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name: pipelines description: "Guides DeepPavlov configuration, CLI, training, evaluation, registry, and custom-component workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Pipelines

Use this sub-skill for DeepPavlov’s configuration-driven workflows: building a pipeline, training or evaluating it, inspecting config dependencies, and extending the registry with custom components or metrics.

Owns

• CLI modes: install, download, train, evaluate, interact, predict, and crossval.
• Python APIs: build_model, train_model, evaluate_model, train_evaluate_model_from_config, read_data_by_config, get_iterator_from_config, parse_config, and deep_download.
• Config mechanics: chainer.in, chainer.out, chainer.pipe, in, out, in_y, id, ref, nested config_path, overwrite, metadata.variables, metadata.imports, and metadata.requirements.
• Training config mechanics: dataset_reader, dataset_iterator, train, metrics, fit_on, save_path, load_path, main, and recursive.
• Registry and metric extension patterns for custom components.

Does Not Own

• Model-family selection, input/output shapes, and dataset layout choices for classifiers, NER, embeddings, ODQA, KBQA, ranking, or SQuAD-style tasks. Route those questions to ../text-models/SKILL.md or ../retrieval-qa/SKILL.md.
• REST or socket serving. Route riseapi and risesocket questions to ../serving/SKILL.md.
• Cross-cutting install, backend, cache, or download problems that affect the whole package. Use the root skill’s troubleshooting reference when the issue is not specific to config parsing or training.

Read This First

1. references/config-workflows.md for end-to-end config loading, CLI modes, training, evaluation, cross-validation, and parameter search.
2. references/api-reference.md for verified public signatures and config-field semantics.
3. references/custom-components.md for registry, metric, id/ref, and metadata.imports workflows.
4. references/troubleshooting.md for config, registry, and CLI failure modes.
5. scripts/inspect_config_requirements.py when you need a no-download summary of a config’s nested pipelines, class names, requirement files, and download references.

Typical Entry Points

• Start with build_model(<config>) when you need an inference-ready pipeline.
• Use train_model(<config>) when you need a trained Chainer back after the training run.
• Use evaluate_model(<config>) or train_evaluate_model_from_config(<config>, to_train=False) when you need metrics on the configured validation/test split.
• Use python -m deeppavlov train <config> for CLI training, evaluate for metrics only, and predict for stdin/file batch inference.
• Use python -m deeppavlov.paramsearch <config> --folds ... for grid search over search_choice values.
• Use python -m deeppavlov crossval <config> --folds ... for plain cross-validation over the union of train and validation data.

Workflow Hints

• If the config name is only a stem, let DeepPavlov resolve it; aliases may be mapped to a newer config and emit a warning.
• If a config nests another config with config_path, inspect the nested config first. overwrite uses dot notation, and list positions are numeric path segments.
• If a component is shared across branches, give it an id and reuse it with ref or #id-style references in later params.
• If a config needs custom code, prefer metadata.imports or a fully qualified module.submodule:ClassName / module.submodule:function_name reference.
• If predict says to use interact, you are on a terminal TTY and should switch modes instead of forcing stdin handling.

Routing Notes

• Use ../../references/troubleshooting.md for package-wide install/import issues once the root skill exists; keep config-specific symptoms here.
• Use the sibling model-family skills for task-specific data shapes and backend-heavy model choices.
• Keep this sub-skill focused on how pipelines are wired and executed, not on which model family to choose.