Merge pull request #136 from Microsoft/master

merge master

docs/en_US/Builtin_Assessors.md

@@ -5,13 +5,11 @@ NNI provides state-of-the-art tuning algorithm in our builtin-assessors and make
 Note: Click the **Assessor's name** to get a detailed description of the algorithm, click the corresponding **Usage** to get the Assessor's installation requirements, suggested scenario and using example.
 
 Currently we support the following Assessors:
-* [Medianstop](medianstopAssessor.md)
-* [Curvefitting](curvefittingAssessor.md)
 
 |Assessor|Brief Introduction of Algorithm|
 |---|---|
-|__Medianstop__[(Usage)](#MedianStop)|Medianstop is a simple early stopping rule. It stops a pending trial X at step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S. [Reference Paper](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf)|
-|__Curvefitting__[(Usage)](#Curvefitting)|Curve Fitting Assessor is a LPA(learning, predicting, assessing) algorithm. It stops a pending trial X at step S if the prediction of final epoch's performance worse than the best final performance in the trial history. In this algorithm, we use 12 curves to fit the accuracy curve. [Reference Paper](http://aad.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf)|
+|[__Medianstop__](#MedianStop)|Medianstop is a simple early stopping rule. It stops a pending trial X at step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S. [Reference Paper](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf)|
+|[__Curvefitting__](#Curvefitting)|Curve Fitting Assessor is a LPA(learning, predicting, assessing) algorithm. It stops a pending trial X at step S if the prediction of final epoch's performance worse than the best final performance in the trial history. In this algorithm, we use 12 curves to fit the accuracy curve. [Reference Paper](http://aad.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf)|
 
 ## Usage of Builtin Assessors
 

docs/en_US/Builtin_Tuner.md

@@ -5,29 +5,19 @@ NNI provides state-of-the-art tuning algorithm as our builtin-tuners and makes t
 Note: Click the **Tuner's name** to get a detailed description of the algorithm, click the corresponding **Usage** to get the Tuner's installation requirements, suggested scenario and using example.
 
 Currently we support the following algorithms:
-* [TPE](hyperoptTuner.md)
-* [Random Search](hyperoptTuner.md)
-* [Anneal](hyperoptTuner.md)
-* [Naive Evolution](evolutionTuner.md)
-* [SMAC](smacTuner.md)
-* [Batch tuner](batchTuner.md)
-* [Grid Search](gridsearchTuner.md)
-* [Hyperband](hyperbandAdvisor.md)
-* [Network Morphism](networkmorphismTuner.md)
-* [Metis Tuner](metisTuner.md)
 
 |Tuner|Brief Introduction of Algorithm|
 |---|---|
-|__TPE__ [(Usage)](#TPE)|The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach. SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements, and then subsequently choose new hyperparameters to test based on this model. [Reference Paper](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf)|
-|__Random Search__ [(Usage)](#Random)|In Random Search for Hyper-Parameter Optimization show that Random Search might be surprisingly simple and effective. We suggest that we could use Random Search as the baseline when we have no knowledge about the prior distribution of hyper-parameters. [Reference Paper](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)|
-|__Anneal__ [(Usage)](#Anneal)|This simple annealing algorithm begins by sampling from the prior, but tends over time to sample from points closer and closer to the best ones observed. This algorithm is a simple variation on the random search that leverages smoothness in the response surface. The annealing rate is not adaptive.|
-|__Naive Evolution__ [(Usage)](#Evolution)|Naive Evolution comes from Large-Scale Evolution of Image Classifiers. It randomly initializes a population-based on search space. For each generation, it chooses better ones and does some mutation (e.g., change a hyperparameter, add/remove one layer) on them to get the next generation. Naive Evolution requires many trials to works, but it's very simple and easy to expand new features. [Reference paper](https://arxiv.org/pdf/1703.01041.pdf)|
-|__SMAC__ [(Usage)](#SMAC)|SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on the SMAC3 Github repo. Notice, SMAC need to be installed by `nnictl package` command. [Reference Paper,](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) [Github Repo](https://github.com/automl/SMAC3)|
-|__Batch tuner__ [(Usage)](#Batch)|Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in search space spec.|
-|__Grid Search__ [(Usage)](#GridSearch)|Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are choice, quniform, qloguniform. The number q in quniform and qloguniform has special meaning (different from the spec in search space spec). It means the number of values that will be sampled evenly from the range low and high.|
-|__Hyperband__ [(Usage)](#Hyperband)|Hyperband tries to use the limited resource to explore as many configurations as possible, and finds out the promising ones to get the final result. The basic idea is generating many configurations and to run them for the small number of STEPs to find out promising one, then further training those promising ones to select several more promising one.[Reference Paper](https://arxiv.org/pdf/1603.06560.pdf)|
-|__Network Morphism__ [(Usage)](#NetworkMorphism)|Network Morphism provides functions to automatically search for architecture of deep learning models. Every child network inherits the knowledge from its parent network and morphs into diverse types of networks, including changes of depth, width, and skip-connection. Next, it estimates the value of a child network using the historic architecture and metric pairs. Then it selects the most promising one to train. [Reference Paper](https://arxiv.org/abs/1806.10282)|
-|__Metis Tuner__ [(Usage)](#MetisTuner)|Metis offers the following benefits when it comes to tuning parameters: While most tools only predict the optimal configuration, Metis gives you two outputs: (a) current prediction of optimal configuration, and (b) suggestion for the next trial. No more guesswork. While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to re-sample a particular hyper-parameter. [Reference Paper](https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/)|
+|[__TPE__](#TPE)|The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach. SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements, and then subsequently choose new hyperparameters to test based on this model. [Reference Paper](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf)|
+|[__Random Search__](#Random)|In Random Search for Hyper-Parameter Optimization show that Random Search might be surprisingly simple and effective. We suggest that we could use Random Search as the baseline when we have no knowledge about the prior distribution of hyper-parameters. [Reference Paper](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)|
+|[__Anneal__](#Anneal)|This simple annealing algorithm begins by sampling from the prior, but tends over time to sample from points closer and closer to the best ones observed. This algorithm is a simple variation on the random search that leverages smoothness in the response surface. The annealing rate is not adaptive.|
+|[__Naive Evolution__](#Evolution)|Naive Evolution comes from Large-Scale Evolution of Image Classifiers. It randomly initializes a population-based on search space. For each generation, it chooses better ones and does some mutation (e.g., change a hyperparameter, add/remove one layer) on them to get the next generation. Naive Evolution requires many trials to works, but it's very simple and easy to expand new features. [Reference paper](https://arxiv.org/pdf/1703.01041.pdf)|
+|[__SMAC__](#SMAC)|SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on the SMAC3 Github repo. Notice, SMAC need to be installed by `nnictl package` command. [Reference Paper,](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) [Github Repo](https://github.com/automl/SMAC3)|
+|[__Batch tuner__](#Batch)|Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in search space spec.|
+|[__Grid Search__](#GridSearch)|Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are choice, quniform, qloguniform. The number q in quniform and qloguniform has special meaning (different from the spec in search space spec). It means the number of values that will be sampled evenly from the range low and high.|
+|[__Hyperband__](#Hyperband)|Hyperband tries to use the limited resource to explore as many configurations as possible, and finds out the promising ones to get the final result. The basic idea is generating many configurations and to run them for the small number of STEPs to find out promising one, then further training those promising ones to select several more promising one.[Reference Paper](https://arxiv.org/pdf/1603.06560.pdf)|
+|[__Network Morphism__](#NetworkMorphism)|Network Morphism provides functions to automatically search for architecture of deep learning models. Every child network inherits the knowledge from its parent network and morphs into diverse types of networks, including changes of depth, width, and skip-connection. Next, it estimates the value of a child network using the historic architecture and metric pairs. Then it selects the most promising one to train. [Reference Paper](https://arxiv.org/abs/1806.10282)|
+|[__Metis Tuner__](#MetisTuner)|Metis offers the following benefits when it comes to tuning parameters: While most tools only predict the optimal configuration, Metis gives you two outputs: (a) current prediction of optimal configuration, and (b) suggestion for the next trial. No more guesswork. While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to re-sample a particular hyper-parameter. [Reference Paper](https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/)|
 
 <br>
 

docs/en_US/assessors.rst

@@ -13,5 +13,7 @@ Here is an experimental result of MNIST after using 'Curvefitting' Assessor in '
 Like Tuners, users can either use built-in Assessors, or customize an Assessor on their own. Please refer to the following tutorials for detail:
 
 ..  toctree::
-    Builtin Assessors<Builtin_Assessors>
+    :maxdepth: 2
+
+    Builtin Assessors<builtinAssessor>
     Customized Assessors<Customize_Assessor>

docs/en_US/builtinAssessor.rst

+Builtin-Assessors
+=================
+
+..  toctree::
+    :maxdepth: 1
+
+    Overview<Builtin_Assessors>
+    Medianstop<medianstopAssessor>
+    Curvefitting<curvefittingAssessor>
\ No newline at end of file

docs/en_US/builtinTuner.rst

+Builtin-Tuners
+==================
+
+..  toctree::
+    :maxdepth: 1
+    
+    Overview<Builtin_Tuner>
+    TPE<hyperoptTuner>
+    Random Search<hyperoptTuner>
+    Anneal<hyperoptTuner>
+    Naive Evolution<evolutionTuner>
+    SMAC<smacTuner>
+    Batch Tuner<batchTuner>
+    Grid Search<gridsearchTuner>
+    Hyperband<hyperbandAdvisor>
+    Network Morphism<networkmorphismTuner>
+    Metis Tuner<metisTuner>
\ No newline at end of file

docs/en_US/conf.py

@@ -186,9 +186,9 @@ epub_exclude_files = ['search.html']
 
 
 # -- Extension configuration -------------------------------------------------
-github_doc_root = 'https://github.com/Microsoft/nni/tree/master/doc/'
 def setup(app):
     app.add_config_value('recommonmark_config', {
-        'enable_auto_toc_tree': True,
-    }, True)
-    app.add_transform(AutoStructify)
+        'enable_eval_rst': True,
+        'enable_auto_toc_tree': False,
+            }, True)
+    app.add_transform(AutoStructify)
\ No newline at end of file

docs/en_US/tuners.rst

@@ -11,6 +11,8 @@ In NNI, we support two approaches to set the tuner: first is directly use builti
 For details, please refer to the following tutorials:
 
 ..  toctree::
-    Builtin Tuners<Builtin_Tuner>
+    :maxdepth: 2
+
+    Builtin Tuners<builtinTuner>
     Customized Tuners<Customize_Tuner>
     Customized Advisor<Customize_Advisor>
\ No newline at end of file