Created
July 10, 2021 01:54
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| from multiprocessing import Pool | |
| import time | |
| import plotly.express as px | |
| import plotly | |
| import pandas as pd | |
| from joblib import Parallel, delayed | |
| def f(x): | |
| time.sleep(2) | |
| return x**2 | |
| def runner(list_length): | |
| print(f"Size of List:{list_length}") | |
| t0 = time.time() | |
| result1 = Parallel(n_jobs=8)(delayed(f)(i) for i in range(list_length)) | |
| t1 = time.time() | |
| print(f"With joblib we ran the function in {t1 - t0:0.4f} seconds") | |
| time_without_multiprocessing = t1-t0 | |
| t0 = time.time() | |
| pool = Pool(8) | |
| result2 = pool.map(f,list(range(list_length))) | |
| pool.close() | |
| t1 = time.time() | |
| print(f"With multiprocessing we ran the function in {t1 - t0:0.4f} seconds") | |
| time_with_multiprocessing = t1-t0 | |
| return time_without_multiprocessing, time_with_multiprocessing | |
| if __name__ == '__main__': | |
| times_taken = [] | |
| for i in range(1,16): | |
| list_length = i | |
| time_without_multiprocessing, time_with_multiprocessing = runner(list_length) | |
| times_taken.append([list_length, 'No Mutiproc', time_without_multiprocessing]) | |
| times_taken.append([list_length, 'Multiproc', time_with_multiprocessing]) | |
| timedf = pd.DataFrame(times_taken,columns = ['list_length', 'type','time_taken']) | |
| fig = px.line(timedf,x = 'list_length',y='time_taken',color='type') | |
| plotly.offline.plot(fig, filename='comparison_bw_multiproc.html') |
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