path,
+                                      datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
     rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0
-    args.logger = get_logger(args.outputs_dir, rank_id)
+    config.logger = get_logger(config.outputs_dir, rank_id)
 
     context.reset_auto_parallel_context()
     parallel_mode = ParallelMode.STAND_ALONE
     context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=1)
 
-    args.logger.info('Creating Network....')
+    config.logger.info('Creating Network....')
     network = YOLOV3DarkNet53(is_training=False)
 
-    args.logger.info(args.pretrained)
-    if os.path.isfile(args.pretrained):
-        param_dict = load_checkpoint(args.pretrained)
+    config.logger.info(config.pretrained)
+    if os.path.isfile(config.pretrained):
+        param_dict = load_checkpoint(config.pretrained)
         param_dict_new = {}
         for key, values in param_dict.items():
             if key.startswith('moments.'):
@@ -270,32 +294,31 @@ def test():
             else:
                 param_dict_new[key] = values
         load_param_into_net(network, param_dict_new)
-        args.logger.info('load_model {} success'.format(args.pretrained))
+        config.logger.info('load_model %s success', config.pretrained)
     else:
-        args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))
-        assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))
+        config.logger.info('%s not exists or not a pre-trained file', config.pretrained)
+        assert FileNotFoundError('{} not exists or not a pre-trained file'.format(config.pretrained))
         exit(1)
 
-    data_root = args.data_root
-    ann_file = args.annFile
+    data_root = config.data_root
+    ann_file = config.annFile
 
-    config = ConfigYOLOV3DarkNet53()
-    if args.testing_shape:
-        config.test_img_shape = conver_testing_shape(args)
+    if config.testing_shape:
+        config.test_img_shape = conver_testing_shape(config)
 
-    ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=args.per_batch_size,
+    ds, data_size = create_yolo_dataset(data_root, ann_file, is_training=False, batch_size=config.per_batch_size,
                                         max_epoch=1, device_num=1, rank=rank_id, shuffle=False,
                                         config=config)
 
-    args.logger.info('testing shape : {}'.format(config.test_img_shape))
-    args.logger.info('totol {} images to eval'.format(data_size))
+    config.logger.info('testing shape : %s', config.test_img_shape)
+    config.logger.info('totol %d images to eval', data_size)
 
     network.set_train(False)
 
     # init detection engine
-    detection = DetectionEngine(args)
+    detection = DetectionEngine(config)
 
-    args.logger.info('Start inference....')
+    config.logger.info('Start inference....')
     for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):
         image = data["image"]
 
@@ -310,20 +333,21 @@ def test():
         image_id = image_id.asnumpy()
         image_shape = image_shape.asnumpy()
 
-        detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, image_id)
+        detection.detect([output_small, output_me, output_big], config.per_batch_size, image_shape, image_id)
         if i % 1000 == 0:
-            args.logger.info('Processing... {:.2f}% '.format(i * args.per_batch_size / data_size * 100))
+            config.logger.info('Processing... {:.2f}% '.format(i * config.per_batch_size / data_size * 100))
 
-    args.logger.info('Calculating mAP...')
+    config.logger.info('Calculating mAP...')
     detection.do_nms_for_results()
     result_file_path = detection.write_result()
-    args.logger.info('result file path: {}'.format(result_file_path))
+    config.logger.info('result file path: %s', result_file_path)
     eval_result = detection.get_eval_result()
 
     cost_time = time.time() - start_time
-    args.logger.info('\n=============coco eval result=========\n' + eval_result)
-    args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
+    eval_print_str = '\n=============coco eval result=========\n' + eval_result
+    config.logger.info(eval_print_str)
+    config.logger.info('testing cost time %.2f h', cost_time / 3600.)
 
 
 if __name__ == "__main__":
-    test()
+    run_test()
diff --git a/model_zoo/official/cv/yolov3_darknet53/export.py b/model_zoo/official/cv/yolov3_darknet53/export.py
index 7f2c3ce696f..c00a20fc708 100644
--- a/model_zoo/official/cv/yolov3_darknet53/export.py
+++ b/model_zoo/official/cv/yolov3_darknet53/export.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import argparse
 import numpy as np
 
 import mindspore as ms
@@ -20,32 +19,21 @@ from mindspore import context, Tensor
 from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
 
 from src.yolo import YOLOV3DarkNet53
-from src.config import ConfigYOLOV3DarkNet53
+from model_utils.config import config
 
-parser = argparse.ArgumentParser(description="yolov3_darknet53 export")
-parser.add_argument("--device_id", type=int, default=0, help="Device id")
-parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="yolov3_darknet53", help="output file name.")
-parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="AIR", help="file format")
-parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
-                    help="device target")
-args = parser.parse_args()
-
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-if args.device_target == "Ascend":
-    context.set_context(device_id=args.device_id)
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+if config.device_target == "Ascend":
+    context.set_context(device_id=config.device_id)
 
 if __name__ == "__main__":
     network = YOLOV3DarkNet53(is_training=False)
 
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(network, param_dict)
 
-    config = ConfigYOLOV3DarkNet53()
     network.set_train(False)
 
-    shape = [args.batch_size, 3] + config.test_img_shape
+    shape = [config.batch_size, 3] + config.test_img_shape
     input_data = Tensor(np.zeros(shape), ms.float32)
 
-    export(network, input_data, file_name=args.file_name, file_format=args.file_format)
+    export(network, input_data, file_name=config.file_name, file_format=config.file_format)
diff --git a/model_zoo/official/cv/yolov3_darknet53/model_utils/__init__.py b/model_zoo/official/cv/yolov3_darknet53/model_utils/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/model_zoo/official/cv/yolov3_darknet53/model_utils/config.py b/model_zoo/official/cv/yolov3_darknet53/model_utils/config.py
new file mode 100644
index 00000000000..2c191e9f748
--- /dev/null
+++ b/model_zoo/official/cv/yolov3_darknet53/model_utils/config.py
@@ -0,0 +1,127 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members.
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
+    """
+    Parse command line arguments to the configuration according to the default yaml.
+
+    Args:
+        parser: Parent parser.
+        cfg: Base configuration.
+        helper: Helper description.
+        cfg_path: Path to the default yaml config.
+    """
+    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file.
+
+    Args:
+        yaml_path: Path to the yaml config.
+    """
+    with open(yaml_path, 'r') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments.
+
+    Args:
+        args: Command line arguments.
+        cfg: Base configuration.
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments.
+    """
+    parser = argparse.ArgumentParser(description="default name", add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../default_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    pprint(default)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+    return Config(final_config)
+
+config = get_config()
diff --git a/model_zoo/official/cv/yolov3_darknet53/model_utils/device_adapter.py b/model_zoo/official/cv/yolov3_darknet53/model_utils/device_adapter.py
new file mode 100644
index 00000000000..7c5d7f837dd
--- /dev/null
+++ b/model_zoo/official/cv/yolov3_darknet53/model_utils/device_adapter.py
@@ -0,0 +1,27 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Device adapter for ModelArts"""
+
+from .config import config
+
+if config.enable_modelarts:
+    from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
+]
diff --git a/model_zoo/official/cv/yolov3_darknet53/model_utils/local_adapter.py b/model_zoo/official/cv/yolov3_darknet53/model_utils/local_adapter.py
new file mode 100644
index 00000000000..769fa6dc78e
--- /dev/null
+++ b/model_zoo/official/cv/yolov3_darknet53/model_utils/local_adapter.py
@@ -0,0 +1,36 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return "Local Job"
diff --git a/model_zoo/official/cv/yolov3_darknet53/model_utils/moxing_adapter.py b/model_zoo/official/cv/yolov3_darknet53/model_utils/moxing_adapter.py
new file mode 100644
index 00000000000..25838a7da99
--- /dev/null
+++ b/model_zoo/official/cv/yolov3_darknet53/model_utils/moxing_adapter.py
@@ -0,0 +1,116 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from .config import config
+
+_global_sync_count = 0
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local path
+    Upload data from local directory to remote obs in contrast.
+    """
+    import moxing as mox
+    import time
+    global _global_sync_count
+    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
+    _global_sync_count += 1
+
+    # Each server contains 8 devices as most.
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print("from path: ", from_path)
+        print("to path: ", to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print("===finish data synchronization===")
+        try:
+            os.mknod(sync_lock)
+        except IOError:
+            pass
+        print("===save flag===")
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+
+    print("Finish sync data from {} to {}.".format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs.
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print("Dataset downloaded: ", os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print("Preload downloaded: ", os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print("Workspace downloaded: ", os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            # Run the main function
+            run_func(*args, **kwargs)
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print("Start to copy output directory")
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train.sh
index 1af73c7f5e2..324def5051b 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train.sh
@@ -64,7 +64,9 @@ do
     rm -rf ./train_parallel$i
     mkdir ./train_parallel$i
     cp ../*.py ./train_parallel$i
+    cp ../*.yaml ./train_parallel$i
     cp -r ../src ./train_parallel$i
+    cp -r ../model_utils ./train_parallel$i
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train_gpu.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train_gpu.sh
index 41d00b30513..8205c33cc63 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train_gpu.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_distribute_train_gpu.sh
@@ -50,7 +50,9 @@ export DEVICE_NUM=8
 rm -rf ./train_parallel
 mkdir ./train_parallel
 cp ../*.py ./train_parallel
+cp ../*.yaml ./train_parallel
 cp -r ../src ./train_parallel
+cp -r ../model_utils ./train_parallel
 cd ./train_parallel || exit
 env > env.log
 mpirun --allow-run-as-root -n ${DEVICE_NUM} --output-filename log_output --merge-stderr-to-stdout \
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval.sh
index 0ac33dc60be..d5976c52fca 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval.sh
@@ -55,7 +55,9 @@ then
 fi
 mkdir ./eval
 cp ../*.py ./eval
+cp ../*.yaml ./eval
 cp -r ../src ./eval
+cp -r ../model_utils ./eval
 cd ./eval || exit
 env > env.log
 echo "start inferring for device $DEVICE_ID"
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh
index 5729e24cf67..e6620449fec 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_eval_gpu.sh
@@ -55,7 +55,9 @@ then
 fi
 mkdir ./eval
 cp ../*.py ./eval
+cp ../*.yaml ./eval
 cp -r ../src ./eval
+cp -r ../model_utils ./eval
 cd ./eval || exit
 env > env.log
 echo "start inferring for device $DEVICE_ID"
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train.sh
index 7d6c9c0a6b7..50f2cb54d9c 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train.sh
@@ -56,7 +56,9 @@ then
 fi
 mkdir ./train
 cp ../*.py ./train
+cp ../*.yaml ./train
 cp -r ../src ./train
+cp -r ../model_utils ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
diff --git a/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train_gpu.sh b/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train_gpu.sh
index 293d97af4a6..3aaa74705b9 100644
--- a/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train_gpu.sh
+++ b/model_zoo/official/cv/yolov3_darknet53/scripts/run_standalone_train_gpu.sh
@@ -56,7 +56,9 @@ then
 fi
 mkdir ./train
 cp ../*.py ./train
+cp ../*.yaml ./train
 cp -r ../src ./train
+cp -r ../model_utils ./train
 cd ./train || exit
 echo "start training for device $DEVICE_ID"
 env > env.log
diff --git a/model_zoo/official/cv/yolov3_darknet53/src/config.py b/model_zoo/official/cv/yolov3_darknet53/src/config.py
deleted file mode 100644
index 8cf6d18d99b..00000000000
--- a/model_zoo/official/cv/yolov3_darknet53/src/config.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Config parameters for Darknet based yolov3_darknet53 models."""
-
-
-class ConfigYOLOV3DarkNet53:
-    """
-    Config parameters for the yolov3_darknet53.
-
-    Examples:
-        ConfigYOLOV3DarkNet53()
-    """
-    # train_param
-    # data augmentation related
-    hue = 0.1
-    saturation = 1.5
-    value = 1.5
-    jitter = 0.3
-
-    resize_rate = 1
-    multi_scale = [[320, 320],
-                   [352, 352],
-                   [384, 384],
-                   [416, 416],
-                   [448, 448],
-                   [480, 480],
-                   [512, 512],
-                   [544, 544],
-                   [576, 576],
-                   [608, 608]
-                   ]
-
-    num_classes = 80
-    max_box = 50
-
-    backbone_input_shape = [32, 64, 128, 256, 512]
-    backbone_shape = [64, 128, 256, 512, 1024]
-    backbone_layers = [1, 2, 8, 8, 4]
-
-    # confidence under ignore_threshold means no object when training
-    ignore_threshold = 0.7
-
-    # h->w
-    anchor_scales = [(10, 13),
-                     (16, 30),
-                     (33, 23),
-                     (30, 61),
-                     (62, 45),
-                     (59, 119),
-                     (116, 90),
-                     (156, 198),
-                     (373, 326)]
-    out_channel = 3 * (num_classes + 5)
-
-    # test_param
-    test_img_shape = [416, 416]
diff --git a/model_zoo/official/cv/yolov3_darknet53/src/distributed_sampler.py b/model_zoo/official/cv/yolov3_darknet53/src/distributed_sampler.py
index d31048ee9b4..339253bb588 100644
--- a/model_zoo/official/cv/yolov3_darknet53/src/distributed_sampler.py
+++ b/model_zoo/official/cv/yolov3_darknet53/src/distributed_sampler.py
@@ -29,7 +29,7 @@ class DistributedSampler:
             rank = 0
         self.dataset_size = dataset_size
         self.num_replicas = num_replicas
-        self.rank = rank
+        self.rank = rank if num_replicas > 1 else 0
         self.epoch = 0
         self.num_samples = int(math.ceil(dataset_size * 1.0 / self.num_replicas))
         self.total_size = self.num_samples * self.num_replicas
diff --git a/model_zoo/official/cv/yolov3_darknet53/src/yolo.py b/model_zoo/official/cv/yolov3_darknet53/src/yolo.py
index 98be509a62d..6fa9543b42f 100644
--- a/model_zoo/official/cv/yolov3_darknet53/src/yolo.py
+++ b/model_zoo/official/cv/yolov3_darknet53/src/yolo.py
@@ -25,9 +25,8 @@ from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 
 from src.darknet import DarkNet, ResidualBlock
-from src.config import ConfigYOLOV3DarkNet53
 from src.loss import XYLoss, WHLoss, ConfidenceLoss, ClassLoss
-
+from model_utils.config import config as default_config
 
 def _conv_bn_relu(in_channel,
                   out_channel,
@@ -164,17 +163,17 @@ class DetectionBlock(nn.Cell):
 
      Args:
          scale: Character.
-         config: ConfigYOLOV3DarkNet53, Configuration instance.
+         config: Configuration.
          is_training: Bool, Whether train or not, default True.
 
      Returns:
          Tuple, tuple of output tensor,(f1,f2,f3).
 
      Examples:
-         DetectionBlock(scale='l',stride=32)
+         DetectionBlock(scale='l',stride=32,config=config)
      """
 
-    def __init__(self, scale, config=ConfigYOLOV3DarkNet53(), is_training=True):
+    def __init__(self, scale, config=None, is_training=True):
         super(DetectionBlock, self).__init__()
         self.config = config
         if scale == 's':
@@ -275,7 +274,7 @@ class YoloLossBlock(nn.Cell):
     """
     Loss block cell of YOLOV3 network.
     """
-    def __init__(self, scale, config=ConfigYOLOV3DarkNet53()):
+    def __init__(self, scale, config=None):
         super(YoloLossBlock, self).__init__()
         self.config = config
         if scale == 's':
@@ -362,9 +361,9 @@ class YOLOV3DarkNet53(nn.Cell):
         YOLOV3DarkNet53(True)
     """
 
-    def __init__(self, is_training):
+    def __init__(self, is_training, config=default_config):
         super(YOLOV3DarkNet53, self).__init__()
-        self.config = ConfigYOLOV3DarkNet53()
+        self.config = config
         self.tenser_to_array = P.TupleToArray()
 
         # YOLOv3 network
@@ -376,9 +375,9 @@ class YOLOV3DarkNet53(nn.Cell):
                                   out_channel=self.config.out_channel)
 
         # prediction on the default anchor boxes
-        self.detect_1 = DetectionBlock('l', is_training=is_training)
-        self.detect_2 = DetectionBlock('m', is_training=is_training)
-        self.detect_3 = DetectionBlock('s', is_training=is_training)
+        self.detect_1 = DetectionBlock('l', is_training=is_training, config=self.config)
+        self.detect_2 = DetectionBlock('m', is_training=is_training, config=self.config)
+        self.detect_3 = DetectionBlock('s', is_training=is_training, config=self.config)
 
     def construct(self, x):
         input_shape = F.shape(x)[2:4]
@@ -393,10 +392,10 @@ class YOLOV3DarkNet53(nn.Cell):
 
 class YoloWithLossCell(nn.Cell):
     """YOLOV3 loss."""
-    def __init__(self, network):
+    def __init__(self, network, config=default_config):
         super(YoloWithLossCell, self).__init__()
         self.yolo_network = network
-        self.config = ConfigYOLOV3DarkNet53()
+        self.config = config
         self.tenser_to_array = P.TupleToArray()
         self.loss_big = YoloLossBlock('l', self.config)
         self.loss_me = YoloLossBlock('m', self.config)
diff --git a/model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py b/model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py
index c4e4466f8d6..30d3624cadb 100644
--- a/model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py
+++ b/model_zoo/official/cv/yolov3_darknet53/src/yolo_dataset.py
@@ -155,12 +155,12 @@ def create_yolo_dataset(image_dir, anno_path, batch_size, max_epoch, device_num,
 
     yolo_dataset = COCOYoloDataset(root=image_dir, ann_file=anno_path, filter_crowd_anno=filter_crowd,
                                    remove_images_without_annotations=remove_empty_anno, is_training=is_training)
-    distributed_sampler = DistributedSampler(len(yolo_dataset), device_num, rank, shuffle=shuffle)
     hwc_to_chw = CV.HWC2CHW()
 
     config.dataset_size = len(yolo_dataset)
     cores = multiprocessing.cpu_count()
     num_parallel_workers = int(cores / device_num)
+    distributed_sampler = DistributedSampler(len(yolo_dataset), device_num, rank, shuffle=shuffle)
     if is_training:
         multi_scale_trans = MultiScaleTrans(config, device_num)
         dataset_column_names = ["image", "annotation", "bbox1", "bbox2", "bbox3",
diff --git a/model_zoo/official/cv/yolov3_darknet53/train.py b/model_zoo/official/cv/yolov3_darknet53/train.py
index c0998e8e0b8..4f6f722889d 100644
--- a/model_zoo/official/cv/yolov3_darknet53/train.py
+++ b/model_zoo/official/cv/yolov3_darknet53/train.py
@@ -15,7 +15,6 @@
 """YoloV3 train."""
 import os
 import time
-import argparse
 import datetime
 
 from mindspore.context import ParallelMode
@@ -36,9 +35,12 @@ from src.util import AverageMeter, get_param_groups
 from src.lr_scheduler import get_lr
 from src.yolo_dataset import create_yolo_dataset
 from src.initializer import default_recurisive_init, load_yolov3_params
-from src.config import ConfigYOLOV3DarkNet53
 from src.util import keep_loss_fp32
 
+from model_utils.config import config
+from model_utils.moxing_adapter import moxing_wrapper
+from model_utils.device_adapter import get_device_id, get_device_num
+
 set_seed(1)
 
 class BuildTrainNetwork(nn.Cell):
@@ -53,79 +55,6 @@ class BuildTrainNetwork(nn.Cell):
         return loss
 
 
-def parse_args():
-    """Parse train arguments."""
-    parser = argparse.ArgumentParser('mindspore coco training')
-
-    # device related
-    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
-                        help='device where the code will be implemented. (Default: Ascend)')
-
-    # dataset related
-    parser.add_argument('--data_dir', type=str, help='Train dataset directory.')
-    parser.add_argument('--per_batch_size', default=32, type=int, help='Batch size for Training. Default: 32.')
-
-    # network related
-    parser.add_argument('--pretrained_backbone', default='', type=str,
-                        help='The ckpt file of DarkNet53. Default: "".')
-    parser.add_argument('--resume_yolov3', default='', type=str,
-                        help='The ckpt file of YOLOv3, which used to fine tune. Default: ""')
-
-    # optimizer and lr related
-    parser.add_argument('--lr_scheduler', default='exponential', type=str,
-                        help='Learning rate scheduler, options: exponential, cosine_annealing. Default: exponential')
-    parser.add_argument('--lr', default=0.001, type=float, help='Learning rate. Default: 0.001')
-    parser.add_argument('--lr_epochs', type=str, default='220,250',
-                        help='Epoch of changing of lr changing, split with ",". Default: 220,250')
-    parser.add_argument('--lr_gamma', type=float, default=0.1,
-                        help='Decrease lr by a factor of exponential lr_scheduler. Default: 0.1')
-    parser.add_argument('--eta_min', type=float, default=0., help='Eta_min in cosine_annealing scheduler. Default: 0')
-    parser.add_argument('--T_max', type=int, default=320, help='T-max in cosine_annealing scheduler. Default: 320')
-    parser.add_argument('--max_epoch', type=int, default=320, help='Max epoch num to train the model. Default: 320')
-    parser.add_argument('--warmup_epochs', default=0, type=float, help='Warmup epochs. Default: 0')
-    parser.add_argument('--weight_decay', type=float, default=0.0005, help='Weight decay factor. Default: 0.0005')
-    parser.add_argument('--momentum', type=float, default=0.9, help='Momentum. Default: 0.9')
-
-    # loss related
-    parser.add_argument('--loss_scale', type=int, default=1024, help='Static loss scale. Default: 1024')
-    parser.add_argument('--label_smooth', type=int, default=0, help='Whether to use label smooth in CE. Default:0')
-    parser.add_argument('--label_smooth_factor', type=float, default=0.1,
-                        help='Smooth strength of original one-hot. Default: 0.1')
-
-    # logging related
-    parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
-    parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
-    parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')
-
-    parser.add_argument('--is_save_on_master', type=int, default=1,
-                        help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1')
-
-    # distributed related
-    parser.add_argument('--is_distributed', type=int, default=1,
-                        help='Distribute train or not, 1 for yes, 0 for no. Default: 1')
-    parser.add_argument('--rank', type=int, default=0, help='Local rank of distributed. Default: 0')
-    parser.add_argument('--group_size', type=int, default=1, help='World size of device. Default: 1')
-
-    # profiler init
-    parser.add_argument('--need_profiler', type=int, default=0,
-                        help='Whether use profiler. 0 for no, 1 for yes. Default: 0')
-
-    # reset default config
-    parser.add_argument('--training_shape', type=str, default="", help='Fix training shape. Default: ""')
-    parser.add_argument('--resize_rate', type=int, default=None,
-                        help='Resize rate for multi-scale training. Default: None')
-
-    args, _ = parser.parse_known_args()
-    if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.T_max:
-        args.T_max = args.max_epoch
-
-    args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
-    args.data_root = os.path.join(args.data_dir, 'train2014')
-    args.annFile = os.path.join(args.data_dir, 'annotations/instances_train2014.json')
-
-    return args
-
-
 def conver_training_shape(args):
     training_shape = [int(args.training_shape), int(args.training_shape)]
     return training_shape
@@ -151,6 +80,7 @@ def network_init(args):
             init("nccl")
         args.rank = get_rank()
         args.group_size = get_group_size()
+
     # select for master rank save ckpt or all rank save, compatible for model parallel
     args.rank_save_ckpt_flag = 0
     if args.is_save_on_master:
@@ -175,47 +105,105 @@ def parallel_init(args):
         degree = get_group_size()
     context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=degree)
 
-def train():
+
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    def unzip(zip_file, save_dir):
+        import zipfile
+        s_time = time.time()
+        if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
+            zip_isexist = zipfile.is_zipfile(zip_file)
+            if zip_isexist:
+                fz = zipfile.ZipFile(zip_file, 'r')
+                data_num = len(fz.namelist())
+                print("Extract Start...")
+                print("unzip file num: {}".format(data_num))
+                data_print = int(data_num / 100) if data_num > 100 else 1
+                i = 0
+                for file in fz.namelist():
+                    if i % data_print == 0:
+                        print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
+                    i += 1
+                    fz.extract(file, save_dir)
+                print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
+                                                     int(int(time.time() - s_time) % 60)))
+                print("Extract Done.")
+            else:
+                print("This is not zip.")
+        else:
+            print("Zip has been extracted.")
+
+    if config.need_modelarts_dataset_unzip:
+        zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
+        save_dir_1 = os.path.join(config.data_path)
+
+        sync_lock = "/tmp/unzip_sync.lock"
+
+        # Each server contains 8 devices as most.
+        if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+            print("Zip file path: ", zip_file_1)
+            print("Unzip file save dir: ", save_dir_1)
+            unzip(zip_file_1, save_dir_1)
+            print("===Finish extract data synchronization===")
+            try:
+                os.mknod(sync_lock)
+            except IOError:
+                pass
+
+        while True:
+            if os.path.exists(sync_lock):
+                break
+            time.sleep(1)
+
+        print("Device: {}, Finish sync unzip data from {} to {}.".format(get_device_id(), zip_file_1, save_dir_1))
+
+    config.ckpt_path = os.path.join(config.output_path, config.ckpt_path)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_train():
     """Train function."""
-    args = parse_args()
-    profiler = network_init(args)
+    if config.lr_scheduler == 'cosine_annealing' and config.max_epoch > config.T_max:
+        config.T_max = config.max_epoch
+    config.lr_epochs = list(map(int, config.lr_epochs.split(',')))
+    config.data_root = os.path.join(config.data_dir, 'train2014')
+    config.annFile = os.path.join(config.data_dir, 'annotations/instances_train2014.json')
+
+    profiler = network_init(config)
 
     loss_meter = AverageMeter('loss')
-    parallel_init(args)
+    parallel_init(config)
 
     network = YOLOV3DarkNet53(is_training=True)
     # default is kaiming-normal
     default_recurisive_init(network)
-    load_yolov3_params(args, network)
+    load_yolov3_params(config, network)
 
     network = YoloWithLossCell(network)
-    args.logger.info('finish get network')
+    config.logger.info('finish get network')
 
-    config = ConfigYOLOV3DarkNet53()
-    config.label_smooth = args.label_smooth
-    config.label_smooth_factor = args.label_smooth_factor
+    config.label_smooth = config.label_smooth
+    config.label_smooth_factor = config.label_smooth_factor
 
-    if args.training_shape:
-        config.multi_scale = [conver_training_shape(args)]
-    if args.resize_rate:
-        config.resize_rate = args.resize_rate
+    if config.training_shape:
+        config.multi_scale = [conver_training_shape(config)]
 
-    ds, data_size = create_yolo_dataset(image_dir=args.data_root, anno_path=args.annFile, is_training=True,
-                                        batch_size=args.per_batch_size, max_epoch=args.max_epoch,
-                                        device_num=args.group_size, rank=args.rank, config=config)
-    args.logger.info('Finish loading dataset')
+    ds, data_size = create_yolo_dataset(image_dir=config.data_root, anno_path=config.annFile, is_training=True,
+                                        batch_size=config.per_batch_size, max_epoch=config.max_epoch,
+                                        device_num=config.group_size, rank=config.rank, config=config)
+    config.logger.info('Finish loading dataset')
 
-    args.steps_per_epoch = int(data_size / args.per_batch_size / args.group_size)
+    config.steps_per_epoch = int(data_size / config.per_batch_size / config.group_size)
 
-    if not args.ckpt_interval:
-        args.ckpt_interval = args.steps_per_epoch
+    if config.ckpt_interval <= 0:
+        config.ckpt_interval = config.steps_per_epoch
 
-    lr = get_lr(args)
+    lr = get_lr(config)
     opt = Momentum(params=get_param_groups(network),
                    learning_rate=Tensor(lr),
-                   momentum=args.momentum,
-                   weight_decay=args.weight_decay,
-                   loss_scale=args.loss_scale)
+                   momentum=config.momentum,
+                   weight_decay=config.weight_decay,
+                   loss_scale=config.loss_scale)
     is_gpu = context.get_context("device_target") == "GPU"
     if is_gpu:
         loss_scale_value = 1.0
@@ -224,18 +212,18 @@ def train():
                                           level="O2", keep_batchnorm_fp32=False)
         keep_loss_fp32(network)
     else:
-        network = TrainingWrapper(network, opt, sens=args.loss_scale)
+        network = TrainingWrapper(network, opt, sens=config.loss_scale)
         network.set_train()
 
-    if args.rank_save_ckpt_flag:
+    if config.rank_save_ckpt_flag:
         # checkpoint save
-        ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval
-        ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
+        ckpt_max_num = config.max_epoch * config.steps_per_epoch // config.ckpt_interval
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval,
                                        keep_checkpoint_max=ckpt_max_num)
-        save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
+        save_ckpt_path = os.path.join(config.outputs_dir, 'ckpt_' + str(config.rank) + '/')
         ckpt_cb = ModelCheckpoint(config=ckpt_config,
                                   directory=save_ckpt_path,
-                                  prefix='{}'.format(args.rank))
+                                  prefix='{}'.format(config.rank))
         cb_params = _InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
@@ -250,7 +238,7 @@ def train():
     for i, data in enumerate(data_loader):
         images = data["image"]
         input_shape = images.shape[2:4]
-        args.logger.info('iter[{}], shape{}'.format(i, input_shape[0]))
+        config.logger.info('iter[{}], shape{}'.format(i, input_shape[0]))
 
         images = Tensor.from_numpy(images)
 
@@ -265,34 +253,34 @@ def train():
                        batch_gt_box2)
         loss_meter.update(loss.asnumpy())
 
-        if args.rank_save_ckpt_flag:
+        if config.rank_save_ckpt_flag:
             # ckpt progress
             cb_params.cur_step_num = i + 1  # current step number
             cb_params.batch_num = i + 2
             ckpt_cb.step_end(run_context)
 
-        if i % args.log_interval == 0:
+        if i % config.log_interval == 0:
             time_used = time.time() - t_end
-            epoch = int(i / args.steps_per_epoch)
-            per_step_time = time_used/args.log_interval
-            fps = args.per_batch_size * (i - old_progress) * args.group_size / time_used
-            if args.rank == 0:
-                args.logger.info(
+            epoch = int(i / config.steps_per_epoch)
+            per_step_time = time_used/config.log_interval
+            fps = config.per_batch_size * (i - old_progress) * config.group_size / time_used
+            if config.rank == 0:
+                config.logger.info(
                     'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{},'
                     ' per_step_time:{}'.format(epoch, i, loss_meter, fps, lr[i], per_step_time))
             t_end = time.time()
             loss_meter.reset()
             old_progress = i
 
-        if (i + 1) % args.steps_per_epoch == 0 and args.rank_save_ckpt_flag:
+        if (i + 1) % config.steps_per_epoch == 0 and config.rank_save_ckpt_flag:
             cb_params.cur_epoch_num += 1
 
-        if args.need_profiler:
+        if config.need_profiler:
             if i == 10:
                 profiler.analyse()
                 break
-    args.logger.info('==========end training===============')
+    config.logger.info('==========end training===============')
 
 
 if __name__ == "__main__":
-    train()
+    run_train()
diff --git a/tests/st/model_zoo_tests/yolov3_darknet53/test_yolov3_darknet53.py b/tests/st/model_zoo_tests/yolov3_darknet53/test_yolov3_darknet53.py
index 13cf728179e..0c413f67802 100644
--- a/tests/st/model_zoo_tests/yolov3_darknet53/test_yolov3_darknet53.py
+++ b/tests/st/model_zoo_tests/yolov3_darknet53/test_yolov3_darknet53.py
@@ -226,11 +226,11 @@ def test_yolov3_darknet_8p():
     cur_model_path = os.path.join(cur_path, model_name)
     train_file = os.path.join(cur_model_path, "train.py")
     old_list = ["--lr_scheduler=cosine_annealing"]
-    new_list = ["--lr_scheduler=cosine_annealing --training_shape=416"]
+    new_list = ["--lr_scheduler=cosine_annealing --training_shape=416 --log_interval=10"]
     utils.exec_sed_command(old_list, new_list,
                            os.path.join(cur_model_path, "scripts/run_distribute_train.sh"))
-    old_list = ["default=100", "max_epoch=args.max_epoch"]
-    new_list = ["default=10", "max_epoch=1"]
+    old_list = ["max_epoch=config.max_epoch"]
+    new_list = ["max_epoch=1"]
     utils.exec_sed_command(old_list, new_list, train_file)
     old_list = ["sampler=distributed_sampler"]
     new_list = ["sampler=distributed_sampler, num_samples=100*batch_size"]