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use 4gpu machine for kmeans #816

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use 4gpu machine for kmeans #816

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72c20f1
use 4gpu machine for kmeans
sh1ng Dec 30, 2019
b28c046
change partition_size
sh1ng Jan 7, 2020
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2 changes: 1 addition & 1 deletion ci/Jenkinsfile-ppc64le-cuda10
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Expand Up @@ -24,7 +24,7 @@ def linuxwheel = "ppc64le-centos7-cuda10.whl"
def testtype = "dotest-single-gpu"
def testtype_multi_gpu = "dotest-multi-gpu"
def labelbuild = "ibm-power-gpu"
def labeltest_multi_gpu = "ibm-power-gpu && !mr-0xp2"
def labeltest_multi_gpu = "mr-0xp2"
def labeltest = "ibm-power-gpu && !mr-0xp2"
def labelruntime = "ibm-power-gpu"
def doingbenchmark = "0"
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2 changes: 1 addition & 1 deletion ci/base/Jenkinsfile-ppc64le-cuda10.base
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Expand Up @@ -18,7 +18,7 @@ def linuxwheel = "ppc64le-centos7-cuda10.whl"
def testtype = "dotest-single-gpu"
def testtype_multi_gpu = "dotest-multi-gpu"
def labelbuild = "ibm-power-gpu"
def labeltest_multi_gpu = "ibm-power-gpu && !mr-0xp2"
def labeltest_multi_gpu = "mr-0xp2"
def labeltest = "ibm-power-gpu && !mr-0xp2"
def labelruntime = "ibm-power-gpu"
def doingbenchmark = "0"
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2 changes: 1 addition & 1 deletion tests/python/open_data/gbm/test_xgboost.py
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Expand Up @@ -113,7 +113,7 @@ def test_xgboost_covtype_multi_gpu():
with LocalCUDACluster(n_workers=n_gpus, threads_per_worker=1) as cluster:
with Client(cluster) as client:
# Convert input data from numpy to XGBoost format
partition_size = 100000
partition_size = 10000

# remove when https://github.com/dmlc/xgboost/issues/4987 is fixed
dask_X_train = da.from_array(X_train, partition_size)
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