Support saving and loading from remote paths in Fabric

Nobody has claimed this yet.

### Bug description

Hello everyone,

I am training a model using FSDP with Fabric.

When saving the model to an S3 bucket calling the following function:
```
fabric.save(
"s3://model-training-us/models/experiment/checkpoint",
state
)
```

I get the following error:
```
───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /vision-language-model/vlm/train_vlm_fabric.py:520 in │
│ │
│ 517 │ │
│ 518 │ cfg = Config(args) │
│ 519 │ │
│ ❱ 520 │ main(cfg) │
│ 521 │
│ │
│ /vision-language-model/vlm/train_vlm_fabric.py:387 in main │
│ │
│ 384 │ │ "step_count": 0, │
│ 385 │ } │
│ 386 │ │
│ ❱ 387 │ fabric.save( │
│ 388 │ │ "s3://model-training-us/models/experiment1/t │
│ 389 │ │ state, │
│ 390 │ ) │
│ │
│ /usr/lib/python3/dist-packages/lightning/fabric/fabric.py:738 in save │
│ │
│ 735 │ │ │ for k, v in filter.items(): │
│ 736 │ │ │ │ if not callable(v): │
│ 737 │ │ │ │ │ raise TypeError(f"Expected `fabric.save(filter=.. │
│ ❱ 738 │ │ self._strategy.save_checkpoint(path=path, state=_unwrap_objec │
│ 739 │ │ self.barrier() │
│ 740 │ │
│ 741 │ def load( │
│ │
│ /usr/lib/python3/dist-packages/lightning/fabric/strategies/fsdp.py:498 in │
│ save_checkpoint │
│ │
│ 495 │ │ │ │ │ _apply_filter(key, filter or {}, converted, full_s │
│ 496 │ │ │ │
│ 497 │ │ │ if self.global_rank == 0: │
│ ❱ 498 │ │ │ │ torch.save(full_state, path) │
│ 499 │ │ else: │
│ 500 │ │ │ raise ValueError(f"Unknown state_dict_type: {self._state_d │
│ 501 │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:618 in save │
│ │
│ 615 │ _check_save_filelike(f) │
│ 616 │ │
│ 617 │ if _use_new_zipfile_serialization: │
│ ❱ 618 │ │ with _open_zipfile_writer(f) as opened_zipfile: │
│ 619 │ │ │ _save(obj, opened_zipfile, pickle_module, pickle_protocol │
│ 620 │ │ │ return │
│ 621 │ else: │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:492 in │
│ _open_zipfile_writer │
│ │
│ 489 │ │ container = _open_zipfile_writer_file │
│ 490 │ else: │
│ 491 │ │ container = _open_zipfile_writer_buffer │
│ ❱ 492 │ return container(name_or_buffer) │
│ 493 │
│ 494 │
│ 495 def _is_compressed_file(f) -> bool: │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:463 in __init__ │
│ │
│ 460 │ │ │ self.file_stream = io.FileIO(self.name, mode='w') │
│ 461 │ │ │ super().__init__(torch._C.PyTorchFileWriter(self.file_str │
│ 462 │ │ else: │
│ ❱ 463 │ │ │ super().__init__(torch._C.PyTorchFileWriter(self.name)) │
│ 464 │ │
│ 465 │ def __exit__(self, *args) -> None: │
│ 466 │ │ self.file_like.write_end_of_file() │
╰──────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Parent directory
s3:/model-training-us/models/experiment does not exist.
```

The path `s3://model-training-us/models/` exists.

Isn't saving to an S3 bucket supported by fabric.save? Or am I encountering a weird bug?

Thank you in advance for your attention.

Kind regards,
Claudio

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

v2.1

### How to reproduce the bug

_No response_

### Error messages and logs

```
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /vision-language-model/vlm/train_vlm_fabric.py:520 in │
│ │
│ 517 │ │
│ 518 │ cfg = Config(args) │
│ 519 │ │
│ ❱ 520 │ main(cfg) │
│ 521 │
│ │
│ /vision-language-model/vlm/train_vlm_fabric.py:387 in main │
│ │
│ 384 │ │ "step_count": 0, │
│ 385 │ } │
│ 386 │ │
│ ❱ 387 │ fabric.save( │
│ 388 │ │ "s3://model-training-us/models/experiment1/t │
│ 389 │ │ state, │
│ 390 │ ) │
│ │
│ /usr/lib/python3/dist-packages/lightning/fabric/fabric.py:738 in save │
│ │
│ 735 │ │ │ for k, v in filter.items(): │
│ 736 │ │ │ │ if not callable(v): │
│ 737 │ │ │ │ │ raise TypeError(f"Expected `fabric.save(filter=.. │
│ ❱ 738 │ │ self._strategy.save_checkpoint(path=path, state=_unwrap_objec │
│ 739 │ │ self.barrier() │
│ 740 │ │
│ 741 │ def load( │
│ │
│ /usr/lib/python3/dist-packages/lightning/fabric/strategies/fsdp.py:498 in │
│ save_checkpoint │
│ │
│ 495 │ │ │ │ │ _apply_filter(key, filter or {}, converted, full_s │
│ 496 │ │ │ │
│ 497 │ │ │ if self.global_rank == 0: │
│ ❱ 498 │ │ │ │ torch.save(full_state, path) │
│ 499 │ │ else: │
│ 500 │ │ │ raise ValueError(f"Unknown state_dict_type: {self._state_d │
│ 501 │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:618 in save │
│ │
│ 615 │ _check_save_filelike(f) │
│ 616 │ │
│ 617 │ if _use_new_zipfile_serialization: │
│ ❱ 618 │ │ with _open_zipfile_writer(f) as opened_zipfile: │
│ 619 │ │ │ _save(obj, opened_zipfile, pickle_module, pickle_protocol │
│ 620 │ │ │ return │
│ 621 │ else: │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:492 in │
│ _open_zipfile_writer │
│ │
│ 489 │ │ container = _open_zipfile_writer_file │
│ 490 │ else: │
│ 491 │ │ container = _open_zipfile_writer_buffer │
│ ❱ 492 │ return container(name_or_buffer) │
│ 493 │
│ 494 │
│ 495 def _is_compressed_file(f) -> bool: │
│ │
│ /usr/lib/python3/dist-packages/torch/serialization.py:463 in __init__ │
│ │
│ 460 │ │ │ self.file_stream = io.FileIO(self.name, mode='w') │
│ 461 │ │ │ super().__init__(torch._C.PyTorchFileWriter(self.file_str │
│ 462 │ │ else: │
│ ❱ 463 │ │ │ super().__init__(torch._C.PyTorchFileWriter(self.name)) │
│ 464 │ │
│ 465 │ def __exit__(self, *args) -> None: │
│ 466 │ │ self.file_like.write_end_of_file() │
╰──────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Parent directory
s3:/model-training-us/models/experiment does not exist.
```

### Environment

* Lightning:
- lightning: 2.1.0
- lightning-utilities: 0.9.0
- pytorch-lightning: 2.1.0
- pytorch-ranger: 0.1.1
- torch: 2.1.0
- torch-optimizer: 0.3.0
- torchmetrics: 1.0.3
- torchvision: 0.16.0

### More info

_No response_

cc @borda @awaelchli @carmocca @justusschock

Start at Fabric.save and the FSDP strategy's save_checkpoint entry point shown in the traceback, then inspect how the path reaches torch.save. Check the corresponding load path as well. Done should include saving and loading checkpoints through the reported S3-style path, with behavior verified by tests.

Written by the indexing model from the issue text.