tqdm

I have a multithreaded function that I would like a status bar for using tqdm . Is there an easy way to show a status bar with ThreadPoolExecutor ? It is the parallelization part that is confusing me. import concurrent.futures def f(x): return f**2 my_iter = range(1000000) def run(f,my_iter): with concurrent.futures.ThreadPoolExecutor() as executor: function = list(executor.map(f, my_iter)) return results run(f, my_iter) # wrap tqdr around this function? You can wrap tqdm around the executor as the following to track the progress: list(tqdm(executor.map(f, iter), total=len(iter)) Here is your

I'm trying to use tqdm through multi processes. And the behavior is not as expected. I think the point is that the value of pbar doesn't update through the processes. So how to deal with this problem? I have also tried to use Value to update pbar.n manually, but still failed. It seems tqdm doesn't support update value and render manually. def test(lock, pbar): for i in range(10000): sleep(0.1) lock.acquire() pbar.update() lock.release() pbar = tqdm(total = 10000) lock = Lock() for i in range(5): Process(target = test, args = (lock, pbar)) Generally, each process has its own data, independent

I want to run a function in parallel, and wait until all parallel nodes are done, using joblib. Like in the example: from math import sqrt from joblib import Parallel, delayed Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in range(10)) But, I want that the execution will be seen in a single progressbar like with tqdm , showing how many jobs has been completed. How would you do that? If your problem consists of many parts, you could split the parts into k subgroups, run each subgroup in parallel and update the progressbar in between, resulting in k updates of the progress. This is demonstrated

I am doing some parallel processing, as follows: with mp.Pool(8) as tmpPool: results = tmpPool.starmap(my_function, inputs) where inputs look like: [(1,0.2312),(5,0.52) ...] i.e., tuples of an int and a float. The code runs nicely, yet I cannot seem to wrap it around a loading bar (tqdm), such as can be done with e.g., imap method as follows: tqdm.tqdm(mp.imap(some_function,some_inputs)) Can this be done for starmap also? Thanks! It's not possible with starmap() , but it's possible with a patch adding Pool.istarmap() . It's based on the code for imap() . All you have to do, is create the

recently I want to add a simple progress bar to my script, I use tqdm to that, but what puzzle me is that the output is different when I am in the IDLE or in the cmd for example this from tqdm import tqdm import time def test(): for i in tqdm( range(100) ): time.sleep(0.1) give the expected output in the cmd 30%|███ | 30/100 [00:03<00:07, 9.14it/s] but in the IDLE the output is like this 0%| | 0/100 [00:00<?, ?it/s] 1%|1 | 1/100 [00:00<00:10, 9.14it/s] 2%|2 | 2/100 [00:00<00:11, 8.77it/s] 3%|3 | 3/100 [00:00<00:11, 8.52it/s] 4%|4 | 4/100 [00:00<00:11, 8.36it/s] 5%|5 | 5/100 [00:00<00:11, 8

I'm attempting to integrate a tqdm progress bar to monitor POST requests generated with aiohttp in Python 3.5. I have a working progress bar but can't seem to gather results using as_completed() . Pointers gratefully received. Examples I've found suggest using the following pattern, which is incompatible with Python 3.5 async def definitions: for f in tqdm.tqdm(asyncio.as_completed(tasks), total=len(coros)): yield from f Working (albeit redacted) async code without the progress bar: def async_classify(records): async def fetch(session, name, sequence): url = 'https://app.example.com/api/v0