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name: training-wrappers-and-customization description: "Routes CogDL Trainer, wrapper matching, configuration, and checkpoint/logging workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

CogDL Training Wrappers and Customization

Use this sub-skill when the task is about CogDL's unified trainer, model/data wrappers, default wrapper matching, best-config handling, or checkpoint/logging behavior.

Typical triggers:

• "Why does CogDL ask for mw and dw?"
• "How do I resume a CogDL checkpoint?"
• "What wrapper does this model use by default?"
• "How does use_best_config change the run?"

Read references/trainer-and-wrappers.md for the verified Trainer signature, wrapper families, and the default mapping table. Read references/configuration-and-best-configs.md for BEST_CONFIGS, the dataset-specific overrides, and the role of use_best_config. Read references/troubleshooting.md when wrapper mismatches, checkpoint writes, logger selection, or distributed/device issues appear.

Run scripts/inspect_wrapper_match.py to print the wrapper pair for one or more models without launching a run. Run scripts/trainer_config_template.py when you want a reusable config skeleton for experiment(...) and Trainer(...) arguments.

Route these elsewhere:

• ../experiments-and-cli/SKILL.md for experiment(...), CLI flags, and AutoML orchestration.
• ../graph-data-and-datasets/SKILL.md for graph schemas, masks, and custom fixture creation.
• ../models-layers-and-operators/SKILL.md for model and layer code.
• ../pipelines-and-applications/SKILL.md for pipeline() apps.

What this sub-skill covers

• Trainer construction and the major training-mode flags.
• fetch_model_wrapper, fetch_data_wrapper, get_wrappers_name, and the default wrapper configuration map.
• Wrapper families such as node classification, graph classification, network embedding, heterogeneous tasks, triple link prediction, traffic, and pretraining.
• set_best_config(args) and the BEST_CONFIGS dataset/model overrides.
• Checkpoint, embedding, logging, device, CPU/inference, and distributed settings that affect the training loop.

Decision rules

• Pick the wrapper pair before tuning the model itself.
• If the user only needs to know the right wrapper names, use the inspection script instead of explaining the whole trainer.
• If a model is supported but does not have a special wrapper, fall back to the default wrapper table or route back to the experiment/CLI sub-skill for the exact command.
• Treat checkpoint/resume paths as file-write surfaces and make the location explicit.