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name: message-passing-training description: "Use DGL message passing, PyTorch GNN layers, full-graph training loops, heterograph modules, readout, and DGL sparse APIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Message Passing Training

Use this sub-skill when a task asks you to implement or debug DGL message passing, full-graph GNN training, built-in DGL PyTorch layers, heterograph relation modules, readout/pooling, or dgl.sparse operations.

Start Here

• For API and layer selection, read references/api-reference.md.
• For full-graph node, edge/link, graph, heterograph, and sparse workflows, read references/workflows.md.
• For DGL-specific failure modes, read references/troubleshooting.md.
• To verify a local DGL/PyTorch install before editing a model, run python scripts/training_smoke.py from this sub-skill directory.
• To verify optional dgl.sparse availability, run python scripts/sparse_api_smoke.py from this sub-skill directory.

Route Elsewhere

• CSV/custom dataset layout, feature fields, train/validation/test masks, and graph save/load setup belong in ../datasets-and-io/.
• Stochastic neighbor sampling, dgl.dataloading.DataLoader, NeighborSampler, GraphBolt datapipes, and minibatch block training belong in ../dataloading-graphbolt/.
• Generated training projects, YAML config generation, and DGL-Go commands belong in ../dglgo-cli/.
• Distributed launches, DDP trainer setup, partitioning, and multi-process execution belong in ../distributed-tools/.

Safety Defaults

• Prefer CPU-safe synthetic graphs for smoke checks and examples; do not download datasets unless the user explicitly asks.
• Keep model code framework-specific: this sub-skill covers PyTorch DGL APIs.
• Prefer DGL built-ins (dgl.function, dgl.nn.pytorch, dgl.sparse) over reimplementing kernels unless a custom UDF or layer is required.