update nccl installation method and write readme

README.md

@@ -22,8 +22,16 @@ 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.
 
-The distributed expert feature is enabled by default. If you want to disable
-it, pass environment variable `USE_NCCL=0` to the setup script.
+The distributed expert feature is disabled by default. If you want to disable
+it, pass environment variable `USE_NCCL=1` to the setup script.
+
+Note that an extra NCCL developer package is needed, which has to be consistant
+with your PyTorch's NCCL version, which can be inspected by running
+`torch.cuda.nccl.version()`. The [official PyTorch docker image]() is
+recommended, as the environment is well-setup there. Otherwise, you can access
+the [download link of all NCCL
+versions](https://developer.nvidia.com/nccl/nccl-legacy-downloads) to download
+the NCCL package that is suitable for you.
 
 ## Usage 
 

setup.py

@@ -10,6 +10,7 @@ cxx_flags = [
 ext_libs = []
 if os.environ.get('USE_NCCL', '0') == '1':
     cxx_flags.append('-DMOE_USE_NCCL')
+    ext_libs.append('nccl')
 
 
 if __name__ == '__main__':