Add device_name classmethod in Accelerator.

[GdoongMathew]

What does this PR do?

Fixes #17355

• 1 GPU

GPU available: NVIDIA GeForce RTX 3050 4GB Laptop GPU, using: 1 devices.
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

• 2 GPUs (Kaggle)

>>> t = Trainer(devices=2)
INFO: GPU available: Tesla T4, using: 2 devices.
INFO: TPU available: False, using: 0 TPU cores
INFO: HPU available: False, using: 0 HPUs

• No cuda GPU available

GPU available: False, using: 0 devices.
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

• MPS

GPU available: Apple M1 Pro, using: 1 devices.
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs

• XLA (Kaggle)

GPU available: False, using: 0 devices.
TPU available: v3-8, using: 8 TPU cores
HPU available: False, using: 0 HPUs

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• Did you list all the breaking changes introduced by this pull request?
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---

📚 Documentation preview 📚: https://pytorch-lightning--21112.org.readthedocs.build/en/21112/

[GdoongMathew]

Results in Kaggle:

• CPU: https://www.kaggle.com/code/gdoong/lightning-cpu?scriptVersionId=258513715&cellId=2
• GPU x 2: https://www.kaggle.com/code/gdoong/lightning-gpu?scriptVersionId=258514966&cellId=2
• TPU: https://www.kaggle.com/code/gdoong/lightning-tpu?scriptVersionId=258515194&cellId=2

[justusschock]

How would you handle heterogeneous gpu types? E.g. say I have one 3090 and one 4090 in my workstation. This doesn't handle it at all.

[GdoongMathew]

How would you handle heterogeneous gpu types? E.g. say I have one 3090 and one 4090 in my workstation. This doesn't handle it at all.

Hi @justusschock , based on the change, the output should look something like

GPU available: RTX 3090, RTX 4090, using: 2 devices.

that being said, if the setup is more complex - for example 2 x 3090 and 1 x 4090 - the current output might not fully reflect that.

[GdoongMathew]

Hi @Borda , a friendly ping for a review ~

[Borda]

Hi @Borda , a friendly ping for a review ~

unfortunatly, I am not in a position to approve a feature type PR, lest ping @ethanwharris

[codecov]

Codecov Report

❌ Patch coverage is 74.00000% with 13 lines in your changes missing coverage. Please review.
✅ Project coverage is 79%. Comparing base (7d2de87) to head (ea2767d).
⚠️ Report is 46 commits behind head on master.
✅ All tests successful. No failed tests found.

❗ There is a different number of reports uploaded between BASE (7d2de87) and HEAD (ea2767d). Click for more details.

HEAD has 1312 uploads less than BASE

Flag	BASE (7d2de87)	HEAD (ea2767d)
cpu	328	33
python	30	3
lightning_fabric	88	0
pytest	163	0
python3.10	30	3
lightning	150	15
python3.13	29	3
python3.12	89	9
python3.11	60	6
python3.12.7	90	9
pytorch2.1	30	6
pytest-full	165	33
pytorch2.3	15	3
pytorch2.5.1	15	3
pytorch2.2.2	15	3
pytorch2.8	30	6
pytorch_lightning	90	18
pytorch2.9	15	3
pytorch2.7	15	3
pytorch2.6	15	3
pytorch2.4.1	15	3

Additional details and impacted files

@@            Coverage Diff            @@
##           master   #21112     +/-   ##
=========================================
- Coverage      87%      79%     -8%     
=========================================
  Files         270      267      -3     
  Lines       24071    24055     -16     
=========================================
- Hits        20867    18986   -1881     
- Misses       3204     5069   +1865