Why only one GPU is getting used in the kaggle kernel

Nobody has claimed this yet.

### Bug description


i initialized my trainer
```
trainer = L.Trainer(max_epochs=5,
devices=2,
strategy='ddp_notebook',
num_sanity_val_steps=0,
profiler='simple',
default_root_dir="/kaggle/working",
callbacks=[DeviceStatsMonitor(),
StochasticWeightAveraging(swa_lrs=1e-2),
#EarlyStopping(monitor='train_Loss', min_delta=0.001, patience=100, verbose=False, mode='min'),
],
enable_progress_bar=True,
enable_model_summary=True,
)
```

distributed is initialized for both the GPUs but only one is getting hit.

Also for validation loop the GPU are not in usage


How can I resolve the situation to use 2 GPUs and fasten my training.

### What version are you seeing the problem on?

v2.4

### How to reproduce the bug

_No response_

### Error messages and logs

```
# Error messages and logs here please
```

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.4.0):
#- PyTorch Version (e.g., 2.4):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```

### More info

_No response_

Start with the reported L.Trainer configuration using devices=2 and strategy='ddp_notebook', then collect the missing Lightning, PyTorch, CUDA, GPU, and installation details from the Environment section. Reproduce the training and validation behavior in the Kaggle kernel; done means identifying why only one GPU is used and documenting or validating a fix for two-GPU training.

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