mirror of
https://github.com/clearml/clearml-serving
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29 lines
695 B
Python
29 lines
695 B
Python
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import xgboost as xgb
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from clearml import Task
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task = Task.init(project_name="serving examples", task_name="train xgboost model", output_uri=True)
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X, y = load_iris(return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=100
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)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test, label=y_test)
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params = {"objective": "reg:squarederror", "eval_metric": "rmse"}
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bst = xgb.train(
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params,
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dtrain,
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num_boost_round=100,
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evals=[(dtrain, "train"), (dtest, "test")],
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verbose_eval=0,
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)
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bst.save_model("xgb_model")
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