gist:9994217 (original) (raw)
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| import multiprocessing |
|---|
| from sklearn.linear_model import LogisticRegression |
| import numpy as np |
| import math |
| def func(iterator): |
| function = LogisticRegression() |
| function.fit(np.random.randn(88, 110), np.random.rand(88) < .1) |
| return function |
| tasks = [1,2,3] |
| pool = multiprocessing.Pool(1) |
| results = pool.imap(func, tasks) |
| for results in results: |
| print("function executed succesfully") |
| pool.close() |
| pool.join() |