conda 降级 numpy 版本

Question

我需要降级 numpy 版本：

python -c "import numpy; print(numpy.__version__)"
1.16.4

conda install numpy==1.14.3

Collecting package metadata (current_repodata.json): done
Solving environment: failed with current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed
Initial quick solve with frozen env failed.  Unfreezing env and trying again.
Solving environment: failed

UnsatisfiableError: The following specifications were found to be incompatible with a past
explicit spec that is not an explicit spec in this operation (numpy):

  - numpy==1.14.3

The following specifications were found to be incompatible with each other:



Package numpy-base conflicts for:
mkl_random -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_fft[version='>=1.0.6,<2.0a0'] -> numpy-base[version='>=1.0.6,<2.0a0']
mkl_fft -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_random[version='>=1.0.2,<2.0a0'] -> numpy-base[version='>=1.0.2,<2.0a0']
numpy-base
pytorch==1.1.0 -> numpy[version='>=1.11.3,<2.0a0'] -> mkl_random[version='>=1.0.2,<2.0a0'] -> numpy-base[version='>=1.0.2,<2.0a0']
numpy==1.14.3 -> mkl_random[version='>=1.0.2,<2.0a0'] -> numpy-base[version='>=1.0.2,<2.0a0']
Package numpy conflicts for:
mkl_fft -> numpy[version='>=1.11.3,<2.0a0']
mkl_random -> numpy[version='>=1.11.3,<2.0a0']
pytorch==1.1.0 -> numpy[version='>=1.11.3,<2.0a0']

不知道为什么会发生这种情况numpy==1.14.3 在numpy[version='>=1.11.3,<2.0a0'] 范围内，如何解决？

更新：

通过conda uninstall numpy-base卸载会删除其他不需要的包：

conda uninstall numpy-base
Collecting package metadata (repodata.json): done
Solving environment: done

  removed specs:
    - numpy-base


The following packages will be REMOVED:

  blas-1.0-mkl
  cffi-1.12.3-py36h2e261b9_0
  cudatoolkit-10.0.130-0
  cudnn-7.6.0-cuda10.0_0
  intel-openmp-2019.4-243
  libgfortran-ng-7.3.0-hdf63c60_0
  mkl-2019.4-243
  mkl-service-2.0.2-py36h7b6447c_0
  mkl_fft-1.0.14-py36ha843d7b_0
  mkl_random-1.0.2-py36hd81dba3_0
  ninja-1.9.0-py36hfd86e86_0
  numpy-1.16.4-py36h7e9f1db_0
  numpy-base-1.16.4-py36hde5b4d6_0
  pycparser-2.19-py36_0
  pytorch-1.1.0-cuda100py36he554f03_0
  six-1.12.0-py36_0

Answer 1

您可以简单地使用命令安装正确的版本

conda install -c conda-forge numpy=1.16.4

conda 会自动处理正确降级到您的版本

Answer 2

如果在 conda 解决环境时降级到特定版本的 numpy 需要很长时间，或者 conda 无法解决冲突，您可以使用 conda-tree 检查依赖关系，然后使用 conda 手动卸载（或尝试降级) 不兼容的软件包。但是请注意，如果有很多依赖项，使用正确的 numpy 版本创建新环境可能会更快（您可以使用 mamba 来加快进程）。

conda install -c conda-forge conda-tree
conda-tree whoneeds -t numpy

这将显示一个树，其中包含每个依赖包支持的 numpy 版本：

numpy==1.20.3
  ├─ h5py 3.2.1 [required: >=1.16.6,<2.0a0]
  │  └─ tensorflow-base 2.5.0 [required: >=3.1.0]
  │     └─ tensorflow 2.5.0 [required: 2.5.0, gpu_py37hb3da07e_0]
  │        └─ tensorflow-gpu 2.5.0 [required: 2.5.0]
  ├─ keras-preprocessing 1.1.2 [required: >=1.9.1]
  │  └─ tensorflow-base 2.5.0 [required: >=1.1.2]
  │     └─ dependent packages of tensorflow-base displayed above
  ├─ matplotlib-base 3.4.2 [required: >=1.17.5,<2.0a0]
  │  └─ matplotlib 3.4.2 [required: >=3.4.2,<3.4.3.0a0]
  ├─ opt_einsum 3.3.0 [required: any]
  │  └─ tensorflow-base 2.5.0 [required: 3.3.0.*]
  │     └─ dependent packages of tensorflow-base displayed above
  ├─ pandas 1.2.5 [required: >=1.20.2,<2.0a0]
  │  └─ statsmodels 0.12.2 [required: >=0.21]
  ├─ patsy 0.5.1 [required: >=1.4.0]
  │  └─ statsmodels 0.12.2 [required: >=0.5.1]
  ├─ scipy 1.6.2 [required: >=1.16.6,<2.0a0]
  │  ├─ keras-preprocessing 1.1.2 [required: >=0.14]
  │  │  └─ dependent packages of keras-preprocessing displayed above
  │  ├─ patsy 0.5.1 [required: any]
  │  │  └─ dependent packages of patsy displayed above
  │  ├─ statsmodels 0.12.2 [required: >=1.0]
  │  └─ tensorflow-base 2.5.0 [required: >=1.6.2]
  │     └─ dependent packages of tensorflow-base displayed above
  ├─ statsmodels 0.12.2 [required: >=1.17.0,<2.0a0]
  ├─ tensorboard 2.5.0 [required: >=1.12.0]
  │  ├─ tensorflow 2.5.0 [required: >=2.5.0]
  │  │  └─ dependent packages of tensorflow displayed above
  │  └─ tensorflow-base 2.5.0 [required: >=2.5.0,<2.6]
  │     └─ dependent packages of tensorflow-base displayed above
  ├─ tensorflow-base 2.5.0 [required: >=1.20]
  │  └─ dependent packages of tensorflow-base displayed above
  └─ tensorflow-estimator 2.5.0 [required: >=1.16.1]
     ├─ tensorflow 2.5.0 [required: >=2.5.0]
     │  └─ dependent packages of tensorflow displayed above
     └─ tensorflow-base 2.5.0 [required: >=2.5.0,<2.6]
        └─ dependent packages of tensorflow-base displayed above