mindspore.dataset.DistributedSampler

class mindspore.dataset.DistributedSampler(num_shards, shard_id, shuffle=True, num_samples=None, offset=- 1)

A sampler that accesses a shard of the dataset.

Parameters

• num_shards (int) – Number of shards to divide the dataset into.

• shard_id (int) – Shard ID of the current shard within num_shards.

• shuffle (bool, optional) – If True, the indices are shuffled (default=True).

• num_samples (int, optional) – The number of samples to draw (default=None, all elements).

• offset (int, optional) – The starting shard ID where the elements in the dataset are sent to (default=-1), which should be no more than num_shards.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset_dir = "path/to/imagefolder_directory"
>>>
>>> # creates a distributed sampler with 10 shards in total. This shard is shard 5.
>>> sampler = ds.DistributedSampler(10, 5)
>>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler)

Raises

• ValueError – If num_shards is not positive.

• ValueError – If shard_id is smaller than 0 or equal to num_shards or larger than num_shards.

• ValueError – If shuffle is not a boolean value.

• ValueError – If offset is greater than num_shards.

get_num_samples()

All samplers can contain a numeric num_samples value (or it can be set to None). A child sampler can exist or be None. If a child sampler exists, then the child sampler count can be a numeric value or None. These conditions impact the resultant sampler count that is used. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns

int, the number of samples, or None