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Created by: hemangjoshi37a

2022-03-14 00:17:24.473694: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory 2022-03-14 00:17:24.473730: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Traceback (most recent call last): File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/api/api_utils/api_composer.py", line 325, in fit_and_check_correctness initial_pipelines[0].fit(data) File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/pipeline.py", line 156, in fit train_predicted = self._fit(input_data=copied_input_data, File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/pipeline.py", line 119, in _fit train_predicted = self.root_node.fit(input_data=input_data) File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/node.py", line 257, in fit return super().fit(input_data) File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/node.py", line 160, in fit self.fitted_operation, operation_predict = self.operation.fit(params=self.content['params'], File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/operations/operation.py", line 82, in fit self.fitted_operation = self._eval_strategy.fit(train_data=data) File "/home/hemang/anaconda3/lib/python3.8/site-packages/fedot/core/operations/evaluation/evaluation_interfaces.py", line 186, in fit operation_implementation.fit(train_data.features, train_data.target) File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/core.py", line 506, in inner_f return f(**kwargs) File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py", line 1231, in fit train_dmatrix, evals = _wrap_evaluation_matrices( File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py", line 286, in _wrap_evaluation_matrices train_dmatrix = create_dmatrix( File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py", line 1245, in create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs), File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/core.py", line 506, in inner_f return f(**kwargs) File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/core.py", line 616, in init handle, feature_names, feature_types = dispatch_data_backend( File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/data.py", line 763, in dispatch_data_backend return _from_numpy_array(data, missing, threads, feature_names, File "/home/hemang/anaconda3/lib/python3.8/site-packages/xgboost/data.py", line 179, in _from_numpy_array _LIB.XGDMatrixCreateFromDense( File "/home/hemang/anaconda3/lib/python3.8/ctypes/init.py", line 394, in getattr func = self.getitem(name) File "/home/hemang/anaconda3/lib/python3.8/ctypes/init.py", line 399, in getitem func = self._FuncPtr((name_or_ordinal, self)) AttributeError: /home/hemang/anaconda3/lib/libxgboost.so: undefined symbol: XGDMatrixCreateFromDense

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AttributeError Traceback (most recent call last) ~/anaconda3/lib/python3.8/site-packages/fedot/api/api_utils/api_composer.py in fit_and_check_correctness(initial_pipelines, data, logger) 324 _, data_test = train_test_data_setup(data) --> 325 initial_pipelines[0].fit(data) 326 initial_pipelines[0].predict(data_test)

~/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/pipeline.py in fit(self, input_data, use_fitted, time_constraint) 155 if time_constraint is None: --> 156 train_predicted = self._fit(input_data=copied_input_data, 157 use_fitted_operations=use_fitted)

~/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/pipeline.py in _fit(self, input_data, use_fitted_operations, process_state_dict, fitted_operations) 118 self.computation_time is None --> 119 train_predicted = self.root_node.fit(input_data=input_data) 120 if computation_time_update:

~/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/node.py in fit(self, input_data, **kwargs) 256 self.node_data = input_data --> 257 return super().fit(input_data) 258

~/anaconda3/lib/python3.8/site-packages/fedot/core/pipelines/node.py in fit(self, input_data) 159 if self.fitted_operation is None: --> 160 self.fitted_operation, operation_predict = self.operation.fit(params=self.content['params'], 161 data=input_data,

~/anaconda3/lib/python3.8/site-packages/fedot/core/operations/operation.py in fit(self, params, data, is_fit_pipeline_stage) 81 ---> 82 self.fitted_operation = self._eval_strategy.fit(train_data=data) 83

~/anaconda3/lib/python3.8/site-packages/fedot/core/operations/evaluation/evaluation_interfaces.py in fit(self, train_data) 185 else: --> 186 operation_implementation.fit(train_data.features, train_data.target) 187 return operation_implementation

~/anaconda3/lib/python3.8/site-packages/xgboost/core.py in inner_f(*args, **kwargs) 505 kwargs[k] = arg --> 506 return f(**kwargs) 507

~/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py in fit(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks) 1230 model, feval, params = self._configure_fit(xgb_model, eval_metric, params) -> 1231 train_dmatrix, evals = _wrap_evaluation_matrices( 1232 missing=self.missing,

~/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py in _wrap_evaluation_matrices(missing, X, y, group, qid, sample_weight, base_margin, feature_weights, eval_set, sample_weight_eval_set, base_margin_eval_set, eval_group, eval_qid, create_dmatrix, enable_categorical, label_transform) 285 """ --> 286 train_dmatrix = create_dmatrix( 287 data=X,

~/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py in (**kwargs) 1244 eval_qid=None, -> 1245 create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs), 1246 enable_categorical=self.enable_categorical,

~/anaconda3/lib/python3.8/site-packages/xgboost/core.py in inner_f(*args, **kwargs) 505 kwargs[k] = arg --> 506 return f(**kwargs) 507

~/anaconda3/lib/python3.8/site-packages/xgboost/core.py in init(self, data, label, weight, base_margin, missing, silent, feature_names, feature_types, nthread, group, qid, label_lower_bound, label_upper_bound, feature_weights, enable_categorical) 615 --> 616 handle, feature_names, feature_types = dispatch_data_backend( 617 data,

~/anaconda3/lib/python3.8/site-packages/xgboost/data.py in dispatch_data_backend(data, missing, threads, feature_names, feature_types, enable_categorical) 762 if _is_numpy_array(data): --> 763 return _from_numpy_array(data, missing, threads, feature_names, 764 feature_types)

~/anaconda3/lib/python3.8/site-packages/xgboost/data.py in _from_numpy_array(data, missing, nthread, feature_names, feature_types) 178 _check_call( --> 179 _LIB.XGDMatrixCreateFromDense( 180 _array_interface(data),

~/anaconda3/lib/python3.8/ctypes/init.py in getattr(self, name) 393 raise AttributeError(name) --> 394 func = self.getitem(name) 395 setattr(self, name, func)

~/anaconda3/lib/python3.8/ctypes/init.py in getitem(self, name_or_ordinal) 398 def getitem(self, name_or_ordinal): --> 399 func = self._FuncPtr((name_or_ordinal, self)) 400 if not isinstance(name_or_ordinal, int):

AttributeError: /home/hemang/anaconda3/lib/libxgboost.so: undefined symbol: XGDMatrixCreateFromDense

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) /tmp/ipykernel_6827/2888392436.py in 5 6 # fit model without optimisation - single XGBoost node is used ----> 7 xgb_pipeline = baseline_model.fit(features=train_data_path, target='tgt_high', predefined_model='xgboost') 8 9 # evaluate the prediction with test data

~/anaconda3/lib/python3.8/site-packages/fedot/api/main.py in fit(self, features, target, predefined_model) 143 if predefined_model is not None: 144 # Fit predefined model and return it without composing --> 145 self.current_pipeline = self._process_predefined_model(predefined_model) 146 else: 147 if self.timeout_set_in_init is not None:

~/anaconda3/lib/python3.8/site-packages/fedot/api/main.py in _process_predefined_model(self, predefined_model) 414 415 # Perform fitting --> 416 fit_and_check_correctness(pipelines, data=self.train_data, 417 logger=self.params.api_params['logger']) 418 return pipelines[0]

~/anaconda3/lib/python3.8/site-packages/fedot/api/api_utils/api_composer.py in fit_and_check_correctness(initial_pipelines, data, logger) 334 logger.info(fit_failed_info) 335 print(traceback.format_exc()) --> 336 raise ValueError(advice_info) 337 338

ValueError: Initial pipeline fit was failed due to: /home/hemang/anaconda3/lib/libxgboost.so: undefined symbol: XGDMatrixCreateFromDense. Check pipeline structure and the correctness of the data