What Is In This Container?

This container image includes the complete source of the NVIDIA version of PyG in /opt/pyg/pytorch_geometric. It is prebuilt and installed as a system Python module. The /workspace/examples folder is copied from /opt/pyg/pytorch_geometric/examples for users starting to run PyG. For an introductory example about training a GCN, try python /workspace/examples/gcn.py

See /workspace/README.md for details.

This container is built on the NVIDIA PyTorch container.\nFor the full list of contents see the PyG Container Release Notes.

The NVIDIA PyG Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration:

The software stack in this container has been validated for compatibility, and does not require any additional installation or compilation from the end user. This container can help accelerate your deep learning workflow from end to end.

Open source code

Running the container

Use the following commands to run the container, where <xx.xx> is the container version.

docker run --gpus all -it --rm nvcr.io/nvidia/pyg:xx.xx-py3\n

For example, 24.07 for the July 2024 release:

docker run --gpus all -it --rm nvcr.io/nvidia/pyg:24.07-py3\n

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this container meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

License

By pulling and using the container, you accept the terms and conditions of this End User License Agreement and Product-Specific Terms.

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It is prebuilt and installed as a system Python module. The /workspace/examples folder is copied from /opt/pyg/pytorch_geometric/examples for users starting to run PyG. For an introductory example about training a GCN, try python /workspace/examples/gcn.py\n\nSee /workspace/README.md for details.\n\nThis container is built on the NVIDIA PyTorch container.\nFor the full list of contents see the [PyG Container Release Notes.](https://docs.nvidia.com/deeplearning/frameworks/pyg-release-notes/index.html)\n\nThe NVIDIA PyG Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration:\n\n- [CUDA](https://developer.nvidia.com/cuda-toolkit)\n- [cuBLAS](https://developer.nvidia.com/cublas)\n- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn)\n- [NVIDIA NCCL](https://developer.nvidia.com/nccl) (optimized for [NVLink](https://www.nvidia.com/object/nvlink.html))\n- [RAPIDS](https://rapids.ai)\n- [NVIDIA Data Loading Library (DALI)](https://developer.nvidia.com/dali)\n- [TensorRT](https://developer.nvidia.com/tensorrt)\n- [Torch-TensorRT](https://nvidia.github.io/Torch-TensorRT/)\n\nThe software stack in this container has been validated for compatibility, and does not require any additional installation or compilation from the end user. This container can help accelerate your deep learning workflow from end to end.\n\n[Open source code](https://developer.download.nvidia.com/assets/dlfw/)\n\n\n### Running the container\n\nUse the following commands to run the container, where is the container version. \n\n```\ndocker run --gpus all -it --rm nvcr.io/nvidia/pyg:xx.xx-py3\n```\nFor example, 24.07 for the July 2024 release:\n```\ndocker run --gpus all -it --rm nvcr.io/nvidia/pyg:24.07-py3\n```\n### Suggested Reading\nFor the latest Release Notes, see the [PyG Release Notes](https://docs.nvidia.com/deeplearning/frameworks/pyg-release-notes/index.html).\n\n\nFor a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the [Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html). \nFor more information about PyG, including tutorials, documentation, and examples, see:\n* __[PyG website](https://www.pyg.org/)__\n* __[PyG project](https://github.com/pyg-team/pytorch_geometric)__\n* __[PyG Documentation](https://pytorch-geometric.readthedocs.io/en/latest/)__\n* __[PyG Tutorials](https://www.pyg.org/blogs-and-tutorials)__\n\n### Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\n\n### Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this container meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n\n### License\n\nBy pulling and using the container, you accept the terms and conditions of this [End User License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).","PyG",["D",1785175164816],[279,277,275,180,272,270,268,266,264,262,260,258,256,254,252,250,247],"/api/image/nvidia/-/containers/pyg",14246927143,"hasSignedTag",[],[307,308,309,310,311,312,313,314,315,316,317,318,319],[],[299],[],{"_90":300,"_301":300,"_302":303,"_304":305},"signed images","value","filter",{"_306":305,"_301":300},"category","system","field",{"_90":359,"_301":360,"_302":361,"_304":323},{"_90":356,"_301":357,"_302":358,"_304":323},{"_90":353,"_301":354,"_302":355,"_304":323},{"_90":350,"_301":351,"_302":352,"_304":323},{"_90":347,"_301":348,"_302":349,"_304":323},{"_90":345,"_301":345,"_302":346,"_304":323},{"_90":342,"_301":343,"_302":344,"_304":323},{"_90":338,"_301":339,"_302":340,"_304":323},{"_90":335,"_301":336,"_302":337,"_304":323},{"_90":331,"_301":332,"_302":333,"_304":323},{"_90":328,"_301":329,"_302":330,"_304":323},{"_90":325,"_301":326,"_302":327,"_304":323},{"_90":320,"_301":321,"_302":322,"_304":323},"Synthetic Data Generation","uscs_synthetic_data_generation",{"_306":324,"_301":321},"label","application","Recommendation","uscs_recommedation",{"_306":324,"_301":326},"Graph Neural Networks","uscs_graph_neural_networks",{"_306":324,"_301":329},"Genomics","soln_genomics",{"_306":334,"_301":332},"technology","Genome Sequencing","uscs_genome_sequencing",{"_306":324,"_301":336},"Financial Services","indus_financial_services",{"_306":341,"_301":339},"industry","Energy","indus_energy",{"_306":341,"_301":343},"DLFW",{"_306":323,"_301":345},"DL","soln_dl",{"_306":334,"_301":348},"Developer Tools","soln_developer_tools",{"_306":334,"_301":351},"Data Center Simulation Platform","soln_data_center_simulation_platform",{"_306":334,"_301":354},"AI","soln_ai",{"_306":334,"_301":357},"Academia / Higher Education","indus_academia_higher_education",{"_306":341,"_301":360},{"_147":1339,"_149":1340},"ngcApps",[1292],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1273":1274,"_1275":1276,"_1277":1278,"_1279":1280,"_1281":373,"_179":401,"_1282":1283,"_1284":51,"_1285":125,"_1286":125,"_1287":1288,"_1289":125,"_1290":1291},"amplitude",{"_1270":1271,"_915":-7,"_1272":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1264":1265,"_1266":1267,"_1268":1269},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1786883400664,"1.11.0","defaultSearchSuggestions",{"_1155":103,"_1156":1157,"_1158":1159,"_1160":1161},"announcements",[],"consentState",{"_440":1145,"_1146":125,"_1147":26,"_1148":26,"_1149":1150},"consentConfig",{"_422":423,"_424":425,"_426":427,"_428":429,"_430":431,"_432":433,"_434":435,"_436":437,"_438":439,"_440":441},"showContactAdmin",1786883400666,[],[],[415,416,417,418,419,420,421],{"_40":61,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":62,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":49,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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These cookies may store a unique ID so that our system will remember you when you return. 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