Merge remote-tracking branch 'github/master' into v1.3-rc0

README.md

@@ -348,6 +348,7 @@ With authors' permission, we listed a set of NNI usage examples and relevant art
   * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
   * [Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI](https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1)
   * **Blog (in Chinese)** - [AutoML tools (Advisor, NNI and Google Vizier) comparison](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90) by [@gaocegege](https://github.com/gaocegege) - 总结与分析 section of design and implementation of kubeflow/katib
+  * **Blog (in Chinese)** - [A summary of NNI new capabilities in 2019](https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA) by @squirrelsc
 
 ## **Feedback**
 * Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.

docs/en_US/Compressor/Overview.md

-# Compressor
+# Model Compression with NNI
 As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications. Model compression can be used to address this problem. 
 
 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.
@@ -333,3 +333,11 @@ class YourQuantizer(Quantizer):
 ```
 
 If you do not customize `QuantGrad`, the default backward is Straight-Through Estimator. 
+_Coming Soon_ ...
+
+## **Reference and Feedback**
+* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
+* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
+* To know more about [Feature Engineering with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md);
+* To know more about [NAS with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md);
+* To know more about [Hyperparameter Tuning with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md);

docs/en_US/FeatureEngineering/Overview.md

-# FeatureEngineering
+# Feature Engineering with NNI
 
 We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
 
@@ -256,3 +256,10 @@ The dataset of benchmark could be download in [here](https://www.csie.ntu.edu.tw
 )
 
 The code could be refenrence `/examples/feature_engineering/gradient_feature_selector/benchmark_test.py`.
+
+## **Reference and Feedback**
+* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
+* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
+* To know more about [Neural Architecture Search with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md);
+* To know more about [Model Compression with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md);
+* To know more about [Hyperparameter Tuning with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md);

docs/en_US/NAS/Overview.md

@@ -50,3 +50,10 @@ NNI proposed API is [here](https://github.com/microsoft/nni/tree/master/src/sdk/
 [3]: https://arxiv.org/abs/1806.09055
 [4]: https://arxiv.org/abs/1806.10282
 [5]: https://arxiv.org/abs/1703.01041
+
+## **Reference and Feedback**
+* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
+* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
+* To know more about [Feature Engineering with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md);
+* To know more about [Model Compression with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md);
+* To know more about [Hyperparameter Tuning with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md);

docs/en_US/Tuner/BuiltinTuner.md

-# Built-in Tuners
+# Built-in Tuners for Hyperparameter Tuning
 
-NNI provides state-of-the-art tuning algorithm as our built-in tuners and makes them easy to use. Below is the brief summary of NNI currently built-in tuners:
+NNI provides state-of-the-art tuning algorithms as our built-in tuners and makes them easy to use. Below is the brief summary of NNI currently built-in tuners:
 
 Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario and using example. The link for a detailed description of the algorithm is at the end of the suggested scenario of each tuner. Here is an [article](../CommunitySharings/HpoComparision.md) about the comparison of different Tuners on several problems.
 
@@ -453,3 +453,9 @@ tuner:
   classArgs:
     optimize_mode: maximize
 ```
+## **Reference and Feedback**
+* To [report a bug](https://github.com/microsoft/nni/issues/new?template=bug-report.md) for this feature in GitHub;
+* To [file a feature or improvement request](https://github.com/microsoft/nni/issues/new?template=enhancement.md) for this feature in GitHub;
+* To know more about [Feature Engineering with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md);
+* To know more about [NAS with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md);
+* To know more about [Model Compression with NNI](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md);