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_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEE_ZTSNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEE_ZTINSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEE_ZTSNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEE_ZTINSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEE_ZTSNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEE_ZTINSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEE_ZTSN4dmlc12ScopedThreadE_ZTIN4dmlc12ScopedThreadE_ZTSN4dmlc8DataIterINS_8RowBlockIjfEEEE_ZTIN4dmlc8DataIterINS_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_ZTSN4dmlc4data14TextPar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TB Tk6@@@$@ g*@F@P@-9 @=!~z3sT+@z;J@Wz@ @ g@@F@@|9@+q@@bijCG@@ҡT[BcC{ɨ_SAҧSAkDSk+Q?{c([>C@@GҶ Sh|@)`|@ Ӛ?  TB Tk6@@@$@f*@F@Q@-9 9" z3sT+@v[z@ @C@f@F@F@G@+}A@Lb@@i8J@"Ax3fH@FC@i@| h8""y6TSAkD+@>CG@@ҡT[BcC{ɨ_SAҧSAkDSk+>{s(S {s(S {[(>Cc@@Gҷ Sf|@^|@ Ӛ?  TB Tk6@@@$@`e*@F@P@m} 5yn!^z3sT+@w;J@Zz@ @Ie@E@Fl@M@|$9@q@yj{@A@C!^#z35eA#@FE@d@C~ xb!^x7TSAkD+@>CG@@ҡT[BcC{ɨ_SAҧSAkDSk+={c([>C@@GҶ SSf|@i^|@ Ӛ?  TB Tk6@@@$@d*@F@Q@m} 5yn" z3sT+@v[z@ @C@d@E@F@N@|+)A@{@ykJ@"a{3@dA#@FE@g@C| xd"x6TSAkD+@>CG@@ҡT[BcC{ɨ_SAҧSAkDSk+={[(>Cc@@Gҗ Se|@]|@ Ӛ? T" Tk6@@@$@td*@F@Q@m} 5yx" z3sT+@w[z@ @C@]d@E@Ff@9@+}L-O@E@yx"2 @x3JdA#@FG@i@d|xx""y7!TSAkD+@>CG@@ҡT[BcC{ɨ_SAҨSAkDSk+< {[(>Cc@@Gҷ Sge|@}]|@ Ӛ?  TB Tk6@@@$@c*@F@P@m} 5yn|!~z3sT+@w;J@Zz@ @c@E@Fl@M@|$9@q@yj|{@A@C!~#z3cA#@FE@d@C~ xb|!~x7TSAkD+@>CG@@ҡT[BcC{ɨ_SAҧSAkDSk+/<{c([>C@@GҶ Sd|@]|@ Ӛ?  TB Tk6@@@$@c*@F@Q@m} 5yn# z3sT+@v[z@ @C@qc@E@F@N@|+)A@{@ykJ@#a{3@]cA#@FE@g@C| xd#x6TSAkD+@>CG@@ҡT[BcC{ɨ_SAҧSAkDSk+;{[(>Cc@@Gҷ S{d|@\|@ Ӛ?  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A?T? @G @C|sB@9o@@Tc7G(< A##<*0&C@d@R@5e>C@@TSA{è_ֵ!`!bRy*藰A%W;D`5BR;O'OM{Sd>Cc@@!L藟ATf>C@@ҁTSA{è_փ!`!RG*~A'%;@B`5 ;'b;O&OL {㶍c۶cmök$`@[AS!Is$D|`T[zT]7 AAj;_Tj;k @ gB9p@ BqT @q9Ac@D!dB?T @!)gj@_{&@d~f2@%B91i T`C,5 5*_F| +  &{c7@[B (TA'T@9c9?i48t@|@@NT[>((T%T@99Bi48t&@|"@@LT[>'Ta#T@9c9i48Rqj@p2@@E) 4,[@rMR@)[(H99a`*49[@A@09A!`5B+9*&9M[@<A@TU45A (@q TAqT@0"5@B@TC4@@T>4@Bs T84?T7@@P藀 53@Ã@9/5SI?T  #@*C @T&4 @+ @T!49A T4藟TK@t4@B @T4@@T 4w@B@T42TTT@9gB9wvh78`vVB@ZTS>VT!ST@9j9ulh58`B`"RD_@@XTS>HRTPTM@9mB9t&h48G{@k@{?Tc;  H 2@S@[`?k@s@cC`#@ăIT@T\1`@h@TW1`A@TS1k@7koJ@TBc܂?t@u@@ NTO>2T!2T@99h48u@|@@LTO>4T/T@9#9h58Bt&@|"@@ITO>H0TA-T@99j48qj@`2@?q $T4@1TB3@@@`GTO>1Ta1T@9B9h587@@@ETO>(3T.T7@@99h58B@"@@@ETO>H/TA,T@9B9&h58*@A@2jK+@B@T0@@T0@s@T0aT_JT `"@zB@T0`@g@T0sAT0TS@S20k@{@?!ToN@@w# T@"@B @T0藠@ @T0`A T0藿T"HT@T?`T@#@LC @T~0@@M @Ty0ZA,Ts0?T`T"@B@Tj0@@Te0ւ AT_0Tc@ @w@#_bT2#`@{CP0?ITT `TT`TT`T`@?0`@=0`@;0`@90`@70`@50`@30?Ts@-0`\+0@)0`]'0`^%0`^#0`_!0`_0?Tc@0ЅTT@9B9h587@@@`$TO>TAT7@@99h58B@"@@"TO>TT@9B9&h58{@{4%TҳFO@iO@@dҦFO@\O@@x?@ҙFO@OO@@Z;@7r(藫57@% ;@7|FO@ 2藀@O@hoFO@"*%藀"@O@cFO@藀@O@b;@7Dwu&7@5KFO@藀@O@l;@7C@@7@ @@+;ˊClCKEiE /9p=TJ@3X T<ҡ.藸@ @yBy@_@@@T3?hTTL@9lB9xzh88`@z@`@`T3? TTn@9n9xph88`B&@`""@@T3? TT@9qB9{&h;8>C&@7@@!#@ TSA[BcCkD+@{Ǩ_@0{H _h T_Y ^? T }B.'@A &@ ?Tb4藀@-藘fyj@JSKK zj{[ C3@`o`@3@C3@`m `@3@C3@`"r*`"@3@A(#)A3 TTA4Td#`94藺A !D藿`B2\&`@2Y&B2V&`@_@T,`@?@T,藔&@@,':>(C{{_basic_string::_M_construct null not valid/__w/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccSaving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified.USE_RMMXGDMatrixCreateFromCSREx2.0.0XGDMatrixCreateFromCSRDMLC_LOG_STACK_TRACE_DEPTH%zuInvalid pointer argument: json_configDMatrix/Booster has not been initialized or has already been disposed.keysuccessCheck failed: p_outversionbufout_dptrout_lenXGBoosterGetModelRaw1.6.0XGBoosterSaveModelToBufferjson_parametersfieldXGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupCheck failed: type >= 1 && type <= 4fnamebinary saving only supported by SimpleDMatrixXGDMatrixCreateFromCSCExXGDMatrixCreateFromCSCindptrindicesCheck failed: mCurrent DMatrix type does not support set data.c_interface_str/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.name_strCheck failed: static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureout_strBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPGCC_VERSIONDEBUGUSE_FEDERATEDwjsonubjXGDeviceQuantileDMatrixCreateFromCallback1.7.0XGQuantileDMatrixCreateFromCallbackdtraingradhessconfigout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits::max()/__w/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrCheck failed: page.page/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.hn_threads >= 1feature_namefeature_typeUnknown field for Booster feature info:lenout_resultdeprecatedUnknown format: `uriXGDMatrixCreateFromURIsilentdata_split_modecache_prefixXGDMatrixCreateFromCallbacknthreadnextresetCheck failed: _refInvalid handle to ref.nbatchXGDMatrixCreateFromArrowCallbackout_featuresdmatsevnamesDMatrix has not been initialized or has already been disposed.c_json_configXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapeInplacePredictImplCheck failed: p_predtp_predt->Size() >= n_samplesCheck failed: proxyInvalid input type for inplace predict.array_interfacestr.size() >= 3str[0] == '{'ftypetextfmap+sCheck failed: std::string(schema->format) == "+s"Check failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falseColumn data type not supported by XGBoostCheck failed: !schema_->columns.empty()Cannot import record batch without a schemaThe quantile cut hasn't been generated yet. Unless this is a `QuantileDMatrix`, quantile cut is generated during training.cpui < data.size()end <= data.size()n_numeric == n_features - n_categoriesindptr.back() == vals.size() + mins.size() - n_categoriescudaimportance_typeXGBoosterFeatureScorefeature_mapfeature_namestree_idxidx < names_.size()FeatureMap feature index exceed boundout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresCheck failed: eCheck failed: ec == std::errc()json_strfloat32float64bool8int32int8int16int64Unknown data table type.%ldfalseUnknown global parameters: { }%02d:%02d:%02d] basic_string::appendOnly %d dimensional array is valid.cannot create std::vector larger than max_size()Stack trace: _Z +basic_string::basic_string%s: __pos (which is %zu) > this->size() (which is %zu) [bt] () /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset RecordBatchesIterAdapterBit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOther/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hInvalid type code: in `typestr' of input array. Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html Or open an issue.- is not supported.Cannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Slicing DMatrix columns is not supported for Proxy DMatrix./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer length vs. /__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/io.hcurr_ptr_ + size <= buffer_size_/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/../c_api/c_api_utils.hCheck failed: pp_mInvalid DMatrix handleCheck failed: p_mProxy DMatrix cannot return data batch.vector::_M_realloc_insertvector::_M_default_appendchunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rowslearnergradient_boostergblineargbtree_model_paramnum_parallel_treestoiUnknown booster:Missing `version' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.typestrMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4`typestr' should be of format .shapeMissing `shape' field for array interfaceMissing `data' field for array interfacemaskEmpty data passed in.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)map::atj_shape.size() == 1basic_string::at: __n (which is %zu) >= this->size() (which is %zu)type_length == 1mask 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.stridesstrides.size() == 1get(strides.at(0)) == type_lengthMemory should be contiguous.Invalid type for: ``, expecting one of the: {`}, got: `Argument `` is required for ``, missingArgument `missing` is required.stream%lu</__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())floatunknown feature type, use i for indicator and q for quantitycustom_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_featuresout_modelsi < Dj_strides.size() == j_shape.size()stride and shape don't match.Half type is not supported.size == 0Empty data with non-zero shape.ptr % alignment == 0Input pointer misalignment.Check failed: mask_it == array.cend() || IsA(mask_it->second)Masked array is not yet supported.Check failed: m == 1 || n == 1n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.get(array).size() == 1Column: __Cannot find argument '', Possible Arguments: ---------------- : 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_policyE2EEN4dmlc5ErrorEN4dmlc10ParamErrorEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data20CSCArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSCArrayAdapterBatchEEEN7xgboost4data15CSCArrayAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_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_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13ExtSparsePageEEELN9__gnu_cxx12_Lock_policyE2EErankXGBOOST_COMMUNICATORrabitfederatedin-memoryin-memory-ncclUnknown communicator type xgboost_communicator/__w/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support./__w/ml-builds/ml-builds/xgboost/src/collective/communicator.hUnknown data type./__w/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hUnknown allreduce operationFloating point types do not support bitwise operations.Unknown data typeIN_MEMORY_WORLD_SIZEIN_MEMORY_RANKin_memory_world_sizein_memory_rank/__w/ml-builds/ml-builds/xgboost/src/collective/in_memory_communicator.hFederated world size must be set.Federated rank must be set.World size is less than 1.Rank is less than 0. is greater than world_size - 1: =1=0Failed to initialize RabitN7xgboost10collective12CommunicatorEN7xgboost10collective20InMemoryCommunicatorEN7xgboost10collective16NoOpCommunicatorEN7xgboost10collective17RabitCommunicatorE/__w/ml-builds/ml-builds/xgboost/src/collective/in_memory_handler.ccCheck failed: world_size_ < world_sizeIn memory handler already initialized.Check failed: world_size_ > 0In memory handler already shutdown.AllgatherAllreduceBroadcastInvalid reduce operationInvalid data type: waiting for current sequence number: handling requestbasic_string::replace: all requests received: waiting for all clients: sending reply: all replies sent/__w/ml-builds/ml-builds/xgboost/src/collective/socket.ccFailed to get addr info for: 127.0.0.10.0.0.0::1::socketgetsockopt(handle_, SOL_SOCKET, SO_DOMAIN, reinterpret_cast(&domain), &len)Unknown IA family.static_cast(socket.Domain()) == static_cast(addr.Domain())system::CloseSocket(handle_)Check failed: !this->IsClosed()str.size() < std::numeric_limits::max()sendthis->SendAll(&len, sizeof(len)) == sizeof(len)Failed to send string length.bytes == str.size()Failed to send string.recvthis->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to recv string length.bytes == lenFailed to recv string./__w/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.h): Failed to call ``: NaN0E0@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!00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899/__w/ml-builds/ml-builds/xgboost/src/common/../data/../common/io.hw_n_bytes == n_bytesw_n_bytes == remaining/__w/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valbad_malloc: Failed to allocate bytes./__w/ml-builds/ml-builds/xgboost/src/common/../data/../common/ref_resource_view.hmem_->Size() >= nreinterpret_cast(ptr) % std::alignment_of_v == 0St19_Sp_make_shared_tagN7xgboost6common14MallocResourceESt23_Sp_counted_ptr_inplaceIN7xgboost6common14MallocResourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE\\\\"\b\f\n\r\t\u%04x/__w/ml-builds/ml-builds/xgboost/src/common/error_msg.ccYou have manually specified the `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.Empty dataset at worker: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead. E.g. tree_method = "hist", device = "cuda" Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: /__w/ml-builds/ml-builds/xgboost/include/xgboost/context.hUnknown device., while the input data is on: Potential solutions: - Use a data structure that matches the device ordinal in the booster. - Set the device for booster before call to inplace_predict. This warning will only be shown once. ` is deprecated since, use `` instead.device E.g. device=cpu/cuda/cuda:0/__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)entries_per_column.size() == info.num_col_SortedSketchContainerCheck failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))invalid init parametervector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.ccCheck failed: !offsetsCheck failed: offsets/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachableSt15_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()basic_string::substr/__w/ml-builds/ml-builds/xgboost/src/common/io.ccCheck failed: outmunmap(handle_->base_ptr, handle_->base_size) != -1Faled to call munmap: close(handle_->fd) != -1Faled to close: pointer_ <= buffer_.size()://file://Opening failed: fd >= 0Failed to open:ptr != ((void *) -1)Failed to map: /__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamEN7xgboost6common15ResourceHandlerEN7xgboost6common12MmapResourceEN7xgboost6common25AlignedResourceReadStreamEN7xgboost6common22PrivateMmapConstStreamEN7xgboost6common18AlignedWriteStreamEN7xgboost6common22AlignedFileWriteStreamEN7xgboost6common21AlignedMemWriteStreamENumberArrayU8Array/__w/ml-builds/ml-builds/xgboost/src/common/json.ccObject of type can not be indexed by Integer. can not be indexed by string., around character position: , " "/__w/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hbeg <= size_\0Unknown construct Unknown escapenullExpecting null value "null"Expecting digitf64 is not supported.High precision number is not supported.Check failed: ret.ec == std::errc()` is not supported for typed array.ch != -1cursor_.Pos(): raw_str_.size():Expecting: "", got: "EOF"\0"N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterEN7xgboost9UBJReaderEN7xgboost9UBJWriterEN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEEN7xgboost14JsonTypedArrayIhLNS_5Value9ValueKindE8EEEN7xgboost14JsonTypedArrayIiLNS_5Value9ValueKindE9EEEN7xgboost14JsonTypedArrayIlLNS_5Value9ValueKindE10EEE/__w/ml-builds/ml-builds/xgboost/src/common/common.h optional, default=, requiredvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to Required parameter of is not presentedInvalid Parameter format for expect but value='/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h*p == ')'Invalid NAN literalOut of range valueNo conversion could be performed/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()Some trailing characters could not be parsed: 'Out of range value for , value='PesudoHuberParamhuber_slopekey has already been registered in The delta term in Pseudo-Huber loss./__w/ml-builds/ml-builds/xgboost/src/common/quantile.cchessian.size() == info.num_row_group_ptr.size() >= 2group_ptr.back() == hessian.size()PushColPageweights.size() == info.num_row_/__w/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categoriesMaximum cateogry should not be lesser than the total number of categories.sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=HostSketchContainerworker_segments.size() >= 1info.weights_.Size() == n_groupsSize of weight must equal to the number of query groups when ranking group is used.group_ptr.back() == n_samplesInvalid query group structure. The number of rows obtained from group doesn't equal to the number of rows from the data.sketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=feature_ptr.front() == 0rank_size == totalbase_rowid < group_ptr.back()Row: is not found in any group.src.size == 0this->size == 0Check failed: a.dataCheck failed: a.data && reduced[fidx].datamingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combine check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nquantile: too many large chunk/__w/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hCheck failed: worker_feature.data()count <= total_entriesh_weights.size() == batch.Size()Invalid size of sample weight.PushRowPagen_threads_ >= 1sketches_.size() == n_columnsAllReducen_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0MakeCutsp_cuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > p_cuts->cut_ptrs_.HostVector().back()SketchContainerImplcolumns_size_.size() != 0Check failed: reduced[i].data>alphaQuantileLossParamquantile_alphaList of quantiles for quantile loss.N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIN7xgboost6common15ParamFloatArrayEEES5_EEN4dmlc9parameter10FieldEntryIN7xgboost6common15ParamFloatArrayEEE/__w/ml-builds/ml-builds/xgboost/src/common/algorithm.hCheck failed: ctx->IsCPU()/__w/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/__w/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0Check failed: ctx_St23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE߰߰%u[-]?@/__w/ml-builds/ml-builds/xgboost/src/common/ranking_utils.hCheck failed: is_binary can only be used with binary labels.Check failed: label_is_integerWhen using relevance degree as target, label must be either 0 or positive integer.Check failed: label_is_validRelevance degress must be lesser than or equal to when the exponential NDCG gain function is used. Set `ndcg_exp_gain` to false to use custom DCG gain./__w/ml-builds/ml-builds/xgboost/src/common/ranking_utils.ccrank.size() == predt.size()LambdaRankParamg_rank.size() == sorted_idx.size()optional, default=enum_back_map_.count(value) != 0UValue not found in enum declaredInvalid Input: '', valid values are: Enum exisit!) Enums: ), lambdarank_pair_methodMethod for constructing pairs.lambdarank_num_pair_per_samplelong (non-negative)Number of pairs for each sample in the list.lambdarank_unbiasedbooleanUnbiased lambda mart. Use extended IPW to debias click positionlambdarank_bias_normdoubleLp regularization for unbiased lambdarank.ndcg_exp_gainWhen set to true, the label gain is 2^rel - 1, otherwise it's rel.N4dmlc9parameter10FieldEntryIN7xgboost3ltr10PairMethodEEEHgY?@/__w/ml-builds/ml-builds/xgboost/src/common/stats.cct_v.Shape(1) != 0/__w/ml-builds/ml-builds/xgboost/src/common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.haft_loss_distributionnormalextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time modelN4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEElimit >= 1Invalid thread limit for OpenMP./sys/fs/cgroup/cpu/cpu.cfs_quota_us/sys/fs/cgroup/cpu/cpu.cfs_period_us/__w/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor ( ========Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again./__w/ml-builds/ml-builds/xgboost/src/common/version.ccfi->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: bad optional access/__w/ml-builds/ml-builds/xgboost/src/context.ccGot: `ContextBoth `device` and `gpu_id` are specified. Use `device` instead.Check failed: opt_id.has_value()Invalid value for `gpu_id`. Got:Invalid argument for `device`. Expected to be one of the following: - cpu - cuda - cuda: # e.g. cuda:0 - gpu - gpu: # e.g. gpu:0 gpu(:[0-9]+)?|cuda(:[0-9]+)?|cpugpuopt_id.value() <= std::numeric_limits::max()Ordinal value too large.new_d.ordinal == this->device_.ordinalthis->device_.ordinal == kCpuIdthis->device_.ordinal > kCpuIdNULSOHSTXETXEOTENQACKalertbackspacetabnewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1spaceexclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodslashzerotwothreefourfivesixseveneightninecolonsemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atABCDFGHIJKMNOPQRSVWXZleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentbjqvxzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrldigitgraphlowerprintpunctupperxdigitUnexpected end of regex when escaping.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character. has not been registered in Alias seedlongRandom number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.stringDevice ordinal.Number of threads to use.n_jobsfail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.Unexpected escape character.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected end of regex when in brace expression.Unexpected character in brace expression.Unexpected end of character class.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.^$\.*+?()[]{}|.[\()*+?{|^$.[\*^$.[\()*+?{|^$ .[\*^$ Number of NFA states exceeds limit. Please use shorter regex string, or use smaller brace expression, or make _GLIBCXX_REGEX_STATE_LIMIT larger.Unexpected back-reference in polynomial mode.Back-reference index exceeds current sub-expression count.Back-reference referred to an opened sub-expression.cannot create std::deque larger than max_size()Invalid range in bracket expression.Nothing to repeat before a quantifier.Unexpected token in brace expression.Unexpected end of brace expression.Invalid range in brace expression.Invalid collate element.Invalid equivalence class.Invalid character 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.St19bad_optional_accessN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESt23_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""//\\abf n r bad any_castauto/__w/ml-builds/ml-builds/xgboost/src/data/data.ccInvalid device ordinal. Data is associated with a different device ordinal than the booster. The device ordinal of the data is: ; the device ordinal of the Booster is: labelbase_marginlabel_lower_boundlabel_upper_boundfeature_weightsUnknown float field name: group_ptrUnknown uint32 field name: Unknown data type for getting meta info./__w/ml-builds/ml-builds/xgboost/src/data/../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hself_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()XGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workerscache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..r matrix with entries loaded from Load part of data partsColumn-wise data split is not support for external memory.Splitting data by columnUnknown feature info: this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.array.valid.Capacity() == 0group_ptr_.size() == weights_.Size() + 1group_ptr_.back() == num_row_the actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels.Shape(0) == num_row_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.num_rownum_colnum_nonzerolabelslabels_lower_boundlabels_upper_boundfeature_typesfield_cnt == kNumFieldWrong number of fieldsAll feature_types must be one of {int, float, i, q, c}.size == this->num_col_Length of must be equal to number of columns.Check failed: infoUnknown feature info name: MetaInfo: Invalid format for Check failed: strm->Read(&name)name == expected_name Expected field: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0transpose.offset.Size() == num_columns + 1Meta info like label or weight can not have missing value.Check failed: t_out.CContiguous()Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.this->labels.Size() % this->num_row_ == 0Incorrect size for labels.Label contains NaN, infinity or a value too large./__w/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_groupInvalid group structure.Check failed: non_dec`qid` must be sorted in non-decreasing order along with data.Weights must be positive values.Feature weight must be greater than 0.Unknown key for MetaInfo: Check failed: keyCheck failed: t.CContiguous() to be a tensor; got a scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field)Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.major >= 1minor >= 6Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.std::future_error: /__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: page_/__w/ml-builds/ml-builds/xgboost/src/data/../data/array_interface.h/__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hCheck failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix./__w/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hshape[i] == r.Shape(i)this->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification./__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: parser_qidkey >= builder_base_row_offsetCheck failed: !cache_info_->writtenraw/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.hUnknown format type format.front() == '.'wbab MB written in seconds.Check failed: fmt->Read(page.get(), fi.get())Check failed: !at_end_n_prefetch_batches > 0total batches:Check failed: curr_exce_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: !(*ring_)[count_].valid()Check failed: validInput data contains `inf` or a value too large, while `missing` is not set to `inf`/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: cache_info_->offset.size() == n_batches_ + 1count_ == n_batches_count_ >= 1Check failed: proxy_this->count_ >= 1source_->Iter() == this->count_St19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EEN7xgboost10SparsePageEN7xgboost7DMatrixEN7xgboost4data11FileAdapterESt12bad_any_castNSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2ESt10shared_ptrIN7xgboost4data15CSRArrayAdapterEESt10shared_ptrIN7xgboost4data12ArrayAdapterEEN7xgboost17BatchIteratorImplINS_11EllpackPageEEEN7xgboost4data20SparsePageSourceImplINS_11EllpackPageEEEN7xgboost4data18PageSourceIncMixInINS_11EllpackPageEEEN7xgboost4data17EllpackPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt23_Sp_counted_ptr_inplaceIN7xgboost6common12MmapResourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost10SparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE /__w/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.ccname_args_cache.size() <= 2Only one `#` is allowed in file path for cachefile specificationname_args.size() == 2URI parameter `format` is required for loading text data: filename?format=csvCheck failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg ?/__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_n_bytes >= this->data.size()Check failed: this->data.empty()Check failed: resource->Type() == common::ResourceHandler::kMallocCheck failed: malloc_resource/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalrow_ptr.size() == 0Check failed: p_fmat->SingleColBlock()Check failed: !sorted_sketch/__w/ml-builds/ml-builds/xgboost/src/data/../common/io.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/ref_resource_view.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hCheck failed: storage.Resource()->Type() == ResourceHandler::kMalloc[Internal Error]: Cannot grow the vector when external memory is used.m_size >= storage.size()Check failed: resourcenum_nonzeros_.size() == n_features/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index_format.ccCheck failed: fiCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizeRaw 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/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_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccpage.row_ptr.size() == h_offset.size()/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/hist_util.hCheck failed: refCheck failed: p_cutsref->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.param.max_bin == batch_.max_binInconsistent `max_bin`. `max_bin` should be the same across different QuantileDMatrix, and consistent with the Booster being trained.Check failed: !param.regen && param.hess.empty()Check failed: !detail::RegenGHist(param, batch_)`QuantileDMatrix` not initialized.`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.n_features == num_cols()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.n_features >= 1Data must has at least 1 column.h_ft.size() == n_ftaccumulated_rows == Info().num_row_Check failed: p_sketchh_ft.size() == n_featuresrbegin == Info().num_row_this->ghist_->Features() == Info().num_col_proxy->Info().labels.Size() == 0Iterative DMatrix must have at least 1 batch./__w/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hNot implemented.Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.Slicing DMatrix is not supported for Quantile DMatrix.Slicing DMatrix columns is not supported for Quantile DMatrix./__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: proxy_handleInvalid proxy handle.Check failed: typedCheck failed: !std::isnan(sparse_thresh)St14default_deleteIN7xgboost16GHistIndexMatrixEEN7xgboost4data16IterativeDMatrixEN7xgboost4data23SimpleBatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost17BatchIteratorImplINS_13ExtSparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13ExtSparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEESt19_Sp_counted_deleterIPN7xgboost16GHistIndexMatrixESt14default_deleteIS1_ESaIvELN9__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: p_fmatFailed to fallback.Check failed: on_hostNot implemented on device./__w/ml-builds/ml-builds/xgboost/src/data/array_interface.hSt15_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: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatch/__w/ml-builds/ml-builds/xgboost/src/data/batch_utils.hCheck failed: r.Initialized()Batch parameter is not initialized.batch_param_.max_bin == param.max_binCheck failed: !detail::RegenGHist(batch_param_, param)Inconsistent sparse threshold.Generating new Gradient Index.param.max_bin >= 2batch_param_.hess.data() == param.hess.data()/__w/ml-builds/ml-builds/xgboost/src/data/../common/error_msg.hSample size too large for the current updater. Maximum number of samples:. Consider using a different updater or tree_method.offset_vec.back() == info_.num_nonzero_Generating new Ellpack page./__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_Column is emptyCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: (std::is_same::value || std::is_same::value)Expecting CSCAdapterN7xgboost7CSCPageEN7xgboost13SortedCSCPageEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost4data13SimpleDMatrixEN7xgboost17BatchIteratorImplINS_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_policyE2EE.row.page/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: iter_.Next()Must have at least 1 batch.DMatrix-rinfo_.num_col_ != 0.col.pagethis->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Couldn't remove external memory cache file ; you may want to remove it manually/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Slicing DMatrix columns is not supported for external memory./__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.hCan not obtain a single CSR page for external memory DMatrix/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.hCheck failed: kv.secondWaitthis->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_ptrIN7xgboost13SortedCSCPageEEEEN7xgboost16GHistIndexMatrixENSt13__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_EEEEEERaw binary data format./__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()page->offset.Size() != 0UInvalid SparsePage fileN7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_Egpu_coord_descentlayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamLinear booster, implement generalized linear model./__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by the current booster.Inplace predict is not supported by the current booster./__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf indexmodelDoBoost/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)get(in["name"]) == "gblinear"/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hCheck failed: !model_.weight.empty()Model is not initializedCheck failed: trees.empty()gblinear doesn't support number of trees for feature importance.importance_type == "weight"gblinear only has `weight` defined for feature importance.PredictBatchInternalbase_margin.Size() == nsize * ngroupPredictBatch { "bias": [ ], "weight": [ ] }bias: weight: updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.gblinear_train_paramCheck failed: this->updater_/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hdevice="cuda", updater="coord_descent"coord_descentGBLinearN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE_Eboosted_roundsDeprecatedGBLinearModelParamdeprecated_num_featureint (non-negative)deprecated_num_output_groupN7xgboost3gbm13GBLinearModelEUnknown gbm type /__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccCommitModel/__w/ml-builds/ml-builds/xgboost/src/gbm/../tree/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/gbm/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccCheck failed: cpu_predictor_Check failed: !updaters_.empty()model_.param.num_parallel_tree == trees.size()model_.param.num_parallel_tree == 1Boosting random forest is not supported for current objective.Check failed: !trees.front()->IsMultiTarget()Update tree leaf support for multi-target tree is not yet implemented.trees.size() == model_.param.num_parallel_treeout_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees.Unknown device type:Inplace predict accepts only DMatrixProxy as input.Unknown data type for inplace prediction:get(in["name"]) == "gbtree"Check failed: p_gbtreeCheck failed: this->model_.learner_model_param->Initialized()end != beginEmpty slice is not allowed.this->model_.trees_to_update.size() == this->model_.trees.size()Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees.out_model.iteration_indptr.front() == 0GBTreeModelParamnum_treesNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.GBTreeTrainParamDartTrainParamgbtree_train_paramtree_train_paramspecified_updaterCheck failed: !updaters_.front()->CanModifyTree()Updater: `` 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 roundsn_out % gpair->Size() == 0Mismatching size between number of rows from input data and size of gradient vector.gpair->Size() == n_outCheck failed: tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAutoOnly the hist tree method is supported for building multi-target trees with vector leaf.Check failed: ctx_->IsCPU()GPU is not yet supported for vector leaf.BoostNewTreesn_groups != 0Current objective doesn't support external memory.in_gpair->Size() % n_groups == 0Umust have exactly ngroup * nrow gpairs 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`./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/error_msg.hIf you are loading a serialized model (like pickle in Python, RDS in R) or configuration generated by an older version of 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/stable/tutorials/saving_model.html for more details about differences between saving model and serializing. grow_gpu_histgrow_quantile_histmakerChanging updater from `grow_gpu_hist` to `grow_quantile_histmaker`.cpu_predictorUsing tree method: grow_histmakerThe `exact` tree method is not supported on GPU.grow_colmaker,pruneUnknown tree_method: `GBTreeTree booster, gradient boosted trees.Tree booster, dart.weight_drop/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.hiteration_indptr.size() == 1/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hCheck failed: !model.iteration_indptr.empty()end <= model.BoostedRounds()Out of range for tree layers.tree_begin <= tree_endtree_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.step >= 1out_l == n_layersget(in["name"]) == "dart"Check failed: !this->model_.learner_model_param->IsVectorLeaf()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()Check failed: successUnsupported data type for inplace predict.predts.predictions.Size() == p_out_preds->predictions.Size()Check failed: tree_param_.GetInitialised()drop trees, weight = Check failed: p_dartCheck failed: p_dart->weight_drop_.empty()vector::reserveupdater_seqTree updater sequence.process_typedefaultupdateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.tree_methodapproxexacthistgpu_histChoice of tree construction method.sample_typeuniformweightedDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.one_dropWhether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.dart_train_paramidx <= total_n_treesInvalid tree index.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: N4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartE?*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE0_E/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())model.trees.size() == model.param.num_treesmodel.tree_info.size() == model.param.num_treesmodel.iteration_indptr.back() == model.param.num_treeslayer_trees != 0Check failed: !iteration_indptr.empty()iteration_indptr.back() == param.num_treesparam.num_trees == static_cast(trees.size())treestree_infoiteration_indptrfi->Read(¶m, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treestrees_json.size() == param.num_treestree_info_json.size() == param.num_treesN7xgboost3gbm11GBTreeModelEverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoostGlobalConfiguration/__w/ml-builds/ml-builds/xgboost/src/learner.ccmulti-target-multi-class is not yet supported. Output classes:, output targets:Check failed: std::as_const(base_score_).Data()->HostCanRead()base_score_.Size() == 1Model 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()LearnerModelParamLegacyLearnerTrainParamCheck failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: !this->need_configuration_/__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective function/__w/ml-builds/ml-builds/xgboost/include/xgboost/cache.hqueue_.size() == container_.size()learner_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_/__w/ml-builds/ml-builds/xgboost/src/collective/aggregator.hCheck failed: ret.ec == std::errc{}base_scorenum_featurenum_classnum_targetboost_from_averageCheck failed: ptrCall Configure before saving model.learner_model_paramattributesModelConfig/__w/ml-builds/ml-builds/xgboost/src/common/error_msg.hGlobal 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 output targets. Can be set automatically if not specified.Whether we should calculate the base score from training data.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableboosterGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.multi_strategyone_output_per_treemulti_output_treeStrategy used for training multi-target models. `multi_output_tree` means building one single tree for all targets.multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7learner_train_parammetricsgeneric_paramCheck failed: !this->learner_model_param_.IsVectorLeaf()Please use JSON/UBJ format for model serialization with multi-output models.;binfUnsupported prediction type:Check failed: it != container_.cend()remained.size() + expired.size() == container_.size()Check failed: matrix.first.ptrCheck failed: !matrix.second.ref.expired()n_features <= max_n_featuresUnfortunately, XGBoost does not support data matrices with features or greatermparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface?Check failed: this->obj_Check failed: n_targets == t || 1 == tInconsistent labels.Check failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: tparam_.GetInitialised()learner_model_param_.BaseScore(Ctx()).Size() != 0base_score.Size() == 1Check failed: !std::isnan(mparam_.base_score)Check failed: std::equal(data.cbegin(), data.cend(), sync.cbegin())Different model parameter across workers.Check failed: !std::isinf(mparam_.base_score)Check failed: IsA(in)Invalid version string in configget(objective_fn["name"]) == tparam_.objectiveFound 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.Check failed: that.lock_.try_lock()container_.size() < max_size__Map_base::atmultiple_predictions <= 1Perform one kind of prediction at a time.Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterUpdateOneIterPredictRawPredictionsGetGradientGradientsLearnerCONFIG-offset:this->learner_model_param_.num_feature != 0begin >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scoreEvalOneIterInvalid parameter "" contains whitespace. Parameters: { ", } are not used. Configurefixed_size->Read(&buffer[0], size) == sizebs64header != "bs64"Base64 format is no longer supported in brick.fp.Read(&header[0], 4) == 4UInvalid model formatfi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)count_poisson_max_delta_stepmulti:data_.Size() == detail::CalcSize(shape_)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility%uInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0N4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13MultiStrategyEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplESt23_Sp_counted_ptr_inplaceIN7xgboost20PredictionCacheEntryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Unknown linear updater /__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccLinearTrainParamlearning_rateLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.etalambdaCoordinateParamUpdate linear model according to coordinate descent algorithm./__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/updater_coordinate.ccSave config for CPU updater.linear_train_paramcoordinate_param/__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: CoordinateUpdaterN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*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() != 0labels.Size() == predts.size()auc <= 1.0fReceiver Operating Characteristic Area Under the Curve.Area under PR curve for both classification and rank./__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/../collective/aggregator.h/__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./__w/ml-builds/ml-builds/xgboost/src/metric/../common/algorithm.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()auc <= 1Total AUC across groups: , valid groups: auc <= 1.0Dataset is empty, or contains only positive or negative samples.PR-AUC supports only binary relevance for learning to rank.N7xgboost6MetricEN7xgboost13MetricNoCacheEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE?*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_Emphequantilegamma-nloglikgamma-deviancepoisson-nloglikloglossmapemaermslermse/__w/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cuCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricRooted mean square error.Rooted mean square log error.Mean absolute error.Mean absolute percentage error.Negative loglikelihood for logistic regression.Mean Pseudo-huber error.Negative loglikelihood for poisson regression.Residual deviance for gamma regression.Negative log-likelihood for gamma regression.Binary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression.Quantile regression error.tweedie-nloglik@errorinfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.preds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0Check failed: !alpha_.Empty()dat[1] > 0n_targets != 0quantile_loss_parampseudo_huber_paramname == "quantile"/__w/ml-builds/ml-builds/xgboost/src/metric/../common/quantile_loss_utils.hCheck failed: GetInitialised()Check failed: !quantile_alpha.Get().empty()quantile alpha must be in the range [0.0, 1.0].N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEEN7xgboost6metric13QuantileErrorEư>*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_E*N7xgboost6metricUlPKcE10_E/__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function merrormloglossMulticlass classification error.Multiclass negative loglikelihood./__w/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchnclass >= 1Umlogloss 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), num_class= but found in labelN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_Ecox-nloglikscore <= 1.0 + kRtEpsInvalid output score, might be caused by invalid query group weight.amsAMS metric for higgs.Negative log partial likelihood of Cox proportional hazards model.preprecision@k for rank.map@k for ranking.ndcgndcg@k for ranking./__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccCheck failed: !collective::IsDistributed()Cox metric does not support distributed evaluationAMS must be in format ams@kams@/__w/ml-builds/ml-builds/xgboost/src/metric/../common/ranking_utils.hCheck failed: param_.GetInitialised()the size of label.Groups() == info.weights_.Size()weight.weights.size() == p_cache->Groups()metric AMS do not support distributed evaluationbest-ams-ratio=lambdarank_paramCheck failed: p_cache->Param() == param_St23_Sp_counted_ptr_inplaceIN7xgboost3ltr8MAPCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost3ltr9NDCGCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost3ltr8PreCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEN7xgboost6metric7EvalAMSEN7xgboost6metric7EvalCoxEN7xgboost6metric17EvalRankWithCacheINS_3ltr8PreCacheEEEN7xgboost6metric13EvalPrecisionEN7xgboost6metric17EvalRankWithCacheINS_3ltr9NDCGCacheEEEN7xgboost6metric8EvalNDCGEN7xgboost6metric17EvalRankWithCacheINS_3ltr8MAPCacheEEEN7xgboost6metric12EvalMAPScoreEzo ?*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_Eaft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model./__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuCheck failed: metric_AFT metric must be configured first, with distribution type and scaleaft_loss_paramUnknown probability distributionpreds.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-q=' @;f?*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E/__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.h/__w/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.cck + 1 < h_node_ptr.size()y_idx <= info.labels.Shape(1)/__w/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: h_row_set.empty()begin_pos <= sorted_pos.size()nptr.size() > 0n_unique == n_leafCheck failed: !position.empty()h_node_ptr.back() <= info.num_row_/__w/ml-builds/ml-builds/xgboost/src/objective/../collective/aggregator.h/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/__w/ml-builds/ml-builds/xgboost/src/objective/../common/algorithm.hAFT loss functionsurvival:aft/__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() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distributionN7xgboost3obj6AFTObjEؗҜ<*N7xgboost3objUlvE_EHinge loss. Expects labels to be in [0,1f]binary:hinge/__w/ml-builds/ml-builds/xgboost/src/objective/hinge.cuinfo.labels.Size() != 0Ulabel set cannot be emptylabels are not correctly providedpreds.size=, label.size=N7xgboost3obj8HingeObjE*N7xgboost3objUlvE_E/__w/ml-builds/ml-builds/xgboost/src/objective/init_estimation.hInvalid shape of labels.info.weights_.Size() == info.num_row_N7xgboost3obj12FitInterceptE/__w/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.cch_n_rel.size() == gptr.back()h_n_rel.size() == label.Size()rank:ndcgLambdaRank with NDCG loss as objectiverank:pairwiseLambdaRank with RankNet loss as objectiverank:mapLambdaRank with MAP loss as objective./__w/ml-builds/ml-builds/xgboost/src/objective/../common/ranking_utils.hUnreachable.Check failed: param_.ndcg_exp_gainNDCG gain can not be set for the MAP objective.NDCG gain can not be set for the pairwise objective./__w/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.hg_rank.size() == g_label.Size()info.labels.Size() == predt.Size()The size of label doesn't match the size of prediction.iter == 0ti+tj-info.labels.Shape(1) <= 1multi-output for LTR is not yet supported.St23_Sp_counted_ptr_inplaceIN7xgboost3ltr12RankingCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEN7xgboost3obj13LambdaRankObjINS0_14LambdaRankNDCGENS_3ltr9NDCGCacheEEEN7xgboost3obj14LambdaRankNDCGEN7xgboost3obj13LambdaRankObjINS0_13LambdaRankMAPENS_3ltr8MAPCacheEEEN7xgboost3obj13LambdaRankMAPEN7xgboost3obj13LambdaRankObjINS0_18LambdaRankPairwiseENS_3ltr12RankingCacheEEEN7xgboost3obj18LambdaRankPairwiseE{Gz?^7*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_ESoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/__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. label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class).N7xgboost3obj20SoftmaxMultiClassObjEҜ<*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E/__w/ml-builds/ml-builds/xgboost/src/objective/objective.ccCheck failed: base_scoreObjective candidate: Unknown objective function: `` N7xgboost11ObjFunctionERegression with quantile loss./__w/ml-builds/ml-builds/xgboost/src/objective/quantile_obj.cualpha.size() == alpha_.Size()The objective is not yet configured.info.labels.Shape(1) == 1Multi-target is not yet supported by the quantile loss.Check failed: !alpha.empty()reg:quantileerrorget(in["name"]) == Name()/__w/ml-builds/ml-builds/xgboost/src/objective/../common/quantile_loss_utils.hparam_.quantile_alpha.Get().size() == alpha_.Size()n_alphas != 0n_targets >= n_alphaspreds.Size() == info.num_row_ * n_targetsh_weights.size() == h_labels.Size()N7xgboost3obj18QuantileRegressionE*N7xgboost3objUlvE_E/__w/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cureg:linear is now deprecated in favor of reg:squarederror.info.labels.Size() == preds.Size()RegLossParamscale_pos_weightScale the weight of positive examples by this factorPoissonRegressionParamMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.TweedieRegressionParamtweedie_variance_powerTweedie variance power. Must be between in range [1, 2).Regression with squared error.reg:squaredlogerrorRegression with root mean squared logarithmic error.reg:logisticLogistic regression for probability regression task.binary:logisticLogistic regression for binary classification task.binary:logitrawLogistic regression for classification, output score before logistic transformation.reg:linearRegression Pseudo Huber error.Poisson regression for count data.Cox regression for censored survival data (negative labels are considered censored).Gamma regression for severity data.Tweedie regression for insurance data.Mean absoluate error.reg:absoluteerrorStringView{get(in["name"])} == StringView{"reg:absoluteerror"}Check failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!/__w/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: base_margin->Size() == 1Sum of weights is close to 0.0, skipping base score estimation.reg:gammasurvival:coxreg:pseudohubererrorpoisson_regression_paramreg:tweedietweedie_regression_paramPoissonRegression: label must be nonnegativeGammaRegression: label must be positive.TweedieRegression: label must be nonnegativereg_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_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionEN7xgboost3obj17MeanAbsoluteErrorEư>?@ffffB$*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_E*N7xgboost3objUlvE10_E/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hCheck failed: IsMultiTarget()/__w/ml-builds/ml-builds/xgboost/include/xgboost/multi_target_tree_model.hCheck failed: IsLeaf(nidx)Make predictions using CPU./__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccbase_margin.Shape(1) == ngroupCheck failed: !model.learner_model_param->IsVectorLeaf()Predict interaction contributionCheck failed: !p_fmat->Info().IsColumnSplit()Predict interaction contribution support for column-wise data split is not yet implemented./__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/../data/proxy_dmatrix.hCheck failed: xgboost::collective::IsDistributed()column-split prediction is only supported for distributed trainingout_preds->size() == p_fmat->Info().num_row_ * model_.learner_model_param->num_output_groupout_preds->size() == p_fmat->Info().num_row_ * (tree_end_ - tree_begin_)Predict contributionPredict contribution support for column-wise data split is not yet implemented.ngroup != 0ncolumns != 0predict instancePredict instance with column splitPredict leaf with column splitm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Check failed: !p_m->Info().IsColumnSplit()Inplace predict support for column-wise data split is not yet implemented.Predict DMatrix with column splitout_preds->size() == n_samples * n_groupsSt19__shared_ptr_accessIN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2EEN7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE?*N7xgboost9predictorUlPKNS_7ContextEE_E/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_treeshap.ccunique_path[i].pweight == 0Unique path must have zero weightUnknown predictor type /__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccmodel.learner_model_param->num_output_group != 0Invalid shape of base_margin. Expected: (margin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsN7xgboost9PredictorE/__w/ml-builds/ml-builds/xgboost/src/tree/constraints.ccnewsize != 0Failed to parse feature interaction constraint: With error: /__w/ml-builds/ml-builds/xgboost/src/tree/fit_stump.ccn_targets == gpair.Shape(1)/__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hCheck failed: h_sum.CContiguous()/__w/ml-builds/ml-builds/xgboost/src/tree/../common/common.hư>ư>/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.ccnodes_to_build.size() == valid_candidates.size()debug_synchronizeCheck if all distributed tree are identical after tree construction.max_cached_hist_nodeMaximum number of nodes in CPU histogram cache. Only for internal usage.HistMakerTrainParam/__w/ml-builds/ml-builds/xgboost/src/tree/hist/param.ccCheck failed: *local_tree == ref_tree/__w/ml-builds/ml-builds/xgboost/src/tree/multi_target_tree_model.ccCheck failed: this->IsLeaf(nidx)Collapsing a split node to leaf weight.Size() == this->NumTarget()weights_.size() >= next_nidx * weight.Size()param_->size_leaf_vector > 1parent_.size() >= 1parent_.size() == left_.size()left_.size() == right_.size()split_idx < this->param_->num_featureparent_[nidx] != InvalidNodeId()p_weight.Size() == base_weight.Size()l_weight.Size() == left_weight.Size()r_weight.Size() == right_weight.Size()nidx < left_.size()nidx < right_.size()nidx < parent_.size()nidx < split_index_.size()in_weight.Size() == weight_out.size()loss_changessum_hessianbase_weightssplit_indicessplit_conditionsdefault_leftparentsleft_childrenright_childrenN7xgboost15MultiTargetTreeE/__w/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:/__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.cc{param_.num_nodes == static_cast(nodes_.size())param_.num_nodes == static_cast(stats_.size())param_.deprecated_num_roots == 1param_.num_nodes != 0Check failed: !IsMultiTarget()Please use JSON/UBJSON for saving models with multi-target trees.Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.mean_values->size() > 0UCheck failed: node.IsLeaf()split_index < this->param_.num_featureCheck failed: this->p_mt_tree_param_.size_leaf_vector > 1fi->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_deletedthis->split_types_.size() == this->Size()this->GetSplitCategoriesPtr().size() == this->Size()split_typesize != 0categories_segmentscategories_sizescategories_nodescategoriesUnknown Model Builder:GraphvizParamTreeParamtree_paramget(out["tree_param"]["num_nodes"]) == std::to_string(param_.num_nodes)this->split_types_.size() == param_.num_nodesstatic_cast(deleted_nodes_.size()) == param_.num_deletedthis->split_categories_segments_.size() == param_.num_nodesDump text representation of treeDump json representation of treedotDump graphviz representation of tree Line , around ^`/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/json.hch == '\"'Error at, Expect '"' but get 'unknown string escape \, Expect '"' but reach end of line ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'Check failed: is_numerical in feature map is categorical but tree node is numerical.Check failed: is_categorical in feature map is numerical but tree node is categorical.Unknown feature map type.pos != std::string::nposparam_.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31ch == '{', Expect '{' but get 'j_end - j_begin > 0std::numeric_limits::min() != max_catn_nodes != 0loss_changes.size() == n_nodessum_hessian.size() == n_nodesbase_weights.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesdefault_left.size() == n_nodesyes_color#0000FFEdge color when meets the node condition.no_color#FF0000Edge color when doesn't meet the node condition.rankdirTBPassed to graphiz via graph_attr.condition_node_paramsConditional node configurationleaf_node_paramsLeaf node configurationgraph_attrsAny other extra attributes for graphviz `graph_attr`.num_nodesNumber of features used in tree construction.num_deletedsize_leaf_vectorSize of leaf vector, reserved for vector treeLearning rate(step size) of update.min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree 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)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum 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 constraintsubsampleRow subsample ratio of training instance.sampling_methodgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints 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 informationsparse_thresholdpercentage threshold for treating a feature as sparsegamma,gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{parent}{stat} {left} {right}{parent}{stat}{left}{parent} {left} {right}{tabs}{nid}{fname}{cond}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},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}] "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{yes}{no}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties} {indent}, "gain": {loss_chg}, "cover": {sum_hess} {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}{nodes}{newline}{indent}{nodes}{newline}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{nid}:[{fname}] yes={yes},no={no}yesno, missing {nid} -> {child} [label="{branch}" color="{color}"] {child}{color}{branch} {nid} [ label="{fname}{<}{cond}" {params}] {<}{params}{tabs}{nid}:leaf={leaf}{stats},cover={cover}{cover}{stats} {nid} [ label="{fname}:{cond}" {params}] {nodes} " edge graph [ {key}="{value}" ] {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_EUnknown tree updater /__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccGlobalApproxUpdaterTree constructor that uses approximate histogram construction for each node./__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/io.h/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hpartition > 0/__w/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hid != kMax/__w/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0Ui < first_dimension_.size()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/sampler.hexponent <= mod/__w/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hCheck failed: tree.IsLeaf(node.node_id)ptr_offset <= row_set.Data()->size()i < ranges_.size()end > beginnid < nodes_begin < end/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hCheck failed: !nodes_to_build.empty()n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endtid < nthreads_/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/ref_resource_view.hnidx != kRootvector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../split_evaluator.hp.monotone_constraints.size() <= n_featuresThe size of monotone constraint should be less or equal to the number of features.cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())out_preds.DeviceIdx() == Context::kCpuIdpart.Size() == n_nodesout_preds.Size() > 0UCheck failed: p_last_treeout_preds.Shape(1) == n_targetsUpdatePredictionCache/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccout_preds.Size() == data->Info().num_row_/__w/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hupper_bound < static_cast(std::numeric_limits::max())p_tree->LeftChild(nidx) + 1 == p_tree->RightChild(nidx)hist_train_paramnodes == targeted_hists.size()row_ptr_[nid] == kMaxCheck failed: !features.empty()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/hist_cache.hcurrent_size_ == new_sizeCheck failed: p_nodes_to_buildCheck failed: p_nodes_to_subCheck failed: !this->hist_.HasExceeded()gpair.Shape(1) == n_targetsp_fmat->Info().num_row_ == gpair.Shape(0)target_builders_.size() == n_targetsCheck failed: dummy_sub.empty()Check failed: gpair.Contiguous()InitRootnodes_to_build.size() >= nodes_to_sub.size()nodes_to_sub.size() + nodes_to_build.size() == valid_candidates.size() * 2gpair.Shape(1) == p_tree->NumTargets()t_gpair.Shape(0) == p_fmat->Info().num_row_/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hInitDatan_total_bins == page.cut.TotalBins()n_targets >= 1base_rowid == gmat.base_rowidgidx > -1any_missing == trueUpdatePositionBuildHistogramEvaluateSplitsLeafPartitionCheck failed: hist_param_.GetInitialised()Check failed: out.Contiguous()param.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.FbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterEPFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost6common6ColumnIhEEN7xgboost6common15DenseColumnIterIhLb1EEEN7xgboost6common16SparseColumnIterIhEEN7xgboost6common6ColumnItEEN7xgboost6common15DenseColumnIterItLb1EEEN7xgboost6common16SparseColumnIterItEEN7xgboost6common6ColumnIjEEN7xgboost6common15DenseColumnIterIjLb1EEEN7xgboost6common16SparseColumnIterIjEESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_Egrow_colmakerColMakerTrainParamGrow tree with parallelization over columns./__w/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccCheck failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0Check failed: this->ctx_ridx < position_.size()ridx exceed bound ridx= pos=opt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault 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./__w/ml-builds/ml-builds/xgboost/src/tree/split_evaluator.hN7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlPKNS_7ContextET_E_EpruneTreePrunerPruner that prune the tree according to statistics./__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()nid >= 1PrunerUpdatetree pruning end, extra nodes, pruned nodes, max_depth=N7xgboost4tree10TreePrunerE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_EGrow tree using quantized histogram.Check failed: p_tree->IsMultiTarget()left_child > candidate.nidright_child > candidate.nidstats_.Shape(1) == n_targets/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccout_preds.Size() == data->Info().num_row_ * p_last_tree_->NumTargets()root_sum_tloc.Shape(1) == gpair.Shape(1)Check failed: root_sum.CContiguous()row_ptr.size() >= 2UpdateTreep_tree->LeftChild(candidate.nid) > candidate.nidCheck failed: param->monotone_constraints.empty()monotone constraintMultiTargetHistBuilderHistUpdaterFbRKN7xgboost4tree16MultiExpandEntryES3_EN7xgboost4tree17QuantileHistMakerEPFbRKN7xgboost4tree16MultiExpandEntryES3_EN7xgboost6common15DenseColumnIterIhLb0EEEN7xgboost6common15DenseColumnIterItLb0EEEN7xgboost6common15DenseColumnIterIjLb0EEE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_Erefresh/__w/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits::max()Refresher that refreshes the weight and statistics according to data.N7xgboost4tree13TreeRefresherE*N7xgboost4treeUlPKNS_7ContextET_E_EsyncSyncher that synchronize the tree in all distributed nodes.N7xgboost4tree11TreeSyncherE*N7xgboost4treeUlPKNS_7ContextET_E_ENULL/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccrabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_reduce_buffer%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}DMLC_WORKER_CONNECT_RETRYrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delayConnecting to (failed): [] 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 failureReConnectLink failure 3tracker.Send(xgboost::StringView{task_id}) == task_id.size()shutdownFailed to shutdown due to%sworker256MBDMLC_NUM_ATTEMPTtask connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rank got new rank bind(handle_, handle, sizeof(std::remove_reference_t))getsockname(handle_, reinterpret_cast(&res_addr), &addrlen)ReConnectLink fail to bind the ports specifiedlisten(handle_, backlog)getsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len)ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14acceptReConnectLink failure 15ReConnectLink: bad socketfcntlsetsockopt(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 linkfailed in ReconnectLink mapred_tip_id%[^=]=%smapreduce_task_idhadoop_mode is set but cannot find mapred_task_idmapred_task_idrabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc workercan only call Init oncegethostname(&buf[0], HOST_NAME_MAX)startneed to assume rank structurewrite ptr boundary checktoo large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary check[%d] read_ptr boundary checkBroadcast: root should be smaller than world sizebuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary error/__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutFailed to poll.AllgatherRing failedBroadcast failedAllreduce failedN5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseEFailed to initialize RABIT.Failed to shutdown RABIT worker.unknown data_typeunknown enum_opDataType does not support bitwise AND operationDataType does not support bitwise OR operationDataType does not support bitwise XOR operationthe rabit has not been initializedLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at /__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 ParseNext/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hCheck failed: base <= 10 && base >= 2sign == true/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.h.split.part/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationname_args.size() == 1Ulibsvm/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type File 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.csvFile format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0No thread/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check 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)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)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))/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hMB read, MB/secfinish reading at %g MB/secCheck 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() == 0 || 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()Check failed: out->label.size() + 1 == out->offset.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_EEEEEEeAhdfs:///__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccviewfs://Please compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOunknown input split type rbBeforeFirst is not supported/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hResetPartition 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: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeCheck 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)) != 0invalid record io format(reinterpret_cast(begin) & 3UL) == 0U(reinterpret_cast(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast(chunk->begin) & 3UL) == 0U(reinterpret_cast(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagicN4dmlc2io16RecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index filesN4dmlc2io23IndexedRecordIOSplitterE/__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()curr=,begin=,end=,fileptr=,fileoffset=offset[]=file offset not calculated correctly bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern Check failed: files_[i].size % align_bytes == 0file do not align by bytesN4dmlc2io14InputSplitBaseE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdoutCheck failed: allow_null LocalFileSystem::Open "": LocalFileSystem.ListDirectory ..Check failed: !std::fseek(fp_, static_cast(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamE/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::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 TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory N4dmlc2io10FileSystemE;t4G44J50ND5 V5j5,6p<6|66P!6&47h_t70w7~7H 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