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, registered_model_name=None)[source]

Note

Autologging is known to be compatible with the following package versions: 2.3.1 <= lightgbm <= 3.3.2. 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 or early_stopping

    callback is set).

  • 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 now 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.

  • registered_model_name – If given, each time a model is trained, it is registered as a new model version of the registered model with this name. The registered model is created if it does not already exist.

mlflow.lightgbm.get_default_conda_env(include_cloudpickle=False)[source]
Returns

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

mlflow.lightgbm.get_default_pip_requirements(include_cloudpickle=False)[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.lightgbm.load_model(model_uri, dst_path=None)[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.

  • 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

A LightGBM model (an instance of lightgbm.Booster) or a LightGBM scikit-learn model, depending on the saved model class specification.

mlflow.lightgbm.log_model(lgb_model, artifact_path, conda_env=None, code_paths=None, registered_model_name=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, scipy.sparse.csr.csr_matrix, scipy.sparse.csc.csc_matrix]] = None, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, **kwargs)[source]

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

Parameters
  • lgb_model – LightGBM model (an instance of lightgbm.Booster) or models that implement the scikit-learn API 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 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=3.7.0",
            {
                "pip": [
                    "lightgbm==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.

  • 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

    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 – 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.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["lightgbm", "-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.

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

Returns

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

mlflow.lightgbm.save_model(lgb_model, path, conda_env=None, code_paths=None, mlflow_model=None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, scipy.sparse.csr.csr_matrix, scipy.sparse.csc.csc_matrix]] = None, pip_requirements=None, extra_pip_requirements=None)[source]

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

Parameters
  • lgb_model – LightGBM model (an instance of lightgbm.Booster) or models that implement the scikit-learn API 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 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=3.7.0",
            {
                "pip": [
                    "lightgbm==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.

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

  • signature

    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 – 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.

  • pip_requirements – Either an iterable of pip requirement strings (e.g. ["lightgbm", "-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.