Error while using distributed_backed = "ddp"
My code works perfectly fine with distributed_backend='dp', but fails when I use distributed_backend='ddp' with the following error:
Traceback (most recent call last):
  File "/scratch/nvarshn2/explore/test_ddp.py", line 89, in <module>
    trainer.fit(model, train_data, val_data)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 510, in fit
    results = self.accelerator_backend.train()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 158, in train
    results = self.ddp_train(process_idx=self.task_idx, model=model)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 307, in ddp_train
    results = self.train_or_test()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 74, in train_or_test
    results = self.trainer.train()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 561, in train
    self.train_loop.run_training_epoch()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 541, in run_training_epoch
    for batch_idx, (batch, is_last_batch) in train_dataloader:
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/profiler/profilers.py", line 85, in profile_iterable
    value = next(iterator)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 45, in _with_is_last
    it = iter(iterable)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 352, in __iter__
    return self._get_iterator()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 294, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 801, in __init__
    w.start()
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/multiprocessing/process.py", line 105, in start
    self._popen = self._Popen(self)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/multiprocessing/context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/multiprocessing/context.py", line 277, in _Popen
    return Popen(process_obj)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/home/nvarshn2/.conda/envs/pytorch_lightning_with_deepseed_env/lib/python3.6/multiprocessing/popen_fork.py", line 66, in _launch
    self.pid = os.fork()
OSError: [Errno 12] Cannot allocate memory

Code:
import os
import torch
from torch.utils.data import Dataset
from pytorch_lightning import LightningModule, Trainer

class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def step(self, x):
        x = self.layer(x)
        out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
        return out

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()

    def test_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"y": loss}

    def test_epoch_end(self, outputs) -> None:
        torch.stack([x["y"] for x in outputs]).mean()

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]

if __name__ == '__main__':
    train_data = torch.utils.data.DataLoader(RandomDataset(32, 64), num_workers=8)
    val_data = torch.utils.data.DataLoader(RandomDataset(32, 64), num_workers=8)
    model = BoringModel()
    trainer = Trainer(
        limit_train_batches=1,
        limit_val_batches=1,
        max_epochs=1,
        gpus=-1,
        distributed_backend="ddp",
    )
    trainer.fit(model, train_data, val_data)

Note: I am using 4 gpus and a single machine
What could be the reason behind this?