Update CIFAR10 tutorial device selection for CUDA, MPS, and CPU

[amit-chaubey]

Description

This PR updates beginner_source/blitz/cifar10_tutorial.py to make device selection explicit across CUDA, MPS, and CPU.

Changes

• Select device in this order:
  • cuda:0 if CUDA is available
  • mps if Apple MPS backend is available
  • cpu otherwise
• Update nearby tutorial comments to refer to generic device usage instead of CUDA-only wording.

Why

The tutorial currently frames this section as CUDA-specific. This change keeps the same tutorial flow while making guidance clearer for Apple Silicon and CPU-only users.

Checklist

• The issue that is being fixed is referred in the description (no exact matching open issue linked for this change)
• Only one issue is addressed in this pull request
• Labels from the issue that this PR is fixing are added to this pull request
• No unnecessary issues are included into this pull request.

cc @subramen @albanD @jbschlosser

[pytorch-bot]

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[svekars]

@claude

[claude]

Claude finished @svekars's task in 2m 15s —— View job

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Review of PR #3826

• Read the changed file and PR diff
• Review the changes for correctness, style, and consistency
• Check for consistency with other tutorials in the repo
• Post review feedback

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Summary

The intent of this PR is good — making the CIFAR10 tutorial device-agnostic benefits Apple Silicon and CPU-only users. However, the implementation uses an older device-selection pattern rather than the modern torch.accelerator API that most other tutorials in this repo have already adopted.

Recommendation: Use torch.accelerator instead

Most beginner tutorials (including the closely related quickstart_tutorial.py, buildmodel_tutorial.py, transfer_learning_tutorial.py, nn_tutorial.py, and others) have already migrated to the torch.accelerator API:

device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"

This single line replaces the 5-line if/elif/else block in this PR and automatically handles CUDA, MPS, XPU, and other accelerator backends without needing explicit checks for each one. It's also future-proof for new hardware backends.

The current PR uses the hasattr(torch.backends, "mps") pattern, which is only found in one other file (reinforcement_q_learning.py) and is a legacy approach.

Suggested change at cifar10_tutorial.py:302-309 — replace:

# 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")

with:

# Let's first select a device. This picks the fastest available accelerator,
# or falls back to CPU.
device = (
    torch.accelerator.current_accelerator().type
    if torch.accelerator.is_available()
    else "cpu"
)

Fix this →

Other comments are fine

The remaining changes — updating prose to say "accelerator device" instead of "CUDA device", "tensors on device" instead of "CUDA tensors", and "device too" instead of "GPU too" — are all good and appropriate regardless of which device selection approach is used.

Minor nit

The hasattr(torch.backends, "mps") guard is overly cautious for the PyTorch versions this tutorial targets. torch.backends.mps has existed since PyTorch 1.12. But this becomes moot if using torch.accelerator.

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[amit-chaubey]

Thanks! @claude Updated to use torch.accelerator (same one-liner style as quickstart_tutorial.py), adjusted the surrounding prose so it stays accurate when device is "cpu", and kept num_workers=0 for direct script runs on macOS/Windows. Pushed in the latest commit.