Source code for mlflow.models.flavor_backend

from abc import ABCMeta, abstractmethod

[docs]class FlavorBackend: """ Abstract class for Flavor Backend. This class defines the API interface for local model deployment of MLflow model flavors. """ __metaclass__ = ABCMeta def __init__(self, config, **kwargs): # pylint: disable=unused-argument self._config = config
[docs] @abstractmethod def predict(self, model_uri, input_path, output_path, content_type, json_format): """ Generate predictions using a saved MLflow model referenced by the given URI. Input and output are read from and written to a file or stdin / stdout. :param model_uri: URI pointing to the MLflow model to be used for scoring. :param input_path: Path to the file with input data. If not specified, data is read from stdin. :param output_path: Path to the file with output predictions. If not specified, data is written to stdout. :param content_type: Specifies the input format. Can be one of {``json``, ``csv``} :param json_format: Only applies if ``content_type == json``. Specifies how is the input data encoded in json. Can be one of {``split``, ``records``} mirroring the behavior of Pandas orient attribute. The default is ``split`` which expects dict like data: ``{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}``, where index is optional. For more information see """ pass
[docs] @abstractmethod def serve( self, model_uri, port, host, timeout, enable_mlserver, synchronous=True, stdout=None, stderr=None, ): """ Serve the specified MLflow model locally. :param model_uri: URI pointing to the MLflow model to be used for scoring. :param port: Port to use for the model deployment. :param host: Host to use for the model deployment. Defaults to ``localhost``. :param timeout: Timeout in seconds to serve a request. Defaults to 60. :param enable_mlserver: Whether to use MLServer or the local scoring server. :param synchronous: If True, wait until server process exit and return 0, if process exit with non-zero return code, raise exception. If False, return the server process `Popen` instance immediately. :param stdout: Redirect server stdout :param stderr: Redirect server stderr """ pass
[docs] def prepare_env(self, model_uri, capture_output=False): """ Performs any preparation necessary to predict or serve the model, for example downloading dependencies or initializing a conda environment. After preparation, calling predict or serve should be fast. """ pass
[docs] @abstractmethod def can_score_model(self): """ Check whether this flavor backend can be deployed in the current environment. :return: True if this flavor backend can be applied in the current environment. """ pass
[docs] def can_build_image(self): """ :return: True if this flavor has a `build_image` method defined for building a docker container capable of serving the model, False otherwise. """ return callable(getattr(self.__class__, "build_image", None))