Update docs

README.md

@@ -207,10 +207,12 @@ Here is a list of models built on `Qlib`.
 Your PR of new Quant models is highly welcomed.
 
 ## Run a single model
+All the models listed above are runnable with ``Qlib``. Users can find the config files we provide and some details about the model through the [benchmarks](examples/benchmarks) folder. More information can be retrieved at the model files listed above.
+
 `Qlib` provides three different ways to run a single model, users can pick the one that fits their cases best:
 - User can use the tool `qrun` mentioned above to run a model's workflow based from a config file.
 - User can create a `workflow_by_code` python script based on the [one](examples/workflow_by_code.py) listed in the `examples` folder.
-- User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
+- User can use the script [`run_all_model.py`](examples/run_all_model.py) listed in the `examples` folder to run a model. Here is an example of the specific shell command to be used: `python run_all_model.py --models=lightgbm`, where the `--models` arguments can take any number of models listed above. For more use cases, please refer to the file's [docstrings](examples/run_all_model.py).
 
 ## Run multiple models
 `Qlib` also provides a script [`run_all_model.py`](examples/run_all_model.py) which can run multiple models for several iterations. (**Note**: the script only supprots *Linux* now. Other OS will be supported in the future.)

docs/component/data.rst

@@ -321,7 +321,7 @@ Dataset
 
 The ``Dataset`` module in ``Qlib`` aims to prepare data for model training and inferencing.
 
-The motivation of this module is that we want to maximize the flexibility of of different models to handle data that are suitable for themselves. This module gives the model the rights to process their data in an unique way. For instance, models such as ``GBDT`` may work well on data that contains `nan` or `None` value, while neural networks such as ``DNN`` will break down on such data. 
+The motivation of this module is that we want to maximize the flexibility of of different models to handle data that are suitable for themselves. This module gives the model the rights to process their data in an unique way. For instance, models such as ``GBDT`` may work well on data that contains `nan` or `None` value, while neural networks such as ``MLP`` will break down on such data. 
 
 The ``DatasetH`` class is the `dataset` with `Data Handler`. Here is the most important interface of the class:
 

docs/component/model.rst

@@ -63,7 +63,7 @@ For other interfaces such as `finetune`, please refer to `Model API <../referenc
 Example
 ==================
 
-``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``DNN``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. The following steps show how to run`` LightGBM`` as an independent module.
+``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. The following steps show how to run`` LightGBM`` as an independent module.
 
 - Initialize ``Qlib`` with `qlib.init` first, please refer to `Initialization <../start/initialization.html>`_.
 - Run the following code to get the `prediction score` `pred_score`

docs/introduction/quick.rst

@@ -91,4 +91,4 @@ Auto Quant Research Workflow
 Custom Model Integration
 ===============================================
 
-``Qlib`` provides several models such as ``lightGBM`` and ``DNN`` model as the baseline of ``Interday Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``. If users are interested in the custom model, please refer to `Custom Model Integration <../start/integration.html>`_.
+``Qlib`` provides several models such as ``lightGBM`` and ``MLP`` model as the baseline of ``Interday Model``. In addition to the default model, users can integrate their own custom models into ``Qlib``. If users are interested in the custom model, please refer to `Custom Model Integration <../start/integration.html>`_.

docs/start/integration.rst

@@ -5,7 +5,7 @@ Custom Model Integration
 Introduction
 ===================
 
-``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``DNN``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
+``Qlib``'s `Model Zoo` includes models such as ``LightGBM``, ``MLP``, ``LSTM``, etc.. These models are treated as the baselines of ``Interday Model``. In addition to the default models ``Qlib`` provide, users can integrate their own custom models into ``Qlib``.
 
 Users can integrate their own custom models according to the following steps.
 

examples/run_all_model.py

@@ -204,16 +204,16 @@ def run(times=1, models=None, exclude=False):
         python run_all_model.py 3
 
         # Case 2 - run specific models multiple times
-        python run_all_model.py 3 dnn
+        python run_all_model.py 3 mlp
 
         # Case 3 - run other models except those are given as arguments for multiple times
-        python run_all_model.py 3 [dnn,tft,lstm] True
+        python run_all_model.py 3 [mlp,tft,lstm] True
 
         # Case 4 - run specific models for one time
-        python run_all_model.py --models=[dnn,lightgbm]
+        python run_all_model.py --models=[mlp,lightgbm]
 
         # Case 5 - run other models except those are given as aruments for one time
-        python run_all_model.py --models=[dnn,tft,sfm] --exclude=True
+        python run_all_model.py --models=[mlp,tft,sfm] --exclude=True
 
     """
     # get all folders