cugraph-dgl-cu11

Name	cugraph-dgl-cu11 JSON
Version	24.8.0 JSON
	download
home_page	None
Summary	cugraph extensions for DGL
upload_time	2024-08-08 16:09:48
maintainer	None
docs_url	None
author	NVIDIA Corporation
requires_python	>=3.9
license	Apache 2.0
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # cugraph_dgl

## Description

[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.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:

```
conda install mamba -n base -c conda-forge
mamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam
```

## 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
```diff

+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)
```

            

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    "description": "# cugraph_dgl\n\n## Description\n\n[RAPIDS](https://rapids.ai) cugraph_dgl provides a duck-typed version of the [DGLGraph](https://docs.dgl.ai/api/python/dgl.DGLGraph.html#dgl.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.\n\n## Conda\n\nInstall and update cugraph-dgl and the required dependencies using the command:\n\n```\nconda install mamba -n base -c conda-forge\nmamba install cugraph-dgl -c rapidsai-nightly -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam\n```\n\n## Build from Source\n\n### Create the conda development environment\n```\nmamba env create -n cugraph_dgl_dev --file conda/cugraph_dgl_dev_11.6.yml\n```\n\n### Install  in editable mode\n```\npip install -e .\n```\n\n### Run tests\n\n```\npytest tests/*\n```\n\n\n## Usage\n```diff\n\n+from cugraph_dgl.convert import cugraph_storage_from_heterograph\n+cugraph_g = cugraph_storage_from_heterograph(dgl_g)\n\nsampler = dgl.dataloading.NeighborSampler(\n        [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])\n\ntrain_dataloader = dgl.dataloading.DataLoader(\n- dgl_g,\n+ cugraph_g,\ntrain_idx,\nsampler,\ndevice=device,\nbatch_size=1024,\nshuffle=True,\ndrop_last=False,\nnum_workers=0)\n```\n",
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