mlflow.h2o

The mlflow.h2o module provides an API for logging and loading H2O models. This module exports H2O models with the following flavors:

H20 (native) format
This is the main flavor that can be loaded back into H2O.
mlflow.pyfunc
Produced for use by generic pyfunc-based deployment tools and batch inference.
mlflow.h2o.get_default_conda_env()
Returns:The default Conda environment for MLflow Models produced by calls to save_model() and log_model().
mlflow.h2o.load_model(model_uri)

Load an H2O model from a local file (if run_id is None) or a run. This function expects there is an H2O instance initialised with h2o.init.

Parameters:model_uri

The location, in URI format, of the MLflow model. For example:

  • /Users/me/path/to/local/model
  • relative/path/to/local/model
  • s3://my_bucket/path/to/model
  • runs:/<mlflow_run_id>/run-relative/path/to/model

For more information about supported URI schemes, see Referencing Artifacts.

Returns:An H2OEstimator model object.
mlflow.h2o.log_model(h2o_model, artifact_path, conda_env=None, registered_model_name=None, **kwargs)

Log an H2O model as an MLflow artifact for the current run.

Parameters:
  • h2o_model – H2O model to be saved.
  • artifact_path – Run-relative artifact path.
  • conda_env

    Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env() environment is added to the model. The following is an example dictionary representation of a Conda environment:

    {
        'name': 'mlflow-env',
        'channels': ['defaults'],
        'dependencies': [
            'python=3.7.0',
            'pip': [
                'h2o==3.20.0.8'
            ]
        ]
    }
    
  • 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 – kwargs to pass to h2o.save_model method.
mlflow.h2o.save_model(h2o_model, path, conda_env=None, mlflow_model=<mlflow.models.Model object>, settings=None)

Save an H2O model to a path on the local file system.

Parameters:
  • h2o_model – H2O model to be saved.
  • path – Local path where the model is to be saved.
  • conda_env

    Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in get_default_conda_env(). If None, the default get_default_conda_env() environment is added to the model. The following is an example dictionary representation of a Conda environment:

    {
        'name': 'mlflow-env',
        'channels': ['defaults'],
        'dependencies': [
            'python=3.7.0',
            'pip': [
                'h2o==3.20.0.8'
            ]
        ]
    }
    
  • mlflow_modelmlflow.models.Model this flavor is being added to.