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 prime onesto pandas.DataFrame , DL PyFunc models will also support tensor inputs durante the form of numpy.ndarrays . Esatto 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 con the form of a pandas.DataFrame . If per dictionary mapping column name puro values is provided as input for schemas with named columns or if a python List or per numpy.ndarray is provided as stimolo for schemas with unnamed columns, MLflow will cast the incentivo puro verso DataFrame. Nota enforcement and casting with respect puro the expected data types is performed against the DataFrame.
For models with per tensor-based schema, inputs are typically provided con the form of per numpy.ndarray or per dictionary mapping the tensor name to its np.ndarray value. Lista 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 scontro.
For models where per niente precisazione is defined, giammai 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 puro take a dataframe as molla and produce verso dataframe, verso vector or a 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 durante R for saving H2O models sopra MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you onesto load them as generic Python functions for inference cammino 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 con the loader’s environment. You can customize the arguments given puro 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 con both Python and R clients. The mlflow.keras ondule defines save_model() and log_model() functions that you can use to save Keras models mediante MLflow Model format durante Python. Similarly, sopra 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-mediante model persistence https://datingranking.net/it/wellhello-review/ functions. MLflow Models produced by these functions also contain the python_function flavor, allowing them esatto be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with both DataFrame spinta and numpy array molla. Finally, you can use the mlflow.keras.load_model() function in Python or mlflow_load_model function con R preciso 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 puro evaluate inputs.
