---

name: paddlex description: "Operate PaddleX 3.7.2 for low-code AI pipelines, module custom development, deployment, installation, and troubleshooting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

PaddleX operating skill

Use this skill when a task involves PaddleX, the PaddlePaddle low-code toolkit for pre-trained pipelines, model/module development, and deployment.

This skill is self-contained. Do not rely on the original PaddleX repository checkout while using it; use the bundled references and scripts here.

Route by user intent

User intent	Read
install PaddleX, verify imports, choose extras, or fix package setup	references/installation.md, references/troubleshooting.md, scripts/check_paddlex_install.py
run a ready-made AI pipeline through Python or CLI	sub-skills/pipelines/
export/get/edit a pipeline config, set devices/engines, save pipeline outputs	sub-skills/pipelines/
use create_model, validate a dataset, train, evaluate, export, predict, or convert module weights	sub-skills/modules/
deploy or accelerate a pipeline/model with HPI, serving, Paddle2ONNX, GenAI client/server, or hardware-specific runtimes	sub-skills/deployment/
decide whether this skill is stale for a new checkout	references/repo-provenance.md

Core public entry points

Installed PaddleX 3.7.2 exports these high-value APIs:

from paddlex import create_pipeline, create_predictor, create_model
from paddlex import build_dataset_checker, build_trainer, build_evaluator

Verified signatures during construction:

create_pipeline(pipeline=None, *, config=None, device=None, engine=None,
                engine_config=None, pp_option=None, use_hpip=None,
                hpi_config=None, **kwargs)
create_predictor(model_name, *, model_dir=None, device=None, engine=None,
                 engine_config=None, batch_size=1, pp_option=None,
                 use_hpip=False, hpi_config=None, genai_config=None, **kwargs)
create_model(model_name, model_dir=None, *args, **kwargs)

Installed CLI entry points:

paddlex --help
paddlex --pipeline image_classification --input demo.jpg --save_path output --device cpu
paddlex --get_pipeline_config image_classification
paddlex --serve --pipeline image_classification --host 0.0.0.0 --port 8080
paddlex --paddle2onnx --paddle_model_dir ./inference_model --onnx_model_dir ./onnx
paddlex_genai_server --help

Baseline install check

After installing PaddlePaddle and PaddleX, run:

python scripts/check_paddlex_install.py

A healthy baseline shows PaddleX importable, a PaddlePaddle version, CLI entry points, and whether Paddle is CPU-only or GPU-enabled.

Common decision rules

• Use pipelines for pre-trained pipeline inference and pipeline YAML orchestration.
• Use modules for custom datasets, model configs, training/evaluation/export, and create_model.
• Use deployment only after the user asks for serving, acceleration, conversion, GenAI hosting, or hardware packaging.
• CPU import success is not proof of GPU/HPI/accelerator readiness. Verify the specific PaddlePaddle wheel, plugin, and backend.
• Avoid full training, model downloads, server starts, or plugin installs until the user supplies data, runtime budget, and backend constraints.

Bundled references

• references/installation.md — install order, extras, PaddlePaddle wheel choice, plugins.
• references/troubleshooting.md — cross-cutting package/CLI/data/backend problems.
• references/pipeline-catalog.md — root summary of pipeline capabilities.
• references/module-overview.md — root summary of module capabilities.
• references/deployment-overview.md — root summary of deployment capabilities.
• references/repo-provenance.md — source commit/evidence/staleness information.
• references/repo-routing-metadata.json — router metadata for managed import.