【发布时间】：2021-08-16 14:48:00
【问题描述】：
每当我尝试运行一个程序时，前 8 行在哪里
import pandas as pd
import numpy as np
import keras
import sklearn
from math import sqrt
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
我收到了回复It seems that scikit-learn has not been built correctly.
但是手动（一个接一个）运行它似乎可以工作
Python 3.6.9 (default, Jan 26 2021, 15:33:00)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sklearn
>>> from sklearn.metrics import mean_squared_error
>>> from sklearn.model_selection import train_test_split
>>> from math import sqrt
>>> from matplotlib import pyplot
>>> import pandas
>>> import numpy
>>> import keras
2021-05-28 12:38:06.355731: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.2
>>> exit()
为什么会发生这种情况，我该如何解决？我已经尝试重新安装每个软件包（也在不同的版本上）
编辑：我错过了添加信息，这是在 Jetson Nano 上运行的，所以操作系统是 Linux，编译器是默认的（程序通过 SSH 运行）。使用python3 -VV 时，它会响应Python 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]，而且我没有使用虚拟环境，也没有使用pip3 以外的其他包管理器。已使用命令sudo pip3 install scikit-learn 安装了 Sci-kit learn
安装日志：
sudo pip3 install scikit-learn Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting scikit-learn
Downloading scikit_learn-0.24.2-cp36-cp36m-manylinux2014_aarch64.whl (24.0 MB)
|████████████████████████████████| 24.0 MB 20.5 MB/s
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/nano/.local/lib/python3.6/site-packages (from scikit-learn) (2.1.0)
Requirement already satisfied: joblib>=0.11 in /home/nano/.local/lib/python3.6/site-packages (from scikit-learn) (1.0.1)
Requirement already satisfied: numpy>=1.13.3 in /home/nano/.local/lib/python3.6/site-packages (from scikit-learn) (1.19.0)
Requirement already satisfied: scipy>=0.19.1 in /home/nano/.local/lib/python3.6/site-packages (from scikit-learn) (1.5.4)
Installing collected packages: scikit-learn
Successfully installed scikit-learn-0.24.2
完整的错误信息：
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/sklearn/__check_build/__init__.py", line 44, in <module>
from ._check_build import check_build # noqa
ImportError: /usr/local/lib/python3.6/dist-packages/sklearn/__check_build/../../scikit_learn.libs/libgomp-d22c30c5.so.1.0.0: cannot allocate memory in static TLS block
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "LSTMMVMSTSF.py", line 4, in <module>
import sklearn
File "/usr/local/lib/python3.6/dist-packages/sklearn/__init__.py", line 81, in <module>
from . import __check_build # noqa: F401
File "/usr/local/lib/python3.6/dist-packages/sklearn/__check_build/__init__.py", line 46, in <module>
raise_build_error(e)
File "/usr/local/lib/python3.6/dist-packages/sklearn/__check_build/__init__.py", line 41, in raise_build_error
%s""" % (e, local_dir, ''.join(dir_content).strip(), msg))
ImportError: /usr/local/lib/python3.6/dist-packages/sklearn/__check_build/../../scikit_learn.libs/libgomp-d22c30c5.so.1.0.0: cannot allocate memory in static TLS block
___________________________________________________________________________
Contents of /usr/local/lib/python3.6/dist-packages/sklearn/__check_build:
__init__.py _check_build.cpython-36m-aarch64-linux-gnu.so__pycache__
setup.py
___________________________________________________________________________
It seems that scikit-learn has not been built correctly.
If you have installed scikit-learn from source, please do not forget
to build the package before using it: run `python setup.py install` or
`make` in the source directory.
If you have used an installer, please check that it is suited for your
Python version, your operating system and your platform.
【问题讨论】：
-
请提供有关您系统的更多信息。你的操作系统是什么？您使用的是虚拟环境还是 pip 以外的其他包管理器？你是如何安装 scikit-learn 包的？发布您从安装包中获得的日志记录。
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@DiegoPalacios 我已经编辑了问题，感谢您让我注意到
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运行脚本时的完整错误是什么？您是否从可能与 sklearn 名称冲突的地方运行它？
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@CJR 我已经用完整的错误消息更新了这个问题。它不应该有任何碰撞。它在机器的桌面和文件夹内运行，没有其他文件在其名称中包含任何与 sklearn 相关的内容，甚至是其内容
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@CJR 非常感谢！该链接帮助我弄清楚了。问题实际上只是进口的顺序。现在我将其更改为先导入 sklearn 并且它可以工作。如果您将其发布为答案，我很乐意接受。
标签： python numpy scikit-learn sklearn-pandas