mlflow.h2o

MLflow integration for H2O.

mlflow.h2o.load_model(path, run_id=None)

Load a H2O model from a local file (if run_id is None) or a run.

This function expects there is a h2o instance initialised with h2o.init().

mlflow.h2o.load_pyfunc(path)

Loads an H2O model from the directory at path as a Python Function model. Note that this method calls h2o.init(...), so the right version of h2o(-py) must be in the environment. The arguments given to h2o.init(...) can be customized in path/h2o.yaml under the key init.

mlflow.h2o.log_model(h2o_model, artifact_path, **kwargs)

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

mlflow.h2o.save_model(h2o_model, path, conda_env=None, mlflow_model=<mlflow.models.Model object>, settings=None)

Save a 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.
• mlflow_model – MLflow model config this flavor is being added to.