Add doc about frameworks and libraries (#1407)

Add doc about frameworks and libraries

docs/en_US/CommunitySharings/HpoComparision.md

-# Hyperparameter Optimization Comparison
+# Hyper Parameter Optimization Comparison
 *Posted by Anonymous Author*
 
-Comparison of Hyperparameter Optimization algorithms on several problems.
+Comparison of Hyperparameter Optimization (HPO) algorithms on several problems.
 
 Hyperparameter Optimization algorithms are list below:
 

docs/en_US/SupportedFramework_Library.md

+# Framework and Library Supports 
+With the built-in Python API, NNI naturally supports the hyper parameter tuning and neural network search for all the AI frameworks and libraries who support Python models(`version >= 3.5`). NNI had also provided a set of examples and tutorials for some of the popular scenarios to make jump start easier.
+
+## Supported AI Frameworks
+
+* <b>[PyTorch]</b> https://github.com/pytorch/pytorch
+    <ul> 
+      <li><a href="../../examples/trials/mnist-distributed-pytorch">MNIST-pytorch</a><br/></li>
+      <li><a href="TrialExample/Cifar10Examples.md">CIFAR-10</a><br/></li>
+      <li><a href="../../examples/trials/kaggle-tgs-salt/README.md">TGS salt identification chanllenge</a><br/></li>
+      <li><a href="../../examples/trials/network_morphism/README.md">Network_morphism</a><br/></li>
+    </ul>
+* <b>[TensorFlow]</b> https://github.com/tensorflow/tensorflow
+    <ul> 
+      <li><a href="../../examples/trials/mnist-distributed">MNIST-tensorflow</a><br/></li>
+       <li><a href="../../examples/trials/ga_squad/README.md">Squad</a><br/></li>
+    </ul>
+* <b>[Keras]</b> https://github.com/keras-team/keras
+    <ul>
+      <li><a href="../../examples/trials/mnist-keras">MNIST-keras</a><br/></li>
+      <li><a href="../../examples/trials/network_morphism/README.md">Network_morphism</a><br/></li>
+    </ul>
+* <b>[MXNet]</b> https://github.com/apache/incubator-mxnet
+* <b>[Caffe2]</b> https://github.com/BVLC/caffe
+* <b>[CNTK (Python language)]</b> https://github.com/microsoft/CNTK
+* <b>[Spark MLlib]</b> http://spark.apache.org/mllib/
+* <b>[Chainer]</b> https://chainer.org/
+* <b>[Theano]</b> https://pypi.org/project/Theano/ <br/>
+
+You are encouraged to [contribute more examples](Tutorial/Contributing.md) for other NNI users. 
+
+## Supported Library
+NNI also supports all libraries written in python.Here are some common libraries, including some algorithms based on GBDT: XGBoost, CatBoost and lightGBM.
+* <b>[Scikit-learn]</b> https://scikit-learn.org/stable/
+    <ul>
+    <li><a href="TrialExample/SklearnExamples.md">Scikit-learn</a><br/></li>
+    </ul>
+* <b>[XGBoost]</b> https://xgboost.readthedocs.io/en/latest/
+* <b>[CatBoost]</b> https://catboost.ai/
+* <b>[LightGBM]</b> https://lightgbm.readthedocs.io/en/latest/
+    <ul>
+    <li><a href="TrialExample/GbdtExample.md">Auto-gbdt</a><br/></li>
+    </ul>
+Here is just a small list of libraries that supported by NNI. If you are interested in NNI, you can refer to the [tutorial](TrialExample/Trials.md) to complete your own hacks.
+
+
+
+In addition to the above examples, we also welcome more and more users to apply NNI to your own work, if you have any doubts, please refer [Write a Trial Run on NNI](TrialExample/Trials.md). In particular, if you want to be a contributor of NNI, whether it is the sharing of examples , writing of Tuner or otherwise, we are all looking forward to your participation.More information please refer to [here](Tutorial/Contributing.md).