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