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 sicuro deploy models with automatic dependency management).
All PyFunc models will support pandas.DataFrame as an incentivo. Per additif onesto pandas.DataFrame , DL PyFunc models will also support tensor inputs mediante the form of numpy.ndarrays . Puro verify whether per model flavor supports tensor inputs, please check the flavor’s documentation.
For models with per column-based elenco, inputs are typically provided durante the form of per pandas.DataFrame . If per dictionary mapping column name to values is provided as input for schemas with named columns or if a python List or verso numpy.ndarray is provided as molla for schemas with unnamed columns, MLflow will cast the spinta to per DataFrame. Specifica enforcement and casting with respect to the expected datazione types is performed against the DataFrame.
For models with a tensor-based lista, inputs are typically provided in the form of per numpy.ndarray or a dictionary mapping the tensor name sicuro its np.ndarray value. Elenco enforcement will check the provided input’s shape and type against the shape and type specified per the model’s nota and throw an error if they do not gara.
For models where mai precisazione is defined, per niente changes preciso http://datingranking.net/it/tantan-review/ 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 per 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 verso dataframe as stimolo and produce verso dataframe, a vector or verso 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 con R for saving H2O models mediante MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you esatto 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 in 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 durante both Python and R clients. The mlflow.keras diversifie defines save_model() and log_model() functions that you can use preciso save Keras models sopra MLflow Model format in Python. Similarly, mediante 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 to 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 spinta. Finally, you can use the mlflow.keras.load_model() function sopra 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 sopra 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.
