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name: recbole description: "Use RecBole for recommender-system data preparation, model selection, training, evaluation, tuning, and customization workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

RecBole

Use this repo skill when the task is about RecBole, the recbole Python package, or recommender-system experiments that need RecBole-style atomic data, configuration, models, training/evaluation, hyperparameter tuning, or extension points.

RecBole is a PyTorch-based recommendation library covering general, sequential, context-aware/CTR, and knowledge-aware recommendation. This skill is self-contained operating guidance for using the installed package; do not rely on the original repository checkout at runtime.

Start here

1. Confirm the active environment can import RecBole and PyTorch:

   python -c "import recbole, torch; print(recbole.__version__); print(torch.__version__)"
   

2. For a richer smoke check, run the bundled helper from this skill:

   python scripts/check_recbole_env.py --models BPR SASRec --check-optional
   

3. Route the task to the narrowest sub-skill below. Read references/repo-provenance.md before deciding whether this skill is current for a different RecBole checkout.

Sub-skill routes

Configuration and data

Read configuration-and-data when the user needs to:

• create or debug Config(model, dataset, config_file_list, config_dict);
• understand YAML/dict/CLI priority;
• prepare RecBole atomic files such as .inter, .user, .item, .kg, .link, or .net;
• fix load_col, data_path, field-type headers, dataset filtering/splitting, saved dataset, or saved dataloader issues;
• validate a dataset with the bundled atomic-file checker.

Training, evaluation, and tuning

Read training-evaluation-and-tuning when the user needs to:

• run BPR, SASRec, FM, KGAT, or another model with run or run_recbole;
• build a no-checkpoint CPU smoke run or a saved experiment run;
• interpret valid/test metrics, grouped model results, or significance tests;
• use load_data_and_model, full_sort_topk, or full_sort_scores;
• run HyperTuning/Hyperopt/Ray or diagnose HPO/GPU/distributed failures.

Models and customization

Read models-and-customization when the user needs to:

• choose among general, sequential, context-aware/CTR, knowledge-aware, or external-library model families;
• resolve model class names with get_model/get_trainer;
• understand model-property defaults and task-specific data prerequisites;
• implement or diagnose custom recommenders, trainers, dataloaders, samplers, or metrics.

Shared references and helpers

• references/api-surface.md lists verified public API signatures and how root/sub-skill guidance uses them.
• references/troubleshooting.md covers cross-cutting install/import, optional dependency, backend, and route-choice failures.
• scripts/check_recbole_env.py is a safe import/model/backend diagnostic helper.

Default operating stance

• Prefer CPU-safe examples first: set use_gpu: False, small epochs, show_progress: False, and saved=False unless the user explicitly wants checkpoints.
• Treat CUDA, multi-GPU/distributed training, Ray GPU trials, W&B, and long HPO as optional/budget-sensitive paths. Verify hardware, credentials, and output directories before enabling them.
• Put complex data layout and load_col work in the configuration/data route before training.
• Put model-family decisions before experiment execution. CTR/side-feature requests usually belong to context-aware models; knowledge-graph requests require KG/link data; sequence/session requests belong to sequential models.
• If a workflow spans multiple areas, handle it in this order: data/config → model selection/customization → training/evaluation/tuning.

Do not use this skill for

• Generic PyTorch training not using RecBole.
• Editing RecBole source internals as a maintainer task; use a repository maintenance route if the user asks to modify the repo itself.
• Recommendation frameworks outside RecBole unless the user is translating a workflow into RecBole terms.