Fixes broken LM links (#4117)

docs/source/callbacks.rst

@@ -22,7 +22,7 @@ Callback
 A callback is a self-contained program that can be reused across projects.
 
 Lightning has a callback system to execute callbacks when needed. Callbacks should capture NON-ESSENTIAL
-logic that is NOT required for your :class:`~pytorch_lightning.core.LightningModule` to run.
+logic that is NOT required for your :ref:`lightning_module` to run.
 
 Here's the flow of how the callback hooks are executed:
 
@@ -63,7 +63,7 @@ Example:
     trainer is init now
 
 We successfully extended functionality without polluting our super clean
-:class:`~pytorch_lightning.core.LightningModule` research code.
+:ref:`lightning_module` research code.
 
 -----------
 

docs/source/converting.rst

@@ -16,7 +16,7 @@ To enable your code to work with Lightning, here's how to organize PyTorch into
 
 1. Move your computational code
 ===============================
-Move the model architecture and forward pass to your :class:`~pytorch_lightning.core.LightningModule`.
+Move the model architecture and forward pass to your :ref:`lightning_module`.
 
 .. testcode::
 
@@ -115,4 +115,4 @@ The test loop will not be used until you call.
 
 6. Remove any .cuda() or to.device() calls
 ==========================================
-Your :class:`~pytorch_lightning.core.LightningModule` can automatically run on any hardware!
+Your :ref:`lightning_module` can automatically run on any hardware!

docs/source/introduction_guide.rst

@@ -57,7 +57,7 @@ The research
 The Model
 ---------
 
-The :class:`~pytorch_lightning.core.LightningModule` holds all the core research ingredients:
+The :ref:`lightning_module` holds all the core research ingredients:
 
 - The model
 
@@ -98,7 +98,7 @@ Let's first start with the model. In this case we'll design a 3-layer neural net
         x = F.log_softmax(x, dim=1)
         return x
 
-Notice this is a :class:`~pytorch_lightning.core.LightningModule` instead of a ``torch.nn.Module``. A LightningModule is
+Notice this is a :ref:`lightning_module` instead of a ``torch.nn.Module``. A LightningModule is
 equivalent to a pure PyTorch Module except it has added functionality. However, you can use it **EXACTLY** the same as you would a PyTorch Module.
 
 .. testcode::

docs/source/logging.rst

@@ -59,7 +59,7 @@ Lightning offers automatic log functionalities for logging scalars, or manual lo
 
 Automatic logging
 =================
-Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to log from anywhere in a :class:`~pytorch_lightning.core.LightningModule`.
+Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to log from anywhere in a :ref:`lightning_module`.
 
 .. code-block:: python
 

docs/source/lr_finder.rst

@@ -37,10 +37,10 @@ initial lr.
 Using Lightning's built-in LR finder
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To enable the learning rate finder, your :class:`~pytorch_lightning.core.LightningModule` needs to have a ``learning_rate`` or ``lr`` property.
+To enable the learning rate finder, your :ref:`lightning_module` needs to have a ``learning_rate`` or ``lr`` property.
 Then, set ``Trainer(auto_lr_find=True)`` during trainer construction,
 and then call ``trainer.tune(model)`` to run the LR finder. The suggested ``learning_rate``
-will be written to the console and will be automatically set to your :class:`~pytorch_lightning.core.LightningModule`,
+will be written to the console and will be automatically set to your :ref:`lightning_module`,
 which can be accessed via ``self.learning_rate`` or ``self.lr``.
 
 .. code-block:: python

docs/source/new-project.rst

@@ -122,7 +122,7 @@ Step 1: Define LightningModule
 
 **SYTEM VS MODEL**
 
-A :class:`~pytorch_lightning.core.LightningModule` defines a *system* not a model.
+A :ref:`lightning_module` defines a *system* not a model.
 
 .. figure:: https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/model_system.png
     :width: 400
@@ -198,7 +198,7 @@ First, define the data however you want. Lightning just needs a :class:`~torch.u
     dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
     train_loader = DataLoader(dataset)
     
-Next, init the :class:`~pytorch_lightning.core.LightningModule` and the PyTorch Lightning :class:`~pytorch_lightning.trainer.Trainer`,
+Next, init the :ref:`lightning_module` and the PyTorch Lightning :class:`~pytorch_lightning.trainer.Trainer`,
 then call fit with both the data and model.
 
 .. code-block:: python

docs/source/slurm.rst

@@ -17,7 +17,7 @@ Multi-node training
 -------------------
 To train a model using multiple nodes, do the following:
 
-1.  Design your :class:`~pytorch_lightning.core.LightningModule`.
+1.  Design your :ref:`lightning_module`.
 
 2.  Enable ddp in the trainer
 

docs/source/test_set.rst

@@ -70,7 +70,7 @@ running the test set (ie: 16-bit, dp, ddp, etc...)
 Test with additional data loaders
 ---------------------------------
 You can still run inference on a test set even if the `test_dataloader` method hasn't been
-defined within your :class:`~pytorch_lightning.core.LightningModule` instance. This would be the case when your test data
+defined within your :ref:`lightning_module` instance. This would be the case when your test data
 is not available at the time your model was declared.
 
 .. code-block:: python