Rapids CUML Random Forest Regression Model doesn't return the scoring function value

Question

I used the code below to tune the model and search for the best combination using the grid search algorithm. However, the model does not evaluate and return the score value of each combination.

I used the same thing with the SVM regressor model and it return the score during the training.

Here is the code:

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer, mean_squared_error
param_grid2 = {
    
    'bootstrap': [True, False],
    'max_depth': [16, 30, 50, 80, 90, 100 ],
    'max_features': ['auto', 'sqrt', 'log2'],
    'min_samples_leaf': [1, 3, 4, 5],
    'min_samples_split': [2,4, 8, 10, 12],
    'n_estimators': [100, 200, 300, 400 ,500]
    }

from cuml.ensemble import RandomForestRegressor



# Create a based model
rfr_cu = RandomForestRegressor() # remove the output_type. Check this link for more details: https://docs.rapids.ai/api/cuml/stable/api.html#output-data-type-configuration

# Instantiate the grid search model
rfr_tune_cu = GridSearchCV(estimator = rfr_cu, param_grid = param_grid2, scoring='neg_mean_squared_error',  
                           cv = 3, n_jobs = -1, verbose = 3, error_score= 'raise',  return_train_score =True )

The output message

RuntimeError                              Traceback (most recent call last)
Input In [80], in <cell line: 2>()
      1 # Fit the grid search to the data
----> 2 rfr_tune_cu.fit(cu_X_train, cu_y_train)

File /usr/local/lib/python3.9/dist-packages/sklearn/model_selection/_search.py:875, in BaseSearchCV.fit(self, X, y, groups, **fit_params)
    869     results = self._format_results(
    870         all_candidate_params, n_splits, all_out, all_more_results
    871     )
    873     return results
--> 875 self._run_search(evaluate_candidates)
    877 # multimetric is determined here because in the case of a callable
    878 # self.scoring the return type is only known after calling
    879 first_test_score = all_out[0]["test_scores"]

File /usr/local/lib/python3.9/dist-packages/sklearn/model_selection/_search.py:1375, in GridSearchCV._run_search(self, evaluate_candidates)
   1373 def _run_search(self, evaluate_candidates):
   1374     """Search all candidates in param_grid"""
-> 1375     evaluate_candidates(ParameterGrid(self.param_grid))

File /usr/local/lib/python3.9/dist-packages/sklearn/model_selection/_search.py:822, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    814 if self.verbose > 0:
    815     print(
    816         "Fitting {0} folds for each of {1} candidates,"
    817         " totalling {2} fits".format(
    818             n_splits, n_candidates, n_candidates * n_splits
    819         )
    820     )
--> 822 out = parallel(
    823     delayed(_fit_and_score)(
    824         clone(base_estimator),
    825         X,
    826         y,
    827         train=train,
    828         test=test,
    829         parameters=parameters,
    830         split_progress=(split_idx, n_splits),
    831         candidate_progress=(cand_idx, n_candidates),
    832         **fit_and_score_kwargs,
    833     )
    834     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    835         enumerate(candidate_params), enumerate(cv.split(X, y, groups))
    836     )
    837 )
    839 if len(out) < 1:
    840     raise ValueError(
    841         "No fits were performed. "
    842         "Was the CV iterator empty? "
    843         "Were there no candidates?"
    844     )

File /usr/local/lib/python3.9/dist-packages/joblib/parallel.py:1056, in Parallel.__call__(self, iterable)
   1053     self._iterating = False
   1055 with self._backend.retrieval_context():
-> 1056     self.retrieve()
   1057 # Make sure that we get a last message telling us we are done
   1058 elapsed_time = time.time() - self._start_time

File /usr/local/lib/python3.9/dist-packages/joblib/parallel.py:935, in Parallel.retrieve(self)
    933 try:
    934     if getattr(self._backend, 'supports_timeout', False):
--> 935         self._output.extend(job.get(timeout=self.timeout))
    936     else:
    937         self._output.extend(job.get())

File /usr/local/lib/python3.9/dist-packages/joblib/_parallel_backends.py:542, in LokyBackend.wrap_future_result(future, timeout)
    539 """Wrapper for Future.result to implement the same behaviour as
    540 AsyncResults.get from multiprocessing."""
    541 try:
--> 542     return future.result(timeout=timeout)
    543 except CfTimeoutError as e:
    544     raise TimeoutError from e

File /usr/lib/python3.9/concurrent/futures/_base.py:446, in Future.result(self, timeout)
    444     raise CancelledError()
    445 elif self._state == FINISHED:
--> 446     return self.__get_result()
    447 else:
    448     raise TimeoutError()

File /usr/lib/python3.9/concurrent/futures/_base.py:391, in Future.__get_result(self)
    389 if self._exception:
    390     try:
--> 391         raise self._exception
    392     finally:
    393         # Break a reference cycle with the exception in self._exception
    394         self = None

RuntimeError: CUDA error encountered at: file=/project/cpp/src/decisiontree/batched-levelalgo/quantiles.cuh line=52: call='cub::DeviceRadixSort::SortKeys( nullptr, temp_storage_bytes, data, sorted_column.data(), n_rows, 0, 8 * sizeof(T), stream)', Reason=cudaErrorInvalidDeviceFunction:invalid device function