torch.utils.data

At the heart of PyTorch data loading utility is the torch.utils.data.DataLoader class. It represents a Python iterable over a dataset, with support for

• map-style and iterable-style datasets,

• customizing data loading order,

• automatic batching,

• single- and multi-process data loading,

• automatic memory pinning.

These options are configured by the constructor arguments of a DataLoader, which has signature:

DataLoader(dataset, batch_size=1, shuffle=False, sampler=None,
           batch_sampler=None, num_workers=0, collate_fn=None,
           pin_memory=False, drop_last=False, timeout=0,
           worker_init_fn=None)

The sections below describe in details the effects and usages of these options.

Dataset Types

The most important argument of DataLoader constructor is dataset, which indicates a dataset object to load data from. PyTorch supports two different types of datasets:

• map-style datasets,

• iterable-style datasets.

Map-style datasets

A map-style dataset is one that implements the __getitem__() and __len__() protocols, and represents a map from (possibly non-integral) indices/keys to data samples.

For example, such a dataset, when accessed with dataset[idx], could read the idx-th image and its corresponding label from a folder on the disk.

See Dataset for more details.

Iterable-style datasets

An iterable-style dataset is an instance of a subclass of IterableDataset that implements the __iter__() protocol, and represents an iterable over data samples. This type of datasets is particularly suitable for cases where random reads are expensive or even improbable, and where the batch size depends on the fetched data.

For example, such a dataset, when called iter(dataset), could return a stream of data reading from a database, a remote server, or even logs generated in real time.

See IterableDataset for more details.

Note

When using an IterableDataset with multi-process data loading. The same dataset object is replicated on each worker process, and thus the replicas must be configured differently to avoid duplicated data. See IterableDataset documentations for how to achieve this.

Data Loading Order and Sampler

For iterable-style datasets, data loading order is entirely controlled by the user-defined iterable. This allows easier implementations of chunk-reading and dynamic batch size (e.g., by yielding a batched sample at each time).

The rest of this section concerns the case with map-style datasets. torch.utils.data.Sampler classes are used to specify the sequence of indices/keys used in data loading. They represent iterable objects over the indices to datasets. E.g., in the common case with stochastic gradient decent (SGD), a Sampler could randomly permute a list of indices and yield each one at a time, or yield a small number of them for mini-batch SGD.

A sequential or shuffled sampler will be automatically constructed based on the shuffle argument to a DataLoader. Alternatively, users may use the sampler argument to specify a custom Sampler object that at each time yields the next index/key to fetch.

A custom Sampler that yields a list of batch indices at a time can be passed as the batch_sampler argument. Automatic batching can also be enabled via batch_size and drop_last arguments. See the next section for more details on this.

Note

Neither sampler nor batch_sampler is compatible with iterable-style datasets, since such datasets have no notion of a key or an index.

Loading Batched and Non-Batched Data

DataLoader supports automatically collating individual fetched data samples into batches via arguments batch_size, drop_last, and batch_sampler.

Automatic batching (default)

This is the most common case, and corresponds to fetching a minibatch of data and collating them into batched samples, i.e., containing Tensors with one dimension being the batch dimension (usually the first).

When batch_size (default 1) is not None, the data loader yields batched samples instead of individual samples. batch_size and drop_last arguments are used to specify how the data loader obtains batches of dataset keys. For map-style datasets, users can alternatively specify batch_sampler, which yields a list of keys at a time.

Note

The batch_size and drop_last arguments essentially are used to construct a batch_sampler from sampler. For map-style datasets, the sampler is either provided by user or constructed based on the shuffle argument. For iterable-style datasets, the sampler is a dummy infinite one. See this section on more details on samplers.

Note

When fetching from iterable-style datasets with multi-processing, the drop_last argument drops the last non-full batch of each worker’s dataset replica.

After fetching a list of samples using the indices from sampler, the function passed as the collate_fn argument is used to collate lists of samples into batches.

In this case, loading from a map-style dataset is roughly equivalent with:

for indices in batch_sampler:
    yield collate_fn([dataset[i] for i in indices])

and loading from an iterable-style dataset is roughly equivalent with:

dataset_iter = iter(dataset)
for indices in batch_sampler:
    yield collate_fn([next(dataset_iter) for _ in indices])

A custom collate_fn can be used to customize collation, e.g., padding sequential data to max length of a batch. See this section on more about collate_fn.

Disable automatic batching

In certain cases, users may want to handle batching manually in dataset code, or simply load individual samples. For example, it could be cheaper to directly load batched data (e.g., bulk reads from a database or reading continuous chunks of memory), or the batch size is data dependent, or the program is designed to work on individual samples. Under these scenarios, it’s likely better to not use automatic batching (where collate_fn is used to collate the samples), but let the data loader directly return each member of the dataset object.

When both batch_size and batch_sampler are None (default value for batch_sampler is already None), automatic batching is disabled. Each sample obtained from the dataset is processed with the function passed as the collate_fn argument.

When automatic batching is disabled, the default collate_fn simply converts NumPy arrays into PyTorch Tensors, and keeps everything else untouched.

In this case, loading from a map-style dataset is roughly equivalent with:

for index in sampler:
    yield collate_fn(dataset[index])

and loading from an iterable-style dataset is roughly equivalent with:

for data in iter(dataset):
    yield collate_fn(data)

See this section on more about collate_fn.

Working with collate_fn

The use of collate_fn is slightly different when automatic batching is enabled or disabled.

When automatic batching is disabled, collate_fn is called with each individual data sample, and the output is yielded from the data loader iterator. In this case, the default collate_fn simply converts NumPy arrays in PyTorch tensors.

When automatic batching is enabled, collate_fn is called with a list of data samples at each time. It is expected to collate the input samples into a batch for yielding from the data loader iterator. The rest of this section describes behavior of the default collate_fn in this case.

For instance, if each data sample consists of a 3-channel image and an integral class label, i.e., each element of the dataset returns a tuple (image, class_index), the default collate_fn collates a list of such tuples into a single tuple of a batched image tensor and a batched class label Tensor. In particular, the default collate_fn has the following properties:

• It always prepends a new dimension as the batch dimension.

• It automatically converts NumPy arrays and Python numerical values into PyTorch Tensors.

• It preserves the data structure, e.g., if each sample is a dictionary, it outputs a dictionary with the same set of keys but batched Tensors as values (or lists if the values can not be converted into Tensors). Same for list s, tuple s, namedtuple s, etc.

Users may use customized collate_fn to achieve custom batching, e.g., collating along a dimension other than the first, padding sequences of various lengths, or adding support for custom data types.

Single- and Multi-process Data Loading

A DataLoader uses single-process data loading by default.

Within a Python process, the Global Interpreter Lock (GIL) prevents true fully parallelizing Python code across threads. To avoid blocking computation code with data loading, PyTorch provides an easy switch to perform multi-process data loading by simply setting the argument num_workers to a positive integer.

Single-process data loading (default)

In this mode, data fetching is done in the same process a DataLoader is initialized. Therefore, data loading may block computing. However, this mode may be preferred when resource(s) used for sharing data among processes (e.g., shared memory, file descriptors) is limited, or when the entire dataset is small and can be loaded entirely in memory. Additionally, single-process loading often shows more readable error traces and thus is useful for debugging.

Multi-process data loading

Setting the argument num_workers as a positive integer will turn on multi-process data loading with the specified number of loader worker processes.

In this mode, each time an iterator of a DataLoader is created (e.g., when you call enumerate(dataloader)), num_workers worker processes are created. At this point, the dataset, collate_fn, and worker_init_fn are passed to each worker, where they are used to initialize, and fetch data. This means that dataset access together with its internal IO, transforms (including collate_fn) runs in the worker process.

torch.utils.data.get_worker_info() returns various useful information in a worker process (including the worker id, dataset replica, initial seed, etc.), and returns None in main process. Users may use this function in dataset code and/or worker_init_fn to individually configure each dataset replica, and to determine whether the code is running in a worker process. For example, this can be particularly helpful in sharding the dataset.

For map-style datasets, the main process generates the indices using sampler and sends them to the workers. So any shuffle randomization is done in the main process which guides loading by assigning indices to load.

For iterable-style datasets, since each worker process gets a replica of the dataset object, naive multi-process loading will often result in duplicated data. Using torch.utils.data.get_worker_info() and/or worker_init_fn, users may configure each replica independently. (See IterableDataset documentations for how to achieve this. ) For similar reasons, in multi-process loading, the drop_last argument drops the last non-full batch of each worker’s iterable-style dataset replica.

Workers are shut down once the end of the iteration is reached, or when the iterator becomes garbage collected.

Warning

It is generally not recommended to return CUDA tensors in multi-process loading because of many subtleties in using CUDA and sharing CUDA tensors in multiprocessing (see CUDA in multiprocessing). Instead, we recommend using automatic memory pinning (i.e., setting pin_memory=True), which enables fast data transfer to CUDA-enabled GPUs.

Platform-specific behaviors

Since workers rely on Python multiprocessing, worker launch behavior is different on Windows compared to Unix.

• On Unix, fork() is the default multiprocessing start method. Using fork(), child workers typically can access the dataset and Python argument functions directly through the cloned address space.

• On Windows, spawn() is the default multiprocessing start method. Using spawn(), another interpreter is launched which runs your main script, followed by the internal worker function that receives the dataset, collate_fn and other arguments through pickle serialization.

This separate serialization means that you should take two steps to ensure you are compatible with Windows while using multi-process data loading:

• Wrap most of you main script’s code within if __name__ == '__main__': block, to make sure it doesn’t run again (most likely generating error) when each worker process is launched. You can place your dataset and DataLoader instance creation logic here, as it doesn’t need to be re-executed in workers.

• Make sure that any custom collate_fn, worker_init_fn or dataset code is declared as top level definitions, outside of the __main__ check. This ensures that they are available in worker processes. (this is needed since functions are pickled as references only, not bytecode.)

Randomness in multi-process data loading

By default, each worker will have its PyTorch seed set to base_seed + worker_id, where base_seed is a long generated by main process using its RNG (thereby, consuming a RNG state mandatorily). However, seeds for other libraries may be duplicated upon initializing workers (e.g., NumPy), causing each worker to return identical random numbers. (See this section in FAQ.).

In worker_init_fn, you may access the PyTorch seed set for each worker with either torch.utils.data.get_worker_info().seed or torch.initial_seed(), and use it to seed other libraries before data loading.

Memory Pinning

Host to GPU copies are much faster when they originate from pinned (page-locked) memory. See Use pinned memory buffers for more details on when and how to use pinned memory generally.

For data loading, passing pin_memory=True to a DataLoader will automatically put the fetched data Tensors in pinned memory, and thus enables faster data transfer to CUDA-enabled GPUs.

The default memory pinning logic only recognizes Tensors and maps and iterables containing Tensors. By default, if the pinning logic sees a batch that is a custom type (which will occur if you have a collate_fn that returns a custom batch type), or if each element of your batch is a custom type, the pinning logic will not recognize them, and it will return that batch (or those elements) without pinning the memory. To enable memory pinning for custom batch or data type(s), define a pin_memory() method on your custom type(s).

See the example below.

Example:

class SimpleCustomBatch:
    def __init__(self, data):
        transposed_data = list(zip(*data))
        self.inp = torch.stack(transposed_data[0], 0)
        self.tgt = torch.stack(transposed_data[1], 0)

    # custom memory pinning method on custom type
    def pin_memory(self):
        self.inp = self.inp.pin_memory()
        self.tgt = self.tgt.pin_memory()
        return self

def collate_wrapper(batch):
    return SimpleCustomBatch(batch)

inps = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
tgts = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
dataset = TensorDataset(inps, tgts)

loader = DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper,
                    pin_memory=True)

for batch_ndx, sample in enumerate(loader):
    print(sample.inp.is_pinned())
    print(sample.tgt.is_pinned())

class torch.utils.data.DataLoader(dataset: torch.utils.data.dataset.Dataset[+T_co][T_co], batch_size: Optional[int] = 1, shuffle: bool = False, sampler: Optional[torch.utils.data.sampler.Sampler[int][int]] = None, batch_sampler: Optional[torch.utils.data.sampler.Sampler[typing.Sequence[int]][Sequence[int]]] = None, num_workers: int = 0, collate_fn: Callable[[List[T]], Any] = None, pin_memory: bool = False, drop_last: bool = False, timeout: float = 0, worker_init_fn: Callable[[int], None] = None, multiprocessing_context=None, generator=None)

Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset.

The DataLoader supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching (collation) and memory pinning.

See torch.utils.data documentation page for more details.

Parameters

• dataset (Dataset) – dataset from which to load the data.

• batch_size (int, optional) – how many samples per batch to load (default: 1).

• shuffle (bool, optional) – set to True to have the data reshuffled at every epoch (default: False).

• sampler (Sampler or Iterable, optional) – defines the strategy to draw samples from the dataset. Can be any Iterable with __len__ implemented. If specified, shuffle must not be specified.

• batch_sampler (Sampler or Iterable, optional) – like sampler, but returns a batch of indices at a time. Mutually exclusive with batch_size, shuffle, sampler, and drop_last.

• num_workers (int, optional) – how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0)

• collate_fn (callable, optional) – merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset.

• pin_memory (bool, optional) – If True, the data loader will copy Tensors into CUDA pinned memory before returning them. If your data elements are a custom type, or your collate_fn returns a batch that is a custom type, see the example below.

• drop_last (bool, optional) – set to True to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If False and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: False)

• timeout (numeric, optional) – if positive, the timeout value for collecting a batch from workers. Should always be non-negative. (default: 0)

• worker_init_fn (callable, optional) – If not None, this will be called on each worker subprocess with the worker id (an int in [0, num_workers - 1]) as input, after seeding and before data loading. (default: None)

Warning

If the spawn start method is used, worker_init_fn cannot be an unpicklable object, e.g., a lambda function. See Multiprocessing best practices on more details related to multiprocessing in PyTorch.

Note

len(dataloader) heuristic is based on the length of the sampler used. When dataset is an IterableDataset, len(dataset) (if implemented) is returned instead, regardless of multi-process loading configurations, because PyTorch trust user dataset code in correctly handling multi-process loading to avoid duplicate data. See Dataset Types for more details on these two types of datasets and how IterableDataset interacts with Multi-process data loading.

class torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader.

Note

DataLoader by default constructs a index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

class torch.utils.data.IterableDataset

An iterable Dataset.

All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream.

All subclasses should overwrite __iter__(), which would return an iterator of samples in this dataset.

When a subclass is used with DataLoader, each item in the dataset will be yielded from the DataLoader iterator. When num_workers > 0, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. get_worker_info(), when called in a worker process, returns information about the worker. It can be used in either the dataset’s __iter__() method or the DataLoader ‘s worker_init_fn option to modify each copy’s behavior.

Example 1: splitting workload across all workers in __iter__():

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         worker_info = torch.utils.data.get_worker_info()
...         if worker_info is None:  # single-process data loading, return the full iterator
...             iter_start = self.start
...             iter_end = self.end
...         else:  # in a worker process
...             # split workload
...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
...             worker_id = worker_info.id
...             iter_start = self.start + worker_id * per_worker
...             iter_end = min(iter_start + per_worker, self.end)
...         return iter(range(iter_start, iter_end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]

>>> # Mult-process loading with two worker processes
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
[3, 4, 5, 6]

Example 2: splitting workload across all workers using worker_init_fn:

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         return iter(range(self.start, self.end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>>
>>> # Directly doing multi-process loading yields duplicate data
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 3, 4, 4, 5, 5, 6, 6]

>>> # Define a `worker_init_fn` that configures each dataset copy differently
>>> def worker_init_fn(worker_id):
...     worker_info = torch.utils.data.get_worker_info()
...     dataset = worker_info.dataset  # the dataset copy in this worker process
...     overall_start = dataset.start
...     overall_end = dataset.end
...     # configure the dataset to only process the split workload
...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
...     worker_id = worker_info.id
...     dataset.start = overall_start + worker_id * per_worker
...     dataset.end = min(dataset.start + per_worker, overall_end)
...

>>> # Mult-process loading with the custom `worker_init_fn`
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]

class torch.utils.data.TensorDataset(*tensors: torch.Tensor)

Dataset wrapping tensors.

Each sample will be retrieved by indexing tensors along the first dimension.

Parameters

*tensors (Tensor) – tensors that have the same size of the first dimension.

class torch.utils.data.ConcatDataset(datasets: Iterable[torch.utils.data.dataset.Dataset])

Dataset as a concatenation of multiple datasets.

This class is useful to assemble different existing datasets.

Parameters

datasets (sequence) – List of datasets to be concatenated

class torch.utils.data.ChainDataset(datasets: Iterable[torch.utils.data.dataset.Dataset])

Dataset for chainning multiple IterableDataset s.

This class is useful to assemble different existing dataset streams. The chainning operation is done on-the-fly, so concatenating large-scale datasets with this class will be efficient.

Parameters

datasets (iterable of IterableDataset) – datasets to be chained together

class torch.utils.data.Subset(dataset: torch.utils.data.dataset.Dataset[+T_co][T_co], indices: Sequence[int])

Subset of a dataset at specified indices.

Parameters

• dataset (Dataset) – The whole Dataset

• indices (sequence) – Indices in the whole set selected for subset

torch.utils.data.get_worker_info()

Returns the information about the current DataLoader iterator worker process.

When called in a worker, this returns an object guaranteed to have the following attributes:

• id: the current worker id.

• num_workers: the total number of workers.

• seed: the random seed set for the current worker. This value is determined by main process RNG and the worker id. See DataLoader’s documentation for more details.

• dataset: the copy of the dataset object in this process. Note that this will be a different object in a different process than the one in the main process.

When called in the main process, this returns None.

Note

When used in a worker_init_fn passed over to DataLoader, this method can be useful to set up each worker process differently, for instance, using worker_id to configure the dataset object to only read a specific fraction of a sharded dataset, or use seed to seed other libraries used in dataset code (e.g., NumPy).

torch.utils.data.random_split(dataset: torch.utils.data.dataset.Dataset[~T][T], lengths: Sequence[int], generator: Optional[torch._C.Generator] = <torch._C.Generator object>) → List[torch.utils.data.dataset.Subset[~T][T]]

Randomly split a dataset into non-overlapping new datasets of given lengths. Optionally fix the generator for reproducible results, e.g.:

>>> random_split(range(10), [3, 7], generator=torch.Generator().manual_seed(42))

Parameters

• dataset (Dataset) – Dataset to be split

• lengths (sequence) – lengths of splits to be produced

• generator (Generator) – Generator used for the random permutation.

class torch.utils.data.Sampler(data_source: Optional[Sized])

Base class for all Samplers.

Every Sampler subclass has to provide an __iter__() method, providing a way to iterate over indices of dataset elements, and a __len__() method that returns the length of the returned iterators.

Note

The __len__() method isn’t strictly required by DataLoader, but is expected in any calculation involving the length of a DataLoader.

class torch.utils.data.SequentialSampler(data_source)

Samples elements sequentially, always in the same order.

Parameters

data_source (Dataset) – dataset to sample from

class torch.utils.data.RandomSampler(data_source: Sized, replacement: bool = False, num_samples: Optional[int] = None, generator=None)

Samples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify num_samples to draw.

Parameters

• data_source (Dataset) – dataset to sample from

• replacement (bool) – samples are drawn with replacement if True, default=``False``

• num_samples (int) – number of samples to draw, default=`len(dataset)`. This argument is supposed to be specified only when replacement is True.

• generator (Generator) – Generator used in sampling.

class torch.utils.data.SubsetRandomSampler(indices: Sequence[int], generator=None)

Samples elements randomly from a given list of indices, without replacement.

Parameters

• indices (sequence) – a sequence of indices

• generator (Generator) – Generator used in sampling.

class torch.utils.data.WeightedRandomSampler(weights: Sequence[float], num_samples: int, replacement: bool = True, generator=None)

Samples elements from [0,..,len(weights)-1] with given probabilities (weights).

Parameters

• weights (sequence) – a sequence of weights, not necessary summing up to one

• num_samples (int) – number of samples to draw

• replacement (bool) – if True, samples are drawn with replacement. If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row.

• generator (Generator) – Generator used in sampling.

Example

>>> list(WeightedRandomSampler([0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True))
[4, 4, 1, 4, 5]
>>> list(WeightedRandomSampler([0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False))
[0, 1, 4, 3, 2]

class torch.utils.data.BatchSampler(sampler: torch.utils.data.sampler.Sampler[int][int], batch_size: int, drop_last: bool)

Wraps another sampler to yield a mini-batch of indices.

Parameters

• sampler (Sampler or Iterable) – Base sampler. Can be any iterable object

• batch_size (int) – Size of mini-batch.

• drop_last (bool) – If True, the sampler will drop the last batch if its size would be less than batch_size

Example

>>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
>>> list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]

class torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=None, rank=None, shuffle=True, seed=0)

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

It is especially useful in conjunction with torch.nn.parallel.DistributedDataParallel. In such a 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.

Parameters

• dataset – Dataset used for sampling.

• num_replicas (int, optional) – Number of processes participating in distributed training. By default, rank is retrieved from the current distributed group.

• rank (int, optional) – Rank of the current process within num_replicas. By default, rank is retrieved from the current distributed group.

• shuffle (bool, optional) – If True (default), sampler will shuffle the indices.

• seed (int, optional) – random seed used to shuffle the sampler if shuffle=True. This number should be identical across all processes in the distributed group. Default: 0.

Warning

In distributed mode, calling the set_epoch() method at the beginning of each epoch before creating the DataLoader iterator is necessary to make shuffling work properly across multiple epochs. Otherwise, the same ordering will be always used.

Example:

>>> sampler = DistributedSampler(dataset) if is_distributed else None
>>> loader = DataLoader(dataset, shuffle=(sampler is None),
...                     sampler=sampler)
>>> for epoch in range(start_epoch, n_epochs):
...     if is_distributed:
...         sampler.set_epoch(epoch)
...     train(loader)