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=None, 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, model_format='xgb', extra_tags=None)[source]

Note

Autologging is known to be compatible with the following package versions: 1.4.2 <= xgboost <= 2.1.0. Autologging may not succeed when used with package versions outside of this range.

Enables (or disables) and configures autologging 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 now supported.

Parameters

importance_types – Importance types to log. If unspecified, defaults to ["weight"].
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. 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 XGBoost 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 XGBoost autologging integration. If False, enables the XGBoost 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 xgboost 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 XGBoost autologging. If False, show all events and warnings during XGBoost 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.
model_format – File format in which the model is to be saved.
extra_tags – A dictionary of extra tags to set on each managed run created by autologging.

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.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.xgboost.load_model(model_uri, dst_path=None)[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.
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 XGBoost model. An instance of either xgboost.Booster or XGBoost scikit-learn models, depending on the saved model class specification.

mlflow.xgboost.log_model(xgb_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, model_format='xgb', metadata=None, **kwargs)[source]

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

Parameters

xgb_model – XGBoost model (an instance of xgboost.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": [
                "xgboost==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.

You can leave code_paths argument unset but set infer_code_paths to True to let MLflow infer the model code paths. See infer_code_paths argument doc for details.

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. ["xgboost", "-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.
model_format – File format in which the model is to be saved.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.
kwargs – kwargs to pass to xgboost.Booster.save_model method.

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

mlflow.xgboost.save_model(xgb_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, model_format='xgb', metadata=None)[source]

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

Parameters

xgb_model – XGBoost model (an instance of xgboost.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": [
                "xgboost==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.

You can leave code_paths argument unset but set infer_code_paths to True to let MLflow infer the model code paths. See infer_code_paths argument doc for details.

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. ["xgboost", "-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.
model_format – File format in which the model is to be saved.
metadata – Custom metadata dictionary passed to the model and stored in the MLmodel file.