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