Doc fix: fix typo, rewording and correct format. (#697)

Change pai to OpenPAI
reformat for translation
fix some typo.
rewording.

README.md

@@ -75,7 +75,7 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
         <li><a href="docs/RemoteMachineMode.md">Remote Servers</a></li>
         <li><a href="docs/PAIMode.md">OpenPAI</a></li>
         <li><a href="docs/KubeflowMode.md">Kubeflow</a></li>
-        <li><a href="docs/KubeflowMode.md">FrameworkController on K8S (AKS etc.)</a></li>
+        <li><a href="docs/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a></li>
       </ul>
       </td>
     </tr>

docs/NNICTLDOC.md

@@ -131,7 +131,7 @@ nnictl support commands:
 
     * Description
 
-      You can use this command to update an experiment's concurrency.  
+      You can use this command to update an experiment's duration.  
 
     * Usage
 

docs/Overview.md

@@ -3,7 +3,7 @@
 NNI (Neural Network Intelligence) is a toolkit to help users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or complex system's parameters, in an efficient and automatic way. NNI has several appealing properties: easy-to-use, scalability, flexibility, and efficiency.
 
 * **Easy-to-use**: NNI can be easily installed through python pip. Only several lines need to be added to your code in order to use NNI's power. You can use both commandline tool and WebUI to work with your experiments.
-* **Scalability**: Tuning hyperparameters or neural architecture often demands large amount of computation resource, while NNI is designed to fully leverage different computation resources, such as remote machines, training platforms (e.g., PAI, Kubernetes). Thousands of trials could run in parallel by depending on the capacity of your configured training platforms.
+* **Scalability**: Tuning hyperparameters or neural architecture often demands large amount of computation resource, while NNI is designed to fully leverage different computation resources, such as remote machines, training platforms (e.g., OpenPAI, Kubernetes). Hundreds of trials could run in parallel by depending on the capacity of your configured training platforms.
 * **Flexibility**: Besides rich built-in algorithms, NNI allows users to customize various hyperparameter tuning algorithms, neural architecture search algorithms, early stopping algorithms, etc. Users could also extend NNI with more training platforms, such as virtual machines, kubernetes service on the cloud. Moreover, NNI can connect to external environments to tune special applications/models on them.
 * **Efficiency**: We are intensively working on more efficient model tuning from both system level and algorithm level. For example, leveraging early feedback to speedup tuning procedure.
 
@@ -27,7 +27,7 @@ The figure below shows high-level architecture of NNI.
 
 * *Assessor*: Assessor analyzes trial's intermediate results (e.g., periodically evaluated accuracy on test dataset) to tell whether this trial can be early stopped or not.
 
-* *Training Platform*: It means where trials are executed. Depending on your experiment's configuration, it could be your local machine, or remote servers, or large-scale training platform (e.g., PAI, Kubernetes).
+* *Training Platform*: It means where trials are executed. Depending on your experiment's configuration, it could be your local machine, or remote servers, or large-scale training platform (e.g., OpenPAI, Kubernetes).
 
 Basically, an experiment runs as follows: Tuner receives search space and generates configurations. These configurations will be submitted to training platforms, such as local machine, remote machines, or training clusters. Their performances are reported back to Tuner. Then, new configurations are generated and submitted.
 

docs/PAIMode.md

@@ -61,7 +61,7 @@ Once complete to fill NNI experiment config file and save (for example, save as
 nnictl create --config exp_pai.yml
 ```
 to start the experiment in pai mode. NNI will create OpenPAI job for each trial, and the job name format is something like `nni_exp_{experiment_id}_trial_{trial_id}`. 
-You can see the pai jobs created by NNI in your OpenPAI cluster's web portal, like:
+You can see jobs created by NNI in the OpenPAI cluster's web portal, like:
 ![](./img/nni_pai_joblist.jpg)
 
 Notice: In pai mode, NNIManager will start a rest server and listen on a port which is your NNI WebUI's port plus 1. For example, if your WebUI port is `8080`, the rest server will listen on `8081`, to receive metrics from trial job running in Kubernetes. So you should `enable 8081` TCP port in your firewall rule to allow incoming traffic. 

docs/QuickStart.md

@@ -24,15 +24,15 @@ Here is an example script to train a CNN on MNIST dataset **without NNI**:
 def run_trial(params):
     # Input data
     mnist = input_data.read_data_sets(params['data_dir'], one_hot=True)
-    # Build MNIST network
+    # Build network
     mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'], channel_2_num=params['channel_2_num'], conv_size=params['conv_size'], hidden_size=params['hidden_size'], pool_size=params['pool_size'], learning_rate=params['learning_rate'])
     mnist_network.build_network()
 
     test_acc = 0.0
     with tf.Session() as sess:
-        # Train MNIST network
+        # Train network
         mnist_network.train(sess, mnist)
-        # Evaluate MNIST network
+        # Evaluate network
         test_acc = mnist_network.evaluate(mnist)
 
 if __name__ == '__main__':
@@ -104,7 +104,7 @@ If you want to use NNI to automatically train your model and find the optimal hy
 
 *Implemented code directory: [mnist.py](https://github.com/Microsoft/nni/tree/master/examples/trials/mnist/mnist.py)*
 
-**Step 3**: Define a `config` file in YAML, which declare the `path` to search space and trial, also give `other information` such as tuning algorithm, max trial number and max runtime arguments.
+**Step 3**: Define a `config` file in YAML, which declare the `path` to search space and trial, also give `other information` such as tuning algorithm, max trial number and max duration arguments.
 
 ```yaml
 authorName: default
@@ -209,7 +209,7 @@ Click the tab "Trial Duration" to see the bar graph.
 Below is the status of the all trials. Specifically:
 
 * Trial detail: trial's id, trial's duration, start time, end time, status, accuracy and search space file.
-* If you run a pai experiment, you can also see the hdfsLogPath.
+* If you run on the OpenPAI platform, you can also see the hdfsLogPath.
 * Kill: you can kill a job that status is running.
 * Support to search for a specific trial.
 

docs/RELEASE.md

@@ -20,7 +20,7 @@
 
 #### User Experience improvements
 
-* A better trial logging support for NNI experiments in PAI, Kubeflow and FrameworkController mode:
+* A better trial logging support for NNI experiments in OpenPAI, Kubeflow and FrameworkController mode:
   * An improved logging architecture to send stdout/stderr of trials to NNI manager via Http post. NNI manager will store trial's stdout/stderr messages in local log file.
   * Show the link for trial log file on WebUI.
 * Support to show final result's all key-value pairs.
@@ -83,9 +83,9 @@
 * Docker file update, add pytorch library 
 * Refactor 'nnictl stop' process, send SIGTERM to nni manager process, rather than calling stop Rest API. 
 * OpenPAI training service bug fix
-  * Support NNI Manager IP configuration(nniManagerIp) in PAI cluster config file, to fix the issue that user’s machine has no eth0 device 
+  * Support NNI Manager IP configuration(nniManagerIp) in OpenPAI cluster config file, to fix the issue that user’s machine has no eth0 device 
   * File number in codeDir is capped to 1000 now, to avoid user mistakenly fill root dir for codeDir
-  * Don’t print useless ‘metrics is empty’ log int PAI job’s stdout. Only print useful message once new metrics are recorded, to reduce confusion when user checks PAI trial’s output for debugging purpose
+  * Don’t print useless ‘metrics is empty’ log in OpenPAI job’s stdout. Only print useful message once new metrics are recorded, to reduce confusion when user checks OpenPAI trial’s output for debugging purpose
   * Add timestamp at the beginning of each log entry in trial keeper.
 
 ## Release 0.3.0 - 11/2/2018
@@ -146,7 +146,7 @@
 
 ### Major Features
 
-* Support [OpenPAI](https://github.com/Microsoft/pai) (aka pai) Training Service (See [here](./PAIMode.md) for instructions about how to submit NNI job in pai mode)
+* 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)

docs/SQuAD_evolution_examples.md

@@ -25,7 +25,7 @@ Also we have another version which time cost is less and performance is better.
 Execute the following command to download needed files
 using the downloading script:
 
-```
+```bash
 chmod +x ./download.sh
 ./download.sh
 ```
@@ -34,14 +34,14 @@ Or Download manually
 
 1. download "dev-v1.1.json" and "train-v1.1.json" in https://rajpurkar.github.io/SQuAD-explorer/
 
-```
+```bash
 wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
 wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
 ```
 
 2. download "glove.840B.300d.txt" in https://nlp.stanford.edu/projects/glove/
 
-```
+```bash
 wget http://nlp.stanford.edu/data/glove.840B.300d.zip
 unzip glove.840B.300d.zip
 ```
@@ -49,7 +49,7 @@ unzip glove.840B.300d.zip
 ### 2.2 Update configuration
 Modify `nni/examples/trials/ga_squad/config.yml`, here is the default configuration:
 
-```
+```yaml
 authorName: default
 experimentName: example_ga_squad
 trialConcurrency: 1
@@ -75,7 +75,7 @@ In the "trial" part, if you want to use GPU to perform the architecture search,
 
 ### 2.3 submit this job
 
-```
+```bash
 nnictl create --config ~/nni/examples/trials/ga_squad/config.yml
 ```
 
@@ -84,9 +84,10 @@ nnictl create --config ~/nni/examples/trials/ga_squad/config.yml
 Due to the memory limitation of upload, we only upload the source code and complete the data download and training on OpenPAI. This experiment requires sufficient memory that `memoryMB >= 32G`, and the training may last for several hours.
 
 ### 3.1 Update configuration
+
 Modify `nni/examples/trials/ga_squad/config_pai.yml`, here is the default configuration:
 
-```
+```yaml
 authorName: default
 experimentName: example_ga_squad
 trialConcurrency: 1
@@ -110,18 +111,18 @@ trial:
   gpuNum: 0
   cpuNum: 1
   memoryMB: 32869
-  #The docker image to run nni job on pai
+  #The docker image to run nni job on OpenPAI
   image: msranni/nni:latest
-  #The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
+  #The hdfs directory to store data on OpenPAI, format 'hdfs://host:port/directory'
   dataDir: hdfs://10.10.10.10:9000/username/nni
   #The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
   outputDir: hdfs://10.10.10.10:9000/username/nni
 paiConfig:
-  #The username to login pai
+  #The username to login OpenPAI
   userName: username
-  #The password to login pai
+  #The password to login OpenPAI
   passWord: password
-  #The host of restful server of pai
+  #The host of restful server of OpenPAI
   host: 10.10.10.10
 ```
 
@@ -133,13 +134,14 @@ In the "trial" part, if you want to use GPU to perform the architecture search,
 
 ### 3.2 submit this job
 
-```
+```bash
 nnictl create --config ~/nni/examples/trials/ga_squad/config_pai.yml
 ```
 
 ## 4. Technical details about the trial
 
 ### 4.1 How does it works
+
 The evolution-algorithm based architecture for question answering has two different parts just like any other examples: the trial and the tuner.
 
 ### 4.2 The trial
@@ -157,7 +159,7 @@ Among those files, `trial.py` and `graph_to_tf.py` are special.
 
 `graph_to_tf.py` has a function named as `graph_to_network`, here is its skeleton code:
 
-```
+```python
 def graph_to_network(input1,
                      input2,
                      input1_lengths,
@@ -190,13 +192,13 @@ def graph_to_network(input1,
 
 As we can see, this function is actually a compiler, that converts the internal model DAG configuration (which will be introduced in the `Model configuration format` section) `graph`, to a Tensorflow computation graph.
 
-```
+```python
 topology = graph.is_topology()
 ```
 
 performs topological sorting on the internal graph representation, and the code inside the loop:
 
-```
+```python
 for _, topo_i in enumerate(topology):
 ```
 
@@ -206,7 +208,7 @@ performs actually conversion that maps each layer to a part in Tensorflow comput
 
 The tuner is much more simple than the trial. They actually share the same `graph.py`. Besides, the tuner has a `customer_tuner.py`, the most important class in which is `CustomerTuner`:
 
-```
+```python
 class CustomerTuner(Tuner):
     # ......
 
@@ -241,7 +243,7 @@ class CustomerTuner(Tuner):
 
 As we can see, the overloaded method `generate_parameters` implements a pretty naive mutation algorithm. The code lines:
 
-```
+```python
             if self.population[0].result > self.population[1].result:
                 self.population[0] = self.population[1]
             indiv = copy.deepcopy(self.population[0])
@@ -253,7 +255,7 @@ controls the mutation process. It will always take two random individuals in the
 
 Here is an example of the model configuration, which is passed from the tuner to the trial in the architecture search procedure.
 
-```
+```json
 {
     "max_layer_num": 50,
     "layers": [
@@ -300,9 +302,9 @@ Here is an example of the model configuration, which is passed from the tuner to
 
 Every model configuration will have a "layers" section, which is a JSON list of layer definitions. The definition of each layer is also a JSON object, where:
 
- * `type` is the type of the layer. 0, 1, 2, 3, 4 corresponds to attention, self-attention, RNN, input and output layer respectively.
- * `size` is the length of the output. "x", "y" correspond to document length / question length, respectively.
- * `input_size` is the number of inputs the layer has.
- * `input` is the indices of layers taken as input of this layer.
- * `output` is the indices of layers use this layer's output as their input.
- * `is_delete` means whether the layer is still available.
+* `type` is the type of the layer. 0, 1, 2, 3, 4 corresponds to attention, self-attention, RNN, input and output layer respectively.
+* `size` is the length of the output. "x", "y" correspond to document length / question length, respectively.
+* `input_size` is the number of inputs the layer has.
+* `input` is the indices of layers taken as input of this layer.
+* `output` is the indices of layers use this layer's output as their input.
+* `is_delete` means whether the layer is still available.

docs/SearchSpaceSpec.md

@@ -8,7 +8,7 @@ To define a search space, users should define the name of variable, the type of
 
 * A example of search space definition as follow:
 
-```python
+```yaml
 {
     "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
     "conv_size":{"_type":"choice","_value":[2,3,5,7]},
@@ -19,8 +19,7 @@ To define a search space, users should define the name of variable, the type of
 
 ```
 
-
-Take the first line as an example. ```dropout_rate``` is defined as a variable whose priori distribution is a uniform distribution of a range from ```0.1``` and ```0.5```.
+Take the first line as an example. `dropout_rate` is defined as a variable whose priori distribution is a uniform distribution of a range from `0.1` and `0.5`.
 
 ## Types
 
@@ -28,50 +27,39 @@ All types of sampling strategies and their parameter are listed here:
 
 * {"_type":"choice","_value":options}
   * Which means the variable value is one of the options, which should be a list. The elements of options can themselves be [nested] stochastic expressions. In this case, the stochastic choices that only appear in some of the options become conditional parameters.
-<br/>
 
 * {"_type":"randint","_value":[upper]}
   * Which means the variable value is a random integer in the range [0, upper). The semantics of this distribution is that there is no more correlation in the loss function between nearby integer values, as compared with more distant integer values. This is an appropriate distribution for describing random seeds for example. If the loss function is probably more correlated for nearby integer values, then you should probably use one of the "quantized" continuous distributions, such as either quniform, qloguniform, qnormal or qlognormal. Note that if you want to change lower bound, you can use `quniform` for now.
-<br/>
 
 * {"_type":"uniform","_value":[low, high]}
   * Which means the variable value is a value uniformly between low and high.
   * When optimizing, this variable is constrained to a two-sided interval.
-<br/>
 
 * {"_type":"quniform","_value":[low, high, q]}
   * Which means the variable value is a value like round(uniform(low, high) / q) * q
   * Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose integer from a range [low, high], you can write `_value` like this: `[low, high, 1]`.
-<br/>
 
 * {"_type":"loguniform","_value":[low, high]}
   * Which means the variable value is a value drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
   * When optimizing, this variable is constrained to be positive.
-<br/>
 
 * {"_type":"qloguniform","_value":[low, high, q]}
   * Which means the variable value is a value like round(loguniform(low, high)) / q) * q
   * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
-<br/>
 
 * {"_type":"normal","_value":[label, mu, sigma]}
   * Which means the variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
-<br/>
 
 * {"_type":"qnormal","_value":[label, mu, sigma, q]}
   * Which means the variable value is a value like round(normal(mu, sigma) / q) * q
   * Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.
-<br/>
 
 * {"_type":"lognormal","_value":[label, mu, sigma]}
   * Which means the variable value is a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
-<br/>
 
 * {"_type":"qlognormal","_value":[label, mu, sigma, q]}
   * Which means the variable value is a value like round(exp(normal(mu, sigma)) / q) * q
   * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
-<br/>
-
 
 ## Search Space Types Supported by Each Tuner
 
@@ -87,7 +75,7 @@ All types of sampling strategies and their parameter are listed here:
 | Hyperband Advisor | &#10003; | &#10003; | &#10003; | &#10003;  | &#10003;    | &#10003;     | &#10003; | &#10003; | &#10003;   | &#10003;    |
 | Metis Tuner   | &#10003; | &#10003; | &#10003; | &#10003;  |     |      |  |  |    |     |
 
-Note that In GridSearch Tuner, for users' convenience, the definition of `quniform` and `qloguniform` change, where q here specifies the number of values that will be sampled. Details about them are listed as follows
+Note that In Grid Search Tuner, for users' convenience, the definition of `quniform` and `qloguniform` change, where q here specifies the number of values that will be sampled. Details about them are listed as follows
 
 * Type 'quniform' will receive three values [low, high, q], where [low, high] specifies a range and 'q' specifies the number of values that will be sampled evenly. Note that q should be at least 2. It will be sampled in a way that the first sampled value is 'low', and each of the following values is (high-low)/q larger that the value in front of it.
 * Type 'qloguniform' behaves like 'quniform' except that it will first change the range to [log(low), log(high)] and sample and then change the sampled value back.

docs/WebUI.md

@@ -41,7 +41,7 @@ Click the tab "Trials Detail" to see the status of the all trials. Specifically:
 
 ![](./img/webui-img/detail-local.png)
 
-* If you run a pai or kubeflow experiment, you can also see the hdfsLog.
+* If you run on OpenPAI or Kubeflow platform, you can also see the hdfsLog.
 
 ![](./img/webui-img/detail-pai.png)
 

docs/gbdt_example.md

@@ -5,7 +5,6 @@ Gradient boosting decision tree has many popular implementations, such as [light
 
 NNI is a great platform for tuning hyper-parameters, you could try various builtin search algorithm in nni and run multiple trials concurrently. 
 
-
 ## 1. Search Space in GBDT
 There are many hyper-parameters in GBDT, but what kind of parameters will affect the performance or speed? Based on some practical experience, some suggestion here(Take lightgbm as example):
 
@@ -37,8 +36,7 @@ There are many hyper-parameters in GBDT, but what kind of parameters will affect
 * `num_leaves`.
 
 Reference link:
-[lightgbm](https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html) and 
-[autoxgoboost](https://github.com/ja-thomas/autoxgboost/blob/master/poster_2018.pdf)
+[lightgbm](https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html) and [autoxgoboost](https://github.com/ja-thomas/autoxgboost/blob/master/poster_2018.pdf)
 
 ## 2. Task description
 Now we come back to our example "auto-gbdt" which run in lightgbm and nni. The data including [train data](https://github.com/Microsoft/nni/blob/master/examples/trials/auto-gbdt/data/regression.train) and [test data](https://github.com/Microsoft/nni/blob/master/examples/trials/auto-gbdt/data/regression.train).
@@ -47,9 +45,10 @@ Given the features and label in train data, we train a GBDT regression model and
 ## 3. How to run in nni
 
 ### 3.1 Prepare your trial code
+
 You need to prepare a basic code as following:
-``` python
 
+```python
 ...
 
 def get_default_parameters():
@@ -88,9 +87,9 @@ if __name__ == '__main__':
 ```
 
 ### 3.2 Prepare your search space.
-If you like to tune `num_leaves`, `learning_rate`, `bagging_fraction` and `bagging_freq`, 
-you could write a [search_space.json](https://github.com/Microsoft/nni/blob/master/examples/trials/auto-gbdt/search_space.json) as follow:
-```
+If you like to tune `num_leaves`, `learning_rate`, `bagging_fraction` and `bagging_freq`, you could write a [search_space.json](https://github.com/Microsoft/nni/blob/master/examples/trials/auto-gbdt/search_space.json) as follow:
+
+```json
 {
     "num_leaves":{"_type":"choice","_value":[31, 28, 24, 20]},
     "learning_rate":{"_type":"choice","_value":[0.01, 0.05, 0.1, 0.2]},
@@ -102,6 +101,7 @@ you could write a [search_space.json](https://github.com/Microsoft/nni/blob/mast
 More support variable type you could reference [here](https://github.com/Microsoft/nni/blob/master/docs/SearchSpaceSpec.md).
 
 ### 3.3 Add SDK of nni into your code.
+
 ```diff
 +import nni
 ...
@@ -147,6 +147,7 @@ if __name__ == '__main__':
 ```
 
 ### 3.4 Write a config file and run it.
+
 In the config file, you could set some settings including:
 
 * Experiment setting: `trialConcurrency`, `maxExecDuration`, `maxTrialNum`, `trial gpuNum`, etc.
@@ -155,6 +156,7 @@ In the config file, you could set some settings including:
 * Algorithm setting: select `tuner` algorithm, `tuner optimize_mode`, etc.
 
 An config.yml as follow:
+
 ```yaml
 authorName: default
 experimentName: example_auto-gbdt
@@ -180,6 +182,7 @@ trial:
 ```
 
 Run this experiment with command as follow:
-```
+
+```bash
 nnictl create --config ./config.yml
 ```
\ No newline at end of file

docs/sklearn_examples.md

 # Scikit-learn in NNI
+
 [Scikit-learn](https://github.com/scikit-learn/scikit-learn) is a pupular meachine learning tool for data mining and data analysis. It supports many kinds of meachine learning models like LinearRegression, LogisticRegression, DecisionTree, SVM etc. How to make the use of scikit-learn more efficiency is a valuable topic.  
 NNI supports many kinds of tuning algorithms to search the best models and/or hyper-parameters for scikit-learn, and support many kinds of environments like local machine, remote servers and cloud.
 
 ## 1. How to run the example
+
 To start using NNI, you should install the nni package, and use the command line tool `nnictl` to start an experiment. For more information about installation and preparing for the environment,  please [refer](QuickStart.md).
 After you installed NNI, you could enter the corresponding folder and start the experiment using following commands:
-```
+
+```bash
 nnictl create --config ./config.yml
 ```
 
 ## 2. Description of the example
 
-
 ### 2.1 classification
+
 This example uses the dataset of digits, which is made up of 1797 8x8 images, and each image is a hand-written digit, the goal is to classify these images into 10 classes.  
 In this example, we use SVC as the model, and choose some parameters of this model, including `"C", "keral", "degree", "gamma" and "coef0"`. For more information of these parameters, please [refer](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html).
 
-
 ### 2.2 regression
+
 This example uses the Boston Housing Dataset, this dataset consists of price of houses in various places in Boston and the information such as Crime (CRIM), areas of non-retail business in the town (INDUS), the age of people who own the house (AGE) etc to predict the house price of boston.
 In this example, we tune different kinds of regression models including `"LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"` and some parameters like `"svr_kernel", "knr_weights"`. You could get more details about these models from [here](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning).
 
 ## 3. How to write sklearn code using nni
+
 It is easy to use nni in your sklearn code, there are only a few steps.
+
 * __step 1__
+
   Prepare a search_space.json to storage your choose spaces.
   For example, if you want to choose different models, you may try:
-  ```
+
+  ```json
   {
     "model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]}
   }
   ```
+
   If you want to choose different models and parameters, you could put them together in a search_space.json file.
-  ```
+
+  ```json
   {
     "model_name":{"_type":"choice","_value":["LinearRegression", "SVR", "KNeighborsRegressor", "DecisionTreeRegressor"]},
     "svr_kernel": {"_type":"choice","_value":["linear", "poly", "rbf"]},
     "knr_weights": {"_type":"choice","_value":["uniform", "distance"]}
   }
   ```
+
   Then you could read these values as a dict from your python code, please get into the step 2.
 * __step 2__  
   At the beginning of your python code, you should `import nni` to insure the packages works normally.
   First, you should use `nni.get_next_parameter()` function to get your parameters given by nni. Then you could use these parameters to update your code.
   For example, if you define your search_space.json like following format:
-  ```
+
+  ```json
   {
     "C": {"_type":"uniform","_value":[0.1, 1]},
     "keral": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]},
@@ -53,8 +64,10 @@ It is easy to use nni in your sklearn code, there are only a few steps.
     "coef0 ": {"_type":"uniform","_value":[0.01, 0.1]}
   }
   ```
+
   You may get a parameter dict like this:
-  ```
+
+  ```python
   params = {
         'C': 1.0,
         'keral': 'linear',
@@ -63,6 +76,7 @@ It is easy to use nni in your sklearn code, there are only a few steps.
         'coef0': 0.01
   }
   ```
+
   Then you could use these variables to write your scikit-learn code.
 * __step 3__  
   After you finished your training, you could get your own score of the model, like your percision, recall or MSE etc. NNI needs your score to tuner algorithms and generate next group of parameters, please report the score back to NNI and start next trial job.  

examples/trials/ga_squad/README.md

@@ -114,18 +114,18 @@ trial:
   gpuNum: 0
   cpuNum: 1
   memoryMB: 32869
-  #The docker image to run NNI job on pai
+  #The docker image to run NNI job on OpenPAI
   image: msranni/nni:latest
-  #The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
+  #The hdfs directory to store data on OpenPAI, format 'hdfs://host:port/directory'
   dataDir: hdfs://10.10.10.10:9000/username/nni
   #The hdfs directory to store output data generated by NNI, format 'hdfs://host:port/directory'
   outputDir: hdfs://10.10.10.10:9000/username/nni
 paiConfig:
-  #The username to login pai
+  #The username to login OpenPAI
   userName: username
-  #The password to login pai
+  #The password to login OpenPAI
   passWord: password
-  #The host of restful server of pai
+  #The host of restful server of OpenPAI
   host: 10.10.10.10
 ```