Update parallel tpe

docs/en_US/CommunitySharings/ParallelizingTpeSearch.md

@@ -29,7 +29,7 @@ Here is a simulation of the TPE algorithm in a two-dimensional search space. The
 
 ![](../../img/parallel_tpe_search_ei2.PNG)
 
-TPE perfors well when we use it in sequential, but if we provide a larger concurrency, then **there will be a large number of points produced in the same EI state**, too concentrated points will reduce the exploration ability of the tuner, resulting in resources waste.
+TPE performs well when we use it in sequential, but if we provide a larger concurrency, then **there will be a large number of points produced in the same EI state**, too concentrated points will reduce the exploration ability of the tuner, resulting in resources waste.
 
 Here is the simulation figure when we set `concurrency=60`, It can be seen that this phenomenon is obvious.
 

src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py

@@ -198,9 +198,11 @@ def __init__(self, algorithm_name, optimize_mode='minimize',
         algorithm_name : str
             algorithm_name includes "tpe", "random_search" and anneal".
         optimize_mode : str
-        parallel_optimize : bool
+        parallel_optimize : bool 
+            More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
         constant_liar_type : str
             constant_liar_type including "min", "max" and "mean"
+            More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
         """
         self.algorithm_name = algorithm_name
         self.optimize_mode = OptimizeMode(optimize_mode)