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.Model(artifact_path=None, run_id=None, utc_time_created=datetime.datetime(2018, 9, 27, 19, 34, 43, 76848), flavors=None)

Bases: object

An MLflow Model that can support multiple model flavors.

add_flavor(name, **params)

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

classmethod load(path)

Load a model from its YAML representation.

classmethod log(artifact_path, flavor, **kwargs)

Log model using supplied flavor module.

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.
• kwargs – Extra args passed to the model flavor.

save(path)

Write the model as a local YAML file.

to_yaml(stream=None)