Dataset.iter_batches(*, prefetch_blocks: int = 0, batch_size: Optional[int] = 256, batch_format: str = 'default', drop_last: bool = False, local_shuffle_buffer_size: Optional[int] = None, local_shuffle_seed: Optional[int] = None) Iterator[Union[List[ray.data.block.T], pyarrow.Table, pandas.DataFrame, bytes, numpy.ndarray, Dict[str, numpy.ndarray]]][source]#

Return a local batched iterator over the dataset.


This operation will trigger execution of the lazy transformations performed on this dataset, and will block until execution completes.


>>> import ray
>>> for batch in ray.data.range(1000000).iter_batches(): 
...     print(batch) 

Time complexity: O(1)

  • prefetch_blocks – The number of blocks to prefetch ahead of the current block during the scan.

  • batch_size – The number of rows in each batch, or None to use entire blocks as batches (blocks may contain different number of rows). The final batch may include fewer than batch_size rows if drop_last is False. Defaults to 256.

  • batch_format – The format in which to return each batch. Specify “default” to use the default block format (promoting tables to Pandas and tensors to NumPy), “pandas” to select pandas.DataFrame, “pyarrow” to select pyarrow.Table, or “numpy” to select numpy.ndarray for tensor datasets and Dict[str, numpy.ndarray] for tabular datasets. Default is “default”.

  • drop_last – Whether to drop the last batch if it’s incomplete.

  • local_shuffle_buffer_size – If non-None, the data will be randomly shuffled using a local in-memory shuffle buffer, and this value will serve as the minimum number of rows that must be in the local in-memory shuffle buffer in order to yield a batch. When there are no more rows to add to the buffer, the remaining rows in the buffer will be drained.

  • local_shuffle_seed – The seed to use for the local random shuffle.


An iterator over record batches.