mlflow.models

The mlflow.models module provides an API for saving machine learning models in “flavors” that can be understood by different downstream tools.

The built-in flavors are:

• mlflow.pyfunc

• mlflow.h2o

• mlflow.keras

• mlflow.pytorch

• mlflow.sklearn

• mlflow.spark

• mlflow.tensorflow

For details, see MLflow Models.

class mlflow.models.FlavorBackend(config, **kwargs)

Bases: object

Abstract class for Flavor Backend. This class defines the API interface for local model deployment of MLflow model flavors.

can_build_image()

Returns

True if this flavor has a build_image method defined for building a docker container capable of serving the model, False otherwise.

abstract can_score_model()

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.

abstract predict(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.

Parameters

• 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}

• 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 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_json.html

prepare_env(model_uri)

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.

abstract serve(model_uri, port, host)

Serve the specified MLflow model locally.

Parameters

• 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.

class mlflow.models.Model(artifact_path=None, run_id=None, utc_time_created=None, flavors=None, **kwargs)

Bases: object

An MLflow Model that can support multiple model flavors. Provides APIs for implementing new Model flavors.

add_flavor(name, **params)

Add an entry for how to serve the model in a given format.

classmethod from_dict(model_dict)

Load a model from its YAML representation.

classmethod load(path)

Load a model from its YAML representation.

classmethod log(artifact_path, flavor, registered_model_name=None, **kwargs)

Log model using supplied flavor module. If no run is active, this method will create a new active run.

Parameters

• artifact_path – Run relative path identifying the model.

• flavor – Flavor module to save the model with. The module must have the save_model function that will persist the model as a valid MLflow model.

• registered_model_name – Note:: Experimental: This argument may change or be removed in a future release without warning. If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.

• kwargs – Extra args passed to the model flavor.

save(path)

Write the model as a local YAML file.

to_dict()

to_json()

to_yaml(stream=None)