This repository contains a collection of Jupyter Notebooks that outline how to run various cuGraph analytics. The notebooks do not address a complete data science problem. The notebooks are simply examples of how to run the graph analytics. Manipulation of the data before or after the graph analytic is not covered here. Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended

Visit the main RAPIDS notebooks repo for a listing of all notebooks across all RAPIDS libraries.

Running the example in these notebooks requires:

• The latest version of RAPIDS with cuGraph.

  • Download via Docker, Conda (See Getting Started)

• cuGraph is dependent on the latest version of cuDF. Please install all components of RAPIDS

• Python 3.8+

• A system with an NVIDIA GPU: Pascal architecture or better

• CUDA 11.4+

• NVIDIA driver 450.51+

The easiest way to run the notebooks is to get the latest rapidsai/notebooks docker image with matching cuda version and run a container based on the image.

For example, get the latest (as of writing the document) nightly image (a after the version number indicates that an image is nightly) with cuda 12.0 using

docker pull rapidsai/notebooks:24.04a-cuda12.0-py3.9

And, then run a container based on the image using

docker run --rm  -it --pull always --gpus all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864   -p 8888:8888 rapidsai/notebooks:24.04a-cuda12.0-py3.9

You are all set. Run and edit cugraph notebooks from a browser at url http://127.0.0.1:8888/lab/tree/cugraph/cugraph_benchmarks

If you want to run the container in a remote machine which has access to GPUs, you can use ssh tunneling to run/edit the notebooks locally as explained above.

Login to your remote machine with ssh tunneling/port forwarding

ssh -L  127.0.0.1:8888:127.0.0.1:8888 [USER_NAME@][REMOTE_HOST_NAME or REMOTE_HOST_IP]

and then run the container in your remote machine.

docker pull rapidsai/notebooks:24.04a-cuda12.0-py3.9
docker run --rm  -it --pull always --gpus all --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p 8888:8888 rapidsai/notebooks:24.04a-cuda12.0-py3.9

You can run and edit cugraph notebooks at url http://127.0.0.1:8888/lab/tree/cugraph/cugraph_benchmarks as if they are running locally.

The following notebooks are not tested as part of the standard cuGraph continuous integration process. There is a plan to start testing these notebooks weekly, but until then there is no guarantee that they will work with the nightly release. The following table list the notebook funtion, where to find the notebook, and the environment used to test the notebook.

If any notebook doesn't run as detailed here, please file an issue in cuGraph

Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.