diff --git a/demo/components/profiler_test.py b/demo/components/profiler_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c467bc0717848438bb4f3170989793be656334cb
--- /dev/null
+++ b/demo/components/profiler_test.py
@@ -0,0 +1,131 @@
+import numpy as np
+import paddle
+import paddle.nn.functional as F
+import paddle.profiler as profiler
+from paddle.vision.transforms import ToTensor
+
+transform = ToTensor()
+cifar10_train = paddle.vision.datasets.Cifar10(
+    mode='train', transform=transform)
+cifar10_test = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+
+class MyNet(paddle.nn.Layer):
+    def __init__(self, num_classes=1):
+        super(MyNet, self).__init__()
+
+        self.conv1 = paddle.nn.Conv2D(
+            in_channels=3, out_channels=32, kernel_size=(3, 3))
+        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
+
+        self.conv2 = paddle.nn.Conv2D(
+            in_channels=32, out_channels=64, kernel_size=(3, 3))
+        self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
+
+        self.conv3 = paddle.nn.Conv2D(
+            in_channels=64, out_channels=64, kernel_size=(3, 3))
+
+        self.flatten = paddle.nn.Flatten()
+
+        self.linear1 = paddle.nn.Linear(in_features=1024, out_features=64)
+        self.linear2 = paddle.nn.Linear(
+            in_features=64, out_features=num_classes)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.pool1(x)
+
+        x = self.conv2(x)
+        x = F.relu(x)
+        x = self.pool2(x)
+
+        x = self.conv3(x)
+        x = F.relu(x)
+
+        x = self.flatten(x)
+        x = self.linear1(x)
+        x = F.relu(x)
+        x = self.linear2(x)
+        return x
+
+
+epoch_num = 10
+batch_size = 32
+learning_rate = 0.001
+val_acc_history = []
+val_loss_history = []
+
+
+def train(model):
+    print('start training ... ')
+    # turn into training mode
+    model.train()
+
+    opt = paddle.optimizer.Adam(
+        learning_rate=learning_rate, parameters=model.parameters())
+
+    train_loader = paddle.io.DataLoader(
+        cifar10_train, shuffle=True, batch_size=batch_size, num_workers=1)
+
+    valid_loader = paddle.io.DataLoader(cifar10_test, batch_size=batch_size)
+
+    # 创建性能分析器相关的代码
+    def my_on_trace_ready(prof):  # 定义回调函数，性能分析器结束采集数据时会被调用
+        callback = profiler.export_chrome_tracing(
+            './profiler_demo')  # 创建导出性能数据到 profiler_demo 文件夹的回调函数
+        callback(prof)  # 执行该导出函数
+        prof.summary(
+            sorted_by=profiler.SortedKeys.GPUTotal)  # 打印表单，按 GPUTotal 排序表单项
+
+    p = profiler.Profiler(
+        scheduler=[3, 14], on_trace_ready=my_on_trace_ready,
+        timer_only=False)  # 初始化 Profiler 对象
+
+    p.start()  # 性能分析器进入第 0 个 step
+
+    for epoch in range(epoch_num):
+        for batch_id, data in enumerate(train_loader()):
+            x_data = data[0]
+            y_data = paddle.to_tensor(data[1])
+            y_data = paddle.unsqueeze(y_data, 1)
+
+            logits = model(x_data)
+            loss = F.cross_entropy(logits, y_data)
+
+            if batch_id % 1000 == 0:
+                print("epoch: {}, batch_id: {}, loss is: {}".format(
+                    epoch, batch_id, loss.numpy()))
+            loss.backward()
+            opt.step()
+            opt.clear_grad()
+
+            p.step()  # 指示性能分析器进入下一个 step
+            if batch_id == 19:
+                p.stop()  # 关闭性能分析器
+                exit()  # 做性能分析时，可以将程序提前退出
+
+        # evaluate model after one epoch
+        model.eval()
+        accuracies = []
+        losses = []
+        for batch_id, data in enumerate(valid_loader()):
+            x_data = data[0]
+            y_data = paddle.to_tensor(data[1])
+            y_data = paddle.unsqueeze(y_data, 1)
+
+            logits = model(x_data)
+            loss = F.cross_entropy(logits, y_data)
+            acc = paddle.metric.accuracy(logits, y_data)
+            accuracies.append(acc.numpy())
+            losses.append(loss.numpy())
+
+        avg_acc, avg_loss = np.mean(accuracies), np.mean(losses)
+        print("[validation] accuracy/loss: {}/{}".format(avg_acc, avg_loss))
+        val_acc_history.append(avg_acc)
+        val_loss_history.append(avg_loss)
+        model.train()
+
+
+model = MyNet(num_classes=10)
+train(model)