ray.data.Dataset.repeat#

Dataset.repeat(times: Optional[int] = None) DatasetPipeline[T][source]#

Convert this into a DatasetPipeline by looping over this dataset.

Transformations prior to the call to repeat() are evaluated once. Transformations done on the returned pipeline are evaluated on each loop of the pipeline over the base dataset.

Note that every repeat of the dataset is considered an “epoch” for the purposes of DatasetPipeline.iter_epochs().

Examples

>>> import ray
>>> # Infinite pipeline of numbers [0, 5)
>>> ray.data.range(5).repeat().take()
[0, 1, 2, 3, 4, 0, 1, 2, 3, 4, ...]
>>> # Can apply transformations to the pipeline.
>>> ray.data.range(5).repeat().map(lambda x: -x).take()
[0, -1, -2, -3, -4, 0, -1, -2, -3, -4, ...]
>>> # Can shuffle each epoch (dataset) in the pipeline.
>>> ray.data.range(5).repeat().random_shuffle().take() 
[2, 3, 0, 4, 1, 4, 0, 2, 1, 3, ...]
Parameters

times – The number of times to loop over this dataset, or None to repeat indefinitely.