merge master and resolve conflict

docs/en_US/CommunitySharings/SptagAutoTune.md

+# Automatically tuning SPTAG with NNI
+
+[SPTAG](https://github.com/microsoft/SPTAG) (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario released by [Microsoft Research (MSR)](https://www.msra.cn/) and [Microsoft Bing](https://www.bing.com/).
+
+This library assumes that the samples are represented as vectors and that the vectors can be compared by L2 distances or cosine distances. Vectors returned for a query vector are the vectors that have smallest L2 distance or cosine distances with the query vector.
+SPTAG provides two methods: kd-tree and relative neighborhood graph (SPTAG-KDT) and balanced k-means tree and relative neighborhood graph (SPTAG-BKT). SPTAG-KDT is advantageous in index building cost, and SPTAG-BKT is advantageous in search accuracy in very high-dimensional data.
+
+In SPTAG, there are tens of parameters that can be tuned for specified scenarios or datasets. NNI is a great tool for automatically tuning those parameters. The authors of SPTAG tried NNI for the auto tuning and found good-performing parameters easily, thus, they shared the practice of tuning SPTAG on NNI in their document [here](https://github.com/microsoft/SPTAG/blob/master/docs/Parameters.md). Please refer to it for detailed tutorial.
\ No newline at end of file

docs/en_US/CommunitySharings/community_sharings.rst

@@ -8,6 +8,7 @@ In addtion to the official tutorilas and examples, we encourage community contri
     :maxdepth: 2
 
     NNI in Recommenders <RecommendersSvd>
+    Automatically tuning SPTAG with NNI <SptagAutoTune>
     Neural Architecture Search Comparison <NasComparision>
     Hyper-parameter Tuning Algorithm Comparsion <HpoComparision>
     Parallelizing Optimization for TPE <ParallelizingTpeSearch>

docs/en_US/Tuner/BuiltinTuner.md

@@ -117,6 +117,11 @@ tuner:
 
 Its requirement of computation resource is relatively high. Specifically, it requires large initial population to avoid falling into local optimum. If your trial is short or leverages assessor, this tuner is a good choice. And, it is more suggested when your trial code supports weight transfer, that is, the trial could inherit the converged weights from its parent(s). This can greatly speed up the training progress. [Detailed Description](./EvolutionTuner.md)
 
+**Requirement of classArg**
+
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
+
+* **population_size** (*int value(should >0), optional, default = 20*) - the initial size of the population(trial num) in evolution tuner.
 
 **Usage example**
 
@@ -126,6 +131,7 @@ tuner:
   builtinTunerName: Evolution
   classArgs:
     optimize_mode: maximize
+    population_size: 100
 ```
 
 <br>