diff --git a/model_zoo/official/cv/googlenet/README.md b/model_zoo/official/cv/googlenet/README.md
index 8e71bfd0568..7e78d94fe74 100644
--- a/model_zoo/official/cv/googlenet/README.md
+++ b/model_zoo/official/cv/googlenet/README.md
@@ -120,6 +120,53 @@ After installing MindSpore via the official website, you can start training and
 
 We use CIFAR-10 dataset by default. Your can also pass `$dataset_type` to the scripts so that select different datasets. For more details, please refer the specify script.
 
+- ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start training as follows)
+
+    - Train imagenet 8p on ModelArts
+
+      ```python
+      # (1) Add "config_path='/path_to_code/imagenet_config.yaml'" on the website UI interface.
+      # (2) Perform a or b.
+      #       a. Set "enable_modelarts=True" on imagenet_config.yaml file.
+      #          Set "dataset_name='imagenet'" on imagenet_config.yaml file.
+      #          Set "train_data_path='/cache/data/ImageNet/train/'" on imagenet_config.yaml file.
+      #          Set other parameters on imagenet_config.yaml file you need.
+      #       b. Add "enable_modelarts=True" on the website UI interface.
+      #          Add "dataset_name=imagenet" on the website UI interface.
+      #          Add "train_data_path=/cache/data/ImageNet/train/" on the website UI interface.
+      #          Add other parameters on the website UI interface.
+      # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
+      # (4) Set the code directory to "/path/googlenet" on the website UI interface.
+      # (5) Set the startup file to "train.py" on the website UI interface.
+      # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
+      # (7) Create your job.
+      ```
+
+    - Eval imagenet on ModelArts
+
+      ```python
+      # (1) Add "config_path='/path_to_code/imagenet_config.yaml'" on the website UI interface.
+      # (2) Perform a or b.
+      #       a. Set "enable_modelarts=True" on imagenet_config.yaml file.
+      #          Set "dataset_name='imagenet'" on imagenet_config.yaml file.
+      #          Set "val_data_path='/cache/data/ImageNet/val/'" on imagenet_config.yaml file.
+      #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on imagenet_config.yaml file.
+      #          Set "checkpoint_path='/cache/checkpoint_path/model.ckpt'" on imagenet_config.yaml file.
+      #          Set other parameters on imagenet_config.yaml file you need.
+      #       b. Add "enable_modelarts=True" on the website UI interface.
+      #          Add "dataset_name=imagenet" on the website UI interface.
+      #          Add "val_data_path=/cache/data/ImageNet/val/" on the website UI interface.
+      #          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+      #          Add "checkpoint_path='/cache/checkpoint_path/model.ckpt'" 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/googlenet" 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)
 
 ## [Script and Sample Code](#contents)
diff --git a/model_zoo/official/cv/googlenet/README_CN.md b/model_zoo/official/cv/googlenet/README_CN.md
index 48080e72320..0c4b73e104b 100644
--- a/model_zoo/official/cv/googlenet/README_CN.md
+++ b/model_zoo/official/cv/googlenet/README_CN.md
@@ -127,6 +127,53 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
 
 默认使用CIFAR-10数据集。您也可以将`$dataset_type`传入脚本，以便选择其他数据集。如需查看更多详情，请参考指定脚本。
 
+- 在 ModelArts 进行训练 (如果你想在modelarts上运行，可以参考以下文档 [modelarts](https://support.huaweicloud.com/modelarts/))
+
+    - 在 ModelArts 上使用8卡训练 ImageNet 数据集
+
+      ```python
+      # (1) 在网页上设置 "config_path='/path_to_code/imagenet_config.yaml'"
+      # (2) 执行a或者b
+      #       a. 在 imagenet_config.yaml 文件中设置 "enable_modelarts=True"
+      #          在 imagenet_config.yaml 文件中设置 "dataset_name='imagenet'"
+      #          在 imagenet_config.yaml 文件中设置 "train_data_path='/cache/data/ImageNet/train/'"
+      #          在 imagenet_config.yaml 文件中设置 其他参数
+      #       b. 在网页上设置 "enable_modelarts=True"
+      #          在网页上设置 "dataset_name=imagenet"
+      #          在网页上设置 "train_data_path=/cache/data/ImageNet/train/"
+      #          在网页上设置 其他参数
+      # (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/googlenet"
+      # (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 上使用单卡验证 ImageNet 数据集
+
+      ```python
+      # (1) 在网页上设置 "config_path='/path_to_code/imagenet_config.yaml'"
+      # (2) 执行a或者b
+      #       a. 在 imagenet_config.yaml 文件中设置 "enable_modelarts=True"
+      #          在 imagenet_config.yaml 文件中设置 "dataset_name='imagenet'"
+      #          在 imagenet_config.yaml 文件中设置 "val_data_path='/cache/data/ImageNet/val/'"
+      #          在 imagenet_config.yaml 文件中设置 "checkpoint_url='s3://dir_to_trained_ckpt/'"
+      #          在 imagenet_config.yaml 文件中设置 "checkpoint_path='/cache/checkpoint_path/model.ckpt'"
+      #          在 imagenet_config.yaml 文件中设置 其他参数
+      #       b. 在网页上设置 "enable_modelarts=True"
+      #          在网页上设置 "dataset_name=imagenet"
+      #          在网页上设置 "val_data_path=/cache/data/ImageNet/val/"
+      #          在网页上设置 "checkpoint_url='s3://dir_to_trained_ckpt/'"
+      #          在网页上设置 "checkpoint_path='/cache/checkpoint_path/model.ckpt'"
+      #          在网页上设置 其他参数
+      # (3) 上传你的预训练模型到 S3 桶上
+      # (4) 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。)
+      # (5) 在网页上设置你的代码路径为 "/path/googlenet"
+      # (6) 在网页上设置启动文件为 "eval.py"
+      # (7) 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
+      # (8) 创建训练作业
+      ```
+
 # 脚本说明
 
 ## 脚本及样例代码
diff --git a/model_zoo/official/cv/googlenet/cifar10_config.yaml b/model_zoo/official/cv/googlenet/cifar10_config.yaml
new file mode 100644
index 00000000000..329eef50ff2
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/cifar10_config.yaml
@@ -0,0 +1,51 @@
+# 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"
+need_modelarts_dataset_unzip: True
+modelarts_dataset_unzip_name: "cifar10"
+
+# ==============================================================================
+# options
+dataset_name: "cifar10"
+name: "cifar10"
+pre_trained: False
+num_classes: 10
+lr_init: 0.1
+batch_size: 128
+epoch_size: 125
+momentum: 0.9
+weight_decay: 0.0005 #5e-4
+image_height: 224
+image_width: 224
+train_data_path: "/cache/data/cifar10/"
+val_data_path: "/cache/data/cifar10/"
+keep_checkpoint_max: 10
+checkpoint_path: "./train_googlenet_cifar10-125_390.ckpt"
+onnx_filename: "googlenet"
+air_filename: "googlenet"
+ckpt_save_dir: "./ckpt/"
+
+# export option
+ckpt_file: ""
+file_name: "googlenet"
+file_format: "AIR"
+#batch_size: 1
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+enable_profiling: 'Whether enable profiling while training, default: False'
diff --git a/model_zoo/official/cv/googlenet/eval.py b/model_zoo/official/cv/googlenet/eval.py
index c774d17e489..a9dac3cf637 100644
--- a/model_zoo/official/cv/googlenet/eval.py
+++ b/model_zoo/official/cv/googlenet/eval.py
@@ -16,7 +16,8 @@
 ##############test googlenet example on cifar10#################
 python eval.py
 """
-import argparse
+import os
+import time
 
 import mindspore.nn as nn
 from mindspore import context
@@ -25,58 +26,105 @@ from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import set_seed
 
-from src.config import cifar_cfg, imagenet_cfg
 from src.dataset import create_dataset_cifar10, create_dataset_imagenet
 
 from src.googlenet import GoogleNet
 from src.CrossEntropySmooth import CrossEntropySmooth
 
+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)
 
-parser = argparse.ArgumentParser(description='googlenet')
-parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
-                    help='dataset name.')
-parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
-args_opt = parser.parse_args()
+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 __name__ == '__main__':
+    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)
 
-    if args_opt.dataset_name == 'cifar10':
-        cfg = cifar_cfg
-        dataset = create_dataset_cifar10(cfg.data_path, 1, False)
+        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_eval():
+    if config.dataset_name == 'cifar10':
+        dataset = create_dataset_cifar10(config.val_data_path, 1, False, cifar_cfg=config)
         loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-        net = GoogleNet(num_classes=cfg.num_classes)
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
-                       weight_decay=cfg.weight_decay)
+        net = GoogleNet(num_classes=config.num_classes)
+        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, config.momentum,
+                       weight_decay=config.weight_decay)
         model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
 
-    elif args_opt.dataset_name == "imagenet":
-        cfg = imagenet_cfg
-        dataset = create_dataset_imagenet(cfg.val_data_path, 1, False)
-        if not cfg.use_label_smooth:
-            cfg.label_smooth_factor = 0.0
+    elif config.dataset_name == "imagenet":
+        dataset = create_dataset_imagenet(config.val_data_path, 1, False, imagenet_cfg=config)
+        if not config.use_label_smooth:
+            config.label_smooth_factor = 0.0
         loss = CrossEntropySmooth(sparse=True, reduction="mean",
-                                  smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
-        net = GoogleNet(num_classes=cfg.num_classes)
+                                  smooth_factor=config.label_smooth_factor, num_classes=config.num_classes)
+        net = GoogleNet(num_classes=config.num_classes)
         model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
 
     else:
         raise ValueError("dataset is not support.")
 
-    device_target = cfg.device_target
-    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
+    device_target = config.device_target
+    context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
     if device_target == "Ascend":
-        context.set_context(device_id=cfg.device_id)
-
-    if args_opt.checkpoint_path is not None:
-        param_dict = load_checkpoint(args_opt.checkpoint_path)
-        print("load checkpoint from [{}].".format(args_opt.checkpoint_path))
-    else:
-        param_dict = load_checkpoint(cfg.checkpoint_path)
-        print("load checkpoint from [{}].".format(cfg.checkpoint_path))
+        context.set_context(device_id=get_device_id())
 
+    param_dict = load_checkpoint(config.checkpoint_path)
+    print("load checkpoint from [{}].".format(config.checkpoint_path))
     load_param_into_net(net, param_dict)
+
     net.set_train(False)
 
     acc = model.eval(dataset)
     print("accuracy: ", acc)
+
+
+if __name__ == '__main__':
+    run_eval()
diff --git a/model_zoo/official/cv/googlenet/export.py b/model_zoo/official/cv/googlenet/export.py
index e701a406d3a..0dc4b25dcab 100644
--- a/model_zoo/official/cv/googlenet/export.py
+++ b/model_zoo/official/cv/googlenet/export.py
@@ -16,7 +16,6 @@
 ##############export checkpoint file into air, onnx, mindir models#################
 python export.py
 """
-import argparse
 import numpy as np
 
 import mindspore as ms
@@ -25,26 +24,17 @@ from mindspore import Tensor, load_checkpoint, load_param_into_net, export, cont
 from src.config import cifar_cfg, imagenet_cfg
 from src.googlenet import GoogleNet
 
-parser = argparse.ArgumentParser(description='Classification')
-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="googlenet", 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")
-parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
-                    help='dataset name.')
-args = parser.parse_args()
+from model_utils.config import config
+from model_utils.device_adapter import get_device_id
 
-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=get_device_id())
 
 if __name__ == '__main__':
-    if args.dataset_name == 'cifar10':
+    if config.dataset_name == 'cifar10':
         cfg = cifar_cfg
-    elif args.dataset_name == 'imagenet':
+    elif config.dataset_name == 'imagenet':
         cfg = imagenet_cfg
     else:
         raise ValueError("dataset is not support.")
@@ -52,8 +42,8 @@ if __name__ == '__main__':
     net = GoogleNet(num_classes=cfg.num_classes)
 
     assert cfg.checkpoint_path is not None, "cfg.checkpoint_path is None."
-    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, cfg.image_height, cfg.image_width]), 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, cfg.image_height, cfg.image_width]), ms.float32)
+    export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
diff --git a/model_zoo/official/cv/googlenet/imagenet_config.yaml b/model_zoo/official/cv/googlenet/imagenet_config.yaml
new file mode 100644
index 00000000000..1e54721aa40
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/imagenet_config.yaml
@@ -0,0 +1,67 @@
+# 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"
+need_modelarts_dataset_unzip: True
+modelarts_dataset_unzip_name: "ImageNet"
+
+# ==============================================================================
+# options
+dataset_name: "imagenet"
+name: "imagenet"
+pre_trained: False
+num_classes: 1000
+lr_init: 0.1
+batch_size: 256
+epoch_size: 300
+momentum: 0.9
+weight_decay: 0.0001 #1e-4
+image_height: 224
+image_width: 224
+train_data_path: "/cache/data/ImageNet/train/"
+val_data_path: "/cache/data/ImageNet/validation_preprocess/"
+keep_checkpoint_max: 10
+checkpoint_path: ""
+onnx_filename: "googlenet"
+air_filename: "googlenet"
+ckpt_save_dir: "./ckpt/"
+
+# optimizer and lr related
+lr_scheduler: "exponential"
+lr_epochs: [70, 140, 210, 280]
+lr_gamma: 0.3
+eta_min: 0.0
+T_max: 150
+warmup_epochs: 0
+
+# loss related
+is_dynamic_loss_scale: 0
+loss_scale: 1024
+label_smooth_factor: 0.1
+use_label_smooth: True
+
+# export option
+ckpt_file: ""
+file_name: "googlenet"
+file_format: "AIR"
+#batch_size: 1
+
+---
+
+# Help description for each configuration
+enable_modelarts: "Whether training on modelarts, default: False"
+data_url: "Url for modelarts"
+train_url: "Url for modelarts"
+data_path: "The location of the input data."
+output_path: "The location of the output file."
+device_target: 'Target device type'
+enable_profiling: 'Whether enable profiling while training, default: False'
+
+file_format: "choices in ['AIR', 'ONNX', 'MINDIR']"
\ No newline at end of file
diff --git a/model_zoo/official/cv/googlenet/model_utils/__init__.py b/model_zoo/official/cv/googlenet/model_utils/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/model_zoo/official/cv/googlenet/model_utils/config.py b/model_zoo/official/cv/googlenet/model_utils/config.py
new file mode 100644
index 00000000000..2c191e9f748
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/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/googlenet/model_utils/device_adapter.py b/model_zoo/official/cv/googlenet/model_utils/device_adapter.py
new file mode 100644
index 00000000000..7c5d7f837dd
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/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/googlenet/model_utils/local_adapter.py b/model_zoo/official/cv/googlenet/model_utils/local_adapter.py
new file mode 100644
index 00000000000..769fa6dc78e
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/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/googlenet/model_utils/moxing_adapter.py b/model_zoo/official/cv/googlenet/model_utils/moxing_adapter.py
new file mode 100644
index 00000000000..25838a7da99
--- /dev/null
+++ b/model_zoo/official/cv/googlenet/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/googlenet/scripts/run_eval.sh b/model_zoo/official/cv/googlenet/scripts/run_eval.sh
index dd7a4d453b9..1f4ad1d7193 100644
--- a/model_zoo/official/cv/googlenet/scripts/run_eval.sh
+++ b/model_zoo/official/cv/googlenet/scripts/run_eval.sh
@@ -30,5 +30,7 @@ then
     fi
     dataset_type=$1
 fi
+config_path="./${dataset_type}_config.yaml"
+echo "config path is : ${config_path}"
 
-python ${BASEPATH}/../eval.py --dataset_name=$dataset_type > ./eval.log 2>&1 &
+python ${BASEPATH}/../eval.py --config_path=$config_path --dataset_name=$dataset_type > ./eval.log 2>&1 &
diff --git a/model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh b/model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh
index 0f422ec775c..e908e6a99d0 100644
--- a/model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh
+++ b/model_zoo/official/cv/googlenet/scripts/run_eval_gpu.sh
@@ -39,6 +39,8 @@ then
     fi
     dataset_type=$2
 fi
+config_path="./${dataset_type}_config.yaml"
+echo "config path is : ${config_path}"
 
 BASEPATH=$(cd "`dirname $0`" || exit; pwd)
 export PYTHONPATH=${BASEPATH}:$PYTHONPATH
@@ -51,4 +53,4 @@ fi
 mkdir ../eval
 cd ../eval || exit
 
-python3 ${BASEPATH}/../eval.py --checkpoint_path=$1 --dataset_name=$dataset_type > ./eval.log 2>&1 &
+python3 ${BASEPATH}/../eval.py --config_path=$config_path --checkpoint_path=$1 --dataset_name=$dataset_type > ./eval.log 2>&1 &
diff --git a/model_zoo/official/cv/googlenet/scripts/run_train.sh b/model_zoo/official/cv/googlenet/scripts/run_train.sh
index 1823e6ecc22..e5c72b607b7 100644
--- a/model_zoo/official/cv/googlenet/scripts/run_train.sh
+++ b/model_zoo/official/cv/googlenet/scripts/run_train.sh
@@ -37,14 +37,15 @@ then
     fi
     dataset_type=$2
 fi
-
+config_path="./${dataset_type}_config.yaml"
+echo "config path is : ${config_path}"
 
 ulimit -u unlimited
 export DEVICE_NUM=8
 export RANK_SIZE=8
-RANK_TABLE_FILE=$(realpath $1)
-export RANK_TABLE_FILE
-echo "RANK_TABLE_FILE=${RANK_TABLE_FILE}"
+PATH1=$(realpath $1)
+export RANK_TABLE_FILE=$PATH1
+echo "RANK_TABLE_FILE=${PATH1}"
 
 export SERVER_ID=0
 rank_start=$((DEVICE_NUM * SERVER_ID))
@@ -55,10 +56,12 @@ do
     rm -rf ./train_parallel$i
     mkdir ./train_parallel$i
     cp -r ./src ./train_parallel$i
+    cp -r ./model_utils ./train_parallel$i
+    cp -r ./*.yaml ./train_parallel$i
     cp ./train.py ./train_parallel$i
     echo "start training for rank $RANK_ID, device $DEVICE_ID, $dataset_type"
     cd ./train_parallel$i ||exit
     env > env.log
-    python train.py --device_id=$i --dataset_name=$dataset_type> log 2>&1 &
+    python train.py --config_path=$config_path --dataset_name=$dataset_type> log 2>&1 &
     cd ..
 done
diff --git a/model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh b/model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh
index 75f37c905a9..4ba5756e326 100644
--- a/model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh
+++ b/model_zoo/official/cv/googlenet/scripts/run_train_gpu.sh
@@ -53,12 +53,14 @@ then
     fi
     dataset_type=$3
 fi
+config_path="./${dataset_type}_config.yaml"
+echo "config path is : ${config_path}"
 
 
 if [ $1 -gt 1 ]
 then
     mpirun -n $1 --allow-run-as-root --output-filename log_output --merge-stderr-to-stdout \
-    python3 ${BASEPATH}/../train.py --dataset_name=$dataset_type > train.log 2>&1 &
+    python3 ${BASEPATH}/../train.py --config_path=$config_path --dataset_name=$dataset_type > train.log 2>&1 &
 else
-    python3 ${BASEPATH}/../train.py --dataset_name=$dataset_type > train.log 2>&1 &
+    python3 ${BASEPATH}/../train.py --config_path=$config_path --dataset_name=$dataset_type > train.log 2>&1 &
 fi
diff --git a/model_zoo/official/cv/googlenet/src/config.py b/model_zoo/official/cv/googlenet/src/config.py
deleted file mode 100644
index 13873d86c88..00000000000
--- a/model_zoo/official/cv/googlenet/src/config.py
+++ /dev/null
@@ -1,73 +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
-
-cifar_cfg = edict({
-    'name': 'cifar10',
-    'pre_trained': False,
-    'num_classes': 10,
-    'lr_init': 0.1,
-    'batch_size': 128,
-    'epoch_size': 125,
-    'momentum': 0.9,
-    'weight_decay': 5e-4,
-    'image_height': 224,
-    'image_width': 224,
-    'data_path': './cifar10',
-    'device_target': 'Ascend',
-    'device_id': 0,
-    'keep_checkpoint_max': 10,
-    'checkpoint_path': './train_googlenet_cifar10-125_390.ckpt',
-    'onnx_filename': 'googlenet',
-    'air_filename': 'googlenet'
-})
-
-imagenet_cfg = edict({
-    'name': 'imagenet',
-    'pre_trained': False,
-    'num_classes': 1000,
-    'lr_init': 0.1,
-    'batch_size': 256,
-    'epoch_size': 300,
-    'momentum': 0.9,
-    'weight_decay': 1e-4,
-    'image_height': 224,
-    'image_width': 224,
-    'data_path': './ImageNet_Original/train/',
-    'val_data_path': './ImageNet_Original/val/',
-    'device_target': 'Ascend',
-    'device_id': 0,
-    'keep_checkpoint_max': 10,
-    'checkpoint_path': None,
-    'onnx_filename': 'googlenet',
-    'air_filename': 'googlenet',
-
-    # optimizer and lr related
-    'lr_scheduler': 'exponential',
-    'lr_epochs': [70, 140, 210, 280],
-    'lr_gamma': 0.3,
-    'eta_min': 0.0,
-    'T_max': 150,
-    'warmup_epochs': 0,
-
-    # loss related
-    'is_dynamic_loss_scale': 0,
-    'loss_scale': 1024,
-    'label_smooth_factor': 0.1,
-    'use_label_smooth': True,
-})
diff --git a/model_zoo/official/cv/googlenet/src/dataset.py b/model_zoo/official/cv/googlenet/src/dataset.py
index e95170b9232..e7a82f05d16 100644
--- a/model_zoo/official/cv/googlenet/src/dataset.py
+++ b/model_zoo/official/cv/googlenet/src/dataset.py
@@ -21,20 +21,18 @@ import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.vision.c_transforms as vision
-from src.config import cifar_cfg, imagenet_cfg
 
-
-def create_dataset_cifar10(data_home, repeat_num=1, training=True):
+def create_dataset_cifar10(data_home, repeat_num=1, training=True, cifar_cfg=None):
     """Data operations."""
     data_dir = os.path.join(data_home, "cifar-10-batches-bin")
     if not training:
         data_dir = os.path.join(data_home, "cifar-10-verify-bin")
 
-    rank_size, rank_id = _get_rank_info()
     if training:
+        rank_size, rank_id = _get_rank_info()
         data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=True)
     else:
-        data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, shuffle=False)
+        data_set = ds.Cifar10Dataset(data_dir, shuffle=False)
 
     resize_height = cifar_cfg.image_height
     resize_width = cifar_cfg.image_width
@@ -67,7 +65,7 @@ def create_dataset_cifar10(data_home, repeat_num=1, training=True):
 
 
 def create_dataset_imagenet(dataset_path, repeat_num=1, training=True,
-                            num_parallel_workers=None, shuffle=None):
+                            num_parallel_workers=None, shuffle=None, imagenet_cfg=None):
     """
     create a train or eval imagenet2012 dataset for resnet50
 
@@ -81,14 +79,15 @@ def create_dataset_imagenet(dataset_path, repeat_num=1, training=True,
     Returns:
         dataset
     """
-
-    device_num, rank_id = _get_rank_info()
-
-    if device_num == 1:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle)
+    if training:
+        device_num, rank_id = _get_rank_info()
+        if device_num == 1:
+            data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle)
+        else:
+            data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle,
+                                             num_shards=device_num, shard_id=rank_id)
     else:
-        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle,
-                                         num_shards=device_num, shard_id=rank_id)
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=num_parallel_workers, shuffle=shuffle)
 
     assert imagenet_cfg.image_height == imagenet_cfg.image_width, "image_height not equal image_width"
     image_size = imagenet_cfg.image_height
diff --git a/model_zoo/official/cv/googlenet/train.py b/model_zoo/official/cv/googlenet/train.py
index 835a21af87f..6940b962182 100644
--- a/model_zoo/official/cv/googlenet/train.py
+++ b/model_zoo/official/cv/googlenet/train.py
@@ -16,15 +16,15 @@
 #################train googlent example on cifar10########################
 python train.py
 """
-import argparse
 import os
+import time
 
 import numpy as np
 
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore import context
-from mindspore.communication.management import init, get_rank
+from mindspore.communication.management import init
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
@@ -33,11 +33,14 @@ from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import set_seed
 
-from src.config import cifar_cfg, imagenet_cfg
 from src.dataset import create_dataset_cifar10, create_dataset_imagenet
 from src.googlenet import GoogleNet
 from src.CrossEntropySmooth import CrossEntropySmooth
 
+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)
 
 def lr_steps_cifar10(global_step, lr_max=None, total_epochs=None, steps_per_epoch=None):
@@ -86,53 +89,107 @@ def lr_steps_imagenet(_cfg, steps_per_epoch):
     return _lr
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Classification')
-    parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
-                        help='dataset name.')
-    parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
-    args_opt = parser.parse_args()
-
-    if args_opt.dataset_name == "cifar10":
-        cfg = cifar_cfg
-    elif args_opt.dataset_name == "imagenet":
-        cfg = imagenet_cfg
-    else:
-        raise ValueError("Unsupported dataset.")
-
-    # set context
-    device_target = cfg.device_target
-
-    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
-    device_num = int(os.environ.get("DEVICE_NUM", 1))
-
-    rank = 0
-    if device_target == "Ascend":
-        if args_opt.device_id is not None:
-            context.set_context(device_id=args_opt.device_id)
+def get_param_groups(network):
+    """ get param groups """
+    decay_params = []
+    no_decay_params = []
+    for x in network.trainable_params():
+        parameter_name = x.name
+        if parameter_name.endswith('.bias'):
+            # all bias not using weight decay
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.gamma'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
+        elif parameter_name.endswith('.beta'):
+            # bn weight bias not using weight decay, be carefully for now x not include BN
+            no_decay_params.append(x)
         else:
-            context.set_context(device_id=cfg.device_id)
+            decay_params.append(x)
 
+    return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
+
+
+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_save_dir = os.path.join(config.output_path, config.ckpt_save_dir)
+
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_train():
+    cfg = config
+    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
+    device_num = get_device_num()
+
+    if cfg.device_target == "Ascend":
+        context.set_context(device_id=get_device_id())
         if device_num > 1:
             context.reset_auto_parallel_context()
             context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             init()
-            rank = get_rank()
-    elif device_target == "GPU":
+    elif cfg.device_target == "GPU":
         if device_num > 1:
             init()
             context.reset_auto_parallel_context()
             context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
-            rank = get_rank()
     else:
         raise ValueError("Unsupported platform.")
 
-    if args_opt.dataset_name == "cifar10":
-        dataset = create_dataset_cifar10(cfg.data_path, 1)
-    elif args_opt.dataset_name == "imagenet":
-        dataset = create_dataset_imagenet(cfg.data_path, 1)
+    if cfg.dataset_name == "cifar10":
+        dataset = create_dataset_cifar10(cfg.train_data_path, 1, cifar_cfg=cfg)
+    elif cfg.dataset_name == "imagenet":
+        dataset = create_dataset_imagenet(cfg.train_data_path, 1, imagenet_cfg=cfg)
     else:
         raise ValueError("Unsupported dataset.")
 
@@ -145,7 +202,7 @@ if __name__ == '__main__':
         load_param_into_net(net, param_dict)
 
     loss_scale_manager = None
-    if args_opt.dataset_name == 'cifar10':
+    if cfg.dataset_name == 'cifar10':
         lr = lr_steps_cifar10(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()),
                        learning_rate=Tensor(lr),
@@ -153,31 +210,9 @@ if __name__ == '__main__':
                        weight_decay=cfg.weight_decay)
         loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 
-    elif args_opt.dataset_name == 'imagenet':
+    elif cfg.dataset_name == 'imagenet':
         lr = lr_steps_imagenet(cfg, batch_num)
 
-
-        def get_param_groups(network):
-            """ get param groups """
-            decay_params = []
-            no_decay_params = []
-            for x in network.trainable_params():
-                parameter_name = x.name
-                if parameter_name.endswith('.bias'):
-                    # all bias not using weight decay
-                    no_decay_params.append(x)
-                elif parameter_name.endswith('.gamma'):
-                    # bn weight bias not using weight decay, be carefully for now x not include BN
-                    no_decay_params.append(x)
-                elif parameter_name.endswith('.beta'):
-                    # bn weight bias not using weight decay, be carefully for now x not include BN
-                    no_decay_params.append(x)
-                else:
-                    decay_params.append(x)
-
-            return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
-
-
         if cfg.is_dynamic_loss_scale:
             cfg.loss_scale = 1
 
@@ -201,13 +236,17 @@ if __name__ == '__main__':
 
     config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
     time_cb = TimeMonitor(data_size=batch_num)
-    ckpt_save_dir = "./ckpt_" + str(rank) + "/"
-    ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_" + args_opt.dataset_name, directory=ckpt_save_dir,
+    ckpoint_cb = ModelCheckpoint(prefix="train_googlenet_" + cfg.dataset_name, directory=config.ckpt_save_dir,
                                  config=config_ck)
     loss_cb = LossMonitor()
 
     cbs = [time_cb, ckpoint_cb, loss_cb]
-    if device_num > 1 and rank != 0:
+    device_id = get_device_id()
+    if device_num > 1 and device_id != 0:
         cbs = [time_cb, loss_cb]
     model.train(cfg.epoch_size, dataset, callbacks=cbs)
     print("train success")
+
+
+if __name__ == '__main__':
+    run_train()