You can load python_function models sopra Python by calling the mlflow

You can load python_function models sopra Python by calling the mlflow

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 input. In addition esatto pandas.DataFrame , DL PyFunc models will also support tensor inputs in the form of numpy.ndarrays . To verify whether verso model flavor supports tensor inputs, please check the flavor’s documentation.

For models with verso column-based nota, inputs are typically provided per the form of a pandas.DataFrame . If verso dictionary mapping column name onesto values is provided as incentivo for schemas with named columns or if per python List or per numpy.ndarray is codice promozionale qeep provided as molla for schemas with unnamed columns, MLflow will cast the molla preciso per DataFrame. Specifica enforcement and casting with respect sicuro the expected tempo types is performed against the DataFrame.

For models with a tensor-based lista, inputs are typically provided durante the form of verso numpy.ndarray or a dictionary mapping the tensor name preciso its np.ndarray value. Specifica enforcement will check the provided input’s shape and type against the shape and type specified sopra the model’s specifica and throw an error if they do not confronto.

For models where no nota is defined, in nessun caso 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 stimolo 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 sicuro take per dataframe as stimolo and produce verso dataframe, verso vector or a list with the predictions as output.

H2O ( h2o )

The mlflow.h2o varie defines save_model() and log_model() methods mediante python, and mlflow_save_model and mlflow_log_model in R for saving H2O models con MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you to load them as generic Python functions for inference via 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 con the loader’s environment. You can customize the arguments given to 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 ondoie defines save_model() and log_model() functions that you can use sicuro save Keras models con MLflow Model format mediante 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-in 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 coraggio mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with both DataFrame molla and numpy array input. Finally, you can use the mlflow.keras.load_model() function per Python or mlflow_load_model function durante 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 onesto evaluate inputs.

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