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name: deep-gcns-torch description: "Use the DeepGCNs PyTorch operating guide for graph convolution layers, dynamic and dilated KNN blocks, point-cloud classification or segmentation, PPI, OGB/DeeperGCN, and reversible memory-efficient GNN workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepGCNs Torch

Use this router when a task names DeepGCNs, DeeperGCN, deep_gcns_torch, gcn_lib, GENConv, dynamic KNN graph blocks, ModelNet40/S3DIS/PartNet point clouds, PPI, OGB graph benchmarks, RevGNN, or RevGAT.

Operating boundary

This is a self-contained guide distilled from a historical PyTorch repository. It does not download datasets, fetch external checkpoints, launch long training, start distributed jobs, or open visualization windows. First stage caller-owned data and checkpoints, then follow the relevant workflow. Read repository provenance when deciding whether a source checkout has drifted, and read troubleshooting when installation or version behavior is unclear.

Minimal environment check

The source repository has no pyproject.toml, setup.py, or console entry point. Use a coherent PyTorch/PyG stack rather than the obsolete repository installer. At minimum, install PyTorch, PyTorch Geometric, matching torch-scatter and torch-cluster wheels, and add ogb, h5py, scikit-learn, or tensorboard only for the selected workflows. Run the bundled checks before using a real model:

python scripts/check_env.py
python scripts/checkpoint_roundtrip.py

For a CUDA task, use python scripts/check_env.py --cuda; CUDA availability is not proof of dataset-scale memory or benchmark reproduction. Match every compiled PyG extension to the installed PyTorch and CPU/CUDA build. Do not run the old one-shot installer: it mutates environments and pins obsolete CUDA 10.2-era packages.

Route by the user's task

• Layer or architecture API — sparse/dense GraphConv, DynConv, GENConv, aggregators, KNN/dilation, blocks, or reversible primitives: read graph-layers.
• Point clouds — ModelNet40, S3DIS dense/sparse, PartNet, segmentation layouts, point-cloud checkpoints, or optional OBJ visualization: read point-cloud-workflows.
• OGB/DeeperGCN — ogbn-*, ogbg-*, ogbl-collab, graph pooling, partitions, atom/bond features, RevGNN, or DGL RevGAT: read ogb-workflows.
• PPI — PPI multilabel node classification, F1 metrics, or PPI checkpoint alignment: read ppi-workflows.

If a request crosses routes, start here, state the data layout and backend, then read each owning sub-skill. Generic layer semantics belong to graph-layers; task-specific data and metrics remain with their workflow.

Fast safety gates

1. Identify CPU versus CUDA, PyG extension compatibility, data root, checkpoint availability, and expected metric before running anything.
2. Use the owning sub-skill's bundled synthetic helper first. These helpers are offline, deterministic, and do not import the original checkout.
3. Never treat a CPU shape smoke as proof of GPU memory, a historical benchmark number, or a dataset split result.
4. Keep the source task's working-directory import quirks and external download requirements explicit; do not turn them into hidden runtime dependencies.
5. For a changed source revision or package API, compare provenance and use a refresh workflow rather than silently applying stale claims.

Shared references and checks

• API overview summarizes shared tensor, module, and utility contracts before choosing a focused route.
• Troubleshooting covers cross-cutting install, import, data, checkpoint, backend, and version failures.
• Environment check is a safe dependency/backend probe.
• Checkpoint roundtrip verifies basic PyTorch checkpoint serialization without touching caller data.