1from wmill import task
2
3import pandas as pd
4import numpy as np
5
6@task()
7# You can specify tag to run the task on a specific type of worker, e.g. @task(tag="custom_tag")
8def heavy_compute(n: int):
9    df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
10    return df.sum().sum()
11
12
13@task
14def send_result(res: int, email: str):
15    # logs of the subtask are available in the main task logs
16    print(f"Sending result {res} to {email}")
17    return "OK"
18
19def main(n: int):
20    l = []
21
22    # to run things in parallel, simply use multiprocessing Pool map instead: https://docs.python.org/3/library/multiprocessing.html
23    for i in range(n):
24        l.append(heavy_compute(i))
25    print(l)
26    return send_result(sum(l), "[email protected]")
27