predict

MultiModalEndpoint.predict(data: str | list[str] | DataFrame, **inference_kwargs: Any) → Series[source]

Predict data with the deployed endpoint.

This is intended for low-latency inference. For larger inputs, use autogluon.cloud.MultiModalCloudPredictor.predict() instead.

Parameters:

  • data (str | list[str] | pd.DataFrame) –

    Data to predict. One of:

    • a pd.DataFrame or a local path to a data file.

    • a local path to a single image file, or a list of local paths to image files.

  • **inference_kwargs (Any) – Additional args passed to the predict call of the AutoGluon predictor on the endpoint. If data has an image column, pass image_column to name the column with absolute paths to local images; the images are encoded and sent with the request.

Returns:

pd.Series – Predictions for data.

SageMaker API

  • InvokeEndpoint: sends the data to the endpoint and returns the predictions. The payload is limited to 6 MB (4 MB for serverless endpoints).