mlflow.xgboost

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

XGBoost (native) format: This is the main flavor that can be loaded back into XGBoost.
mlflow.pyfunc: Produced for use by generic pyfunc-based deployment tools and batch inference.

mlflow.xgboost.autolog(importance_types=['weight'], log_input_examples=False, log_model_signatures=True)[source]

Note

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

Enables automatic logging from XGBoost to MLflow. Logs the following.

parameters specified in xgboost.train.
metrics on each iteration (if evals specified).
metrics at the best iteration (if early_stopping_rounds specified).
feature importance 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

importance_types – importance types to log.
log_input_examples – If True, input examples from training datasets are collected and logged along with XGBoost model artifacts during training. If False, input examples are not logged.
log_model_signatures – If True, ModelSignatures describing model inputs and outputs are collected and logged along with XGBoost model artifacts during training. If False, signatures are not logged.

mlflow.xgboost.get_default_conda_env()[source]

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

mlflow.xgboost.load_model(model_uri)[source]

Load an XGBoost 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

An XGBoost model (an instance of xgboost.Booster)

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

Log an XGBoost model as an MLflow artifact for the current run.

Parameters

xgb_model – XGBoost model (an instance of xgboost.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': [
            'xgboost==0.90'
        ]
    ]
}
```
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 xgboost.Booster.save_model method.

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

Save an XGBoost model to a path on the local file system.

Parameters

xgb_model – XGBoost model (an instance of xgboost.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': [
            'xgboost==0.90'
        ]
    ]
}
```
mlflow_model – mlflow.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.