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cogdl

Routes CogDL graph-learning workflows for experiments, graph data, models, training wrappers, and pipeline apps.

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VectorSpaceLab/AREX-Skill
설치 방법 보기

포함된 파일(37)

  • SKILL.md2.9 KB
  • references/package-overview.md3.2 KB
  • references/repo-provenance.md1.4 KB
  • references/repo-routing-metadata.json478 B
  • references/troubleshooting.md3.8 KB
  • scripts/check_cogdl_environment.py3.0 KB
  • sub-skills/experiments-and-cli/references/api-and-cli.md4.2 KB
  • sub-skills/experiments-and-cli/references/troubleshooting.md3.7 KB
  • sub-skills/experiments-and-cli/references/workflows.md3.7 KB
  • sub-skills/experiments-and-cli/scripts/cogdl_cli_smoke.py1.6 KB
  • sub-skills/experiments-and-cli/scripts/run_tiny_experiment.py4.0 KB
  • sub-skills/experiments-and-cli/SKILL.md2.9 KB
  • sub-skills/graph-data-and-datasets/references/built-in-datasets.md7.2 KB
  • sub-skills/graph-data-and-datasets/references/data-formats.md6.8 KB
  • sub-skills/graph-data-and-datasets/references/graph-and-dataset-api.md9.6 KB
  • sub-skills/graph-data-and-datasets/references/troubleshooting.md6.1 KB
  • sub-skills/graph-data-and-datasets/scripts/create_tiny_graph_dataset.py10.4 KB
  • sub-skills/graph-data-and-datasets/scripts/validate_graph_masks.py13.3 KB
  • sub-skills/graph-data-and-datasets/SKILL.md2.7 KB
  • sub-skills/models-layers-and-operators/references/custom-gnn.md2.4 KB
  • sub-skills/models-layers-and-operators/references/model-registry-and-layers.md3.1 KB
  • sub-skills/models-layers-and-operators/references/operator-compatibility.md1.9 KB
  • sub-skills/models-layers-and-operators/references/troubleshooting.md2.2 KB
  • sub-skills/models-layers-and-operators/scripts/custom_gnn_smoke.py1.7 KB
  • sub-skills/models-layers-and-operators/scripts/inspect_model_registry.py1.3 KB
  • sub-skills/models-layers-and-operators/SKILL.md2.7 KB
  • sub-skills/pipelines-and-applications/references/oagbert.md1.9 KB
  • sub-skills/pipelines-and-applications/references/pipeline-recipes.md2.8 KB
  • sub-skills/pipelines-and-applications/references/troubleshooting.md2.9 KB
  • sub-skills/pipelines-and-applications/scripts/pipeline_smoke.py1.7 KB
  • sub-skills/pipelines-and-applications/SKILL.md2.3 KB
  • sub-skills/training-wrappers-and-customization/references/configuration-and-best-configs.md2.2 KB
  • sub-skills/training-wrappers-and-customization/references/trainer-and-wrappers.md3.5 KB
  • sub-skills/training-wrappers-and-customization/references/troubleshooting.md2.5 KB
  • sub-skills/training-wrappers-and-customization/scripts/inspect_wrapper_match.py1.3 KB
  • sub-skills/training-wrappers-and-customization/scripts/trainer_config_template.py2.9 KB
  • sub-skills/training-wrappers-and-customization/SKILL.md2.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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 .
  1. For a published wheel instead of a checkout install, use:
pip install cogdl
  1. 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.

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