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[cherry-pick] sharing -> sharding (#1787) #1788

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Jun 14, 2022
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[cherry-pick] sharing -> sharding (#1787) #1788

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2 changes: 1 addition & 1 deletion docs/source/datasets.rst
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Expand Up @@ -42,7 +42,7 @@ torchtext.datasets
- All workers (DDP workers *and* DataLoader workers) see a different part
of the data. The datasets are already wrapped inside `ShardingFilter
<https://pytorch.org/data/main/generated/torchdata.datapipes.iter.ShardingFilter.html>`_
and you may need to call ``dp.apply_sharing(num_shards, shard_id)`` in order to shard the
and you may need to call ``dp.apply_sharding(num_shards, shard_id)`` in order to shard the
data across ranks (DDP workers) and DataLoader workers. One way to do this
is to create ``worker_init_fn`` that calls ``apply_sharding`` with appropriate
number of shards (DDP workers * DataLoader workers) and shard id (inferred through rank
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