update readme

README.md

 Fast MoE
 ===
 
+## Introduction
+
 An easy-to-use but efficient implementation of the Mixture of Experts (MoE) 
-model for PyTorch
+model for PyTorch. 
+
+## Installation
+
+PyTorch with CUDA is supported. The repository is currently tested with PyTorch
+v1.6.0 and CUDA 10, with designed compatibility to other versions.
+
+Fast MoE contains a set of PyTorch customized opearators, including both C and
+Python components. Use `python setup.py install` to easily install and enjoy
+using Fast MoE for training.
+
+## Usage 
+
+### Using Fast MoE as a PyTorch module
+
+Examples can be seen in [examples](examples/). The easist way is to replace the
+feed forward layer by the `FMoE` layer.
+
+### Using Fast MoE in Parallel
+
+For data parallel, nothing else is needed.
+
+For expert parallel, in which experts are located separately across workers,
+NCCL and MPI backend are required to be built with PyTorch. Use environment
+variable `USE_NCCL=1` to `setup.py` to enable distributing experts across
+workers. Note that the arguments of the MoE layers should then be excluded from
+the data parallel parameter synchronization list.