tqdm_multiprocess_pool_parallel_tasks_with_progress_bars.py

1import time
2import random
3from tqdm_multiprocess import TqdmMultiProcessPool
4
5def task(name, iterations):
6    """
7    Example task that simulates work and updates its own progress bar.
8    """
9    # The first two arguments of the task must be (tqdm_func, global_tqdm)
10    # followed by any custom arguments.
11    def run_task(tqdm_func, global_tqdm, name, iterations):
12        # Create a progress bar for this specific process
13        pbar = tqdm_func(total=iterations, desc=f"Process {name}", position=name)
14        for i in range(iterations):
15            time.sleep(random.random() * 0.1)  # Simulate work
16            pbar.update(1)
17        pbar.close()
18        return f"Task {name} completed"
19
20if __name__ == '__main__':
21    # Define tasks: (function, arguments_tuple)
22    tasks = [(task, (i, random.randint(10, 50))) for i in range(5)]
23
24    # Initialize the pool
25    pool = TqdmMultiProcessPool(processes=4)
26
27    # Execute tasks and collect results
28    # The library handles the tqdm overhead across multiple processes
29    results = pool.map(task, [(i, random.randint(50, 100)) for i in range(10)])
30
31    print("\nResults:", results)