diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md
index 8d45551e76b9c03202191f3bcf624e0f9622e4be..4c61f175a3a0f9f2b5e403c7c40eaaa86f5f266f 100644
--- a/tutorials/source_en/advanced_use/distributed_training.md
+++ b/tutorials/source_en/advanced_use/distributed_training.md
@@ -256,11 +256,13 @@ device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=device_id) # set device_id
 
-def test_train_cifar(num_classes=10, epoch_size=10):
+def test_train_cifar(epoch_size=10):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True)
     loss_cb = LossMonitor()
-    dataset = create_dataset(epoch_size)
-    net = resnet50(32, num_classes)
+    dataset = create_dataset(data_path, epoch_size)
+    batch_size = 32
+    num_classes = 10
+    net = resnet50(batch_size, num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
@@ -342,17 +344,14 @@ The running time is about 5 minutes, which is mainly occupied by operator compil
 Log files are saved in the device directory. The env.log file records environment variable information. The train.log file records the loss function information. The following is an example:
 
 ```
-resnet50_distributed_training.py::test_train_feed ===============ds_num 195
-global_step: 194, loss: 1.997
-global_step: 389, loss: 1.655
-global_step: 584, loss: 1.723
-global_step: 779, loss: 1.807
-global_step: 974, loss: 1.417
-global_step: 1169, loss: 1.195
-global_step: 1364, loss: 1.238
-global_step: 1559, loss: 1.456
-global_step: 1754, loss: 0.987
-global_step: 1949, loss: 1.035
-end training
-PASSED
+epoch: 1 step: 156, loss is 2.0084016
+epoch: 2 step: 156, loss is 1.6407638
+epoch: 3 step: 156, loss is 1.6164391
+epoch: 4 step: 156, loss is 1.6838071
+epoch: 5 step: 156, loss is 1.6320667
+epoch: 6 step: 156, loss is 1.3098773
+epoch: 7 step: 156, loss is 1.3515002
+epoch: 8 step: 156, loss is 1.2943741
+epoch: 9 step: 156, loss is 1.2316195
+epoch: 10 step: 156, loss is 1.1533381
 ```
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md
index dab05866784faa207701883517327640db44fc8a..4ca5fb796cd59a1dda3ebec43bf50bef749ed95c 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md
@@ -254,11 +254,13 @@ device_id = int(os.getenv('DEVICE_ID'))
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_id=device_id) # set device_id
 
-def test_train_cifar(num_classes=10, epoch_size=10):
+def test_train_cifar(epoch_size=10):
     context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, mirror_mean=True)
     loss_cb = LossMonitor()
-    dataset = create_dataset(epoch_size)
-    net = resnet50(32, num_classes)
+    dataset = create_dataset(data_path, epoch_size)
+    batch_size = 32
+    num_classes = 10
+    net = resnet50(batch_size, num_classes)
     loss = SoftmaxCrossEntropyExpand(sparse=True)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
@@ -340,17 +342,14 @@ cd ../
 日志文件保存device目录下，env.log中记录了环境变量的相关信息，关于Loss部分结果保存在train.log中，示例如下：
 
 ```
-resnet50_distributed_training.py::test_train_feed ===============ds_num 195
-global_step: 194, loss: 1.997
-global_step: 389, loss: 1.655
-global_step: 584, loss: 1.723
-global_step: 779, loss: 1.807
-global_step: 974, loss: 1.417
-global_step: 1169, loss: 1.195
-global_step: 1364, loss: 1.238
-global_step: 1559, loss: 1.456
-global_step: 1754, loss: 0.987
-global_step: 1949, loss: 1.035
-end training
-PASSED
+epoch: 1 step: 156, loss is 2.0084016
+epoch: 2 step: 156, loss is 1.6407638
+epoch: 3 step: 156, loss is 1.6164391
+epoch: 4 step: 156, loss is 1.6838071
+epoch: 5 step: 156, loss is 1.6320667
+epoch: 6 step: 156, loss is 1.3098773
+epoch: 7 step: 156, loss is 1.3515002
+epoch: 8 step: 156, loss is 1.2943741
+epoch: 9 step: 156, loss is 1.2316195
+epoch: 10 step: 156, loss is 1.1533381
 ```