modify docs (#1759)

* modify docs

* "nni_search_space.json" -> "search_space.json"

docs/en_US/TrainingService/LocalMode.md

@@ -96,7 +96,7 @@ This command will be filled in the YAML configure file below. Please refer to [h
 
 **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
 
@@ -112,6 +112,9 @@ maxTrialNum: 100
 # choice: local, remote
 trainingServicePlatform: local
 
+# search space file
+searchSpacePath: search_space.json
+
 # choice: true, false
 useAnnotation: true
 tuner:

docs/en_US/TrainingService/PaiMode.md

@@ -19,6 +19,8 @@ maxExecDuration: 3h
 maxTrialNum: 100
 # choice: local, remote, pai
 trainingServicePlatform: pai
+# search space file
+searchSpacePath: search_space.json
 # choice: true, false
 useAnnotation: true
 tuner:

docs/en_US/TrainingService/RemoteMachineMode.md

@@ -28,6 +28,8 @@ maxExecDuration: 1h
 maxTrialNum: 10
 #choice: local, remote, pai
 trainingServicePlatform: remote
+# search space file
+searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: true
 tuner:

docs/zh_CN/TrainingService/LocalMode.md

@@ -98,33 +98,36 @@
 *builtinTunerName* 用来指定 NNI 中的 Tuner，*classArgs* 是传入到 Tuner的参数（内置 Tuner 在[这里](../Tuner/BuiltinTuner.md)），*optimization_mode* 表明需要最大化还是最小化 Trial 的结果。
 
 **准备配置文件**：实现 Trial 的代码，并选择或实现自定义的 Tuner 后，就要准备 YAML 配置文件了。 NNI 为每个 Trial 样例都提供了演示的配置文件，用命令`cat ~/nni/examples/trials/mnist-annotation/config.yml` 来查看其内容。 大致内容如下：
-
-    authorName: your_name
-    experimentName: auto_mnist
-    
-    # 并发运行数量
-    trialConcurrency: 2
-    
-    # Experiment 运行时间
-    maxExecDuration: 3h
-    
-    # 可为空，即数量不限
-    maxTrialNum: 100
-    
-    # 可选值为: local, remote
-    trainingServicePlatform: local
-    
-    # 可选值为: true, false
-    useAnnotation: true
-    tuner:
-      builtinTunerName: TPE
-      classArgs:
-        optimize_mode: maximize
-    trial:
-      command: python mnist.py
-      codeDir: ~/nni/examples/trials/mnist-annotation
-      gpuNum: 0
-    
+```yaml
+authorName: your_name
+experimentName: auto_mnist
+
+# 并发运行数量
+trialConcurrency: 2
+
+# Experiment 运行时间
+maxExecDuration: 3h
+
+# 可为空，即数量不限
+maxTrialNum: 100
+
+# 可选值为: local, remote
+trainingServicePlatform: local
+
+# 搜索空间文件
+searchSpacePath: search_space.json
+
+# 可选值为: true, false
+useAnnotation: true
+tuner:
+  builtinTunerName: TPE
+  classArgs:
+    optimize_mode: maximize
+trial:
+  command: python mnist.py
+  codeDir: ~/nni/examples/trialsmnist-annotation
+  gpuNum: 0
+ ```   
 
 因为这个 Trial 代码使用了 NNI Annotation 的方法（参考[这里](../Tutorial/AnnotationSpec.md) ），所以*useAnnotation* 为 true。 *command* 是运行 Trial 代码所需要的命令，*codeDir* 是 Trial 代码的相对位置。 命令会在此目录中执行。 同时，也需要提供每个 Trial 进程所需的 GPU 数量。
 

docs/zh_CN/TrainingService/PaiMode.md

@@ -21,6 +21,8 @@ maxExecDuration: 3h
 maxTrialNum: 100
 # 可选项: local, remote, pai
 trainingServicePlatform: pai
+# 搜索空间文件
+searchSpacePath: search_space.json
 # 可选项: true, false
 useAnnotation: true
 tuner:

docs/zh_CN/TrainingService/RemoteMachineMode.md

@@ -28,6 +28,8 @@ maxExecDuration: 1h
 maxTrialNum: 10
 #可选项: local, remote, pai
 trainingServicePlatform: remote
+# 搜索空间文件
+searchSpacePath: search_space.json
 #可选项: true, false
 useAnnotation: true
 tuner: