Worker init and exit

When you want to initialize a worker you can make use of the worker_init parameter of any map function. This will call the initialization function only once per worker. Similarly, if you need to clean up the worker at the end of its lifecycle you can use the worker_exit parameter. Additionally, the exit function can return anything you like, which can be collected using mpire.WorkerPool.get_exit_results() after the workers are done.

Both init and exit functions receive the worker ID, shared objects, and worker state in the same way as the task function does, given they’re enabled.

For example:

def init_func(worker_state):
    # Initialize a counter for each worker
    worker_state['count_even'] = 0

def square_and_count_even(worker_state, x):
    # Count number of even numbers and return the square
    if x % 2 == 0:
        worker_state['count_even'] += 1
    return x * x

def exit_func(worker_state):
    # Return the counter
    return worker_state['count_even']

with WorkerPool(n_jobs=4, use_worker_state=True) as pool:, range(100), worker_init=init_func, worker_exit=exit_func)
    print(pool.get_exit_results())  # Output, e.g.: [13, 13, 12, 12]
    print(sum(pool.get_exit_results()))  # Output: 50


When the worker_lifespan option is used to restart workers during execution, the exit function will be called for the worker that’s shutting down and the init function will be called again for the new worker. Therefore, the number of elements in the list that’s returned from mpire.WorkerPool.get_exit_results() does not always equal n_jobs.


When keep_alive is enabled the workers won’t be terminated after a map call. This means the exit function won’t be called until it’s time for cleaning up the entire pool. You will have to explicitly call mpire.WorkerPool.stop_and_join() to receive the exit results.