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name: deployment description: "Deploy PaddleX pipelines and models with high-performance inference, serving, Paddle2ONNX, GenAI client/server, backend plugins, and hardware-specific runtime caveats." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

PaddleX deployment

Use this sub-skill when the user wants to deploy, accelerate, serve, convert, or host PaddleX outputs. Typical requests mention high-performance inference (HPI), use_hpip, serving, high-stability serving, Paddle2ONNX, ONNX/TensorRT/OpenVINO, paddlex --install, paddlex_genai_server, GenAI client/server, or hardware-specific runtime setup.

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

• ordinary pre-trained pipeline prediction or pipeline config export — use ../pipelines/ first.
• dataset checking, training, evaluation, export from a module config, or create_model prediction — use ../modules/ first.

Start here

1. Identify the deployment family: HPI, serving, high-stability serving, Paddle2ONNX, GenAI client/server, or on-device/accelerator-specific packaging.
2. Confirm the source artifact: built-in pipeline name/YAML, exported module inference directory, or GenAI model/backend config.
3. Confirm plugins and backends before running deployment commands. PaddleX separates the base package from deployment plugins.
4. Keep GPU/TensorRT/vendor-accelerator claims conditional on the installed PaddlePaddle/backend stack.

Read references/deployment-overview.md for command patterns and backend boundaries. Read references/deployment-troubleshooting.md for plugin, HPI, server, GenAI, Paddle2ONNX, and hardware failures.

Common plugin installs

paddlex --install serving
paddlex --install paddle2onnx
paddlex --install hpi-cpu
paddlex --install hpi-gpu
paddlex --install genai-client
paddlex --install genai-vllm-server
paddlex --install genai-sglang-server

Install only the plugin needed for the selected workflow. Do not install GPU/HPI/GenAI server stacks merely for a CPU pipeline smoke.

Minimal command patterns

# Pipeline serving.
paddlex --serve --pipeline image_classification --host 0.0.0.0 --port 8080

# Pipeline prediction with HPI enabled when the environment supports it.
paddlex --pipeline image_classification --input demo.jpg --save_path output --use_hpip

# Paddle2ONNX conversion of an exported Paddle model directory.
paddlex --paddle2onnx --paddle_model_dir ./inference_model --onnx_model_dir ./onnx --opset_version 7

# GenAI server entry point for supported backends/models.
paddlex_genai_server --help

Bundled helper

Use scripts/check_deployment_options.py to inspect installed plugin/import availability and print the deployment commands this skill expects.

python scripts/check_deployment_options.py

This helper is read-only; it does not install plugins or start servers.

Boundary reminders

• HPI and serving are pipeline/runtime deployment concerns; they do not replace module training or export.
• Paddle2ONNX expects an exported Paddle inference model directory, not an arbitrary training checkpoint.
• GenAI client pipelines need a server URL or equivalent backend configuration when using remote/server-backed models.
• On-device and vendor accelerator paths require platform-specific SDK/toolchain prerequisites and are reference/advanced workflows unless explicitly prepared.