Source code for 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.
:py:mod:`mlflow.pyfunc`
    Produced for use by generic pyfunc-based deployment tools and batch inference.
"""
import os
import yaml

import mlflow
from mlflow import pyfunc
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import ModelSignature
from mlflow.models.utils import ModelInputExample, _save_example
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.environment import _mlflow_conda_env
from mlflow.utils.model_utils import _get_flavor_configuration

FLAVOR_NAME = "h2o"


[docs]def get_default_conda_env(): """ :return: The default Conda environment for MLflow Models produced by calls to :func:`save_model()` and :func:`log_model()`. """ import h2o return _mlflow_conda_env( additional_conda_deps=None, additional_pip_deps=["h2o=={}".format(h2o.__version__)], additional_conda_channels=None, )
[docs]def save_model( h2o_model, path, conda_env=None, mlflow_model=None, settings=None, signature: ModelSignature = None, input_example: ModelInputExample = None, ): """ Save an H2O model to a path on the local file system. :param h2o_model: H2O model to be saved. :param path: Local path where the model is to be saved. :param conda_env: Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this describes the environment this model should be run in. At minimum, it should specify the dependencies contained in :func:`get_default_conda_env()`. If ``None``, the default :func:`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' ] ] } :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>` describes model input and output :py:class:`Schema <mlflow.types.Schema>`. The model signature can be :py:func:`inferred <mlflow.models.infer_signature>` from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example: .. code-block:: python from mlflow.models.signature import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions) :param input_example: (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded. :param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to. """ import h2o path = os.path.abspath(path) if os.path.exists(path): raise Exception("Path '{}' already exists".format(path)) model_data_subpath = "model.h2o" model_data_path = os.path.join(path, model_data_subpath) os.makedirs(model_data_path) if mlflow_model is None: mlflow_model = Model() if signature is not None: mlflow_model.signature = signature if input_example is not None: _save_example(mlflow_model, input_example, path) # Save h2o-model h2o_save_location = h2o.save_model(model=h2o_model, path=model_data_path, force=True) model_file = os.path.basename(h2o_save_location) # Save h2o-settings if settings is None: settings = {} settings["full_file"] = h2o_save_location settings["model_file"] = model_file settings["model_dir"] = model_data_path with open(os.path.join(model_data_path, "h2o.yaml"), "w") as settings_file: yaml.safe_dump(settings, stream=settings_file) conda_env_subpath = "conda.yaml" if conda_env is None: conda_env = get_default_conda_env() elif not isinstance(conda_env, dict): with open(conda_env, "r") as f: conda_env = yaml.safe_load(f) with open(os.path.join(path, conda_env_subpath), "w") as f: yaml.safe_dump(conda_env, stream=f, default_flow_style=False) pyfunc.add_to_model( mlflow_model, loader_module="mlflow.h2o", data=model_data_subpath, env=conda_env_subpath ) mlflow_model.add_flavor(FLAVOR_NAME, h2o_version=h2o.__version__, data=model_data_subpath) mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
[docs]def log_model( h2o_model, artifact_path, conda_env=None, registered_model_name=None, signature: ModelSignature = None, input_example: ModelInputExample = None, **kwargs ): """ Log an H2O model as an MLflow artifact for the current run. :param h2o_model: H2O model to be saved. :param artifact_path: Run-relative artifact path. :param conda_env: Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decsribes the environment this model should be run in. At minimum, it should specify the dependencies contained in :func:`get_default_conda_env()`. If ``None``, the default :func:`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' ] ] } :param registered_model_name: (Experimental) If given, create a model version under ``registered_model_name``, also creating a registered model if one with the given name does not exist. :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>` describes model input and output :py:class:`Schema <mlflow.types.Schema>`. The model signature can be :py:func:`inferred <mlflow.models.infer_signature>` from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example: .. code-block:: python from mlflow.models.signature import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions) :param input_example: (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded. :param kwargs: kwargs to pass to ``h2o.save_model`` method. """ Model.log( artifact_path=artifact_path, flavor=mlflow.h2o, registered_model_name=registered_model_name, h2o_model=h2o_model, conda_env=conda_env, signature=signature, input_example=input_example, **kwargs )
def _load_model(path, init=False): import h2o path = os.path.abspath(path) with open(os.path.join(path, "h2o.yaml")) as f: params = yaml.safe_load(f.read()) if init: h2o.init(**(params["init"] if "init" in params else {})) h2o.no_progress() return h2o.load_model(os.path.join(path, params["model_file"])) class _H2OModelWrapper: def __init__(self, h2o_model): self.h2o_model = h2o_model def predict(self, dataframe): import h2o predicted = self.h2o_model.predict(h2o.H2OFrame(dataframe)).as_data_frame() predicted.index = dataframe.index return predicted def _load_pyfunc(path): """ Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``. :param path: Local filesystem path to the MLflow Model with the ``h2o`` flavor. """ return _H2OModelWrapper(_load_model(path, init=True))
[docs]def 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``. :param 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`` - ``models:/<model_name>/<model_version>`` - ``models:/<model_name>/<stage>`` For more information about supported URI schemes, see `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html# artifact-locations>`_. :return: An `H2OEstimator model object <http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/intro.html#models>`_. """ local_model_path = _download_artifact_from_uri(artifact_uri=model_uri) flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME) # Flavor configurations for models saved in MLflow version <= 0.8.0 may not contain a # `data` key; in this case, we assume the model artifact path to be `model.h2o` h2o_model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.h2o")) return _load_model(path=h2o_model_file_path)