from abc import ABCMeta, abstractmethod
from mlflow.utils.annotations import developer_stable
[docs]@developer_stable
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):
self._config = config
[docs] @abstractmethod
def predict(self, model_uri, input_path, output_path, content_type):
"""
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.
Args:
model_uri: URI pointing to the MLflow model to be used for scoring.
input_path: Path to the file with input data. If not specified, data is read from
stdin.
output_path: Path to the file with output predictions. If not specified, data is
written to stdout.
content_type: Specifies the input format. Can be one of {``json``, ``csv``}
"""
[docs] @abstractmethod
def serve(
self,
model_uri,
port,
host,
timeout,
enable_mlserver,
synchronous=True,
stdout=None,
stderr=None,
):
"""
Serve the specified MLflow model locally.
Args:
model_uri: URI pointing to the MLflow model to be used for scoring.
port: Port to use for the model deployment.
host: Host to use for the model deployment. Defaults to ``localhost``.
timeout: Timeout in seconds to serve a request. Defaults to 60.
enable_mlserver: Whether to use MLServer or the local scoring server.
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.
stdout: Redirect server stdout
stderr: Redirect server stderr
"""
[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.
"""
[docs] @abstractmethod
def build_image(
self, model_uri, image_name, install_mlflow, mlflow_home, enable_mlserver, base_image=None
): ...
[docs] @abstractmethod
def generate_dockerfile(
self, model_uri, output_path, install_mlflow, mlflow_home, enable_mlserver, base_image=None
): ...
[docs] @abstractmethod
def can_score_model(self):
"""
Check whether this flavor backend can be deployed in the current environment.
Returns:
True if this flavor backend can be applied in the current environment.
"""
[docs] def can_build_image(self):
"""
Returns:
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))