Source code for mlflow.deployments

Exposes functionality for deploying MLflow models to custom serving tools.

Note: model deployment to AWS Sagemaker can currently be performed via the
:py:mod:`mlflow.sagemaker` module. Model deployment to Azure can be performed by using the
`azureml library <>`_.

MLflow does not currently provide built-in support for any other deployment targets, but support
for custom targets can be installed via third-party plugins. See a list of known plugins
`here <>`_.

This page largely focuses on the user-facing deployment APIs. For instructions on implementing
your own plugin for deployment to a custom serving tool, see
`plugin docs <>`_.
import json

from mlflow.exceptions import MlflowException
from mlflow.deployments.base import BaseDeploymentClient
from mlflow.deployments.interface import get_deploy_client, run_local
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE

[docs]class PredictionsResponse(dict): """ Represents the predictions and metadata returned in response to a scoring request, such as a REST API request sent to the ``/invocations`` endpoint of an MLflow Model Server. """
[docs] def get_predictions(self, predictions_format="dataframe", dtype=None): """ Get the predictions returned from the MLflow Model Server in the specified format. :param predictions_format: The format in which to return the predictions. Either ``"dataframe"`` or ``"ndarray"``. :param dtype: The NumPy datatype to which to coerce the predictions. Only used when the ``"ndarray"`` ``predictions_format`` is specified. :throws: Exception if the predictions cannot be represented in the specified format. :return: The predictions, represented in the specified format. """ import numpy as np import pandas as pd if predictions_format == "dataframe": predictions = self["predictions"] if isinstance(predictions, str): return pd.DataFrame(data=[predictions]) return pd.DataFrame(data=predictions) elif predictions_format == "ndarray": return np.array(self["predictions"], dtype) else: raise MlflowException( f"Unrecognized predictions format: '{predictions_format}'", INVALID_PARAMETER_VALUE, )
[docs] def to_json(self, path=None): """ Get the JSON representation of the MLflow Predictions Response. :param path: If specified, the JSON representation is written to this file path. :return: If ``path`` is unspecified, the JSON representation of the MLflow Predictions Response. Else, None. """ if path is not None: with open(path, "w") as f: json.dump(dict(self), f) else: return json.dumps(dict(self))
@classmethod def from_json(cls, json_str): try: parsed_response = json.loads(json_str) except Exception as e: raise MlflowException("Predictions response contents are not valid JSON") from e if not isinstance(parsed_response, dict) or "predictions" not in parsed_response: raise MlflowException( f"Invalid response. Predictions response contents must be a dictionary" f" containing a 'predictions' field. Instead, received: {parsed_response}" ) return PredictionsResponse(parsed_response)
__all__ = ["get_deploy_client", "run_local", "BaseDeploymentClient", "PredictionsResponse"]