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ataIterINS_8RowBlockIjfEEEE_ZTSN4dmlc12RowBlockIterIjfEE_ZTIN4dmlc12RowBlockIterIjfEE_ZTSN4dmlc8DataIterINS_8RowBlockImfEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImfEEEE_ZTSN4dmlc12RowBlockIterImfEE_ZTIN4dmlc12RowBlockIterImfEE_ZTSN4dmlc8DataIterINS_8RowBlockIjiEEEE_ZTIN4dmlc8DataIterINS_8RowBlockIjiEEEE_ZTSN4dmlc12RowBlockIterIjiEE_ZTIN4dmlc12RowBlockIterIjiEE_ZTSN4dmlc8DataIterINS_8RowBlockImiEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImiEEEE_ZTSN4dmlc12RowBlockIterImiEE_ZTIN4dmlc12RowBlockIterImiEE_ZTSN4dmlc8DataIterINS_8RowBlockIjlEEEE_ZTIN4dmlc8DataIterINS_8RowBlockIjlEEEE_ZTSN4dmlc12RowBlockIterIjlEE_ZTIN4dmlc12RowBlockIterIjlEE_ZTSN4dmlc8DataIterINS_8RowBlockImlEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImlEEEE_ZTSN4dmlc12RowBlockIterImlEE_ZTIN4dmlc12RowBlockIterImlEE_ZTSN4dmlc6ParserIjfEE_ZTIN4dmlc6ParserIjfEE_ZTSN4dmlc6ParserImfEE_ZTIN4dmlc6ParserImfEE_ZTSN4dmlc6ParserIjiEE_ZTIN4dmlc6ParserIjiEE_ZTSN4dmlc6ParserImiEE_ZTIN4dmlc6ParserImiEE_ZTSN4dmlc6ParserIjlEE_ZTIN4dmlc6ParserIjlEE_ZTSN4dmlc6ParserImlEE_ZTIN4dmlc6ParserImlEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEE_ZTSN4dmlc4data11DiskRowIterIjfEE_ZTIN4dmlc4data11DiskRowIterIjfEE_ZTSN4dmlc4data12BasicRowIterIjfEE_ZTIN4dmlc4data12BasicRowIterIjfEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc4data11DiskRowIterImfEE_ZTIN4dmlc4data11DiskRowIterImfEE_ZTSN4dmlc4data12BasicRowIterImfEE_ZTIN4dmlc4data12BasicRowIterImfEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc4data11DiskRowIterIjiEE_ZTIN4dmlc4data11DiskRowIterIjiEE_ZTSN4dmlc4data12BasicRowIterIjiEE_ZTIN4dmlc4data12BasicRowIterIjiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc4data11DiskRowIterImiEE_ZTIN4dmlc4data11DiskRowIterImiEE_ZTSN4dmlc4data12BasicRowIterImiEE_ZTIN4dmlc4data12BasicRowIterImiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc4data11DiskRowIterIjlEE_ZTIN4dmlc4data11DiskRowIterIjlEE_ZTSN4dmlc4data12BasicRowIterIjlEE_ZTIN4dmlc4data12BasicRowIterIjlEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc4data11DiskRowIterImlEE_ZTIN4dmlc4data11DiskRowIterImlEE_ZTSN4dmlc4data12BasicRowIterImlEE_ZTIN4dmlc4data12BasicRowIterImlEE_ZTSN4dmlc4data10ParserImplIjfEE_ZTIN4dmlc4data10ParserImplIjfEE_ZTSN4dmlc4data14TextParserBaseIjfEE_ZTIN4dmlc4data14TextParserBaseIjfEE_ZTSN4dmlc4data12LibSVMParserIjfEE_ZTIN4dmlc4data12LibSVMParserIjfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc4data14ThreadedParserIjfEE_ZTIN4dmlc4data14ThreadedParserIjfEE_ZTSN4dmlc4data10ParserImplImfEE_ZTIN4dmlc4data10ParserImplImfEE_ZTSN4dmlc4data14TextParserBaseImfEE_ZTIN4dmlc4data14TextParserBaseImfEE_ZTSN4dmlc4data12LibSVMParserImfEE_ZTIN4dmlc4data12LibSVMParserImfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTSN4dmlc4data14ThreadedParserImfEE_ZTIN4dmlc4data14ThreadedParserImfEE_ZTSN4dmlc4data11LibFMParserIjfEE_ZTIN4dmlc4data11LibFMParserIjfEE_ZTSN4dmlc4data11LibFMParserImfEE_ZTIN4dmlc4data11LibFMParserImfEE_ZTSN4dmlc4data9CSVParserIjfEE_ZTIN4dmlc4data9CSVParserIjfEE_ZTSN4dmlc4data9CSVParserImfEE_ZTIN4dmlc4data9CSVParserImfEE_ZTSN4dmlc4data10ParserImplIjiEE_ZTIN4dmlc4data10ParserImplIjiEE_ZTSN4dmlc4data14TextParserBaseIjiEE_ZTIN4dmlc4data14TextParserBaseIjiEE_ZTSN4dmlc4data9CSVParserIjiEE_ZTIN4dmlc4data9CSVParserIjiEE_ZTSN4dmlc4data10ParserImplImiEE_ZTIN4dmlc4data10ParserImplImiEE_ZTSN4dmlc4data14TextParserBaseImiEE_ZTIN4dmlc4data14TextParserBaseImiEE_ZTSN4dmlc4data9CSVParserImiEE_ZTIN4dmlc4data9CSVParserImiEE_ZTSN4dmlc4data10Pa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Use `XGQuantileDMatrixCreateFromCallback` instead.XGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workersstatic_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureCheck failed: ec == std::errc()basic_string::_M_createDMLC_LOG_STACK_TRACE_DEPTH%zuUSE_CUDAUSE_NCCLUSE_RMMInvalid pointer argument: BUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPGCC_VERSIONDEBUGUSE_FEDERATEDCheck failed: limit >= 1c_interface_strCheck failed: mfnameout_lenout_dptrjson_parametersout_strdtraingradhessstr.size() >= 3str[0] == '{'jsonubjbufCheck failed: p_outname_strfeature_namefeature_typedmatsevnamesout_featuresout_indptrout_indicesout_dataCheck failed: page.pagen_threads >= 1deprecatedUnknown format: `out_resultc_json_configiteration_beginiteration_endntree_limittrainingstrict_shapeout_dimout_shapecache_idCache ID is not supported yetCheck failed: p_predtCheck failed: proxyarray_interfaceftypetextfmap+sCheck failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseimportance_typefeature_mapfeature_namestree_idxidx < names_.size()out_n_featuresout_scoresautocache_prefixnthreadnextresetInvalid handle to ref.Check failed: _refmax_binnbatchCheck failed: ejson_str%ldUnknown global parameters: { Stack trace: +basic_string::basic_string [bt] () %s: __pos (which is %zu) > this->size() (which is %zu) vs. basic_string::appendOnly %d dimensional array is valid.vector::_M_default_appendbasic_string::_M_replace%02d:%02d:%02d] /__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer lengthwrite position exceed fixed buffer size/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hNot implemented.Slicing DMatrix is not supported for Proxy DMatrix./__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support./__w/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hIncorrect type for: ``, expecting: ``, got: ``.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. Or open an issue.-basic_string::_M_replace_aux/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html ,LllCannot reset RecordBatchesIterAdapterCheck failed: pp_mInvalid DMatrix handleCheck failed: p_mchunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rows++ll/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from Argument `` is required for `learnergradient_boostergblineargbtree_model_paramnum_parallel_treestoiUnknown booster:Invalid missing value: Argument `missing` is required.Missing `version' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.Missing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4`typestr' should be of format .Missing `shape' field for array interfaceMissing `data' field for array interfacetypestrEmpty data passed in.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)Empty data with non-zero shape./__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())unknown feature type, use i for indicator and q for quantityfloatcustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.feature_map.Size() == n_features%luout_modelsxR`S`S`S`S`SPS`S`S@S`S`S`S`S`S`S0S`S`S`S`S`S`S`S`S`S`S`S`S`S`S`S S`S`SSS`SR`S`SR`S`S`S`S`S`SRXGBoosterFeatureScoreXGBoosterGetModelRawXGBoosterSaveModelToBufferInplacePredictImplXGBoosterPredictFromDMatrixXGDMatrixCreateFromArrowCallbackXGDeviceQuantileDMatrixCreateFromCallbackXGDMatrixCreateFromCallbackmap::atmaskj_shape.size() == 1type_length == 1stridesstrides.size() == 1Memory should be contiguous.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.get(strides.at(0)) == type_lengthj_strides.size() == j_shape.size()stride and shape don't match.i < Dptr % alignment == 0Input pointer misalignment.streamCheck failed: mask_it == array.cend() || IsA(mask_it->second)Masked array is not yet supported.f|p\f|\f|Check failed: m == 1 || n == 1n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting. :Z :Z :get(array).size() == 1Column: vector::_M_range_insertCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data18ArrowColumnarBatchEN4dmlc8DataIterISt6vectorISt10unique_ptrIN7xgboost4data18ArrowColumnarBatchESt14default_deleteIS5_EESaIS8_EEEEN7xgboost4data24RecordBatchesIterAdapterEN7xgboost4data12DMatrixProxyEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13ExtSparsePageEEELN9__gnu_cxx12_Lock_policyE2EE dimensional arr is not supporte ?0C?C/__w/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hUnknown allreduce operationUnknown data type()l)) *\*,(|(=1=0Failed to initialize RabitWorld size is less than 1.Rank is less than 0./__w/ml-builds/ml-builds/xgboost/src/collective/communicator.h is greater than world_size - 1: XGBOOST_COMMUNICATORrabitfederatedUnknown communicator type /__w/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.N7xgboost10collective12CommunicatorEN7xgboost10collective16NoOpCommunicatorEN7xgboost10collective17RabitCommunicatorEXGBOOST_COMMUNICxgboost_communic_Z/__w/ml-builds/ml-builds/xgboost/src/collective/socket.ccCheck failed: !this->IsClosed()/__w/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.hthis->RecvAll(&len, sizeof(len)) == sizeof(len)str.size() < std::numeric_limits::max()this->SendAll(&len, sizeof(len)) == sizeof(len)getsockopt(handle_, SOL_SOCKET, SO_DOMAIN, reinterpret_cast(&domain), &len)static_cast(socket.Domain()) == static_cast(addr.Domain())Failed to get addr info for: 127.0.0.10.0.0.0::1::): Failed to call ``: recvFailed to recv string length.bytes == lenFailed to recv string.sendFailed to send string length.bytes == str.size()Failed to send string.Unknown IA family.system::CloseSocket(handle_)@je _vH@0ļ4&k7y؅W4Ngm=`X@xPMJ- yCx(,*42q0/'|!1錠9Y>R$n`F"x|+MraB ydiWC*fJ\)c7]`xxֲK}Yq\Ck 3TgffffffQ~jt*Ǻ"<8b->Nc{-1+x#c^- Knu7X%Y^_GzK`eq]p J&gչ o"4%QttS`؈$C(ӦfHRl ۩#j+vv=IJ+10Q=ѬVZbu)j}և]:1VӾa'miK$!Q㢢P@9j>`KUq݉ [qB1h?(SuYQ!"GMk9X 3D۸N <C.{$~ۃ6/l##ՂF!00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899vector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val?/__w/ml-builds/ml-builds/xgboost/src/common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast(n_columns)column_sizes_tloc[i].size() == static_cast(n_columns)/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.ccentries_per_column.size() == info.num_col_Check failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))invalid init parameter/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachableSortedSketchContainerSt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EE)A@/__w/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.size()Size() == other.Size()/__w/ml-builds/ml-builds/xgboost/src/common/io.ccpointer_ <= buffer_.size()Check failed: outfile://Opening basic_string::substr/__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamE/__w/ml-builds/ml-builds/xgboost/src/common/json.cc can not be indexed by string. can not be indexed by Integer.Check failed: ret.ec == std::errc()High precision number is not supported.` is not supported for typed array.NumberNullF32ArrayU8ArrayI32ArrayI64Array\\\"\b\f\n\r\t\u%04xObject of type , around character position: , "\0Unknown construct Unknown escapeExpecting null value "null"Expecting digitf64 is not supported.ch != -1cursor_.Pos(): raw_str_.size():ȋ؋ 8\Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)Invaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it agai matrix with entries loaded from num_rownum_colnum_nonzerolabelsgroup_ptrbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightsfield_cnt == kNumFieldWrong number of fieldslabel_lower_boundlabel_upper_boundUnknown float field name: Unknown uint32 field name: Device that data is on: `gpu_id` for XGBoost: group_ptr_.back() == num_row_weights_.Size() == num_row_labels.Shape(0) == num_row_self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()size == this->num_col_Length of Check failed: infoUnknown feature info name: Unknown feature info: .rLoad part of data partscsvlibsvm . Encountered parser error: that.group_ptr_.size() != 0Check failed: t.CContiguous()Incorrect size for labels.Check failed: non_decUnknown key for MetaInfo: Check failed: keyMetaInfo: Invalid format for name == expected_name Expected field: , got: Expected field of type: got field type: Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: !is_scalar to be a vector; got a scalarshape.second == 1 to be a tensor; got a scalar is no longer supported. again.major >= 1minor >= 6MetaInfo: invalid formatnum_field >= expectedfields.)/__w/ml-builds/ml-builds/xgboost/src/data/data.ccUnknown data type for getting meta info.Check failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.Size of weights must equal to number of rows.Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.self_offset.size() == other_offset.size()All feature_types must be one of {int, float, i, q, c}. must be equal to number of columns./__w/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hthis->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.No format parameter is provided in input uri, but found file extension: Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csvthis->num_col_ == that.num_col_Number of columns must be consistent across batches.transpose.offset.Size() == num_columns + 1Check failed: array.valid.Size() == 0Meta info like label or weight can not have missing value.Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.this->labels.Size() % this->num_row_ == 0Label contains NaN, infinity or a value too large.`qid` must be sorted in non-decreasing order along with data.Weights must be positive values.Feature weight must be greater than 0.Check failed: strm->Read(&name)Check failed: strm->Read(&type_val)Check failed: type == expected_typeCheck failed: strm->Read(&is_scalar)Check failed: strm->Read(field)Check failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.Check failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field)Binary DMatrix generated by XGBoost: Please process and save your data in current version: Check failed: fi->Read(&num_field)MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains MetaInfo: the given binary file contains extra fields which will be ignored.xЁ @XЀPԄ ,Dd|t؇0Ph܊ ,Dd|LPp8` T4ԣLLH0إXX̨lLḑ ,ḑ`HpЩ(pЩTData()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification.shape[i] == r.Shape(i)/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix./__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././././any.hCheck failed: type_ != nullptrCheck failed: *(type_->ptype_info) == typeid(T)The any container is empty requested=The stored type mismatch stored=format.front() == '.'/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()/__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hkey >= builder_base_row_offset0Px 0 d<ܘDԙCheck failed: page_T<ħ4l@@Xxstd::future_error: <Check failed: parser_qidCheck failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.Check failed: validInput data contains `inf` or `nan`/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: /__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.hUnknown format type fi->Tell() == offsetRead a page in seconds.Check failed: fmt->Read(page.get(), fi.get())Check failed: !cache_info_->written MB written in count_ == n_batches_Check failed: proxy_this->count_ == this->n_batches_source_->Iter() == this->count_this->count_ >= 1SingleColBlock()Check failed: columns_Check failed: !sorted_sketchrow_ptr.size() == 0/__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizevector::_M_fill_insert//,// /.-...-..@.(...----`.9T9$9888l8D88797666P6 6555(587Raw GHistIndex binary data format.N7xgboost4data16SparsePageFormatINS_16GHistIndexMatrixEEEN7xgboost4data19GHistIndexRawFormatE*N7xgboost4dataUlvE_E/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././any.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hN7xgboost17BatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost4data20SparsePageSourceImplINS_16GHistIndexMatrixEEEN7xgboost4data18PageSourceIncMixInINS_16GHistIndexMatrixEEEN7xgboost4data23GradientIndexPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost16GHistIndexMatrixELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost16GHistIndexMatrixESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEEInconsistent max_bin between Quantile DMatrix and Booster:Check failed: !param.regen && param.hess.empty()/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.cc`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.page.row_ptr.size() == h_offset.size()/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/hist_util.href_->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.Data must has at least 1 column.accumulated_rows == Info().num_row_proxy->Info().labels.Size() == 0Iterative DMatrix must have at least 1 batch.All batch should be on the same device.Check failed: ellpack_Check failed: ref_Check failed: p_cutsn_features == num_cols()n_features >= 1Check failed: p_sketchrbegin == Info().num_row_d == batch_param_.gpu_idoooooto\oDo,otnorrrxr`r8r(rHrr`qrduuuLu4u utut4ttu8xxx`x xxwwwwwHx,t\ԻĻ<`H08p|d<,LlPh|dT<$ЅhȄ(lT<, Check failed: proxy_handleInvalid proxy handle.Check failed: typed/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.h/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hSlicing DMatrix is not supported for Quantile DMatrix.Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`./__w/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrCheck failed: !std::isnan(sparse_thresh)\,Lp@P $d4T)h)8))((x(H((&)St14default_deleteIN7xgboost16GHistIndexMatrixEEN7xgboost4data16IterativeDMatrixEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost17BatchIteratorImplINS_13ExtSparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13ExtSparsePageEEESt19_Sp_counted_deleterIPN7xgboost16GHistIndexMatrixESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13ExtSparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE?/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./__w/ml-builds/ml-builds/xgboost/src/data/array_interface.h   St19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EE/__w/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: r != BatchParam{}Batch parameter is not initialized.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_param.gpu_id >= 0param.max_bin >= 2param.gpu_id == -1Check failed: castedinvalid input file formattmagic == kMagic@ p`/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: (std::is_same::value)Expecting CSCAdapterCheck failed: data_Column is empty/__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hN7xgboost7CSCPageEN7xgboost13SortedCSCPageEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost4data13SimpleDMatrixEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEESt15_Sp_counted_ptrIPN7xgboost7CSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost13SortedCSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11EllpackPageELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIaEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIhEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIsEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnItEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIiEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIjEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIlEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnImEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIfEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIdEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_7CSCPageEEELN9__gnu_cxx12_Lock_policyE2EEGGGG/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: ghist_index_source_.row.pageiter_.Next() == 1Must have at least 1 batch.DMatrix-rinfo_.num_col_ != 0this->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0Couldn't remove external memory cache file ; you may want to remove it manually/__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hCan not obtain a single CSR page for external memory DMatrixSlicing DMatrix is not supported for external memory.Check failed: kv.secondthis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size()Make cache:N7xgboost4data20SparsePageSourceImplINS_10SparsePageEEEN7xgboost4data16SparsePageSourceEN7xgboost4data20SparsePageSourceImplINS_7CSCPageEEEN7xgboost4data18PageSourceIncMixInINS_7CSCPageEEEN7xgboost4data13CSCPageSourceEN7xgboost4data20SparsePageSourceImplINS_13SortedCSCPageEEEN7xgboost4data18PageSourceIncMixInINS_13SortedCSCPageEEEN7xgboost4data19SortedCSCPageSourceEN7xgboost4data17SparsePageDMatrixENSt13__future_base7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENSt13__future_base7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost4data5CacheELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data23GradientIndexPageSourceELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data16SparsePageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data13CSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data19SortedCSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost7CSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13SortedCSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()Raw binary data format.page->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)N7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_E/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccLinear booster does not support prediction range.Linear booster, implement generalized linear model.layer_begin == 0GBLinearTrainParamgpu_coord_descentmodelDoBoost/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hgblinear does not support prediction of leaf index/__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hInplace predict is not supported by current booster.Slice is not supported by current booster.PredictBatchInternalbase_margin.Size() == nsize * ngroupPredictBatch/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)Check failed: !model_.weight.empty()gblinear doesn't support number of trees for feature importance.gblinear only has `weight` defined for feature importance.Model is not initializedCheck failed: trees.empty()importance_type == "weight"get(in["name"]) == "gblinear" { "bias": [ ], "weight": [ ] }bias: weight: shotguntolerancemax_row_perbatchlong (non-negative)Maximum rows per batch.Update algorithm for linear model. One of shotgun/coord_descentStop if largest weight update is smaller than this number./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hGBLineargblinear_train_paramCheck failed: this->updater_N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_Eboosted_roundsdeprecated_num_featureint (non-negative)deprecated_num_output_groupN7xgboost3gbm13GBLinearModelEDeprecatedGBLine/__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type /__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccTree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.Check failed: !updaters_.empty()XGBoost version not compiled with OneAPI support.Data is on CUDA device, but XGBoost is not compiled with CUDA support.tree_end <= model_.trees.size()Tree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.get(in["name"]) == "gbtree"ups.size() == updaters_.size() mismatched updater sequence. this->model_.learner_model_param->num_feature != 0this->model_.trees_to_update.size() == this->model_.trees.size() trees remain. Slice the model before making update if you only want to update a portion of trees.Check failed: !updaters_.front()->CanModifyTree()can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.Check failed: this->configured_Check failed: tparam_.GetInitialised()Current objective doesn't support external memory.in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsLoading from a raw memory buffer on CPU only machine. Changing predictor to auto. Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only machine. Consider using `save_model/load_model` instead. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. Changing `tree_method` to `hist`.Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.Tree booster, gradient boosted trees.CommitModelUsing tree method: Check failed: cpu_predictor_out_preds->version == 0Invalid number of trees.grow_histmakergrow_colmaker,prunegrow_quantile_histmakerUnknown tree_method () detectedInternal Error: Specified updaters: Actual updaters: Check failed: p_gbtreelayer_trees != 0layer_end > layer_beginstep >= 1Not all trees are updated, GBTreeModelParamGBTreeTrainParamDartTrainParamUpdater: `` GBTreeUsing updaters: BoostNewTreesngroup != 0gbtree_train_paramspecified_updatergrow_gpu_histTree booster, dart.weight_dropdrop trees, weight = /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.h/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endCheck failed: configured_model_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0tree_begin == 0Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.cpu_predictorgpu_predictorCheck failed: successCurrent Predictor: Unsupported data type for inplace predict.Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()predts.predictions.Size() == p_out_preds->predictions.Size()*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE0_E/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hget(in["name"]) == "dart"in_it < tree_endCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()num_treessize_leaf_vectorNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.Reserved option for vector tree. has not been registered in Alias updater_seqTree updater sequence.process_typedefaultupdateoneapi_predictorPredictor algorithm typetree_methodapproxexactWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.Choice of tree construction method.sample_typeuniformweightednormalize_typeforestrate_dropone_dropbooleanskip_droplearning_rateetaDifferent types of sampling algorithm.Different types of normalization algorithm.Fraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.Probability of skipping the dropout during a boosting iteration.Learning rate(step size) of update.idx <= total_n_treesInvalid tree index.total_gaintotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: dart_train_paramvector::reserveN4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartEA;?/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())fi->Read(¶m, sizeof(param)) == sizeof(param)fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast(trees.size())GBTree: invalid model fileCheck failed: ctx_tree_infoN7xgboost3gbm11GBTreeModelEGlobalConfiguratFlag to print out detailed breakdown of runtime.Whether to use RAPIDS Memory Manager to allocate GPU memory in X, n_targets:base_score_.Size() == 1Check failed: ptrLearnerModelParamLegacyGenericParameterLearnerTrainParam/__w/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:Check failed: common::AsConst(base_score_).Data()->HostCanRead()Model is not yet initialized (not fitted).Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead() : Check failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0ConfigCheck failed: ret.ec == std::errc{}num_classnum_targetboost_from_averageCheck failed: !this->need_configuration_/__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective functionlearner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Empty dataset at worker: Check failed: gbm_ != nullptrPredict must happen after Load or configurationEvalOneIterCheck failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.BoostOneIterCheck failed: this->obj_Inconsistent labels.Check failed: n_targets == t || 1 == tCheck failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)multiple_predictions <= 1Perform one kind of prediction at a time.UpdateOneIterPredictRawPredictionsGetGradientGradientsCall Configure before saving model.learner_model_paramobjectiveattributesGlobal bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Number of target for multi-target regression.Whether we should calculate the base score from training data.seedlongrandom_stateseed_per_iterationNumber of threads to use.n_jobsfail_on_invalid_gpu_idvalidate_parametersRandom number seed during training.Seed PRNG determnisticly via iterator number.The primary GPU device ordinal.Fail with error when gpu_id is invalid.Enable checking whether parameters are used or not.dsplitdisable_default_eval_metricreg:squarederrorData split mode for distributed training.Flag to disable default metric. Set to >0 to disableGradient booster used for training.Objective function used for obtaining gradient.Check failed: matrix.first features or greatermparam_.num_feature != 0Check failed: !matrix.second.ref.expired()num_col <= static_cast(std::numeric_limits::max())Unfortunately, XGBoost does not support data matrices with 0 feature is supplied. Are you using raw Booster interface?multi:softprobmulti:softmaxcount:poisson0.7__Cannot find argument '', Possible Arguments: ---------------- Learnernum_roundCONFIG-offset:this->learner_model_param_.num_feature != 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0begin_layer >= 0best_iterationbest_scoreCheck failed: IsA(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. If you are loading a serialized model (like pickle in Python, RDS in R) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. learner_train_parammetricsgeneric_param;binfUnsupported prediction type:Invalid parameter "" contains whitespace. Parameters: { ", } are not used. Configurefixed_size->Read(&buffer[0], size) == sizebs64header != "bs64"fp.Read(&header[0], 4) == 4UInvalid model formatSAVED_PARAM_count_poisson_max_delta_stepmulti:Base64 format is no longer supported in brick.fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)data_.Size() == detail::CalcSize(shape_)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityInvalid serialization file.sz > 0fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)N4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplE/__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater Learning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.Update linear model according to coordinate descent algorithm.CoordinateParam/__w/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeaturetop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features./__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: CoordinateUpdater^~J>coordinate_paramN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_EUpdate linear model according to shotgun coordinate descent algorithm./__w/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}-N7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_EWARNING: DEBUG: INFO: aucaucpr/__w/ml-builds/ml-builds/xgboost/src/metric/../common/common.hlabels.Size() == predts.size()Receiver Operating Characteristic Area Under the Curve.Area under PR curve for both classification and rank.labels.Size() != 0auc <= 1.0f/__w/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples./__w/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/metric/auc.ccinfo.group_ptr_.size() >= 2n_classes != 0labels.Shape(1) == 1AUC doesn't support multi-target model.P&'&&'info.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()Dataset is empty, or contains only positive or negative samples.auc <= 1Total AUC across groups: , valid groups: auc <= local_areaPR-AUC supports only binary relevance for learning to rank.N7xgboost6MetricEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E?mphegamma-nloglikgamma-deviancepoisson-nloglikmapemaermslermse/__w/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cusscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)Mean absolute percentage error.Negative loglikelihood for logistic regression.Negative loglikelihood for poisson regression.Residual deviance for gamma regression.Negative log-likelihood for gamma regression.tweedie-nloglik@rho for tweedie regression.%fRooted mean square error.Rooted mean square log error.Mean absolute error.Mean Pseudo-huber error.Binary classification error.tweedie-nlogliktweedie-nloglik@info.labels.Shape(0) == info.num_row_slope for pseudo huber cannot be 0.slope != 0.0pseudo_huber_parampreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_E@$tI$tɕ$ư>Cannot find a GPU metric builder for metric . Resorting to the CPU builder/__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function /src/metric/metrmerrormloglossMulticlass classification error.Multiclass negative loglikelihood./__w/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cuCheck failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchmlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast(n_class)MultiClassEvaluation: label must be in [0, num_class),preds.Size() == 0nclass >= 1U num_class= but found in labelN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E]Bcox-nloglikNegative log partial likelihood of Cox proportional hazards model.preAMS metric for higgs.precision@k for rank.ndcg@k for rank.map@k for rank./__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@label size predict size not matchmust specify group when constructing rank fileEvalRank: group structure must match number of predictiongptr.size() != 0Ugptr.back() == preds.Size()Check failed: tparam_%u[-]?Check failed: !collective::IsDistributed()Cox metric does not support distributed evaluationmetric AMS do not support distributed evaluationbest-ams-ratio=<$,p N7xgboost6metric14EvalRankConfigEN7xgboost6metric7EvalAMSEN7xgboost6metric8EvalRankEN7xgboost6metric13EvalPrecisionEN7xgboost6metric8EvalNDCGEN7xgboost6metric7EvalMAPEN7xgboost6metric7EvalCoxE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E$@aft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model./__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuAFT metric must be configured first, with distribution type and scaleCheck failed: metric_Unknown probability distributionaft_loss_parampreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E-q=' @;f?/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.cc/__w/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: alpha >= 0 && alpha <= 1Check failed: h_row_set.empty()begin_pos <= sorted_pos.size()Check failed: !position.empty()h_node_ptr.back() <= info.num_row_/__w/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.hk + 1 < h_node_ptr.size()k >= 0nptr.size() > 0n_unique == n_leaf/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf(),)l)'T))AFT loss function/__w/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /__w/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndataNumber of weights should be equal to number of data points.info.weights_.Size() == ndataUnrecognized distributionsurvival:aftN7xgboost3obj6AFTObjE*N7xgboost3objUlvE_E..@ؗҜ<@@pApHinge loss. Expects labels to be in [0,1f]/__w/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedinfo.labels.Size() != 0Ulabel set cannot be emptypreds.size=, label.size=binary:hingeN7xgboost3obj8HingeObjE*N7xgboost3objUlvE_ENumber of output class in the multi-class classification.Softmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.SoftmaxMultiClassParam/__w/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. SoftmaxMultiClassObj: label must be in [0, num_class).label.Size() * num_class: preds.Size(): softmax_multiclass_paramN7xgboost3obj20SoftmaxMultiClassObjE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_EҜ 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: /__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.hStringView{get(in["name"])} == StringView{"reg:absoluteerror"}PoissonRegression: label must be nonnegativeGammaRegression: label must be positive.TweedieRegression: label must be nonnegativetweedie_regression_parampoisson_regression_paramCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!t_v.Shape(1) != 0Sum of weights is close to 0.0, skipping base score estimation.reg_loss_paramlabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionEN7xgboost3obj17MeanAbsoluteErrorE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_E*N7xgboost3objUlvE10_EffB????ư>Make predictions using CPU./__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farbase_margin.Shape(1) == ngroup p X @ (   ncolumns != 0m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Inplace predict accepts only DMatrixProxy as input.out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_group |l\4<,L4p@0P0td D4T H0xN7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE*N7xgboost9predictorUlPKNS_16GenericParameterEE_E?/__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()model.learner_model_param->num_output_group != 0Unknown predictor type _Map_base::at[Internal error]: DMatrix: has expired.%umargin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsInvalid shape of base_margin. Expected: (N7xgboost9PredictorE/__w/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: newsize != 0With error: _K/__w/ml-builds/ml-builds/xgboost/src/tree/param.ccFound floating point number in interaction constraintsUnknown value type for interaction constraint:std::floor(d) == d/__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccfi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam)fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast(nodes_.size())param.num_nodes == static_cast(stats_.size())param.deprecated_num_roots == 1Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.this->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodesget(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)static_cast(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodesDump text representation of treeDump json representation of treeDump graphviz representation of tree{param.num_nodes != 0categories_segmentscategories_sizescategories_nodesmean_values->size() > 0Uunique_path[i].pweight == 0Unique path must have zero weightCheck failed: node.IsLeaf()Unknown Model Builder:GraphvizParamTreeParamtree_paramsplit_indicessplit_typeloss_changessum_hessianbase_weightsleft_childrenright_childrenparentssplit_conditionsdefault_leftdot Line , around ^`pos != std::string::nposbasic_string::replaceCheck failed: is_numerical in feature map is categorical but tree node is numerical.Check failed: is_categoricalUnknown feature map type. in feature map is numerical but tree node is categorical.ULUTLUU/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././json.h, Expect '"' but reach end of line ch == '\"'Error at, Expect '"' but get 'unknown string escape \ch == ','ch == ':', Expect ':' but get ', JSON object expect '}' or ',' 'ch == '{', Expect '{' but get '/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31j_end - j_begin != 0std::numeric_limits::min() != max_catyes_color#0000FFno_color#FF0000rankdircondition_node_paramsleaf_node_paramsLeaf node configurationgraph_attrsEdge color when meets the node condition.Edge color when doesn't meet the node condition.Passed to graphiz via graph_attr.Conditional node configurationAny other extra attributes for graphviz `graph_attr`.num_nodesnum_deletedNumber of features used in tree construction.Size of leaf vector, reserved for vector treemin_split_lossmax_depthmax_leavesgrow_policydepthwiselossguidemax_cat_to_onehotmax_cat_thresholdmin_child_weightsubsamplesampling_methodgradient_basedcolsample_bynodecolsample_bylevelcolsample_bytreesketch_ratiocache_optrefresh_leafmonotone_constraintsinteraction_constraintssparse_thresholddoubleMinimum loss reduction required to make a further partition.Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policyMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featureTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)Maximum number of categories to use one-hot encoding based split.Maximum number of categories considered for split. Used only by partition-basedsplits.Minimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintRow subsample ratio of training instance.Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)Subsample ratio of columns, resample on each node (split).Subsample ratio of columns, resample on each level.Subsample ratio of columns, resample on each tree construction.EXP Param: Sketch accuracy related parameter of approximate algorithm.EXP Param: Cache aware optimization.Whether the refresh updater needs to update leaf values.Constraint of variable monotonicityConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationpercentage threshold for treating a feature as sparse{tabs}{nid}{fname}{cond}{left}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}{nid}:[{fname}] yes={yes},no={no}{yes}{no} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{sum_hess}{stat}{newline}{indent}{nodes}{newline}{indent}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties}{tabs}{nid}:leaf={leaf}{stats},cover={cover}{stats}{parent}{stat} {left} {right}{parent}{nodes} , missingyesno{child}{color}{branch} {nid} -> {child} [label="{branch}" color="{color}"] {<}{params} {nid} [ label="{fname}{<}{cond}" {params}] {nid} [ label="{fname}:{cond}" {params}] {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}, "gain": {loss_chg}, "cover": {sum_hess}{loss_chg},gain={loss_chg},cover={sum_hess}n_nodes != 0sum_hessian.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesloss_changes.size() == n_nodesbase_weights.size() == n_nodesdefault_left.size() == n_nodesn_nodes == split_categories_segments.size()" edge{key}{value} graph [ {key}="{value}" ] The following parameters for graphviz are not recognized: Failed to parse graphviz parameters: N7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_EHgY?@חA?/__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node./__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/threading_utils.hbegin < endnthreads >= 1i < ranges_.size()i < first_dimension_.size()/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/hist_util.hid != kMaxend > beginnid < nodes_/__w/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hupper_bound < static_cast(std::numeric_limits::max())/__w/ml-builds/ml-builds/xgboost/src/tree/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/row_set.helem_of_each_node_.size() == 0Un_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.end(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()tid < nthreads_row_ptr_[nid] == kMax/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccn_total_bins == page.cut.TotalBins()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hout_preds.DeviceIdx() == GenericParameter::kCpuIdout_preds.Size() > 0UCheck failed: p_last_treepart.Size() == n_nodesout_preds.Size() == data->Info().num_row_Check failed: !features.empty()nodes == targeted_hists.size()base_rowid == gmat.base_rowid/__w/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == trueCheck failed: tree[node.node_id].IsLeaf()ptr_offset <= row_set.Data()->size()partition > 0cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())n_nodes > 0n_batches_ >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hUpdatePositionEvaluateSplitsCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method.LeafPartitionBuildHistogramInitRootInitDataUpdatePredictionCacheGlobalApproxUpdater*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterEN7xgboost6common6ColumnIhEEN7xgboost6common16SparseColumnIterIhEEPFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost6common6ColumnItEEN7xgboost6common16SparseColumnIterItEEN7xgboost6common6ColumnIjEEN7xgboost6common16SparseColumnIterIjEESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EEgrow_colmakerGrow tree with parallelization over columns.ColMakerTrainParam/__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccCheck failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0ridx < position_.size()ridx exceed bound ridx= pos=opt_dense_coldefault_directionlearnrightEXP Param: speed optimization for dense column.Default direction choice when encountering a missing valuecolmaker_train_paramfmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.param_.max_depth > 0exact tree method doesn't support unlimited depth./__w/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.N7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_Enid >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()tree pruning end, extra nodes, pruned nodes, max_depth=Pruner that prune the tree according to statistics.N7xgboost4tree10TreePrunerE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_E/src/tree/update/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/histogram.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/transform.hGrow tree using quantized histogram.exponent <= modrow_ptr.size() >= 2Quantile::Builder/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/hist_util.h/__w/ml-builds/ml-builds/xgboost/src/tree/./common_row_partitioner.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/row_set.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/evaluate_splits.h/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/partition_builder.hInitDataInitSamplingUpdatePredictionCacheUpdateTreeExpandTreeLeafPartition*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EN7xgboost4tree17QuantileHistMakerE::max()N7xgboost4tree13TreeRefresherE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_EsyncSyncher that synchronize the tree in all distributed nodes.N7xgboost4tree11TreeSyncherE*N7xgboost4treeUlPKNS_16GenericParameterENS_7ObjInfoEE_E/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccrabit_reduce_ring_mincount should be greater than 0invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}rabit_timeout_sec should be non negative second Retry connecting to IP(retry time: tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failuretracker.Send(xgboost::StringView{task_id}) == task_id.size()must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rankbind(handle_, handle, sizeof(std::remove_reference_t))getsockname(handle_, reinterpret_cast(&res_addr), &addrlen)ReConnectLink fail to bind the ports specifiedgetsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len)ReConnectLink failure, link rank inconsistentOverride a link that is activesetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&tcp_no_delay), sizeof(tcp_no_delay))cannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkhadoop_mode is set but cannot find mapred_task_idhadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workergethostname(&buf[0], HOST_NAME_MAX)too large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkBroadcast: root should be smaller than world sizerabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_buffer%lu%cinvalid format for %sDMLC_WORKER_CONNECT_RETRYrabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_enable_tcp_no_delayworker256MBDMLC_NUM_ATTEMPTConnecting to (failed): [] ReConnectLink failure 3shutdownFailed to shutdown due toprinttask connected to the trackerReConnectLink failure 4 got new rank listen(handle_, backlog)ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure 14acceptReConnectLink failure 15ReConnectLink: bad socketfcntlfailed in ReconnectLink %[^=]=%smapred_tip_idmapreduce_task_idmapred_task_idrabit_num_trialmapred_map_tasksmapreduce_job_mapscan only call Init oncestartneed to assume rank structurewrite ptr boundary checkAllreduce: boundary check[%d] read_ptr boundary checkbuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorPoll timeoutFailed to poll./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hAllgatherRing failedBroadcast failedAllreduce failedN5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseEFailed to shutdown RABIT worker.DataType does not support bitwise or operationFailed to initialize RABIT.unknown data_typeunknown enum_opxH 8بxHhh8ةxȪȫh8ت(PPȮȮȮthe rabit has not been initialized/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file finish reading at No thread/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: out_data_ != NULLCalling Value at beginning or end?/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNextCheck failed: base <= 10 && base >= 2sign == true.split.partname_cache.size() == 1UInvalid uri argument format for key in arg for value in arg name_args.size() == 1U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.honly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Check failed: std::getline(is, kv.second)Unknown data type /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccFile formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.libfmIf >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_columndelimiterweight_columnColumn index (0-based) that will put into label.Delimiter used in the csv file.Column index that will put into instance weights./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()Check failed: offset.back() == value.size() || value.size() == 0offset.back() == index.size()param_.format == "csv"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hCheck failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsCheck failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroy/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"Check failed: fi->Read(&label)Check failed: fi->Read(&weight)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))Bad RowBlock formatCheck failed: fi->Read(&qid)MB read,finish reading at %g MB/sec/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Delimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()Check failed: out->field.size() == out->index.size()qid:/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/././text_parser.hchunk.size != 0UNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEEN4dmlc12ScopedThreadEN4dmlc8DataIterINS_8RowBlockIjfEEEEN4dmlc12RowBlockIterIjfEEN4dmlc8DataIterINS_8RowBlockImfEEEEN4dmlc12RowBlockIterImfEEN4dmlc8DataIterINS_8RowBlockIjiEEEEN4dmlc12RowBlockIterIjiEEN4dmlc8DataIterINS_8RowBlockImiEEEEN4dmlc12RowBlockIterImiEEN4dmlc8DataIterINS_8RowBlockIjlEEEEN4dmlc12RowBlockIterIjlEEN4dmlc8DataIterINS_8RowBlockImlEEEEN4dmlc12RowBlockIterImlEEN4dmlc6ParserIjfEEN4dmlc6ParserImfEEN4dmlc6ParserIjiEEN4dmlc6ParserImiEEN4dmlc6ParserIjlEEN4dmlc6ParserImlEEN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEEN4dmlc4data11DiskRowIterIjfEEN4dmlc4data12BasicRowIterIjfEEN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEEN4dmlc4data11DiskRowIterImfEEN4dmlc4data12BasicRowIterImfEEN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEEN4dmlc4data11DiskRowIterIjiEEN4dmlc4data12BasicRowIterIjiEEN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEEN4dmlc4data11DiskRowIterImiEEN4dmlc4data12BasicRowIterImiEEN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEEN4dmlc4data11DiskRowIterIjlEEN4dmlc4data12BasicRowIterIjlEEN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEEN4dmlc4data11DiskRowIterImlEEN4dmlc4data12BasicRowIterImlEEN4dmlc4data10ParserImplIjfEEN4dmlc4data14TextParserBaseIjfEEN4dmlc4data12LibSVMParserIjfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc4data14ThreadedParserIjfEEN4dmlc4data10ParserImplImfEEN4dmlc4data14TextParserBaseImfEEN4dmlc4data12LibSVMParserImfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc4data14ThreadedParserImfEEN4dmlc4data11LibFMParserIjfEEN4dmlc4data11LibFMParserImfEEN4dmlc4data9CSVParserIjfEEN4dmlc4data9CSVParserImfEEN4dmlc4data10ParserImplIjiEEN4dmlc4data14TextParserBaseIjiEEN4dmlc4data9CSVParserIjiEEN4dmlc4data10ParserImplImiEEN4dmlc4data14TextParserBaseImiEEN4dmlc4data9CSVParserImiEEN4dmlc4data10ParserImplIjlEEN4dmlc4data14TextParserBaseIjlEEN4dmlc4data9CSVParserIjlEEN4dmlc4data10ParserImplImlEEN4dmlc4data14TextParserBaseImlEEN4dmlc4data9CSVParserImlEEZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImlEEE_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerIjfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerImfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfsPlease compile with DMLC_USE_S3=1 to use S3Please compile with DMLC_USE_AZURE=1 to use AzureSingleFileSplit: fail to open invalid input parameter for InputSplit::Createneed to pass index file to use IndexedRecordIOhdfs://viewfs://s3://http://https://azure://unknown filesystem protocol stdinCheck failed: fp_ != NULLCheck failed: part < nsplitindexed_recordiounknown input split type rb/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedResetPartition is not supported in CachedInputSplit/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support writeCheck failed: this->InitCachedIter()Failed to initialize CachedIterFvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != endN4dmlc2io12LineSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0(reinterpret_cast(begin) & 3UL) == 0U(reinterpret_cast(end) & 3UL) == 0UCheck failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end(reinterpret_cast(chunk->begin) & 3UL) == 0U(reinterpret_cast(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: p[0] == RecordIOWriter::kMagicinvalid record io formatCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: cflag == 1UN4dmlc2io16RecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccIndexedRecordIOSplitter does not support multiple index filesexpanded_list.size() == 1ulN4dmlc2io23IndexedRecordIOSplitterEcurr=,begin=,end=,fileptr=,fileoffset=offset[]= bad regex files_.size() != 0Ufile do not align by bytesNULSOHSTXETXEOTENQACKalertbackspacenewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1exclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodonetwothreefourfivesixsevenninesemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atJSVWleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrlgraphlowerpunctupperxdigit/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size()file offset not calculated correctlyThis could due to compiler version, g++-4.9 is neededCannot find any files that matches the URI pattern Check failed: files_[i].size % align_bytes == 0Number of NFA states exceeds limit. Please use shorter regex string, or use smaller brace expression, or make _GLIBCXX_REGEX_STATE_LIMIT larger.Unexpected escape character.Unexpected end of regex when escaping.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character.Unexpected end of character class.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.Unexpected character in brace expression.^$\.*+?()[]{}|.[\*^$.[\()*+?{|^$.[\*^$ .[\()*+?{|^$ Nothing to repeat before a quantifier.Unexpected back-reference in polynomial mode.Back-reference index exceeds current sub-expression count.Back-reference referred to an opened sub-expression.hHd$DLdInvalid character class.Invalid range in bracket expression.Unexpected token in brace expression.Unexpected end of brace expression.Invalid range in brace expression.Invalid collate element.Invalid equivalence class.Unexpected dash in bracket expression. For POSIX syntax, a dash is not treated literally only when it is at beginning or end.Character is expected after a dash.Unexpected character in bracket expression.Parenthesis is not closed.N4dmlc2io14InputSplitBaseESt23_Sp_counted_ptr_inplaceINSt8__detail4_NFAINSt7__cxx1112regex_traitsIcEEEESaIS5_ELN9__gnu_cxx12_Lock_policyE2EENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEEww error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": ../__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink LocalFileSystem.ListDirectory Check failed: !std::fseek(fp_, static_cast(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamElstat(path.c_str(), &sb) == 0Couldn't remove file ~TemporaryDirectory(): /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hdmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectorySuccessfully deleted temporary directory Could not remove temporary directory N4dmlc2io10FileSystemE;<X(3X33353o4X*4T4~4"4u468 "6.(69@74Dj7hkp77778F*8HNT8~888R8[&9\f9Pi9\9&:X::d:|:h(;X;;p;5;\ghpX>Pq>>tH@lx@P@ @(AC8APDAKDLE0cFgFhGhIIpIHJJhLl8LMMXP<ZPBZEH[,J[xKH^$SXcehcmc\ufĈ8fhfgxhاitXll(oLozW{P|Ы|}(8}<x`؀ "X?؃?hB腼> ? 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