`TerminatedWorkerError` when working with `n_jobs=-1` in `GridSearchCV`

### Describe the bug

`TerminatedWorkerError` when working with `n_jobs=-1` or even `n_jobs=4` in `GridSearchCV`.

I just migrated to a Macbook Pro M3 from an Ubuntu. This info is probably relevant since it seems to be related to how the OS terminate a process? I'm able to run this on my older Ubuntu laptop with older version of Python (v3.8) and Sklearn (v1.0.2). With my Ubuntu laptop, I never encountered this issue before. 

I noticed that this issue happens randomly on my Macbook, which means if the `TerminatedWorkerError` raised, I can re-run the fitting code and it might finish without issue. Sometimes it also happens early in the fitting or a bit later. 

My code is very similar to the attached code below. But, it seems I couldn't reproduce the same error with that code on a fresh jupyter notebook!

It would be great if someone could advise how to debug further, or advise if there is any settings I could change to reduce the 'likelihood' of `TerminatedWorkerError`.

### Steps/Code to Reproduce

```python
# Note: I couldn't reproduce this error with the code below, could be differences in data?
# But this is very similar to what my actual code looks like

import numpy as np
from imblearn.pipeline import Pipeline
from sklearn import tree, datasets
from sklearn.model_selection import GridSearchCV, RepeatedStratifiedKFold
from sklearn.feature_selection import SequentialFeatureSelector, SelectFromModel
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import RandomOverSampler

class CustomGridSearchCV(GridSearchCV):
    @property
    def feature_importances_(self):
        return self.best_estimator_.feature_importances_

rs=42

breast_cancer = datasets.load_breast_cancer()
grid_search_cv = RepeatedStratifiedKFold(random_state=rs, n_repeats=100, n_splits=5)

tree_params = {'criterion': ['gini', 'entropy'],
              'max_depth': range(2, 6),
              'min_samples_leaf': range(3, 5, 1)}

tree_pipe = Pipeline([
    ("scaling", StandardScaler()), 
    ("oversampling", RandomOverSampler(random_state=rs)),
    ("feature_selection", "passthrough"),
    ("clf", tree.DecisionTreeClassifier()),
])

parameters = {
    'feature_selection': [
        SelectFromModel(
            CustomGridSearchCV(
                param_grid=tree_params,
                estimator=DecisionTreeClassifier(random_state=rs), 
                cv=5,
                scoring='balanced_accuracy',
            )
        ),
    ],
    'oversampling__shrinkage': np.arange(0, 1, 0.3),
    'clf__criterion': ['gini', 'entropy'],
    'clf__max_depth': range(2, 10),
    'clf__min_samples_leaf': range(3, 10, 1)
}
clf = GridSearchCV(tree_pipe, parameters, n_jobs=-1, cv=grid_search_cv, verbose=1, error_score='raise')
clf.fit(breast_cancer.data, breast_cancer.target)
```

### Expected Results

No error is thrown.

### Actual Results

```python-traceback
---------------------------------------------------------------------------
TerminatedWorkerError                     Traceback (most recent call last)
File <timed exec>:158

File ~/miniconda3/lib/python3.12/site-packages/sklearn/base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1466     estimator._validate_params()
   1468 with config_context(
   1469     skip_parameter_validation=(
   1470         prefer_skip_nested_validation or global_skip_validation
   1471     )
   1472 ):
-> 1473     return fit_method(estimator, *args, **kwargs)

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1018, in BaseSearchCV.fit(self, X, y, **params)
   1012     results = self._format_results(
   1013         all_candidate_params, n_splits, all_out, all_more_results
   1014     )
   1016     return results
-> 1018 self._run_search(evaluate_candidates)
   1020 # multimetric is determined here because in the case of a callable
   1021 # self.scoring the return type is only known after calling
   1022 first_test_score = all_out[0]["test_scores"]

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1572, in GridSearchCV._run_search(self, evaluate_candidates)
   1570 def _run_search(self, evaluate_candidates):
   1571     """Search all candidates in param_grid"""
-> 1572     evaluate_candidates(ParameterGrid(self.param_grid))

File ~/miniconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:964, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    956 if self.verbose > 0:
    957     print(
    958         "Fitting {0} folds for each of {1} candidates,"
    959         " totalling {2} fits".format(
    960             n_splits, n_candidates, n_candidates * n_splits
    961         )
    962     )
--> 964 out = parallel(
    965     delayed(_fit_and_score)(
    966         clone(base_estimator),
    967         X,
    968         y,
    969         train=train,
    970         test=test,
    971         parameters=parameters,
    972         split_progress=(split_idx, n_splits),
    973         candidate_progress=(cand_idx, n_candidates),
    974         **fit_and_score_kwargs,
    975     )
    976     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    977         enumerate(candidate_params),
    978         enumerate(cv.split(X, y, **routed_params.splitter.split)),
    979     )
    980 )
    982 if len(out) < 1:
    983     raise ValueError(
    984         "No fits were performed. "
    985         "Was the CV iterator empty? "
    986         "Were there no candidates?"
    987     )

File ~/miniconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:74, in Parallel.__call__(self, iterable)
     69 config = get_config()
     70 iterable_with_config = (
     71     (_with_config(delayed_func, config), args, kwargs)
     72     for delayed_func, args, kwargs in iterable
     73 )
---> 74 return super().__call__(iterable_with_config)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:2007, in Parallel.__call__(self, iterable)
   2001 # The first item from the output is blank, but it makes the interpreter
   2002 # progress until it enters the Try/Except block of the generator and
   2003 # reaches the first `yield` statement. This starts the asynchronous
   2004 # dispatch of the tasks to the workers.
   2005 next(output)
-> 2007 return output if self.return_generator else list(output)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1650, in Parallel._get_outputs(self, iterator, pre_dispatch)
   1647     yield
   1649     with self._backend.retrieval_context():
-> 1650         yield from self._retrieve()
   1652 except GeneratorExit:
   1653     # The generator has been garbage collected before being fully
   1654     # consumed. This aborts the remaining tasks if possible and warn
   1655     # the user if necessary.
   1656     self._exception = True

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1754, in Parallel._retrieve(self)
   1747 while self._wait_retrieval():
   1748 
   1749     # If the callback thread of a worker has signaled that its task
   1750     # triggered an exception, or if the retrieval loop has raised an
   1751     # exception (e.g. `GeneratorExit`), exit the loop and surface the
   1752     # worker traceback.
   1753     if self._aborting:
-> 1754         self._raise_error_fast()
   1755         break
   1757     # If the next job is not ready for retrieval yet, we just wait for
   1758     # async callbacks to progress.

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:1789, in Parallel._raise_error_fast(self)
   1785 # If this error job exists, immediately raise the error by
   1786 # calling get_result. This job might not exists if abort has been
   1787 # called directly or if the generator is gc'ed.
   1788 if error_job is not None:
-> 1789     error_job.get_result(self.timeout)

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:745, in BatchCompletionCallBack.get_result(self, timeout)
    739 backend = self.parallel._backend
    741 if backend.supports_retrieve_callback:
    742     # We assume that the result has already been retrieved by the
    743     # callback thread, and is stored internally. It's just waiting to
    744     # be returned.
--> 745     return self._return_or_raise()
    747 # For other backends, the main thread needs to run the retrieval step.
    748 try:

File ~/miniconda3/lib/python3.12/site-packages/joblib/parallel.py:763, in BatchCompletionCallBack._return_or_raise(self)
    761 try:
    762     if self.status == TASK_ERROR:
--> 763         raise self._result
    764     return self._result
    765 finally:

TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.

The exit codes of the workers are {SIGBUS(-10)}
```

### Versions

```shell
System:
    python: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 10:07:17) [Clang 14.0.6 ]
executable: /Users/darren/miniconda3/bin/python
   machine: macOS-14.5-arm64-arm-64bit

Python dependencies:
      sklearn: 1.5.1
          pip: 24.1.2
   setuptools: 69.5.1
        numpy: 2.0.1
        scipy: 1.14.0
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.9.1
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: libomp
       filepath: /Users/darren/miniconda3/lib/python3.12/site-packages/sklearn/.dylibs/libomp.dylib
        version: None
```

COMMENT:
Hi, I am reopening this as I just faced the same issue on my other dataset using `pre_dispatch='1.5*n_jobs'`. Do I need to keep lowering the pre_dispatch? Will there be any issues in performance/training speed? 

```
The exit codes of the workers are {SIGBUS(-10)}
```

FYI, below is the dimension of my training data and I added imputation in the pipeline early on. 

![image](https://github.com/user-attachments/assets/6494cbb9-ada5-4f74-ad0d-a6af2cd9f081)

Here is my memory stats when fitting the model.

![image](https://github.com/user-attachments/assets/d3e07623-3a0e-4bfb-8b42-8363428fa735)

Here is the stats in `top` command of the spawned processes, it seems more of CPU heavy and doesn't use a lot of memory, which is expected.

![image](https://github.com/user-attachments/assets/777c9bac-6ee0-46d8-8f47-28358c5535cd)

Any help would be greatly appreciated.

This old issue seems to be related. https://github.com/joblib/joblib/issues/908

Labels: Bug, Needs Reproducible Code