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    AI

    Easy methods to Construct Progress Monitoring Utilizing Superior tqdm for Async, Parallel, Pandas, Logging, and Excessive-Efficiency Workflows

    Naveed AhmadBy Naveed Ahmad08/03/2026Updated:08/03/2026No Comments1 Min Read
    blog banner23 26


    print("5) Concurrency progress: thread_map / process_map")
    def cpuish(n: int) -> int:
       x = 0
       for i in vary(50_000):
           x = (x + (n * i)) % 1_000_003
       return x
    
    
    nums = record(vary(80))
    thread_results = thread_map(cpuish, nums, max_workers=8, desc="thread_map")
    print("thread_map finished:", len(thread_results))
    
    
    proc_results = process_map(cpuish, nums[:20], max_workers=2, chunksize=2, desc="process_map")
    print("process_map finished:", len(proc_results))
    print()
    
    
    print("6) logging_redirect_tqdm (logs received’t break bars)")
    logger = logging.getLogger("demo")
    logger.setLevel(logging.INFO)
    handler = logging.StreamHandler()
    handler.setFormatter(logging.Formatter("%(levelname)s: %(message)s"))
    logger.handlers = [handler]
    
    
    with logging_redirect_tqdm():
       for ok in tqdm(vary(60), desc="Work with logs"):
           time.sleep(0.01)
           if ok in (5, 25, 45):
               logger.information(f"checkpoint ok={ok}")
    print()
    
    
    print("7) asyncio progress (as_completed) — Colab/Jupyter-safe")
    async def io_task(i: int):
       await asyncio.sleep(random.uniform(0.02, 0.12))
       return i, random.random()
    
    
    async def run_async():
       duties = [asyncio.create_task(io_task(i)) for i in range(80)]
       outcomes = []
       for fut in tqdm(asyncio.as_completed(duties), whole=len(duties), desc="async duties"):
           outcomes.append(await fut)
       return outcomes
    
    
    outcomes = await run_async()
    print("async finished:", len(outcomes), "outcomes")



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    Naveed Ahmad

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