composer.datasets

DataloaderHparams contains the torch.utils.data.dataloader settings that are common across both training and eval datasets:

• num_workers

• prefetch_factor

• persistent_workers

• pin_memory

• timeout

Each DatasetHparams is then reponsible for returning a DataloaderSpec, which is a namedtuple of dataset-specific settings such as:

• dataset

• drop_last

• shuffle

• collate_fn

This indirection (instead of directly creating the dataloader at the start) is needed because for multi-GPU training, dataloaders require the global rank to initialize their torch.utils.data.distributed.DistributedSampler.

As a result, our trainer uses the DataloaderSpec and DataloaderHparams to create the dataloaders after DDP has forked the processes.

Base Classes and Hyperparameters

DataloaderHparams
DataloaderSpec
DatasetHparams

Datasets

MNISTDatasetHparams
CIFAR10DatasetHparams
ImagenetDatasetHparams
LMDatasetHparams	Defines a generic dataset class for autoregressive language models.
SyntheticDatasetHparams
BratsDatasetHparams