23 lines
651 B
Python
23 lines
651 B
Python
import rmm
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from sklearn.datasets import make_classification
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import xgboost as xgb
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# Initialize RMM pool allocator
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rmm.reinitialize(pool_allocator=True)
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# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
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# xgb.set_config(use_rmm=True)
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X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
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dtrain = xgb.DMatrix(X, label=y)
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params = {
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"max_depth": 8,
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"eta": 0.01,
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"objective": "multi:softprob",
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"num_class": 3,
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"tree_method": "gpu_hist",
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}
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# XGBoost will automatically use the RMM pool allocator
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bst = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, "train")])
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