Add changing default device recipe (#2220)

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Update build.sh

* Fix #2219 and disabling failing tutorials

* Update validate_tutorials_built.py

* Update validate_tutorials_built.py

* Add changing default device recipe

Signed-off-by: Edward Z. Yang <[email protected]>

* Update changing_default_device.py

* Update changing_default_device.py

* Update .jenkins/build.sh

* Update .jenkins/validate_tutorials_built.py

* Update beginner_source/introyt/autogradyt_tutorial.py

---------

Signed-off-by: Edward Z. Yang <[email protected]>
Co-authored-by: Svetlana Karslioglu <[email protected]>
Co-authored-by: Nikita Shulga <[email protected]>

Author: 3 people

recipes_source/recipes/changing_default_device.py

@@ -0,0 +1,47 @@
+"""
+Changing default device
+=======================
+
+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.
+Typically, to do this you might have used if-statements and ``cuda()`` calls
+to do this:
+
+"""
+import torch
+
+USE_CUDA = False
+
+mod = torch.nn.Linear(20, 30)
+if USE_CUDA:
+    mod.cuda()
+
+device = 'cpu'
+if USE_CUDA:
+    device = 'cuda'
+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. 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 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
+# is causing problems for you, please comment on
+# `this issue <https://github.com/pytorch/pytorch/issues/92701>`__