physicalai-runtime-configuring-inference-pipeline

# Configuring the Inference Pipeline Pipeline order: observation → preprocessors → runner → postprocessors → action output. See `docs/how-to/inference/configure-pre-post-processing.md`. Core code: - `src/physicalai/inference/component_factory.py` — `ComponentRegistry`, `instantiate_component`, `_MAX_COMPONENT_DEPTH`. - `src/physicalai/inference/model.py` — builds processor chains from manifest specs. - Built-ins under `preprocessors/` and `postprocessors/`; runners under `runners/`. ## Workflow 1. **Read the manifest slice** for `preprocessors`, `postprocessors`, and `model.runner`. - Done when: you know whether specs use `type` (registry short name) or `class_path`. 2. **Prefer `type` for built-ins** registered in `component_factory` (e.g. normalize/denormalize patterns in docs). 3. **Use `class_path` + `init_args`** for explicit classes: ```yaml preprocessors: - class_path: physicalai.inference.preprocessors.StatsNormalizer init_args: artifact: stats.safetensors ``` - Done when: `init_args` paths resolve relative to the export directory via `resolve_artifact`. 4. **Add a new built-in processor**: - Implement subclass of `Preprocessor` / `Postprocessor` in the appropriate package. - Register a short `type` name in `component_factory` if manifest-friendly aliases are needed. - Add unit tests under `tests/unit/inference/preprocessors/` or `postprocessors/`. - Done when: manifest using `type` or `class_path` instantiates in a minimal `InferenceModel` test. 5. **Nested components** in `init_args` must stay within `_MAX_COMPONENT_DEPTH`; avoid cyclic specs. ## Validation loop ```bash uv run pytest tests/unit/inference/preprocessors tests/unit/inference/postprocessors tests/unit/inference/test_manifest.py -q ``` ## Required checks - Processor order matches training/export semantics (normalization before runner, denormalization after). - Artifact file names in manifests do not traverse paths (`..`, absolute paths). - New public processors appear in `docs/reference/inference-api.md` or how-to docs when user-visible. - Runner choice (`SinglePass`, chunking runners) stays consistent with `predict_action_chunk` vs `select_action` docs. ## References - `docs/reference/manifest-schema.md` - `docs/how-to/inference/use-manifest.md`