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dgl/docs/source/api/python/dgl.dataloading.rst

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.. _api-dataloading:
dgl.dataloading
=================================
.. currentmodule:: dgl.dataloading
The ``dgl.dataloading`` package provides two primitives to compose a data pipeline
for loading from graph data. ``Sampler`` represents algorithms
to generate subgraph samples from the original graph, and ``DataLoader``
represents the iterable over these samples.
DGL provides a number of built-in samplers that subclass :class:`~dgl.dataloading.Sampler`.
Creating new samplers follow the same paradigm. Read our user guide chapter
:ref:`guide-minibatch` for more examples and explanations.
The entire package only works for PyTorch backend.
DataLoaders
-----------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
DataLoader
GraphDataLoader
.. _api-dataloading-neighbor-sampling:
Samplers
--------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
Sampler
NeighborSampler
LaborSampler
MultiLayerFullNeighborSampler
ClusterGCNSampler
ShaDowKHopSampler
SAINTSampler
Sampler Transformations
-----------------------
.. autosummary::
:toctree: ../../generated/
as_edge_prediction_sampler
BlockSampler
.. _api-dataloading-negative-sampling:
Negative Samplers for Link Prediction
-------------------------------------
.. currentmodule:: dgl.dataloading.negative_sampler
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
Uniform
PerSourceUniform
GlobalUniform
Utility Class and Functions for Feature Prefetching
---------------------------------------------------
.. currentmodule:: dgl.dataloading.base
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
set_node_lazy_features
set_edge_lazy_features
set_src_lazy_features
set_dst_lazy_features
LazyFeature