DDPStrategy under windows is complaining about missing libuv

[benHeid]

Bug description

When using DDPStrategy with windows, the training fails and complains about that PyTorch is not built with libuv. While, I agree that this is not a main issue of PyTorch-lightning, I see the need that the parameters that could be used to set the old TCPStore should exposed to the users.

This is only affecting PyTorch 2.4.0 due to https://pytorch.org/tutorials/intermediate/TCPStore_libuv_backend.html

What version are you seeing the problem on?

v2.4

How to reproduce the bug

import pytorch_lightning as pl
import numpy as np
import torch
from torch.nn import MSELoss
from torch.optim import Adam
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn


class SimpleDataset(Dataset):
    def __init__(self):
        X = np.arange(10000)
        y = X * 2
        X = [[_] for _ in X]
        y = [[_] for _ in y]
        self.X = torch.Tensor(X)
        self.y = torch.Tensor(y)

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        return {"X": self.X[idx], "y": self.y[idx]}


class MyModel(pl.LightningModule):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(1, 1)
        self.criterion = MSELoss()

    def forward(self, inputs_id, labels=None):
        outputs = self.fc(inputs_id)
        loss = 0
        if labels is not None:
            loss = self.criterion(outputs, labels)
        return loss, outputs

    def train_dataloader(self):
        dataset = SimpleDataset()
        return DataLoader(dataset, batch_size=1000)

    def training_step(self, batch, batch_idx):
        input_ids = batch["X"]
        labels = batch["y"]
        loss, outputs = self(input_ids, labels)
        return {"loss": loss}

    def configure_optimizers(self):
        optimizer = Adam(self.parameters())
        return optimizer


if __name__ == '__main__':
    model = MyModel()
    trainer = pl.Trainer(
        max_epochs=1, 
        accelerator="cpu",
        strategy="ddp")
    trainer.fit(model)

    X = torch.Tensor([[1.0], [51.0], [89.0]])
    _, y = model(X)
    print(y)

Error messages and logs

    return TCPStore(
RuntimeError: use_libuv was requested but PyTorch was build without libuv support

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

Issue affecting for example CI runs in pytorch-forecasting: sktime/pytorch-forecasting#1623