Prepare a dataloader for distributed training. The dataloader will be wrapped by `torch.utils.data.DataLoader`
(
self,
dataset: Dataset,
batch_size: int,
shuffle: bool = False,
seed: int = 1024,
drop_last: bool = False,
pin_memory: bool = False,
num_workers: int = 0,
**kwargs,
)
| 75 | |
| 76 | @abstractmethod |
| 77 | def prepare_dataloader( |
| 78 | self, |
| 79 | dataset: Dataset, |
| 80 | batch_size: int, |
| 81 | shuffle: bool = False, |
| 82 | seed: int = 1024, |
| 83 | drop_last: bool = False, |
| 84 | pin_memory: bool = False, |
| 85 | num_workers: int = 0, |
| 86 | **kwargs, |
| 87 | ): |
| 88 | """Prepare a dataloader for distributed training. The dataloader will be wrapped by |
| 89 | `torch.utils.data.DataLoader` |
| 90 | """ |
no outgoing calls