torch.utils.data.distributed.DistributedSampler Class Reference

Inheritance diagram for torch.utils.data.distributed.DistributedSampler:

Public Member Functions
def	__init__ (self, dataset, num_replicas=None, rank=None)
def	__iter__ (self)
def	__len__ (self)
def	set_epoch (self, epoch)

Public Attributes
	dataset
	num_replicas
	rank
	epoch
	num_samples
	total_size

Detailed Description

Sampler that restricts data loading to a subset of the dataset.

It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.

.. note::
    Dataset is assumed to be of constant size.

Arguments:
    dataset: Dataset used for sampling.
    num_replicas (optional): Number of processes participating in
        distributed training.
    rank (optional): Rank of the current process within num_replicas.

Definition at line 7 of file distributed.py.

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The documentation for this class was generated from the following file:

• torch/utils/data/distributed.py