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from xgboost import dask as dxgb
from xgboost import testing as tm
import dask.array as da
import dask.distributed
def train_result(client, param, dtrain, num_rounds):
result = dxgb.train(
client,
param,
dtrain,
num_rounds,
verbose_eval=False,
evals=[(dtrain, "train")],
)
return result
class TestSYCLDask:
# The simplest test verify only one node training.
def test_simple(self):
cluster = dask.distributed.LocalCluster(n_workers=1)
client = dask.distributed.Client(cluster)
param = {}
param["tree_method"] = "hist"
param["device"] = "sycl"
param["verbosity"] = 0
param["objective"] = "reg:squarederror"
# X and y must be Dask dataframes or arrays
num_obs = int(1e4)
num_features = 20
rng = da.random.RandomState(1994)
X = rng.random_sample((num_obs, num_features), chunks=(1000, -1))
y = X.sum(axis=1)
dtrain = dxgb.DaskDMatrix(client, X, y)
result = train_result(client, param, dtrain, 10)
assert tm.non_increasing(result["history"]["train"]["rmse"])
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