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GraphScope leverages the distributed GNN training framework, graphlearn-for-pytorch (GLTorch), to facilitate large-scale distributed GNN training. GLTorch is model-layer compatible with PyG and enables the extension of PyG-based GNN training to large distributed graphs.
To address the challenge of training GNNs on graphs that exceed the available memory of a single machine, PyG has introduced a pluggable Remote Backend mechanism. This mechanism, through abstractions like FeatureStore and GraphStore, supports integration with third-party graph storage engines. The FeatureStore permits utilization of node/edge features stored remotely, while the GraphStore facilitates access to graph structure information held externally. This project aims to implement a PyG Remote Backend based on GraphScope for PyG to provide a user-friendly experience for conducting distributed GNN training with GraphScope for PyG users.
Deliverables:
Implement the PyG FeatureStore and GraphStore abstractions within GraphScope
Complete the end-to-end integration of GraphScope and PyG via the Remote Backend
The text was updated successfully, but these errors were encountered:
GraphScope leverages the distributed GNN training framework, graphlearn-for-pytorch (GLTorch), to facilitate large-scale distributed GNN training. GLTorch is model-layer compatible with PyG and enables the extension of PyG-based GNN training to large distributed graphs.
To address the challenge of training GNNs on graphs that exceed the available memory of a single machine, PyG has introduced a pluggable Remote Backend mechanism. This mechanism, through abstractions like FeatureStore and GraphStore, supports integration with third-party graph storage engines. The FeatureStore permits utilization of node/edge features stored remotely, while the GraphStore facilitates access to graph structure information held externally. This project aims to implement a PyG Remote Backend based on GraphScope for PyG to provide a user-friendly experience for conducting distributed GNN training with GraphScope for PyG users.
Deliverables:
The text was updated successfully, but these errors were encountered: