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pipelines-and-models

pipelines-and-models

Use ModelScope inference pipelines, model/preprocessor registries, output keys, and safe local custom pipeline smoke checks.

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name: pipelines-and-models description: "Use ModelScope inference pipelines, model/preprocessor registries, output keys, and safe local custom pipeline smoke checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Pipelines and Models

Use this sub-skill when a task needs ModelScope inference or registry behavior: constructing pipeline(...), loading Model.from_pretrained(...) or Preprocessor.from_pretrained(...), choosing task constants, reading standard OutputKeys, validating batching behavior, or creating a local custom pipeline smoke check.

Fast routing

  • Start with references/workflows.md for the decision flow: default task model, explicit hub/local model, local config, explicit preprocessor, custom pipeline_name, device, revision, optional dependency, and trust_remote_code choices.
  • Use references/api-reference.md for signatures, call flow, registry construction, config loading, and trust boundaries.
  • Use references/task-output-reference.md for Tasks, input checking, output key conventions, and safe result handling.
  • Use references/troubleshooting.md when a registry lookup, optional backend import, CUDA/device selection, hub download, Python config, plugin, or output-key check fails.
  • Run scripts/custom_pipeline_smoke.py to verify local custom pipeline registration without network, training, downloads, CUDA, or writes outside a temporary directory.

Scope boundaries

This sub-skill covers inference and registry workflows only. For template-file creation with ModelScope CLI, route to ../customization-and-development/SKILL.md. For training, evaluation loops, metrics-in-trainer usage, or checkpoint training outputs, route to ../training-and-evaluation/SKILL.md. For detailed dataset loading, MsDataset.load(...), dataset schemas, or dataset cache behavior, route to ../datasets-config/SKILL.md.

CUDA, domain-specific accelerator execution, and heavyweight model downloads are optional and unverified in this production scope. Prefer explicit CPU execution for portable smoke checks.

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