Multiprocessing : use tqdm to display a progress bar

Multiprocessing : use tqdm to display a progress bar

Using tqdm to display a progress bar when using the multiprocessing module in Python requires some careful handling due to the interaction between multiprocessing and the way Jupyter Notebook handles outputs. Here's how you can achieve it:

from multiprocessing import Pool from tqdm import tqdm import time def worker_function(item): # Simulate some work time.sleep(1) return item * 2 def main(): num_processes = 4 items = list(range(10)) with Pool(num_processes) as pool: results = list(tqdm(pool.imap(worker_function, items), total=len(items))) print("Results:", results) if __name__ == "__main__": main()

In this example, worker_function simulates some processing. We use pool.imap to distribute the work across processes and iterate over the results using tqdm. The total argument of tqdm specifies the total number of iterations to expect, which allows it to display an accurate progress bar.

Keep in mind that running this code directly in a Jupyter Notebook cell might not display the progress bar correctly due to how Jupyter handles standard output in multiprocessing environments. If you encounter issues with the progress bar not updating as expected, consider running the code in a standalone Python script outside of the Jupyter Notebook environment.

If you intend to run this code in a Jupyter Notebook and still want to display a progress bar, you might need to explore alternative solutions like using the concurrent.futures module, which handles outputs more gracefully in Jupyter environments.

Examples

  1. "Python multiprocessing tqdm example"

    • Description: This query demonstrates how to use tqdm to display a progress bar for tasks executed in parallel using Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm as a context manager to wrap imap with tqdm(total=num_tasks) as pbar: for result in pool.imap(task, range(num_tasks)): pbar.update(1)

  2. "Python multiprocessing tqdm progress bar with map"

    • Description: This query illustrates how to use tqdm to display a progress bar when using map function in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm to wrap map and provide total number of tasks for _ in tqdm(pool.map(task, range(num_tasks)), total=num_tasks): pass

  3. "Python multiprocessing tqdm progress bar with starmap"

    • Description: This query demonstrates using tqdm to show a progress bar while utilizing starmap function in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x, y): """Example task.""" time.sleep(0.1) return x * y if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm to wrap starmap and specify total number of tasks for _ in tqdm(pool.starmap(task, [(i, i+1) for i in range(num_tasks)]), total=num_tasks): pass

  4. "Python multiprocessing tqdm progress bar with apply_async"

    • Description: This query illustrates using tqdm to display a progress bar while using apply_async function in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm to manually update the progress bar with apply_async results = [] for _ in range(num_tasks): results.append(pool.apply_async(task, args=(_,))) with tqdm(total=num_tasks) as pbar: for res in results: res.get() pbar.update(1)

  5. "Python multiprocessing tqdm progress bar with imap_unordered"

    • Description: This query demonstrates utilizing tqdm to show a progress bar while using imap_unordered function in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm with imap_unordered and specify total number of tasks for _ in tqdm(pool.imap_unordered(task, range(num_tasks)), total=num_tasks): pass

  6. "Python multiprocessing tqdm progress bar with apply"

    • Description: This query illustrates using tqdm to display a progress bar while using apply function in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm to manually update the progress bar with apply results = [] for _ in range(num_tasks): results.append(pool.apply_async(task, args=(_,))) with tqdm(total=num_tasks) as pbar: for res in results: res.get() pbar.update(1)

  7. "Python multiprocessing tqdm progress bar with async_result"

    • Description: This query demonstrates using tqdm to display a progress bar while utilizing AsyncResult objects in Python multiprocessing.
    from multiprocessing import Pool from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with Pool() as pool: # Use tqdm with async_result objects to track progress results = [] for _ in range(num_tasks): results.append(pool.apply_async(task, args=(_,))) with tqdm(total=num_tasks) as pbar: while results: for res in results[:]: if res.ready(): res.get() results.remove(res) pbar.update(1)

  8. "Python multiprocessing tqdm progress bar with Process"

    • Description: This query illustrates using tqdm to display a progress bar while utilizing Process objects in Python multiprocessing.
    from multiprocessing import Process from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 processes = [] with tqdm(total=num_tasks) as pbar: for i in range(num_tasks): p = Process(target=lambda q, arg1: q.put(task(arg1)), args=(i,)) p.start() processes.append(p) for p in processes: p.join() pbar.update(1)

  9. "Python multiprocessing tqdm progress bar with concurrent.futures"

    • Description: This query demonstrates using tqdm to display a progress bar while utilizing concurrent.futures module in Python multiprocessing.
    from concurrent.futures import ProcessPoolExecutor from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with ProcessPoolExecutor() as executor: # Use tqdm as a context manager to wrap executor.map with tqdm(total=num_tasks) as pbar: for _ in executor.map(task, range(num_tasks)): pbar.update(1)

  10. "Python multiprocessing tqdm progress bar with ThreadPoolExecutor"

    • Description: This query illustrates using tqdm to display a progress bar while utilizing ThreadPoolExecutor from concurrent.futures module in Python multiprocessing.
    from concurrent.futures import ThreadPoolExecutor from tqdm import tqdm import time def task(x): """Example task.""" time.sleep(0.1) return x * x if __name__ == "__main__": num_tasks = 100 with ThreadPoolExecutor() as executor: # Use tqdm as a context manager to wrap executor.map with tqdm(total=num_tasks) as pbar: for _ in executor.map(task, range(num_tasks)): pbar.update(1)

More Tags

spatial-query jmeter-4.0 tlist system.diagnostics coap point-cloud-library metricbeat touchablehighlight azure-cli2 acl

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