I have a list of tasks that each take a different amount of time. Let's say, I have 3 tasks, with durations close to 1x, 5x, 10*x. My tqdm code is something like:
from tqdm import tqdm def create_task(n): def fib(x): if x == 1 or x == 0: return 1 return fib(x - 1) + fib(x - 2) return lambda: fib(n) n = 1 tasks = [create_task(n), create_task(5*n), create_task(10*n)] for task in tqdm(tasks): task.run()
The problem is that tqdm thinks each iteration takes the same amount of time. As the first takes approximately 1/10 of the time, the ETA is unreliable.
My question: is it possible to somehow add a hint to tqdm to inform how much each iteration takes compared to the first? Something like informing the duration weights of each iteration...