8442321b9b7a7cef7e3806b525965c36cea1549e,examples/60_search/example_parallel_manual_spawning.py,,,#,97

Before Change

processes = []
spawn_classifier = get_spawn_classifier(X_train, y_train)
for i in range(4):  // set this at roughly half of your cores
    p = multiprocessing.Process(
        target=spawn_classifier,
        args=(i, "breast_cancer"),
    )
    p.start()
    processes.append(p)
for p in processes:
    p.join()

After Change

        print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

        // Wait until all workers are closed
        process_python_worker.join()
        process_cli_worker.join()

Instances