Address feedback for new Lite docs (#16330)

docs/source-pytorch/fabric/fabric.rst

@@ -24,28 +24,30 @@ With only a few changes to your code, Fabric allows you to:
 
     + from lightning.fabric import Fabric
 
-      class MyModel(nn.Module):
+      class PyTorchModel(nn.Module):
           ...
 
-      class MyDataset(Dataset):
+      class PyTorchDataset(Dataset):
           ...
 
     + fabric = Fabric(accelerator="cuda", devices=8, strategy="ddp")
     + fabric.launch()
 
     - device = "cuda" if torch.cuda.is_available() else "cpu
-      model = MyModel(...)
+      model = PyTorchModel(...)
       optimizer = torch.optim.SGD(model.parameters())
     + model, optimizer = fabric.setup(model, optimizer)
-      dataloader = DataLoader(MyDataset(...), ...)
+      dataloader = DataLoader(PyTorchDataset(...), ...)
     + dataloader = fabric.setup_dataloaders(dataloader)
       model.train()
 
       for epoch in range(num_epochs):
           for batch in dataloader:
-    -         batch.to(device)
+              input, target = batch
+    -         input, target = input.to(device), target.to(device)
               optimizer.zero_grad()
-              loss = model(batch)
+              output = model(input)
+              loss = loss_fn(output, target)
     -         loss.backward()
     +         fabric.backward(loss)
               optimizer.step()

docs/source-pytorch/fabric/fundamentals/convert.rst

@@ -39,7 +39,7 @@ Here are five easy steps to let :class:`~lightning_fabric.fabric.Fabric` scale y
 
 .. code-block:: bash
 
-    lightning run model path/to/train.py``
+    lightning run model path/to/train.py
 
 or use the :meth:`~lightning_fabric.fabric.Fabric.launch` method in a notebook.
 Learn more about :doc:`launching distributed training <launch>`.
@@ -56,28 +56,30 @@ All steps combined, this is how your code will change:
 
     + from lightning.fabric import Fabric
 
-      class MyModel(nn.Module):
+      class PyTorchModel(nn.Module):
           ...
 
-      class MyDataset(Dataset):
+      class PyTorchDataset(Dataset):
           ...
 
     + fabric = Fabric(accelerator="cuda", devices=8, strategy="ddp")
     + fabric.launch()
 
     - device = "cuda" if torch.cuda.is_available() else "cpu
-      model = MyModel(...)
+      model = PyTorchModel(...)
       optimizer = torch.optim.SGD(model.parameters())
     + model, optimizer = fabric.setup(model, optimizer)
-      dataloader = DataLoader(MyDataset(...), ...)
+      dataloader = DataLoader(PyTorchDataset(...), ...)
     + dataloader = fabric.setup_dataloaders(dataloader)
       model.train()
 
       for epoch in range(num_epochs):
           for batch in dataloader:
-    -         batch.to(device)
+              input, target = batch
+    -         input, target = input.to(device), target.to(device)
               optimizer.zero_grad()
-              loss = model(batch)
+              output = model(input)
+              loss = loss_fn(output, target)
     -         loss.backward()
     +         fabric.backward(loss)
               optimizer.step()
@@ -85,3 +87,41 @@ All steps combined, this is how your code will change:
 
 That's it! You can now train on any device at any scale with a switch of a flag.
 Check out our before-and-after example for `image classification <https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/image_classifier/README.md>`_ and many more :ref:`examples <Fabric Examples>` that use Fabric.
+
+**********
+Next steps
+**********
+
+.. raw:: html
+
+    <div class="display-card-container">
+        <div class="row">
+
+.. displayitem::
+    :header: Examples
+    :description: See examples across computer vision, NLP, RL, etc.
+    :col_css: col-md-4
+    :button_link: ../fabric.html#examples
+    :height: 150
+    :tag: basic
+
+.. displayitem::
+    :header: Accelerators
+    :description: Take advantage of your hardware with a switch of a flag
+    :button_link: accelerators.html
+    :col_css: col-md-4
+    :height: 150
+    :tag: intermediate
+
+.. displayitem::
+    :header: Build your own Trainer
+    :description: Learn how to build a trainer tailored for you
+    :col_css: col-md-4
+    :button_link: ../fabric.html#build-your-own-trainer
+    :height: 150
+    :tag: intermediate
+
+.. raw:: html
+
+        </div>
+    </div>