NEWS · CODING · #298
Google releases TensorFlow GNN 1.0 for production-scale graph neural networks
Google announces TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building and training graph neural networks in TensorFlow with first-class GraphTensor support, Keras integration, and flexible dynamic or batch subgraph sampling—scalable from interactive Colab use to distributed sampling via Apache Beam for datasets of up to hundreds of millions of nodes and billions of edges.
KEY POINTS
- Google announces TensorFlow GNN 1.0 (TF-GNN), a production-tested library for building and training graph neural networks in TensorFlow with first-class GraphTensor support, Keras integration, and flexible dynamic or batch subgraph sampling—scalable from interactive Colab use to distributed sampling via Apache Beam for datasets of up to hundreds of millions of nodes and billions of edges.
- TF-GNN makes heterogeneous graphs a first-class citizen in TensorFlow and provides scalable sampling and training tools, lowering the barrier to deploying GNNs on large real-world graphs.
- Graph neural networks in TensorFlow
WHY IT MATTERS
TF-GNN makes heterogeneous graphs a first-class citizen in TensorFlow and provides scalable sampling and training tools, lowering the barrier to deploying GNNs on large real-world graphs.