Installing lightning changes my pytorch version from cuda118 to cpu

Nobody has claimed this yet.

### Bug description

Installing lightning changes my pytorch version from cuda118 to cpu. I just did `python -m pip install lightning` and I ended up with pytorch-cpu and torch does not recognise my GPUs

This has also affected my pytorch-geometric/scatter/sparse installations

Hope there is a hassle-free way of installing lightning.
@awaelchli

Here is my torch environment before installing lightning (`python -m torch.utils.collect_env`):
```
Collecting environment information...
PyTorch version: 2.0.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: CentOS Linux release 8.1.1911 (Core) (x86_64)
GCC version: (GCC) 8.3.1 20190507 (Red Hat 8.3.1-4)
Clang version: Could not collect
CMake version: version 3.27.4
Libc version: glibc-2.28

Python version: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-147.8.1.el8_2.arc.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 PCIe
GPU 1: NVIDIA H100 PCIe

Nvidia driver version: 525.116.04
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Thread(s) per core: 1
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz
Stepping: 6
CPU MHz: 2070.674
BogoMIPS: 5600.00
Virtualization: VT-x
L1d cache: 48K
L1i cache: 32K
L2 cache: 1280K
L3 cache: 36864K
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] performer-pytorch==1.1.4
[pip3] torch==2.0.0+cu118
[pip3] torch-geometric==2.3.1
[pip3] torch-scatter==2.1.2+pt20cu118
[pip3] torchaudio==2.0.1+cu118
[pip3] torchvision==0.15.1+cu118
[conda] blas 1.0 mkl
[conda] mkl 2023.1.0 h213fc3f_46343
[conda] numpy 1.25.2 pypi_0 pypi
[conda] performer-pytorch 1.1.4 pypi_0 pypi
[conda] pytorch 2.0.1 py3.9_cpu_0 pytorch
[conda] pytorch-mutex 1.0 cpu pytorch
[conda] torch 2.0.0+cu118 pypi_0 pypi
[conda] torch-geometric 2.3.1 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt20cu118 pypi_0 pypi
[conda] torchaudio 2.0.1+cu118 pypi_0 pypi
[conda] torchvision 0.15.1+cu118 pypi_0 pypi
```

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

master

### How to reproduce the bug

```python
python -m pip install lightning
```

### Error messages and logs

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

### Environment

```
Collecting environment information...
PyTorch version: 2.0.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: CentOS Linux release 8.1.1911 (Core) (x86_64)
GCC version: (GCC) 8.3.1 20190507 (Red Hat 8.3.1-4)
Clang version: Could not collect
CMake version: version 3.27.4
Libc version: glibc-2.28

Python version: 3.9.17 (main, Jul 5 2023, 20:41:20) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.18.0-147.8.1.el8_2.arc.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 PCIe
GPU 1: NVIDIA H100 PCIe

Nvidia driver version: 525.116.04
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Thread(s) per core: 1
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz
Stepping: 6
CPU MHz: 2070.674
BogoMIPS: 5600.00
Virtualization: VT-x
L1d cache: 48K
L1i cache: 32K
L2 cache: 1280K
L3 cache: 36864K
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] performer-pytorch==1.1.4
[pip3] torch==2.0.0+cu118
[pip3] torch-geometric==2.3.1
[pip3] torch-scatter==2.1.2+pt20cu118
[pip3] torchaudio==2.0.1+cu118
[pip3] torchvision==0.15.1+cu118
[conda] blas 1.0 mkl
[conda] mkl 2023.1.0 h213fc3f_46343
[conda] numpy 1.25.2 pypi_0 pypi
[conda] performer-pytorch 1.1.4 pypi_0 pypi
[conda] pytorch 2.0.1 py3.9_cpu_0 pytorch
[conda] pytorch-mutex 1.0 cpu pytorch
[conda] torch 2.0.0+cu118 pypi_0 pypi
[conda] torch-geometric 2.3.1 pypi_0 pypi
[conda] torch-scatter 2.1.2+pt20cu118 pypi_0 pypi
[conda] torchaudio 2.0.1+cu118 pypi_0 pypi
[conda] torchvision 0.15.1+cu118 pypi_0 pypi
```

### More info

_No response_

Start by reproducing `python -m pip install lightning` in an isolated Python 3.9 environment matching the reported CUDA 11.8 setup, then inspect which dependency causes the CPU PyTorch packages to be selected. The issue is done when installing Lightning preserves a compatible CUDA-enabled PyTorch installation and does not break the related PyTorch Geometric packages.

Written by the indexing model from the issue text.