CogDL

CogDL is a graph deep learning toolkit for node classification, graph classification, link prediction, graph embedding, heterogeneous graphs, traffic prediction, and application-style pipelines such as dataset inspection and OAG-BERT.

Use this root router when a user asks about CogDL itself, names one of its public APIs, or needs help choosing which part of the package to open first.

Quick start

1. Install PyTorch first with a CPU or CUDA wheel that matches the host.
2. Install CogDL in editable mode for local inspection:

pip install -e .

3. For a published wheel instead of a checkout install, use:

pip install cogdl

4. If the task needs optional package families, add them explicitly:
   • ogb for OGB datasets and benchmarks
   • transformers and sentencepiece for OAG-BERT
   • the repo already declares optuna, gensim, grave, tabulate, numba, and ninja as runtime dependencies

Minimal import check

python -c "import cogdl; from cogdl import experiment, pipeline; print(cogdl.__version__)"
python scripts/check_cogdl_environment.py --show-registries

Use python -m pip check after install to catch incompatible dependencies.

Route map

• sub-skills/experiments-and-cli/: experiment(), get_default_args, CLI flags, scripts/train.py, variants, checkpoint/log/embedding flags, and AutoML.
• sub-skills/graph-data-and-datasets/: Graph, Adjacency, Dataset, NodeDataset, GraphDataset, DataLoader, masks, schemas, and tiny fixtures.
• sub-skills/models-layers-and-operators/: model registry, layers, custom GNNs, and sparse/message operators.
• sub-skills/training-wrappers-and-customization/: Trainer, wrappers, default wrapper matching, configs, checkpoint/resume, and logging.
• sub-skills/pipelines-and-applications/: pipeline() apps, embedding generation, recommendation, and OAG-BERT.

Shared references

Read these before refreshing the skill or checking whether a checkout is current:

• references/package-overview.md
• references/troubleshooting.md
• references/repo-provenance.md

Shared smoke script

Run scripts/check_cogdl_environment.py when you want a fast, no-download check of the installed package, registry counts, and optional CUDA/Graph smoke probes.

Scope notes

• Built-in datasets may download or populate caches on first use.
• OAG-BERT weights and archives are optional, cache- or network-dependent resources.
• CUDA acceleration is optional in this skill tree; CPU-safe workflows are the baseline unless a sub-skill explicitly says otherwise.
• For maintainer-only repository edits, CI, or release tasks, use a different workflow; this runtime skill is for research-side usage.