问题描述
我最近设置了一台新机器，以帮助减少拟合模型和数据整理的运行时间.
I recently set up a new machine to aid in decreasing run times for fitting models and data wrangling.
我做了一些初步的基准测试，一切都很顺利，但是当我尝试在 scikit learn 中启用多进程工作者时遇到了一个障碍.
I did some preliminary benchmarks and everything is mostly smoothe, but I ran into a snag when I tried enabling multi-process workers with in scikit learn.
我已将错误简化为与我的原始代码无关，因为我在另一台机器和 VM 上启用了此功能而没有问题.
I've simplified the error to not be associated with my original code as I enabled this feature without a problem on a different machine and a VM.
我还进行了内存分配检查，以确保我的机器没有用完可用 RAM.我有 16GB 的 RAM，所以应该没有问题，但我已经留下了测试的输出，以防我错过了什么.
I've also done memory allocation checks to make sure my machine wasn't running out of available RAM. I have 16gb of RAM so there should be no issue, but I've left the output of the test incase I missed something.
鉴于附近的回溯错误，我可以告诉我的操作系统正在杀死它，但对于我的生活，我无法弄清楚原因.据我所知，我的代码只会在仅使用单个 CPU 内核时运行.
Given the traceback error near I can tell my OS is killing this, but for the life of me I can't figure out why. Near as I can tell my code will ONLY run when it is just using a single CPU core.
我运行的是 Windows 10、AMD ryzen 7 2700x、16GB RAM
I'm running Windows 10, AMD ryzen 7 2700x, 16GB RAM
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，只是拟合模型的返回分数.
The expected results from this are pretty obvious, just a returned score with the fit model.
[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.
内存输出
[ 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
推荐答案
我发现我的 scipy 模块与我的 Windows 10 C++ 可再发行版本不兼容.
I figured out the my scipy module was incompatible with my windows 10 C++ redistributable version.
我所做的只是下载最新的 Visual Studio 并安装了单个组件"部分中列出的 C++ 可再发行更新.
All i did was download the latest visual studio and installed the C++ redistributable update that is listed in the "individual components" section.
安装后，我重新启动计算机并运行.
Once I installed that I restarted my computer and ran.
import scipy
scipy.test()
在实际运行后，我尝试了上面的代码块并修复了.
Once that was actually running I attempted my code block above and it fixed.
我认为这归结为使用全新版本的 python 和 scipy 安装旧版本的 Windows 10
I think what this boils down to is installing an old build of windows 10 with a brand new version of python and scipy
这花了很长时间来解决和调试.希望它有所帮助.
This took a LONG time to solve and debug. Hopefully it helps.
这篇关于如何修复/调试 scikit 中引发的多进程终止工作错误学习的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！