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()[source]

Returns: The default Conda environment for MLflow Models produced by calls to save_model() and log_model().

mlflow.h2o.get_default_pip_requirements()[source]

Returns: A list of default pip requirements for MLflow Models produced by this flavor. Calls to save_model() and log_model() produce a pip environment that, at minimum, contains these requirements.

mlflow.h2o.load_model(model_uri, dst_path=None)[source]

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
- models:/<model_name>/<model_version>
- models:/<model_name>/<stage>
For more information about supported URI schemes, see Referencing Artifacts.
dst_path – The local filesystem path to which to download the model artifact. This directory must already exist. If unspecified, a local output path will be created.

Returns

An H2OEstimator model object.

mlflow.h2o.log_model(h2o_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None, **kwargs)[source]

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 describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "h2o==x.y.z"
            ],
        },
    ],
}
```
code_paths –
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
registered_model_name – If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.
signature –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["h2o", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.
kwargs – kwargs to pass to h2o.save_model method.

Returns

A ModelInfo instance that contains the metadata of the logged model.

mlflow.h2o.save_model(h2o_model, path, conda_env=None, code_paths=None, mlflow_model=None, settings=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes, tuple] = None, pip_requirements=None, extra_pip_requirements=None, metadata=None)[source]

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 describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements(). pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
```
{
    "name": "mlflow-env",
    "channels": ["conda-forge"],
    "dependencies": [
        "python=3.8.15",
        {
            "pip": [
                "h2o==x.y.z"
            ],
        },
    ],
}
```
code_paths –
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.

For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
mlflow_model – mlflow.models.Model this flavor is being added to.
settings – Settings to pass to h2o.init() when loading the model.
signature –
an instance of the ModelSignature class that describes the model’s inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
```
from mlflow.models import infer_signature

train = df.drop_column("target_label")
predictions = ...  # compute model predictions
signature = infer_signature(train, predictions)
```
input_example – one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements – Either an iterable of pip requirement strings (e.g. ["h2o", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements(). Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
extra_pip_requirements –
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the user’s current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t be specified at the same time:
- conda_env
- pip_requirements
- extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.