The RAPIDS cuGraph library is a collection of graph analytics that process data found in GPU Dataframes - see cuDF. cuGraph aims to provide a NetworkX-like API that will be familiar to data scientists, so they can now build GPU-accelerated workflows more easily.
For more project details, see rapids.ai.
NOTE: For the latest stable README.md ensure you are on the
import cugraph # assuming that data has been loaded into a cuDF (using read_csv) Dataframe # create a Graph using the source (src) and destination (dst) vertex pairs the GDF G = cugraph.Graph() G.add_edge_list(gdf["src"], gdf["dst"]) # Call cugraph.pagerank to get the pagerank scores gdf_page = cugraph.pagerank(G) for i in range(len(gdf_page)): print("vertex " + str(gdf_page['vertex'][i]) + " PageRank is " + str(gdf_page['pagerank'][i]))
|Spectral Clustering - Balanced-Cut||Single-GPU|
|Spectral Clustering - Modularity Maximization||Single-GPU|
|Basic Graph Statistics||Single-GPU|
cuGraph 0.7 Notice
cuGraph version 0.7 has some limitations:
- Only Int32 Vertex ID are supported
- Only float (FP32) edge data is supported
- Vertex numbering is assumed to start at zero
These limitations are being addressed and will be fixed future versions.
There are 3 ways to get cuGraph :
Please see the Demo Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries: cuDF, cuML, and cuGraph.
Install and update cuGraph using the conda command:
# CUDA 9.2 conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=9.2 # CUDA 10.0 conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cugraph cudatoolkit=10.0
Note: This conda installation only applies to Linux and Python versions 3.6/3.7.
Build from Source and Contributing
Please see our guide for building and contributing to cuGraph.
Python API documentation can be generated from docs directory.
The RAPIDS suite of open source software libraries aim 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.
Apache Arrow on GPU
The GPU version of Apache Arrow 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.