diff --git a/docs-site/docs/optimizer.md b/docs-site/docs/optimizer.md
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+---
+id: optimizer
+title: Optimizer
+---
+
+<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you 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.  -->
+
+SINGA supports various popular optimizers including stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad, etc. For each of the optimizer, it supports to use a decay schedular to schedule the learning rate to be applied in different epochs. The optimizers and the decay schedulers are included in `singa/opt.py`.
+
+## Create an optimizer
+
+1. SGD with momentum
+
+```python
+# define hyperparameter learning rate
+lr = 0.001
+# define hyperparameter momentum
+momentum = 0.9
+# define hyperparameter weight decay 
+weight_decay = 0.0001
+
+from singa import opt
+sgd = opt.SGD(lr=lr, momentum=momentum, weight_decay=weight_decay)
+```
+
+2. RMSProp
+
+```python
+# define hyperparameter learning rate
+lr = 0.001
+# define hyperparameter rho
+rho = 0.9
+# define hyperparameter epsilon 
+epsilon = 1e-8
+# define hyperparameter weight decay 
+weight_decay = 0.0001
+
+from singa import opt
+sgd = opt.RMSProp(lr=lr, rho=rho, epsilon=epsilon, weight_decay=weight_decay)
+```
+
+3. AdaGrad
+
+```python
+# define hyperparameter learning rate
+lr = 0.001
+# define hyperparameter epsilon 
+epsilon = 1e-8
+# define hyperparameter weight decay 
+weight_decay = 0.0001
+
+from singa import opt
+sgd = opt.AdaGrad(lr=lr, epsilon=epsilon, weight_decay=weight_decay)
+```
+
+4. Adam
+
+```python
+# define hyperparameter learning rate
+lr = 0.001
+# define hyperparameter beta 1 
+beta_1= 0.9
+# define hyperparameter beta 2 
+beta_1= 0.999
+# define hyperparameter epsilon 
+epsilon = 1e-8
+# define hyperparameter weight decay 
+weight_decay = 0.0001
+
+from singa import opt
+sgd = opt.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, weight_decay=weight_decay)
+```
+
+## Create a Decay Scheduler
+
+```python
+from singa import opt
+
+# define initial learning rate
+lr_init = 0.001
+# define the rate of decay in the decay scheduler
+decay_rate = 0.95
+# define whether the learning rate schedule is a staircase shape
+staircase=True
+# define the decay step of the decay scheduler (in this example the lr is decreased at every 2 steps)
+decay_steps = 2
+
+# create the decay scheduler, the schedule of lr becomes lr_init * (decay_rate ^ (step // decay_steps) )
+lr = opt.ExponentialDecay(0.1, 2, 0.5, True)
+# Use the lr to create an optimizer
+sgd = opt.SGD(lr=lr, momentum=0.9, weight_decay=0.0001)
+```
+
+## Use the optimizer in Model API
+
+When we create the model, we need to attach the optimizer to the model.
+
+```python
+# create a CNN using the Model API
+model = CNN()
+
+# initialize optimizer and attach it to the model
+sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
+model.set_optimizer(sgd)
+```
+
+Then, when we call the model, it runs the `train_one_batch` method that utilizes the optimizer.
+
+Hence, an example of an iterative loop to optimize the model is:
+
+```python
+for b in range(num_train_batch):
+    # generate the next mini-batch
+    x, y = ...
+
+    # Copy the data into input tensors
+    tx.copy_from_numpy(x)
+    ty.copy_from_numpy(y)
+
+    # Training with one batch
+    out, loss = model(tx, ty)
+```
\ No newline at end of file
diff --git a/docs-site/docs/time-profiling.md b/docs-site/docs/time-profiling.md
new file mode 100644
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+++ b/docs-site/docs/time-profiling.md
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+---
+id: time-profiling
+title: Time Profiling
+---
+
+<!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you 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.  -->
+
+SINGA supports time profiling of each of the operator buffered in the graph. To utilize the time profiling function, 
+
+
+
+
diff --git a/docs-site/website/sidebars.json b/docs-site/website/sidebars.json
index 2db3746..7bbe66a 100644
--- a/docs-site/website/sidebars.json
+++ b/docs-site/website/sidebars.json
@@ -9,9 +9,11 @@
       "device",
       "tensor",
       "autograd",
+      "optimizer",
       "graph",
       "onnx",
-      "dist-train"
+      "dist-train",
+      "time-profiling"
     ],
     "Development": [
       "download-singa",