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

@@ -2,11 +2,14 @@
 ===
 
 ## **Installation**
+
 * __Dependencies__
 
+  ```bash
   python >= 3.5
   git
   wget
+  ```
 
   python pip should also be correctly installed. You could use "python3 -m pip -v" to check in Linux.
 
@@ -14,15 +17,20 @@
 
 * __Install NNI through pip__
 
+  ```bash
   python3 -m pip install --user --upgrade nni
+  ```
 
 * __Install NNI through source code__
 
+  ```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:
+```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
 
@@ -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/Installation.md

@@ -6,21 +6,27 @@ Currently we only support installation on Linux & Mac.
 ## **Installation**
 * __Dependencies__
 
+  ```bash
   python >= 3.5
   git
   wget
+  ```
 
   python pip should also be correctly installed. You could use "python3 -m pip -v" to check pip version. 
 
 * __Install NNI through pip__
 
+  ```bash
   python3 -m pip install --user --upgrade nni
+  ```
 
 * __Install NNI through source code__
 
+  ```bash
   git clone -b v0.4.1 https://github.com/Microsoft/nni.git
   cd nni
   source install.sh
+  ```
 
 * __Install NNI in docker image__
 
@@ -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
-

docs/NNICTLDOC.md

 nnictl
+
 ===
+
 ## Introduction
+
 __nnictl__ is a command line tool, which can be used to control experiments, such as start/stop/resume an experiment, start/stop NNIBoard, etc.
 
 ## Commands
+
 nnictl support commands:
-```
+
+```bash
 nnictl create
 nnictl stop
 nnictl update
@@ -19,21 +24,22 @@ nnictl tensorboard
 nnictl top
 nnictl --version
 ```
+
 ### Manage an experiment
+
 * __nnictl create__
+
   * Description
 
-		  You can use this command to create a new experiment, using the configuration specified in config file. 
-          After this command is successfully done, the context will be set as this experiment, 
-          which means the following command you issued is associated with this experiment, 
-          unless you explicitly changes the context(not supported yet). 
+    You can use this command to create a new experiment, using the configuration specified in config file. After this command is successfully done, the context will be set as this experiment, which means the following command you issued is associated with this experiment, unless you explicitly changes the context(not supported yet).
 
   * Usage
 
+    ```bash
     nnictl create [OPTIONS]
+    ```
 
     Options:  
-    
     | Name, shorthand | Required|Default | Description |
     | ------ | ------ | ------ |------ |
     | --config, -c|  True| |yaml configure file of the experiment|
@@ -47,7 +53,10 @@ nnictl --version
 
   * Usage
 
+    ```bash
     nnictl resume [OPTIONS]
+    ```
+
     Options:
 
     | Name, shorthand | Required|Default | Description |
@@ -55,9 +64,6 @@ nnictl --version
     | id|  False| |The id of the experiment you want to resume|  
     | --port, -p|  False| |Rest port of the experiment you want to resume|
 
-     
-      
-
 * __nnictl stop__
   * Description
 
@@ -65,16 +71,23 @@ nnictl --version
 
   * Usage
 
+    ```bash
     nnictl stop [id]
+    ```
   
   * Detail
 
-        1.If there is an id specified, and the id matches the running experiment, nnictl will stop the corresponding experiment, or will print error message.
-        2.If there is no id specified, and there is an experiment running, stop the running experiment, or print error message.
-        3.If the id ends with *, nnictl will stop all experiments whose ids matchs the regular.
-        4.If the id does not exist but match the prefix of an experiment id, nnictl will stop the matched experiment.
-        5.If the id does not exist but match multiple prefix of the experiment ids, nnictl will give id information.
-        6.Users could use 'nnictl stop all' to stop all experiments  
+    1. If there is an id specified, and the id matches the running experiment, nnictl will stop the corresponding experiment, or will print error message.
+
+    2. If there is no id specified, and there is an experiment running, stop the running experiment, or print error message.
+
+    3. If the id ends with *, nnictl will stop all experiments whose ids matchs the regular.
+
+    4. If the id does not exist but match the prefix of an experiment id, nnictl will stop the matched experiment.
+
+    5. If the id does not exist but match multiple prefix of the experiment ids, nnictl will give id information.
+
+    6. Users could use 'nnictl stop all' to stop all experiments  
 
 * __nnictl update__
 
@@ -85,7 +98,9 @@ nnictl --version
 
     * Usage
 
+      ```bash
       nnictl update searchspace [OPTIONS]
+      ```
 
       Options:
 
@@ -101,7 +116,9 @@ nnictl --version
 
     * Usage
 
+      ```bash
       nnictl update concurrency [OPTIONS]
+      ```
 
       Options:
 
@@ -113,11 +130,13 @@ nnictl --version
   * __nnictl update duration__  
     * Description
 
-               You can use this command to update an experiment's concurrency.  
+      You can use this command to update an experiment's duration.  
 
     * Usage
 
+      ```bash
       nnictl update duration [OPTIONS]
+      ```
 
       Options:
 
@@ -133,8 +152,9 @@ nnictl --version
 
     * Usage
 
+      ```bash
       nnictl update trialnum [OPTIONS]
-
+      ```
       Options:
 
       | Name, shorthand | Required|Default | Description |
@@ -142,17 +162,19 @@ nnictl --version
       | id|  False| |ID of the experiment you want to set|
       | --value, -v|  True| |the new number of maxtrialnum you want to set|
 
-     
-
 * __nnictl trial__
+
   * __nnictl trial ls__
+
     * Description
 
       You can use this command to show trial's information.
 
     * Usage
   
+      ```bash
       nnictl trial ls
+      ```
 
       Options:
 
@@ -164,10 +186,12 @@ nnictl --version
     * Description
 
       You can use this command to kill a trial job.
+
     * Usage
 
+      ```bash  
       nnictl trial kill [OPTIONS]
-    
+      ```  
       Options:  
 
       | Name, shorthand | Required|Default | Description |
@@ -183,7 +207,9 @@ nnictl --version
 
     * Usage
 
+      ```bash  
       nnictl top
+      ```  
 
       Options:  
 
@@ -192,17 +218,19 @@ nnictl --version
       | id|  False| |ID of the experiment you want to set|
       | --time, -t|  False| |The interval to update the experiment status, the unit of time is second, and the default value is 3 second.|
 
-          
-
 ### Manage experiment information
 
 * __nnictl experiment show__
+
   * Description
 
     Show the information of experiment.
+
   * Usage
 
+    ```bash
     nnictl experiment show
+    ```
 
     Options:
 
@@ -210,14 +238,17 @@ nnictl --version
     | ------ | ------ | ------ |------ |
     | id|  False| |ID of the experiment you want to set|
 
-
 * __nnictl experiment status__
+
   * Description
 
     Show the status of experiment.
+
   * Usage
 
+    ```bash
     nnictl experiment status
+    ```
 
     Options:
 
@@ -225,14 +256,16 @@ nnictl --version
     | ------ | ------ | ------ |------ |
     | id|  False| |ID of the experiment you want to set|
 
-
 * __nnictl experiment list__
   * Description
 
     Show the information of all the (running) experiments.
+
   * Usage
 
+    ```bash
     nnictl experiment list
+    ```
 
     Options:
 
@@ -240,8 +273,6 @@ nnictl --version
     | ------ | ------ | ------ |------ |
     | all|  False| False|Show all of experiments, including stopped experiments.|
 
- 
-
 * __nnictl config show__
   * Description
 
@@ -249,18 +280,23 @@ nnictl --version
 
   * Usage
 
+    ```bash
     nnictl config show
-        
+    ```
 
 ### Manage log
+
 * __nnictl log stdout__
+
   * Description
 
     Show the stdout log content.
 
   * Usage
 
+    ```bash
     nnictl log stdout [options]
+    ```
 
     Options:
 
@@ -271,7 +307,6 @@ nnictl --version
     | --tail, -t|  False| |show tail lines of stdout|
     | --path, -p|  False| |show the path of stdout file|
 
-	 
 * __nnictl log stderr__
   * Description
 
@@ -279,7 +314,9 @@ nnictl --version
   
   * Usage
 
+    ```bash
     nnictl log stderr [options]
+    ```
 
     Options:
 
@@ -290,7 +327,6 @@ nnictl --version
     | --tail, -t|  False| |show tail lines of stderr|
     | --path, -p|  False| |show the path of stderr file|
 
-
 * __nnictl log trial__
   * Description
   
@@ -298,7 +334,9 @@ nnictl --version
   
   * Usage
 
+    ```bash  
     nnictl log trial [options]
+    ```
 
     Options:
 
@@ -306,16 +344,19 @@ nnictl --version
     | ------ | ------ | ------ |------ |
     | id| False| |the id of trial|
 
-
 ### Manage webui
+
 * __nnictl webui url__
+
   * Description
 
     Show the urls of the experiment.
 
   * Usage
 
+    ```bash
     nnictl webui url
+    ```
 
     Options:
 
@@ -323,16 +364,19 @@ nnictl --version
     | ------ | ------ | ------ |------ |
     | id|  False| |ID of the experiment you want to set|
 
-
 ### Manage tensorboard
+
 * __nnictl tensorboard start__
+
   * Description
 
     Start the tensorboard process.
   
   * Usage
 
+    ```bash
     nnictl tensorboard start
+    ```
 
     Options:
 
@@ -357,7 +401,9 @@ nnictl --version
 
   * Usage
 
+    ```bash
     nnictl tensorboard stop
+    ```
 
     Options:
 
@@ -368,10 +414,13 @@ nnictl --version
 ### Check nni version
 
 * __nnictl --version__
+
   * Description
 
     Describe the current version of nni installed.
   
   * Usage
 
+    ```bash
     nnictl --version
+    ```

docs/Overview.md

@@ -22,7 +22,7 @@ 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.
 
-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. 
+NNI provides a set of examples in the package to get you familiar with the above process.
 
 ## Key Concepts
 

docs/PAIMode.md

@@ -7,7 +7,8 @@ Install NNI, follow the install guide [here](GetStarted.md).
 
 ## Run an experiment
 Use `examples/trials/mnist-annotation` as an example. The nni config yaml file's content is like:
-```
+
+```yaml
 authorName: your_name
 experimentName: auto_mnist
 # how many trials could be concurrently running
@@ -39,6 +40,7 @@ paiConfig:
   passWord: your_pai_password
   host: 10.1.1.1
 ```
+
 Note: You should set `trainingServicePlatform: pai` in nni config yaml file if you want to start experiment in pai mode.
 
 Compared with LocalMode and [RemoteMachineMode](RemoteMachineMode.md), trial configuration in pai mode have five additional keys:
@@ -58,7 +60,7 @@ Once complete to fill nni experiment config file and save (for example, save as
 ```
 nnictl create --config exp_pai.yaml
 ```
-to start the experiment in pai mode. NNI will create OpanPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`. 
+to start the experiment in pai mode. NNI will create OpenPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`. 
 You can see the pai jobs created by NNI in your OpenPAI cluster's web portal, like:
 ![](./img/nni_pai_joblist.jpg)
 
@@ -77,4 +79,3 @@ You can see there're three fils in output folder: stderr, stdout, and trial.log
 If you also want to save trial's other output into HDFS, like model files, you can use environment variable `NNI_OUTPUT_DIR` in your trial code to save your own output files, and NNI SDK will copy all the files in `NNI_OUTPUT_DIR` from trial's container to HDFS. 
 
 Any problems when using NNI in pai mode, plesae create issues on [NNI github repo](https://github.com/Microsoft/nni), or send mail to nni@microsoft.com
-

docs/RELEASE.md

@@ -101,5 +101,3 @@ Initial release of Neural Network Intelligence (NNI).
 
 ## Known Issues
 [Known Issues in release 0.1.0](https://github.com/Microsoft/nni/labels/nni010knownissues).
-      
-   

docs/RemoteMachineMode.md

 **Run an Experiment on Multiple Machines**
+
 ===
+
 NNI supports running an experiment on multiple machines through SSH channel, called `remote` mode. NNI assumes that you have access to those machines, and already setup the environment for running deep learning training code.
 
 e.g. Three machines and you login in with account `bob` (Note: the account is not necessarily the same on different machine):
@@ -11,19 +13,24 @@ e.g. Three machines and you login in with account `bob` (Note: the account is no
 | 10.1.1.3 | bob | bob123    |
 
 ## Setup NNI environment
+
 Install NNI on each of your machines following the install guide [here](GetStarted.md).
 
 For remote machines that are used only to run trials but not the nnictl, you can just install python SDK:
 
 * __Install python SDK through pip__
 
+  ```bash
   python3 -m pip install --user --upgrade nni-sdk
+  ```
 
 ## Run an experiment
+
 Install NNI on another machine which has network accessibility to those three machines above, or you can just use any machine above to run nnictl command line tool.
 
 We use `examples/trials/mnist-annotation` as an example here. `cat ~/nni/examples/trials/mnist-annotation/config_remote.yml` to see the detailed configuration file:
-```
+
+```yaml
 authorName: default
 experimentName: example_mnist
 trialConcurrency: 1
@@ -58,8 +65,11 @@ machineList:
     username: bob
     passwd: bob123
 ```
+
 Simply filling the `machineList` section and then run:
-```
+
+```bash
 nnictl create --config ~/nni/examples/trials/mnist-annotation/config_remote.yml
 ```
+
 to start the experiment.

docs/SearchSpaceSpec.md

@@ -15,7 +15,7 @@
 
 ```
 
-The example define ```dropout_rate``` as variable which priori distribution is uniform distribution, and its value from ```0.1``` and ```0.5```.
+The example define `dropout_rate` as variable which priori distribution is uniform distribution, and its value from `0.1` and `0.5`.
 The tuner will sample parameters/architecture by understanding the search space first.
 
 User should define the name of variable, type and candidate value of variable.
@@ -69,6 +69,6 @@ The candidate type and value for variable is here:
 
 Note that SMAC only supports a subset of the types above, including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`. In the current version, SMAC does not support cascaded search space (i.e., conditional variable in SMAC).
 
-Note that GridSearch Tuner only supports a subset of the types above, including `choic`, `quniform` and `qloguniform`, where q here specifies the number of values that will be sampled. Details about the last two type as follows
+Note that GridSearch Tuner only supports a subset of the types above, including `choice`, `quniform` and `qloguniform`, where q here specifies the number of values that will be sampled. Details about the last two type as follows
 * Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the number of values that will be sampled evenly. Note that q should be at least 2. It will be sampled in a way that the first sampled value is 'low', and each of the following values is (high-low)/q larger that the value in front of it.
 * Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log(low), log(high)] and sample and then change the sampled value back.

docs/SetupNNIDeveloperEnvironment.md

 **Set up NNI developer environment**
+
 ===
+
 ## Best practice for debug NNI source code
 
 For debugging NNI source code, your development environment should be under Ubuntu 16.04 (or above) system with python 3 and pip 3 installed, then follow the below steps.
@@ -7,42 +9,52 @@ For debugging NNI source code, your development environment should be under Ubun
 **1. Clone the source code**
 
 Run the command
+
 ```
 git clone https://github.com/Microsoft/nni.git
 ```
+
 to clone the source code
 
 **2. Prepare the debug environment and install dependencies**
 
 Change directory to the source code folder, then run the command
+
 ```
 make install-dependencies
 ```
+
 to install the dependent tools for the environment
 
 **3. Build source code**
 
 Run the command
+
 ```
 make build
 ```
+
 to build the source code
 
 **4. Install NNI to development environment**
 
 Run the command
+
 ```
 make dev-install
 ```
+
 to install the distribution content to development environment, and create cli scripts
 
 **5. Check if the environment is ready**
 
 Now, you can try to start an experiment to check if your environment is ready.
 For example, run the command
+
 ```
 nnictl create --config ~/nni/examples/trials/mnist/config.yml
 ```
+
 And open WebUI to check if everything is OK
 
 **6. Redeploy**

docs/StartExperiment.md

@@ -2,7 +2,9 @@ How to start an experiment
 ===
 ## 1.Introduce
 There are few steps to start an new experiment of nni, here are the process.
+
 <img src="./img/experiment_process.jpg" width="50%" height="50%" />
+
 ## 2.Details
 ### 2.1 Check environment
 1. Check if there is an old experiment running 
@@ -20,7 +22,7 @@ Check whether restful server process is successfully started and could get a res
 Call restful server to set experiment config before starting an experiment, experiment config includes the config values in config yaml file.
 
 ### 2.5 Check experiment cofig
-Check the response content of restful srver,  if the status code of response is 200, the config is successfully set.
+Check the response content of restful server,  if the status code of response is 200, the config is successfully set.
 
 ### 2.6 Start Experiment
 Call restful server process to setup an experiment.

docs/howto_1_WriteTrial.md

@@ -6,10 +6,12 @@ A **Trial** in NNI is an individual attempt at applying a set of parameters on a
 To define a NNI trial, you need to firstly define the set of parameters and then update the model. NNI provide two approaches for you to define a trial: `NNI API` and `NNI Python annotation`.
 
 ## NNI API
+
 >Step 1 - Prepare a SearchSpace parameters file. 
 
 An example is shown below:
-```
+
+```json
 {
     "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
     "conv_size":{"_type":"choice","_value":[2,3,5,7]},
@@ -17,9 +19,11 @@ An example is shown below:
     "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
 }
 ```
-Refer to [SearchSpaceSpec.md](SearchSpaceSpec.md) to learn more about search space.
+
+Refer to [SearchSpaceSpec.md](./SearchSpaceSpec.md) to learn more about search space.
 
 >Step 2 - Update model codes
+
 ~~~~
 2.1 Declare NNI API
     Include `import nni` in your trial code to use NNI APIs. 
@@ -48,6 +52,7 @@ Refer to [SearchSpaceSpec.md](SearchSpaceSpec.md) to learn more about search spa
 ~~~~
 
 **NOTE**:
+
 ~~~~
 accuracy - The `accuracy` could be any python object, but  if you use NNI built-in tuner/assessor, `accuracy` should be a numerical variable (e.g. float, int).
 assessor - The assessor will decide which trial should early stop based on the history performance of trial (intermediate result of one trial).
@@ -63,11 +68,12 @@ useAnnotation: false
 searchSpacePath: /path/to/your/search_space.json
 ```
 
-You can refer to [here](ExperimentConfig.md) for more information about how to set up experiment configurations.
+You can refer to [here](./ExperimentConfig.md) for more information about how to set up experiment configurations.
 
-(../examples/trials/README.md) for more information about how to write trial code using NNI APIs.
+You can refer to [here](../examples/trials/README.md) for more information about how to write trial code using NNI APIs.
 
 ## NNI Python Annotation
+
 An alternative to write a trial is to use NNI's syntax for python. Simple as any annotation, NNI annotation is working like comments in your codes. You don't have to make structure or any other big changes to your existing codes. With a few lines of NNI annotation, you will be able to:
 * annotate the variables you want to tune 
 * specify in which range you want to tune the variables
@@ -118,9 +124,11 @@ with tf.Session() as sess:
 
 >Step 2 - Enable NNI Annotation
 In the yaml configure file, you need to set *useAnnotation* to true to enable NNI annotation:
-```
+
+```yaml
 useAnnotation: true
 ```
 
 ## More Trial Example
+
 * [Automatic Model Architecture Search for Reading Comprehension.](../examples/trials/ga_squad/README.md)

docs/howto_2_CustomizedTuner.md

@@ -4,13 +4,14 @@
 
 So, if user want to implement a customized Tuner, she/he only need to:
 
-1) Inherit a tuner of a base Tuner class
-2) Implement receive_trial_result and generate_parameter function
-3) Configure your customized tuner in experiment yaml config file
+1. Inherit a tuner of a base Tuner class
+1. Implement receive_trial_result and generate_parameter function
+1. Configure your customized tuner in experiment yaml config file
 
-Here ia an example:
+Here is an example:
 
 **1) Inherit a tuner of a base Tuner class**
+
 ```python
 from nni.tuner import Tuner
 
@@ -20,6 +21,7 @@ class CustomizedTuner(Tuner):
 ```
 
 **2) Implement receive_trial_result and generate_parameter function**
+
 ```python
 from nni.tuner import Tuner
 
@@ -46,12 +48,14 @@ class CustomizedTuner(Tuner):
     return your_parameters
     ...
 ```
-```receive_trial_result``` will receive ```the parameter_id, parameters, value``` as parameters input. Also, Tuner will receive the ```value``` object are exactly same value that Trial send.
 
-The ```your_parameters``` return from ```generate_parameters``` function, will be package as json object by NNI SDK. NNI SDK will unpack json object so the Trial will receive the exact same ```your_parameters``` from Tuner.
+`receive_trial_result` will receive the `parameter_id, parameters, value` as parameters input. Also, Tuner will receive the `value` object are exactly same value that Trial send.
+
+The `your_parameters` return from `generate_parameters` function, will be package as json object by NNI SDK. NNI SDK will unpack json object so the Trial will receive the exact same `your_parameters` from Tuner.
 
 For example:
-If the you implement the ```generate_parameters``` like this:
+If the you implement the `generate_parameters` like this:
+
 ```python
     def generate_parameters(self, parameter_id):
         '''
@@ -61,23 +65,28 @@ If the you implement the ```generate_parameters``` like this:
         # your code implements here.
         return {"dropout": 0.3, "learning_rate": 0.4}
 ```
- It means your Tuner will always generate parameters ```{"dropout": 0.3, "learning_rate": 0.4}```. Then Trial will receive ```{"dropout": 0.3, "learning_rate": 0.4}``` by calling API ```nni.get_next_parameter()```. Once the trial ends with a result (normally some kind of metrics), it can send the result to Tuner by calling API ```nni.report_final_result()```, for example ```nni.report_final_result(0.93)```. Then your Tuner's ```receive_trial_result``` function will receied the result like：
-```
+
+It means your Tuner will always generate parameters `{"dropout": 0.3, "learning_rate": 0.4}`. Then Trial will receive `{"dropout": 0.3, "learning_rate": 0.4}` by calling API `nni.get_next_parameter()`. Once the trial ends with a result (normally some kind of metrics), it can send the result to Tuner by calling API `nni.report_final_result()`, for example `nni.report_final_result(0.93)`. Then your Tuner's `receive_trial_result` function will receied the result like：
+
+```python
 parameter_id = 82347
 parameters = {"dropout": 0.3, "learning_rate": 0.4}
 value = 0.93
 ```
 
-**Note that** if you want to access a file (e.g., ```data.txt```) in the directory of your own tuner, you cannot use ```open('data.txt', 'r')```. Instead, you should use the following:
-```
+**Note that** if you want to access a file (e.g., `data.txt`) in the directory of your own tuner, you cannot use `open('data.txt', 'r')`. Instead, you should use the following:
+
+```python
 _pwd = os.path.dirname(__file__)
 _fd = open(os.path.join(_pwd, 'data.txt'), 'r')
 ```
-This is because your tuner is not executed in the directory of your tuner (i.e., ```pwd``` is not the directory of your own tuner).
+
+This is because your tuner is not executed in the directory of your tuner (i.e., `pwd` is not the directory of your own tuner).
 
 **3) Configure your customized tuner in experiment yaml config file**
 
 NNI needs to locate your customized tuner class and instantiate the class, so you need to specify the location of the customized tuner class and pass literal values as parameters to the \_\_init__ constructor.
+
 ```yaml
 tuner:
   codeDir: /home/abc/mytuner
@@ -90,9 +99,11 @@ tuner:
 ```
 
 More detail example you could see:
+
 > * [evolution-tuner](../src/sdk/pynni/nni/evolution_tuner)
 > * [hyperopt-tuner](../src/sdk/pynni/nni/hyperopt_tuner)
 > * [evolution-based-customized-tuner](../examples/tuners/ga_customer_tuner)
 
 ## Write a more advanced automl algorithm
-The methods above are usually enough to write a general tuner. However, users may also want more methods, for example, intermediate results, trials' state (e.g., the methods in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called `advisor` which directly inherits from `MsgDispatcherBase` in [`src/sdk/pynni/nni/msg_dispatcher_base.py`](../src/sdk/pynni/nni/msg_dispatcher_base.py). Please refer to [here](./howto_3_CustomizedAdvisor.md) for how to write a customized advisor.
+
+The information above are usually enough to write a general tuner. However, users may also want more information, for example, intermediate results, trials' state (e.g., the information in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called `advisor` which directly inherits from `MsgDispatcherBase` in [`src/sdk/pynni/nni/msg_dispatcher_base.py`](../src/sdk/pynni/nni/msg_dispatcher_base.py). Please refer to [here](./howto_3_CustomizedAdvisor.md) for how to write a customized advisor.

docs/howto_3_CustomizedAdvisor.md

 # **How To** - Customize Your Own Advisor
 
-*Advisor targets the scenario that the automl algorithm wants the methods of both tuner and assessor. Advisor is similar to tuner on that it receives trial configuration request, final results, and generate trial configurations. Also, it is similar to assessor on that it receives intermediate results, trial's end state, and could send trial kill command. Note that, if you use Advisor, tuner and assessor are not allowed to be used at the same time.*
+*Advisor targets the scenario that the automl algorithm wants the methods of both tuner and assessor. Advisor is similar to tuner on that it receives trial parameters request, final results, and generate trial parameters. Also, it is similar to assessor on that it receives intermediate results, trial's end state, and could send trial kill command. Note that, if you use Advisor, tuner and assessor are not allowed to be used at the same time.*
 
 So, if user want to implement a customized Advisor, she/he only need to:
 
-1) Define an Advisor inheriting from the MsgDispatcherBase class
-2) Implement the methods with prefix `handle_` except `handle_request`
-3) Configure your customized Advisor in experiment yaml config file
+1. Define an Advisor inheriting from the MsgDispatcherBase class
+1. Implement the methods with prefix `handle_` except `handle_request`
+1. Configure your customized Advisor in experiment yaml config file
 
-Here ia an example:
+Here is an example:
 
 **1) Define an Advisor inheriting from the MsgDispatcherBase class**
+
 ```python
 from nni.msg_dispatcher_base import MsgDispatcherBase
 

docs/tutorial_1_CR_exp_local_api.md

@@ -83,9 +83,9 @@ Let's use a simple trial example, e.g. mnist, provided by NNI. After you install
 
       python ~/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) for how to write your own trial.
+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](CustomizedTuner.md)), but for simplicity, here we choose a tuner provided by NNI as below:
+**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
@@ -133,7 +133,7 @@ With all these steps done, we can run the experiment with the following command:
 You can refer to [here](NNICTLDOC.md) for more usage guide of *nnictl* command line tool.
 
 ## View experiment results
-The experiment has been running now. Oher than *nnictl*, NNI also provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features.
+The experiment has been running now. Other than *nnictl*, NNI also provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features.
 
 ## Using multiple local GPUs to speed up search
 The following steps assume that you have 4 NVIDIA GPUs installed at local and [tensorflow with GPU support](https://www.tensorflow.org/install/gpu). The demo enables 4 concurrent trail jobs and each trail job uses 1 GPU. 

docs/tutorial_3_tryTunersAndAssessors.md

@@ -12,12 +12,12 @@ NNI provides an easy to adopt approach to set up parameter tuning algorithms as
         required fields: codeDirectory, classFileName, className and classArgs.
 
 ### **Learn More about tuners**
-* For detailed defintion and usage aobut the required field, please refer to [Config an experiment](ExperimentConfig.md)
+* For detailed defintion and usage about the required field, please refer to [Config an experiment](ExperimentConfig.md)
 * [Tuners in the latest NNI release](HowToChooseTuner.md)
 * [How to implement your own tuner](howto_2_CustomizedTuner.md)
 
 
-**Assessor** specifies the algorithm you use to apply early stop policy. In NNI, there are two approaches to set theassessor. 
+**Assessor** specifies the algorithm you use to apply early stop policy. In NNI, there are two approaches to set the assessor.
 1. Directly use assessor provided by nni sdk
 
         required fields: builtinAssessorName and classArgs.