clean up docs (#1614)

* fixed hparams section

* docs clean up

Author: williamFalcon

README.md

@@ -54,7 +54,8 @@ pip install pytorch-lightning
 [MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
 
 ## What is it?
-Lightning is a way to organize your PyTorch code to decouple the science code from the engineering. It's more of a style-guide than a framework.
+Lightning is a way to organize your PyTorch code to decouple the science code from the engineering.
+It's more of a PyTorch style-guide than a framework. 
 
 In Lightning, you organize your code into 3 distinct categories:
 
@@ -69,6 +70,8 @@ Here's an example of how to refactor your research code into a [LightningModule]
 The rest of the code is automated by the [Trainer](https://pytorch-lightning.readthedocs.io/en/latest/trainer.html)!
 ![PT to PL](docs/source/_images/lightning_module/pt_trainer.png)
 
+[READ THIS QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/latest/new-project.html)
+
 ## Testing Rigour
 All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
 

docs/source/hyperparameters.rst

@@ -27,9 +27,9 @@ Argparser Best Practices
 ^^^^^^^^^^^^^^^^^^^^^^^^
 It is best practice to layer your arguments in three sections.
 
-    1. Trainer args (gpus, num_nodes, etc...)
-    2. Model specific arguments (layer_dim, num_layers, learning_rate, etc...)
-    3. Program arguments (data_path, cluster_email, etc...)
+1.  Trainer args (gpus, num_nodes, etc...)
+2.  Model specific arguments (layer_dim, num_layers, learning_rate, etc...)
+3.  Program arguments (data_path, cluster_email, etc...)
 
 We can do this as follows. First, in your LightningModule, define the arguments
 specific to that module. Remember that data splits or data paths may also be specific to
@@ -84,15 +84,11 @@ Finally, make sure to start the training like so:
 
     # YES
     model = LitModel(hparams)
-
-    # NO
-    # model = LitModel(learning_rate=hparams.learning_rate, ...)
-
-    # YES
     trainer = Trainer.from_argparse_args(hparams, early_stopping_callback=...)
 
     # NO
-    trainer = Trainer(gpus=hparams.gpus, ...)
+    # model = LitModel(learning_rate=hparams.learning_rate, ...)
+    #trainer = Trainer(gpus=hparams.gpus, ...)
 
 
 LightiningModule hparams
@@ -144,8 +140,8 @@ Now pass in the params when you init your model
 The line `self.hparams = hparams` is very special. This line assigns your hparams to the LightningModule.
 This does two things:
 
-    1. It adds them automatically to tensorboard logs under the hparams tab.
-    2. Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
+1.  It adds them automatically to tensorboard logs under the hparams tab.
+2.  Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
 
 Trainer args
 ^^^^^^^^^^^^

docs/source/introduction_guide.rst

@@ -604,71 +604,7 @@ Notice the epoch is MUCH faster!
 
 ---------
 
-Hyperparameters
----------------
-Normally, we don't hard-code the values to a model. We usually use the command line to
-modify the network.
-
-.. code-block:: python
-
-    from argparse import ArgumentParser
-
-    parser = ArgumentParser()
-
-    # parametrize the network
-    parser.add_argument('--layer_1_dim', type=int, default=128)
-    parser.add_argument('--layer_2_dim', type=int, default=256)
-    parser.add_argument('--batch_size', type=int, default=64)
-
-    args = parser.parse_args()
-
-Now we can parametrize the LightningModule.
-
-.. code-block:: python
-    :emphasize-lines: 5,6,7,12,14
-
-    class LitMNIST(pl.LightningModule):
-      def __init__(self, hparams):
-        super().__init__()
-        self.hparams = hparams
-
-        self.layer_1 = torch.nn.Linear(28 * 28, hparams.layer_1_dim)
-        self.layer_2 = torch.nn.Linear(hparams.layer_1_dim, hparams.layer_2_dim)
-        self.layer_3 = torch.nn.Linear(hparams.layer_2_dim, 10)
-
-      def forward(self, x):
-        ...
-
-      def train_dataloader(self):
-        ...
-        return DataLoader(mnist_train, batch_size=self.hparams.batch_size)
-
-      def configure_optimizers(self):
-        return Adam(self.parameters(), lr=self.hparams.learning_rate)
-
-    hparams = parse_args()
-    model = LitMNIST(hparams)
-
-.. note:: Bonus! if (hparams) is in your module, Lightning will save it into the checkpoint and restore your
-    model using those hparams exactly.
-
-And we can also add all the flags available in the Trainer to the Argparser.
-
-.. code-block:: python
-
-    # add all the available Trainer options to the ArgParser
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-And now you can start your program with
-
-.. code-block:: bash
-
-    # now you can use any trainer flag
-    $ python main.py --num_nodes 2 --gpus 8
-
-
-For a full guide on using hyperparameters, `check out the hyperparameters docs <hyperparameters.rst>`_.
+.. include:: hyperparameters.rst
 
 ---------