使用pyenv+tox时无法安装指定版本numpy的问题求助

环境信息 #

• 已安装pyenv，可用Python版本：

pyenv versions
  system
  3.8.0
  3.9.16
* 3.10.1 (set by /home/me/.pyenv/version)

• 系统默认Python版本：3.6.9

tox.ini配置 #

[tox]
envlist = unit_tests
skipsdist = True


[testenv]
install_command = pip install {opts} {packages}
deps =
    -rrequirements.txt

commands=
    py.test


[testenv:unit_tests]
envdir = {toxworkdir}/unit_tests
deps =
     {[testenv]deps}

setenv =
  PYTHONPATH=.

commands =
     python gradient_boosting_model/train_pipeline.py
     pytest \
           -s \
           -vv \
           {posargs:tests/}


[testenv:train]
envdir = {toxworkdir}/train
deps =
     {[testenv]deps}

setenv =
  PYTHONPATH=.

commands =
     python gradient_boosting_model/train_pipeline.py

requirements.txt内容 #

# ML requirements
numpy>=1.20.0,<1.21.0
pandas>=1.3.5,<1.4.0
scikit-learn>=1.0.2,<1.1.0
feature-engine>=1.0.2,<1.1.0
joblib>=1.0.1,<1.1.0

# config parsing
strictyaml>=1.3.2,<1.4.0
ruamel.yaml==0.16.12
pydantic>=1.8.1,<1.9.0

# validation
marshmallow>=3.2.2,<4.0

# packaging
setuptools>=41.4.0,<42.0.0
wheel>=0.33.6,<0.34.0

# testing requirements
pytest>=5.3.2,<6.0.0

运行tox时的错误 #

tox
unit_tests create: /media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests
unit_tests installdeps: -rrequirements.txt
ERROR: invocation failed (exit code 1), logfile: /media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/log/unit_tests-1.log
==================================================================== log start ====================================================================
ERROR: Could not find a version that satisfies the requirement numpy<1.21.0,>=1.20.0 (from versions: 1.3.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.6.1, 1.6.2, 1.7.0, 1.7.1, 1.7.2, 1.8.0, 1.8.1, 1.8.2, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.10.0.post2, 1.10.1, 1.10.2, 1.10.4, 1.11.0, 1.11.1, 1.11.2, 1.11.3, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 1.13.3, 1.14.0, 1.14.1, 1.14.2, 1.14.3, 1.14.4, 1.14.5, 1.14.6, 1.15.0, 1.15.1, 1.15.2, 1.15.3, 1.15.4, 1.16.0, 1.16.1, 1.16.2, 1.16.3, 1.16.4, 1.16.5, 1.16.6, 1.17.0, 1.17.1, 1.17.2, 1.17.3, 1.17.4, 1.17.5, 1.18.0, 1.18.1, 1.18.2, 1.18.3, 1.18.4, 1.18.5, 1.19.0, 1.19.1, 1.19.2, 1.19.3, 1.19.4, 1.19.5)
ERROR: No matching distribution found for numpy<1.21.0,>=1.20.0

===================================================================== log end =====================================================================
ERROR: could not install deps [-rrequirements.txt]; v = InvocationError('/media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/bin/pip install -rrequirements.txt', 1)
_____________________________________________________________________ summary _____________________________________________________________________
ERROR:   unit_tests: could not install deps [-rrequirements.txt]; v = InvocationError('/media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/bin/pip install -rrequirements.txt', 1)

核心原因 #

你用pyenv切换了Python版本，但tox默认可能仍调用系统旧版本的pip，或者没有正确关联pyenv管理的Python环境。旧版pip无法检索到numpy 1.20+的版本（该版本支持Python3.8+，你的环境是符合要求的）。

具体解决步骤 #

1. 强制tox使用pyenv管理的Python版本
   修改tox.ini，给testenv指定明确的Python版本：

[tox]
envlist = unit_tests
skipsdist = True

[testenv]
basepython = python3.10  # 指定你想用的pyenv版本
install_command = pip install --upgrade pip {opts} {packages}  # 先升级pip
deps =
    -rrequirements.txt

# 其余配置保持不变

2. 手动验证pip可用性
   先激活pyenv的目标版本，测试能否安装指定numpy：

pyenv activate 3.10.1
pip install --upgrade pip
pip install numpy>=1.20.0,<1.21.0

如果这个命令成功，说明tox之前没正确调用该环境，调整basepython配置即可解决。

3. 清理tox缓存
   运行以下命令清除旧的缓存环境，避免残留问题：

tox -r

之后重新运行tox。

4. 可选：放宽版本限制
   如果以上步骤仍有问题，可以暂时放宽numpy的版本限制，将requirements.txt中的numpy行改为：

numpy>=1.20.0

内容的提问来源于stack exchange，提问作者KansaiRobot