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Pdf Pyg Nvidia Documentation Hub

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Get support for our latest innovations and see how you can bring them into your own work. This PyG container release is intended for use on the NVIDIA® Hopper Architecture GPU, NVIDIA H100, the NVIDIA® Ampere Architecture GPU, NVIDIA A100, and the associated NVIDIA CUDA® 12 and NVIDIA cuDNN 9 libraries. Release 25.08 is based on CUDA 13.0.0.044 which requires NVIDIA Driver release 570 or later. However, if you are running on a data center GPU (for example, B100, L40, or any other data center GPU), you can use NVIDIA driver release 470.57 (or later R470), 525.85 (or later R525),... The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R450, R460, R510, R520, R530, R545 and R555 and R560 drivers, which are not forward-compatible with CUDA 12.8.

For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. 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 example, to run the gcn.py example:

The container also includes the following: PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple interfaces... Documentation | Paper | Colab Notebooks and Video Tutorials | External Resources | OGB Examples PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple interfaces... Whether you are a machine learning researcher or first-time user of machine learning toolkits, here are some reasons to try out PyG for machine learning on graph-structured data. In this quick tour, we highlight the ease of creating and training a GNN model with only a few lines of code. Select the release of the NVML documentation. Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.

These playbooks provide detailed instructions for: Each playbook includes prerequisites, step-by-step instructions, troubleshooting guidance, and example code. Graph neural network (GNN) frameworks are easy-to-use Python packages that offer building blocks to build GNNs on top of existing deep learning frameworks for a wide range of applications. NVIDIA AI Accelerated GNN frameworks are optimized to deliver high-performance preprocessing, sampling, and training on NVIDIA GPUs. GNN framework containers for Deep Graph Library (DGL) and PyTorch Geometric (PyG) come with the latest NVIDIA RAPIDs, PyTorch, and frameworks that are performance tuned and tested for NVIDIA GPUs. Go from hours to minutes.

With NVIDIA RAPIDS™ integration, cuDF accelerates pandas queries up to 39X faster than CPU so that you can run ETL with GPU-optimized code. Streamline workflows for GNNs, from experimentation to production, with GPU-optimized, tested, and validated examples for fraud detection, recommender systems, and drug discovery.

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Get Support For Our Latest Innovations And See How You

Get support for our latest innovations and see how you can bring them into your own work. This PyG container release is intended for use on the NVIDIA® Hopper Architecture GPU, NVIDIA H100, the NVIDIA® Ampere Architecture GPU, NVIDIA A100, and the associated NVIDIA CUDA® 12 and NVIDIA cuDNN 9 libraries. Release 25.08 is based on CUDA 13.0.0.044 which requires NVIDIA Driver release 570 or later. Ho...

For A Complete List Of Supported Drivers, See The CUDA

For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. 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 u...

The Container Also Includes The Following: PyG (PyTorch Geometric) Is

The container also includes the following: PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it con...

It Consists Of Various Methods For Deep Learning On Graphs

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, torch.compile support, DataPipe support, a large number of common benchmark datasets (based on simple i...

These Playbooks Provide Detailed Instructions For: Each Playbook Includes Prerequisites,

These playbooks provide detailed instructions for: Each playbook includes prerequisites, step-by-step instructions, troubleshooting guidance, and example code. Graph neural network (GNN) frameworks are easy-to-use Python packages that offer building blocks to build GNNs on top of existing deep learning frameworks for a wide range of applications. NVIDIA AI Accelerated GNN frameworks are optimized ...