pyfunc.load_model() function. Note that the load_model function assumes that all dependencies are already available and will not check nor install any dependencies ( see model deployment section for tools preciso deploy models with automatic dependency management).
All PyFunc models will support pandas.DataFrame as an spinta. In prime to pandas.DataFrame , DL PyFunc models will also support tensor inputs mediante the form of numpy.ndarrays . Onesto verify whether per model flavor supports tensor inputs, please check the flavor’s documentation.
For models with per column-based lista, inputs are typically provided sopra the form of a pandas.DataFrame . If a dictionary mapping column name sicuro values is provided as incentivo for schemas with named columns or if a python List or verso numpy.ndarray is provided as stimolo for schemas with unnamed columns, MLflow will cast the spinta to verso DataFrame. Specifica enforcement and casting with respect to the expected data types is performed against the DataFrame.
For models with a tensor-based schema, inputs are typically provided in the form of a numpy.ndarray or verso dictionary mapping the tensor name onesto its np.ndarray value. Specifica enforcement will check the provided input’s shape and type against the shape and type specified con the model’s precisazione and throw an error if they do not confronto. Читать далее You can load python_function models sopra Python by calling the mlflow