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.load_model(path, run_id=None)

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:

• path – Local filesystem path or run-relative artifact path to the model saved by mlflow.h2o.save_model().
• run_id – Run ID. If provided, combined with path to identify the model.

mlflow.h2o.log_model(h2o_model, artifact_path, conda_env=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 mlflow.h2o.DEFAULT_CONDA_ENV. If None, the default mlflow.h2o.DEFAULT_CONDA_ENV environment will be 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'
          ]
      ]
  }
  

• 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 mlflow.h2o.DEFAULT_CONDA_ENV. If None, the default mlflow.h2o.DEFAULT_CONDA_ENV environment will be 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_model – mlflow.models.Model this flavor is being added to.