dmlc--dgl
John Andrilla
a2b8d8e4e7
* Edit for readability * giant_graph_readme edit for grammar * NodeFlow and Sampling edit pass for grammar * Update tutorials/models/1_gnn/9_gat.py * Update tutorials/models/1_gnn/9_gat.py
19 行
882 B
Plaintext
19 行
882 B
Plaintext
.. _tutorials5-index:
|
|
|
|
|
|
Training on giant graphs
|
|
=============================
|
|
|
|
* **Sampling** `[paper] <https://arxiv.org/abs/1710.10568>`__ `[tutorial]
|
|
<5_giant_graph/1_sampling_mx.html>`__ `[MXNet code]
|
|
<https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__ `[Pytorch code]
|
|
<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
|
|
You can perform neighbor sampling and control-variate sampling to train a
|
|
graph convolution network and its variants on a giant graph.
|
|
* **Scale to giant graphs** `[tutorial] <5_giant_graph/2_giant.html>`__
|
|
`[MXNet code] <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__
|
|
`[Pytorch code]
|
|
<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
|
|
You can find two components (graph store and distributed sampler) to scale to
|
|
graphs with hundreds of millions of nodes.
|