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dataloading-graphbolt【数据与研究】Build stochastic minibatch, DGL dataloading, and GraphBolt pipelines for DGL training.
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Use this sub-skill when a task needs stochastic minibatch training, neighbor sampling, block/MFG handoff, GraphBolt datapipes, ItemSet/ItemSampler, OnDiskDataset task sets, feature fetching, or GPU/UVA dataloader choices.
references/api-reference.md for classic dgl.dataloading and dgl.graphbolt constructors, outputs, and object contracts.references/workflows.md for node/link neighbor sampling, DataLoader vs GraphBolt decisions, feature prefetch/fetch, and GPU/UVA setup.references/data-formats.md when preparing or validating GraphBolt metadata.yaml and OnDiskDataset task splits.references/troubleshooting.md for device mismatches, CUDA worker rules, GraphBolt native-library issues, feature-key errors, and empty seed batches.python scripts/graphbolt_pipeline_sanity.py --help for available CPU-safe checks.forward() methods, losses, and training loops belong in ../message-passing-training/.DGLDataset, CSVDataset, graph serialization, and initial OnDiskDataset file preparation belong in ../datasets-and-io/.dgl.to_block() details, batching, and graph transforms belong in ../graph-apis/.../distributed-tools/.dgl.dataloading.DataLoader for GPU neighbor sampling and UVA sampling.