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, log_datasets=True, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, registered_model_name=None, extra_tags=None)[source]

Note

Autologging is known to be compatible with the following package versions: 3.1.1 <= lightgbm <= 4.5.0. 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 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.
log_datasets – If True, train and validation dataset information is logged to MLflow Tracking if applicable. If False, dataset information is not logged.
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.
extra_tags – A dictionary of extra tags to set on each managed run created by autologging.

Example

import mlflow
from lightgbm import LGBMClassifier
from sklearn import datasets


def print_auto_logged_info(run):
    tags = {k: v for k, v in run.data.tags.items() if not k.startswith("mlflow.")}
    artifacts = [
        f.path for f in mlflow.MlflowClient().list_artifacts(run.info.run_id, "model")
    ]
    feature_importances = [
        f.path
        for f in mlflow.MlflowClient().list_artifacts(run.info.run_id)
        if f.path != "model"
    ]
    print(f"run_id: {run.info.run_id}")
    print(f"artifacts: {artifacts}")
    print(f"feature_importances: {feature_importances}")
    print(f"params: {run.data.params}")
    print(f"metrics: {run.data.metrics}")
    print(f"tags: {tags}")


# Load iris dataset
X, y = datasets.load_iris(return_X_y=True, as_frame=True)

# Initialize our model
model = LGBMClassifier(objective="multiclass", random_state=42)

# Auto log all MLflow entities
mlflow.lightgbm.autolog()

# Train the model
with mlflow.start_run() as run:
    model.fit(X, y)

# fetch the auto logged parameters and metrics
print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id))

Output

run_id: e08dd59d57a74971b68cf78a724dfaf6
artifacts: ['model/MLmodel',
            'model/conda.yaml',
            'model/model.pkl',
            'model/python_env.yaml',
            'model/requirements.txt']
feature_importances: ['feature_importance_gain.json',
                      'feature_importance_gain.png',
                      'feature_importance_split.json',
                      'feature_importance_split.png']
params: {'boosting_type': 'gbdt',
         'categorical_feature': 'auto',
         'colsample_bytree': '1.0',
         ...
         'verbose_eval': 'warn'}
metrics: {}
tags: {}

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.

Example

from lightgbm import LGBMClassifier
from sklearn import datasets
import mlflow

# Auto log all MLflow entities
mlflow.lightgbm.autolog()

# Load iris dataset
X, y = datasets.load_iris(return_X_y=True, as_frame=True)

# Initialize our model
model = LGBMClassifier(objective="multiclass", random_state=42)

# Train the model
model.fit(X, y)

# Load model for inference
model_uri = f"runs:/{mlflow.last_active_run().info.run_id}/model"
loaded_model = mlflow.lightgbm.load_model(model_uri)
print(loaded_model.predict(X[:5]))

Output

[0 0 0 0 0]

mlflow.lightgbm.log_model(lgb_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, await_registration_for=300, pip_requirements=None, extra_pip_requirements=None, metadata=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 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": [
                "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. 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.
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.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.
kwargs – kwargs to pass to lightgbm.Booster.save_model method.

Returns

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

Example

from lightgbm import LGBMClassifier
from sklearn import datasets
import mlflow
from mlflow.models import infer_signature

# Load iris dataset
X, y = datasets.load_iris(return_X_y=True, as_frame=True)

# Initialize our model
model = LGBMClassifier(objective="multiclass", random_state=42)

# Train the model
model.fit(X, y)

# Create model signature
predictions = model.predict(X)
signature = infer_signature(X, predictions)

# Log the model
artifact_path = "model"
with mlflow.start_run():
    model_info = mlflow.lightgbm.log_model(model, artifact_path, signature=signature)

# Fetch the logged model artifacts
print(f"run_id: {run.info.run_id}")
client = mlflow.MlflowClient()
artifacts = [f.path for f in client.list_artifacts(run.info.run_id, artifact_path)]
print(f"artifacts: {artifacts}")

Output

artifacts: ['model/MLmodel',
            'model/conda.yaml',
            'model/model.pkl',
            'model/python_env.yaml',
            'model/requirements.txt']

mlflow.lightgbm.save_model(lgb_model, path, conda_env=None, code_paths=None, mlflow_model=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 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 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": [
                "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. 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.
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. ["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.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.

Example

from pathlib import Path
from lightgbm import LGBMClassifier
from sklearn import datasets
import mlflow

# Load iris dataset
X, y = datasets.load_iris(return_X_y=True, as_frame=True)

# Initialize our model
model = LGBMClassifier(objective="multiclass", random_state=42)

# Train the model
model.fit(X, y)

# Save the model
path = "model"
mlflow.lightgbm.save_model(model, path)

# Load model for inference
loaded_model = mlflow.lightgbm.load_model(Path.cwd() / path)
print(loaded_model.predict(X[:5]))

Output

[0 0 0 0 0]