diff --git a/python/paddle/distributed/fleet/base/distributed_strategy.py b/python/paddle/distributed/fleet/base/distributed_strategy.py
index 1fe8bf52c92e1dc234a3e99a78c65396bfcc4f0d..2971617aa705f55f193e512bf7ef75b609588c02 100755
--- a/python/paddle/distributed/fleet/base/distributed_strategy.py
+++ b/python/paddle/distributed/fleet/base/distributed_strategy.py
@@ -307,6 +307,30 @@ class DistributedStrategy(object):
 
     @property
     def amp_configs(self):
+        """
+        Set automatic mixed precision training configurations. In general, amp has serveral configurable
+        settings that can be configured through a dict.
+
+        **Notes**:
+            **init_loss_scaling(float)**: The initial loss scaling factor. Default 32768.
+            **use_dynamic_loss_scaling(bool)**: Whether to use dynamic loss scaling. Default True.
+            **incr_every_n_steps(int)**: Increases loss scaling every n consecutive steps with finite gradients. Default 1000.
+            **decr_every_n_nan_or_inf(int)**: Decreases loss scaling every n accumulated steps with nan or inf gradients. Default 2.
+            **incr_ratio(float)**: The multiplier to use when increasing the loss scaling. Default 2.0.
+            **decr_ratio(float)**: The less-than-one-multiplier to use when decreasing the loss scaling. Default 0.5.
+            **custom_white_list(list[str])**: Users' custom white list which always execution fp16.
+            **custom_black_list(list[str])**: Users' custom black list which forbidden execution fp16.
+
+        Examples:
+          .. code-block:: python
+
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.amp = True
+            strategy.amp_configs = {
+                "init_loss_scaling": 32768,
+                "custom_white_list": ['conv2d']}
+        """
         return get_msg_dict(self.strategy.amp_configs)
 
     @amp_configs.setter
@@ -620,6 +644,20 @@ class DistributedStrategy(object):
 
     @property
     def dgc(self):
+        """
+        Indicating whether we are using Deep Gradient Compression training. For more details, please refer to
+        [Deep Gradient Compression](https://arxiv.org/abs/1712.01887).
+
+        Default Value: False
+
+        Examples:
+          .. code-block:: python
+
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.dgc = True # by default this is false
+
+        """
         return self.strategy.dgc
 
     @dgc.setter
@@ -631,6 +669,28 @@ class DistributedStrategy(object):
 
     @property
     def dgc_configs(self):
+        """
+        Set Deep Gradient Compression training configurations. In general, dgc has serveral configurable
+        settings that can be configured through a dict.
+
+        **Notes**:
+            **rampup_begin_step(int)**: The beginning step from which gradient compression is implemented. Default 0.
+            **rampup_step(int)**: Time steps used in sparsity warm-up periods. Default is 1.
+                For example, if the sparsity is [0.75, 0.9375, 0.984375, 0.996, 0.999], and the rampup_step is 100,
+                it will use 0.75 at 0~19 steps, and 0.9375 at 20~39 steps, and so on. And when reach sparsity array
+                ends, it will use 0.999 then and after.
+            **sparsity(list[float])**: Get top important element from gradient tensor, the ratio is (1 - sparsity).
+                Default is [0.999]. For example, if the sparsity is [0.99, 0.999], the top [1%, 0.1%] important
+                element will be transmitted.
+
+        Examples:
+          .. code-block:: python
+
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.dgc = True
+            strategy.dgc_configs = {"rampup_begin_step": 1252}
+        """
         return get_msg_dict(self.strategy.dgc_configs)
 
     @dgc_configs.setter