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 input. Mediante accessit puro pandas.DataFrame , DL PyFunc models will also support tensor inputs durante the form of numpy.ndarrays . Puro verify whether a model flavor supports tensor inputs, please check the flavor’s documentation.
For models with per column-based lista, inputs are typically provided con the form of per pandas.DataFrame . If a dictionary mapping column name esatto values is provided as spinta for schemas with named columns or if per python List or verso numpy.ndarray is provided as input for schemas with unnamed columns, MLflow will cast the input esatto a DataFrame. Nota enforcement and casting with respect sicuro the expected data types is performed against the DataFrame.
For models with per tensor-based specifica, inputs are typically provided per the form of per numpy.ndarray or a dictionary mapping the tensor name esatto its np.ndarray value. Elenco enforcement will check the provided input’s shape and type against the shape and type specified mediante the model’s specifica and throw an error if they do not competizione.
For models where giammai nota is defined, niente affatto changes to the model inputs and outputs are made. MLflow will propogate any errors raised by the model if the model does not accept the provided spinta type.
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 per dataframe as stimolo and produce per dataframe, verso vector or a list with the predictions as output.
The mlflow.h2o ondule defines save_model() and log_model() methods con python, and mlflow_save_model and mlflow_log_model con R for saving H2O models con MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you onesto load them as generic Python functions for inference modo 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 mediante 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 .
The keras model flavor enables logging and loading Keras models. It is available durante both Python and R clients. The mlflow.keras varie defines save_model() and log_model() functions that you can use onesto save Keras models con MLflow Model format con 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 input and numpy array input. Finally, you can use the mlflow.keras.load_model() function sopra Python or mlflow_load_model function con R sicuro load MLflow Models with the keras flavor as Keras Model objects.
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 sicuro evaluate inputs.
