When I'm trying to run hyper parameter Bayesian optimization using:
from skopt import BayesSearchCV
I'm getting this error on a JupterLab AWS platform with pip environment.
The full error:
---------------------------------------------------------------------------
XGBoostError Traceback (most recent call last)
Input In [49], in <cell line: 1>()
----> 1 opt.fit(X_train, y_train['dr_with_pp_assumption_dpd30'])
File ~/wd/venv/lib/python3.8/site-packages/skopt/searchcv.py:466, in BayesSearchCV.fit(self, X, y, groups, callback, **fit_params)
463 else:
464 self.optimizer_kwargs_ = dict(self.optimizer_kwargs)
--> 466 super().fit(X=X, y=y, groups=groups, **fit_params)
468 # BaseSearchCV never ranked train scores,
469 # but apparently we used to ship this (back-compat)
470 if self.return_train_score:
File ~/wd/venv/lib/python3.8/site-packages/sklearn/model_selection/_search.py:891, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
885 results = self._format_results(
886 all_candidate_params, n_splits, all_out, all_more_results
887 )
889 return results
--> 891 self._run_search(evaluate_candidates)
893 # multimetric is determined here because in the case of a callable
894 # self.scoring the return type is only known after calling
895 first_test_score = all_out[0]["test_scores"]
File ~/wd/venv/lib/python3.8/site-packages/skopt/searchcv.py:512, in BayesSearchCV._run_search(self, evaluate_candidates)
508 while n_iter > 0:
509 # when n_iter < n_points points left for evaluation
510 n_points_adjusted = min(n_iter, n_points)
--> 512 optim_result = self._step(
513 search_space, optimizer,
514 evaluate_candidates, n_points=n_points_adjusted
515 )
516 n_iter -= n_points
518 if eval_callbacks(callbacks, optim_result):
File ~/wd/venv/lib/python3.8/site-packages/skopt/searchcv.py:408, in BayesSearchCV._step(self, search_space, optimizer, evaluate_candidates, n_points)
405 # make lists into dictionaries
406 params_dict = [point_asdict(search_space, p) for p in params]
--> 408 all_results = evaluate_candidates(params_dict)
409 # Feed the point and objective value back into optimizer
410 # Optimizer minimizes objective, hence provide negative score
411 local_results = all_results["mean_test_score"][-len(params):]
File ~/wd/venv/lib/python3.8/site-packages/sklearn/model_selection/_search.py:838, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
830 if self.verbose > 0:
831 print(
832 "Fitting {0} folds for each of {1} candidates,"
833 " totalling {2} fits".format(
834 n_splits, n_candidates, n_candidates * n_splits
835 )
836 )
--> 838 out = parallel(
839 delayed(_fit_and_score)(
840 clone(base_estimator),
841 X,
842 y,
843 train=train,
844 test=test,
845 parameters=parameters,
846 split_progress=(split_idx, n_splits),
847 candidate_progress=(cand_idx, n_candidates),
848 **fit_and_score_kwargs,
849 )
850 for (cand_idx, parameters), (split_idx, (train, test)) in product(
851 enumerate(candidate_params), enumerate(cv.split(X, y, groups))
852 )
853 )
855 if len(out) < 1:
856 raise ValueError(
857 "No fits were performed. "
858 "Was the CV iterator empty? "
859 "Were there no candidates?"
860 )
File ~/wd/venv/lib/python3.8/site-packages/joblib/parallel.py:1085, in Parallel.__call__(self, iterable)
1076 try:
1077 # Only set self._iterating to True if at least a batch
1078 # was dispatched. In particular this covers the edge
(...)
1082 # was very quick and its callback already dispatched all the
1083 # remaining jobs.
1084 self._iterating = False
-> 1085 if self.dispatch_one_batch(iterator):
1086 self._iterating = self._original_iterator is not None
1088 while self.dispatch_one_batch(iterator):
File ~/wd/venv/lib/python3.8/site-packages/joblib/parallel.py:901, in Parallel.dispatch_one_batch(self, iterator)
899 return False
900 else:
--> 901 self._dispatch(tasks)
902 return True
File ~/wd/venv/lib/python3.8/site-packages/joblib/parallel.py:819, in Parallel._dispatch(self, batch)
817 with self._lock:
818 job_idx = len(self._jobs)
--> 819 job = self._backend.apply_async(batch, callback=cb)
820 # A job can complete so quickly than its callback is
821 # called before we get here, causing self._jobs to
822 # grow. To ensure correct results ordering, .insert is
823 # used (rather than .append) in the following line
824 self._jobs.insert(job_idx, job)
File ~/wd/venv/lib/python3.8/site-packages/joblib/_parallel_backends.py:208, in SequentialBackend.apply_async(self, func, callback)
206 def apply_async(self, func, callback=None):
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
210 callback(result)
File ~/wd/venv/lib/python3.8/site-packages/joblib/_parallel_backends.py:597, in ImmediateResult.__init__(self, batch)
594 def __init__(self, batch):
595 # Don't delay the application, to avoid keeping the input
596 # arguments in memory
--> 597 self.results = batch()
File ~/wd/venv/lib/python3.8/site-packages/joblib/parallel.py:288, in BatchedCalls.__call__(self)
284 def __call__(self):
285 # Set the default nested backend to self._backend but do not set the
286 # change the default number of processes to -1
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/wd/venv/lib/python3.8/site-packages/joblib/parallel.py:288, in <listcomp>(.0)
284 def __call__(self):
285 # Set the default nested backend to self._backend but do not set the
286 # change the default number of processes to -1
287 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 288 return [func(*args, **kwargs)
289 for func, args, kwargs in self.items]
File ~/wd/venv/lib/python3.8/site-packages/sklearn/utils/fixes.py:216, in _FuncWrapper.__call__(self, *args, **kwargs)
214 def __call__(self, *args, **kwargs):
215 with config_context(**self.config):
--> 216 return self.function(*args, **kwargs)
File ~/wd/venv/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:680, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
678 estimator.fit(X_train, **fit_params)
679 else:
--> 680 estimator.fit(X_train, y_train, **fit_params)
682 except Exception:
683 # Note fit time as time until error
684 fit_time = time.time() - start_time
File ~/wd/venv/lib/python3.8/site-packages/xgboost/core.py:506, in _deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
504 for k, arg in zip(sig.parameters, args):
505 kwargs[k] = arg
--> 506 return f(**kwargs)
File ~/wd/venv/lib/python3.8/site-packages/xgboost/sklearn.py:789, in XGBModel.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)
786 obj = None
788 model, feval, params = self._configure_fit(xgb_model, eval_metric, params)
--> 789 self._Booster = train(
790 params,
791 train_dmatrix,
792 self.get_num_boosting_rounds(),
793 evals=evals,
794 early_stopping_rounds=early_stopping_rounds,
795 evals_result=evals_result,
796 obj=obj,
797 feval=feval,
798 verbose_eval=verbose,
799 xgb_model=model,
800 callbacks=callbacks,
801 )
803 self._set_evaluation_result(evals_result)
804 return self
File ~/wd/venv/lib/python3.8/site-packages/xgboost/training.py:188, in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks)
115 def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
116 maximize=None, early_stopping_rounds=None, evals_result=None,
117 verbose_eval=True, xgb_model=None, callbacks=None):
118 # pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
119 """Train a booster with given parameters.
120
121 Parameters
(...)
186 Booster : a trained booster model
187 """
--> 188 bst = _train_internal(params, dtrain,
189 num_boost_round=num_boost_round,
190 evals=evals,
191 obj=obj, feval=feval,
192 xgb_model=xgb_model, callbacks=callbacks,
193 verbose_eval=verbose_eval,
194 evals_result=evals_result,
195 maximize=maximize,
196 early_stopping_rounds=early_stopping_rounds)
197 return bst
File ~/wd/venv/lib/python3.8/site-packages/xgboost/training.py:81, in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks, evals_result, maximize, verbose_eval, early_stopping_rounds)
79 if callbacks.before_iteration(bst, i, dtrain, evals):
80 break
---> 81 bst.update(dtrain, i, obj)
82 if callbacks.after_iteration(bst, i, dtrain, evals):
83 break
File ~/wd/venv/lib/python3.8/site-packages/xgboost/core.py:1680, in Booster.update(self, dtrain, iteration, fobj)
1677 self._validate_features(dtrain)
1679 if fobj is None:
-> 1680 _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,
1681 ctypes.c_int(iteration),
1682 dtrain.handle))
1683 else:
1684 pred = self.predict(dtrain, output_margin=True, training=True)
File ~/wd/venv/lib/python3.8/site-packages/xgboost/core.py:218, in _check_call(ret)
207 """Check the return value of C API call
208
209 This function will raise exception when error occurs.
(...)
215 return value from API calls
216 """
217 if ret != 0:
--> 218 raise XGBoostError(py_str(_LIB.XGBGetLastError()))
XGBoostError: [13:46:18] ../src/gbm/gbtree.cc:588: Check failed: common::AllVisibleGPUs() >= 1 (0 vs. 1) : No visible GPU is found for XGBoost.
Stack trace:
[bt] (0) /root/wd/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.so(+0x179459) [0x7f00362cb459]
[bt] (1) /root/wd/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.so(+0x179b82) [0x7f00362cbb82]
[bt] (2) /root/wd/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.so(+0x17a1da) [0x7f00362cc1da]
[bt] (3) /root/wd/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.so(+0x1b46e5) [0x7f00363066e5]
[bt] (4) /root/wd/venv/lib/python3.8/site-packages/xgboost/lib/libxgboost.so(XGBoosterUpdateOneIter+0x68) [0x7f00361eb4e8]
[bt] (5) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call_unix64+0x4c) [0x7f01f861cdae]
[bt] (6) /usr/lib/x86_64-linux-gnu/libffi.so.6(ffi_call+0x22f) [0x7f01f861c71f]
[bt] (7) /root/wd/venv/lib/python3.8/lib-dynload/_ctypes.cpython-38-x86_64-linux-gnu.so(_ctypes_callproc+0x8ce) [0x7f01f883234e]
[bt] (8) /root/wd/venv/lib/python3.8/lib-dynload/_ctypes.cpython-38-x86_64-linux-gnu.so(+0xe4e4) [0x7f01f882d4e4]
Any suggestions how to make it work?
I tried to solve this using the answers from here:
but it didn't work
