{"id":13426955,"url":"https://github.com/rapidsai/cugraph","last_synced_at":"2025-04-23T20:57:41.454Z","repository":{"id":37382193,"uuid":"157752451","full_name":"rapidsai/cugraph","owner":"rapidsai","description":"cuGraph - RAPIDS Graph Analytics Library","archived":false,"fork":false,"pushed_at":"2025-04-23T19:57:36.000Z","size":59640,"stargazers_count":1954,"open_issues_count":180,"forks_count":326,"subscribers_count":45,"default_branch":"branch-25.06","last_synced_at":"2025-04-23T20:57:08.157Z","etag":null,"topics":["complex-networks","cuda","gpu","graph","graph-algorithms","graph-analysis","graph-framework","graphml","nvidia","rapids"],"latest_commit_sha":null,"homepage":"https://docs.rapids.ai/api/cugraph/stable/","language":"Cuda","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rapidsai.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-15T18:07:11.000Z","updated_at":"2025-04-23T19:57:38.000Z","dependencies_parsed_at":"2023-10-14T14:16:42.808Z","dependency_job_id":"36d425d4-56e3-49ef-b0ca-942aedd98b77","html_url":"https://github.com/rapidsai/cugraph","commit_stats":{"total_commits":4374,"total_committers":119,"mean_commits":36.7563025210084,"dds":0.9094650205761317,"last_synced_commit":"99612d75c49310305f893d3d89b34b551ded3091"},"previous_names":[],"tags_count":92,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcugraph","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcugraph/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcugraph/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rapidsai%2Fcugraph/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rapidsai","download_url":"https://codeload.github.com/rapidsai/cugraph/tar.gz/refs/heads/branch-25.06","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250514767,"owners_count":21443208,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["complex-networks","cuda","gpu","graph","graph-algorithms","graph-analysis","graph-framework","graphml","nvidia","rapids"],"created_at":"2024-07-31T00:01:49.344Z","updated_at":"2025-04-23T20:57:41.437Z","avatar_url":"https://github.com/rapidsai.png","language":"Cuda","readme":"# ** NOTICE ** the cuGraph repository has been refactored to make it more efficient to build, maintain and use.\r\n\r\nLibraries supporting GNNs are now located in the [cugraph-gnn repository](https://github.com/rapidsai/cugraph-gnn)\r\n\r\n* [pylibwholegraph](https://github.com/rapidsai/cugraph-gnn/tree/HEAD/python/) - the [Wholegraph](https://docs.rapids.ai/api/cugraph/nightly/wholegraph/) library for client memory management supporting both cuGraph-DGL and cuGraph-PyG for even greater scalability\r\n* [cugraph_dgl](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_dgl.md)  enables the ability to use cugraph Property Graphs with Deep Graph Library (DGL)\r\n* [cugraph_pyg](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_pyg.md) enables the ability to use cugraph Property Graphs with PyTorch Geometric (PyG).\r\n\r\n[RAPIDS nx-cugraph](https://rapids.ai/nx-cugraph/) is now located in the [nx-cugraph repository](https://github.com/rapidsai/nx-cugraph) containing a backend to NetworkX for running supported algorithms with GPU acceleration.\r\n\r\nThe [cugraph-docs repository](https://github.com/rapidsai/cugraph-docs) contains code to generate cuGraph documentation.\r\n\r\n#\r\n\r\n\u003ch1 align=\"center\"; style=\"font-style: italic\";\u003e\r\n  \u003cbr\u003e\r\n  \u003cimg src=\"img/cugraph_logo_2.png\" alt=\"cuGraph\" width=\"500\"\u003e\r\n\u003c/h1\u003e\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n\r\n\u003ca href=\"https://github.com/rapidsai/cugraph/blob/main/LICENSE\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"\u003e\u003c/a\u003e\r\n\u003cimg alt=\"GitHub tag (latest by date)\" src=\"https://img.shields.io/github/v/tag/rapidsai/cugraph\"\u003e\r\n\r\n\u003ca href=\"https://github.com/rapidsai/cugraph/stargazers\"\u003e\r\n    \u003cimg src=\"https://img.shields.io/github/stars/rapidsai/cugraph\"\u003e\u003c/a\u003e\r\n\u003cimg alt=\"Conda\" src=\"https://img.shields.io/conda/dn/rapidsai/cugraph\"\u003e\r\n\u003cimg alt=\"GitHub last commit\" src=\"https://img.shields.io/github/last-commit/rapidsai/cugraph\"\u003e\r\n\r\n\u003cimg alt=\"Conda\" src=\"https://img.shields.io/conda/pn/rapidsai/cugraph\" /\u003e\r\n\r\n\u003ca href=\"https://rapids.ai/\"\u003e\u003cimg src=\"img/rapids_logo.png\" alt=\"RAPIDS\" width=\"125\"\u003e\u003c/a\u003e\r\n\r\n\u003c/div\u003e\r\n\r\n\u003cbr\u003e\r\n\r\n[RAPIDS](https://rapids.ai) cuGraph is a repo that represents a collection of packages focused on GPU-accelerated graph analytics including support for property graphs and remote (graph as a service) operations.  cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n\r\n[Getting cuGraph](https://docs.rapids.ai/api/cugraph/nightly/) *\r\n[Graph Algorithms](https://docs.rapids.ai/api/cugraph/nightly/graph_support/algorithms/) *\r\n[Graph Service](./readme_pages/cugraph_service.md) *\r\n[Property Graph](./readme_pages/property_graph.md) *\r\n\r\n\u003c/div\u003e\r\n\r\n-----\r\n\r\n## Table of contents\r\n- Installation\r\n  - [Getting cuGraph Packages](https://docs.rapids.ai/api/cugraph/stable/installation/getting_cugraph/)\r\n  - [Building from Source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/)\r\n  - [Contributing to cuGraph](https://docs.rapids.ai/contributing/)\r\n- General\r\n  - [Latest News](https://docs.rapids.ai/api/cugraph/nightly/)\r\n  - [Current list of algorithms](https://docs.rapids.ai/api/cugraph/stable/graph_support/algorithms/)\r\n  - [Blogs and Presentation](https://docs.rapids.ai/api/cugraph/nightly/tutorials/cugraph_blogs/)\r\n- Packages\r\n  - [cuGraph Python](./readme_pages/cugraph_python.md)\r\n    - [Property Graph](./readme_pages/property_graph.md)\r\n    - [External Data Types](./readme_pages/data_types.md)\r\n  - [pylibcugraph](./readme_pages/pylibcugraph.md)\r\n  - [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md)\r\n  - [nx-cugraph](https://rapids.ai/nx-cugraph/)\r\n  - [cugraph-service](./readme_pages/cugraph_service.md)\r\n- API Docs\r\n  - Python\r\n    - [Python Nightly](https://docs.rapids.ai/api/cugraph/nightly/api_docs/cugraph/)\r\n    - [Python Stable](https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph/)\r\n  - C\r\n    -  [C Nightly](https://docs.rapids.ai/api/cugraph/nightly/api_docs/cugraph_c/)\r\n    -  [C Stable](https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph_c/)\r\n  - C++\r\n    - [C++ Nightly](https://docs.rapids.ai/api/cugraph/nightly/api_docs/cugraph_cpp/)\r\n    - (Will be available when 25.02 is released)[C++ Stable](https://docs.rapids.ai/api/cugraph/stable/api_docs/cugraph_cpp/)\r\n- References\r\n  - [RAPIDS](https://rapids.ai/)\r\n  - [ARROW](https://arrow.apache.org/)\r\n  - [DASK](https://www.dask.org/)\r\n\r\n\u003cbr\u003e\u003cbr\u003e\r\n\r\n-----\r\n\r\n\u003cimg src=\"img/Stack2.png\" alt=\"Stack\" width=\"800\"\u003e\r\n\r\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.\r\n\r\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.\r\n\r\n**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cugraph/blob/main/README.md) ensure you are on the latest branch.\r\n\r\n\r\n\r\nAs an example, the following Python snippet loads graph data and computes PageRank:\r\n\r\n```python\r\nimport cudf\r\nimport cugraph\r\n\r\n# read data into a cuDF DataFrame using read_csv\r\ngdf = cudf.read_csv(\"graph_data.csv\", names=[\"src\", \"dst\"], dtype=[\"int32\", \"int32\"])\r\n\r\n# We now have data as edge pairs\r\n# create a Graph using the source (src) and destination (dst) vertex pairs\r\nG = cugraph.Graph()\r\nG.from_cudf_edgelist(gdf, source='src', destination='dst')\r\n\r\n# Let's now get the PageRank score of each vertex by calling cugraph.pagerank\r\ndf_page = cugraph.pagerank(G)\r\n\r\n# Let's look at the top 10 PageRank Score\r\ndf_page.sort_values('pagerank', ascending=False).head(10)\r\n\r\n```\r\n\r\n\u003c/br\u003e\r\n\r\n[Why cuGraph does not support Method Cascading](https://docs.rapids.ai/api/cugraph/nightly/basics/cugraph_cascading.html)\r\n\r\n\r\n\r\n------\r\n# Projects that use cuGraph\r\n\r\n(alphabetical order)\r\n* ArangoDB - a free and open-source native multi-model database system  - https://www.arangodb.com/\r\n* CuPy - \"NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python\" -  https://cupy.dev/\r\n* Memgraph - In-memory Graph database - https://memgraph.com/\r\n* NetworkX (via [nx-cugraph](https://rapids.ai/nx-cugraph/) 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/\r\n* PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS \u0026 cuGraph support - http://github.com/graphistry/pygraphistry\r\n* ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/\r\n\r\n(please post an issue if you have a project to add to this list)\r\n\r\n\r\n\r\n------\r\n\u003cbr\u003e\r\n\r\n## \u003cdiv align=\"center\"\u003e\u003cimg src=\"img/rapids_logo.png\" width=\"265px\"/\u003e\u003c/div\u003e Open GPU Data Science \u003ca name=\"rapids\"\u003e\u003c/a\u003e\r\n\r\n\r\nThe 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.\r\n\r\n\u003cp align=\"center\"\u003e\u003cimg src=\"img/rapids_arrow.png\" width=\"50%\"/\u003e\u003c/p\u003e\r\n\r\nFor more project details, see [rapids.ai](https://rapids.ai/).\r\n\r\n\u003cbr\u003e\u003cbr\u003e\r\n### Apache Arrow on GPU  \u003ca name=\"arrow\"\u003e\u003c/a\u003e\r\n\r\nThe 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.\r\n","funding_links":[],"categories":["Jupyter Notebook","Graph","GPU实用程序","Cuda","Software","Applications"],"sub_categories":["Others","C / C++"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frapidsai%2Fcugraph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frapidsai%2Fcugraph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frapidsai%2Fcugraph/lists"}