Update CIFAR10 tutorial device selection for CUDA/MPS/CPU

Author: amit-chaubey

beginner_source/blitz/cifar10_tutorial.py

@@ -299,28 +299,32 @@ def forward(self, x):
 # Just like how you transfer a Tensor onto the GPU, you transfer the neural
 # net onto the GPU.
 #
-# Let's first define our device as the first visible cuda device if we have
-# CUDA available:
+# Let's first select a device. Prefer CUDA when available, otherwise use MPS
+# (Apple Silicon), and fall back to CPU.
+if torch.cuda.is_available():
+    device = torch.device("cuda:0")
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-
-# Assuming that we are on a CUDA machine, this should print a CUDA device:
+# This prints the selected device, e.g. "cuda:0", "mps", or "cpu".
 
 print(device)
 
 ########################################################################
-# The rest of this section assumes that ``device`` is a CUDA device.
+# The rest of this section assumes that ``device`` is an accelerator device.
 #
 # Then these methods will recursively go over all modules and convert their
-# parameters and buffers to CUDA tensors:
+# parameters and buffers to tensors on ``device``:
 #
 # .. code:: python
 #
 #     net.to(device)
 #
 #
 # Remember that you will have to send the inputs and targets at every step
-# to the GPU too:
+# to ``device`` too:
 #
 # .. code:: python
 #