pip install

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)