Updated document for "write a trial" related fixes.

README.md

@@ -88,16 +88,17 @@ You can use these commands to get more information about the experiment
 </table>
 
 ## **Documentation**
-* [Overview](docs/Overview.md)
-* [Get started](docs/GetStarted.md)
+* [NNI overview](docs/Overview.md)
+* [Quick start](docs/GetStarted.md)
+
 ## **How to**
-* [Installation](docs/InstallNNI_Ubuntu.md)
+* [Install NNI](docs/InstallNNI_Ubuntu.md)
 * [Use command line tool nnictl](docs/NNICTLDOC.md)
 * [Use NNIBoard](docs/WebUI.md)
 * [How to define search space](docs/SearchSpaceSpec.md)
+* [How to define a trial](docs/howto_1_WriteTrial.md)
 * [Config an experiment](docs/ExperimentConfig.md)
-* [Use annotation](docs/AnnotationSpec.md)
-* [Debug](docs/HowToDebug.md)
+* [How to use annotation](docs/howto_1_WriteTrial.md#nni-python-annotation)
 ## **Tutorials**
 * [Run an experiment on local (with multiple GPUs)?](docs/tutorial_1_CR_exp_local_api.md)
 * [Run an experiment on multiple machines?](docs/tutorial_2_RemoteMachineMode.md)

docs/Overview.md

@@ -2,6 +2,13 @@
 
 NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. For each experiment, user only need to define a search space and update a few lines of code, and then leverage NNI build-in algorithms and training services to search the best hyper parameters and/or neural architecture.
 
+>Step 1: [Define search space](SearchSpaceSpec.md)
+
+>Step 2: [Update model codes](howto_1_WriteTrial.md)
+
+>Step 3: [Define Experiment](ExperimentConfig.md)
+
+
 <p align="center">
 <img src="./img/3_steps.jpg" alt="drawing"/>
 </p>
@@ -15,11 +22,6 @@ After user submits the experiment through a command line tool [nnictl](../tools/
 
 User can use the nnictl and/or a visualized Web UI nniboard to monitor and debug a given experiment.
 
-<p align="center">
-<img src="./img/overview.jpg" alt="drawing"/>
-</p>
-
-
 NNI provides a set of examples in the package to get you familiar with the above process. In the following example [/examples/trials/mnist], we had already set up the configuration and updated the training codes for you. You can directly run the following command to start an experiment. 
 
 ## Key Concepts
@@ -35,28 +37,13 @@ NNI provides a set of examples in the package to get you familiar with the above
 ### **Assessor**
 **Assessor** in NNI is an implementation of Assessor API for optimizing the execution of experiment.
 
-
 ## Learn More
 * [Get started](GetStarted.md)
-### **How to**
-* [Installation](InstallNNI_Ubuntu.md)
+* [Install NNI](InstallNNI_Ubuntu.md)
 * [Use command line tool nnictl](NNICTLDOC.md)
 * [Use NNIBoard](WebUI.md)
-* [Define search space](InstallNNI_Ubuntu.md)
-* [Use NNI sdk] - *coming soon*
-* [Config an experiment](SearchSpaceSpec.md)
-* [Use annotation](AnnotationSpec.md)
-* [Debug](HowToDebug.md)
+* [Use annotation](howto_1_WriteTrial.md#nni-python-annotation)
 ### **Tutorials**
 * [How to run an experiment on local (with multiple GPUs)?](tutorial_1_CR_exp_local_api.md)
 * [How to run an experiment on multiple machines?](tutorial_2_RemoteMachineMode.md)
-* [How to run an experiment on OpenPAI?](PAIMode.md)
-* [Try different tuners and assessors] - *coming soon*
-* [How to run an experiment on K8S services?] - *coming soon*
-* [Implement a customized tuner] - *coming soon*
-* [Implement a customized assessor] - *coming soon*
-* [Implement a custmoized weight sharing algorithm] - *coming soon*
-* [How to integrate NNI with your own custmoized training service] - *coming soon*
-### **Best practice**
-* [Compare different AutoML algorithms] - *coming soon*
-* [Serve NNI as a capability of a ML Platform] - *coming soon*
+* [How to run an experiment on OpenPAI?](PAIMode.md)