Readme and release page for NNIv1 (#1512)

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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/releases) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **NNI [v1.0](https://github.com/Microsoft/nni/blob/readme-v1/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>**
 
 <p align="center">
   <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -347,19 +347,25 @@ Before providing your hacks, you can review the [Contributing Instruction](docs/
 
 ## **External Repositories and References**
 With authors' permission, we listed a set of NNI usage examples and relevant articles.
-
-* **Repo** - Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
-* **Repo** - Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
-* **Repo** - [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI 
-* **Repo** - [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
-* **Blog (in Chinese)** - AutoML tools (Advisor, NNI and Google Vizier) comparison by [@gaocegege]( https://github.com/gaocegege ) - [总结与分析 section of design and implementation of kubeflow/katib](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)
+* ### **External Repositories** ###
+   * Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
+   * Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
+   * [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI 
+   * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
+
+* ### **Relevant Articles** ###
+
+  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
+  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
+  * [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
+  * [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
+  * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)
+  * **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
 
 ## **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
-* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true)
+* 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.
 * [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-
-
+* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).
 
 
 ## **License**

docs/en_US/Release_v1.0.md

@@ -0,0 +1,32 @@
+
+<p align="center">
+<img src=".././img/release-1-title-1.png" width="100%" />
+</p>
+
+
+From September 2018 to September 2019, We are still moving on …
+
+
+ **Great news!**&nbsp;&nbsp;With the tag of **Scalability** and **Ease of Use**, NNI v1.0 is comming. Based on the various types of [Tuning Algorithms](./Tuner/BuiltinTuner.md), NNI has supported the Hyperparameter tuning, Neural Architecture search and Auto-Feature-Engineering, which is an exciting news for algorithmic engineers; besides these, NNI v1.0 has made many improvements in the optimization of tuning algorithm, [WebUI's simplicity and intuition](./Tutorial/WebUI.md) and [platform diversification](./TrainingService/SupportTrainingService.md). NNI has grown into a more intelligent automated machine learning (AutoML) toolkit.
+
+
+
+<br/>
+<br/>
+<br/>
+<p align="center">
+<img src=".././img/nni-1.png" width="80%" />
+</p>
+
+<br />
+<br />
+
+<p align="center">
+<img src=".././img/release-1-title-2.png" width="100%" />
+</p>
+
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step one**: Start with the [Tutorial Doc](./Tutorial/Installation.md), and install NNI v1.0 first.<br>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step two**: Find a " Hello world example", follow the [Tutorial Doc](./Tutorial/QuickStart.md) and have a Quick Start. <br>
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**Step three**: Get familiar with the [WebUI Tutorial](./Tutorial/WebUI.md) and let NNI better assists with your tuning tour.<br>
+
+The fully automated tool greatly improves the efficiency of the tuning process. For more detail about the 1.0 updates, you can refer to [Release 1.0](https://github.com/microsoft/nni/releases). More of our advance plan, you can refer to our [Roadmap](https://github.com/microsoft/nni/wiki/Roadmap). Besides, we also welcome more and more contributors to join us, there are many ways to participate, please refer to [How to contribute](./Tutorial/Contributing.md) for more details.