Update doc (#1647)

* add announcement to model compression doc

* update version number to 1.1

README.md

@@ -18,7 +18,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users run automated machi
 The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
 
 
-### **NNI [v1.0](https://github.com/Microsoft/nni/blob/master/docs/en_US/Release_v1.0.md) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **NNI v1.1 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 <p align="center">
   <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -211,7 +211,7 @@ Linux and MacOS
 * Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
 
 ```bash
-    git clone -b v1.0 https://github.com/Microsoft/nni.git
+    git clone -b v1.1 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -221,7 +221,7 @@ Windows
 * Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
 
 ```bash
-  git clone -b v1.0 https://github.com/Microsoft/nni.git
+  git clone -b v1.1 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -237,7 +237,7 @@ The following example is an experiment built on TensorFlow. Make sure you have *
 * Download the examples via clone the source code.
 
 ```bash
-    git clone -b v1.0 https://github.com/Microsoft/nni.git
+    git clone -b v1.1 https://github.com/Microsoft/nni.git
 ```
 
 Linux and MacOS

docs/en_US/Compressor/Overview.md

 # Compressor
+
+We are glad to announce the alpha release for model compression toolkit on top of NNI, it's still in the experiment phase which might evolve based on usage feedback. We'd like to invite you to use, feedback and even contribute.
+
 NNI provides an easy-to-use toolkit to help user design and use compression algorithms. It supports Tensorflow and PyTorch with unified interface. For users to compress their models, they only need to add several lines in their code. There are some popular model compression algorithms built-in in NNI. Users could further use NNI's auto tuning power to find the best compressed model, which is detailed in [Auto Model Compression](./AutoCompression.md). On the other hand, users could easily customize their new compression algorithms using NNI's interface, refer to the tutorial [here](#customize-new-compression-algorithms).
 
 ## Supported algorithms