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modify automatic batching doc

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This commit is contained in:
Dario Coscia
2025-03-17 12:29:40 +01:00
committed by FilippoOlivo
parent ea21754d53
commit 01ea27d405
2 changed files with 19 additions and 2 deletions
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11
pina/data/data_module.py
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@@ -81,7 +81,16 @@ class Collator:
:param dict max_conditions_lengths: ``dict`` containing the maximum
number of data points to consider in a single batch for
each condition.
:param bool automatic_batching: Whether to enable automatic batching.
:param bool automatic_batching: Whether to enable automatic batching.
If ``True``, automatic PyTorch batching
is performed, which consists of extracting one element at a time
from the dataset and collating them into a batch. This is useful
when the dataset is too large to fit into memory. On the other hand,
if ``False``, the items are retrieved from the dataset all at once
avoind the overhead of collating them into a batch and reducing the
__getitem__ calls to the dataset. This is useful when the dataset
fits into memory. Avoid using automatic batching when ``batch_size``
is large. Default is ``False``.
:param PinaDataset dataset: The dataset where the data is stored.
"""

10
pina/trainer.py
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@@ -170,7 +170,15 @@ class Trainer(lightning.pytorch.Trainer):
validation dataset.
:param int batch_size: The number of samples per batch to load.
:param bool automatic_batching: Whether to perform automatic batching
with PyTorch.
with PyTorch. If ``True``, automatic PyTorch batching
is performed, which consists of extracting one element at a time
from the dataset and collating them into a batch. This is useful
when the dataset is too large to fit into memory. On the other hand,
if ``False``, the items are retrieved from the dataset all at once
avoind the overhead of collating them into a batch and reducing the
__getitem__ calls to the dataset. This is useful when the dataset
fits into memory. Avoid using automatic batching when ``batch_size``
is large. Default is ``False``.
:param bool pin_memory: Whether to use pinned memory for faster data
transfer to GPU.
:param int num_workers: The number of worker threads for data loading.