Update changing_default_device.py

Author: Svetlana Karslioglu

recipes_source/recipes/changing_default_device.py

@@ -1,12 +1,13 @@
 """
 Changing default device
 =======================
-It is common to want to write PyTorch code in a device agnostic way,
+
+It is common practice to write PyTorch code in a device-agnostic way,
 and then switch between CPU and CUDA depending on what hardware is available.
-Traditionally, to do this you might have used if-statements and cuda() calls
+Typically, to do this you might have used if-statements and cuda() calls
 to do this:
-"""
 
+"""
 import torch
 
 USE_CUDA = False
@@ -21,23 +22,26 @@
 inp = torch.randn(128, 20, device=device)
 print(mod(inp).device)
 
+###################################################################
 # PyTorch now also has a context manager which can take care of the
-# device transfer automatically.
+# device transfer automatically. Here is an example:
 
 with torch.device('cuda'):
     mod = torch.nn.Linear(20, 30)
     print(mod.weight.device)
     print(mod(torch.randn(128, 20)).device)
 
-# You can also set it globally
+#########################################
+# You can also set it globally like this: 
 
 torch.set_default_device('cuda')
 
 mod = torch.nn.Linear(20, 30)
 print(mod.weight.device)
 print(mod(torch.randn(128, 20)).device)
 
+################################################################
 # This function imposes a slight performance cost on every Python
-# call to the torch API (not just factory functions).  If this
+# call to the torch API (not just factory functions). If this
 # is causing problems for you, please comment on
-# https://github.com/pytorch/pytorch/issues/92701
+# `this issue <https://github.com/pytorch/pytorch/issues/92701>`__