host_device_vector.cu:100: Check failed: Size() == other->Size() (0 vs. 115)

[allen20200111]

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[allen20200111]

---------------------------------------------------------------------------
XGBoostError                              Traceback (most recent call last)
<ipython-input-18-a0b9f07acd28> in <module>
    604 
    605     # grid_search_xgb()
--> 606     train_xgb(dataset12, dataset3)
    607 
    608     #gbm,dataset12_xx, dataset3_xx = train_lgb(dataset12, dataset3)

<ipython-input-18-a0b9f07acd28> in train_xgb(dataset12, dataset3)
    530     cvresult = xgb.cv(params, train_dmatrix, num_boost_round=10000, nfold=3, metrics='auc', seed=0, callbacks=[
    531         xgb.callback.print_evaluation(show_stdv=False),
--> 532         xgb.callback.early_stop(50)
    533     ])
    534 

/usr/local/lib/python3.6/dist-packages/xgboost/training.py in cv(params, dtrain, num_boost_round, nfold, stratified, folds, metrics, obj, feval, maximize, early_stopping_rounds, fpreproc, as_pandas, verbose_eval, show_stdv, seed, callbacks, shuffle)
    470     results = {}
    471     cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc,
--> 472                       stratified, folds, shuffle)
    473 
    474     # setup callbacks

/usr/local/lib/python3.6/dist-packages/xgboost/training.py in mknfold(dall, nfold, param, seed, evals, fpreproc, stratified, folds, shuffle)
    361     for k in range(nfold):
    362         # perform the slicing using the indexes determined by the above methods
--> 363         dtrain = dall.slice(in_idset[k])
    364         dtest = dall.slice(out_idset[k])
    365         # run preprocessing on the data set if needed

/usr/local/lib/python3.6/dist-packages/xgboost/core.py in slice(self, rindex, allow_groups)
    821                 c_bst_ulong(len(rindex)),
    822                 ctypes.byref(res.handle),
--> 823                 ctypes.c_int(1 if allow_groups else 0),
    824             )
    825         )

/usr/local/lib/python3.6/dist-packages/xgboost/core.py in _check_call(ret)
    188     """
    189     if ret != 0:
--> 190         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
    191 
    192 

XGBoostError: [09:40:10] /root/xgboost/src/common/host_device_vector.cu:100: Check failed: Size() == other->Size() (0 vs. 115) : 
Stack trace:
  [bt] (0) /usr/local/lib/python3.6/dist-packages/xgboost/lib/libxgboost.so(+0x391da2) [0x7fd4a6acbda2]
  [bt] (1) /usr/local/lib/python3.6/dist-packages/xgboost/lib/libxgboost.so(xgboost::HostDeviceVector<xgboost::FeatureType>::Copy(xgboost::HostDeviceVector<xgboost::FeatureType> const&)+0x203) [0x7fd4a6af1fe3]
  [bt] (2) /usr/local/lib/python3.6/dist-packages/xgboost/lib/libxgboost.so(xgboost::MetaInfo::Slice(xgboost::common::Span<int const, 18446744073709551615ul>) const+0x841) [0x7fd4a690d131]
  [bt] (3) /usr/local/lib/python3.6/dist-packages/xgboost/lib/libxgboost.so(xgboost::data::SimpleDMatrix::Slice(xgboost::common::Span<int const, 18446744073709551615ul>)+0xa5d) [0x7fd4a6944a7d]
  [bt] (4) /usr/local/lib/python3.6/dist-packages/xgboost/lib/libxgboost.so(XGDMatrixSliceDMatrixEx+0x81) [0x7fd4a68adf11]
  [bt] (5) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7fd56a425dae]
  [bt] (6) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x22f) [0x7fd56a42571f]
  [bt] (7) /usr/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(_ctypes_callproc+0x2b4) [0x7fd56a6395c4]
  [bt] (8) /usr/lib/python3.6/lib-dynload/_ctypes.cpython-36m-x86_64-linux-gnu.so(+0x11c33) [0x7fd56a639c33]

[trivialfis]

Could you please share the used parameters and type of input data?

[allen20200111]

OS is： Ubuntu 18.0.4
code is:
#train_dmatrix = xgb.DMatrix(X_train, label=y_train)
train_dmatrix = xgb.DMatrix(dataset12_x, label=dataset12.label)
predict_dmatrix = xgb.DMatrix(dataset3_x)
# xgboost模型训练
params = {'booster': 'gbtree',
'objective': 'binary:logistic',
'eval_metric': 'auc',
'gamma': 0.1,
'min_child_weight': 1.1,
'max_depth': 5,
'lambda': 10,
'subsample': 0.7,
'colsample_bytree': 0.7,
'colsample_bylevel': 0.7,
'eta': 0.01,
#'tree_method': 'hist',
'tree_method': 'gpu_hist',
#'n_gpus': '-1',
'seed': 0,
'nthread': cpu_jobs,
'predictor': 'gpu_predictor'
}

#使用xgb.cv优化num_boost_round参数

cvresult = xgb.cv(params, train_dmatrix, num_boost_round=10000, nfold=3, metrics='auc', seed=0, callbacks=[
    xgb.callback.print_evaluation(show_stdv=False),
    xgb.callback.early_stop(50)
])

Could you please share the used parameters and type of input data?

[allen20200111]

when used xgb.cv a error happen, but xgb train is ok

[allen20200111]

dataset12_x is input data, and the type of the data below:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 383386 entries, 0 to 252585
Columns: 115 entries, Distance to on_u13
dtypes: float64(94), int64(21)
memory usage: 339.3 MB