366f3cb9d8
* Ensure RMM is 0.18 or later * Add use_rmm flag to global configuration * Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True * Update the demo * [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap
17 lines
610 B
Python
17 lines
610 B
Python
import xgboost as xgb
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import rmm
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from sklearn.datasets import make_classification
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# Initialize RMM pool allocator
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rmm.reinitialize(pool_allocator=True)
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# Inform XGBoost that RMM is used for GPU memory allocation
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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 = {'max_depth': 8, 'eta': 0.01, 'objective': 'multi:softprob', 'num_class': 3,
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'tree_method': 'gpu_hist'}
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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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