Hi SciKit-Learn folks, I am building a stacked generalization classifier using the multilayer perceptron classifier as one of it's submodels. All data have been preprocessed appropriately and I am tuning each submodel's hyperparameters with a customized randomized search protocol (very similar to sklearn's RandomizedSearchCV). Importantly, I am using Python's Multiprocessing.Pool() to parallelize this search. When I start the hyperparameter search, jobs/threads do indeed spawn appropriately. Tuning other submodels (RandomForestClassifier, SVC, GradientBoostingClassifier, SDGClassifier) works perfectly, which each job (model with particular randomized parameters) being scored with cross_val_score and returning when the Pool of workers is complete. All is well until I reach the MLPClassifier model. Jobs spawn as with the other models, however, System CPU (Linux Kernel) processes surge and overwhelm my server. Approximately 20% of the CPUs are running User processes, while the other 80% of CPUS are running System/Kernel processes, causing immense slow-down. Again, this only happens with the MLPClassifier - all other models run appropriately with ~98% User processes and ~2% System/Kernel processes. Is there something unique in the MLPClassifier/Regressor models that causes increased System/Kernel processes compared to other models? In an attempt to troubleshoot, I used sklearn's RandomizedSearchCV instead of my custom implementation and the same problems happen (with n_jobs specified in the same way). Any help with why the MLP models are behaving this way during multiprocessing is much appreciated. Best, Taylor Keding