Change all master links to v1.9 (#3005)

docs/en_US/Tuner/PPOTuner.md

@@ -10,12 +10,12 @@ We had successfully tuned the mnist-nas example and has the following result:
 
 ![](../../img/ppo_mnist.png)
 
-We also tune [the macro search space for image classification in the enas paper](https://github.com/microsoft/nni/tree/master/examples/trials/nas_cifar10) (with a limited epoch number for each trial, i.e., 8 epochs), which is implemented using the NAS interface and tuned with PPOTuner. Here is Figure 7 from the [enas paper](https://arxiv.org/pdf/1802.03268.pdf) to show what the search space looks like
+We also tune [the macro search space for image classification in the enas paper](https://github.com/microsoft/nni/tree/v1.9/examples/trials/nas_cifar10) (with a limited epoch number for each trial, i.e., 8 epochs), which is implemented using the NAS interface and tuned with PPOTuner. Here is Figure 7 from the [enas paper](https://arxiv.org/pdf/1802.03268.pdf) to show what the search space looks like
 
 ![](../../img/enas_search_space.png)
 
 The figure above was the chosen architecture. Each square is a layer whose operation was chosen from 6 options. Each dashed line is a skip connection, each square layer can choose 0 or 1 skip connections, getting the output from a previous layer. __Note that__, in original macro search space, each square layer could choose any number of skip connections, while in our implementation, it is only allowed to choose 0 or 1.
 
-The results are shown in figure below (see the experimenal config [here](https://github.com/microsoft/nni/blob/master/examples/trials/nas_cifar10/config_ppo.yml):
+The results are shown in figure below (see the experimenal config [here](https://github.com/microsoft/nni/blob/v1.9/examples/trials/nas_cifar10/config_ppo.yml):
 
 ![](../../img/ppo_cifar10.png)

docs/en_US/Tutorial/Contributing.md

@@ -47,10 +47,10 @@ A person looking to contribute can take up an issue by claiming it as a comment/
  - Internal Guideline on Writing Standards](https://ribokit.github.io/docs/text/)
 
 ## Documentation
-Our documentation is built with [sphinx](http://sphinx-doc.org/), supporting [Markdown](https://guides.github.com/features/mastering-markdown/) and [reStructuredText](http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) format. All our documentations are placed under [docs/en_US](https://github.com/Microsoft/nni/tree/master/docs).
+Our documentation is built with [sphinx](http://sphinx-doc.org/), supporting [Markdown](https://guides.github.com/features/mastering-markdown/) and [reStructuredText](http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) format. All our documentations are placed under [docs/en_US](https://github.com/Microsoft/nni/tree/v1.9/docs).
 
 * Before submitting the documentation change, please __build homepage locally__: `cd docs/en_US && make html`, then you can see all the built documentation webpage under the folder `docs/en_US/_build/html`. It's also highly recommended taking care of __every WARNING__ during the build, which is very likely the signal of a __deadlink__ and other annoying issues.
 
 * For links, please consider using __relative paths__ first. However, if the documentation is written in Markdown format, and:
     * It's an image link which needs to be formatted with embedded html grammar, please use global URL like `https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png`, which can be automatically generated by dragging picture onto [Github Issue](https://github.com/Microsoft/nni/issues/new) Box.
-    * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at `https://github.com/Microsoft/nni/tree/master/` ([mnist.py](https://github.com/Microsoft/nni/blob/master/examples/trials/mnist-tfv1/mnist.py) for example).
+    * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at `https://github.com/Microsoft/nni/tree/v1.9/` ([mnist.py](https://github.com/Microsoft/nni/blob/v1.9/examples/trials/mnist-tfv1/mnist.py) for example).

docs/en_US/Tutorial/InstallationLinux.md

@@ -26,7 +26,7 @@ Installation on Linux and macOS follow the same instructions, given below.
 
 ### Use NNI in a docker image
 
-  You can also install NNI in a docker image. Please follow the instructions [here](https://github.com/Microsoft/nni/tree/master/deployment/docker/README.md) to build an NNI docker image. The NNI docker image can also be retrieved from Docker Hub through the command `docker pull msranni/nni:latest`.
+  You can also install NNI in a docker image. Please follow the instructions [here](https://github.com/Microsoft/nni/tree/v1.9/deployment/docker/README.md) to build an NNI docker image. The NNI docker image can also be retrieved from Docker Hub through the command `docker pull msranni/nni:latest`.
 
 ## Verify installation
 

docs/en_US/Tutorial/QuickStart.md

@@ -71,7 +71,7 @@ if __name__ == '__main__':
     run_trial(params)
 ```
 
-If you want to see the full implementation, please refer to [examples/trials/mnist-tfv1/mnist_before.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist_before.py).
+If you want to see the full implementation, please refer to [examples/trials/mnist-tfv1/mnist_before.py](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1/mnist_before.py).
 
 The above code can only try one set of parameters at a time; if we want to tune learning rate, we need to manually modify the hyperparameter and start the trial again and again.
 
@@ -108,7 +108,7 @@ If you want to use NNI to automatically train your model and find the optimal hy
 + }
 ```
 
-*Example: [search_space.json](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/search_space.json)*
+*Example: [search_space.json](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1/search_space.json)*
 
 **Step 2**: Modify your `Trial` file to get the hyperparameter set from NNI and report the final result to NNI.
 
@@ -133,7 +133,7 @@ If you want to use NNI to automatically train your model and find the optimal hy
       run_trial(params)
 ```
 
-*Example: [mnist.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/mnist.py)*
+*Example: [mnist.py](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1/mnist.py)*
 
 **Step 3**: Define a `config` file in YAML which declares the `path` to the search space and trial files. It also gives other information such as the tuning algorithm, max trial number, and max duration arguments.
 
@@ -160,9 +160,9 @@ trial:
 .. Note:: If you are planning to use remote machines or clusters as your :doc:`training service <../TrainingService/Overview>`, to avoid too much pressure on network, we limit the number of files to 2000 and total size to 300MB. If your codeDir contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation <https://git-scm.com/docs/gitignore#_pattern_format>`_.
 ```
 
-*Example: [config.yml](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/config.yml) [.nniignore](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1/.nniignore)*
+*Example: [config.yml](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1/config.yml) [.nniignore](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1/.nniignore)*
 
-All the code above is already prepared and stored in [examples/trials/mnist-tfv1/](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist-tfv1).
+All the code above is already prepared and stored in [examples/trials/mnist-tfv1/](https://github.com/Microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1).
 
 #### Linux and macOS
 

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -30,7 +30,7 @@ All types of sampling strategies and their parameter are listed here:
 * `{"_type": "choice", "_value": options}`
 
   * The variable's value is one of the options. Here `options` should be a list of numbers or a list of strings. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
-  * `options` can also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space can be seen as conditional variables. Here is an simple [example of nested search space definition](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json). If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a `_name` key in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.
+  * `options` can also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space can be seen as conditional variables. Here is an simple [example of nested search space definition](https://github.com/microsoft/nni/tree/v1.9/examples/trials/mnist-nested-search-space/search_space.json). If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a `_name` key in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/v1.9/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.
 
 * `{"_type": "randint", "_value": [lower, upper]}`
   * Choosing a random integer between `lower` (inclusive) and `upper` (exclusive).

docs/en_US/_templates/index.html

@@ -107,11 +107,11 @@
         <ul class="firstUl">
           <li><b>Examples</b></li>
           <ul class="circle">
-            <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
+            <li><a href="https://github.com/microsoft/nni/tree/v1.9/examples/trials/mnist-pytorch">MNIST-pytorch</li>
             </a>
-            <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv1">MNIST-tensorflow</li>
+            <li><a href="https://github.com/microsoft/nni/tree/v1.9/examples/trials/mnist-tfv1">MNIST-tensorflow</li>
             </a>
-            <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
+            <li><a href="https://github.com/microsoft/nni/tree/v1.9/examples/trials/mnist-keras">MNIST-keras</li></a>
             <li><a href="{{ pathto('TrialExample/GbdtExample') }}">Auto-gbdt</a></li>
             <li><a href="{{ pathto('TrialExample/Cifar10Examples') }}">Cifar10-pytorch</li></a>
             <li><a href="{{ pathto('TrialExample/SklearnExamples') }}">Scikit-learn</a></li>
@@ -393,7 +393,7 @@ You can use these commands to get more information about the experiment
     <li>Run <a href="{{ pathto('NAS/ENAS') }}">ENAS</a> with NNI</li>
     <li>
       <a
-        href="https://github.com/microsoft/nni/blob/master/examples/feature_engineering/auto-feature-engineering/README.md">Automatic
+        href="https://github.com/microsoft/nni/blob/v1.9/examples/feature_engineering/auto-feature-engineering/README.md">Automatic
         Feature Engineering</a> with NNI
     </li>
     <li><a
@@ -504,7 +504,7 @@ You can use these commands to get more information about the experiment
 <!-- License -->
 <div>
   <h1 class="title">License</h1>
-  <p>The entire codebase is under <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT license</a></p>
+  <p>The entire codebase is under <a href="https://github.com/microsoft/nni/blob/v1.9/LICENSE">MIT license</a></p>
 </div>
 </div>
 {% endblock %}

docs/en_US/builtin_assessor.rst

@@ -7,7 +7,7 @@ Assessor receives the intermediate result from a trial and decides whether the t
 
 Here is an experimental result of MNIST after using the 'Curvefitting' Assessor in 'maximize' mode. You can see that Assessor successfully **early stopped** many trials with bad hyperparameters in advance. If you use Assessor, you may get better hyperparameters using the same computing resources.
 
-*Implemented code directory: [config_assessor.yml](https://github.com/Microsoft/nni/blob/master/examples/trials/mnist-tfv1/config_assessor.yml)*
+*Implemented code directory: [config_assessor.yml](https://github.com/Microsoft/nni/blob/v1.9/examples/trials/mnist-tfv1/config_assessor.yml)*
 
 ..  image:: ../img/Assessor.png