mlflow.lightgbm

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

LightGBM (native) format

This is the main flavor that can be loaded back into LightGBM.

mlflow.pyfunc

Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.lightgbm.autolog(log_input_examples=False, log_model_signatures=True, log_models=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False)[source]

Note

Experimental: This method may change or be removed in a future release without warning.

Note

Autologging is known to be compatible with the following package versions: 2.3.1 <= lightgbm <= 3.2.0. Autologging may not succeed when used with package versions outside of this range.

Enables (or disables) and configures autologging from LightGBM to MLflow. Logs the following:

  • parameters specified in lightgbm.train.

  • metrics on each iteration (if valid_sets specified).

  • metrics at the best iteration (if early_stopping_rounds specified).

  • feature importance (both “split” and “gain”) as JSON files and plots.

  • trained model, including:
    • an example of valid input.

    • inferred signature of the inputs and outputs of the model.

Note that the scikit-learn API is not supported.

Parameters
  • log_input_examples – If True, input examples from training datasets are collected and logged along with LightGBM model artifacts during training. If False, input examples are not logged. Note: Input examples are MLflow model attributes and are only collected if log_models is also True.

  • log_model_signatures – If True, ModelSignatures describing model inputs and outputs are collected and logged along with LightGBM model artifacts during training. If False, signatures are not logged. Note: Model signatures are MLflow model attributes and are only collected if log_models is also True.

  • log_models – If True, trained models are logged as MLflow model artifacts. If False, trained models are not logged. Input examples and model signatures, which are attributes of MLflow models, are also omitted when log_models is False.

  • disable – If True, disables the LightGBM autologging integration. If False, enables the LightGBM autologging integration.

  • exclusive – If True, autologged content is not logged to user-created fluent runs. If False, autologged content is logged to the active fluent run, which may be user-created.

  • disable_for_unsupported_versions – If True, disable autologging for versions of lightgbm that have not been tested against this version of the MLflow client or are incompatible.

  • silent – If True, suppress all event logs and warnings from MLflow during LightGBM autologging. If False, show all events and warnings during LightGBM autologging.

mlflow.lightgbm.get_default_conda_env()[source]
Returns

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

mlflow.lightgbm.load_model(model_uri)[source]

Load a LightGBM model from a local file or a run.

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

A LightGBM model (an instance of lightgbm.Booster).

mlflow.lightgbm.log_model(lgb_model, artifact_path, conda_env=None, registered_model_name=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list]] = None, await_registration_for=300, **kwargs)[source]

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

Parameters
  • lgb_model – LightGBM model (an instance of lightgbm.Booster) to be saved. Note that models that implement the scikit-learn API are not supported.

  • 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 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': [
                'lightgbm==2.3.0'
            ]
        ]
    }
    

  • 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.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred 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:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • 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.

  • await_registration_for – Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.

  • kwargs – kwargs to pass to lightgbm.Booster.save_model method.

mlflow.lightgbm.save_model(lgb_model, path, conda_env=None, mlflow_model=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list]] = None)[source]

Save a LightGBM model to a path on the local file system.

Parameters
  • lgb_model – LightGBM model (an instance of lightgbm.Booster) to be saved. Note that models that implement the scikit-learn API are not supported.

  • 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 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': [
                'lightgbm==2.3.0'
            ]
        ]
    }
    

  • mlflow_modelmlflow.models.Model this flavor is being added to.

  • signature

    (Experimental) ModelSignature describes model input and output Schema. The model signature can be inferred 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:

    from mlflow.models.signature import infer_signature
    train = df.drop_column("target_label")
    predictions = ... # compute model predictions
    signature = infer_signature(train, predictions)
    

  • 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.