ModelInferenceAPI

class lightning.app.components.serve.serve.ModelInferenceAPI(input=None, output=None, host='127.0.0.1', port=7777, workers=0)

Bases: LightningWork, ABC

The ModelInferenceAPI Class enables to easily get your model served.

Parameters

• input (Optional[str]) – Optional input to be provided. This would make provide a built-in deserializer.

• output (Optional[str]) – Optional output to be provided. This would make provide a built-in serializer.

• host (str) – Address to be used to serve the model.

• port (int) – Port to be used to serve the model.

• workers (int) – Number of workers for the uvicorn. Warning, this won’t work if your subclass takes more arguments.

abstract build_model()

Override to define your model.

Return type

Any

configure_layout()

Configure the UI of this LightningWork.

You can either :rtype: str

1. Return a single Frontend object to serve a user interface for this Work.

2. Return a string containing a URL to act as the user interface for this Work.

3. Return None to indicate that this Work doesn’t currently have a user interface.

Example: Serve a static directory (with at least a file index.html inside).

from lightning.app.frontend import StaticWebFrontend


class Work(LightningWork):
    def configure_layout(self):
        return StaticWebFrontend("path/to/folder/to/serve")

Example: Arrange the UI of my children in tabs (default UI by Lightning).

class Work(LightningWork):
    def configure_layout(self):
        return [
            dict(name="First Tab", content=self.child0),
            dict(name="Second Tab", content=self.child1),
            dict(name="Lightning", content="https://lightning.ai"),
        ]

If you don’t implement configure_layout, Lightning will use self.url.

Note

This hook gets called at the time of app creation and then again as part of the loop. If desired, a returned URL can depend on the state. This is not the case if the work returns a Frontend. These need to be provided at the time of app creation in order for the runtime to start the server.

abstract predict(data)

Override to add your predict logic.

Return type

Any

run()

Override to add your own logic.

Raises

LightningPlatformException – If resource exceeds platform quotas or other constraints.