| Name | cugraph-cu11 JSON |
| Version |
24.8.0
JSON |
| download |
| home_page | None |
| Summary | cuGraph - RAPIDS GPU Graph Analytics |
| upload_time | 2024-08-08 15:46:59 |
| 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.
|
<h1 align="center"; style="font-style: italic";>
<br>
<img src="img/cugraph_logo_2.png" alt="cuGraph" width="500">
</h1>
<div align="center">
<a href="https://github.com/rapidsai/cugraph/blob/main/LICENSE">
<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License"></a>
<img alt="GitHub tag (latest by date)" src="https://img.shields.io/github/v/tag/rapidsai/cugraph">
<a href="https://github.com/rapidsai/cugraph/stargazers">
<img src="https://img.shields.io/github/stars/rapidsai/cugraph"></a>
<img alt="Conda" src="https://img.shields.io/conda/dn/rapidsai/cugraph">
<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/rapidsai/cugraph">
<img alt="Conda" src="https://img.shields.io/conda/pn/rapidsai/cugraph" />
<a href="https://rapids.ai/"><img src="img/rapids_logo.png" alt="RAPIDS" width="125"></a>
</div>
<br>
[RAPIDS](https://rapids.ai) cuGraph is a monorepo that represents a collection of packages focused on GPU-accelerated graph analytics, including support for property graphs, remote (graph as a service) operations, and graph neural networks (GNNs). cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.
<div align="center">
[Getting cuGraph](./docs/cugraph/source/installation/getting_cugraph.md) *
[Graph Algorithms](./docs/cugraph/source/graph_support/algorithms.md) *
[Graph Service](./readme_pages/cugraph_service.md) *
[Property Graph](./readme_pages/property_graph.md) *
[GNN Support](./readme_pages/gnn_support.md)
</div>
-----
## News
___NEW!___ _[nx-cugraph](./python/nx-cugraph/README.md)_, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change.
```
> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
> export NETWORKX_AUTOMATIC_BACKENDS=cugraph
```
That's it. NetworkX now leverages cuGraph for accelerated graph algorithms.
-----
## Table of contents
- Installation
- [Getting cuGraph Packages](./docs/cugraph/source/installation/getting_cugraph.md)
- [Building from Source](./docs/cugraph/source/installation/source_build.md)
- [Contributing to cuGraph](./readme_pages/CONTRIBUTING.md)
- General
- [Latest News](./readme_pages/news.md)
- [Current list of algorithms](./docs/cugraph/source/graph_support/algorithms.md)
- [Blogs and Presentation](./docs/cugraph/source/tutorials/cugraph_blogs.rst)
- [Performance](./readme_pages/performance/performance.md)
- Packages
- [cuGraph Python](./readme_pages/cugraph_python.md)
- [Property Graph](./readme_pages/property_graph.md)
- [External Data Types](./readme_pages/data_types.md)
- [pylibcugraph](./readme_pages/pylibcugraph.md)
- [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md)
- [nx-cugraph](./python/nx-cugraph/README.md)
- [cugraph-service](./readme_pages/cugraph_service.md)
- [cugraph-dgl](./readme_pages/cugraph_dgl.md)
- [cugraph-ops](./readme_pages/cugraph_ops.md)
- API Docs
- Python
- [Python Nightly](https://docs.rapids.ai/api/cugraph/nightly/)
- [Python Stable](https://docs.rapids.ai/api/cugraph/stable/)
- C++
- [C++ Nightly](https://docs.rapids.ai/api/libcugraph/nightly/)
- [C++ Stable](https://docs.rapids.ai/api/libcugraph/stable/)
- References
- [RAPIDS](https://rapids.ai/)
- [ARROW](https://arrow.apache.org/)
- [DASK](https://www.dask.org/)
<br><br>
-----
<img src="img/Stack2.png" alt="Stack" width="800">
[RAPIDS](https://rapids.ai) cuGraph is a collection of GPU-accelerated graph algorithms and services. At the Python layer, cuGraph operates on [GPU DataFrames](https://github.com/rapidsai/cudf), thereby allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. To simplify integration, cuGraph also supports data found in [Pandas DataFrame](https://pandas.pydata.org/), [NetworkX Graph Objects](https://networkx.org/) and several other formats.
While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.
**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cugraph/blob/main/README.md) ensure you are on the latest branch.
As an example, the following Python snippet loads graph data and computes PageRank:
```python
import cudf
import cugraph
# read data into a cuDF DataFrame using read_csv
gdf = cudf.read_csv("graph_data.csv", names=["src", "dst"], dtype=["int32", "int32"])
# We now have data as edge pairs
# create a Graph using the source (src) and destination (dst) vertex pairs
G = cugraph.Graph()
G.from_cudf_edgelist(gdf, source='src', destination='dst')
# Let's now get the PageRank score of each vertex by calling cugraph.pagerank
df_page = cugraph.pagerank(G)
# Let's look at the top 10 PageRank Score
df_page.sort_values('pagerank', ascending=False).head(10)
```
</br>
[Why cuGraph does not support Method Cascading](https://docs.rapids.ai/api/cugraph/nightly/basics/cugraph_cascading.html)
------
# Projects that use cuGraph
(alphabetical order)
* ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/
* CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" - https://cupy.dev/
* Memgraph - In-memory Graph database - https://memgraph.com/
* NetworkX (via [nx-cugraph](./python/nx-cugraph/README.md) backend) - an extremely popular, free and open-source package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks - https://networkx.org/
* PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS & cuGraph support - http://github.com/graphistry/pygraphistry
* ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/
(please post an issue if you have a project to add to this list)
------
<br>
## <div align="center"><img src="img/rapids_logo.png" width="265px"/></div> Open GPU Data Science <a name="rapids"></a>
The RAPIDS suite of open source software libraries aims to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
<p align="center"><img src="img/rapids_arrow.png" width="50%"/></p>
For more project details, see [rapids.ai](https://rapids.ai/).
<br><br>
### Apache Arrow on GPU <a name="arrow"></a>
The GPU version of [Apache Arrow](https://arrow.apache.org/) is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.
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"description": "<h1 align=\"center\"; style=\"font-style: italic\";>\n <br>\n <img src=\"img/cugraph_logo_2.png\" alt=\"cuGraph\" width=\"500\">\n</h1>\n\n<div align=\"center\">\n\n<a href=\"https://github.com/rapidsai/cugraph/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"></a>\n<img alt=\"GitHub tag (latest by date)\" src=\"https://img.shields.io/github/v/tag/rapidsai/cugraph\">\n\n<a href=\"https://github.com/rapidsai/cugraph/stargazers\">\n <img src=\"https://img.shields.io/github/stars/rapidsai/cugraph\"></a>\n<img alt=\"Conda\" src=\"https://img.shields.io/conda/dn/rapidsai/cugraph\">\n<img alt=\"GitHub last commit\" src=\"https://img.shields.io/github/last-commit/rapidsai/cugraph\">\n\n<img alt=\"Conda\" src=\"https://img.shields.io/conda/pn/rapidsai/cugraph\" />\n\n<a href=\"https://rapids.ai/\"><img src=\"img/rapids_logo.png\" alt=\"RAPIDS\" width=\"125\"></a>\n\n</div>\n\n<br>\n\n[RAPIDS](https://rapids.ai) cuGraph is a monorepo that represents a collection of packages focused on GPU-accelerated graph analytics, including support for property graphs, remote (graph as a service) operations, and graph neural networks (GNNs). cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.\n\n<div align=\"center\">\n\n[Getting cuGraph](./docs/cugraph/source/installation/getting_cugraph.md) *\n[Graph Algorithms](./docs/cugraph/source/graph_support/algorithms.md) *\n[Graph Service](./readme_pages/cugraph_service.md) *\n[Property Graph](./readme_pages/property_graph.md) *\n[GNN Support](./readme_pages/gnn_support.md)\n\n</div>\n\n-----\n## News\n\n___NEW!___ _[nx-cugraph](./python/nx-cugraph/README.md)_, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change.\n```\n> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com\n> export NETWORKX_AUTOMATIC_BACKENDS=cugraph\n```\nThat's it. NetworkX now leverages cuGraph for accelerated graph algorithms.\n\n-----\n\n## Table of contents\n- Installation\n - [Getting cuGraph Packages](./docs/cugraph/source/installation/getting_cugraph.md)\n - [Building from Source](./docs/cugraph/source/installation/source_build.md)\n - [Contributing to cuGraph](./readme_pages/CONTRIBUTING.md)\n- General\n - [Latest News](./readme_pages/news.md)\n - [Current list of algorithms](./docs/cugraph/source/graph_support/algorithms.md)\n - [Blogs and Presentation](./docs/cugraph/source/tutorials/cugraph_blogs.rst)\n - [Performance](./readme_pages/performance/performance.md)\n- Packages\n - [cuGraph Python](./readme_pages/cugraph_python.md)\n - [Property Graph](./readme_pages/property_graph.md)\n - [External Data Types](./readme_pages/data_types.md)\n - [pylibcugraph](./readme_pages/pylibcugraph.md)\n - [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md)\n - [nx-cugraph](./python/nx-cugraph/README.md)\n - [cugraph-service](./readme_pages/cugraph_service.md)\n - [cugraph-dgl](./readme_pages/cugraph_dgl.md)\n - [cugraph-ops](./readme_pages/cugraph_ops.md)\n- API Docs\n - Python\n - [Python Nightly](https://docs.rapids.ai/api/cugraph/nightly/)\n - [Python Stable](https://docs.rapids.ai/api/cugraph/stable/)\n - C++\n - [C++ Nightly](https://docs.rapids.ai/api/libcugraph/nightly/)\n - [C++ Stable](https://docs.rapids.ai/api/libcugraph/stable/)\n- References\n - [RAPIDS](https://rapids.ai/)\n - [ARROW](https://arrow.apache.org/)\n - [DASK](https://www.dask.org/)\n\n<br><br>\n\n-----\n\n<img src=\"img/Stack2.png\" alt=\"Stack\" width=\"800\">\n\n[RAPIDS](https://rapids.ai) cuGraph is a collection of GPU-accelerated graph algorithms and services. At the Python layer, cuGraph operates on [GPU DataFrames](https://github.com/rapidsai/cudf), thereby allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. To simplify integration, cuGraph also supports data found in [Pandas DataFrame](https://pandas.pydata.org/), [NetworkX Graph Objects](https://networkx.org/) and several other formats.\n\nWhile the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.\n\n**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cugraph/blob/main/README.md) ensure you are on the latest branch.\n\n\n\nAs an example, the following Python snippet loads graph data and computes PageRank:\n\n```python\nimport cudf\nimport cugraph\n\n# read data into a cuDF DataFrame using read_csv\ngdf = cudf.read_csv(\"graph_data.csv\", names=[\"src\", \"dst\"], dtype=[\"int32\", \"int32\"])\n\n# We now have data as edge pairs\n# create a Graph using the source (src) and destination (dst) vertex pairs\nG = cugraph.Graph()\nG.from_cudf_edgelist(gdf, source='src', destination='dst')\n\n# Let's now get the PageRank score of each vertex by calling cugraph.pagerank\ndf_page = cugraph.pagerank(G)\n\n# Let's look at the top 10 PageRank Score\ndf_page.sort_values('pagerank', ascending=False).head(10)\n\n```\n\n</br>\n\n[Why cuGraph does not support Method Cascading](https://docs.rapids.ai/api/cugraph/nightly/basics/cugraph_cascading.html)\n\n\n\n------\n# Projects that use cuGraph\n\n(alphabetical order)\n* ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/\n* CuPy - \"NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python\" - https://cupy.dev/\n* Memgraph - In-memory Graph database - https://memgraph.com/\n* NetworkX (via [nx-cugraph](./python/nx-cugraph/README.md) backend) - an extremely popular, free and open-source package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks - https://networkx.org/\n* PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS & cuGraph support - http://github.com/graphistry/pygraphistry\n* ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/\n\n(please post an issue if you have a project to add to this list)\n\n\n\n------\n<br>\n\n## <div align=\"center\"><img src=\"img/rapids_logo.png\" width=\"265px\"/></div> Open GPU Data Science <a name=\"rapids\"></a>\n\n\nThe RAPIDS suite of open source software libraries aims to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. 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