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",1780353115365],[271,269,267,265,263,175,260,258,256,254,252,250,248,246,244,241],"/api/image/nvidia/-/containers/pyg",13849780266,"hasSignedTag",[],[299,300,301,302,303,304,305,306,307,308,309,310,311],[],[291],[],{"_91":292,"_293":292,"_294":295,"_296":297},"signed images","value","filter",{"_298":297,"_293":292},"category","system","field",{"_91":351,"_293":352,"_294":353,"_296":315},{"_91":348,"_293":349,"_294":350,"_296":315},{"_91":345,"_293":346,"_294":347,"_296":315},{"_91":342,"_293":343,"_294":344,"_296":315},{"_91":339,"_293":340,"_294":341,"_296":315},{"_91":337,"_293":337,"_294":338,"_296":315},{"_91":334,"_293":335,"_294":336,"_296":315},{"_91":330,"_293":331,"_294":332,"_296":315},{"_91":327,"_293":328,"_294":329,"_296":315},{"_91":323,"_293":324,"_294":325,"_296":315},{"_91":320,"_293":321,"_294":322,"_296":315},{"_91":317,"_293":318,"_294":319,"_296":315},{"_91":312,"_293":313,"_294":314,"_296":315},"Synthetic Data Generation","uscs_synthetic_data_generation",{"_298":316,"_293":313},"label","application","Recommendation","uscs_recommedation",{"_298":316,"_293":318},"Graph Neural Networks","uscs_graph_neural_networks",{"_298":316,"_293":321},"Genomics","soln_genomics",{"_298":326,"_293":324},"technology","Genome Sequencing","uscs_genome_sequencing",{"_298":316,"_293":328},"Financial Services","indus_financial_services",{"_298":333,"_293":331},"industry","Energy","indus_energy",{"_298":333,"_293":335},"DLFW",{"_298":315,"_293":337},"DL","soln_dl",{"_298":326,"_293":340},"Developer Tools","soln_developer_tools",{"_298":326,"_293":343},"Data Center Simulation Platform","soln_data_center_simulation_platform",{"_298":326,"_293":346},"AI","soln_ai",{"_298":326,"_293":349},"Academia / Higher Education","indus_academia_higher_education",{"_298":333,"_293":352},{"_146":1526,"_148":1527},"ngcApps",[1479],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1461":1462,"_1463":1464,"_1465":1466,"_1467":1468,"_1469":365,"_174":389,"_1470":1471,"_1459":51,"_1472":126,"_1473":126,"_1474":1475,"_1476":126,"_1477":1478},"amplitude",{"_1457":1458,"_1459":51,"_1063":-7,"_1460":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1451":1452,"_1453":1454,"_1455":1456},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1338":104,"_1339":1340,"_1341":1342,"_1343":1344},"announcements",[],"consentState",{"_428":1328,"_1329":126,"_1330":26,"_1331":26,"_1332":1333},"consentConfig",{"_410":411,"_412":413,"_414":415,"_416":417,"_418":419,"_420":421,"_422":423,"_424":425,"_426":427,"_428":429},"showContactAdmin",1783392683106,[],[],[403,404,405,406,407,408],{"_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":52,"_42":53,"_44":45,"_46":47,"_48":409,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},3,"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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