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name: datasets-and-customization description: "Route Graphormer dataset source selection, custom registration, preprocessing, and validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Datasets and Customization

Use this sub-skill when you need to:

• choose a built-in DGL, PyG, or OGB dataset
• parse --dataset-name and --dataset-source
• register a custom dataset with register_dataset
• reason about Graphormer preprocessing and batch fields
• validate a user dataset module before training

Route elsewhere

• Training schedules, optimizer choices, and long-run command building: fairseq-training
• Pretrained checkpoints, evaluation, and fine-tuning: pretrained-and-evaluation
• Model, task, criterion, or architecture internals: model-extension

Bundled runtime files

• Dataset sources and formats
• API reference
• Troubleshooting
• Custom dataset validator

Quick rules

• Built-in datasets use --dataset-source plus --dataset-name.
• Custom datasets use --user-data-dir and --dataset-name; do not combine them with --dataset-source.
• Custom registrations must return dataset, train_idx, valid_idx, test_idx, and source.
• Allowed custom source values are dgl and pyg.
• Graphormer preprocessing produces idx, attn_bias, attn_edge_type, spatial_pos, in_degree, out_degree, x, edge_input, and y.
• Batch collation drops graphs above max_nodes, masks long spatial distances, and truncates multi-hop edge history.

Validation path

1. Read the dataset source table and syntax guide.
2. Register or locate the dataset module.
3. Run the bundled validator in list-only mode or full validation mode.
4. If validation fails, use the troubleshooting notes before moving to training or evaluation.
5. Run the bundled validator in list-only mode first; rerun with --execute-registrations only when dataset construction is safe.