Fix regression by white-space PR, and some typos. (#1197)

docs/en_US/ExperimentConfig.md

@@ -4,10 +4,10 @@ A config file is needed when create an experiment, the path of the config file i
 The config file is written in YAML format, and need to be written correctly.
 This document describes the rule to write config file, and will provide some examples and templates.
 
-- [Experiment config reference](#experiment-config-reference)
-  - [Template](#template)
-  - [Configuration spec](#configuration-spec)
-  - [Examples](#examples)
+- [Experiment config reference](#Experiment-config-reference)
+  - [Template](#Template)
+  - [Configuration spec](#Configuration-spec)
+  - [Examples](#Examples)
 
 <a name="Template"></a>
 ## Template
@@ -128,12 +128,14 @@ machineList:
   * Description
 
     __authorName__ is the name of the author who create the experiment.
-   TBD: add default value
+
+    TBD: add default value
 
 * __experimentName__
   * Description
 
     __experimentName__ is the name of the experiment created.
+
     TBD: add default value
 
 * __trialConcurrency__
@@ -153,7 +155,7 @@ machineList:
 * __versionCheck__
   * Description
   
-    NNI will check the version of nniManager process and the version of trialKeeper in remote, pai and kubernetes platform. If you want to disable version check, you could set versionCheck be false. 
+    NNI will check the version of nniManager process and the version of trialKeeper in remote, pai and kubernetes platform. If you want to disable version check, you could set versionCheck be false.
 
 * __debug__
   * Description

docs/en_US/FrameworkControllerMode.md

@@ -1,36 +1,42 @@
-**Run an Experiment on FrameworkController**
+# Run an Experiment on FrameworkController
+
 ===
-NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install kubeflow for specific deeplearning framework like tf-operator or pytorch-operator. Now you can use frameworkcontroller as the training service to run NNI experiment.
+NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator. Now you can use FrameworkController as the training service to run NNI experiment.
 
 ## Prerequisite for on-premises Kubernetes Service
+
 1. A **Kubernetes** cluster using Kubernetes 1.8 or later. Follow this [guideline](https://kubernetes.io/docs/setup/) to set up Kubernetes
-2. Prepare a **kubeconfig** file, which will be used by NNI to interact with your kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the **KUBECONFIG** environment variable. Refer this [guideline]( https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig) to learn more about kubeconfig.
+2. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the **KUBECONFIG** environment variable. Refer this [guideline]( https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig) to learn more about kubeconfig.
 3. If your NNI trial job needs GPU resource, you should follow this [guideline](https://github.com/NVIDIA/k8s-device-plugin) to configure **Nvidia device plugin for Kubernetes**.
 4. Prepare a **NFS server** and export a general purpose mount (we recommend to map your NFS server path in `root_squash option`, otherwise permission issue may raise when NNI copies files to NFS. Refer this [page](https://linux.die.net/man/5/exports) to learn what root_squash option is), or **Azure File Storage**.
 5. Install **NFS client** on the machine where you install NNI and run nnictl to create experiment. Run this command to install NFSv4 client:
-    ```
+
+    ```bash
     apt-get install nfs-common
     ```
 
 6. Install **NNI**, follow the install guide [here](QuickStart.md).
 
 ## Prerequisite for Azure Kubernetes Service
-1. NNI support kubeflow based on Azure Kubernetes Service, follow the [guideline](https://azure.microsoft.com/en-us/services/kubernetes-service/) to set up Azure Kubernetes Service.
+
+1. NNI support Kubeflow based on Azure Kubernetes Service, follow the [guideline](https://azure.microsoft.com/en-us/services/kubernetes-service/) to set up Azure Kubernetes Service.
 2. Install [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and __kubectl__.  Use `az login` to set azure account, and connect kubectl client to AKS, refer this [guideline](https://docs.microsoft.com/en-us/azure/aks/kubernetes-walkthrough#connect-to-the-cluster).
 3. Follow the [guideline](https://docs.microsoft.com/en-us/azure/storage/common/storage-quickstart-create-account?tabs=portal) to create azure file storage account. If you use Azure Kubernetes Service, NNI need Azure Storage Service to store code files and the output files.
 4. To access Azure storage service, NNI need the access key of the storage account, and NNI uses [Azure Key Vault](https://azure.microsoft.com/en-us/services/key-vault/) Service to protect your private key. Set up Azure Key Vault Service, add a secret to Key Vault to store the access key of Azure storage account. Follow this [guideline](https://docs.microsoft.com/en-us/azure/key-vault/quick-create-cli) to store the access key.
 
+## Setup FrameworkController
 
-## Set up FrameworkController
-Follow the [guideline](https://github.com/Microsoft/frameworkcontroller/tree/master/example/run) to set up frameworkcontroller in the kubernetes cluster, NNI supports frameworkcontroller by the statefulset mode.
+Follow the [guideline](https://github.com/Microsoft/frameworkcontroller/tree/master/example/run) to set up FrameworkController in the Kubernetes cluster, NNI supports FrameworkController by the stateful set mode.
 
 ## Design
-Please refer the design of [kubeflow training service](./KubeflowMode.md), frameworkcontroller training service pipeline is similar.
+
+Please refer the design of [Kubeflow training service](./KubeflowMode.md), FrameworkController training service pipeline is similar.
 
 ## Example
 
-The frameworkcontroller config file format is:
-```
+The FrameworkController config file format is:
+
+```yaml
 authorName: default
 experimentName: example_mnist
 trialConcurrency: 1
@@ -71,8 +77,10 @@ frameworkcontrollerConfig:
     server: {your_nfs_server}
     path: {your_nfs_server_exported_path}
 ```
+
 If you use Azure Kubernetes Service, you should  set `frameworkcontrollerConfig` in your config YAML file as follows:
-```
+
+```yaml
 frameworkcontrollerConfig:
   storage: azureStorage
   keyVault:
@@ -82,22 +90,27 @@ frameworkcontrollerConfig:
     accountName: {your_storage_account_name}
     azureShare: {your_azure_share_name}
 ```
+
 Note: You should explicitly set `trainingServicePlatform: frameworkcontroller` in NNI config YAML file if you want to start experiment in frameworkcontrollerConfig mode.
 
-The trial's config format for NNI frameworkcontroller mode is a simple version of frameworkcontroller's offical config, you could refer the [tensorflow example of frameworkcontroller](https://github.com/Microsoft/frameworkcontroller/blob/master/example/framework/scenario/tensorflow/cpu/tensorflowdistributedtrainingwithcpu.yaml) for deep understanding.
+The trial's config format for NNI frameworkcontroller mode is a simple version of FrameworkController's official config, you could refer the [Tensorflow example of FrameworkController](https://github.com/Microsoft/frameworkcontroller/blob/master/example/framework/scenario/tensorflow/cpu/tensorflowdistributedtrainingwithcpu.yaml) for deep understanding.
+
 Trial configuration in frameworkcontroller mode have the following configuration keys:
-* taskRoles: you could set multiple task roles in config file, and each task role is a basic unit to process in kubernetes cluster.
-   * name: the name of task role specified, like "worker", "ps", "master".
-   * taskNum: the replica number of the task role.
-   * command: the users' command to be used in the container.
-   * gpuNum: the number of gpu device used in container.
-   * cpuNum: the number of cpu device used in container.
-   * memoryMB: the memory limitaion to be specified in container.
-   * image: the docker image used to create pod and run the program.
-   * frameworkAttemptCompletionPolicy: the policy to run framework, please refer the [user-manual](https://github.com/Microsoft/frameworkcontroller/blob/master/doc/user-manual.md#frameworkattemptcompletionpolicy) to get the specific information. Users could use the policy to control the pod, for example, if ps does not stop, only worker stops, this completionpolicy could helps stop ps.
+
+* taskRoles: you could set multiple task roles in config file, and each task role is a basic unit to process in Kubernetes cluster.
+  * name: the name of task role specified, like "worker", "ps", "master".
+  * taskNum: the replica number of the task role.
+  * command: the users' command to be used in the container.
+  * gpuNum: the number of gpu device used in container.
+  * cpuNum: the number of cpu device used in container.
+  * memoryMB: the memory limitaion to be specified in container.
+  * image: the docker image used to create pod and run the program.
+  * frameworkAttemptCompletionPolicy: the policy to run framework, please refer the [user-manual](https://github.com/Microsoft/frameworkcontroller/blob/master/doc/user-manual.md#frameworkattemptcompletionpolicy) to get the specific information. Users could use the policy to control the pod, for example, if ps does not stop, only worker stops, The completion policy could helps stop ps.
 
 ## How to run example
-After you prepare a config file, you could run your experiment by nnictl. The way to start an experiment on frameworkcontroller is similar to kubeflow, please refer the [document](./KubeflowMode.md) for more information.
+
+After you prepare a config file, you could run your experiment by nnictl. The way to start an experiment on FrameworkController is similar to Kubeflow, please refer the [document](./KubeflowMode.md) for more information.
 
 ## version check
-NNI support version check feature in since version 0.6, [refer](PaiMode.md)
+
+NNI support version check feature in since version 0.6, [refer](PaiMode.md)

docs/en_US/HowToImplementTrainingService.md

@@ -2,12 +2,13 @@
 ===
 
 ## Overview
-TrainingService is a module related to platform management and job schedule in NNI. TrainingService is designed to be easily implemented, we define an abstract class TrainingService as the parent class of all kinds of TrainignService, users just need to  inherit the parent class and complete their own clild class if they want to implement customized TrainingService.
+TrainingService is a module related to platform management and job schedule in NNI. TrainingService is designed to be easily implemented, we define an abstract class TrainingService as the parent class of all kinds of TrainingService, users just need to inherit the parent class and complete their own child class if they want to implement customized TrainingService.
 
 ## System architecture
 ![](../img/NNIDesign.jpg)
 
 The brief system architecture of NNI is shown in the picture. NNIManager is the core management module of system, in charge of calling TrainingService to manage trial jobs and the communication between different modules. Dispatcher is a message processing center responsible for message dispatch. TrainingService is a module to manage trial jobs, it communicates with nniManager module, and has different instance according to different training platform. For the time being, NNI supports local platfrom, [remote platfrom](RemoteMachineMode.md), [PAI platfrom](PaiMode.md), [kubeflow platform](KubeflowMode.md) and [FrameworkController platfrom](FrameworkController.md).
+
 In this document, we introduce the brief design of TrainingService. If users want to add a new TrainingService instance, they just need to complete a child class to implement TrainingService, don't need to understand the code detail of NNIManager, Dispatcher or other modules.
 
 ## Folder structure of code
@@ -63,6 +64,7 @@ abstract class TrainingService {
 The parent class of TrainingService has a few abstract functions, users need to inherit the parent class and implement all of these abstract functions.
 
 __setClusterMetadata(key: string, value: string)__
+
 ClusterMetadata is the data related to platform details, for examples, the ClusterMetadata defined in remote machine server is:
 ```
 export class RemoteMachineMeta {
@@ -91,9 +93,11 @@ export class RemoteMachineMeta {
 The metadata includes the host address, the username or other configuration related to the platform. Users need to define their own metadata format, and set the metadata instance in this function. This function is called before the experiment is started to set the configuration of remote machines.
 
 __getClusterMetadata(key: string)__
+
 This function will return the metadata value according to the values, it could be left empty if users don't need to use it.
 
 __submitTrialJob(form: JobApplicationForm)__
+
 SubmitTrialJob is a function to submit new trial jobs, users should generate a job instance in TrialJobDetail type. TrialJobDetail is defined as follow:
 ```
 interface TrialJobDetail {
@@ -113,37 +117,49 @@ interface TrialJobDetail {
 According to different kinds of implementation, users could put the job detail into a job queue, and keep  fetching the job from the queue and start preparing and running them. Or they could finish preparing and running process in this function, and return job detail after the submit work.
 
 __cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean)__
+
 If this function is called, the trial started by the platform should be canceled. Different kind of platform has diffenent methods to calcel a running job, this function should be implemented according to specific platform.
 
 __updateTrialJob(trialJobId: string, form: JobApplicationForm)__
+
 This function is called to update the trial job's status, trial job's status should be detected according to different platform, and be updated to `RUNNING`, `SUCCEED`, `FAILED` etc.
 
 __getTrialJob(trialJobId: string)__
+
 This function returns a trialJob detail instance according to trialJobId.
 
 __listTrialJobs()__
+
 Users should put all of trial job detail information into a list, and return the list.
 
 __addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void)__
+
 NNI will hold an EventEmitter to get job metrics, if there is new job metrics detected, the EventEmitter will be triggered. Users should start the EventEmitter in this function.
 
 __removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void)__
+
 Close the EventEmitter.
 
 __run()__
+
 The run() function is a main loop function in TrainingService, users could set a while loop to execute their logic code, and finish executing them when the experiment is stopped.
 
 __cleanUp()__
+
 This function is called to clean up the environment when a experiment is stopped. Users should do the platform-related cleaning operation in this function.
 
 ## TrialKeeper tool
 
 NNI offers a TrialKeeper tool to help maintaining trial jobs. Users can find the source code in `nni/tools/nni_trial_tool`. If users want to run trial jobs in cloud platform, this tool will be a fine choice to help keeping trial running in the platform.
+
 The running architecture of TrialKeeper is show as follow:
+
 ![](../img/trialkeeper.jpg)
+
 When users submit a trial job to cloud platform, they should wrap their trial command into TrialKeeper, and start a TrialKeeper process in cloud platform. Notice that TrialKeeper use restful server to communicate with TrainingService, users should start a restful server in local machine to receive metrics sent from TrialKeeper. The source code about restful server could be found in `nni/src/nni_manager/training_service/common/clusterJobRestServer.ts`.
 
 ## Reference
 
 For more information about how to debug, please [refer](HowToDebug.md).
-The guide line of how to contribute, please [refer](Contributing.md).
+
+The guideline of how to contribute, please [refer](Contributing.md).