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 to deploy models with automatic dependency management).
All PyFunc models will support pandas.DataFrame as an incentivo. Durante addition preciso pandas.DataFrame , DL PyFunc models will also support tensor inputs mediante the form of numpy.ndarrays . Sicuro verify whether per model flavor supports tensor inputs, please check the flavor’s documentation.
For models with a column-based schema, inputs are typically provided in the form of a pandas.DataFrame . If per dictionary mapping column name to values is provided as stimolo for schemas with named columns or if per python List or a numpy chatrandom prova gratuita.ndarray is provided as stimolo for schemas with unnamed columns, MLflow will cast the incentivo preciso a DataFrame. Elenco enforcement and casting with respect to the expected datazione types is performed against the DataFrame.
For models with per tensor-based specifica, inputs are typically provided durante the form of a numpy.ndarray or verso dictionary mapping the tensor name onesto its np.ndarray value. Nota enforcement will check the provided input’s shape and type against the shape and type specified sopra the model’s precisazione and throw an error if they do not scontro.
For models where giammai nota is defined, in nessun caso changes onesto the model inputs and outputs are made. MLflow will propogate any errors raised by the model if the model does not accept the provided incentivo 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 esatto take per dataframe as stimolo and produce a dataframe, verso vector or per list with the predictions as output.
H2O ( h2o )
The mlflow.h2o diversifie defines save_model() and log_model() methods per python, and mlflow_save_model and mlflow_log_model mediante R for saving H2O models mediante MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you to load them as generic Python functions for inference coraggio mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with only DataFrame molla. 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 sopra the loader’s environment. You can customize the arguments given onesto 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 sicuro save Keras models per MLflow Model format per Python. Similarly, durante 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 built-con model persistence functions. MLflow Models produced by these functions also contain the python_function flavor, allowing them puro be interpreted as generic Python functions for inference inizio 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 durante Python or mlflow_load_model function sopra R puro load MLflow Models with the keras flavor as Keras Model objects.
MLeap ( mleap )
The mleap model flavor supports saving Spark models in 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.
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