xgboost/demo/rmm_plugin/rmm_singlegpu.py
Jiaming Yuan 275da176ba
Document for device ordinal. (#9398)
- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots.
- Add GPU demos into the sphinx gallery.
- Add RMM demos into the sphinx gallery.
- Test for firing threads with different device ordinals.
2023-07-22 15:26:29 +08:00

28 lines
745 B
Python

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