---

name: pipelines description: "Use PaddleX pre-trained pipelines through create_pipeline, the paddlex pipeline CLI, config export, prediction, result saving, devices, engines, and parallel inference." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

PaddleX pipelines

Use this sub-skill when the user wants to run or configure a pre-trained PaddleX pipeline: image/CV pipelines, OCR/document pipelines, time-series pipelines, speech/video pipelines, retrieval/face pipelines, 3D pipelines, or VLM/document-understanding pipelines.

Route away from this sub-skill when the task is:

• single-model custom development, dataset checking, training, evaluation, export, or create_model usage — use ../modules/.
• serving, high-performance inference deployment, Paddle2ONNX, GenAI server/client setup, or plugin installation — use ../deployment/.

Start here

1. Identify whether the user already has a built-in pipeline name or a local pipeline YAML.
2. For Python API usage, use from paddlex import create_pipeline.
3. For CLI usage, use paddlex --pipeline ... or paddlex --get_pipeline_config ....
4. Decide device and engine separately from the pipeline name. CPU is the safest baseline; GPU/HPI and GenAI-backed routes require matching install extras or plugins.
5. For multi-artifact document pipelines, save to a directory instead of forcing a single output file.

Read references/pipeline-catalog.md for supported family names, common workflow patterns, and output behavior. Read references/pipeline-troubleshooting.md when pipeline creation, input parsing, result saving, backend selection, or remote service setup fails.

Minimal Python pattern

from paddlex import create_pipeline

pipeline = create_pipeline(
    pipeline="image_classification",  # or a local YAML path / config dict
    device="cpu",
)
for result in pipeline.predict("demo.jpg"):
    result.print()
    result.save_to_json("output")
    result.save_to_img("output")

Verified installed signature for PaddleX 3.7.2:

create_pipeline(pipeline=None, *, config=None, device=None, engine=None,
                engine_config=None, pp_option=None, use_hpip=None,
                hpi_config=None, **kwargs) -> BasePipeline

Important precedence rule: explicit Python/CLI arguments such as device, engine, use_hpip, engine_config, or pipeline-specific keyword arguments override defaults from a named pipeline or exported YAML config.

Minimal CLI patterns

# Export a built-in pipeline config before editing it.
paddlex --get_pipeline_config image_classification

# Run prediction from the CLI.
paddlex --pipeline image_classification --input demo.jpg --save_path output --device cpu

# Use a local YAML config.
paddlex --pipeline ./pipeline.yaml --input demo.jpg --save_path output

Bundled helper

Use scripts/run_pipeline_smoke.py when you need a small reusable wrapper that checks importability, creates a pipeline, runs predict, and tries common save methods without depending on the original PaddleX checkout.

Examples:

python scripts/run_pipeline_smoke.py --dry-run
python scripts/run_pipeline_smoke.py --pipeline image_classification --input demo.jpg --save-path output --device cpu
python scripts/run_pipeline_smoke.py --pipeline OCR --input demo.png --save-path output/ocr --kwargs-json '{"use_doc_orientation_classify": false}'

The helper may trigger model downloads when you create a real pipeline. Use --dry-run for a no-download install/API check.

High-value checks before answering a user

• Is the pipeline name exact? Names are case-sensitive in several document pipelines (OCR, PP-StructureV3, PaddleOCR-VL).
• Is the input type supported by that pipeline? Document pipelines may accept images, PDFs, URLs, or directories; time-series pipelines usually expect CSV/tabular inputs.
• Does the task require remote LLM/GenAI credentials or a running server (PP-DocTranslation, PP-ChatOCRv4, PaddleOCR-VL variants)? If yes, route deployment details to ../deployment/.
• Does the user ask for high-performance inference, serving, or Paddle2ONNX? Use ../deployment/ even if the source object is a pipeline.