+ QV-jR^RIHt^RIHtHt^RIHtHt]!R]]4tRRlt R#))TypeVar)Dataset _split_by_node_map_style_dataset)IterableDataset_split_by_node_iterable_dataset DatasetTypecHV^8dQhR\R\R\R\/#)datasetrank world_sizereturn)rint)formats"e/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/datasets/distributed.py __annotate__r s.!Z!Z;!Zc!Zs!Z{!Zcb\V\4'd\WVR7#\WVR7#)a Split a dataset for the node at rank `rank` in a pool of nodes of size `world_size`. For map-style datasets: Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset. To maximize data loading throughput, chunks are made of contiguous data on disk if possible. For iterable datasets: If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.num_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples. > [!WARNING] > If you shuffle your iterable dataset in a distributed setup, make sure to set a fixed `seed` in [`IterableDataset.shuffle`] so the same shuffled list of shards is used on every node to know which shards the node should skip. Args: dataset ([`Dataset`] or [`IterableDataset`]): The dataset to split by node. rank (`int`): Rank of the current node. world_size (`int`): Total number of nodes. Returns: [`Dataset`] or [`IterableDataset`]: The dataset to be used on the node at rank `rank`. )r r ) isinstancerrr)r r r s&&&rsplit_dataset_by_noder s+<'7##/zZZ.wjYYrN) typingr arrow_datasetrriterable_datasetrrrrrrrs$DNmWo> !Zr