pull code

README.md

@@ -14,10 +14,10 @@
 
 [简体中文](README_zh_CN.md)
 
-NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments. 
+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 [v0.7](https://github.com/Microsoft/nni/releases) has been released!**
+### **NNI [v0.8](https://github.com/Microsoft/nni/releases) has been released!**
 <p align="center">
   <a href="#nni-v05-has-been-released"><img src="docs/img/overview.svg" /></a>
 </p>
@@ -57,20 +57,20 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
           <li><a href="docs/en_US/BuiltinTuner.md#TPE">TPE</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#Random">Random Search</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#Anneal">Anneal</a></li>
-          <li><a href="docs/en_US/BuiltinTuner.md#Evolution">Naive Evolution</a></li>
+          <li><a href="docs/en_US/BuiltinTuner.md#Evolution">Naïve Evolution</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#SMAC">SMAC</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#Batch">Batch</a></li>
-          <li><a href="docs/en_US/BuiltinTuner.md#Grid">Grid Search</a></li>
+          <li><a href="docs/en_US/BuiltinTuner.md#GridSearch">Grid Search</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#Hyperband">Hyperband</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
           <li><a href="examples/tuners/enas_nni/README.md">ENAS</a></li>
-          <li><a href="docs/en_US/BuiltinTuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
+          <li><a href="docs/en_US/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
           <li><a href="docs/en_US/BuiltinTuner.md#BOHB">BOHB</a></li>
         </ul>
-          <a href="docs/en_US/BuiltinAssessors.md#assessor">Assessor</a> 
+          <a href="docs/en_US/BuiltinAssessor.md">Assessor</a>
         <ul>
-          <li><a href="docs/en_US/BuiltinAssessors.md#Medianstop">Median Stop</a></li>
-          <li><a href="docs/en_US/BuiltinAssessors.md#Curvefitting">Curve Fitting</a></li>
+          <li><a href="docs/en_US/BuiltinAssessor.md#Medianstop">Median Stop</a></li>
+          <li><a href="docs/en_US/BuiltinAssessor.md#Curvefitting">Curve Fitting</a></li>
         </ul>
       </td>
       <td>
@@ -106,13 +106,7 @@ We encourage researchers and students leverage these projects to accelerate the
 
 ## **Install & Verify**
 
-If you are using NNI on Windows and use PowerShell to run script for the first time, you need to **run PowerShell as administrator** with this command first:
-
-```bash
-    Set-ExecutionPolicy -ExecutionPolicy Unrestricted
-```
-
-**Install through pip** 	
+**Install through pip**
 
 * We support Linux, MacOS and Windows(local, remote and pai mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 along with Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
 
@@ -136,14 +130,14 @@ Note:
 
 **Install through source code**
 
-* We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows (10.1809) in our current stage. 
+* We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows (10.1809) in our current stage.
 
 Linux and MacOS
 
 * Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
 
 ```bash
-    git clone -b v0.7 https://github.com/Microsoft/nni.git
+    git clone -b v0.8 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -153,9 +147,9 @@ Windows
 * Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
 
 ```bash
-  git clone -b v0.7 https://github.com/Microsoft/nni.git
+  git clone -b v0.8 https://github.com/Microsoft/nni.git
   cd nni
-  powershell .\install.ps1
+  powershell -ExecutionPolicy Bypass -file install.ps1
 ```
 
 For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Installation.md)
@@ -169,7 +163,7 @@ The following example is an experiment built on TensorFlow. Make sure you have *
 * Download the examples via clone the source code.
 
 ```bash
-    git clone -b v0.7 https://github.com/Microsoft/nni.git
+    git clone -b v0.8 https://github.com/Microsoft/nni.git
 ```
 
 Linux and MacOS

README_zh_CN.md

@@ -10,7 +10,7 @@
 
 NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包。 它通过多种调优的算法来搜索最好的神经网络结构和（或）超参，并支持单机、本地多机、云等不同的运行环境。
 
-### **NNI [v0.7](https://github.com/Microsoft/nni/releases) 已发布！**
+### **NNI [v0.8](https://github.com/Microsoft/nni/releases) 已发布！**
 
 <p align="center">
   <a href="#nni-v05-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -55,14 +55,14 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
           <li><a href="docs/zh_CN/BuiltinTuner.md#Evolution">Naive Evolution（进化算法）</a></li>
           <li><a href="docs/zh_CN/BuiltinTuner.md#SMAC">SMAC</a></li>
           <li><a href="docs/zh_CN/BuiltinTuner.md#Batch">Batch（批处理）</a></li>
-          <li><a href="docs/zh_CN/BuiltinTuner.md#Grid">Grid Search（遍历搜索）</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#GridSearch">Grid Search（遍历搜索）</a></li>
           <li><a href="docs/zh_CN/BuiltinTuner.md#Hyperband">Hyperband</a></li>
           <li><a href="docs/zh_CN/BuiltinTuner.md#NetworkMorphism">Network Morphism</a></li>
           <li><a href="examples/tuners/enas_nni/README_zh_CN.md">ENAS</a></li>
-          <li><a href="docs/zh_CN/BuiltinTuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#MetisTuner">Metis Tuner</a></li>
           <li><a href="docs/zh_CN/BuiltinTuner.md#BOHB">BOHB</a></li>
         </ul>
-          <a href="docs/zh_CN/BuiltinAssessors.md#assessor">Assessor（评估器）</a> 
+          <a href="docs/zh_CN/BuiltinAssessors.md">Assessor（评估器）</a> 
         <ul>
           <li><a href="docs/zh_CN/BuiltinAssessors.md#Medianstop">Median Stop</a></li>
           <li><a href="docs/zh_CN/BuiltinAssessors.md#Curvefitting">Curve Fitting</a></li>
@@ -150,7 +150,7 @@ Windows
 ```bash
   git clone -b v0.7 https://github.com/Microsoft/nni.git
   cd nni
-  powershell ./install.ps1
+  powershell .\install.ps1
 ```
 
 参考[安装 NNI](docs/zh_CN/Installation.md) 了解系统需求。
@@ -180,7 +180,7 @@ Windows
 * 运行 MNIST 示例。
 
 ```bash
-    nnictl create --config nni/examples/trials/mnist/config_windows.yml
+    nnictl create --config nni\examples\trials\mnist\config_windows.yml
 ```
 
 * 在命令行中等待输出 `INFO: Successfully started experiment!`。 此消息表明 Experiment 已成功启动。 通过命令行输出的 `Web UI url` 来访问 Experiment 的界面。

deployment/docker/Dockerfile

@@ -31,7 +31,7 @@ RUN DEBIAN_FRONTEND=noninteractive && \
     vim \
     unzip \
     wget \
-    build-essential \ 
+    build-essential \
     cmake \
     libopenblas-dev \
     automake \
@@ -51,9 +51,9 @@ RUN DEBIAN_FRONTEND=noninteractive && \
 #
 RUN python3 -m pip install --upgrade pip
 
-# numpy 1.14.3  scipy 1.1.0 
+# numpy 1.14.3  scipy 1.1.0
 RUN python3 -m pip --no-cache-dir install \
-    numpy==1.14.3 scipy==1.1.0 
+    numpy==1.14.3 scipy==1.1.0
 
 #
 # Tensorflow 1.10.0
@@ -66,7 +66,7 @@ RUN python3 -m pip --no-cache-dir install tensorflow-gpu==1.10.0
 RUN python3 -m pip --no-cache-dir install Keras==2.1.6
 
 #
-# PyTorch 
+# PyTorch
 #
 RUN python3 -m pip --no-cache-dir install torch==0.4.1
 RUN python3 -m pip install torchvision==0.2.1

deployment/docker/README.md

@@ -14,11 +14,11 @@ pandas 0.23.4
 lightgbm 2.2.2
 NNI v0.7
 ```
-You can take this Dockerfile as a reference for your own customized Dockerfile. 
+You can take this Dockerfile as a reference for your own customized Dockerfile.
 
 ## 2.How to build and run
 __Use the following command from `nni/deployment/docker` to build docker image__
-```    
+```
     docker build -t nni/nni .
 ```
 __Run the docker image__

deployment/pypi/Makefile

@@ -7,7 +7,7 @@ ifeq ($(UNAME_S), Linux)
 else ifeq ($(UNAME_S), Darwin)
 	OS_SPEC := darwin
 	WHEEL_SPEC := macosx_10_9_x86_64
-else 
+else
 	$(error platform $(UNAME_S) not supported)
 endif
 

deployment/pypi/install.ps1

@@ -43,7 +43,7 @@ $env:PATH = $NNI_NODE_FOLDER+';'+$env:PATH
 cd $CWD\..\..\src\nni_manager
 yarn
 yarn build
-cd $CWD\..\..\src\webui 
+cd $CWD\..\..\src\webui
 yarn
 yarn build
 if(Test-Path $CWD\nni){

deployment/pypi/setup.py

@@ -75,7 +75,8 @@
         'numpy',
         'scipy',
         'coverage',
-        'colorama'
+        'colorama',
+        'sklearn'
     ],
     classifiers = [
         'Programming Language :: Python :: 3',

docs/en_US/AdvancedNas.md

@@ -88,4 +88,4 @@ For details, please refer to this [simple weight sharing example](https://github
 [2]: https://arxiv.org/abs/1707.07012
 [3]: https://arxiv.org/abs/1806.09055
 [4]: https://arxiv.org/abs/1806.10282
-[5]: https://arxiv.org/abs/1703.01041 
+[5]: https://arxiv.org/abs/1703.01041

docs/en_US/AnnotationSpec.md

@@ -29,7 +29,7 @@ In NNI, there are mainly four types of annotation:
 
 **Arguments**
 
-- **sampling_algo**: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an `nni.` identification and a search space type specified in [SearchSpaceSpec](SearchSpaceSpec.md) such as `choice` or `uniform`. 
+- **sampling_algo**: Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an `nni.` identification and a search space type specified in [SearchSpaceSpec](SearchSpaceSpec.md) such as `choice` or `uniform`.
 - **name**: The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.
 
 There are 10 types to express your search space as follows:

docs/en_US/BatchTuner.md

@@ -5,4 +5,4 @@ Batch Tuner on NNI
 
 Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](SearchSpaceSpec.md).
 
-Suggested sceanrio: If the configurations you want to try have been decided, you can list them in searchspace file (using choice) and run them using batch tuner.
+Suggested scenario: If the configurations you want to try have been decided, you can list them in SearchSpace file (using choice) and run them using batch tuner.

docs/en_US/BuiltinAssessors.md → docs/en_US/BuiltinAssessor.md

@@ -2,7 +2,7 @@
 
 NNI provides state-of-the-art tuning algorithm in our builtin-assessors and makes them easy to use. Below is the brief overview of NNI current builtin Assessors:
 
-Note: Click the **Assessor's name** to get a detailed description of the algorithm, click the corresponding **Usage** to get the Assessor's installation requirements, suggested scenario and using example.
+Note: Click the **Assessor's name** to get the Assessor'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 Assessor.
 
 Currently we support the following Assessors:
 
@@ -25,7 +25,7 @@ Note: Please follow the format when you write your `config.yml` file.
 
 **Suggested scenario**
 
-It is applicable in a wide range of performance curves, thus, can be used in various scenarios to speed up the tuning progress.
+It is applicable in a wide range of performance curves, thus, can be used in various scenarios to speed up the tuning progress. [Detailed Description](./MedianstopAssessor.md)
 
 **Requirement of classArg**
 
@@ -53,7 +53,7 @@ assessor:
 
 **Suggested scenario**
 
-It is applicable in a wide range of performance curves, thus, can be used in various scenarios to speed up the tuning progress. Even better, it's able to handle and assess curves with similar performance.
+It is applicable in a wide range of performance curves, thus, can be used in various scenarios to speed up the tuning progress. Even better, it's able to handle and assess curves with similar performance. [Detailed Description](./CurvefittingAssessor.md)
 
 **Requirement of classArg**