cugraph-dgl-cu12

Name	cugraph-dgl-cu12 JSON
Version	24.12.0 JSON
	download
home_page	None
Summary	cugraph extensions for DGL
upload_time	2024-12-12 22:26:10
maintainer	None
docs_url	None
author	NVIDIA Corporation
requires_python	>=3.10
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:

```
# 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
```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)
```

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "cugraph-dgl-cu12",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.10",
    "maintainer_email": null,
    "keywords": null,
    "author": "NVIDIA Corporation",
    "author_email": null,
    "download_url": "https://files.pythonhosted.org/packages/0c/23/f0ede509a90cb95a87e3690b942a6c900efc338d612feea6380b9f9bde21/cugraph_dgl_cu12-24.12.0.tar.gz",
    "platform": null,
    "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```\n# CUDA 11\nconda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th23_cu118 cugraph-dgl\n\n# CUDA 12\nconda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th23_cu121 cugraph-dgl\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",
    "bugtrack_url": null,
    "license": "Apache 2.0",
    "summary": "cugraph extensions for DGL",
    "version": "24.12.0",
    "project_urls": {
        "Documentation": "https://docs.rapids.ai/api/cugraph/stable/",
        "Homepage": "https://github.com/rapidsai/cugraph"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "0c23f0ede509a90cb95a87e3690b942a6c900efc338d612feea6380b9f9bde21",
                "md5": "e123aba952e6f320f9bf72402debaeda",
                "sha256": "e0a06158cbe8b962a728ce6c7b1efc2ab9ff5472a5bbd629b70da9b57628a870"
            },
            "downloads": -1,
            "filename": "cugraph_dgl_cu12-24.12.0.tar.gz",
            "has_sig": false,
            "md5_digest": "e123aba952e6f320f9bf72402debaeda",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10",
            "size": 1486,
            "upload_time": "2024-12-12T22:26:10",
            "upload_time_iso_8601": "2024-12-12T22:26:10.751503Z",
            "url": "https://files.pythonhosted.org/packages/0c/23/f0ede509a90cb95a87e3690b942a6c900efc338d612feea6380b9f9bde21/cugraph_dgl_cu12-24.12.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-12-12 22:26:10",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "rapidsai",
    "github_project": "cugraph",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "cugraph-dgl-cu12"
}