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ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc4data11DiskRowIterImfEE_ZTIN4dmlc4data11DiskRowIterImfEE_ZTSN4dmlc4data12BasicRowIterImfEE_ZTIN4dmlc4data12BasicRowIterImfEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc4data11DiskRowIterIjiEE_ZTIN4dmlc4data11DiskRowIterIjiEE_ZTSN4dmlc4data12BasicRowIterIjiEE_ZTIN4dmlc4data12BasicRowIterIjiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc4data11DiskRowIterImiEE_ZTIN4dmlc4data11DiskRowIterImiEE_ZTSN4dmlc4data12BasicRowIterImiEE_ZTIN4dmlc4data12BasicRowIterImiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc4data11DiskRowIterIjlEE_ZTIN4dmlc4data11DiskRowIterIjlEE_ZTSN4dmlc4data12BasicRowIterIjlEE_ZTIN4dmlc4data12BasicRowIterIjlEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc4data11DiskRowIterImlEE_ZTIN4dmlc4data11DiskRowIterImlEE_ZTSN4dmlc4data12BasicRowIterImlEE_ZTIN4dmlc4data12BasicRowIterImlEE_ZTSN4dmlc4data10ParserImplIjfEE_ZTIN4dmlc4data10ParserImplIjfEE_ZTSN4dmlc4data14TextParserBaseIjfEE_ZTIN4dmlc4data14TextParserBaseIjfEE_ZTSN4dmlc4data12LibSVMParserIjfEE_ZTIN4dmlc4data12LibSVMParserIjfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc4data14ThreadedParserIjfEE_ZTIN4dmlc4data14ThreadedParserIjfEE_ZTSN4dmlc4data10ParserImplImfEE_ZTIN4dmlc4data10ParserImplImfEE_ZTSN4dmlc4data14TextParserBaseImfEE_ZTIN4dmlc4data14TextParserBaseImfEE_ZTSN4dmlc4data12LibSVMParserImfEE_ZTIN4dmlc4data12LibSVMParserImfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTSN4dmlc4data14ThreadedParserImfEE_ZTIN4dmlc4data14ThreadedParserImfEE_ZTSN4dmlc4data11LibFMParserIjfEE_ZTIN4dmlc4data11LibFMParserIjfEE_ZTSN4dmlc4data11LibFMParserImfEE_ZTIN4dmlc4data11LibFMParserImfEE_ZTSN4dmlc4data9CSVParserIjfEE_ZTIN4dmlc4data9CSVParserIjfEE_ZTSN4dmlc4data9CSVParserImfEE_ZTIN4dmlc4data9CSVParserImfEE_ZTSN4dmlc4data10ParserImplIjiEE_ZTIN4dmlc4data10ParserImplIjiEE_ZTSN4dmlc4data14TextParserBaseIjiEE_ZTIN4dmlc4data14TextParserBaseIjiEE_ZTSN4dmlc4data9CSVParserIjiEE_ZTIN4dmlc4data9CSVParserIjiEE_ZTSN4dmlc4data10ParserImplImiEE_ZTIN4dmlc4data10ParserImplImiEE_ZTSN4dmlc4data14TextParserBaseImiEE_ZTIN4dmlc4data14TextParserBaseImiEE_ZTSN4dmlc4data9CSVParserImiEE_ZTIN4dmlc4data9CSVParserImiEE_ZTSN4dmlc4data10ParserImplIjlEE_ZTIN4dmlc4data10ParserImplIjlEE_ZTSN4dmlc4data14TextParserBaseIjlEE_ZTIN4dmlc4data14TextParserBaseIjlEE_ZTSN4dmlc4data9CSVParserIjlEE_ZTIN4dmlc4data9CSVParserIjlEE_ZTSN4dmlc4data10ParserImplImlEE_ZTIN4dmlc4data10ParserImplImlEE_ZTSN4dmlc4data14TextParserBaseImlEE_ZTIN4dmlc4data14TextParserBaseImlEE_ZTSN4dmlc4data9CSVParserImlEE_ZTIN4dmlc4data9CSVParserImlEE_ZTSZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE__ZTSZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0__ZTSZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE__ZTSZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0__ZTSZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE__ZTSZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0__ZTSZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE__ZTSZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0__ZTSZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE__ZTSZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0__ZTSZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE__ZTSZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0__ZTSZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE__ZTSZN4dm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TbUl@}gN#@@"2&kpcC"ҡCwv+@'`c V+@`@Lm"}ӢˎAx ?TT)lҡ\v3@`"g*V3@`"@ҡcPv/@`aV/@d@yVTlsҹ#ckZ a'[&@@fok [a`@?@Tf Ѐam@ T fru{{_ St19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data18ArrowColumnarBatchEN4dmlc8DataIterISt6vectorISt10unique_ptrIN7xgboost4data18ArrowColumnarBatchESt14default_deleteIS5_EESaIS8_EEEEN7xgboost4data24RecordBatchesIterAdapterEN7xgboost4data12DMatrixProxyEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data12ArrayAdapterEESt15_Sp_counted_ptrIPN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data15CSRArrayAdapterEEXGBoosterGetModelRawXGBoosterSaveModelToBufferXGBoosterPredictFromDMatrixInplacePredictImplInplacePredictImplXGBoosterFeatureScoreXGDMatrixCreateFromCallbackcache_idget(config["cache_id"]) == 0Cache ID is not supported yetCheck failed: p_predtStack trace: _Z +basic_string::basic_string%s: __pos (which is %zu) > this->size() (which is %zu) [bt] () vs. [%02d:%02d:%02d] read can not have position excceed buffer lengthwrite position exceed fixed buffer size/__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.h/__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())qintfloatunknown feature type, use i for indicator and q for quantitymissingInvalid missing value: Argument `` is required for `n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.get(array).size() == 1Column: /__w/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hCheck failed: chunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), 1, std::multiplies<>{}) == chunksize * rowscustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.%luffeature_map.Size() == n_featuresBit 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.basic_string::appendOnly %d dimensional array is valid.Empty data passed in.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)size == 0Empty data with non-zero shape.maskj_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.Check failed: m == 1 || n == 1j_strides.size() == j_shape.size()stride and shape don't match.i < DversionMissing `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 interfacelearnergradient_boostergblineargbtree_model_paramnum_parallel_treestoiUnknown booster:/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from Check failed: array.find("mask") == array.cend()Masked array is not yet supported.streamNot implemented./__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Cannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset RecordBatchesIterAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.map::atvector::_M_range_insertvector::_M_default_appendCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from basic_string::_M_construct null not validDMLC_LOG_STACK_TRACE_DEPTH%zuUSE_CUDAUSE_NCCLUSE_RMM/__w/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been initialized or has already been disposed.Check failed: mCurrent DMatrix type does not support set data.binary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.group` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.Check failed: p_outwjsonubjCheck failed: outInvalid input pointerBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPGCC_VERSIONDEBUGfeature_namefeature_typeUnknown field for Booster feature info:deprecatedUnknown format: `DMatrix has not been initialized or has already been disposed.iteration_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_shapestr.size() >= 3str[0] == '{'str[str.size() - 2] == '}'text+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 schemaimportance_typefeature_mapfeature_namestree_idxidx < names_.size()FeatureMap feature index exceed boundfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()XGBoost distributed mode detected, will split data among workersautocache_prefixnthread/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/../common/threading_utils.hlimit >= 1Invalid thread limit for OpenMP.static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureCheck failed: eCheck failed: ec == std::errc()%ldfalseUnknown global parameters: { }00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899@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!NaN0E0/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.cc/__w/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()vector::_M_fill_insertN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamE/__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedbasic_string::substr/__w/ml-builds/ml-builds/xgboost/src/common/io.cc://file://Opening failed: pointer_ <= buffer_.size() N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterEN7xgboost9UBJReaderEN7xgboost9UBJWriterEN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEEN7xgboost14JsonTypedArrayIhLNS_5Value9ValueKindE8EEEN7xgboost14JsonTypedArrayIiLNS_5Value9ValueKindE9EEEN7xgboost14JsonTypedArrayIlLNS_5Value9ValueKindE10EEEExpecting: "", got: "\0"vector::_M_realloc_insertNumberArrayNullF32ArrayU8ArrayI32ArrayI64Array/__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_\n\0 Unknown construct \ Unknown escapenullExpecting null value "null"Expecting digitf64 is not supported.High precision number is not supported.Check failed: ret.ec == std::errc()\\\"\b\f\r\t\u%04x` is not supported for typed array.N4dmlc10ParamErrorE/__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='Required parameter of is not presented optional, default=, requiredInvalid Parameter format for expect but value='value for Parameter exceed bound [ should be greater equal to should be smaller equal to PesudoHuberParamhuber_slopekey has already been registered in The delta term in Pseudo-Huber loss.>PushColPagePushRowPagePushRowPageAllReduceMakeCutsAllReduceMakeCutsSketchContainerImplHostSketchContainerSketchContainerImplCheck failed: a.dataCheck failed: a.data && reduced[fidx].dataCheck failed: reduced[i].data check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nquantile: too many large chunkCheck failed: worker_feature.data()sketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=count <= total_entriesn_threads_ >= 1sketches_.size() == n_columns/__w/ml-builds/ml-builds/xgboost/src/common/quantile.hbase_rowid < group_ptr.back()Row: is not found in any group.cuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > cuts->cut_ptrs_.HostVector().back()n_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0columns_size_.size() != 0worker_segments.size() >= 1mingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0/__w/ml-builds/ml-builds/xgboost/src/common/quantile.cc/__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.feature_ptr.front() == 0rank_size == total/__w/ml-builds/ml-builds/xgboost/src/common/threading_utils.hn_threads >= 1group_ptr.size() >= 2group_ptr.back() == n_sampleshessian.size() == info.num_row_group_ptr.back() == hessian.size()weights.size() == info.num_row_Check failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))invalid init parameter߰߰St19_Sp_make_shared_tagSt23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE/__w/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/__w/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0N4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEEEnum exisit!) Enums: ), aft_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 model/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.henum_back_map_.count(value) != 0UValue not found in enum declaredoptional, default=Invalid Input: '', valid values are: /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: N7xgboost7DMatrixEN7xgboost4data11FileAdapterEN7xgboost4data22IterativeDeviceDMatrixEN7xgboost17BatchIteratorImplINS_11EllpackPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEENSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2EN7xgboost4data20SparsePageSourceImplINS_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_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 Check failed: validInput data contains `inf` or `nan`key >= builder_base_row_offsetfloat32float64bool8int32int8int16int64/__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hUnknown data table type./__w/ml-builds/ml-builds/xgboost/src/data/../data/array_interface.hCheck failed: parser_qidCheck failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix./__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type Check failed: proxy_count_ == n_batches_count_ >= 1/__w/ml-builds/ml-builds/xgboost/src/data/../common/common.hthis->count_ == this->n_batches_this->count_ >= 1source_->Iter() == this->count_Check failed: valid_query_groupInvalid group structure./__w/ml-builds/ml-builds/xgboost/src/data/validation.h/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: Check failed: !cache_info_->written MB written in Check failed: !at_end_n_prefetch_batches > 0total batches:fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration./__w/ml-builds/ml-builds/xgboost/src/data/../data/iterative_device_dmatrix.hSlicing DMatrix is not supported for Device DMatrix.format.front() == '.'/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()</__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/dmlc-core/include/dmlc/./././any.hCheck failed: type_ != nullptrThe any container is empty requested=Check failed: *(type_->ptype_info) == typeid(T)The stored type mismatch stored=/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: page_/__w/ml-builds/ml-builds/xgboost/src/data/../data/simple_batch_iterator.hCheck failed: page_ != nullptrstd::future_error: rawfi->Tell() == offsetCheck failed: fmt->Read(page.get(), fi.get())Read a page in seconds./__w/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: v.DeviceIdx() == Context::kCpuId || device == Context::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: num_rownum_colnum_nonzerolabelsgroup_ptrbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightslabellabel_lower_boundlabel_upper_boundUnknown float field name: Unknown uint32 field name: Unknown data type for getting meta info. matrix with entries loaded from All feature_types must be one of {int, float, i, q, c}./__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()size == this->num_col_Length of must be equal to number of columns.Unknown feature info name: group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.group_ptr_.back() == num_row_Invalid group structure. Number of rows obtained from groups doesn't equal to 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.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.Unknown feature info: cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..rXGBOOST_TEST_NPARTLoad part of data parts.group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from csvlibsvmNo format parameter is provided in input uri, but found file extension: . Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csvEncountered parser error: Check failed: array.valid.Size() == 0Meta info like label or weight can not have missing value.this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0transpose.offset.Size() == num_columns + 1Check failed: t.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.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: Unknown data typeMetaInfo: 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. 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./__w/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is requiredSt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EESortedSketchContainer/__w/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hbatch_size < offset_vec.size()gmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSize/__w/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/../common/quantile.hvector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.hibegin + inst.size() == iendcut.Values().size() > 0Ucurent_bin_size == common::kUint32BinsTypeSizerow_ptr.size() == 0Check failed: !sorted_sketchN7xgboost4data16SparsePageFormatINS_16GHistIndexMatrixEEEN7xgboost4data19GHistIndexRawFormatE*N7xgboost4dataUlvE_E/__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizeRaw GHistIndex binary data format.N7xgboost17BatchIteratorImplINS_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/dmlc-core/include/dmlc/././any.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h/__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/array_interface.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device.N7xgboost4data13SimpleDMatrixEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_16GHistIndexMatrixEEESt15_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_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EECheck failed: (std::is_same::value)Expecting CSCAdapterCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the column/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_Column is empty/__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/__w/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: r != BatchParam{}Batch parameter is not initialized.param.gpu_id >= 0param.max_bin >= 2Generating new Gradient Index.param.gpu_id == -1batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatchoffset_vec.back() == info_.num_nonzero_N7xgboost4data20SparsePageSourceImplINS_10SparsePageEEEN7xgboost4data16SparsePageSourceEN7xgboost4data20SparsePageSourceImplINS_7CSCPageEEEN7xgboost4data18PageSourceIncMixInINS_7CSCPageEEEN7xgboost4data13CSCPageSourceEN7xgboost4data20SparsePageSourceImplINS_13SortedCSCPageEEEN7xgboost4data18PageSourceIncMixInINS_13SortedCSCPageEEEN7xgboost4data19SortedCSCPageSourceEN7xgboost4data17SparsePageDMatrixENSt13__future_base7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENSt13__future_base7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost4data5CacheELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data23GradientIndexPageSourceELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data16SparsePageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data13CSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data19SortedCSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost7CSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13SortedCSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEESortedSketchContainer/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.h/__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manually/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.hthis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size()Make cache:/__w/ml-builds/ml-builds/xgboost/src/data/./../common/quantile.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././././any.h.row.page/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: proxy_handleInvalid proxy handle.iter_.Next() == 1Must 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_N7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_E/__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 filen_bytes == (page->data).Size() * sizeof(Entry)Raw binary data format.N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_EupdaterstringshotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchlong (non-negative)Maximum rows per batch./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hgblinear_train_param/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf indexPredictBatchPredictBatchInternalbase_margin.Size() == nsize * ngroup/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)DoBoostGBLinearget(in["name"]) == "gblinear"/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hInplace predict is not supported by current booster./__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster. { "bias": [ ], "weight": [ ] }bias: weight: Check failed: this->updater_Check 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.gpu_coord_descentmodellayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamLinear booster, implement generalized linear model.N7xgboost3gbm13GBLinearModelEDeprecatedGBLinearModelParamboosted_rounds/__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type ?N4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_16GenericParameterEE0_E has not been registered in Alias sample_typeuniformweightedDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.one_dropbooleanWhether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.learning_rateLearning rate(step size) of update.etanum_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.size_leaf_vectorReserved option for vector tree.updater_seqTree updater sequence.process_typedefaultupdateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.predictoroneapi_predictorPredictor algorithm typetree_methodapproxexacthistgpu_histChoice of tree construction method.dart_train_paramdrop trees, weight = get(in["name"]) == "dart"Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.tree_begin == 0Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead./__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin <= tree_endpredts.predictions.Size() == out_preds->predictions.Size()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()Check failed: p_dartCheck failed: p_dart->weight_drop_.empty()weight_dropgaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: model_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0cpu_predictorgpu_predictorUnsupported data type for inplace predict.Check failed: successCurrent Predictor: vector::reserveidx <= total_n_treesInvalid tree index./__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccTree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.Using tree method: grow_histmakergrow_colmaker,pruneTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakerUnknown tree_method () detectedCheck failed: configured_XGBoost version not compiled with OneAPI support.Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.out_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees.get(in["name"]) == "gbtree"CommitModelups.size() == updaters_.size()Internal Error: mismatched updater sequence. Specified updaters: Actual updaters: Check failed: p_gbtreethis->model_.learner_model_param->num_feature != 0layer_trees != 0layer_end > layer_beginstep >= 1this->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.in_it < tree_endGBTreeModelParamGBTreeTrainParamDartTrainParamCheck 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 roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.GBTreeDANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.Check failed: this->configured_Check failed: tparam_.GetInitialised()Using updaters: BoostNewTreesngroup != 0in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsgbtree_train_paramLoading from a raw memory buffer on CPU only machine. Changing predictor to auto.Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist.specified_updaterTree booster, gradient boosted trees.Tree booster, dart.N7xgboost3gbm11GBTreeModelE/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())fi->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_treestreesCheck failed: ctx_tree_infoparam.num_trees == static_cast(trees.size())GlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoostN4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplEEvalOneIterbinary:logitrawauclogloss1.4.0BoostOneIterUpdateOneIterPredictRawPredictionsGetGradientGradientsUnsupported prediction type:Check failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.this->learner_model_param_.num_feature != 0begin_layer >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scoremultiple_predictions <= 1Perform one kind of prediction at a time.seedlongRandom number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.Number of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.dsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableboosterGradient booster used for training.objectivereg:squarederrorObjective function used for obtaining gradient. If you are loading a serialized model (like pickle in Python, RDS in R) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. 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:multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7Invalid parameter "" contains whitespace. Parameters: { ", } might not be used. This could be a false alarm, with some parameters getting used by language bindings but then being mistakenly passed down to XGBoost core, or some parameter actually being used but getting flagged wrongly here. Please open an issue if you find any such cases. Check failed: matrix.firstCheck failed: !matrix.second.ref.expired()num_col <= static_cast(std::numeric_limits::max())Unfortunately, XGBoost does not support data matrices with features or greatermparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface?Configurebase_scoreGlobal bias of the model.num_featureint (non-negative)Number of features in training data, this parameter will be automatically detected by learner.num_classNumber of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.num_targetNumber of target for multi-target regression.__Cannot find argument '', Possible Arguments: ---------------- fixed_size->Read(&buffer[0], size) == sizeInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0bs64header != "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)SAVED_PARAM_count_poisson_max_delta_stepmulti:Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityCheck failed: IsA(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.Learnernum_roundCONFIG-offset: : Check failed: gbm_ != nullptrPredict must happen after Load or configuration/__w/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: !this->need_configuration_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_Empty dataset at worker: /__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective functionlearner_train_parammetricsgeneric_paramModelConfig;binfCall Configure before saving model.learner_model_paramattributesStarting in XGBoost , the default evaluation metric used with the objective '' was changed from '' to ''. Explicitly set eval_metric if you'd like to restore the old behavior.Check failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:, n_targets:LearnerModelParamLegacyGenericParameterLearnerTrainParamLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaalpha/__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater LinearTrainParamN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_E/__w/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.htop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_paramUpdateFeature/__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: CoordinateUpdaterCoordinateParamcoord_descentUpdate linear model according to coordinate descent algorithm.N7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_E/__w/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}Update linear model according to shotgun coordinate descent algorithm.WARNING: DEBUG: INFO: ?N7xgboost6MetricEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E/__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/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples.info.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()auc <= 1Total AUC across groups: , valid groups: auc <= local_areaDataset is empty, or contains only positive or negative samples.PR-AUC supports only binary relevance for learning to rank.aucpr/__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.ư>N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_Epreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0pseduo_huber_paraminfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.maemapermsermslegamma-nloglikgamma-deviancepoisson-nloglikmphetweedie-nloglik@error/__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./__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU builderN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E/__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 labelmerrormloglossMulticlass classification error.Multiclass negative loglikelihood.N7xgboost6metric14EvalRankConfigEN7xgboost6metric7EvalAMSEN7xgboost6metric8EvalRankEN7xgboost6metric13EvalPrecisionEN7xgboost6metric8EvalNDCGEN7xgboost6metric7EvalMAPEN7xgboost6metric7EvalCoxE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_Emetric AMS do not support distributed evaluationbest-ams-ratio=/__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !distributedCox metric does not support distributed evaluationlabel size predict size not matchgptr.size() != 0Umust specify group when constructing rank filegptr.back() == preds.Size()EvalRank: group structure must match number of predictionCheck failed: tparam_%u[-]?cox-nloglikprendcgamsAMS metric for higgs.precision@k for rank.ndcg@k for rank.map@k for rank.Negative log partial likelihood of Cox proportional hazards model.-q=' @;f?N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_Eaft_loss_paramCheck failed: metric_AFT metric must be configured first, with distribution type and scaleUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()/__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuaft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model.ؗҜ(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).SoftmaxMultiClassParamNumber of output class in the multi-class classification.Softmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Objective candidate: /__w/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` N7xgboost3obj13LambdaRankObjINS0_28PairwiseLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_24NDCGLambdaWeightComputerEEEN7xgboost3obj13LambdaRankObjINS0_23MAPLambdaWeightComputerEEE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_ECheck failed: gptr.size() != 0 && gptr.back() == info.labels.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: /__w/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuComputing gradients on CPU.num_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenlambda_rank_paramLambdaRankParamrank:pairwisePairwise rank objective.rank:ndcgLambdaRank with NDCG as objective.rank:mapLambdaRank with MAP as objective.?@N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_Elabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.reg_loss_paramCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be positive.poisson_regression_paramPoissonRegression: label must be nonnegativetweedie_regression_paramTweedieRegression: label must be nonnegative/__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.h/__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: survival:coxreg:gamma/__w/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cureg:linear is now deprecated in favor of reg:squarederror.Invalid shape of labels.info.labels.Size() == preds.Size()info.weights_.Size() == info.num_row_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.Logistic regression for classification, output score before logistic transformation.reg:linearreg:pseudohubererrorRegression Pseudo Huber error.Poisson regression for count data.Cox regression for censored survival data (negative labels are considered censored).Gamma regression for severity data.reg:tweedieTweedie regression for insurance data.N7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE*N7xgboost9predictorUlPKNS_16GenericParameterEE_Ebase_margin.Shape(1) == ngroup/__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farout_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupncolumns != 0/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Make predictions using CPU.N7xgboost9PredictorEInvalid shape of base_margin. Expected: (%umargin.Shape(0) == n_samplesmargin.Shape(1) == n_groups/__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccUnknown predictor type Check failed: container_.find(m) != container_.cend()_Map_base::atCheck failed: container_.at(m).ref.lock()[Internal error]: DMatrix: has expired.model.learner_model_param->num_output_group != 0/__w/ml-builds/ml-builds/xgboost/src/tree/constraints.ccnewsize != 0Failed to parse feature interaction constraint: With error: CPUHistMakerTrainParamsingle_precision_histogramUse single precision to build histograms./__w/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:HgY?@??N7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_E/__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 == '{', Expect '{' but get 'ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'yes_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`.{newline}{indent}{nodes}{newline} {indent}{nodes}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties}n_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_nodesn_nodes == split_categories_segments.size(){parent}{stat} {left} {right}{parent}{nodes} pos != std::string::nposCheck failed: is_categorical in feature map is numerical but tree node is categorical.Unknown feature map type.{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}" edge graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: Failed to parse graphviz parameters: 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.max_binif 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.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_epsEXP Param: Sketch accuracy of approximate algorithm.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_thresholddoublepercentage threshold for treating a feature as sparsegammaj_end - j_begin != 0std::numeric_limits::min() != max_cat/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31num_nodesNumber of features used in tree construction.Size of leaf vector, reserved for vector tree Line , around ^` "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}]{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{stat}, "gain": {loss_chg}, "cover": {sum_hess} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{nid}{fname}{cond}{left}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:leaf={leaf}{stats},cover={cover}{cover}{stats},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{nid}:[{fname}] yes={yes},no={no}{yes}{no}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing} {nid} [ label="{fname}:{cond}" {params}] {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}yesno, missing {nid} -> {child} [label="{branch}" color="{color}"] {child}{color}{branch} {nid} [ label="{fname}{<}{cond}" {params}] {<}{params}Check failed: is_numerical in feature map is categorical but tree node is numerical./__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccbasic_string::replaceUnknown Model Builder:{param.num_nodes == static_cast(nodes_.size())param.num_nodes == static_cast(stats_.size())param.deprecated_num_roots == 1param.num_nodes != 0Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.mean_values->size() > 0UCheck failed: node.IsLeaf()fi->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() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodessize != 0categories_segmentscategories_sizescategories_nodescategoriesunique_path[i].pweight == 0Unique path must have zero weightGraphvizParamTreeParamtree_paramget(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)split_indicessplit_typeloss_changessum_hessianbase_weightsleft_childrenright_childrenparentssplit_conditionsdefault_leftstatic_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/__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater FbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterEPFbRKN7xgboost4tree14CPUExpandEntryES3_ESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEUpdatePredictionCacheUpdatePredictionCacheGlobalApproxUpdaterInitDataInitDataInitRootBuildHistogramInitRootBuildHistogram*N7xgboost4treeUlNS_7ObjInfoEE_ECheck failed: !features.empty()n_nodes > 0n_batches_ >= 1/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hUpdatePositionEvaluateSplitsn_total_bins == page.cut.TotalBins()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hhist_paramCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method./__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.hn_left + n_right == row_set_collection_[nidx].Size()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hout_preds.Size() > 0UCheck failed: p_last_treeout_preds.DeviceIdx() == GenericParameter::kCpuIdpart.Size() == n_nodes/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/threading_utils.hnthreads >= 1row_ptr_[nid] == kMax/__w/ml-builds/ml-builds/xgboost/src/tree/../common/categorical.h/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/row_set.hn_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endnodes == targeted_hists.size()tid < nthreads_begin < end/__w/ml-builds/ml-builds/xgboost/src/tree/param.hMax leaves and max depth cannot both be unconstrained.this->max_depth <= 31max_depth can not be greater than 31 as that might generate 2 ** 32 - 1 nodes.cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccout_preds.Size() == data->Info().num_row_i < first_dimension_.size()i < ranges_.size()/__w/ml-builds/ml-builds/xgboost/src/tree/../common/../data/../common/hist_util.hid != kMaxend > beginnid < nodes_elem_of_each_node_.size() == 0UTree constructor that uses approximate histogram construction for each node.N7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlNS_7ObjInfoEE_Eopt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault direction choice when encountering a missing valuecolmaker_train_paramUpdater `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/updater_colmaker.ccCheck failed: this->ctx_param_.max_depth > 0exact tree method doesn't support unlimited depth./__w/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hfmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist./__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hgrow_colmakerridx < position_.size()ridx exceed bound ridx= pos=Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0ColMakerTrainParamGrow tree with parallelization over columns.N7xgboost4tree9BaseMakerEN7xgboost4tree9HistMakerEN7xgboost4tree11CQHistMakerEZN7xgboost4tree11CQHistMaker10CreateHistERKSt6vectorINS_6detail20GradientPairInternalIfEESaIS5_EEPNS_7DMatrixERKS2_IjSaIjEERKNS_7RegTreeEEUlvE_*N7xgboost4treeUlNS_7ObjInfoEE_ECheck failed: tree[nid].IsLeaf()istart != hist.sizesummary_array_.size() == sketchs_.size()offset == -2this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1/__w/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.ccfv=, hist.size=hist[]=, hist.last=fid * 2 < fminmax_.size()Check failed: param_.max_depth > 0max_depth must be larger than 0/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.h/__w/ml-builds/ml-builds/xgboost/src/tree/./param.hret > 0Ugrow_local_histmaker/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hfid * 2 + 1 < fminmax_.size()FeatHelper fid exceed query bound node2workindex_[nid] == static_cast(wid)fi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry)Tree constructor that uses approximate histogram construction.N7xgboost4tree10TreePrunerE*N7xgboost4treeUlNS_7ObjInfoEE_E/__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: !tree[pid].IsLeaf()PrunerUpdatetree pruning end, extra nodes, pruned nodes, max_depth=nid >= 1pruneTreePrunerPruner that prune the tree according to statistics.N7xgboost4tree17QuantileHistMakerEN7xgboost6common6ColumnIhEEN7xgboost6common11DenseColumnIhLb1EEEN7xgboost6common12SparseColumnIhEEN7xgboost6common6ColumnItEEN7xgboost6common11DenseColumnItLb1EEEN7xgboost6common12SparseColumnItEEN7xgboost6common6ColumnIjEEN7xgboost6common11DenseColumnIjLb1EEEN7xgboost6common12SparseColumnIjEEN7xgboost6common11DenseColumnIhLb0EEEN7xgboost6common11DenseColumnItLb0EEEN7xgboost6common11DenseColumnIjLb0EEEInitSamplingInitSamplingUpdatePredictionCacheUpdatePredictionCacheInitDataInitDataExpandTreeUpdateTreeExpandTreeUpdateTree*N7xgboost4treeUlNS_7ObjInfoEE_E/__w/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hexponent <= modAttempted to load internal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/transform.hbase_rowid == gmat.base_rowidrow_ptr.size() >= 2/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/histogram.hQuantile::Builder/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/row_set.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/evaluate_splits.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/categorical.h/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/partition_builder.hany_missing == true/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../param.hcpu_hist_train_param/__w/ml-builds/ml-builds/xgboost/src/tree/./../common/column_matrix.hsizeof(BinIdxType) == bins_type_size_any_missing == any_missing_/__w/ml-builds/ml-builds/xgboost/src/tree/./hist/../../common/hist_util.h/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.cc(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()upper_bound < static_cast(std::numeric_limits::max())Grow tree using quantized histogram.N7xgboost4tree13TreeRefresherEZN7xgboost4tree13TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKSt6vectorIPNS_7RegTreeESaISC_EEEUlvE0_*N7xgboost4treeUlNS_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.N7xgboost4tree11TreeSyncherE*N7xgboost4treeUlNS_7ObjInfoEE_EsyncSyncher that synchronize the tree in all distributed nodes.N5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseEAllgatherRing failedAllreduce failedBroadcast failedNULLrabit_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_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delaySocket %s Error:%scannot obtain address of %sDoes not support IPv6/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccConnect to (failed): [] ConnectRetry connect to ip(retry time ): [SendAllReConnectLink failure 1RecvAllReConnectLink failure 2sync::Invalid tracker message, init failureReConnectLink failure 3error during send SendStrSocket::Close double close the socket or close without createFailed to shutdown due toprintworker256MBDMLC_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 0.0.0.0TryBindHostReConnectLink fail to bind the ports specifiedGetSockErrorReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8error during send RecvStrReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14AcceptReConnectLink failure 15ReConnectLink: bad socketSetNonBlock-1SetNonBlock-2SetKeepAlivecannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink %[^=]=%smapred_tip_idmapreduce_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 oncefail to get host namestartneed to assume rank structure/__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutPollwrite 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 errorN5rabit5c_api11ReadWrapperEN5rabit5c_api12WriteWrapperERead pickle stringnot implementedFailed to initialize RABIT.Failed to shutdown RABIT worker.unknown data_typeDataType does not support bitwise or operationunknown enum_opthe rabit has not been initializedcannot initialize reduce handle twicemust initialize handle to call AllReduceeANSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEEN4dmlc12ScopedThreadEN4dmlc8DataIterINS_8RowBlockIjfEEEEN4dmlc12RowBlockIterIjfEEN4dmlc8DataIterINS_8RowBlockImfEEEEN4dmlc12RowBlockIterImfEEN4dmlc8DataIterINS_8RowBlockIjiEEEEN4dmlc12RowBlockIterIjiEEN4dmlc8DataIterINS_8RowBlockImiEEEEN4dmlc12RowBlockIterImiEEN4dmlc8DataIterINS_8RowBlockIjlEEEEN4dmlc12RowBlockIterIjlEEN4dmlc8DataIterINS_8RowBlockImlEEEEN4dmlc12RowBlockIterImlEEN4dmlc6ParserIjfEEN4dmlc6ParserImfEEN4dmlc6ParserIjiEEN4dmlc6ParserImiEEN4dmlc6ParserIjlEEN4dmlc6ParserImlEEN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEEN4dmlc4data11DiskRowIterIjfEEN4dmlc4data12BasicRowIterIjfEEN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEEN4dmlc4data11DiskRowIterImfEEN4dmlc4data12BasicRowIterImfEEN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEEN4dmlc4data11DiskRowIterIjiEEN4dmlc4data12BasicRowIterIjiEEN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEEN4dmlc4data11DiskRowIterImiEEN4dmlc4data12BasicRowIterImiEEN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEEN4dmlc4data11DiskRowIterIjlEEN4dmlc4data12BasicRowIterIjlEEN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEEN4dmlc4data11DiskRowIterImlEEN4dmlc4data12BasicRowIterImlEEN4dmlc4data10ParserImplIjfEEN4dmlc4data14TextParserBaseIjfEEN4dmlc4data12LibSVMParserIjfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc4data14ThreadedParserIjfEEN4dmlc4data10ParserImplImfEEN4dmlc4data14TextParserBaseImfEEN4dmlc4data12LibSVMParserImfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc4data14ThreadedParserImfEEN4dmlc4data11LibFMParserIjfEEN4dmlc4data11LibFMParserImfEEN4dmlc4data9CSVParserIjfEEN4dmlc4data9CSVParserImfEEN4dmlc4data10ParserImplIjiEEN4dmlc4data14TextParserBaseIjiEEN4dmlc4data9CSVParserIjiEEN4dmlc4data10ParserImplImiEEN4dmlc4data14TextParserBaseImiEEN4dmlc4data9CSVParserImiEEN4dmlc4data10ParserImplIjlEEN4dmlc4data14TextParserBaseIjlEEN4dmlc4data9CSVParserIjlEEN4dmlc4data10ParserImplImlEEN4dmlc4data14TextParserBaseImlEEN4dmlc4data9CSVParserImlEEZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImlEEE_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerIjfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerImfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hNo threadCheck 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)Check failed: base <= 10 && base >= 2sign == true.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 specificationCheck 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 name_args.size() == 1U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hMB read, MB/secfinish reading at %g MB/secCheck failed: out->field.size() == out->index.size()qid:/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type File 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/././text_parser.hchunk.size != 0U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/__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 weightslibfmIf >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./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"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.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/./libsvm_parser.hparam_.format == "libsvm"Delimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()/__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/./row_block.hlabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.hCheck 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) != kDestroyLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at FvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEEBeforeFirst is not supported/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support writeCheck failed: this->InitCachedIter()Failed to initialize CachedIter/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hResetPartition is not supported in CachedInputSplitCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizehdfs:///__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 rbN4dmlc2io12LineSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != endN4dmlc2io16RecordIOSplitterE/__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::kMagicN4dmlc2io23IndexedRecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index filesN4dmlc2io14InputSplitBaseESt23_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 t v 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.Unexpected character in brace expression.Unexpected end of character class.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected escape character.Unexpected end of regex when escaping.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.^$\.*+?()[]{}|.[\*^$.[\()*+?{|^$.[\*^$ .[\()*+?{|^$ 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.Invalid range in bracket expression.Character is expected after a dash.Unexpected character in bracket expression.Parenthesis is not closed.Nothing to repeat before a quantifier./__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 bytesNULSOHSTXETXEOTENQACKalertbackspacetabnewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1spaceexclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodslashzerotwothreefourfivesixseveneightninecolonsemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atABCDFGHIJKMNOPQRSVWXZleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentbjvxzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrldigitgraphlowerpunctupperxdigitN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamECheck failed: !std::fseek(fp_, static_cast(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incomplete/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: stdoutwbCheck failed: allow_null LocalFileSystem::Open "": LocalFileSystem.ListDirectory ..N4dmlc2io10FileSystemE/__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 ;rD@D@D@]DE@ExEhSE|SEzED@XClFILO0STXUhW|Y\H^`c,Hf@iTkhpn@qtHwz(}<Pdx@/H/8@`X(PXp$8L `(t@xx0X| $8L` t(D@H\ 0@$8ȥ\ȧ8 P@ hd 8 @ H P X `$ h8 pL   H  ȱ ( Ȳ , P d x ( x     , T ȴ д  @ Xp,x@Th|PP ػx T0hPXhxȽнؽ0Dh88@x$Hl,@TH`88(H<h`pt<Pd(@$XH (,0@h0<x  0P<XPhdxx8@x(@$ (08`(`XXp$8L` (@ x( PT Xh    !!(!@fD>Xf@f@xg,AphApi Bj|BlBxmBmCPnCnF0rF vFxv$GwPGxGyGyG|Gp}$H(~H II8JJ@KK@KLh\LؒLL(Lp MHMMؤM(NNxNH0>X>l>> >(>0>8>h>> ? ? 4? 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