Rewrite the definition of “optimize_mode” and rename “LocalMode.md” (#958)

README.md

@@ -74,7 +74,7 @@ The tool dispatches and runs trial jobs generated by tuning algorithms to search
       </td>
       <td>
       <ul>
-        <li><a href="docs/en_US/tutorial_1_CR_exp_local_api.md">Local Machine</a></li>
+        <li><a href="docs/en_US/LocalMode.md">Local Machine</a></li>
         <li><a href="docs/en_US/RemoteMachineMode.md">Remote Servers</a></li>
         <li><a href="docs/en_US/PAIMode.md">OpenPAI</a></li>
         <li><a href="docs/en_US/KubeflowMode.md">Kubeflow</a></li>
@@ -183,7 +183,7 @@ You can use these commands to get more information about the experiment
 * [Config an experiment](docs/en_US/ExperimentConfig.md)
 * [How to use annotation](docs/en_US/Trials.md#nni-python-annotation)
 ## **Tutorials**
-* [Run an experiment on local (with multiple GPUs)?](docs/en_US/tutorial_1_CR_exp_local_api.md)
+* [Run an experiment on local (with multiple GPUs)?](docs/en_US/LocalMode.md)
 * [Run an experiment on multiple machines?](docs/en_US/RemoteMachineMode.md)
 * [Run an experiment on OpenPAI?](docs/en_US/PAIMode.md)
 * [Run an experiment on Kubeflow?](docs/en_US/KubeflowMode.md)

docs/en_US/Builtin_Tuner.md

@@ -39,7 +39,7 @@ TPE, as a black-box optimization, can be used in various scenarios and shows goo
 
 **Requirement of classArg**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 
 **Usage example:**
 
@@ -65,7 +65,7 @@ Random search is suggested when each trial does not take too long (e.g., each tr
 
 **Requirement of classArg:**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 
 **Usage example**
 
@@ -89,7 +89,7 @@ Anneal is suggested when each trial does not take too long, and you have enough
 
 **Requirement of classArg**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 
 **Usage example**
 
@@ -145,7 +145,7 @@ Similar to TPE, SMAC is also a black-box tuner which can be tried in various sce
 
 **Requirement of classArg**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 
 **Usage example**
 
@@ -232,7 +232,7 @@ It is suggested when you have limited computation resource but have relatively l
 
 **Requirement of classArg**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 * **R** (*int, optional, default = 60*) - the maximum STEPS (could be the number of mini-batches or epochs) can be allocated to a trial. Each trial should use STEPS to control how long it runs.
 * **eta** (*int, optional, default = 3*) - `(eta-1)/eta` is the proportion of discarded trials
 
@@ -266,7 +266,7 @@ It is suggested that you want to apply deep learning methods to your task (your
 
 **Requirement of classArg**
 
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 * **task** (*('cv'), optional, default = 'cv'*) - The domain of experiment, for now, this tuner only supports the computer vision(cv) domain.
 * **input_width** (*int, optional, default = 32*) - input image width
 * **input_channel** (*int, optional, default = 3*) - input image channel
@@ -306,7 +306,7 @@ Similar to TPE and SMAC, Metis is a black-box tuner. If your system takes a long
 
 **Requirement of classArg**
 
-* **optimize_mode** (*'maximize' or 'minimize', optional, default = 'maximize'*) - If 'maximize', tuners will return the hyperparameter set with larger expectation. If 'minimize', tuner will return the hyperparameter set with smaller expectation.
+* **optimize_mode** (*'maximize' or 'minimize', optional, default = 'maximize'*) - If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
 
 **Usage example**
 

docs/en_US/Installation.md

@@ -57,7 +57,7 @@ Below are the minimum system requirements for NNI on macOS. Due to potential pro
 * [Use NNIBoard](WebUI.md)
 * [Define search space](SearchSpaceSpec.md)
 * [Config an experiment](ExperimentConfig.md)
-* [How to run an experiment on local (with multiple GPUs)?](tutorial_1_CR_exp_local_api.md)
+* [How to run an experiment on local (with multiple GPUs)?](LocalMode.md)
 * [How to run an experiment on multiple machines?](RemoteMachineMode.md)
 * [How to run an experiment on OpenPAI?](PAIMode.md)
 * [How to run an experiment on Kubernetes through Kubeflow?](KubeflowMode.md)

docs/en_US/Overview.md

@@ -53,7 +53,7 @@ More details about how to run an experiment, please refer to [Get Started](Quick
 * [How to customize your own tuner?](Customize_Tuner.md)
 * [What are assessors supported by NNI?](Builtin_Assessors.md)
 * [How to customize your own assessor?](Customize_Assessor.md)
-* [How to run an experiment on local?](tutorial_1_CR_exp_local_api.md)
+* [How to run an experiment on local?](LocalMode.md)
 * [How to run an experiment on multiple machines?](RemoteMachineMode.md)
 * [How to run an experiment on OpenPAI?](PAIMode.md)
 * [Examples](mnist_examples.md)
\ No newline at end of file

docs/en_US/QuickStart.md

@@ -225,7 +225,7 @@ Below is the status of the all trials. Specifically:
 * [Try different Assessors](Builtin_Assessors.md)
 * [How to use command line tool nnictl](NNICTLDOC.md)
 * [How to write a trial](Trials.md)
-* [How to run an experiment on local (with multiple GPUs)?](tutorial_1_CR_exp_local_api.md)
+* [How to run an experiment on local (with multiple GPUs)?](LocalMode.md)
 * [How to run an experiment on multiple machines?](RemoteMachineMode.md)
 * [How to run an experiment on OpenPAI?](PAIMode.md)
 * [How to run an experiment on Kubernetes through Kubeflow?](KubeflowMode.md)

docs/en_US/training_services.rst

@@ -2,7 +2,7 @@ Introduction to NNI Training Services
 =====================================
 
 ..  toctree::
-    Local<tutorial_1_CR_exp_local_api>
+    Local<LocalMode>
     Remote<RemoteMachineMode>
     OpenPAI<PAIMode>
     Kubeflow<KubeflowMode>