fix docs before r0.5.2 release (#783)

* fix sklearn dependency
* add doc badge to readme
* fix infinite toctree level

README.md

@@ -10,6 +10,7 @@
 [![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
 [![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
 [![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=latest)](https://nni.readthedocs.io/en/latest/?badge=latest)
 
 [简体中文](README_zh_CN.md)
 

docs/en_US/RELEASE.md

@@ -2,8 +2,8 @@
 
 ## Release 0.5.1 - 1/31/2018
 ### Improvements
-* Making [log directory](https://github.com/Microsoft/nni/blob/v0.5.1/docs/ExperimentConfig.md) configurable
-* Support [different levels of logs](https://github.com/Microsoft/nni/blob/v0.5.1/docs/ExperimentConfig.md), making it easier for debugging 
+* Making [log directory](https://github.com/Microsoft/nni/blob/v0.5.1/docs/en_US/ExperimentConfig.md) configurable
+* Support [different levels of logs](https://github.com/Microsoft/nni/blob/v0.5.1/docs/en_US/ExperimentConfig.md), making it easier for debugging 
 
 ### Documentation
 * Reorganized documentation & New Homepage Released: https://nni.readthedocs.io/en/latest/
@@ -20,9 +20,9 @@
 
 #### New tuner and assessor supports
 
-* Support [Metis tuner](./Builtin_Tuner.md#MetisTuner) as a new NNI tuner. Metis algorithm has been proofed to be well performed for **online** hyper-parameter tuning.
+* Support [Metis tuner](metisTuner.md) as a new NNI tuner. Metis algorithm has been proofed to be well performed for **online** hyper-parameter tuning.
 * Support [ENAS customized tuner](https://github.com/countif/enas_nni), a tuner contributed by github community user, is an algorithm for neural network search, it could learn neural network architecture via reinforcement learning and serve a better performance than NAS.
-* Support [Curve fitting assessor](./Builtin_Tuner.md#Curvefitting) for early stop policy using learning curve extrapolation.
+* Support [Curve fitting assessor](curvefittingAssessor.md) for early stop policy using learning curve extrapolation.
 * Advanced Support of [Weight Sharing](./AdvancedNAS.md): Enable weight sharing for NAS tuners, currently through NFS.
 
 #### Training Service Enhancement
@@ -45,7 +45,7 @@
 
 #### New tuner supports
 
-* Support [network morphism](./Builtin_Tuner.md#NetworkMorphism) as a new tuner
+* Support [network morphism](networkmorphismTuner.md) as a new tuner
 
 #### Training Service improvements
 
@@ -79,8 +79,8 @@
 * [Kubeflow Training service](./KubeflowMode.md)
   * Support tf-operator
   * [Distributed trial example](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-distributed/dist_mnist.py) on Kubeflow
-* [Grid search tuner](Builtin_Tuner.md#GridSearch) 
-* [Hyperband tuner](Builtin_Tuner.md#Hyperband)
+* [Grid search tuner](gridsearchTuner.md) 
+* [Hyperband tuner](hyperbandAdvisor.md)
 * Support launch NNI experiment on MAC
 * WebUI
   * UI support for hyperband tuner
@@ -163,7 +163,7 @@
 * Support [OpenPAI](https://github.com/Microsoft/pai) Training Platform (See [here](./PAIMode.md) for instructions about how to submit NNI job in pai mode)
   * Support training services on pai mode. NNI trials will be scheduled to run on OpenPAI cluster
   * NNI trial's output (including logs and model file) will be copied to OpenPAI HDFS for further debugging and checking
-* Support [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner (See [here](Builtin_Tuner.md) for instructions about how to use SMAC tuner)
+* Support [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner (See [here](smacTuner.md) for instructions about how to use SMAC tuner)
   * [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) 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 to handle categorical parameters. The SMAC supported by NNI is a wrapper on [SMAC3](https://github.com/automl/SMAC3)
 * Support NNI installation on [conda](https://conda.io/docs/index.html) and python virtual environment
 * Others

docs/en_US/Tutorials.rst

@@ -3,6 +3,8 @@ Tutorials
 ######################
 
 ..  toctree::
+    :maxdepth: 2
+
     Installation
     Write Trial<Trials>
     Tuners<tuners>

docs/requirements.txt

@@ -9,3 +9,4 @@ json_tricks
 numpy
 scipy
 coverage
+sklearn
\ No newline at end of file