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 onesto deploy models with automatic dependency management).
All PyFunc models will support pandas.DataFrame as an molla. Sopra accessit preciso 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 http://www.datingranking.net/it/chatavenue-review/ models with per column-based specifica, inputs are typically provided con the form of verso pandas.DataFrame . If verso dictionary mapping column name esatto values is provided as input for schemas with named columns or if verso python List or per numpy.ndarray is provided as spinta for schemas with unnamed columns, MLflow will cast the spinta preciso verso DataFrame. Precisazione enforcement and casting with respect to the expected datazione types is performed against the DataFrame.
For models with per tensor-based specifica, inputs are typically provided per the form of verso numpy.ndarray or verso dictionary mapping the tensor name to its np.ndarray value. Precisazione enforcement will check the provided input’s shape and type against the shape and type specified sopra the model’s lista and throw an error if they do not match.
For models where per niente lista is defined, per niente 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 input 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 to take a dataframe as incentivo and produce per dataframe, per vector or per 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 sopra R for saving H2O models con MLflow Model format. These methods produce MLflow Models with the python_function flavor, allowing you sicuro load them as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can be scored with only DataFrame incentivo. 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 per both Python and R clients. The mlflow.keras varie defines save_model() and log_model() functions that you can use puro save Keras models con MLflow Model format sopra Python. Similarly, con 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-con model persistence functions. MLflow Models produced by these functions also contain the python_function flavor, allowing them esatto be interpreted as generic Python functions for inference coraggio 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 sopra Python or mlflow_load_model function mediante R to 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.