Update doc for PAIMode and v0.2 release notes (#153)

* Update v0.2 documentation regards to release note and PAI training service

* Update document to describe NNI docker image

docs/PAIMode.md

@@ -48,6 +48,7 @@ Compared with LocalMode and [RemoteMachineMode](RemoteMachineMode.md), trial con
     * Required key. Should be positive number based on your trial program's memory requirement
 * image
     * Required key. In pai mode, your trial program will be scheduled by OpenPAI to run in [Docker container](https://www.docker.com/). This key is used to specify the Docker image used to create the container in which your traill will run. 
+    * We already build a docker image [nnimsra/nni](https://hub.docker.com/r/msranni/nni/) on [Docker Hub](https://hub.docker.com/). It contains NNI python packages, Node modules and javascript artifact files required to start experiment, and all of NNI dependencies. The docker file used to build this image can be found at [here](../deployment/Dockerfile.build.base). You can either use this image directly in your config file, or build your own image based on it.
 * dataDir
     * Optional key. It specifies the HDFS data direcotry for trial to download data. The format should be something like hdfs://{your HDFS host}:9000/{your data directory}
 * outputDir 

docs/RELEASE.md

 # Release 0.2.0 - 9/29/2018
 ## Major Features
-   * Support for [OpenPAI](https://github.com/Microsoft/pai) (aka pai) Training Service 
+   * Support [OpenPAI](https://github.com/Microsoft/pai) (aka pai) Training Service (See [here](./PAIMode.md) for instructions about how to submit NNI job in pai mode)
       * Support training services on pai mode. NNI trials will be scheduled to run on OpenPAI cluster
       * NNI trial's output (including logs and model file) will be copied to OpenPAI HDFS for further debugging and checking
-   * Support [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner
+   * Support [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner (See [here](../src/sdk/pynni/nni/README.md) for instructions about how to use SMAC tuner)
       * [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO to handle categorical parameters. The SMAC supported by NNI is a wrapper on [SMAC3](https://github.com/automl/SMAC3)
    * Support NNI installation on [conda](https://conda.io/docs/index.html) and python virtual environment
    * Others