Sdk update

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'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}``` 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()```, such as ```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}