merge master

README.md

@@ -14,8 +14,7 @@
 
 [简体中文](README_zh_CN.md)
 
-NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments.
-The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
+**NNI (Neural Network Intelligence)** is an efficient and automatic toolkit to help users design and search neural network architecture, tune machine learning model's parameters or complex system's parameters. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
 
 
 ### **NNI v1.2 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
@@ -359,7 +358,7 @@ With authors' permission, we listed a set of NNI usage examples and relevant art
 * ### **External Repositories** ###
    * Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
    * Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI 
-   * [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI 
+   * [Automatic Feature Engineering](examples/feature_engineering/auto-feature-engineering/README.md) with NNI 
    * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
    * [scikit-nni](https://github.com/ksachdeva/scikit-nni) Hyper-parameter search for scikit-learn pipelines using NNI
 

azure-pipelines.yml

@@ -17,6 +17,11 @@ jobs:
   - script: |
       source install.sh
     displayName: 'Install nni toolkit via source code'
+  - script: |
+      set -e
+      cd src/nni_manager
+      yarn eslint
+    displayName: 'Run eslint'
   - script: |
       python3 -m pip install torch==0.4.1 --user
       python3 -m pip install torchvision==0.2.1 --user

deployment/pypi/setup.py

@@ -62,7 +62,7 @@
         'scipy',
         'coverage',
         'colorama',
-        'scikit-learn==0.20'
+        'scikit-learn>=0.20,<0.22'
     ],
     classifiers = [
         'Programming Language :: Python :: 3',