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@@@@0C0C@@1!ffBNSt3__110__function6__funcIN7xgboost3obj3$_0ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj13LambdaRankObjINS0_28PairwiseLambdaWeightComputerEEEN7xgboost3obj3$_0ENSt3__110__function6__funcIN7xgboost3obj3$_1ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj13LambdaRankObjINS0_24NDCGLambdaWeightComputerEEEN7xgboost3obj3$_1ENSt3__110__function6__funcIN7xgboost3obj3$_2ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj13LambdaRankObjINS0_23MAPLambdaWeightComputerEEEN7xgboost3obj3$_2Eql!aTư>NSt3__110__function6__funcIN7xgboost3obj3$_0ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj3$_0ENSt3__110__function6__funcIN7xgboost3obj3$_1ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj3$_1ENSt3__110__function6__funcIN7xgboost3obj3$_2ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj3$_2ENSt3__110__function6__funcIN7xgboost3obj3$_3ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj3$_3ENSt3__110__function6__funcIN7xgboost3obj3$_4ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj3$_4ENSt3__110__function6__funcIN7xgboost3obj3$_5ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj3$_5ENSt3__110__function6__funcIN7xgboost3obj3$_6ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj3$_6ENSt3__110__function6__funcIN7xgboost3obj3$_7ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj17PoissonRegressionEN7xgboost3obj3$_7ENSt3__110__function6__funcIN7xgboost3obj3$_8ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj13CoxRegressionEN7xgboost3obj3$_8ENSt3__110__function6__funcIN7xgboost3obj3$_9ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj15GammaRegressionEN7xgboost3obj3$_9ENSt3__110__function6__funcIN7xgboost3obj4$_10ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj17TweedieRegressionEN7xgboost3obj4$_10ENSt3__110__function6__funcIN7xgboost3obj4$_11ENS_9allocatorIS4_EEFPNS2_11ObjFunctionEvEEEN7xgboost3obj17MeanAbsoluteErrorEN7xgboost3obj4$_11E?NSt3__110__function6__funcIN7xgboost9predictor3$_0ENS_9allocatorIS4_EEFPNS2_9PredictorEPKNS2_16GenericParameterEEEENSt3__110__function6__baseIFPN7xgboost9PredictorEPKNS2_16GenericParameterEEEEN7xgboost9predictor12CPUPredictorENSt3__110shared_ptrIN7xgboost4data12DenseAdapterEEENSt3__110shared_ptrIN7xgboost4data10CSRAdapterEEEN7xgboost9predictor3$_0EN7xgboost9PredictorEA<aYaVxA8!QEaPHgY?@חA?N7xgboost7RegTreeEN4dmlc9parameter16FieldAccessEntryEN4dmlc10ParamErrorENSt3__119basic_istringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryINSt3__16vectorIiNS2_9allocatorIiEEEEEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt3__16vectorIiNS3_9allocatorIiEEEEEES7_EEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEENSt3__117bad_function_callENSt3__110__function6__funcIN7xgboost3$_1ENS_9allocatorIS3_EEFPNS2_13TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS4_IcEEEEbEEENSt3__110__function6__baseIFPN7xgboost13TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEbEEEN7xgboost13TextGeneratorEN7xgboost13TreeGeneratorEN7xgboost3$_1ENSt3__110__function6__funcIN7xgboost3$_2ENS_9allocatorIS3_EEFPNS2_13TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS4_IcEEEEbEEEN7xgboost13JsonGeneratorEN7xgboost3$_2ENSt3__110__function6__funcIN7xgboost3$_3ENS_9allocatorIS3_EEFPNS2_13TreeGeneratorERKNS2_10FeatureMapENS_12basic_stringIcNS_11char_traitsIcEENS4_IcEEEEbEEEN7xgboost17GraphvizGeneratorEN7xgboost3$_3EN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEENSt3__110__function6__funcIN7xgboost4tree3$_0ENS_9allocatorIS4_EEFPNS2_11TreeUpdaterEPKNS2_16GenericParameterENS2_7ObjInfoEEEENSt3__110__function6__baseIFPN7xgboost11TreeUpdaterEPKNS2_16GenericParameterENS2_7ObjInfoEEEEN7xgboost4tree19GlobalApproxUpdaterEN7xgboost11TreeUpdaterENSt3__120__shared_ptr_emplaceIN7xgboost6common13ColumnSamplerENS_9allocatorIS3_EEEENSt3__110__function6__funcIPFbRKN7xgboost4tree14CPUExpandEntryES6_ENS_9allocatorIS8_EEFbS4_S4_EEENSt3__110__function6__baseIFbN7xgboost4tree14CPUExpandEntryES4_EEEPFbRKN7xgboost4tree14CPUExpandEntryES3_EFbRKN7xgboost4tree14CPUExpandEntryES3_ENSt3__120__shared_ptr_pointerIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoENS_10shared_ptrIS5_E27__shared_ptr_default_deleteIS5_S5_EENS_9allocatorIS5_EEEENSt3__110shared_ptrIN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoEE27__shared_ptr_default_deleteIS5_S5_EEN7xgboost6common15DenseColumnIterItLb1EEEN7xgboost6common6ColumnItEEN7xgboost6common16SparseColumnIterItEEN7xgboost6common15DenseColumnIterIjLb1EEEN7xgboost6common6ColumnIjEEN7xgboost6common16SparseColumnIterIjEEN7xgboost6common6ColumnIhEEN7xgboost6common16SparseColumnIterIhEEN7xgboost6common15DenseColumnIterIhLb1EEEN7xgboost4tree3$_0E1,NSt3__110__function6__funcIN7xgboost4tree3$_0ENS_9allocatorIS4_EEFPNS2_11TreeUpdaterEPKNS2_16GenericParameterENS2_7ObjInfoEEEEN7xgboost4tree8ColMakerEN7xgboost4tree8ColMaker7BuilderEN7xgboost4tree3$_0ENSt3__110__function6__funcIN7xgboost4tree3$_0ENS_9allocatorIS4_EEFPNS2_11TreeUpdaterEPKNS2_16GenericParameterENS2_7ObjInfoEEEEN7xgboost4tree10TreePrunerEN7xgboost4tree3$_0E::allocate(size_t n) 'n' exceeds maximum supported size/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccInvalid pointer argument: outBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPCLANG_VERSIONDEBUGUSE_FEDERATED[] :: %02d:%02d:%02d Stack trace: [bt] () _ZDMLC_LOG_STACK_TRACE_DEPTH%zujson_strfalseCheck failed: ec == std::errc()Unknown global parameters: { , }/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to __Cannot find argument '', Possible Arguments: ---------------- : Check failed: eCheck failed: 0 ( vs. XGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workersfname/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapterCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.configcache_prefixXGDMatrixCreateFromCallbacknthreadnextresetmissing/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hArgument `missing` is required.Invalid missing value: Argument `` is required for ``.Incorrect type for: ``, expecting: ``, got: `/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.hlimit >= 1Invalid thread limit for OpenMP.XGDeviceQuantileDMatrixCreateFromCallback is deprecated. Use `XGQuantileDMatrixCreateFromCallback` instead.Check failed: _refmax_bin/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Not implemented.DMatrix/Booster has not been initialized or has already been disposed.c_interface_strCheck failed: p_mCheck failed: mCurrent DMatrix type does not support set data.Check failed: c_interfaceindptrindicesdatac_json_config/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hget(array).size() == 1Column: typestrn_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.streamversionMissing `version' field for array interfaceMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4shapeMissing `shape' field for array interfaceMissing `data' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.`typestr' should be of format .map::at: key not found- is not supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaObjectStringUnicodeOtherInvalid 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.i < DCheck failed: m == 1 || n == 1stridesj_strides.size() == j_shape.size()stride and shape don't match.size == 0Empty data with non-zero shape.Empty data passed in.maskj_shape.size() == 1type_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.strides.size() == 1get(strides.at(0)) == type_lengthMemory should be contiguous.Only dimensional array is valid.Check failed: array.find("mask") == array.cend()Masked array is not yet supported.+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: rb_Cannot import non-existent record batchCheck failed: !schema_->columns.empty()Cannot import record batch without a schemanbatchXGDMatrixCreateFromArrowCallbackCannot reset RecordBatchesIterAdapterstatic_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structurebinary saving only supported by SimpleDMatrixfieldout_featureslenCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupout_lenout_dptrCheck failed: pp_mout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits::max()Check failed: page.page/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrn_threads >= 1;unknown;unknown;0;0;;dmatsjson_parametersout_strdtrainCheck failed: dtrgradhessevnamesout_resultlearnergradient_boostergblineardartgbtreegbtree_model_paramnum_parallel_treeUnknown booster:Booster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.typeXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapechunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rowsget(config["cache_id"]) == 0Cache ID is not supported yetInplacePredictImplCheck failed: p_predtCheck failed: proxyInvalid input type for inplace predict.array_interfacejsonubjrstr.size() >= 3str[0] == '{'wbufwrite position exceed fixed buffer size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hjson_configformatXGBoosterSaveModelToBufferdeprecatedUnknown format: ``read can not have position excceed buffer lengthXGBoosterGetModelRaw` is deprecated, please use `XGBoosterSaveModelToBuffer` instead.Check failed: p_outtextfmap/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())iqintfloatcunknown feature type, use i for indicator and q for quantityout_modelscustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.ffeature_map.Size() == n_featuresftypesuccesskeyfeaturesfeature_namefeature_typeUnknown field for Booster feature info:importance_typeXGBoosterFeatureScorefeature_mapfeature_namesout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresidx < names_.size()FeatureMap feature index exceed boundname_str/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.hWorld size is less than 1.Rank is less than 0. is greater than world_size - 1: ./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.XGBOOST_COMMUNICATORrabitfederatedUnknown communicator type xgboost_communicator==1=0/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hFailed to initialize RabitUnknown data typeUnknown allreduce operation/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/socket.ccFailed to get addr info for: 127.0.0.10.0.0.0::1::Check failed: !this->IsClosed()str.size() < std::numeric_limits::max()this->SendAll(&len, sizeof(len)) == sizeof(len)Failed to send string length.bytes == str.size()Failed to send string.send/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.hFailed to call ``: this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to recv string length.bytes == lenFailed to recv string.recvstatic_cast(socket.Domain()) == static_cast(addr.Domain())socketsystem::CloseSocket(handle_)NaN0E0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachable/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccentries_per_column.size() == info.num_col_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast(n_columns)column_sizes_tloc[i].size() == static_cast(n_columns)/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.hSortedSketchContainerinvalid init parameterCheck failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccpointer_ <= buffer_.size()Check failed: outfile://://rbOpening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hNot implemented/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.ccCheck failed: ret.ec == std::errc()\\\\"\b\f\n\r\t\u%04xNumberArrayU8ArrayObject of type can not be indexed by string. can not be indexed by Integer.Unknown construct, around character position: , " "\0/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hbeg <= size_ "Unknown escapeExpecting: "", got: "EOF"\0"nullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digit` is not supported for typed array.f64 is not supported.High precision number is not supported./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/common.hPesudoHuberParamhuber_slopeThe delta term in Pseudo-Huber loss.Invalid Parameter format for expect but value='Required parameter of is not presentedOut of range value for , value='/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()Some trailing characters could not be parsed: ''Out of range valueNo conversion could be performed/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h*p == ')'Invalid NAN literalvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default=, requiredkey has already been registered in group_ptr.size() >= 2group_ptr.back() == n_samplescount <= total_entriesmingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0float32float64bool8int32int8int16int64/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hUnknown data table type.SketchContainerImpl/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cccolumns_size_.size() != 0n_threads_ >= 1base_rowid < group_ptr.back()Row: is not found in any group.worker_segments.size() >= 1AllReducen_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0feature_ptr.front() == 0rank_size == totalCheck failed: reduced[i].dataCheck failed: worker_feature.data()PushRowPagesketches_.size() == n_columnsweights.size() == info.num_row_hessian.size() == info.num_row_group_ptr.back() == hessian.size()cuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > cuts->cut_ptrs_.HostVector().back()Check failed: a.dataCheck failed: a.data && reduced[fidx].data check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/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.HostSketchContainerPushColPagesketch->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=/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()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 modelInvalid Input: '', valid values are: optional, default=/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.henum_back_map_.count(value) != 0UValue not found in enum declaredEnum ( exisit!) Enums: ), /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor (): ========/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: validInput data contains `inf` or `nan`key >= builder_base_row_offset/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hnum_rownum_colnum_nonzerolabelsgroup_ptrweightsbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightsfield_cnt == kNumFieldWrong number of fieldsAll feature_types must be one of {int, float, i, q, c}.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.MetaInfo: Invalid format for Check failed: strm->Read(&name)name == expected_name Expected field: , got: 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 tensor; got a scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field) to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.hCheck failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.labelthis->labels.Size() % this->num_row_ == 0Incorrect size for labels.Label contains NaN, infinity or a value too large.qidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.weightWeights must be positive values.label_lower_boundlabel_upper_boundFeature weight must be greater than 0.Unknown key for MetaInfo: Check failed: array.valid.Size() == 0Meta info like label or weight can not have missing value./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_groupInvalid group structure.Check failed: key<Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Check failed: infoUnknown feature info name: Unknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0shape[i] == r.Shape(i)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.Check 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: matrix with entries loaded from cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..rautoLoad part of data partscsvlibsvmNo 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: /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: !type_.empty()Check failed: parser_transpose.offset.Size() == num_columns + 1self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.hCheck failed: p_fmat->SingleColBlock()Check failed: !sorted_sketch/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalrow_ptr.size() == 0rawRaw GHistIndex binary data format.Check failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hCheck 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.fi->Tell() == offsetCheck failed: fmt->Read(page.get(), fi.get())Read a page in seconds./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type format.front() == '.'thread constructor failedcount_ == n_batches_count_ >= 1Check failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.Check failed: proxy_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hUnknown type: /Users/runner/work/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=Check failed: !cache_info_->written MB written in Check failed: page_this->count_ >= 1source_->Iter() == this->count_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccIterative DMatrix must have at least 1 batch.d == batch_param_.gpu_idgpu_idCheck failed: proxy_handleInvalid proxy handle.Check failed: typedCheck failed: ref_Check failed: p_cutsref_->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.n_features == num_cols()Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.n_features >= 1Data must has at least 1 column.accumulated_rows == Info().num_row_Check failed: p_sketchrbegin == Info().num_row_proxy->Info().labels.Size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: !std::isnan(sparse_thresh)Check failed: ellpack_`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hInconsistent max_bin between Quantile DMatrix and Booster:Check failed: !param.regen && param.hess.empty()Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrpage.row_ptr.size() == h_offset.size()Slicing DMatrix is not supported for Quantile DMatrix./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same::value)Expecting CSCAdapter/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hparam.gpu_id >= 0param.max_bin >= 2Check failed: r != BatchParam{}Batch parameter is not initialized.Generating 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_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: data_Column is emptyDMatrix-r/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccinfo_.num_col_ != 0.row.pageCheck failed: sparse_page_source_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hMake cache:iter_.Next() == 1Must have at least 1 batch..col.pagethis->Info().num_col_ != 0.sorted.col.pagethis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size().gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manuallySlicing DMatrix is not supported for external memory.Can not obtain a single CSR page for external memory DMatrixRaw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage filen_bytes == (page->data).Size() * sizeof(Entry)Check failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamupdatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.stringLinear booster, implement generalized linear model.get(in["name"]) == "gblinear"modelgpu_coord_descentcoord_descentLoading configuration on a CPU only machine. Changing updater to `coord_descent`./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by current booster.DoBoostPredictBatchPredictBatchInternalbase_margin.Size() == nsize * ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hInplace predict is not supported by current booster.gblinear does not support prediction of leaf index { "bias": [, ], "weight": [ ] }bias: weight: 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.gblinear_train_paramCheck failed: this->updater_boosted_roundsDeprecatedGBLinearModelParamdeprecated_num_featuredeprecated_num_output_groupint (non-negative)/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictorGBTree/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccDANGER 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: Tree 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_histmakergrow_gpu_histUnknown tree_method () detectedCheck failed: !updaters_.empty()BoostNewTreesngroup != 0Current objective doesn't support external memory.in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsups.size() == updaters_.size()Internal Error: mismatched updater sequence. Specified updaters: Actual updaters: Check 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.CommitModelget(in["name"]) == "gbtree"gbtree_train_paramLoading from a raw memory buffer on CPU only machine. Changing predictor to auto.predictortree_methodLoading from a raw memory buffer on CPU only machine. Changing tree_method to hist.specified_updaterdefaultprocess_typeCheck failed: configured_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./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hin_it < tree_endtree_begin <= tree_endout_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees.Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.XGBoost version not compiled with OneAPI support.GBTreeModelParamnum_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.GBTreeTrainParamupdater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.gpu_predictoroneapi_predictorPredictor algorithm typeapproxexactChoice of tree construction method. has not been registered in Alias DartTrainParamsample_typeuniformDifferent types of sampling algorithm.normalize_typeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.Whether 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.etatrue1booleanTree booster, gradient boosted trees.Tree booster, dart.get(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()Unsupported data type for inplace predict.predts.predictions.Size() == p_out_preds->predictions.Size()Check failed: successCurrent Predictor: dart_train_paramdrop trees, weight = model_.param.num_parallel_tree != 0model_.learner_model_param->num_output_group != 0tree_begin == 0Predict 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.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_treesInvalid tree index./Users/runner/work/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_treesparam.num_trees == static_cast(trees.size())Check failed: ctx_treestree_infoGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: n_classes <= 1 || n_targets <= 1Multi-class multi-output is not yet supported. n_classes:, n_targets:Check failed: common::AsConst(base_score_).Data()->HostCanRead()base_score_.Size() == 1Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()LearnerModelParamLegacybase_scoreGlobal bias of the model.num_featureNumber 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.boost_from_averageWhether we should calculate the base score from training data.LearnerTrainParamdsplitcolrowData 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.GenericParameterRandom 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.eval_metricnum_roundCONFIG-offset:LearnerCheck 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.learner_model_paramattributesCheck failed: !this->need_configuration_Call Configure before saving model.Check failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: ret.ec == std::errc{}ConfigureCheck 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?multi:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: this->obj_Check failed: n_targets == t || 1 == tInconsistent labels.Check failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: ptr_paramInvalid parameter "" contains whitespace. Parameters: { ", } are not used. UpdateOneIterPredictRawGetGradientCheck failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)Check failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.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: Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterEvalOneItermultiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.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_stepmetricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityfixed_size->Read(&buffer[0], size) == sizedata_.Size() == detail::CalcSize(shape_);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_scorelearner_train_paramgeneric_param 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. ModelConfigInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater LinearTrainParamLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.lambdaalphaCoordinateParamtop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.Update linear model according to coordinate descent algorithm.linear_train_paramcoordinate_paramCoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cclabels.Size() != 0labels.Size() == predts.size()auc <= 1.0faucReceiver Operating Characteristic Area Under the Curve.info.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()auc <= 1Total AUC across groups: , valid groups: n_classes != 0auc <= local_areaDataset is empty, or contains only positive or negative samples.info.group_ptr_.size() >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/Users/runner/work/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.labels.Shape(1) == 1AUC doesn't support multi-target model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.haucprArea under PR curve for both classification and rank.PR-AUC supports only binary relevance for learning to rank.rmseRooted mean square error./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.loglossNegative loglikelihood for logistic regression.mpheMean Pseudo-huber error.pseudo_huber_paraminfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.errorBinary classification error.%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metrictweedie-nlogliktweedie-nloglik@rho for tweedie regression.Check failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function Cannot find a GPU metric builder for metric . Resorting to the CPU buildermerrorMulticlass classification error./Users/runner/work/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 labelmloglossMulticlass negative loglikelihood.amsAMS metric for higgs./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !collective::IsDistributed()metric AMS do not support distributed evaluationbest-ams-ratio=preprecision@k for rank.%u[-]?label 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_ndcgndcg@k for rank.mapmap@k for rank.cox-nloglikNegative log partial likelihood of Cox proportional hazards model.Cox metric does not support distributed evaluationaft-nloglikNegative log likelihood of Accelerated Failure Time model.aft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scaleinterval-regression-accuracy/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.ccbegin_pos <= sorted_pos.size()nptr.size() > 0n_unique == n_leafCheck failed: !position.empty()h_node_ptr.back() <= info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.hk + 1 < h_node_ptr.size()Check failed: h_row_set.empty()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0survival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective functionbinary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cuinfo.labels.Size() != 0Ulabel set cannot be emptylabels are not correctly providedpreds.size=, label.size=SoftmaxMultiClassParamNumber of output class in the multi-class classification.Softmax for multi-class classification, output class index.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class).Softmax for multi-class classification, output probability distribution.Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` Check failed: base_scoreLambdaRankParamnum_pairsampleNumber of pair generated for each instance.fix_list_weightNormalize the weight of each list by this value, if equals 0, no effect will happenPairwise rank objective.rank:pairwiselambda_rank_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/rank_obj.cuCheck failed: gptr.size() != 0 && gptr.back() == info.labels.Size()group structure not consistent with #rowsgroup ponter size: labels size: group pointer back: Computing gradients on CPU.LambdaRank with NDCG as objective.rank:ndcgLambdaRank with MAP as objective.rank:mapRegLossParamscale_pos_weightScale the weight of positive examples by this factorRegression with squared error.reg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuinfo.labels.Size() == preds.Size()Invalid shape of labels.info.weights_.Size() == info.num_row_Regression with root mean squared logarithmic error.reg:squaredlogerrorlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.Logistic regression for probability regression task.reg:logisticlabel must be in [0,1] for logistic regression/Users/runner/work/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: Logistic regression for binary classification task.binary:logisticLogistic regression for classification, output score before logistic transformation.binary:logitrawreg:linearreg:linear is now deprecated in favor of reg:squarederror.reg:pseudohubererrorRegression Pseudo Huber error./Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.hPoissonRegressionParamMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.Poisson regression for count data.poisson_regression_paramPoissonRegression: label must be nonnegativesurvival:coxCox regression for censored survival data (negative labels are considered censored).Check failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!reg:gammaGamma regression for severity data.GammaRegression: label must be positive.TweedieRegressionParamtweedie_variance_powerTweedie variance power. Must be between in range [1, 2).reg:tweedieTweedie regression for insurance data.tweedie_regression_paramTweedieRegression: label must be nonnegativereg:absoluteerrorMean absoluate error.StringView{get(in["name"])} == StringView{"reg:absoluteerror"}Sum of weights is close to 0.0, skipping base score estimation.t_v.Shape(1) != 0Make predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccout_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupmodel.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so far/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.hInplace predict accepts only DMatrixProxy as input.m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.ncolumns != 0base_margin.Shape(1) == ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()[Internal error]: DMatrix: has expired.unordered_map::at: key not foundUnknown predictor type model.learner_model_param->num_output_group != 0Invalid shape of base_margin. Expected: ()margin.Shape(0) == n_samplesmargin.Shape(1) == n_groups/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: With error: newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamnum_nodesNumber of features used in tree construction.num_deletedSize of leaf vector, reserved for vector treeTrainParammin_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methodgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsparse_thresholdpercentage threshold for treating a feature as sparsegammadoublecategories_segmentscategories_sizescategories_nodescategories/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccj_end - j_begin != 0std::numeric_limits::min() != max_catUnknown Model Builder:{}Dump text representation of tree{nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::npos{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}Unknown feature map type.Check failed: is_categorical in feature map is numerical but tree node is categorical.Check failed: is_numerical in feature map is categorical but tree node is numerical.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} {left} {right}{parent}{stat}{nodes}Dump json representation of tree "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing}] "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties} { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {newline}{indent}{nodes}{newline}GraphvizParamyes_color#0000FFEdge color when meets the node condition.#FF0000Edge color when doesn't meet the node condition.rankdirPassed to graphiz via graph_attr.condition_node_paramsConditional node configurationleaf_node_paramsLeaf node configurationgraph_attrsAny other extra attributes for graphviz `graph_attr`.dotDump graphviz representation of treeFailed to parse graphviz parameters: " graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./json.hch == '{'Error at, Expect '{' but get ' Line , around ^`ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get 'unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}] {nid} -> {child} [label="{branch}" color="{color}"] noyes, missing{child}{color}{branch} {nid} [ label="{fname}{<}{cond}" {params}] {<} {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}Check failed: node.IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam)param.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast(deleted_nodes_.size()) == param.num_deletedparam.num_nodes == static_cast(nodes_.size())param.num_nodes == static_cast(stats_.size())param.deprecated_num_roots == 1Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.this->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodessize != 0loss_changessplit_indicesstatic_cast(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodestree_paramn_nodes != 0loss_changes.size() == n_nodessum_hessiansum_hessian.size() == n_nodesbase_weightsbase_weights.size() == n_nodesleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_leftdefault_left.size() == n_nodessplit_typen_nodes == split_categories_segments.size()get(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)mean_values->size() > 0Uunique_path[i].pweight == 0Unique path must have zero weight/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node.GlobalApproxUpdatertrain_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccCheck failed: param.sampling_method != TrainParam::kGradientBasedGradient based sampling is not supported for approx tree method.UpdatePositionEvaluateSplitsn_total_bins == page.cut.TotalBins()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0U/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hbegin < endn_batches_ >= 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../data/../common/hist_util.hrow_ptr_[nid] == kMaxn_nodes > 0id != kMaxnodes == targeted_hists.size()i < first_dimension_.size()nthreads >= 1nid < nodes_tid < nthreads_i < ranges_.size()end > begin/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hbase_rowid == gmat.base_rowidupper_bound < static_cast(std::numeric_limits::max())/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == true(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endBuildHistogram/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hCheck failed: !features.empty()partition > 0cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())LeafPartitionCheck failed: tree[node.node_id].IsLeaf()ptr_offset <= row_set.Data()->size()UpdatePredictionCacheout_preds.Size() == data->Info().num_row_out_preds.Size() > 0UCheck failed: p_last_treeout_preds.DeviceIdx() == GenericParameter::kCpuIdpart.Size() == n_nodesColMakerTrainParamopt_dense_colEXP Param: speed optimization for dense column.default_directionlearnrightDefault direction choice when encountering a missing valuegrow_colmakerGrow tree with parallelization over columns.colmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.Check failed: this->ctx_Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0param_.max_depth > 0exact tree method doesn't support unlimited depth.fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.ridx < position_.size()ridx exceed bound ridx= pos=prunePruner that prune the tree according to statistics.TreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Quantile::Builder/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccrow_ptr.size() >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/hist_util.hExpandTreeUpdateTreeInitSampling/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hexponent <= modGrow tree using quantized histogram.refreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits::max()Syncher that synchronize the tree in all distributed nodes.NULLworkerrabit_reduce_buffer256MBDMLC_TASK_IDDMLC_ROLEDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_WORKER_CONNECT_RETRY%[^=]=%smapred_tip_idmapreduce_task_idhadoop_mode is set but cannot find mapred_task_idrabit_task_idrabit_hadoop_modemapred_task_id%drabit_num_trialmapred_map_tasksmapreduce_job_mapshadoop_mode is set but cannot find mapred_map_tasksrabit_world_size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccRabit Module currently only work with dmlc workercan only call Init oncestartgethostname(&buf[0], HOST_NAME_MAX)shutdownFailed to shutdown due to%sprintrabit_tracker_urirabit_tracker_portrabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_ring_mincount should be greater than 0rabit_bootstrap_cacherabit_debugrabit_timeoutrabit_timeout_secrabit_timeout_sec should be non negative secondrabit_enable_tcp_no_delay%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}Connecting to (failed): [] Retry connecting to IP(retry time: ): [tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failureReConnectLink failure 3tracker.Send(xgboost::StringView{task_id}) == task_id.size()task 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 ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure, link rank inconsistentOverride a link that is activeReConnectLink failure 14ReConnectLink failure 15ReConnectLink: bad socketcannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink bind(handle_, handle, sizeof(std::remove_reference_t))getsockname(handle_, reinterpret_cast(&res_addr), &addrlen)listen(handle_, backlog)getsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len)acceptfcntlsetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&tcp_no_delay), sizeof(tcp_no_delay))buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errortoo large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutFailed to poll.ReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeDataType does not support bitwise or operationFailed to initialize RABIT.Failed to shutdown RABIT worker.the rabit has not been initializedLibSVMParserParamFile 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.LibFMParserParamlibfmIf >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.CSVParserParamFile 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./Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck 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))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/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 weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease 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: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type .split.part/Users/runner/work/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/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)unique_lock::unlock: not lockedCheck 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: out_data_ != NULLCalling Value at beginning or end?Check 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) != kDestroyNo thread/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/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::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededCfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[]=file offset not calculated correctly/Users/runner/work/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 /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory ..stdoutwbCheck failed: allow_null LocalFileSystem::Open "": Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast(pos), SEEK_SET) LPXQ QaQX!Q a!QQ.V|` dЮ t0w +* D@`$ U)|`V8)pX\)Yl)pbd)c) e)f)@l)n)p$)sh)`tx)P)`)P)P0)Px)@)0) P) ))0)) @)X)@t)x))0))))0<))p)))0)))@))`L)0d) )) <) )P)p)P)P@)P ) ) !)!)@"8)"\)#)P$)%)%)))-)-)P1)4H) 9);)Ah)D)0F)I8)JL)O)P)PT\)[)p\4)hL)i\)`j|)m)p) r)@s)Pt8)uH) wh)z)0|)P~)`8) )P )PT)@))D))l))`l)0)D)h)))() @))`,)P)t)P)p8)p)P)`)`l**@t*0**P**@*,*|*`* *@**,*@P*Pp*  *` t * * *P * * P *` *# ***-*1|*08* Ct*D*K*M\* R*@T*0[*@``*pe,*g*i4*o4*r*u*Pz*{*`0*d*d**8**X**L**T*Т* *0T*@p**0** *8 *0 *!*@"*@#*#*X$*` %* &*&*0'*'*(*P\(*0p(*(*`P)*0)* )*)* ** ,** @****p+*+*<+*pd+*`+*P+*@+*0,* 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