Merge pull request #114 from Microsoft/master

merge master

.gitignore

@@ -67,3 +67,5 @@ typings/
 __pycache__
 build
 *.egg-info
+
+.vscode

README.md

@@ -134,4 +134,3 @@ We are in construction of the instruction for [How to Debug](docs/HowToDebug.md)
 
 ## **License** 
 The entire codebase is under [MIT license](https://github.com/Microsoft/nni/blob/master/LICENSE)
-

docs/GetStarted.md

@@ -2,27 +2,35 @@
 ===
 
 ## **Installation**
+
 * __Dependencies__
 
-      python >= 3.5
-      git
-      wget
+  ```bash
+  python >= 3.5
+  git
+  wget
+  ```
+
+  python pip should also be correctly installed. You could use "python3 -m pip -v" to check in Linux.
 
-    python pip should also be correctly installed. You could use "python3 -m pip -v" to check in Linux.
-
-    * Note: we don't support virtual environment in current releases.
+  * Note: we don't support virtual environment in current releases.
 
 * __Install NNI through pip__
 
-      python3 -m pip install --user --upgrade nni
+  ```bash
+  python3 -m pip install --user --upgrade nni
+  ```
 
 * __Install NNI through source code__
-
-      git clone -b v0.4.1 https://github.com/Microsoft/nni.git
-      cd nni
-      source install.sh
+
+  ```bash
+  git clone -b v0.4.1 https://github.com/Microsoft/nni.git
+  cd nni
+  source install.sh
+  ```
 
 ## **Quick start: run a customized experiment**
+
 An experiment is to run multiple trial jobs, each trial job tries a configuration which includes a specific neural architecture (or model) and hyper-parameter values. To run an experiment through NNI, you should:
 
 * Provide a runnable trial
@@ -32,22 +40,26 @@ An experiment is to run multiple trial jobs, each trial job tries a configuratio
 
 **Prepare trial**: Let's use a simple trial example, e.g. mnist, provided by NNI. After you installed NNI, NNI examples have been put in ~/nni/examples, run `ls ~/nni/examples/trials` to see all the trial examples. You can simply execute the following command to run the NNI mnist example: 
 
-      python3 ~/nni/examples/trials/mnist-annotation/mnist.py
+```bash
+python3 ~/nni/examples/trials/mnist-annotation/mnist.py
+```
 
 This command will be filled in the yaml configure file below. Please refer to [here](howto_1_WriteTrial.md) for how to write your own trial.
 
 **Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](howto_2_CustomizedTuner.md), but for simplicity, here we choose a tuner provided by NNI as below:
 
-      tuner:
-        builtinTunerName: TPE
-        classArgs:
-          optimize_mode: maximize
+```yaml
+tuner:
+  builtinTunerName: TPE
+    classArgs:
+      optimize_mode: maximize
+```
 
 *builtinTunerName* is used to specify a tuner in NNI, *classArgs* are the arguments pass to the tuner, *optimization_mode* is to indicate whether you want to maximize or minimize your trial's result.
 
 **Prepare configure file**: Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the yaml configure file. NNI provides a demo configure file for each trial example, `cat ~/nni/examples/trials/mnist-annotation/config.yml` to see it. Its content is basically shown below:
 
-```
+```yaml
 authorName: your_name
 experimentName: auto_mnist
 
@@ -73,7 +85,7 @@ trial:
   command: python mnist.py
   codeDir: ~/nni/examples/trials/mnist-annotation
   gpuNum: 0
-``` 
+```
 
 Here *useAnnotation* is true because this trial example uses our python annotation (refer to [here](../tools/annotation/README.md) for details). For trial, we should provide *trialCommand* which is the command to run the trial, provide *trialCodeDir* where the trial code is. The command will be executed in this directory. We should also provide how many GPUs a trial requires.
 
@@ -87,6 +99,7 @@ You can refer to [here](NNICTLDOC.md) for more usage guide of *nnictl* command l
 The experiment has been running now, NNI provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features. The WebUI is opened by default by `nnictl create`.
 
 ## Read more
+
 * [Tuners supported in the latest NNI release](./HowToChooseTuner.md)
 * [Overview](Overview.md)
 * [Installation](Installation.md)

docs/HowToDebug.md

@@ -1,4 +1,4 @@
 **How to Debug in NNI**
 ===
 
-*Coming soon*
+*Coming soon*

docs/Installation.md

@@ -6,25 +6,31 @@ Currently we only support installation on Linux & Mac.
 ## **Installation**
 * __Dependencies__
 
-      python >= 3.5
-      git
-      wget
+  ```bash
+  python >= 3.5
+  git
+  wget
+  ```
 
-    python pip should also be correctly installed. You could use "python3 -m pip -v" to check pip version. 
+  python pip should also be correctly installed. You could use "python3 -m pip -v" to check pip version. 
 
 * __Install NNI through pip__
 
-      python3 -m pip install --user --upgrade nni
+  ```bash
+  python3 -m pip install --user --upgrade nni
+  ```
 
 * __Install NNI through source code__
-
-      git clone -b v0.4.1 https://github.com/Microsoft/nni.git
-      cd nni
-      source install.sh
+
+  ```bash
+  git clone -b v0.4.1 https://github.com/Microsoft/nni.git
+  cd nni
+  source install.sh
+  ```
 
 * __Install NNI in docker image__
 
-    You can also install NNI in a docker image. Please follow the instructions [here](../deployment/docker/README.md) to build NNI docker image. The NNI docker image can also be retrieved from Docker Hub through the command `docker pull msranni/nni:latest`.
+  You can also install NNI in a docker image. Please follow the instructions [here](../deployment/docker/README.md) to build NNI docker image. The NNI docker image can also be retrieved from Docker Hub through the command `docker pull msranni/nni:latest`.
 
 ## **System requirements**
 
@@ -52,8 +58,8 @@ Below are the minimum system requirements for NNI on macOS. Due to potential pro
 |**Internet**|Boardband internet connection|
 |**Resolution**|1024 x 768 minimum display resolution|
 
-
 ## Further reading
+
 * [Overview](Overview.md)
 * [Use command line tool nnictl](NNICTLDOC.md)
 * [Use NNIBoard](WebUI.md)

docs/KubeflowMode.md

@@ -43,7 +43,7 @@ kubeflowConfig:
 ```
 If users want to use tf-operator, he could set `ps` and `worker` in trial config. If users want to use pytorch-operator, he could set `master` and `worker` in trial config. 
 
-## Supported sotrage type
+## Supported storage type
 NNI support NFS and Azure Storage to store the code and output files, users could set storage type in config file and set the corresponding config.  
 The setting for NFS storage are as follows:
 ```
@@ -197,4 +197,3 @@ Notice: In kubeflow mode, NNIManager will start a rest server and listen on a po
 Once a trial job is completed, you can goto NNI WebUI's overview page (like http://localhost:8080/oview) to check trial's information. 
 
 Any problems when using NNI in kubeflow mode, plesae create issues on [NNI github repo](https://github.com/Microsoft/nni), or send mail to nni@microsoft.com
-