dmlc--dgl
Quan (Andy) Gan
701b4fccc2
* initial update * more * more * multi-gpu example * cluster gcn, finalize homogeneous * more explanation * fix * bunch of fixes * fix * RGAT example and more fixes * shadow-gnn sampler and some changes in unit test * fix * wth * more fixes * remove shadow+node/edge dataloader tests for possible ux changes * lints * add legacy dataloading import just in case * fix * update pylint for f-strings * fix * lint * lint * lint again * cherry-picking commit fa9f494 * oops * fix * add sample_neighbors in dist_graph * fix * lint * fix * fix * fix * fix tutorial * fix * fix * fix * fix warning * remove debug * add get_foo_storage apis * lint
29 行
881 B
Python
29 行
881 B
Python
"""The ``dgl.dataloading`` package contains:
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* Data loader classes for iterating over a set of nodes or edges in a graph and generates
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computation dependency via neighborhood sampling methods.
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* Various sampler classes that perform neighborhood sampling for multi-layer GNNs.
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* Negative samplers for link prediction.
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For a holistic explanation on how different components work together.
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Read the user guide :ref:`guide-minibatch`.
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.. note::
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This package is experimental and the interfaces may be subject
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to changes in future releases. It currently only has implementations in PyTorch.
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"""
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from .neighbor import *
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from .dataloader import *
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from .cluster_gcn import *
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from .shadow import *
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from . import negative_sampler
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from .async_transferer import AsyncTransferer
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from .. import backend as F
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if F.get_preferred_backend() == 'pytorch':
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from .pytorch import *
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