项目场景：
项目中有需要用到argoverse模块，本机环境是conda版本23.5.0，Python版本3.10.11，Pip版本23.1.2。安装argoverse采用源码安装方式，源码来自argoverse git仓，安装步骤遵循readme。
问题描述
执行pip install -e .命令时，主要遇到两个报错，如下：
- 关于sklearn的报错
Collecting sklearn (from argoverse==1.0.1)
Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7a/93/e0e1b1e98f39dfca7ec9795cb46f6e09e88a2fd5d4a28e4b3d1f618a2aec/sklearn-0.0.post5.tar.gz (3.7 kB)
Preparing metadata (setup.py) ... error
error: subprocess-exited-with-error
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [18 lines of output]
The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
rather than 'sklearn' for pip commands.
Here is how to fix this error in the main use cases:
- use 'pip install scikit-learn' rather than 'pip install sklearn'
- replace 'sklearn' by 'scikit-learn' in your pip requirements files
(requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
- if the 'sklearn' package is used by one of your dependencies,
it would be great if you take some time to track which package uses
'sklearn' instead of 'scikit-learn' and report it to their issue tracker
- as a last resort, set the environment variable
SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
More information is available at
https://github.com/scikit-learn/sklearn-pypi-package
If the previous advice does not cover your use case, feel free to report it at
https://github.com/scikit-learn/sklearn-pypi-package/issues/new
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
- 关于numpy的报错
error: Command "gcc -pthread -B /home/muhao/anaconda3/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/muhao/anaconda3/include -fPIC -O2 -isystem /home/muhao/anaconda3/include -fPIC -DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DSCIPY_MKL_H -DHAVE_CBLAS -I/usr/local/include -I/usr/include -I/home/muhao/anaconda3/include -Ibuild/src.linux-x86_64-3.10/numpy/core/src/umath -Ibuild/src.linux-x86_64-3.10/numpy/core/src/npymath -Ibuild/src.linux-x86_64-3.10/numpy/core/src/common -Inumpy/core/include -Ibuild/src.linux-x86_64-3.10/numpy/core/include/numpy -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -I/home/muhao/anaconda3/include/python3.10 -Ibuild/src.linux-x86_64-3.10/numpy/core/src/common -Ibuild/src.linux-x86_64-3.10/numpy/core/src/npymath -c build/src.linux-x86_64-3.10/numpy/core/src/multiarray/scalartypes.c -o build/temp.linux-x86_64-3.10/build/src.linux-x86_64-3.10/numpy/core/src/multiarray/scalartypes.o -MMD -MF build/temp.linux-x86_64-3.10/build/src.linux-x86_64-3.10/numpy/core/src/multiarray/scalartypes.o.d" failed with exit status 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for numpy
Failed to build numpy
ERROR: Could not build wheels for numpy, which is required to install pyproject.toml-based projects
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× pip subprocess to install build dependencies did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
原因分析：
sklearn报错原因是因为新版本该模块已经被弃用，需用scikit-learn模块进行替代。numpy报错是因为argoverse安装脚本里指定的numpy依赖版本是1.19，但该本版与3.10的Python版本不兼容。
解决方案：
需修改argoverse仓里的安装脚本setup.py，找到install_requires一栏，将对应的numpy依赖和sklearn依赖注释掉，手动安装适合python3.10的numpy版本（我的是1.24.3）和scikit-learn模块。安装完成后再执行pip install -e .进行安装。