From c63115698aa75f9c1f9fcdf5b00fa5ea44ac5cdb Mon Sep 17 00:00:00 2001 From: quanlu Date: Wed, 28 Aug 2019 09:59:25 +0800 Subject: [PATCH] add doc for sptag --- docs/en_US/CommunitySharings/SptagAutoTune.md | 8 ++++++++ docs/en_US/CommunitySharings/community_sharings.rst | 1 + 2 files changed, 9 insertions(+) create mode 100644 docs/en_US/CommunitySharings/SptagAutoTune.md diff --git a/docs/en_US/CommunitySharings/SptagAutoTune.md b/docs/en_US/CommunitySharings/SptagAutoTune.md new file mode 100644 index 0000000000..a96f9f4991 --- /dev/null +++ b/docs/en_US/CommunitySharings/SptagAutoTune.md @@ -0,0 +1,8 @@ +# 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 diff --git a/docs/en_US/CommunitySharings/community_sharings.rst b/docs/en_US/CommunitySharings/community_sharings.rst index 29e3a46051..2f6edf142c 100644 --- a/docs/en_US/CommunitySharings/community_sharings.rst +++ b/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 + Automatically tuning SPTAG with NNI Neural Architecture Search Comparison Hyper-parameter Tuning Algorithm Comparsion Parallelizing Optimization for TPE