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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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11
tests/test_distributed/test_with_dask/test_with_dask.py
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@@ -4,7 +4,6 @@ import json
import os
import pickle
import socket
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor
from functools import partial
@@ -41,7 +40,7 @@ from distributed import Client, LocalCluster
from toolz import sliding_window # dependency of dask
from xgboost.dask import DaskDMatrix
from xgboost.testing.dask import check_init_estimation
from xgboost.testing.dask import check_init_estimation, check_uneven_nan
dask.config.set({"distributed.scheduler.allowed-failures": False})
@@ -2014,6 +2013,14 @@ def test_init_estimation(client: Client) -> None:
check_init_estimation("hist", client)
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_uneven_nan(tree_method) -> None:
n_workers = 2
with LocalCluster(n_workers=n_workers) as cluster:
with Client(cluster) as client:
check_uneven_nan(client, tree_method, n_workers)
class TestDaskCallbacks:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping(self, client: "Client") -> None: