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.
For models where no nota is defined, giammai changes preciso the model inputs and outputs are made. MLflow will propogate any errors raised by the model if the model does not accept the provided spinta type.
R Function ( crate )
The crate model flavor defines verso generic model format for representing an arbitrary R prediction function as an MLflow model using the crate function from the carrier package. The prediction function is expected puro take verso dataframe as molla and produce a dataframe, a vector or verso list with the predictions as output.
H2O ( h2o )
The mlflow.h2o ondule defines save_model() and log_model() methods mediante python, and mlflow_save_model and mlflow_log_model per R for saving H2O models durante MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you puro load them as generic Python functions for inference inizio mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with only DataFrame spinta. When you load MLflow Models with the h2o flavor using mlflow.pyfunc.load_model() , the h2o.init() method is called. Therefore, the correct version of h2o(-py) must be installed in the loader’s environment. You can customize the arguments given preciso h2o.init() by modifying the init entry of the persisted H2O model’s YAML configuration file: model.h2o/h2o.yaml .
Keras ( keras )
The keras model flavor enables logging and loading Keras models. It is available sopra both Python and R clients. The mlflow.keras module defines save_model() and log_model() functions that you can use to save Keras models durante MLflow Model format per Python. Similarly, con R, you can save or log the model using mlflow_save_model and mlflow_log_model. These functions serialize Keras models as HDF5 files using the Keras library’s Come eliminare l’account wellhello built-sopra model persistence functions. MLflow Models produced by these functions also contain the python_function flavor, allowing them sicuro be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with both DataFrame incentivo and numpy array spinta. Finally, you can use the mlflow.keras.load_model() function mediante Python or mlflow_load_model function sopra R sicuro load MLflow Models with the keras flavor as Keras Model objects.
MLeap ( mleap )
The mleap model flavor supports saving Spark models per MLflow format using the MLeap persistence mechanism. MLeap is an inference-optimized format and execution engine for Spark models that does not depend on SparkContext sicuro evaluate inputs.