update doc: NNI core capacities in Overview.md, standalone mode in Trials.md

docs/en_US/Overview.md

@@ -46,6 +46,33 @@ For each experiment, user only needs to define a search space and update a few l
 
 More details about how to run an experiment, please refer to [Get Started](Tutorial/QuickStart.md).
 
+## Core Features
+
+NNI provides a key capacity to run multiple instances in parallel to find best combinations of parameters. This feature can be used in various domains, like find best hyperparameters for a deep learning model, or find best configuration for database and other complex system with real data.
+
+NNI is also like to provide algorithm toolkits for machine learning and deep learning, especially neural architecture search (NAS) algorithms, model compression algorithms, and feature engineering algorithms.
+
+### Hyperparameter Tuning
+This is a core and basic feature of NNI, we provide many popular [automatic tuning algorithms](Tuner/BuiltinTuner.md) (i.e., tuner) and [early stop algorithms](Assessor/BuiltinAssessor.md) (i.e., assessor). You could follow [Quick Start](Tutorial/QuickStart.md) to tune your model (or system). Basically, there are the above three steps and then start an NNI experiment.
+
+### General NAS Framework
+This NAS framework is for users to easily specify candidate neural architectures, for example, could specify multiple candidate operations (e.g., separable conv, dilated conv) for a single layer, and specify possible skip connections. NNI will find the best candidate automatically. On the other hand, the NAS framework provides simple interface for another type of users (e.g., NAS algorithm researchers) to implement new NAS algorithms. Detailed description and usage can be found [here](NAS/Overview.md).
+
+NNI has supported many one-shot NAS algorithms, such as ENAS, DARTS, through NNI trial SDK. To use these algorithms you do not have to start an NNI experiment. Instead, to import an algorithm in your trial code, and simply run your trial code. If you want to tune the hyperparameters in the algorithms or want to run multiple instances, you could choose a tuner and start an NNI experiment.
+
+Other than one-shot NAS, NAS can also run in a classic mode where each candidate architecture runs as an independent trial job. In this mode, similar to hyperparameter tuning, users have to start an NNI experiment and choose a tuner for NAS.
+
+### Model Compression
+Model Compression on NNI includes pruning algorithms and quantization algorithms. These algorithms are provided through NNI trial SDK. Users could directly use them in their trial code and run the trial code without starting an NNI experiment. Detailed description and usage can be found [here](Compressor/Overview.md).
+
+There are different types of hyperparamters in model compression. One type is the hyperparameters in input configuration, e.g., sparsity, quantization bits, to a compression algorithm. The other type is the hyperparamters in compression algorithms. Here, Hyperparameter tuning of NNI could help a lot in finding the best compressed model automatically. A simple example can be found [here](Compressor/AutoCompression.md).
+
+### Automatic Feature Engineering
+Automatic feature engineering is for users to find the best features for the following tasks. Detailed description and usage can be found [here](FeatureEngineering/Overview.md). It is supported through NNI trial SDK, which means you do not have to create an NNI experiment. Instead, simply import a built-in auto-feature-engineering algorithm in your trial code and directly run your trial code. 
+
+The auto-feature-engineering algorithms usually have a bunch of hyperparameters themselves. If you want to automatically tune those hyperparameters, you can leverage hyperparameter tuning of NNI, that is, choose a tuning algorithm (i.e., tuner) and start an NNI experiment for it.
+
+
 ## Learn More
 * [Get started](Tutorial/QuickStart.md)
 * [How to adapt your trial code on NNI?](TrialExample/Trials.md)
@@ -56,4 +83,7 @@ More details about how to run an experiment, please refer to [Get Started](Tutor
 * [How to run an experiment on local?](TrainingService/LocalMode.md)
 * [How to run an experiment on multiple machines?](TrainingService/RemoteMachineMode.md)
 * [How to run an experiment on OpenPAI?](TrainingService/PaiMode.md)
-* [Examples](TrialExample/MnistExamples.md)
+* [Examples](TrialExample/MnistExamples.md)
+* [Neural Architecture Search on NNI](NAS/Overview.md)
+* [Automatic model compression on NNI](Compressor/Overview.md)
+* [Automatic feature engineering on NNI](FeatureEngineering/Overview.md)

docs/en_US/TrialExample/Trials.md

@@ -127,6 +127,23 @@ In the YAML configure file, you need to set *useAnnotation* to true to enable NN
 useAnnotation: true
 ```
 
+## Standalone mode for debug
+
+NNI supports standalone mode for trial code to run without starting an NNI experiment. This is for finding out bugs in trial code more conveniently. NNI annotation natively supports standalone mode, as the added NNI related lines are comments. For NNI trial APIs, the APIs have changed behaviors in standalone mode, some APIs return dummy values, and some APIs do not really report values. Please refer to the following table for the full list of these APIs.
+```python
+# NOTE: please assign default values to the hyperparameters in your trial code
+nni.get_next_parameter # return {}
+nni.report_final_result # have log printed on stdout, but does not report
+nni.report_intermediate_result # have log printed on stdout, but does not report
+nni.get_experiment_id # return "STANDALONE"
+nni.get_trial_id # return "STANDALONE"
+nni.get_sequence_id # return 0
+```
+
+You can try standalone mode with the [mnist example](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv1). Simply run `python3 mnist.py` under the code directory. The trial code successfully runs with default hyperparameter values.
+
+For more debuggability, please refer to [How to Debug](../Tutorial/HowToDebug.md)
+
 ## Where are my trials?
 
 ### Local Mode

docs/en_US/Tutorial/HowToDebug.md

@@ -84,5 +84,6 @@ A common example of this would be run the mnist example without installing tenso
 
 ![](../../img/trial_error.jpg)
 
-As it shows, every trial has a log path, where you can find trial'log and stderr.
+As it shows, every trial has a log path, where you can find trial's log and stderr.
 
+In addition to experiment level debug, NNI also provides the capability for debugging a single trial without the need to start the entire experiment. Refer to [standalone mode](../TrialExample/Trials.md#standalone-mode-for-debug) for more information about debug single trial code.