Sdk update

docs/AnnotationSpec.md

@@ -4,23 +4,26 @@ For good user experience and reduce user effort, we need to design a good annota
 
 If users use NNI system, they only need to:
 
- 1. Annotation variable in code as:
+ 1. Use nni.next_parameter() to retrieve hyper parameters from Tuner, before using other annotation, use following annotation the begining of trial code:
+    '''@nni.next_parameter()'''
+
+ 2. Annotation variable in code as:
 
     '''@nni.variable(nni.choice(2,3,5,7),name=self.conv_size)'''
 
- 2. Annotation intermediate in code as:
+ 3. Annotation intermediate in code as:
 
     '''@nni.report_intermediate_result(test_acc)'''
 
- 3. Annotation output in code as:
+ 4. Annotation output in code as:
 
     '''@nni.report_final_result(test_acc)'''
 
- 4. Annotation `function_choice` in code as:
+ 5. Annotation `function_choice` in code as:
 
     '''@nni.function_choice(max_pool(h_conv1, self.pool_size),avg_pool(h_conv1, self.pool_size),name=max_pool)'''
 
-In this way, they can easily realize automatic tuning on NNI. 
+In this way, they can easily implement automatic tuning on NNI. 
 
 For `@nni.variable`, `nni.choice` is the type of search space and there are 10 types to express your search space as follows:
 

docs/howto_1_WriteTrial.md

@@ -27,7 +27,7 @@ Refer to [SearchSpaceSpec.md](SearchSpaceSpec.md) to learn more about search spa
 2.2 Get predefined parameters
     Use the following code snippet: 
 
-        RECEIVED_PARAMS = nni.get_parameters()
+        RECEIVED_PARAMS = nni.get_next_parameter()
 
     to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example: 
 

docs/howto_2_CustomizedTuner.md

@@ -61,7 +61,7 @@ If the you implement the ```generate_parameters``` like this:
         # your code implements here.
         return {"dropout": 0.3, "learning_rate": 0.4}
 ```
-It's 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}``` this object will using ```nni.get_parameters()``` API from NNI SDK. After training of Trial, it will send result to Tuner by calling ```nni.report_final_result(0.93)```. Then ```receive_trial_result``` will function will receied these parameters 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：
 ```
 parameter_id = 82347
 parameters = {"dropout": 0.3, "learning_rate": 0.4}