diff --git a/model_zoo/official/cv/inceptionv4/README.md b/model_zoo/official/cv/inceptionv4/README.md
index 23f80335723..306c0f1cec4 100644
--- a/model_zoo/official/cv/inceptionv4/README.md
+++ b/model_zoo/official/cv/inceptionv4/README.md
@@ -78,14 +78,21 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─run_infer_310.sh                  # shell script for 310 inference
     └─run_eval_ascend.sh                # launch evaluating with ascend platform
   ├─src
-    ├─config.py                       # parameter configuration
     ├─dataset.py                      # data preprocessing
     ├─inceptionv4.py                  # network definition
-    └─callback.py                     # eval callback function
-  ├─eval.py                           # eval net
-  ├─export.py                         # export checkpoint, surpport .onnx, .air, .mindir convert
-  ├─postprogress.py                   # post process for 310 inference
-  └─train.py                          # train net
+    ├─callback.py                     # eval callback function
+    └─model_utils
+      ├─config.py               # Processing configuration parameters
+      ├─device_adapter.py       # Get cloud ID
+      ├─local_adapter.py        # Get local ID
+      └─moxing_adapter.py       # Parameter processing
+  ├─default_config.yaml             # Training parameter profile(ascend)
+  ├─default_config_cpu.yaml         # Training parameter profile(cpu)
+  ├─default_config_gpu.yaml         # Training parameter profile(gpu)
+  ├─eval.py                         # eval net
+  ├─export.py                       # export checkpoint, surpport .onnx, .air, .mindir convert
+  ├─postprogress.py                 # post process for 310 inference
+  └─train.py                        # train net
 ```
 
 ## [Script Parameters](#contents)
diff --git a/model_zoo/official/cv/inceptionv4/default_config.yaml b/model_zoo/official/cv/inceptionv4/default_config.yaml
new file mode 100644
index 00000000000..eee0fde951d
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/default_config.yaml
@@ -0,0 +1,74 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+dataset_path: "/cache/data"
+ckpt_path: '/cache/data/'
+checkpoint_path: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+ckpt_file: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+resume: ''
+is_distributed: False
+device_id: 0
+platform: 'Ascend'
+file_name: 'inceptionv4'
+file_format: 'MINDIR'
+width: 299
+height: 299
+
+# fasterrcnn_export
+result_path: '' # "result file path"
+label_file: ''  # "label file"
+
+# Training options
+is_save_on_master: False
+
+batch_size: 128
+epoch_size: 250
+num_classes: 1000
+work_nums: 8
+ds_type: 'imagenet'
+ds_sink_mode: True
+
+loss_scale: 1024
+smooth_factor: 0.1
+weight_decay: 0.00004
+momentum: 0.9
+amp_level: 'O3'
+decay: 0.9
+epsilon: 1.0
+
+keep_checkpoint_max: 10
+save_checkpoint_epochs: 10
+
+lr_init: 0.00004
+lr_end: 0.000004
+lr_max: 0.4
+warmup_epochs: 1
+start_epoch: 1
+    
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+device_target: 'Target device type' 
+enable_profiling: 'Whether enable profiling while training, default: False'
+file_name: 'output file name.'
+file_format: 'file format'
+resume: 'resume training with existed checkpoint'
+
+---
+device_target: ['Ascend', 'GPU', 'CPU']
+file_format: ['AIR', 'ONNX', 'MINDIR']
diff --git a/model_zoo/official/cv/inceptionv4/default_config_cpu.yaml b/model_zoo/official/cv/inceptionv4/default_config_cpu.yaml
new file mode 100644
index 00000000000..e6f403b2230
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/default_config_cpu.yaml
@@ -0,0 +1,67 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+dataset_path: "/cache/data"
+ckpt_path: '/cache/data/'
+checkpoint: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+ckpt_file: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+resume: ''
+is_distributed: False
+device_id: 0
+platform: 'GPU'
+file_name: 'inceptionv3'
+file_format: 'AIR'
+width: 299
+height: 299
+
+# Training options
+batch_size: 128
+epoch_size: 250
+num_classes: 10
+work_nums: 8
+ds_type: 'cifar10'
+ds_sink_mode: False
+
+loss_scale: 1024
+smooth_factor: 0.1
+weight_decay: 0.00004
+momentum: 0.9
+amp_level: 'O0'
+decay: 0.9
+epsilon: 1.0
+
+keep_checkpoint_max: 10
+save_checkpoint_epochs: 10
+
+lr_init: 0.00004
+lr_end: 0.000004
+lr_max: 0.4
+warmup_epochs: 1
+start_epoch: 1
+
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+device_target: 'Target device type' 
+enable_profiling: 'Whether enable profiling while training, default: False'
+file_name: 'inceptionv3 output air name.'
+file_format: 'file format'
+
+---
+device_target: ['Ascend', 'GPU', 'CPU']
+file_format: ['AIR', 'ONNX', 'MINDIR']
diff --git a/model_zoo/official/cv/inceptionv4/default_config_gpu.yaml b/model_zoo/official/cv/inceptionv4/default_config_gpu.yaml
new file mode 100644
index 00000000000..e53162651d1
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/default_config_gpu.yaml
@@ -0,0 +1,69 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+dataset_path: "/cache/data"
+ckpt_path: '/cache/data/'
+checkpoint: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+ckpt_file: '/cache/data/inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'
+resume: ''
+is_distributed: False
+device_id: 0
+platform: 'GPU'
+file_name: 'inceptionv3'
+file_format: 'AIR'
+width: 299
+height: 299
+
+# Training options
+is_save_on_master: False
+
+batch_size: 128
+epoch_size: 250
+num_classes: 1000
+work_nums: 8
+ds_type: 'imagenet'
+ds_sink_mode: True
+
+loss_scale: 1024
+smooth_factor: 0.1
+weight_decay: 0.00004
+momentum: 0.9
+amp_level: 'O0'
+decay: 0.9
+epsilon: 1.0
+
+keep_checkpoint_max: 10
+save_checkpoint_epochs: 10
+
+lr_init: 0.00004
+lr_end: 0.000004
+lr_max: 0.4
+warmup_epochs: 1
+start_epoch: 1
+
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+device_target: 'Target device type' 
+enable_profiling: 'Whether enable profiling while training, default: False'
+file_name: 'output file name.'
+file_format: 'file format'
+
+---
+device_target: ['Ascend', 'GPU', 'CPU']
+file_format: ['AIR', 'ONNX', 'MINDIR']
diff --git a/model_zoo/official/cv/inceptionv4/eval.py b/model_zoo/official/cv/inceptionv4/eval.py
index 64269bb0345..d83836adfe0 100644
--- a/model_zoo/official/cv/inceptionv4/eval.py
+++ b/model_zoo/official/cv/inceptionv4/eval.py
@@ -13,58 +13,100 @@
 # limitations under the License.
 # ============================================================================
 """evaluate_imagenet"""
-import argparse
+import time
 import os
 
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id, get_device_num
+from src.dataset import create_dataset_imagenet, create_dataset_cifar10
+from src.inceptionv4 import Inceptionv4
+
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.config import config_ascend, config_gpu, config_cpu
-from src.dataset import create_dataset_imagenet, create_dataset_cifar10
-from src.inceptionv4 import Inceptionv4
 
-CFG_DICT = {
-    "Ascend": config_ascend,
-    "GPU": config_gpu,
-    "CPU": config_cpu,
-}
+
+def modelarts_process():
+    """ modelarts process """
+    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))
+        print('#' * 200, os.listdir(save_dir_1))
+        print('#' * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
+
+    config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
+
 
 DS_DICT = {
     "imagenet": create_dataset_imagenet,
     "cifar10": create_dataset_cifar10,
 }
 
+@moxing_wrapper(pre_process=modelarts_process)
+def inception_v4_eval():
 
-def parse_args():
-    '''parse_args'''
-    parser = argparse.ArgumentParser(description='image classification evaluation')
-    parser.add_argument('--platform', type=str, default='Ascend', choices=('Ascend', 'GPU', 'CPU'), help='run platform')
-    parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
-    parser.add_argument('--checkpoint_path', type=str, default='', help='checkpoint of inceptionV4')
-    args_opt = parser.parse_args()
-    return args_opt
-
-
-if __name__ == '__main__':
-    args = parse_args()
-
-    if args.platform == 'Ascend':
+    if config.platform == 'Ascend':
         device_id = int(os.getenv('DEVICE_ID', '0'))
         context.set_context(device_id=device_id)
 
-    config = CFG_DICT[args.platform]
     create_dataset = DS_DICT[config.ds_type]
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.platform)
     net = Inceptionv4(classes=config.num_classes)
-    ckpt = load_checkpoint(args.checkpoint_path)
+    ckpt = load_checkpoint(config.checkpoint_path)
     load_param_into_net(net, ckpt)
     net.set_train(False)
     config.rank = 0
     config.group_size = 1
-    dataset = create_dataset(dataset_path=args.dataset_path, do_train=False, cfg=config)
+    dataset = create_dataset(dataset_path=config.dataset_path, do_train=False, cfg=config)
     loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
     eval_metrics = {'Loss': nn.Loss(),
                     'Top1-Acc': nn.Top1CategoricalAccuracy(),
@@ -73,3 +115,8 @@ if __name__ == '__main__':
     print('=' * 20, 'Evalute start', '=' * 20)
     metrics = model.eval(dataset, dataset_sink_mode=config.ds_sink_mode)
     print("metric: ", metrics)
+
+
+if __name__ == '__main__':
+    config.dataset_path = os.path.join(config.dataset_path, 'validation_preprocess')
+    inception_v4_eval()
diff --git a/model_zoo/official/cv/inceptionv4/export.py b/model_zoo/official/cv/inceptionv4/export.py
index eab136ed51a..ea1144d742a 100644
--- a/model_zoo/official/cv/inceptionv4/export.py
+++ b/model_zoo/official/cv/inceptionv4/export.py
@@ -13,43 +13,27 @@
 # limitations under the License.
 # ============================================================================
 """export checkpoint file into air, onnx, mindir models"""
-import argparse
 import numpy as np
 
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+from src.inceptionv4 import Inceptionv4
+
 import mindspore as ms
 from mindspore import Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export, context
 
-from src.config import config_ascend, config_gpu, config_cpu
-from src.inceptionv4 import Inceptionv4
 
-parser = argparse.ArgumentParser(description='inceptionv4 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='inceptionv4 ckpt file.')
-parser.add_argument('--file_name', type=str, default='inceptionv4', help='inceptionv4 output air name.')
-parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
-parser.add_argument('--width', type=int, default=299, help='input width')
-parser.add_argument('--height', type=int, default=299, help='input height')
-parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
-                    help="device target")
-args = parser.parse_args()
+config.batch_size = 1
 
-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)
-
-CFG_DICT = {
-    "Ascend": config_ascend,
-    "GPU": config_gpu,
-    "CPU": config_cpu,
-}
-config = CFG_DICT[args.device_target]
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+if config.device_target == "Ascend":
+    context.set_context(device_id=get_device_id())
 
 if __name__ == '__main__':
     net = Inceptionv4(classes=config.num_classes)
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(net, param_dict)
 
-    input_arr = Tensor(np.ones([args.batch_size, 3, args.width, args.height]), ms.float32)
-    export(net, input_arr, file_name=args.file_name, file_format=args.file_format)
+    input_arr = Tensor(np.ones([config.batch_size, 3, config.width, config.height]), ms.float32)
+    export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
diff --git a/model_zoo/official/cv/inceptionv4/postprocess.py b/model_zoo/official/cv/inceptionv4/postprocess.py
index 0b032de2b3f..1ba9a16a633 100644
--- a/model_zoo/official/cv/inceptionv4/postprocess.py
+++ b/model_zoo/official/cv/inceptionv4/postprocess.py
@@ -14,13 +14,10 @@
 # ============================================================================
 '''post process for 310 inference'''
 import os
-import argparse
 import numpy as np
 
-parser = argparse.ArgumentParser(description='fasterrcnn_export')
-parser.add_argument("--result_path", type=str, required=True, help="result file path")
-parser.add_argument("--label_file", type=str, required=True, help="label file")
-args = parser.parse_args()
+from src.model_utils.config import config
+
 
 def read_label(label_file):
     f = open(label_file, "r")
@@ -55,4 +52,4 @@ def cal_acc(result_path, label_file):
     print("========accuraty:{}========".format(accuracy))
 
 if __name__ == "__main__":
-    cal_acc(args.result_path, args.label_file)
+    cal_acc(config.result_path, config.label_file)
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_ascend.sh b/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_ascend.sh
index 49768b95a64..bf91ed469df 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_ascend.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_ascend.sh
@@ -19,6 +19,8 @@ export RANK_TABLE_FILE=$1
 DATA_DIR=$2
 export RANK_SIZE=8
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config.yaml"
 
 cores=`cat /proc/cpuinfo|grep "processor" |wc -l`
 echo "the number of logical core" $cores
@@ -39,11 +41,13 @@ do
     rm -rf train_parallel$i
     mkdir ./train_parallel$i
     cp  *.py ./train_parallel$i
+    cp  *.yaml ./train_parallel$i
+    cp  -r ./src ./train_parallel$i
     cd ./train_parallel$i || exit
     echo "start training for rank $i, device $DEVICE_ID rank_id $RANK_ID"
 
     env > env.log
-    taskset -c $cmdopt python -u ../train.py \
+    taskset -c $cmdopt python -u ../train.py --config_path=$CONFIG_FILE \
     --device_id $i \
     --dataset_path=$DATA_DIR > log.txt 2>&1 &
     cd ../
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_gpu.sh b/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_gpu.sh
index ff0a892a1a9..ca1ee884bb5 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_gpu.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_distribute_train_gpu.sh
@@ -17,14 +17,17 @@
 rm -rf device
 mkdir device
 cp ./*.py ./device
+cp ./*.yaml ./device
 cp -r ./src ./device
 cd ./device || exit
 
 DATA_DIR=$1
-
 export DEVICE_ID=0
 export RANK_SIZE=8
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config_gpu.yaml"
+
 echo "start training"
 
-mpirun -n $RANK_SIZE --allow-run-as-root python train.py --dataset_path=$DATA_DIR --platform='GPU' > train.log 2>&1 &
+mpirun -n $RANK_SIZE --allow-run-as-root python train.py --config_path=$CONFIG_FILE --dataset_path=$DATA_DIR --platform='GPU' > train.log 2>&1 &
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_eval_ascend.sh b/model_zoo/official/cv/inceptionv4/scripts/run_eval_ascend.sh
index a33555ae249..02f42e11a63 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_eval_ascend.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_eval_ascend.sh
@@ -19,10 +19,13 @@ DATA_DIR=$2
 CHECKPOINT_PATH=$3
 export RANK_SIZE=1
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config.yaml"
+
 rm -rf evaluation_ascend
 mkdir ./evaluation_ascend
 cd ./evaluation_ascend || exit
 echo  "start training for device id $DEVICE_ID"
 env > env.log
-python ../eval.py --platform=Ascend --dataset_path=$DATA_DIR --checkpoint_path=$CHECKPOINT_PATH > eval.log 2>&1 &
+python ../eval.py --config_path=$CONFIG_FILE --platform=Ascend --dataset_path=$DATA_DIR --checkpoint_path=$CHECKPOINT_PATH > eval.log 2>&1 &
 cd ../
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_eval_cpu.sh b/model_zoo/official/cv/inceptionv4/scripts/run_eval_cpu.sh
index 0dd105b77d6..018c0abd39a 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_eval_cpu.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_eval_cpu.sh
@@ -17,12 +17,15 @@
 rm -rf evaluation
 mkdir evaluation
 cp ./*.py ./evaluation
+cp ./*.yaml ./evaluation
 cp -r ./src ./evaluation
 cd ./evaluation || exit
 
 DATA_DIR=$1
 CKPT_DIR=$2
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config_cpu.yaml"
 
 echo "start evaluation"
 
-python eval.py --dataset_path=$DATA_DIR --checkpoint_path=$CKPT_DIR --platform='CPU'  > eval.log 2>&1 &
+python eval.py --config_path=$CONFIG_FILE --dataset_path=$DATA_DIR --checkpoint_path=$CKPT_DIR --platform='CPU'  > eval.log 2>&1 &
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_eval_gpu.sh b/model_zoo/official/cv/inceptionv4/scripts/run_eval_gpu.sh
index cbe12e5b2e4..818b24efe0f 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_eval_gpu.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_eval_gpu.sh
@@ -17,6 +17,7 @@
 rm -rf evaluation
 mkdir evaluation
 cp ./*.py ./evaluation
+cp ./*.yaml ./evaluation
 cp -r ./src ./evaluation
 cd ./evaluation || exit
 
@@ -26,6 +27,9 @@ export RANK_SIZE=1
 DATA_DIR=$1
 CKPT_DIR=$2
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config_gpu.yaml"
+
 echo "start evaluation"
 
-python eval.py --dataset_path=$DATA_DIR --checkpoint_path=$CKPT_DIR --platform='GPU'  > eval.log 2>&1 &
+python eval.py --config_path=$CONFIG_FILE --dataset_path=$DATA_DIR --checkpoint_path=$CKPT_DIR --platform='GPU'  > eval.log 2>&1 &
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_infer_310.sh b/model_zoo/official/cv/inceptionv4/scripts/run_infer_310.sh
index faa08fba0c1..9e6f29790ff 100755
--- a/model_zoo/official/cv/inceptionv4/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_infer_310.sh
@@ -43,6 +43,9 @@ echo $data_path
 echo $label_file
 echo $device_id
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config.yaml"
+
 export ASCEND_HOME=/usr/local/Ascend/
 if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
     export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
@@ -82,7 +85,7 @@ function infer()
     fi
     mkdir result_Files
     mkdir time_Result
-    ../ascend310_infer/out/main --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
+    ../ascend310_infer/out/main --config_path=$CONFIG_FILE --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
 
     if [ $? -ne 0 ]; then
         echo "execute inference failed"
@@ -92,7 +95,7 @@ function infer()
 
 function cal_acc()
 {
-    python ../postprocess.py --label_file=$label_file --result_path=result_Files &> acc.log
+    python ../postprocess.py --config_path=$CONFIG_FILE --label_file=$label_file --result_path=result_Files &> acc.log
     if [ $? -ne 0 ]; then
         echo "calculate accuracy failed"
         exit 1
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_ascend.sh b/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_ascend.sh
index 326f2a7d5fa..0956920983b 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_ascend.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_ascend.sh
@@ -18,12 +18,15 @@ export RANK_SIZE=1
 export DEVICE_ID=$1
 DATA_DIR=$2
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config.yaml"
+
 rm -rf train_standalone
 mkdir ./train_standalone
 cd ./train_standalone || exit
 echo  "start training for device id $DEVICE_ID"
 env > env.log
-python -u ../train.py \
+python -u ../train.py --config_path=$CONFIG_FILE \
     --device_id=$1 \
     --dataset_path=$DATA_DIR > log.txt 2>&1 &
 cd ../
diff --git a/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_cpu.sh b/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_cpu.sh
index 2279d933442..9c7a5b8830c 100644
--- a/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_cpu.sh
+++ b/model_zoo/official/cv/inceptionv4/scripts/run_standalone_train_cpu.sh
@@ -16,10 +16,13 @@
 
 DATA_DIR=$1
 
+BASE_PATH=$(cd ./"`dirname $0`" || exit; pwd)
+CONFIG_FILE="${BASE_PATH}/../default_config_cpu.yaml"
+
 rm -rf train_standalone
 mkdir ./train_standalone
 cd ./train_standalone || exit
 env > env.log
-python -u ../train.py \
+python -u ../train.py --config_path=$CONFIG_FILE \
     --dataset_path=$DATA_DIR --platform=CPU> log.txt 2>&1 &
 cd ../
diff --git a/model_zoo/official/cv/inceptionv4/src/config.py b/model_zoo/official/cv/inceptionv4/src/config.py
deleted file mode 100644
index a2be6987082..00000000000
--- a/model_zoo/official/cv/inceptionv4/src/config.py
+++ /dev/null
@@ -1,100 +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.
-# ============================================================================
-"""
-network config setting, will be used in main.py
-"""
-from easydict import EasyDict as edict
-
-config_ascend = edict({
-    'is_save_on_master': False,
-
-    'batch_size': 128,
-    'epoch_size': 250,
-    'num_classes': 1000,
-    'work_nums': 8,
-    'ds_type': 'imagenet',
-    'ds_sink_mode': True,
-
-    'loss_scale': 1024,
-    'smooth_factor': 0.1,
-    'weight_decay': 0.00004,
-    'momentum': 0.9,
-    'amp_level': 'O3',
-    'decay': 0.9,
-    'epsilon': 1.0,
-
-    'keep_checkpoint_max': 10,
-    'save_checkpoint_epochs': 10,
-
-    'lr_init': 0.00004,
-    'lr_end': 0.000004,
-    'lr_max': 0.4,
-    'warmup_epochs': 1,
-    'start_epoch': 1,
-})
-
-config_gpu = edict({
-    'is_save_on_master': False,
-
-    'batch_size': 128,
-    'epoch_size': 250,
-    'num_classes': 1000,
-    'work_nums': 8,
-    'ds_type': 'imagenet',
-    'ds_sink_mode': True,
-
-    'loss_scale': 1024,
-    'smooth_factor': 0.1,
-    'weight_decay': 0.00004,
-    'momentum': 0.9,
-    'amp_level': 'O0',
-    'decay': 0.9,
-    'epsilon': 1.0,
-
-    'keep_checkpoint_max': 10,
-    'save_checkpoint_epochs': 10,
-
-    'lr_init': 0.00004,
-    'lr_end': 0.000004,
-    'lr_max': 0.4,
-    'warmup_epochs': 1,
-    'start_epoch': 1,
-})
-
-config_cpu = edict({
-    'batch_size': 128,
-    'epoch_size': 250,
-    'num_classes': 10,
-    'work_nums': 8,
-    'ds_type': 'cifar10',
-    'ds_sink_mode': False,
-
-    'loss_scale': 1024,
-    'smooth_factor': 0.1,
-    'weight_decay': 0.00004,
-    'momentum': 0.9,
-    'amp_level': 'O0',
-    'decay': 0.9,
-    'epsilon': 1.0,
-
-    'keep_checkpoint_max': 10,
-    'save_checkpoint_epochs': 10,
-
-    'lr_init': 0.00004,
-    'lr_end': 0.000004,
-    'lr_max': 0.4,
-    'warmup_epochs': 1,
-    'start_epoch': 1,
-})
diff --git a/model_zoo/official/cv/inceptionv4/src/model_utils/__init__.py b/model_zoo/official/cv/inceptionv4/src/model_utils/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/model_zoo/official/cv/inceptionv4/src/model_utils/config.py b/model_zoo/official/cv/inceptionv4/src/model_utils/config.py
new file mode 100644
index 00000000000..7f1ff6e2b8d
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/src/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/inceptionv4/src/model_utils/device_adapter.py b/model_zoo/official/cv/inceptionv4/src/model_utils/device_adapter.py
new file mode 100644
index 00000000000..7c5d7f837dd
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/src/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/inceptionv4/src/model_utils/local_adapter.py b/model_zoo/official/cv/inceptionv4/src/model_utils/local_adapter.py
new file mode 100644
index 00000000000..769fa6dc78e
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/src/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/inceptionv4/src/model_utils/moxing_adapter.py b/model_zoo/official/cv/inceptionv4/src/model_utils/moxing_adapter.py
new file mode 100644
index 00000000000..830d19a6fc9
--- /dev/null
+++ b/model_zoo/official/cv/inceptionv4/src/model_utils/moxing_adapter.py
@@ -0,0 +1,122 @@
+# 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 mindspore.profiler import Profiler
+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()
+
+            if config.enable_profiling:
+                profiler = Profiler()
+
+            run_func(*args, **kwargs)
+
+            if config.enable_profiling:
+                profiler.analyse()
+
+            # 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/inceptionv4/train.py b/model_zoo/official/cv/inceptionv4/train.py
index 4f8dc54614f..8ceaed7dc85 100644
--- a/model_zoo/official/cv/inceptionv4/train.py
+++ b/model_zoo/official/cv/inceptionv4/train.py
@@ -13,12 +13,17 @@
 # limitations under the License.
 # ============================================================================
 """train imagenet"""
-import argparse
+import time
 import math
 import os
-
 import numpy as np
 
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id, get_device_num
+from src.dataset import create_dataset_imagenet, create_dataset_cifar10
+from src.inceptionv4 import Inceptionv4
+
 from mindspore import Model
 from mindspore import Tensor
 from mindspore import context
@@ -31,42 +36,19 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMoni
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.model import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.config import config_ascend, config_gpu, config_cpu
-from src.dataset import create_dataset_imagenet, create_dataset_cifar10
-from src.inceptionv4 import Inceptionv4
+
 
 os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python'
 set_seed(1)
 
-CFG_DICT = {
-    "Ascend": config_ascend,
-    "GPU": config_gpu,
-    "CPU": config_cpu,
-}
-
 DS_DICT = {
     "imagenet": create_dataset_imagenet,
     "cifar10": create_dataset_cifar10,
 }
 
-device_num = int(os.getenv('RANK_SIZE', '1'))
-
-
-def parse_args():
-    '''parse_args'''
-    arg_parser = argparse.ArgumentParser(description='InceptionV4 image classification training')
-    arg_parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
-    arg_parser.add_argument('--device_id', type=int, default=0, help='device id')
-    arg_parser.add_argument('--platform', type=str, default='Ascend', choices=("Ascend", "GPU", "CPU"),
-                            help='Platform, support Ascend, GPU, CPU.')
-    arg_parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
-    args_opt = arg_parser.parse_args()
-    return args_opt
-
-
-args = parse_args()
-
-config = CFG_DICT[args.platform]
+config.device_id = get_device_id()
+config.device_num = get_device_num()
+device_num = config.device_num
 create_dataset = DS_DICT[config.ds_type]
 
 
@@ -107,21 +89,77 @@ def generate_cosine_lr(steps_per_epoch, total_epochs,
     return learning_rate
 
 
+def modelarts_pre_process():
+    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))
+        print('#' * 200, os.listdir(save_dir_1))
+        print('#' * 200, os.listdir(os.path.join(config.data_path, config.modelarts_dataset_unzip_name)))
+
+    config.dataset_path = os.path.join(config.data_path, config.modelarts_dataset_unzip_name)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
 def inception_v4_train():
     """
     Train Inceptionv4 in data parallelism
     """
     print('epoch_size: {} batch_size: {} class_num {}'.format(config.epoch_size, config.batch_size, config.num_classes))
 
-    context.set_context(mode=context.GRAPH_MODE, device_target=args.platform)
-    if args.platform == "Ascend":
-        context.set_context(device_id=args.device_id)
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.platform)
+    if config.platform == "Ascend":
+        context.set_context(device_id=get_device_id())
         context.set_context(enable_graph_kernel=False)
 
     if device_num > 1:
-        if args.platform == "Ascend":
+        if config.platform == "Ascend":
             init(backend_name='hccl')
-        elif args.platform == "GPU":
+        elif config.platform == "GPU":
             init()
         else:
             raise ValueError("Unsupported device target.")
@@ -137,7 +175,7 @@ def inception_v4_train():
         config.group_size = 1
 
     # create dataset
-    train_dataset = create_dataset(dataset_path=args.dataset_path, do_train=True, cfg=config)
+    train_dataset = create_dataset(dataset_path=config.dataset_path, do_train=True, cfg=config)
     train_step_size = train_dataset.get_dataset_size()
 
     # create model
@@ -164,11 +202,11 @@ def inception_v4_train():
     opt = RMSProp(group_params, lr, decay=config.decay, epsilon=config.epsilon, weight_decay=config.weight_decay,
                   momentum=config.momentum, loss_scale=config.loss_scale)
 
-    if args.device_id == 0:
+    if get_device_id() == 0:
         print(lr)
         print(train_step_size)
-    if args.resume:
-        ckpt = load_checkpoint(args.resume)
+    if config.resume:
+        ckpt = load_checkpoint(config.resume)
         load_param_into_net(net, ckpt)
 
     loss_scale_manager = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
@@ -185,7 +223,7 @@ def inception_v4_train():
                                  directory='ckpts_rank_' + str(config.rank), config=config_ck)
     callbacks = [performance_cb, loss_cb]
     if device_num > 1 and config.is_save_on_master:
-        if args.device_id == 0:
+        if get_device_id() == 0:
             callbacks.append(ckpoint_cb)
     else:
         callbacks.append(ckpoint_cb)
@@ -195,5 +233,6 @@ def inception_v4_train():
 
 
 if __name__ == '__main__':
+    config.dataset_path = os.path.join(config.dataset_path, 'train')
     inception_v4_train()
     print('Inceptionv4 training success!')