[doc] misc refactor (#4638)

c6c361d8 · QuanluZhang · GitHub · f355b956 · c6c361d8 · c6c361d8
Unverified Commit c6c361d8 authored Mar 21, 2022 by QuanluZhang Committed by GitHub Mar 21, 2022
11 changed files
--- a/docs/source/CommunitySharings/NNI_colab_support.rst
+++ b/docs/source/CommunitySharings/NNI_colab_support.rst
--- a/docs/source/TrialExample/OpEvoExamples.rst
+++ b/docs/source/TrialExample/OpEvoExamples.rst
--- a/docs/source/CommunitySharings/ParallelizingTpeSearch.rst
+++ b/docs/source/CommunitySharings/ParallelizingTpeSearch.rst
--- a/docs/source/CommunitySharings/perf_compare.rst
+++ b/docs/source/CommunitySharings/perf_compare.rst
-################################################
 Performance Measurement, Comparison and Analysis
-################################################
+================================================
 Performance comparison and analysis can help users decide a proper algorithm (e.g., tuner, NAS algorithm) for their scenario. The following are some measurement and comparison data for users' reference.
 ..  toctree::
    :maxdepth: 1
-    Neural Architecture Search Comparison <NasComparison>
+    Neural Architecture Search Comparison <nas_comparison>
-    Hyper-parameter Tuning Algorithm Comparsion <HpoComparison>
+    Hyper-parameter Tuning Algorithm Comparsion <hpo_comparison>
-    Model Compression Algorithm Comparsion <ModelCompressionComparison>
+    Model Compression Algorithm Comparsion <model_compress_comp>
\ No newline at end of file
--- a/docs/source/CommunitySharings/perf_compare_zh.rst
+++ b/docs/source/CommunitySharings/perf_compare_zh.rst
-.. 7d625bd21018f53834e9db08a619c494
+.. 8f6869e44d85db2e2a969194c1828580
-################################################
 性能测量，比较和分析
-################################################
+====================
 性能比较和分析可以帮助用户在他们的场景中选择适合的算法（例如 Tuner，NAS 算法）。 以下是一些供用户参考的测量和比较数据。
 ..  toctree::
    :maxdepth: 1
-    神经网络结构搜索（NAS）的对比<NasComparison>
+    神经网络结构搜索（NAS）的对比<nas_comparison>
-    超参调优算法的对比<HpoComparison>
+    超参调优算法的对比<hpo_comparison>
-    模型压缩算法的对比<ModelCompressionComparison>
+    模型压缩算法的对比<model_compress_comp>
\ No newline at end of file
--- a/docs/source/CommunitySharings/RecommendersSvd.rst
+++ b/docs/source/CommunitySharings/RecommendersSvd.rst
--- a/docs/source/ResearchPublications.rst
+++ b/docs/source/ResearchPublications.rst
--- a/docs/source/TrialExample/RocksdbExamples.rst
+++ b/docs/source/TrialExample/RocksdbExamples.rst
--- a/docs/source/CommunitySharings/SptagAutoTune.rst
+++ b/docs/source/CommunitySharings/SptagAutoTune.rst
--- a/docs/source/TrialExample/SquadEvolutionExamples.rst
+++ b/docs/source/TrialExample/SquadEvolutionExamples.rst
--- a/nni/algorithms/hpo/tpe_tuner.py
+++ b/nni/algorithms/hpo/tpe_tuner.py
@@ -49,7 +49,7 @@ class TpeArguments(NamedTuple):
        How each liar works is explained in paper's section 6.1.
        In general "best" suit for small trial number and "worst" suit for large trial number.
-        (:doc:`experiment result </CommunitySharings/ParallelizingTpeSearch>`)
+        (:doc:`experiment result </misc/parallelizing_tpe_search>`)
    n_startup_jobs
        The first N hyperparameters are generated fully randomly for warming up.