XGBoost¶

The Snowflake ML Model Registry supports models created using XGBoost (models derived from xgboost.XGBModel or xgboost.Booster).

The following additional options can be used in the options dictionary when you call log_model:

Option	Description
`target_methods`	A list of the names of the methods available on the model object. Models derived from `XGBModel` have the following target methods by default, assuming the method exists: `predict`, `predict_proba`. (Before v1.4.0, `apply` was also included.) Models derived from `Booster` have the `predict` method by default.
`cuda_version`	The version of the CUDA runtime to be used when deploying to a platform with GPU; defaults to 11.8. If manually set to `None`, the model cannot be deployed to a platform having a GPU.

You must specify either the sample_input_data or signatures parameter when logging an XGBoost model so that the registry knows the signatures of the target methods.

Example¶

import xgboost
from sklearn import datasets, model_selection

cal_X, cal_y = datasets.load_breast_cancer(as_frame=True, return_X_y=True)
cal_X_train, cal_X_test, cal_y_train, cal_y_test = model_selection.train_test_split(cal_X, cal_y)
params = dict(n_estimators=100, reg_lambda=1, gamma=0, max_depth=3, objective="binary:logistic")
regressor = xgboost.train(params, xgboost.DMatrix(data=cal_X_train, label=cal_y_train))
model_ref = registry.log_model(
    regressor,
    model_name="xgBooster",
    version_name="v1",
    sample_input_data=cal_X_test,
    options={
        "target_methods": ["predict"],
        "method_options": {
            "predict": {"case_sensitive": True},
        },
    },
)
model_ref.run(cal_X_test[-10:])

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