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Disable dense optimization in hist for distributed training. (#9272)

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This commit is contained in:
Jiaming Yuan
2023-06-10 02:31:34 +08:00
committed by GitHub
parent 8c1065f645
commit ea0deeca68
5 changed files with 44 additions and 10 deletions
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8
tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
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@@ -44,7 +44,7 @@ try:
from dask_cuda import LocalCUDACluster
from xgboost import dask as dxgb
from xgboost.testing.dask import check_init_estimation
from xgboost.testing.dask import check_init_estimation, check_uneven_nan
except ImportError:
pass
@@ -224,6 +224,12 @@ class TestDistributedGPU:
def test_init_estimation(self, local_cuda_client: Client) -> None:
check_init_estimation("gpu_hist", local_cuda_client)
def test_uneven_nan(self) -> None:
n_workers = 2
with LocalCUDACluster(n_workers=n_workers) as cluster:
with Client(cluster) as client:
check_uneven_nan(client, "gpu_hist", n_workers)
@pytest.mark.skipif(**tm.no_dask_cudf())
def test_dask_dataframe(self, local_cuda_client: Client) -> None:
run_with_dask_dataframe(dxgb.DaskDMatrix, local_cuda_client)