36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
import dask
|
|
from dask.distributed import Client
|
|
from dask_cuda import LocalCUDACluster
|
|
from sklearn.datasets import make_classification
|
|
|
|
import xgboost as xgb
|
|
|
|
|
|
def main(client):
|
|
# 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)
|
|
# In pratice one should prefer loading the data with dask collections instead of using
|
|
# `from_array`.
|
|
X = dask.array.from_array(X)
|
|
y = dask.array.from_array(y)
|
|
dtrain = xgb.dask.DaskDMatrix(client, X, label=y)
|
|
|
|
params = {'max_depth': 8, 'eta': 0.01, 'objective': 'multi:softprob', 'num_class': 3,
|
|
'tree_method': 'gpu_hist', 'eval_metric': 'merror'}
|
|
output = xgb.dask.train(client, params, dtrain, num_boost_round=100,
|
|
evals=[(dtrain, 'train')])
|
|
bst = output['booster']
|
|
history = output['history']
|
|
for i, e in enumerate(history['train']['merror']):
|
|
print(f'[{i}] train-merror: {e}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# To use RMM pool allocator with a GPU Dask cluster, just add rmm_pool_size option to
|
|
# LocalCUDACluster constructor.
|
|
with LocalCUDACluster(rmm_pool_size='2GB') as cluster:
|
|
with Client(cluster) as client:
|
|
main(client)
|