cugraph extensions for DGL
Project description
cugraph_dgl
Description
RAPIDS cugraph_dgl provides a duck-typed version of the DGLGraph class, which uses cugraph for storing graph structure and node/edge feature data. Using cugraph as the backend allows DGL users to access a collection of GPU accelerated algorithms for graph analytics, such as centrality computation and community detection.
Conda
Install and update cugraph-dgl and the required dependencies using the command:
# CUDA 11
conda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th23_cu118 cugraph-dgl
# CUDA 12
conda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th23_cu121 cugraph-dgl
Build from Source
Create the conda development environment
mamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml
Install in editable mode
pip install -e .
Run tests
pytest tests/*
Usage
+from cugraph_dgl.convert import cugraph_storage_from_heterograph
+cugraph_g = cugraph_storage_from_heterograph(dgl_g)
sampler = dgl.dataloading.NeighborSampler(
[15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])
train_dataloader = dgl.dataloading.DataLoader(
- dgl_g,
+ cugraph_g,
train_idx,
sampler,
device=device,
batch_size=1024,
shuffle=True,
drop_last=False,
num_workers=0)
Project details
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