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distributed mode detected, will split data among workers/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been intialized or has already been disposed.Check failed: p_m: Check failed: mCurrent DMatrix type does not support set data.Check failed: static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structurebinary saving only supported by SimpleDMatrixCheck failed: type >= 1 && type <= 4XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.groupcache_id == 0Cache ID is not supported yetCheck failed: p_predtjsonstr.size() > 2str[0] == '{'rwCheck failed: p_outtext/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 to allocator::allocate(size_t n) 'n' exceeds maximum supported size/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support./Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Not implemented.[] :%02d:%02d:%02d Stack trace: [bt] () _ZDMLC_LOG_STACK_TRACE_DEPTH%zuwrite position exceed fixed buffer size/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer length/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size()) ( vs. iqintfloatunknown feature type, use i for indicator and q for quantity[xgboost] Condition %s failed. _ptr || _count == 0NaN0E0;unknown;unknown;0;0;;/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccsibling.size() == sizeparent.size() == sizecut.Values().size() > 0Ucurent_bin_size == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hbins_type_size == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.hsizeof(BinIdxType) == bins_type_size_/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrbatch_size < offset_vec.size()ibegin + inst.size() == iendCheck failed: false/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://://Opening failed: /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedrb/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.ccCheck failed: ret.ec == std::errc()\\\\"\b\f\n\r\t\u%04xStringNumberObjectArrayBooleanIntegerObject of type can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first.Unknown construct, around character position: , " "\0 "Unknown escapenullExpecting null value "null"ch != -1cursor_.Pos(): , raw_str_.size():Expecting digit/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hbeg <= size_Expecting: "", got: "EOF"\0"Invalid cast, from HostSketchContainer/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cccolumns_size_.size() != 0count <= total_entriesPushRowPagesketches_.size() == info.num_col_worker_segments.size() >= 1AllReducenum_cuts.size() == 0n_columns == sketches_.size()Number of columns differs across workersCheck failed: reduced[i].dataCheck failed: a.dataCheck failed: a.data && reduced[fid].datacuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > cuts->cut_ptrs_.HostVector().back()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hinvalid init parameterCheck failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))base_rowid < group_ptr.back()Row: is not found in any group.mingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0(_count == dynamic_extent) ? (_offset <= size()) : (_offset + _count <= size())(index_ + n) <= span_->size()index_ < span_->size()index_ != span_->size()Check failed: worker_feature.data()(_idx) < (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 Parameter format for expect but value=' optional, default=, requiredInvalid Input: '', valid values are: 'Required parameter of is not presentedvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to 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 declaredmap::at: key not foundkey has already been registered in Enum ( exisit!) Enums: ), Out of range value for , value='pos <= 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 literalstatisticcountelapsed/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor: ========From rank: version/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).num_rownum_colnum_nonzerolabelsgroup_ptrweightsbase_marginlabels_lower_boundlabels_upper_boundfeature_namesfeature_types/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccfield_cnt == kNumFieldWrong number of fieldscategoricalAll feature_types must be one of {int, float, i, q, categorical}.major == 1Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.Check 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.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.labelUnknown data typeweightCheck failed: validWeights must be positive values.label_lower_boundlabel_upper_boundfeature_weightsFeature weight must be greater than 0.Unknown key for MetaInfo: 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.feature_typefeature_nameUnknown feature info name: Unknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0group_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_.Size() == 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_weigths.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.cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification..r-XGBOOST_TEST_NPARTLoad part of data partsauto matrix with entries 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: .group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from self_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/../common/common.hfloat32float64bool8int32int8int16int64/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hUnknown data table type.Check failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. Device that data is on: `gpu_id` for XGBoost: MetaInfo: Invalid format. 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 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./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCouldn't remove external memory cache file ; you may want to remove it manually.row.pagepage->Size() == 0Check failed: qids.empty() || qids.size() == info.num_row_SparsePageSource Finished writing to cache_shards.size() != 0UrawCache file exists already; Is there another DMatrix with the same cache prefix? It can be caused by previously used DMatrix that hasn't been collected by language environment garbage collector. Otherwise please remove it manually./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hname_shards.size() == format_shards.size()thread constructor failedSparsePageWriter Finished writing to Unknown format type info.name_shards.size() != 0UCheck failed: finfo->Read(&tmagic)tmagic == kMagicinvalid format, magic number mismatchCheck failed: fi->Read(&format)Invalid page format/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 threadCheck failed: mutex_.try_lock()Multiple threads attempting to use prefetcherkey >= builder_base_row_offset/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required.ellpack.page/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPageSource is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same::value)Expecting CSCAdapterCheck failed: param != BatchParam{}Batch parameter is not initialized.param.gpu_id >= 0param.max_bin >= 2Check failed: in_stream->Read(&tmagic)invalid input file format/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hCheck failed: page_ != nullptr/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.cc/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hCheck failed: source_ != nullptr.col.pageWriting to in MB/s, writtenCSCPageSource: Finished writing to Check failed: *out_page == nullptrCheck failed: qrecycle_.Pop(out_page).sorted.col.pageSortedCSCPageSource: Finished writing to /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Raw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage filefi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry)fid + 1 < disk_offset_.size()disk_offset_[fid] > curr_offsetfi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * 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/data/sparse_page_source.ccoffset + n_rows + 1 <= in_offset.size()h_offset.size() > 0n_entries == in_offset.at(offset + n_rows) - in_offset.at(offset)out->Size() != 0GBLinearTrainParamgblinearLinear booster, implement generalized linear model.updatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.string/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccget(in["name"]) == "gblinear"modelgpu_coord_descent__Cannot find argument '', Possible Arguments: ---------------- : /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.DoBoostPredictBatchntree_limit == 0UGBLinear::Predict ntrees is only valid for gbtree predictorPredictBatchInternalbase_margin.size() == nsize * ngroupInplace predict is not supported by current booster.gblinear does not support prediction of leaf indexGBLinear::PredictContribution: ntrees is only valid for gbtree predictor { "bias": [, ], "weight": [ ] }bias: weight: gblinear_train_paramCheck failed: this->updater_DeprecatedGBLinearModelParam/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_histmaker,prunegrow_colmaker,pruneTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.grow_quantile_histmakergrow_gpu_histUnknown tree_method () detectedBoostNewTreesngroup != 0in_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.CommitModelgbtreeget(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_gbtreelayer_end >= layer_beginstep >= 1Check failed: this->model_.trees_to_update.empty()Check failed: cpu_predictor_Data is on CUDA device, but XGBoost is not compiled with CUDA support.GBTreeModelParamGBTreeTrainParamDartTrainParamTree booster, gradient boosted trees.dartTree booster, dart.XGBoost version not compiled with OneAPI support./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.htree_begin < tree_end in_it < tree_endnum_treesNumber of features used for training and prediction.size_leaf_vectorReserved option for vector tree.num_parallel_treeNumber of parallel trees constructed during each iteration. This option is used to support boosted random forest.updater_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 sample_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.etatruefalse10booleanget(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()out_preds.size() == nnum_group == model_.learner_model_param->num_output_groupmodel_.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farpreds.size() == p_fmat->Info().num_row_ * num_groupdart_train_paramdrop trees, weight = /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())gbtree_model_paramtreestree_infoLearnerModelParamLegacyLearnerTrainParamGenericParametergpu_ideval_metricnum_roundCONFIG-offset:Learner/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: IsA(in)learnerlearner_model_paramobjectivegradient_boosterboosterattributesnum_featurenum_classbase_scoreCheck failed: !this->need_configuration_Call Configure before saving model.Configure/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.h n_gpus: n_gpusCheck 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:poisson0.7_paramverbosity Parameters: { } might not be used. This may not be accurate due to some parameters are only used in language bindings but passed down to XGBoost core. Or some parameters are not used but slip through this verification. Please open an issue if you find above cases. UpdateOneIterPredictRawGetGradientCheck 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 configurationBoostOneIterEvalOneIterbinary:logisticerrorloglossmerrorStarting in XGBoost 1.3.0, 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.multiple_predictions <= 1Perform one kind of prediction at a time.valuemarginUnsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4Ufi->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:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibility;begin_layer >= 0learner_train_paramgeneric_paramModelConfigfp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_ If you are loading a serialized model (like pickle in Python) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. There's a simple script for helping the process. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for reference to the script, and more details about differences between saving model and serializing. fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0Global bias of the model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.int (non-negative)dsplitcolrowData split mode for distributed training.disable_default_eval_metricFlag to disable default metric. Set to >0 to disableGradient booster used for training.reg:squarederrorObjective function used for obtaining gradient.Random number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode.nthreadNumber of threads to use.n_jobsThe primary GPU device ordinal.fail_on_invalid_gpu_idFail with error when gpu_id is invalid.gpu_page_sizeGPU page size when running in external memory mode.enable_experimental_json_serializationEnable using JSON for memory serialization (Python Pickle, rabit checkpoints etc.).validate_parametersEnable checking whether parameters are used or not. Deprecated. Single process multi-GPU training is no longer supported. Please switch to distributed training with one process per GPU. This can be done using Dask or Spark. See documentation for details./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.lambdaalphaCoordinateParamcoord_descentUpdate linear model according to coordinate descent algorithm.top_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.linear_train_paramcoordinate_paramCoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: UpdateFeatureUpdate 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}ConsoleLoggerParamWARNING: DEBUG: INFO: Flag to print out detailed breakdown of runtime.debug_verbosermseRooted mean square error.rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.mpheMean Pseudo Huber error.Negative loglikelihood for logistic regression.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.Binary classification error.tweedie-nlogliktweedie-nloglik@rho for tweedie regression./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 classification%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)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 builderMulticlass classification error.mloglossMulticlass negative loglikelihood./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 labelamsAMS metric for higgs.aucArea under curve for both classification and rank.aucprArea under PR curve for both classification and rank.preprecision@k for rank.ndcgndcg@k for rank.mapmap@k for rank.cox-nloglikNegative log partial likelihood of Cox proportioanl hazards model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !distributedmetric AMS do not support distributed evaluationbest-ams-ratio=info.labels_.Size() != 0Ulabel set cannot be emptylabel size predict size not matchgptr.back() == info.labels_.Size()EvalAuc: group structure must match number of predictiondat[1] > 0.0fAUC: the dataset only contains pos or neg samplesEvalAucPR: group structure must match number of predictionCheck failed: !auc_errorAUC-PR: error in calculationAUC-PR: the dataset only contains pos or neg samplesdat[0] <= dat[1]AUC-PR: AUC > 1.0%u[-]?gptr.size() != 0Umust specify group when constructing rank filegptr.back() == preds.Size()EvalRank: group structure must match number of predictionCox metric does not support distributed evaluationaft-nloglikNegative log likelihood of Accelerated Failure Time model.interval-regression-accuracyaft_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 scalesurvival: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: binary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedpreds.size=, label.size=SoftmaxMultiClassParamSoftmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.Number of output class in the multi-class classification.softmax_multiclass_param/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).span_ == rhs.span_Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` LambdaRankParamPairwise rank objective.LambdaRank with NDCG as objective.LambdaRank with MAP as objective.rank:pairwiserank:ndcgrank:mapnum_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_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.RegLossParamRegression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Regression Pseudo Huber error.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.reg:linearPoissonRegressionParamPossion regression for count data.survival:coxCox regression for censored survival data (negative labels are considered censored).reg:gammaGamma regression for severity data.TweedieRegressionParamreg:tweedieTweedie regression for insurance data.reg:squaredlogerrorreg:logisticreg:pseudohubererrorbinary:logitrawscale_pos_weightScale the weight of positive examples by this factorreg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuLoss: label must be greater than -1 for rmsle so that log(label + 1) can be valid.label 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: reg:linear is now deprecated in favor of reg:squarederror.Maximum delta step we allow each weight estimation to be. This parameter is required for possion regression.poisson_regression_paramPoissonRegression: label must be nonnegativeCheck failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!GammaRegression: label must be nonnegativetweedie_variance_powerTweedie variance power. Must be between in range [1, 2).tweedie_regression_paramTweedieRegression: label must be nonnegativeMake predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.cctree_begin == 0predts->version >= tree_beginpredts->version == 0output_groups != 0ntree_limit != 0beg_version <= end_versiondelta <= model.trees.size()Check failed: out_preds->Size() == output_groups * dmat->Info().num_row_ || out_preds->Size() == dmat->Info().num_row_model.learner_model_param->num_output_group != 0out_preds->Size() == nIgnoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. * = . [number of data points], i.e. Instead, all data points will use base_score = out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupmodel.param.size_leaf_vector == 0Data type is not supported by CPU Predictor.m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model./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=ncolumns != 0/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.Unknown predictor type unordered_map::at: key not found/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:TreeParamTrainParam/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccUnknown Model Builder:Dump text representation of treeDump json representation of treeGraphvizParamdotDump graphviz representation of treeCheck failed: node.IsLeaf()fi->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 == 1categories_segmentscategories_sizescategories_nodescategoriesj_end - j_begin != 0std::numeric_limits::min() != max_catthis->split_types_.size() == param.num_nodesthis->GetSplitCategoriesPtr().size() == param.num_nodestree_paramn_nodes != 0loss_changesloss_changes.size() == n_nodessum_hessiansum_hessian.size() == n_nodesbase_weightsbase_weights.size() == n_nodesleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodessplit_indicesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_leftdefault_left.size() == n_nodessplit_typen_nodes == split_categories_segments_.size()parent != RegTree::kInvalidNodeIdstatic_cast(deleted_nodes_.size()) == param.num_deletedthis->split_categories_segments_.size() == param.num_nodesnum_nodesget(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes)this->node_mean_values_.size() > 0Uunique_path[i].pweight == 0Unique path must have zero weight/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 2^31(pos_v.int_pos) < (bits_.size())Number of features used in tree construction.num_deletedSize of leaf vector, reserved for vector treemin_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)min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightdefault_directionlearnrightDefault direction choice when encountering a missing valueMaximum 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 informationsplit_evaluatorelastic_net,monotonicThe criteria to use for ranking splitssparse_thresholdpercentage threshold for treating a feature as sparseenable_feature_groupingif >0, enable feature grouping to ameliorate work imbalance among worker threadsmax_conflict_ratewhen grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary.max_search_groupwhen grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined.gammadouble{nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}pos != std::string::nposidx < names_.size()FeatureMap feature index exceed bound{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{cond}{left}{right}{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.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} {left} {right}{stat}{nodes} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess} "nodeid": {nid}, "depth": {depth}, "split": {fname}, "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties} { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {newline}{indent}{nodes}{newline}yes_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`.Failed 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}"] yesno, missing{child}{color} {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}{parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs}/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater ColMakerTrainParamgrow_colmakerGrow tree with parallelization over columns.opt_dense_colEXP Param: speed optimization for dense column.train_paramcolmaker_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./Users/runner/work/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.Check failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0fmat.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=/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h!common::CheckNAN(mid)grow_local_histmakerTree constructor that uses approximate histogram construction.grow_histmakerTree constructor that uses approximate global of histogram construction./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.hfi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry) 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/tree/updater_histmaker.ccCheck failed: param_.max_depth > 0max_depth must be larger than 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hnode2workindex_[nid] == static_cast(wid)summary_array_.size() == sketchs_.size()offset == -2this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1fid * 2 + 1 < fminmax_.size()FeatHelper fid exceed query bound ret > 0UCheck failed: tree[nid].IsLeaf()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=fid * 2 < fminmax_.size()fv=, hist.size=hist[]=, hist.last=istart != hist.sizethis->qexpand_.size() == 1Uthis->wspace_.rptr.back() == this->wspace_.cut.size()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[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Quantile::BuilderUpdateSubtractionTrickUpdatePredictionCache/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccout_preds.size() > 0UCheck failed: (param_.max_depth > 0 || param_.max_leaves > 0)max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity.max_depth cannot be 0 (unlimited) when grow_policy is depthwise.min_nbins_per_feature > 0UEvaluateSplitsApplySplit(*p_tree)[nid].LeftChild() + 1 == (*p_tree)[nid].RightChild()upper_bound < static_cast(std::numeric_limits::max())InitNewNodeBuildLocalHistogramsSplitSiblingsBuildNodeStatsExpandWithLossGuideCPUHistMakerTrainParamGmatInitializationgrow_fast_histmaker(Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram.Grow tree using quantized histogram./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0UCheck failed: e.begin != nullptrn_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.end/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/column_matrix.hgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valgmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSizecpu_hist_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hAttempted 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.htmlsingle_precision_histogramUse single precision to build histograms.SyncHistogramsSyncHistogramsAllreduce/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hbegin < end/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hend > beginnid < nodes_i < first_dimension_.size()i < ranges_.size()AddHistRowsrow_ptr_[nid] == kMaxCheck failed: (*p_last_tree_)[nid].IsLeaf()id != kMaxcut_ptr[fid] <= static_cast(std::numeric_limits::max())cut_ptr[fid + 1] <= static_cast(std::numeric_limits::max())local_block_ptr != (BlockInfo*)nullptrnodes == targeted_hists.size()tid < nthreads_data_ || size_ == 0grow_fast_histmaker is deprecated, use grow_quantile_histmaker instead.QuantileHistMakerrefreshRefresher 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 oncestartFailed 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_delayConnect to (failed): [] ConnectRetry connect to ip(retry time ): [ReConnectLink failure 1ReConnectLink failure 2sync::Invalid tracker message, init failureReConnectLink failure 3task connected to the trackerReConnectLink failure 4must keep rank to same if the node already have onetracker got overwhelemed and not able to assign correct rank got new rank ReConnectLink fail to bind the ports specifiedReConnectLink 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 sockettcp no delay is not implemented on non unix platformscannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkfailed in ReconnectLink buffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorBroadcast: root should be smaller than world sizeneed to assume rank structurewrite ptr boundary check[%d] read_ptr boundary checkfail to get host nameSocket::Close double close the socket or close without createerror during send SendStr%lu%cinvalid format for %sinvalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}Createcannot obtain address of %sDoes not support IPv6Socket %s Error:%sSendAllRecvAll0.0.0.0TryBindHostGetSockErrorerror during send RecvStrAcceptSetNonBlock-1SetNonBlock-2SetKeepAlivetoo large type_nbytes=%lu, buffer_size=%lu/Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll timeoutPollReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkAllreduce: boundary checkAllgatherRing failedAllreduce failedBroadcast failedunknown enum_opunknown data_typeFailed to initialize RABIT.Failed to shutdown RABIT worker.Read pickle stringnot implementedDataType does not support bitwise or operationthe rabit has not been initializedcannot initialize reduce handle twicemust intialize handle to call AllReduceLibSVMParserParamLibFMParserParamCSVParserParamlibfmformatFile 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.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.File format.label_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/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/secbatch.field[i] <= std::numeric_limits::max()field exceed numeric bound of current typebatch.index[i] <= std::numeric_limits::max()index exceed numeric bound of current typelabel.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.ccUnknown data type /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/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 neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[file offset not calculated correctlyC/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!QaQ QX !QaQ0#1L@#pI pY$pil3KL@@2`]5]@;\^=^?<_A_pET`H` KhaMaO8bPRbTc Wpc[d^Ld`ddTe`feiTfkfPn8g@rg0u@hpxhzDi0}i(jЁjjPlk0ltl m mnnТ(o`o o@pвp@qм\r`r0ss(t@Xt0uuvv0vdw w XxxPXyptyyyPzHzPz|{|$|@||`!|p"|"|@#|#|$}%@}&d}p'}+}2}4}5~p9\~`D~HpN4@ShTVXZ$[h@]|_ `0~  X0h,Ȅp4d0ą@@ 0 @PṗP'p144l`<>ԈBpE,0J<Ll`Q UX,\lu|w`x yy܊p(XЅP̋ЉЎp4dДP،0С@pp0؍p0,P\@؎,Hx0؏@8T0 p(0D```@ 4Pl`̒`$ @\x ؓ 0p L0 h`5$07@8p0;0<ȕC\DEI W@X0[Hbdhko,thw{ؘ L@04lЛУPЫě8йH@ (dPpPx0P 4DX@ܟ P  L8"hp088h`CPEH0IKؤpPTDWp[ȥk`q0{ ̦ܦІl`Xh` PPd T ܪ  LPhPD P4ԭ8 x 4@hP@ԯ@#$,'p*P+ذ155;رE0F@F\Gx@HHI̲PJpP4pV`XP]4ach4lns@tTyȶ{d08Ը L8hкHl X`t P L @x'`* +HP.x/0KPSTpW< YdPZ|\`jmo pq8PtHp|xp$<ЎLpУ00D@tP$8Px p` HPp@p4D  PDXhxP@!!#@#$ $0(`69HK@pZ]m p<@,0Pt0P 8гpT0h0$0p$HPl  `8H`@p0 $T(h0x8`@|p !" 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96S12InitSamplingERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEERKNS_7DMatrixEPNS5_ImNS9_ImEEEE40ApplySplitENSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSA_12ColumnMatrixERKNSA_14HistCollectionIdEEPNS_7RegTreeE7AddSplitsToRowSetERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPNS_7RegTreeE3SplitSiblingsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEEPS9_SC_PNS_7RegTreeE5ExpandWithߴSetHistSynchronizerEPNS0_16HistSynchronizerIdEEFindSplitConditionsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_7RegTreeERKNS_6common16GHistIndexMatrixEPNS5_IiNS7_IiEEEELossGuideERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEEDepthWiseERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixERKNS4_12ColumnMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSI_9allocatorISM_EEEEYYBuildHistERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS4_9allocatorIS8_EEEENS_6common16RowSetCollection4ElemERKNSE_16GHistIndexMatrixERKNSE_21GHistIndexBlockMatrixENSE_4SpanINS7_IdEELm18446744073709551615EEELossGuideENS3_11ExpandEntryES4_YubtractionTrickENS_6common4SpanINS_6detail20GradientPairInternalIdEELm18446744073709551615EEES9_S9_etHistRowsAdderEPNS0_13HistRowsAdderIdEEY1UpdatePredictionCacheEPKNS_7DMatrixEPNS_16HostDeviceVectorIfEE6SplitContainsMissingValuesENS0_9GradStatsERKNS1_9NodeEntryE0BuildLocalHistogramsERKNS_6common16GHistIndexMatrixERKNS4_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSD_9allocatorISH_EEEE2EvaluateAndApplySplitsERKNS_6common16GHistIndexMatrixERKNS4_12ColumnMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSF_9allocatorISH_EEEE4BuildHistogramsLossGuideENS3_11ExpandEntryERKNS_6common16GHistIndexMatrixERKNS5_21GHistIndexBlockMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSE_9allocatorISI_EEEEYYYExpandEntryCInitNewNodeEiRKNS_6common16GHistIndexMatrixERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENS8_9allocatorISC_EEEERKNS_7DMatrixERKNS_7RegTreeE2Eiiifj1EiiifjYYYYYEvaluateSplitsERKNSt3__16vectorINS3_11ExpandEntryENS4_9allocatorIS6_EEEERKNS_6common16GHistIndexMatrixERKNSC_14HistCollectionIdEERKNS_7RegTreeEBuildNodeStatsERKNS_6common16GHistIndexMatrixEPNS_7DMatrixEPNS_7RegTreeERKNSt3__16vectorINS_6detail20GradientPairInternalIfEENSC_9allocatorISG_EEEEZZZZZZZZZZZAddSplitsToTreeERKNS_6common16GHistIndexMatrixEPNS_7RegTreeEPiiPjPNSt3__16vectorINS3_11ExpandEntryENSC_9allocatorISE_EEEESI_PartitionKernelIZhEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeEtEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeEjEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeEЀ[[[hEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeEtEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeEjEEvmmNS_6common7Range1dEiRKNS5_12ColumnMatrixERKNS_7RegTreeE[[[[[[[\^_`a14InitEiPPc8FinalizeEv93AllreduceBase0Allreduce_EPvmmPFvPKvS1_iRKN3MPI8DatatypeEENS0_3mpi8DataTypeENSA_6OpTypeEPFvS1_ES1_2ReduceHandleC4InitEiPPc18S20TryReduceScatterRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEE2Ev1Evaaa4ReConnectLinksEPKc2Tr6TryAllahutdownEvetParamEPKcS3_bbackerPrintERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEybbAllreduceEPvmmPFvPKvS2_iRKN3MPI8DatatypeEEBroadcastEPvmibreducegatherRingEPvmmmmRingEPvmmPFvPKvS2_iRKN3MPI8DatatypeEETreeEPvmmPFvPKvS2_iRKN3MPI8DatatypeEEbbbЁccreduceEPvmNS_6engine3mpi8DataTypeENS3_6OpTypeEPFvS1_ES1_gatherEPvmmmmicc 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