update top README.md

README.md

@@ -14,9 +14,71 @@
 NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments. 
 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 [v0.5](https://github.com/Microsoft/nni/releases) has been released!**
 <p align="center">
-<img src="./docs/img/nni_arch_overview.png" alt="drawing"/>
+  <a href=#><img src="https://rawgit.com/QuanluZhang/nni/update-doc11/overview.svg" /></a>
 </p>
+<table>
+  <tbody>
+    <tr align="center">
+      <td>
+        <b>User Code + SDK( import in )</b>
+        <img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
+      </td>
+      <td>
+        <b>Tunning Algorithm Extensions</b>
+        <img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
+      </td>
+      <td>
+        <b>Training Service Extensions</b>
+        <img src="https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png"/>
+      </td>
+    </tr>
+    <tr/>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li>CNTK</li>
+        <li>Tensorflow</li>
+        <li>PyTorch</li>
+        <li>Keras</li>
+        <li>...</li>
+        </ul>
+        (Python based frameworks)
+      </td>
+      <td>
+        <a href="docs/HowToChooseTuner.md">Tuner</a>
+        <ul>
+          <li><a href="docs/HowToChooseTuner.md#TPE">TPE</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Random">Random Search</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Anneal">Anneal</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Evolution">Naive Evolution</a></li>
+          <li><a href="docs/HowToChooseTuner.md#SMAC">SMAC</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Batch">Batch</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Grid">Grid Search</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Hyperband">Hyperband</a></li>
+          <li><a href="docs/HowToChooseTuner.md#NetworkMorphism">Network Morphism</a></li>
+          <li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
+          <li><a href="docs/HowToChooseTuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
+        </ul> 
+          <a href="docs/HowToChooseTuner.md#assessor">Assessor</a> 
+        <ul>
+          <li><a href="docs/HowToChooseTuner.md#Medianstop">Median Stop</a></li>
+          <li><a href="docs/HowToChooseTuner.md#Curvefitting">Curve Fitting</a></li>
+        </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="docs/tutorial_1_CR_exp_local_api.md">Local Machine</a></li>
+        <li><a href="docs/tutorial_2_RemoteMachineMode.md">Remote Servers</a></li>
+        <li><a href="docs/PAIMode.md">OpenPAI</a></li>
+        <li><a href="docs/KubeflowMode.md">Kubeflow</a></li>
+        <li><a href="docs/KubeflowMode.md">FrameworkController on K8S (AKS etc.)</a></li>
+      </ul>
+      </td>
+    </tr>
+  </tbody>
+</table>
 
 ## **Who should consider using NNI**
 * Those who want to try different AutoML algorithms in their training code (model) at their local machine.

docs/HowToChooseTuner.md

@@ -244,7 +244,7 @@ _Usage_:
       optimize_mode: maximize
 ```
 
-
+<a name="assessor"></a>
 # How to use Assessor that NNI supports?
 
 For now, NNI has supported the following assessor algorithms.

docs/NNICTLDOC.md

@@ -1,6 +1,5 @@
-nnictl
+# nnictl
 
-===
 
 ## Introduction