diff --git a/tutorials/training/source_en/advanced_use/performance_profiling.md b/tutorials/training/source_en/advanced_use/performance_profiling.md
index 4d9242608d6f1c2a876c704eb46d29ff9c70b818..4fbc78b8f8bf7417cb942273e35e5b2295bd55db 100644
--- a/tutorials/training/source_en/advanced_use/performance_profiling.md
+++ b/tutorials/training/source_en/advanced_use/performance_profiling.md
@@ -44,30 +44,19 @@ The sample code is as follows:
 from mindspore.profiler import Profiler
 from mindspore import Model, nn, context
 
+# Init context env
+context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=int(os.environ["DEVICE_ID"]))
 
-def test_profiler():
-    # Init context env
-    context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=int(os.environ["DEVICE_ID"]))
-
-    # Init Profiler
-    # Note that 'data' directory is created in current path by default. To visualize the profiling data by MindInsight,
-    # 'data' directory should be placed under summary-base-dir.
-    profiler = Profiler()
-
-    # Init hyperparameter
-    epoch = 2
-    # Init network and Model
-    net = Net()
-    loss_fn = CrossEntropyLoss()
-    optim = MyOptimizer(learning_rate=0.01, params=network.trainable_params())
-    model = Model(net, loss_fn=loss_fn, optimizer=optim, metrics=None)  
-    # Prepare mindrecord_dataset for training
-    train_ds = create_mindrecord_dataset_for_training()
-    # Model Train
-    model.train(epoch, train_ds)
-
-    # Profiler end
-    profiler.analyse()
+# Init Profiler
+# Note that 'data' directory is created in current path by default. To visualize the profiling data by MindInsight,
+# 'data' directory should be placed under summary-base-dir.
+profiler = Profiler()
+
+# Model Train
+Model.train()
+
+# Profiler end
+profiler.analyse()
 ```
 
 ## Launch MindInsight
diff --git a/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md b/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
index 3a53f103ad98b2f70529dcfc6a28d464c866c19a..fec0808fabb5b9fbe3acf4f9f98ce7ea1f40bd37 100644
--- a/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
+++ b/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
@@ -48,30 +48,19 @@
 from mindspore.profiler import Profiler
 from mindspore import Model, nn, context
 
+# Init context env
+context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=int(os.environ["DEVICE_ID"]))
 
-def test_profiler():
-    # Init context env
-    context.set_context(mode=context.GRAPH_MODE, device_target='Ascend', device_id=int(os.environ["DEVICE_ID"]))
-
-    # Init Profiler
-    # Note that 'data' directory is created in current path by default. To visualize the profiling data by MindInsight,
-    # 'data' directory should be placed under summary-base-dir.
-    profiler = Profiler()
-
-    # Init hyperparameter
-    epoch = 2
-    # Init network and Model
-    net = Net()
-    loss_fn = CrossEntropyLoss()
-    optim = MyOptimizer(learning_rate=0.01, params=network.trainable_params())
-    model = Model(net, loss_fn=loss_fn, optimizer=optim, metrics=None)  
-    # Prepare mindrecord_dataset for training
-    train_ds = create_mindrecord_dataset_for_training()
-    # Model Train
-    model.train(epoch, train_ds)
-
-    # Profiler end
-    profiler.analyse()
+# Init Profiler
+# Note that 'data' directory is created in current path by default. To visualize the profiling data by MindInsight,
+# 'data' directory should be placed under summary-base-dir.
+profiler = Profiler()
+
+# Model Train
+Model.train()
+
+# Profiler end
+profiler.analyse()
 ```
 
 ## 启动MindInsight