Update README.md for the demo of distributed sampler (#577)

examples/mxnet/sampling/dis_sampling/README.md

@@ -15,7 +15,9 @@ pip install mxnet --pre
 
 ### Usage
 
-To run the following demos, you need to start trainer and sampler process on different machines by changing the `--ip`. You can also change the number of sampler by the `--num-sampler` option.
+Assume that the user has already launched two instances (`instance_0` & `instance_1`) on AWS EC2, and also these two instances have the correct authority to access each other by TCP/IP protocol. Now we can treat `instance_0` as `Trainer` and `instance_1` as `Sampler`. Then, the user can start the trainer process and sampler process on these two instances separately. We have already provided a set of scripts to start the trainer and sampler process and users just need to change the `--ip` to their own IP address.
+
+For the sampler instance_0, users can change the `--num-sampler` option to set the number of the sampler. The `sampler.py` script will start `--num-sampler` processes concurrently to maximalize the system utilization. Users can also launch many samplers in parallel across a set of machines. For example, if we have `10` sampler instance and for each instance, we set the `--num-sampler` to `2`, we need to set the `--num-sampler` to `20`.
 
 ### Neighbor Sampling & Skip Connection
 

examples/pytorch/sampling/dis_sampling/README.md

@@ -15,7 +15,9 @@ pip install torch requests
 
 ### Usage
 
-To run the following demos, you need to start trainer and sampler process on different machines by changing the `--ip`. You can also change the number of sampler by the `--num-sampler` option.
+Assume that the user has already launched two instances (`instance_0` & `instance_1`) on AWS EC2, and also these two instances have the correct authority to access each other by TCP/IP protocol. Now we can treat `instance_0` as `Trainer` and `instance_1` as `Sampler`. Then, the user can start the trainer process and sampler process on these two instances separately. We have already provided a set of scripts to start the trainer and sampler process and users just need to change the `--ip` to their own IP address.
+
+For the sampler instance_0, users can change the `--num-sampler` option to set the number of the sampler. The `sampler.py` script will start `--num-sampler` processes concurrently to maximalize the system utilization. Users can also launch many samplers in parallel across a set of machines. For example, if we have `10` sampler instance and for each instance, we set the `--num-sampler` to `2`, we need to set the `--num-sampler` to `20`.
 
 ### Neighbor Sampling & Skip Connection