我最近安装了一台新机器，以帮助减少拟合模型和数据整理的运行时间。

我做了一些初步的基准测试，一切都变得很顺畅，但是当我尝试使用scikit学习启用多进程工作者时遇到了麻烦。

我将错误简化为不与原始代码相关联，因为我启用了此功能，而在其他计算机和VM上都没有问题。

我还做了内存分配检查，以确保我的机器没有耗尽可用的RAM。我有16gb的RAM，所以应该没有问题，但是如果我错过了一些东西，我就保留了测试的输出。

考虑到附近的回溯错误，我可以告诉我的操作系统正在杀死它，但是对于我一生来说，我不知道为什么。据我所知，我的代码仅在使用单个CPU内核时才运行。

我正在运行Windows 10，AMD ryzen 7 2700x，16GB RAM

码

import sklearn
import numpy as np
import tracemalloc
import time


from sklearn.model_selection import cross_val_score
from numpy.random import randn
from sklearn.linear_model import Ridge


##################### memory allocation snapshot

tracemalloc.start()

start_time = time.time()
snapshot1 = tracemalloc.take_snapshot()

###################### model

X = randn(815000, 100)
y = randn(815000, 1)
mod = Ridge()
sc = cross_val_score(mod, X, y,verbose =10, n_jobs=3)

################### Second memory allocation snapshot

snapshot2 = tracemalloc.take_snapshot()
top_stats = snapshot2.compare_to(snapshot1, 'lineno')

print("[ Top 10 ]")
for stat in top_stats[:5]:
print(stat)

由此产生的预期结果非常明显，只是拟合模型返回的分数。

错误输出

[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s
---------------------------------------------------------------------------
TerminatedWorkerError                     Traceback (most recent call last)
<ipython-input-18-b2bdfd425f82> in <module>
     16 y = randn(815000, 1)
     17 mod = Ridge()
---> 18 sc = cross_val_score(mod, X, y,verbose =10, n_jobs=3)

..........

TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. 
This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.

记忆输出

[ Top 5 ]
<ipython-input-18-b2bdfd425f82>:15: size=622 MiB (+622 MiB), count=3 (+3), average=207 MiB
<ipython-input-18-b2bdfd425f82>:16: size=6367 KiB (+6367 KiB), count=3 (+3), average=2122 KiB
~python37\lib\inspect.py:732: size=37.2 KiB (+26.2 KiB), count=596 (+419), average=64 B
~python37\lib\site-packages\sklearn\externals\joblib\numpy_pickle.py:292: size=7072 B (+3808 B), count=13 (+7), average=544 B
~python37\lib\pickle.py:549: size=5728 B (+3408 B), count=14 (+8), average=409 B