diff --git a/example/mobilenetv2_imagenet2012/launch.py b/example/mobilenetv2_imagenet2012/launch.py
index 5a8977c64b24decc3c407a1d943bd11579e97e70..bd28e20149ccabdfda6db8842bd2e60b93092a29 100644
--- a/example/mobilenetv2_imagenet2012/launch.py
+++ b/example/mobilenetv2_imagenet2012/launch.py
@@ -15,7 +15,6 @@
 """launch train script"""
 import os
 import sys
-import subprocess
 import json
 from argparse import ArgumentParser
 
@@ -125,25 +124,19 @@ def main():
     sys.stdout.flush()
 
     # spawn the processes
-    current_env = os.environ.copy()
-    current_env["RANK_SIZE"] = str(args.nproc_per_node)
-    if args.nproc_per_node > 1:
-        current_env["MINDSPORE_HCCL_CONFIG_PATH"] = table_fn
-    processes = []
-    cmds = []
     for rank_id in range(0, args.nproc_per_node):
-        current_env["RANK_ID"] = str(rank_id)
-        current_env["DEVICE_ID"] = visible_devices[rank_id]
-        cmd = [sys.executable, "-u"]
-        cmd.append(args.training_script)
-        cmd.extend(args.training_script_args)
-        process = subprocess.Popen(cmd, env=current_env)
-        processes.append(process)
-        cmds.append(cmd)
-    for process, cmd in zip(processes, cmds):
-        process.wait()
-        if process.returncode != 0:
-            raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
+        device_id = visible_devices[rank_id]
+        device_dir = os.path.join(os.getcwd(), 'device{}'.format(rank_id))
+        rank_process = 'export RANK_SIZE={} && export RANK_ID={} && export DEVICE_ID={} && '.format(args.nproc_per_node,
+                                                                                                    rank_id, device_id)
+        if args.nproc_per_node > 1:
+            rank_process += 'export MINDSPORE_HCCL_CONFIG_PATH={} && '.format(table_fn)
+            rank_process += 'export RANK_TABLE_FILE={} && '.format(table_fn)
+        rank_process += 'rm -rf {dir} && mkdir {dir} && cd {dir} && python {script} '.format(dir=device_dir,
+                                                                                             script=args.training_script
+                                                                                             )
+        rank_process += ' '.join(args.training_script_args) + ' > log{}.log 2>&1 &'.format(rank_id)
+        os.system(rank_process)
 
 
 if __name__ == "__main__":
diff --git a/example/mobilenetv2_imagenet2012/train.py b/example/mobilenetv2_imagenet2012/train.py
index 584e89fe431f26e8f4efe7991ba6611c602ae02f..d97eab5f04bac4e69610df8563ec58be70181cdc 100644
--- a/example/mobilenetv2_imagenet2012/train.py
+++ b/example/mobilenetv2_imagenet2012/train.py
@@ -23,6 +23,7 @@ from lr_generator import get_lr
 from config import config
 from mindspore import context
 from mindspore import Tensor
+from mindspore import nn
 from mindspore.model_zoo.mobilenet import mobilenet_v2
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.nn.optim.momentum import Momentum
@@ -110,16 +111,17 @@ class Monitor(Callback):
 
 if __name__ == '__main__':
     if run_distribute:
-        context.set_context(enable_hccl=True)
         context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           parameter_broadcast=True, mirror_mean=True)
         auto_parallel_context().set_all_reduce_fusion_split_indices([140])
         init()
-    else:
-        context.set_context(enable_hccl=False)
 
     epoch_size = config.epoch_size
     net = mobilenet_v2(num_classes=config.num_classes)
+    net.add_flags_recursive(fp16=True)
+    for _, cell in net.cells_and_names():
+        if isinstance(cell, nn.Dense):
+            cell.add_flags_recursive(fp32=True)
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
 
     print("train args: ", args_opt, "\ncfg: ", config,
@@ -135,8 +137,7 @@ if __name__ == '__main__':
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
                    config.weight_decay, config.loss_scale)
 
-    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, amp_level='O0',
-                  keep_batchnorm_fp32=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale)
 
     cb = None
     if rank_id == 0: