semvec/pypi-packages huggingface dataset:
pylibwholegraph-cu11 25.6.0
pylibwholegraph - GPU Graph Storage for GNN feature and graph structure
License: Apache-2.0
Requires Python: >=3.10

# pylibwholegraph

WholeGraph supports PyTorch and provides a distributed graph and kv store.

Both cuGraph-DGL and cuGraph-PyG can leverage WholeGraph for even greater scalability.
cugraph-dgl-cu11 25.6.0
cugraph extensions for DGL
License: Apache-2.0
Requires Python: >=3.10

# 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/th24_cu118 cugraph-dgl

# CUDA 12
conda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th24_cu124 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)
```
cugraph-dgl-cu12 25.6.0
cugraph extensions for DGL
License: Apache-2.0
Requires Python: >=3.10

# 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/th24_cu118 cugraph-dgl

# CUDA 12
conda install -c rapidsai -c pytorch -c conda-forge -c nvidia -c dglteam/label/th24_cu124 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)
```
cugraph-pyg-cu11 25.6.0
cugraph-pyg - PyG support for cuGraph massive-scale, ultra-fast GPU graph analytics.
License: Apache-2.0
Requires Python: >=3.10

# cugraph-pyg

## Description

cuGraph-PyG supports PyTorch Geometric (PyG) and implements PyG's GraphStore, FeatureStore, Loader, and Sampler interfaces.