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

conda env create -n cugraph_dgl_dev --file conda/environments/all_cuda-125_arch-x86_64.yaml

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(
cugraph_g,
train_idx,
sampler,
device=device,
batch_size=1024,
shuffle=True,
drop_last=False,
num_workers=0)

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Copyright (c) 2023, NVIDIA CORPORATION.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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