XGBoost and scikit-optimize: BayesSearchCV and XGBRegressor are incompatible - why?

I have a very large dataset (7 million rows, 54 features) that I would like to fit a regression model to using XGBoost. To train the best possible model, I want to use BayesSearchCV from scikit-optimize to run the fit repeatedly for different hyperparameter combinations until the best performing set is found.

For a given set of hyperparameters, XGBoost takes a very long time to train a model, so in order to find the best hyperparameters without spending days on every permutation of training folds, hyperparameters, etc., I want to multithread both XGBoost and BayesSearchCV. The relevant part of my code looks like this:

xgb_pipe = Pipeline([('clf', XGBRegressor(random_state = 42,  objective='reg:squarederror', n_jobs = 1))])

xgb_fit_params = {'clf__early_stopping_rounds': 5, 'clf__eval_metric': 'mae', 'clf__eval_set': [[X_val.values, y_val.values]]}

xgb_kfold = KFold(n_splits = 5, random_state = 42)

xgb_unsm_cv = BayesSearchCV(xgb_pipe, xgb_params, cv = xgb_kfold, n_jobs = 2, n_points = 1, n_iter = 15, random_state = 42, verbose = 4, scoring = 'neg_mean_absolute_error', fit_params = xgb_fit_params)

xgb_unsm_cv.fit(X_train.values, y_train.values)

However, I've found that when n_jobs > 1 in the BayesSearchCV call, the fit crashes and I get the following error:

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 {SIGKILL(-9)}

This error persists whenever I use more than 1 thread in the BayesSearchCV call, and is independent of the memory I provide.

Is this some fundamental incompatibility between XGBoost and scikit-optimize, or can both packages be forced to work together somehow? Without some way of multithreading the optimization, I fear that fitting my model will take weeks to perform. What can I do to fix this?

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