Merge pull request #173 from microsoft/master

merge master

README.md

@@ -106,7 +106,7 @@ We encourage researchers and students leverage these projects to accelerate the
 
 ## **Install & Verify**
 
-If you choose NNI Windows local mode and you use PowerShell to run script for the first time, you need to **run PowerShell as administrator** with this command first:
+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
@@ -114,7 +114,7 @@ If you choose NNI Windows local mode and you use PowerShell to run script for th
 
 **Install through pip** 	
 
-* We support Linux, MacOS and Windows(local 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`.
+* 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`.
 
 Linux and MacOS
 
@@ -131,12 +131,12 @@ python -m pip install --upgrade nni
 Note:
 
 * `--user` can be added if you want to install NNI in your home directory, which does not require any special privileges.
-* Currently NNI on Windows only support local mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
+* Currently NNI on Windows support local, remote and pai mode. Anaconda or Miniconda is highly recommended to install NNI on Windows.
 * If there is any error like `Segmentation fault`, please refer to [FAQ](docs/en_US/FAQ.md)
 
 **Install through source code**
 
-* We support Linux (Ubuntu 16.04 or higher), MacOS (10.14.1) and Windows local mode (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
 
@@ -160,7 +160,7 @@ Windows
 
 For the system requirements of NNI, please refer to [Install NNI](docs/en_US/Installation.md)
 
-For NNI Windows local mode, please refer to [NNI Windows local mode](docs/en_US/WindowsLocalMode.md)
+For NNI on Windows, please refer to [NNI on Windows](docs/en_US/NniOnWindows.md)
 
 **Verify install**
 

README_zh_CN.md

@@ -47,32 +47,32 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
         </ul>
       </td>
       <td>
-        <a href="docs/zh_CN/Builtin_Tuner.md">Tuner（调参器）</a>
+        <a href="docs/zh_CN/BuiltinTuner.md">Tuner（调参器）</a>
         <ul>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#TPE">TPE</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Random">Random Search（随机搜索）</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Anneal">Anneal（退火算法）</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Evolution">Naive Evolution（进化算法）</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#SMAC">SMAC</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Batch">Batch（批处理）</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Grid">Grid Search（遍历搜索）</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#Hyperband">Hyperband</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#NetworkMorphism">Network Morphism</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#TPE">TPE</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#Random">Random Search（随机搜索）</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#Anneal">Anneal（退火算法）</a></li>
+          <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#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/Builtin_Tuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
-          <li><a href="docs/zh_CN/Builtin_Tuner.md#BOHB">BOHB</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#NetworkMorphism#MetisTuner">Metis Tuner</a></li>
+          <li><a href="docs/zh_CN/BuiltinTuner.md#BOHB">BOHB</a></li>
         </ul>
-          <a href="docs/zh_CN/Builtin_Assessors.md#assessor">Assessor（评估器）</a> 
+          <a href="docs/zh_CN/BuiltinAssessors.md#assessor">Assessor（评估器）</a> 
         <ul>
-          <li><a href="docs/zh_CN/Builtin_Assessors.md#Medianstop">Median Stop</a></li>
-          <li><a href="docs/zh_CN/Builtin_Assessors.md#Curvefitting">Curve Fitting</a></li>
+          <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>
         </ul>
       </td>
       <td>
       <ul>
         <li><a href="docs/zh_CN/LocalMode.md">本地计算机</a></li>
         <li><a href="docs/zh_CN/RemoteMachineMode.md">远程计算机</a></li>
-        <li><a href="docs/zh_CN/PAIMode.md">OpenPAI</a></li>
+        <li><a href="docs/zh_CN/PaiMode.md">OpenPAI</a></li>
         <li><a href="docs/zh_CN/KubeflowMode.md">Kubeflow</a></li>
         <li><a href="docs/zh_CN/FrameworkControllerMode.md">基于 Kubernetes（AKS 等等）的 FrameworkController</a></li>
       </ul>
@@ -94,7 +94,10 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
 
 * [OpenPAI](https://github.com/Microsoft/pai)：作为开源平台，提供了完整的 AI 模型训练和资源管理能力，能轻松扩展，并支持各种规模的私有部署、云和混合环境。
 * [FrameworkController](https://github.com/Microsoft/frameworkcontroller)：开源的通用 Kubernetes Pod 控制器，通过单个控制器来编排 Kubernetes 上所有类型的应用。
-* [MMdnn](https://github.com/Microsoft/MMdnn)：一个完整、跨框架的解决方案，能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。 我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
+* [MMdnn](https://github.com/Microsoft/MMdnn)：一个完整、跨框架的解决方案，能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。
+* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) 是用于大规模向量的最近邻搜索场景的开源库。
+
+我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
 
 ## **安装和验证**
 
@@ -108,7 +111,7 @@ NNI (Neural Network Intelligence) 是自动机器学习（AutoML）的工具包
 
 * 当前支持 Linux，MacOS 和 Windows（本机模式），在 Ubuntu 16.04 或更高版本，MacOS 10.14.1 以及 Windows 10.1809 上进行了测试。 在 `python >= 3.5` 的环境中，只需要运行 `pip install` 即可完成安装。
 
-Linux 和 MacOS
+Linux 和 macOS
 
 ```bash
 python3 -m pip install --upgrade nni
@@ -123,14 +126,14 @@ python -m pip install --upgrade nni
 注意：
 
 * 如果需要将 NNI 安装到自己的 home 目录中，可使用 `--user`，这样也不需要任何特殊权限。
-* 当前 NNI 在 Windows 上仅支持本机模式。 强烈推荐使用 Anaconda 在 Windows 上安装 NNI。
+* 当前 NNI 在 Windows 上仅支持本机模式。 强烈推荐使用 Anaconda 或 Miniconda 在 Windows 上安装 NNI。
 * 如果遇到如`Segmentation fault` 这样的任何错误请参考[常见问题](docs/zh_CN/FAQ.md)。
 
 **通过源代码安装**
 
 * 当前支持 Linux（Ubuntu 16.04 或更高版本），MacOS（10.14.1）以及 Windows 10（1809 版）下的本机模式。 
 
-Linux 和 MacOS
+Linux 和 macOS
 
 * 在 `python >= 3.5` 的环境中运行命令： `git` 和 `wget`，确保安装了这两个组件。
 
@@ -224,11 +227,11 @@ You can use these commands to get more information about the experiment
 ## **入门**
 
 * [安装 NNI](docs/zh_CN/Installation.md)
-* [使用命令行工具 nnictl](docs/zh_CN/NNICTLDOC.md)
+* [使用命令行工具 nnictl](docs/zh_CN/Nnictl.md)
 * [使用 NNIBoard](docs/zh_CN/WebUI.md)
 * [如何定义搜索空间](docs/zh_CN/SearchSpaceSpec.md)
 * [如何编写 Trial 代码](docs/zh_CN/Trials.md)
-* [如何选择 Tuner、搜索算法](docs/zh_CN/Builtin_Tuner.md)
+* [如何选择 Tuner、搜索算法](docs/zh_CN/BuiltinTuner.md)
 * [配置 Experiment](docs/zh_CN/ExperimentConfig.md)
 * [如何使用 Annotation](docs/zh_CN/Trials.md#nni-python-annotation)
 
@@ -236,12 +239,12 @@ You can use these commands to get more information about the experiment
 
 * [在本机运行 Experiment (支持多 GPU 卡)](docs/zh_CN/LocalMode.md)
 * [在多机上运行 Experiment](docs/zh_CN/RemoteMachineMode.md)
-* [在 OpenPAI 上运行 Experiment](docs/zh_CN/PAIMode.md)
+* [在 OpenPAI 上运行 Experiment](docs/zh_CN/PaiMode.md)
 * [在 Kubeflow 上运行 Experiment。](docs/zh_CN/KubeflowMode.md)
 * [尝试不同的 Tuner](docs/zh_CN/tuners.rst)
 * [尝试不同的 Assessor](docs/zh_CN/assessors.rst)
-* [实现自定义 Tuner](docs/zh_CN/Customize_Tuner.md)
-* [实现自定义 Assessor](docs/zh_CN/Customize_Assessor.md)
+* [实现自定义 Tuner](docs/zh_CN/CustomizeTuner.md)
+* [实现自定义 Assessor](docs/zh_CN/CustomizeAssessor.md)
 * [使用进化算法为阅读理解任务找到好模型](examples/trials/ga_squad/README_zh_CN.md)
 
 ## **贡献**
@@ -250,9 +253,9 @@ You can use these commands to get more information about the experiment
 
 推荐新贡献者从标有 **good first issue** 的简单需求开始。
 
-如要安装 NNI 开发环境，参考： [配置 NNI 开发环境](docs/zh_CN/SetupNNIDeveloperEnvironment.md)。
+如要安装 NNI 开发环境，参考：[配置 NNI 开发环境](docs/zh_CN/SetupNniDeveloperEnvironment.md)。
 
-在写代码之前，请查看并熟悉 NNI 代码贡献指南：[贡献](docs/zh_CN/CONTRIBUTING.md)。
+在写代码之前，请查看并熟悉 NNI 代码贡献指南：[贡献](docs/zh_CN/Contributing.md)。
 
 我们正在编写[如何调试](docs/zh_CN/HowToDebug.md) 的页面，欢迎提交建议和问题。
 

deployment/pypi/Makefile

@@ -20,22 +20,28 @@ ifeq ($(version_ts), true)
 	NNI_VERSION_VALUE := $(NNI_VERSION_VALUE).$(TIME_STAMP)
 endif
 NNI_VERSION_TEMPLATE = 999.0.0-developing
-
+NNI_YARN_TARBALL ?= $(CWD)nni-yarn.tar.gz
+NNI_YARN_FOLDER ?= $(CWD)nni-yarn
+NNI_YARN := PATH=$(CWD)node-$(OS_SPEC)-x64/bin:$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
 .PHONY: build
 build:
 	python3 -m pip install --user --upgrade setuptools wheel
-	wget https://aka.ms/nni/nodejs-download/$(OS_SPEC) -O $(CWD)node-$(OS_SPEC)-x64.tar.xz
+	wget -q https://aka.ms/nni/nodejs-download/$(OS_SPEC) -O $(CWD)node-$(OS_SPEC)-x64.tar.xz
 	rm -rf $(CWD)node-$(OS_SPEC)-x64
 	mkdir $(CWD)node-$(OS_SPEC)-x64
 	tar xf $(CWD)node-$(OS_SPEC)-x64.tar.xz -C node-$(OS_SPEC)-x64 --strip-components 1
-	cd $(CWD)../../src/nni_manager && yarn && yarn build
-	cd $(CWD)../../src/webui && yarn && yarn build
+	wget -q https://aka.ms/yarn-download -O $(NNI_YARN_TARBALL)
+	rm -rf $(NNI_YARN_FOLDER)
+	mkdir $(NNI_YARN_FOLDER)
+	tar -xf $(NNI_YARN_TARBALL) -C $(NNI_YARN_FOLDER) --strip-components 1
+	cd $(CWD)../../src/nni_manager && $(NNI_YARN) && $(NNI_YARN) build
+	cd $(CWD)../../src/webui && $(NNI_YARN) && $(NNI_YARN) build
 	rm -rf $(CWD)nni
 	cp -r $(CWD)../../src/nni_manager/dist $(CWD)nni
 	cp -r $(CWD)../../src/webui/build $(CWD)nni/static
 	cp $(CWD)../../src/nni_manager/package.json $(CWD)nni
 	sed -ie 's/$(NNI_VERSION_TEMPLATE)/$(NNI_VERSION_VALUE)/' $(CWD)nni/package.json
-	cd $(CWD)nni && yarn --prod
+	cd $(CWD)nni && $(NNI_YARN) --prod
 	cd $(CWD) && sed -ie 's/$(NNI_VERSION_TEMPLATE)/$(NNI_VERSION_VALUE)/' setup.py && python3 setup.py bdist_wheel -p $(WHEEL_SPEC)
 	cd $(CWD)
 

docs/en_US/FAQ.md

@@ -36,8 +36,8 @@ Unable to open the WebUI may have the following reasons:
 * If you still can't see the WebUI after you use the server IP, you can check the proxy and the firewall of your machine. Or use the browser on the machine where you start your NNI experiment.
 * Another reason may be your experiment is failed and NNI may fail to get the experiment infomation. You can check the log of NNImanager in the following directory: ~/nni/experiment/[your_experiment_id] /log/nnimanager.log
 
-### Windows local mode problems
-Please refer to [NNI Windows local mode](WindowsLocalMode.md) 
+### NNI on Windows problems
+Please refer to [NNI on Windows](NniOnWindows.md) 
 
 ### Help us improve
 Please inquiry the problem in https://github.com/Microsoft/nni/issues to see whether there are other people already reported the problem, create a new one if there are no existing issues been created.

docs/en_US/Installation.md

 # Installation of NNI
 
-Currently we support installation on Linux, Mac and Windows(local mode).
+Currently we support installation on Linux, Mac and Windows(local, remote and pai mode).
 
 ## **Installation on Linux & Mac**
 

docs/en_US/WindowsLocalMode.md → docs/en_US/NniOnWindows.md

-# Windows Local Mode (experimental feature)
+# NNI on Windows (experimental feature)
 
-Currently we only support local mode on Windows. Windows 10.1809 is well tested and recommended.
+Currently we support local, remote and pai mode on Windows. Windows 10.1809 is well tested and recommended.
 
 ## **Installation on Windows**
 
@@ -25,15 +25,15 @@ Set-ExecutionPolicy -ExecutionPolicy Unrestricted
   Prerequisite: `python >=3.5`, `git`, `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 -file install.ps1
   ```
 
 When these things are done, use the **config_windows.yml** configuration to start an experiment for validation.
 
 ```bash
-nnictl create --config nni/examples/trials/mnist/config_windows.yml
+nnictl create --config nni\examples\trials\mnist\config_windows.yml
 ```
 
 For other examples you need to change trial command `python3` into `python` in each example YAML.

docs/en_US/QuickStart.md

@@ -2,7 +2,7 @@
 
 ## Installation
 
-We support Linux MacOS and Windows(local mode) in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 and Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
+We support Linux MacOS and Windows in current stage, Ubuntu 16.04 or higher, MacOS 10.14.1 and Windows 10.1809 are tested and supported. Simply run the following `pip install` in an environment that has `python >= 3.5`.
 #### Linux and MacOS
 
 ```bash
@@ -10,7 +10,7 @@ We support Linux MacOS and Windows(local mode) in current stage, Ubuntu 16.04 or
 ```
 
 #### Windows
-If you choose Windows local mode and use PowerShell to run script, you need run below PowerShell command as administrator at first time.
+If you are using NNI on Windows, you need run below PowerShell command as administrator at first time.
 ```bash
     Set-ExecutionPolicy -ExecutionPolicy Unrestricted
 ```
@@ -151,7 +151,7 @@ Run the **config.yml** file from your command line to start MNIST experiment.
 #### Windows   
 Run the **config_windows.yml** file from your command line to start MNIST experiment.
 
-**Note**, if you're using windows local mode, it needs to change `python3` to `python` in the config.yml file, or use the config_windows.yml file to start the experiment.
+**Note**, if you're using NNI on Windows, it needs to change `python3` to `python` in the config.yml file, or use the config_windows.yml file to start the experiment.
 
 ```bash
     nnictl create --config nni/examples/trials/mnist/config_windows.yml

docs/en_US/RemoteMachineMode.md

@@ -55,7 +55,8 @@ machineList:
     username: bob
     passwd: bob123
 ```
-
+You can use different systems to run experiments on the remote machine.
+#### Linux and MacOS
 Simply filling the `machineList` section and then run:
 
 ```bash
@@ -64,5 +65,14 @@ nnictl create --config ~/nni/examples/trials/mnist-annotation/config_remote.yml
 
 to start the experiment.
 
+#### Windows
+Simply filling the `machineList` section and then run:
+
+```bash
+nnictl create --config %userprofile%\nni\examples\trials\mnist-annotation\config_remote.yml
+```
+
+to start the experiment.
+
 ## version check
 NNI support version check feature in since version 0.6, [refer](PaiMode.md)
\ No newline at end of file

docs/en_US/WebUI.md

@@ -6,6 +6,8 @@ Click the tab "Overview".
 
 * See the experiment trial profile and search space message.
 * Support to download the experiment result.
+* Support to export nni-manager and dispatcher log file.
+* If you have any question, you can click "Feedback" to report it.
 
 ![](../img/webui-img/over1.png)
 * See good performance trials.
@@ -52,6 +54,14 @@ Click the tab "Trials Detail" to see the status of the all trials. Specifically:
 
 ![](../img/webui-img/detail-local.png)
 
+* The button named "Add column" can select which column to show in the table. If you run an experiment that final result is dict, you can see other keys in the table.
+
+![](../img/webui-img/addColumn.png)
+
+* You can use the button named "Copy as python" to copy trial's parameters.
+
+![](../img/webui-img/copyParameter.png)
+
 * If you run on OpenPAI or Kubeflow platform, you can also see the hdfsLog.
 
 ![](../img/webui-img/detail-pai.png)

docs/zh_CN/AnnotationSpec.md

@@ -9,6 +9,7 @@
 ```python
 '''@nni.variable(nni.choice(0.1, 0.01, 0.001), name=learning_rate)'''
 learning_rate = 0.1
+
 ```
 
 此样例中，NNI 会从 (0.1, 0.01, 0.001) 中选择一个值赋给 learning_rate 变量。 第一行就是 NNI 的 Annotation，是 Python 中的一个字符串。 接下来的一行需要是赋值语句。 NNI 会根据 Annotation 行的信息，来给这一行的变量赋上相应的值。

docs/zh_CN/batchTuner.md → docs/zh_CN/BatchTuner.md

 # Batch Tuner
 
-## Batch Tuner（批量调参器）
+## Batch Tuner（批处理 Tuner）
 
 Batch Tuner 能让用户简单的提供几组配置（如，超参选项的组合）。 当所有配置都执行完后，Experiment 即结束。 Batch Tuner 的[搜索空间](SearchSpaceSpec.md)只支持 `choice`。
 

docs/zh_CN/Blog/index.rst

-######################
-博客
-######################
-
-..  toctree::
-    :maxdepth: 2
-
-    超参优化的对比<HPOComparison>
-    神经网络结构搜索（NAS）的对比<NASComparison>
\ No newline at end of file

docs/zh_CN/bohbAdvisor.md → docs/zh_CN/BohbAdvisor.md

@@ -10,7 +10,7 @@ BOHB 依赖 HB（Hyperband）来决定每次跑多少组参数和每组参数分
 
 ### HB（Hyperband）
 
-按照 Hyperband 的方式来选择每次跑的参数个数与分配多少资源（budget），并继续使用“连续减半（SuccessiveHalving）”策略，更多有关Hyperband算法的细节，请参考[NNI 中的 Hyperband](hyperbandAdvisor.md) 和 [Hyperband 的参考论文](https://arxiv.org/abs/1603.06560)。 下面的伪代码描述了这个过程。
+按照 Hyperband 的方式来选择每次跑的参数个数与分配多少资源（budget），并继续使用“连续减半（SuccessiveHalving）”策略，更多有关Hyperband算法的细节，请参考[NNI 中的 Hyperband](HyperbandAdvisor.md) 和 [Hyperband 的参考论文](https://arxiv.org/abs/1603.06560)。 下面的伪代码描述了这个过程。
 
 ![](../img/bohb_1.png)
 

docs/zh_CN/Builtin_Tuner.md → docs/zh_CN/BuiltinTuner.md

@@ -2,7 +2,7 @@
 
 NNI 提供了先进的调优算法，使用上也很简单。 下面是内置 Tuner 的简单介绍：
 
-注意：点击 **Tuner 的名称**可跳转到算法的详细描述，点击**用法**可看到 Tuner 的安装要求、建议场景和使用样例等等。 [此文章](./Blog/HPOComparison.md)对比了不同 Tuner 在几个问题下的不同效果。
+注意：点击 **Tuner 的名称**可跳转到算法的详细描述，点击**用法**可看到 Tuner 的安装要求、建议场景和使用样例等等。 [此文章](./CommunitySharings/HPOComparison.md)对比了不同 Tuner 在几个问题下的不同效果。
 
 当前支持的 Tuner：