cerebras.modelzoo.data.nlp.gpt.DummyIterableDataProcessor#
Pytorch Generic Iterable Dataloader
Classes
A Generic PyTorch Data Processor. :param dict params: dict containing training input parameters for creating dataset. Expects the following fields: - "batch_size" (int): Batch size. - "shuffle" (bool): Flag to enable data shuffling. - "shuffle_seed" (int): Shuffle seed. - "shuffle_buffer" (int): Size of shuffle buffer in samples. - "num_workers" (int): How many subprocesses to use for data loading. - "drop_last" (bool): If True and the dataset size is not divisible by the batch size, the last incomplete batch will be dropped. - "prefetch_factor" (int): Number of batches loaded in advance by each worker. - "persistent_workers" (bool): If True, the data loader will not shutdown the worker processes after a dataset has been consumed once. |
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A Dummy iterable torch.utils.data.IterableDataset. |
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A Generic PyTorch Data Processor. :param dict params: dict containing training input parameters for creating dataset. Expects the following fields: - "batch_size" (int): Batch size. - "shuffle" (bool): Flag to enable data shuffling. - "shuffle_seed" (int): Shuffle seed. - "shuffle_buffer" (int): Size of shuffle buffer in samples. - "num_workers" (int): How many subprocesses to use for data loading. - "drop_last" (bool): If True and the dataset size is not divisible by the batch size, the last incomplete batch will be dropped. - "prefetch_factor" (int): Number of batches loaded in advance by each worker. - "persistent_workers" (bool): If True, the data loader will not shutdown the worker processes after a dataset has been consumed once. |
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A Dummy iterable torch.utils.data.IterableDataset. |