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conda env create -f environment.yml报错ResolvePackageNotFound和Found conflicts的解决方案【已解决】_resolvepackagenotfound:

作者：Cpp五条 | 2024-02-09 09:22:29

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resolvepackagenotfound:

阅读须知：长文，将近10万字。主要原因是报了太多错，记录了太多bug。

前面的11步骤是我的试错过程，直到第12/13步才解决。没耐心的可以直接从目录跳到第12步最后。

整篇文章简而言之：笨方法在一些时候或许是最好的方法，且是最省时间最省力气的做法。

下面看一看我的一把辛酸泪吧。

————————————————————

事情的起源是想把本机程序配置到服务器运行以减少运行时间。我之前试了pip和pipreqs安装依赖，报错却随着我的修改而越来越多。

于是我决定试一试conda环境配置解决这个问题。

按照CSDN博主：℡ヾNothing-_哥所说，只需要四步，一如大象装冰箱一样简单。就可以搞定移植环境后的程序配置。

Anaconda 复制或移植已有环境（复制到别的服务器上）_anaconda复制环境_℡ヾNothing-_哥的博客-CSDN博客

于是我就按照他的方法搞了起来。

前面的：克隆环境——激活环境——导出配置都顺利完成，唯有最后一步配置环境时候出了问题。

conda env create -f environment.yml

大问题。

下面就是我的报错和解决历程了。

1 报错第一波——ResolvePackageNotFound: 

  1. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
  2. Collecting package metadata (repodata.json): done
  3. Solving environment: failed
  4.  
  5. ResolvePackageNotFound: 
  6.   - lz4-c==1.9.4=h2bbff1b_0
  7.   - git==2.34.1=haa95532_0
  8.   - libtiff==4.4.0=h8a3f274_2
  9.   - sip==4.19.8=py37h6538335_0
  10.   - sqlite==3.35.4=h2bbff1b_0
  11.   - libwebp==1.2.4=h2bbff1b_0
  12.   - libwebp-base==1.2.4=h2bbff1b_0
  13.   - wrapt==1.12.1=py37he774522_1
  14.   - mkl_fft==1.3.0=py37h277e83a_2
  15.   - zstd==1.5.0=h19a0ad4_1
  16.   - matplotlib-base==3.4.3=py37h49ac443_0
  17.   - icc_rt==2019.0.0=h0cc432a_1
  18.   - pyreadline==2.1=py37_1
  19.   - markdown==3.3.4=py37haa95532_0
  20.   - certifi==2022.12.7=py37haa95532_0
  21.   - libbrotlidec==1.0.9=h2bbff1b_7
  22.   - qt==5.9.7=vc14h73c81de_0
  23.   - tk==8.6.12=h2bbff1b_0
  24.   - libbrotlienc==1.0.9=h2bbff1b_7
  25.   - python==3.7.10=h7840368_100_cpython
  26.   - pandas==1.2.4=py37hf11a4ad_0
  27.   - lerc==3.0=hd77b12b_0
  28.   - six==1.15.0=py37haa95532_0
  29.   - cython==0.29.23=py37hd77b12b_0
  30.   - ca-certificates==2022.10.11=haa95532_0
  31.   - libpng==1.6.37=h2a8f88b_0
  32.   - xz==5.2.8=h8cc25b3_0
  33.   - brotli==1.0.9=h2bbff1b_7
  34.   - libdeflate==1.8=h2bbff1b_5
  35.   - mkl_random==1.2.1=py37hf11a4ad_2
  36.   - tensorboard==1.14.0=py37he3c9ec2_0
  37.   - openssl==1.1.1s=h2bbff1b_0
  38.   - wincertstore==0.2=py37_0
  39.   - libprotobuf==3.14.0=h23ce68f_0
  40.   - tornado==6.2=py37h2bbff1b_0
  41.   - brotli-bin==1.0.9=h2bbff1b_7
  42.   - zlib==1.2.11=h62dcd97_4
  43.   - absl-py==0.12.0=py37haa95532_0
  44.   - libbrotlicommon==1.0.9=h2bbff1b_7
  45.   - hdf5==1.10.4=h7ebc959_0
  46.   - pip==21.0.1=py37haa95532_0
  47.   - tensorflow-base==1.14.0=gpu_py37h55fc52a_0
  48.   - astor==0.8.1=py37haa95532_0
  49.   - coverage==5.5=py37h2bbff1b_2
  50.   - pyqt==5.9.2=py37h6538335_2
  51.   - tensorflow==1.14.0=gpu_py37h5512b17_0
  52.   - freetype==2.10.4=hd328e21_0
  53.   - vc==14.2=h21ff451_1
  54.   - jpeg==9b=hb83a4c4_2
  55.   - yaml==0.2.5=he774522_0
  56.   - icu==58.2=ha925a31_3
  57.   - scikit-learn==0.24.1=py37hf11a4ad_0
  58.   - numpy-base==1.16.6=py37h5bb6eb2_3
  59.   - vs2015_runtime==14.27.29016=h5e58377_2

我看到有人说清华源下包可能更齐全，然后就添加了清华源。

  1. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
  2. Writing to /home/LIST_2080Ti/.config/pip/pip.conf

于是迎来了第二波报错，与原来的报错缺包情况相差无几。

2 报错第二波——ResolvePackageNotFound: 

  1. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
  2. Collecting package metadata (repodata.json): done
  3. Solving environment: failed
  4.  
  5. ResolvePackageNotFound: 
  6.   - tornado==6.2=py37h2bbff1b_0
  7.   - absl-py==0.12.0=py37haa95532_0
  8.   - freetype==2.10.4=hd328e21_0
  9.   - brotli-bin==1.0.9=h2bbff1b_7
  10.   - pandas==1.2.4=py37hf11a4ad_0
  11.   - sip==4.19.8=py37h6538335_0
  12.   - zstd==1.5.0=h19a0ad4_1
  13.   - libbrotlicommon==1.0.9=h2bbff1b_7
  14.   - markdown==3.3.4=py37haa95532_0
  15.   - matplotlib-base==3.4.3=py37h49ac443_0
  16.   - tensorboard==1.14.0=py37he3c9ec2_0
  17.   - jpeg==9b=hb83a4c4_2
  18.   - libtiff==4.4.0=h8a3f274_2
  19.   - six==1.15.0=py37haa95532_0
  20.   - tk==8.6.12=h2bbff1b_0
  21.   - libdeflate==1.8=h2bbff1b_5
  22.   - git==2.34.1=haa95532_0
  23.   - certifi==2022.12.7=py37haa95532_0
  24.   - lerc==3.0=hd77b12b_0
  25.   - openssl==1.1.1s=h2bbff1b_0
  26.   - zlib==1.2.11=h62dcd97_4
  27.   - astor==0.8.1=py37haa95532_0
  28.   - libwebp==1.2.4=h2bbff1b_0
  29.   - scikit-learn==0.24.1=py37hf11a4ad_0
  30.   - brotli==1.0.9=h2bbff1b_7
  31.   - tensorflow==1.14.0=gpu_py37h5512b17_0
  32.   - pyqt==5.9.2=py37h6538335_2
  33.   - tensorflow-base==1.14.0=gpu_py37h55fc52a_0
  34.   - mkl_random==1.2.1=py37hf11a4ad_2
  35.   - yaml==0.2.5=he774522_0
  36.   - libbrotlidec==1.0.9=h2bbff1b_7
  37.   - qt==5.9.7=vc14h73c81de_0
  38.   - libpng==1.6.37=h2a8f88b_0
  39.   - vs2015_runtime==14.27.29016=h5e58377_2
  40.   - cython==0.29.23=py37hd77b12b_0
  41.   - wincertstore==0.2=py37_0
  42.   - icu==58.2=ha925a31_3
  43.   - wrapt==1.12.1=py37he774522_1
  44.   - xz==5.2.8=h8cc25b3_0
  45.   - vc==14.2=h21ff451_1
  46.   - sqlite==3.35.4=h2bbff1b_0
  47.   - pip==21.0.1=py37haa95532_0
  48.   - ca-certificates==2022.10.11=haa95532_0
  49.   - python==3.7.10=h7840368_100_cpython
  50.   - pyreadline==2.1=py37_1
  51.   - libbrotlienc==1.0.9=h2bbff1b_7
  52.   - mkl_fft==1.3.0=py37h277e83a_2
  53.   - icc_rt==2019.0.0=h0cc432a_1
  54.   - libwebp-base==1.2.4=h2bbff1b_0
  55.   - coverage==5.5=py37h2bbff1b_2
  56.   - hdf5==1.10.4=h7ebc959_0
  57.   - numpy-base==1.16.6=py37h5bb6eb2_3
  58.   - lz4-c==1.9.4=h2bbff1b_0
  59.   - libprotobuf==3.14.0=h23ce68f_0

3 看来源不怎么影响包是否缺失。

于是决定删除第二步的配置。将ResolvePackageNotFound: 找不到的版本号删掉，然后报错由原来的54个变成了4个。

  1. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ pip config unset global.index-url
  2. Writing to /home/LIST_2080Ti/.config/pip/pip.conf
  3. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
  4. Collecting package metadata (repodata.json): done
  5. Solving environment: failed
  6.  
  7. ResolvePackageNotFound: 
  8.   - pyreadline
  9.   - icc_rt
  10.   - vc
  11.   - vs2015_runtime

4 pip与conda

但是我毫无疑问更改了依赖包的版本，因此并不是太合理，于是决定按照上面参考文章那样，直接将conda无法安装的包改由pip安装。

直接将报错的内容复制到environment.yml的pip后面，并将前面conda内的相关报错删除即可。

后来查询知道，pip包远比conda包多，所以，conda会遇到更多的缺包现象。

conda

pip

包内容

二进制

.whl和源码

是否需要编译

不需要

需要

安装包类型

Python、C、R等任何类型

仅限于Python

是否支持环境管理

是，可以创建多个环境

否，需要借助virtualenv or venv等其它工具

依赖包检查

检查十分严格

检查不严格

包来源

Anaconda repo and cloud

PyPI

包数量

约1500个

约150000个

图来自：【基础知识】pip和conda,你会选择谁？ - 腾讯云开发者社区-腾讯云

pip的包大约是conda包的100倍。

因此把conda安装改为pip安装就有了依据。

这里还有两篇对比conda和pip的文章，写得很好，有空的可以看看。

Anaconda和pip使用总结 conda与pip的区别_taoqick的博客-CSDN博客_anaconda pip

python使用pip与conda 的区别_pip安装和conda安装的区别_weixin_42641188的博客-CSDN博客

pip 和conda_知更鸟k的博客-CSDN博客_pip和conda

5 Found conflicts! Looking for incompatible packages.

当我把conda无法安装的包转到pip安装后，上面的ResolvePackageNotFound消失，但是现在出现了Found conflicts! Looking for incompatible packages.

  1. Found conflicts! Looking for incompatible packages.
  2. This can take several minutes.  Press CTRL-C to abort.
  3. failed                                                                                                                                                                                         /  
  4. Solving environment: | 
  5. Found conflicts! Looking for incompatible packages.
  6. This can take several minutes.  Press CTRL-C to abort.
  7. failed                                                                                                                                                                                         -  
  8.  
  9. UnsatisfiableError: The following specifications were found to be incompatible with each other:
  10.  
  11. Output in format: Requested package -> Available versions

这次就要把版本号删除掉以解决冲突问题。

删除版本号的有：

  1. - tensorflow-base==1.14.0=gpu_py37h55fc52a_0
  2.  
  3. - zlib==1.2.11=h62dcd97_4
  4.  
  5. - blas=1.0=mkl
  6.  
  7. - setuptools==54.2.0
  8.  
  9. - munkres=1.1.4=py_0
  10.  
  11. - numpy==1.16.6
  12.  
  13. Package fftw conflicts for:
  14.  
  15. Package libgcc-ng conflicts for:
  16.  
  17. - werkzeug=1.0.1=pyhd3eb1b0_0
  18.  
  19. - scipy==1.6.3
  20.  
  21. - keras-base=2.3.1=py37_0
  22.  
  23. - six==1.15.0=py37haa95532_0
  24.  
  25. - openssl==1.1.1s=h2bbff1b_0
  26.  
  27. Package system conflicts for:
  28.  
  29. - intel-openmp==2021.2.0
  30.  
  31. - certifi==2022.12.7=py37haa95532_0
  32.  
  33. - python==3.7.10=h7840368_100_cpython
  34.  
  35. - _tflow_select=2.1.0=gpu
  36.  
  37. - mkl_random==1.2.1=py37hf11a4ad_2
  38.  
  39. - pip==21.0.1=py37haa95532_0
  40.  
  41. Package tzdata conflicts for:
  42.  
  43. - keras-applications=1.0.8=py_1
  44.  
  45. - cudatoolkit=10.0.130=0
  46.  
  47. Package libgcc conflicts for:
  48.  
  49. - keras-preprocessing=1.1.2=pyhd3eb1b0_0
  50.  
  51. - gast=0.4.0=py_0
  52.  
  53. - hdf5==1.10.4=h7ebc959_0
  54.  
  55. - libpng==1.6.37=h2a8f88b_0

完整冲突如下：

  1. (base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
  2. Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies.  Conda may not use the correct pip to install your packages, and they may end up in the wrong place.  Please add an explicit pip dependency.  I'm adding one for you, but still nagging you.
  3. Collecting package metadata (repodata.json): done
  4. Solving environment: | 
  5. Found conflicts! Looking for incompatible packages.
  6. This can take several minutes.  Press CTRL-C to abort.
  7. failed                                                                                                                                                                                         /  
  8. Solving environment: | 
  9. Found conflicts! Looking for incompatible packages.
  10. This can take several minutes.  Press CTRL-C to abort.
  11. failed                                                                                                                                                                                         -  
  13. UnsatisfiableError: The following specifications were found to be incompatible with each other:
  15. Output in format: Requested package -> Available versions
  17. Package tensorflow-base conflicts for:
  18. keras==2.3.1=0 -> tensorflow -> tensorflow-base[version='1.13.1|1.13.1|1.13.1|1.13.1|1.13.1|1.13.2|1.14.0|1.14.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.11.0|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.4.3|2.4.3|2.4.3|2.4.3|2.4.1|2.4.1|2.4.1|2.4.1|2.4.0|2.4.0|2.4.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.10.0|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.9.1|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.8.2|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.5.0|2.5.0|2.5.0|2.5.0|2.5.0|2.5.0|2.4.1|2.4.1|2.4.1|2.4.1|2.4.1|2.4.1|2.4.1|2.4.1|2.4.1|2.3.0|2.3.0|2.3.0|2.3.0|2.2.0|2.2.0|2.2.0|2.2.0|2.2.0|2.2.0|2.2.0|2.2.0|2.2.0|2.1.0|2.1.0|2.1.0|2.1.0|2.1.0|2.1.0|2.1.0|2.1.0|2.1.0|2.0.0|2.0.0|2.0.0|2.0.0|2.0.0|2.0.0|2.0.0|2.0.0|2.0.0|1.15.0|1.15.0|1.15.0|1.15.0|1.15.0|1.15.0|1.15.0|1.15.0|1.15.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.13.1|==1.12.0|==1.12.0|==1.12.0|==1.12.0|==1.12.0|==1.12.0|==1.12.0|==1.12.0|==1.11.0|==1.11.0|==1.11.0|==1.11.0|==1.11.0|==1.11.0|==1.11.0|==1.11.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.10.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|==1.9.0|1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|==1.8.0|1.7.0|1.6.0|1.5.0|1.4.1|1.3.0',build='py35hee38f2d_0|py27hee38f2d_0|py35h5f64886_0|py27h5f64886_0|py35h4df133c_0|py36hc1a7637_0|eigen_py27hdfca3bf_0|eigen_py36hdfca3bf_0|mkl_py27h2ca6a6a_0|mkl_py27h3c3e929_0|mkl_py36h3c3e929_0|eigen_py36h4dcebc2_0|eigen_py27h4dcebc2_0|eigen_py35h4dcebc2_0|gpu_py36h3435052_0|gpu_py36h6ecc378_0|eigen_py36h4dcebc2_0|mkl_py27h3c3e929_0|eigen_py27h4dcebc2_0|mkl_py36h3c3e929_0|gpu_py27h8e0ae2d_0|gpu_py27had579c0_0|mkl_py27h3c3e929_0|eigen_py27h4dcebc2_0|mkl_py36h3c3e929_0|gpu_py27h8e0ae2d_0|gpu_py27had579c0_0|eigen_py27hf4a566f_0|gpu_py27h8d69cac_0|gpu_py37h8d69cac_0|gpu_py36h8d69cac_0|gpu_py27h611c6d2_0|gpu_py36h611c6d2_0|gpu_py37h8f37b9b_0|gpu_py36h8f37b9b_0|gpu_py27h8f37b9b_0|eigen_py37hf4a566f_0|eigen_py27hf4a566f_0|eigen_py36hf4a566f_0|gpu_py36h8f37b9b_0|gpu_py37h611c6d2_0|gpu_py36h611c6d2_0|gpu_py36h8d69cac_0|gpu_py37h8d69cac_0|gpu_py27he45bfe2_0|gpu_py36he45bfe2_0|mkl_py37he1670d9_0|eigen_py36h52b156a_0|eigen_py37h52b156a_0|mkl_py27h503033c_0|gpu_py27hf473bbb_0|gpu_py37h9dcbed7_0|mkl_py27had7a488_0|gpu_py27h356bb79_0|mkl_py27hb6fb96e_0|mkl_py37h6d63fb7_0|eigen_py37h0c57e5d_0|gpu_py37h6c5654b_0|eigen_py38h2e5f744_0|eigen_py37haef3446_0|eigen_py36haef3446_0|gpu_py36h8a81be8_0|gpu_py37h8a81be8_0|mkl_py37he9661a2_0|mkl_py39h43e0292_0|mkl_py37h43e0292_0|eigen_py38h17880bf_0|eigen_py39h17880bf_0|mkl_py38h43e0292_0|eigen_py37h17880bf_0|gpu_py39h29c2da4_0|gpu_py37h29c2da4_0|mkl_py39h35b2a3d_0|mkl_py38h35b2a3d_0|eigen_py39h2b86b3d_0|eigen_py38h2b86b3d_0|eigen_py39ha9cc040_0|mkl_py37h3d85931_0|eigen_py38ha9cc040_0|mkl_py38hf890080_0|eigen_py310h980454f_0|mkl_py310hf890080_0|eigen_py37h980454f_0|eigen_py38h980454f_0|gpu_py37h1986732_0|gpu_py39h1986732_0|gpu_py38h1986732_0|gpu_py39h1986732_0|gpu_py38h1986732_0|gpu_py310h1986732_0|gpu_py37h1986732_0|mkl_py39h353358b_0|mkl_py37h353358b_0|mkl_py38h353358b_0|eigen_py39hd99631c_0|eigen_py38hd99631c_0|mkl_py37h353358b_1|eigen_py39hd99631c_1|mkl_py39h353358b_1|mkl_py310h353358b_1|mkl_py38h353358b_1|eigen_py310hd99631c_1|eigen_py38hd99631c_1|gpu_py38h1986732_1|gpu_py37h1986732_1|eigen_py310h1969d1f_0|mkl_py39hb9daa73_0|eigen_py37h1969d1f_0|gpu_py38h6559e04_0|gpu_py39h6559e04_0|py37h5ece82f_4|py37h5ece82f_5|py36h76b4ce7_7|py27h76b4ce7_8|py38h01d9eeb_0|py36h515a7b5_0|py38h83f5f1d_0|py36h312d151_0|py39h23a8cbf_0|py36h312d151_0|py38h83f5f1d_0|py36h312d151_0|py37he2fe834_0|py38h83f5f1d_0|py38he1e5d52_1|cuda102py39h747ea68_2|cuda110py37hb8f09f9_2|cuda102py38h3f41ba3_2|cuda110py39hd7afca0_2|cuda110py38h937a041_2|cpu_py39h7e79a0b_2|cuda112py37hd5a5b6b_2|cuda102py38h11de4e7_0|cuda102py39h32831d4_0|cuda110py38hca4bd6d_0|cuda110py39hd0eac33_0|cuda111py37h8b10f06_0|cuda111py38hcc0b86b_0|cuda112py37h8584d8f_0|cuda112py39h7de589b_0|cpu_py38h113505c_0|cuda111py38h806d141_1|cuda112py38h8955826_1|cuda112py39he9472f8_1|cuda102py38h62eeb6a_1|cuda102py39hcf1dd7e_1|cuda110py37h0ebe739_1|cuda110py38h0c0c5d7_1|cuda110py39h405f49e_1|cuda111py38hf41bb10_2|cuda112py37h8d33417_2|cuda110py37h341a48a_2|cuda110py38h7f44352_2|cuda112py39hc7f77e4_2|cuda110py39h1b3dc91_2|cpu_py37hf9aebbf_2|cpu_py38he70b6e8_2|cuda111py39h2b78b69_0|cuda110py39h0c9afd6_0|cuda110py310hae929b1_0|cuda102py37h44d275c_0|cuda102py39h15c874f_0|cuda102py38h021f141_0|cpu_py37h8697747_0|cpu_py38h48ebf30_0|cpu_py39hf4995fd_0|cpu_py310h8d3bea7_0|cuda111py39h6f4cae7_0|cuda102py39hbb9dcef_0|cuda110py310h1c8d5c9_0|cuda111py310h6b17f32_0|cpu_py38ha28dbe6_0|cuda102py37hc592af7_0|cpu_py39h7e02d9e_0|cpu_py310h75e90da_0|cuda111py39h96f73e6_0|cuda111py310h4626a94_0|cuda112py310hdce628a_0|cuda112py39h99c2b39_0|cuda110py37h9acc0b3_0|cuda110py39h3c9bc52_0|cuda102py38hcbbd5f6_0|cuda102py39h1759960_0|cpu_py310h17449b8_0|cpu_py39h45807a0_0|cuda112py37h45fe353_0|cuda102py37hbbf6b52_0|cuda112py38had2df90_0|cpu_py38hc7a75a0_0|cuda111py39hab2865d_0|cuda112py310h666ff7d_0|cuda102py39h4f2f7b8_0|cuda102py37h0d2b0d7_0|cuda102py310h282d6da_0|cuda110py37h5235c7d_0|cuda110py39h2c4febc_0|cuda110py38hd7529fe_0|cuda111py39hc0859d9_0|cuda111py38h346ca62_0|cuda111py37ha9dc7ab_0|cpu_py39hfe2e05e_0|cuda112py39h81abfd3_0|cpu_py37h50bd216_0|cpu_py38h67fe383_0|cuda112py39h2957820_0|cuda112py38h6b2b66c_0|cuda112py310*_0|cuda112py38*_0|cuda112py39*_0|cpu_py310*_0|cpu_py39*_0|cpu_py38*_0|cuda112py37ha0c8746_0|cpu_py310hc537a0e_0|cpu_py39h16601f7_0|cuda112py310hf679b68_0|cuda112py38h47a61a2_0|cuda112py310hc65a3b4_0|cuda112py37h83f6acc_0|cpu_py37hb97876d_0|cpu_py38hca74540_0|cpu_py310h8df3ab6_0|cuda111py310h12abe6f_0|cuda110py310h31c0a5d_0|cuda102py38hba23241_0|cuda111py310h4e6f299_0|cuda102py38ha005362_0|cuda110py38hb43e109_0|cpu_py37h0ff5a03_0|cuda102py310ha277fc2_0|cuda111py38hf8a263a_0|cuda110py39h0baf056_0|cuda111py37hc702159_0|cuda110py37ha2ed0d1_0|cuda110py310h9e8cd52_0|cuda112py39he716a45_0|cuda102py37h09db7f3_0|cuda110py38h974df97_0|cuda110py310h1d26a15_0|cuda102py39h714d7d1_0|cuda102py310h42bbde6_0|cuda112py37hd7e45b3_0|cpu_py37h4373017_0|cuda112py38h6a3b174_0|cpu_py38hdf8f09a_0|cuda111py38hf76636f_0|cuda111py37hf17b69b_0|cpu_py37h6aa720e_0|cuda110py37he1a3a50_0|cuda112py310h680fca1_0|cuda110py39h7593abd_0|cuda111py38h13b88b6_0|cuda102py310h5611d22_0|cuda110py38h4cd2a3c_0|cpu_py39hfb6d7af_0|cuda102py38h5246720_0|cuda112py38h1f4bd8a_0|cuda111py37hdeab154_0|cpu_py310h643b9b6_0|cuda112py37hf039c21_0|cuda112py39h6917f46_0|cuda102py310hf4be40b_0|cuda110py38h76162fe_0|cuda110py37h3fa1966_0|cuda111py37hf266e69_0|cuda111py38hca068ee_0|cuda111py310h8463a45_0|cuda112py37had06f64_0|cuda112py310h2bd284a_0|cuda112py38hd3dc81e_0|cuda112py39hd98b2dd_0|cpu_py39h6349a3b_2|cuda111py37ha84a828_2|cuda112py38h1eec131_2|cuda102py39h42c91ab_2|cuda111py39h26679cf_2|cuda102py38h8c73509_2|cuda102py37h55054dc_2|cpu_py39h73312ee_1|cpu_py38h8e8016f_1|cpu_py37hfc86a07_1|cuda102py37h9af999e_1|cuda112py37h151f92d_1|cuda111py39h763576d_1|cuda111py37h85699b6_1|cpu_py39hbcb9a37_0|cpu_py37h2c79ba4_0|cuda112py38h30560fc_0|cuda111py39h0d021e8_0|cuda110py37he67c9a8_0|cuda102py37hd5ceeda_0|cuda111py39he6e9a3f_2|cuda111py37h95189bc_2|cuda111py38h152c24c_2|cuda112py38heae9c4c_2|cpu_py37hc5ef7b8_2|cpu_py38h4611ba2_2|cuda102py37hbd7ce69_2|cuda112py39h0b4cdfd_2|py39he745eb5_1|py37h4c77830_1|py39h23a8cbf_0|py39h23a8cbf_0|py37he2fe834_0|py37he2fe834_0|py37h00a14e9_0|py36hc3e5e64_0|py37h4531e10_0|py27h76b4ce7_0|py36h58012e3_6|gpu_py310h6559e04_0|gpu_py37h6559e04_0|eigen_py39h1969d1f_0|mkl_py37hb9daa73_0|mkl_py310hb9daa73_0|mkl_py38hb9daa73_0|eigen_py38h1969d1f_0|gpu_py39h1986732_1|gpu_py310h1986732_1|eigen_py37hd99631c_1|eigen_py310hd99631c_0|mkl_py310h353358b_0|eigen_py37hd99631c_0|gpu_py310h1986732_0|eigen_py39h980454f_0|mkl_py39hf890080_0|mkl_py37hf890080_0|mkl_py38h3d85931_0|mkl_py39h3d85931_0|eigen_py37ha9cc040_0|mkl_py37h35b2a3d_0|eigen_py37h2b86b3d_0|gpu_py38h29c2da4_0|eigen_py37h3b305d7_0|eigen_py38hb57a387_0|mkl_py38hac35e67_0|gpu_py38h83e3d50_0|mkl_py36hd506778_0|mkl_py38h5059a2d_0|mkl_py37hd506778_0|gpu_py36h6c5654b_0|gpu_py27hb9b3ea8_0|mkl_py36h6d63fb7_0|eigen_py27hedad41d_0|eigen_py36h0c57e5d_0|gpu_py36h0ec5d1f_0|gpu_py37h0ec5d1f_0|mkl_py36h9204916_0|mkl_py37h9204916_0|eigen_py37h4ed9498_0|eigen_py36h4ed9498_0|eigen_py27hce92a77_0|gpu_py36h9dcbed7_0|eigen_py27hd4672e3_0|mkl_py36he1670d9_0|gpu_py37he45bfe2_0|gpu_py27h8d69cac_0|gpu_py27h611c6d2_0|gpu_py27h8f37b9b_0|gpu_py37h8f37b9b_0|mkl_py37h7ce6ba3_0|mkl_py27h7ce6ba3_0|mkl_py36h7ce6ba3_0|gpu_py37h611c6d2_0|mkl_py27h7ce6ba3_0|mkl_py37h7ce6ba3_0|eigen_py36hf4a566f_0|mkl_py36h7ce6ba3_0|eigen_py37hf4a566f_0|gpu_py36had579c0_0|gpu_py36h8e0ae2d_0|eigen_py36h4dcebc2_0|gpu_py36had579c0_0|gpu_py36h8e0ae2d_0|gpu_py27h6ecc378_0|gpu_py35h6ecc378_0|gpu_py35h3435052_0|gpu_py27h3435052_0|mkl_py35h3c3e929_0|gpu_py35had579c0_0|gpu_py27had579c0_0|gpu_py36had579c0_0|gpu_py36h9f529ab_1|gpu_py27h9f529ab_1|gpu_py35h9f529ab_1|gpu_py27h6ecc378_0|gpu_py36h6ecc378_0|gpu_py35h6ecc378_0|eigen_py35hdfca3bf_0|mkl_py35h2ca6a6a_0|mkl_py36h2ca6a6a_0|gpu_py27h9f529ab_0|gpu_py36h9f529ab_0|gpu_py35h9f529ab_0|py35hc1a7637_0|py27hc1a7637_0|py36h4df133c_0|py27h4df133c_0|py36h5f64886_0|py36hee38f2d_0']
  19. tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> tensorflow-base[version='1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0|==1.14.0',build='mkl_py27h7ce6ba3_0|eigen_py27hf4a566f_0|eigen_py36hf4a566f_0|gpu_py37h8f37b9b_0|gpu_py36h8f37b9b_0|gpu_py36h611c6d2_0|gpu_py27h611c6d2_0|gpu_py36he45bfe2_0|py36hc3e5e64_0|py37h4531e10_0|gpu_py37he45bfe2_0|gpu_py27he45bfe2_0|gpu_py37h8d69cac_0|gpu_py27h8d69cac_0|gpu_py36h8d69cac_0|gpu_py37h611c6d2_0|gpu_py27h8f37b9b_0|mkl_py37h7ce6ba3_0|mkl_py36h7ce6ba3_0|eigen_py37hf4a566f_0']
  21. Package zlib conflicts for:
  22. keras==2.3.1=0 -> tensorflow -> zlib[version='>=1.2.11,<1.3.0a0']
  23. threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  24. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  25. joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  26. munkres==1.1.4=py_0 -> python -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|1.2.11.*|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  27. zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  28. typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  29. tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> zlib[version='>=1.2.11,<1.3.0a0']
  30. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|1.2.11.*|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  31. wheel==0.36.2=pyhd3eb1b0_0 -> python -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|1.2.11.*|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  32. fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  33. pip -> python[version='>=3.7'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0|1.2.8|1.2.11.*']
  34. keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> zlib[version='>=1.2.11,<1.3.0a0|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  35. keras-applications==1.0.8=py_1 -> python -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|1.2.11.*|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  36. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  37. gast==0.4.0=py_0 -> python -> zlib[version='1.2.*|1.2.11|>=1.2.11,<1.3.0a0|1.2.8|1.2.11.*|>=1.2.13,<1.3.0a0|>=1.2.12,<1.3.0a0']
  39. Package blas conflicts for:
  40. blas==1.0=mkl
  41. keras-applications==1.0.8=py_1 -> numpy[version='>=1.9.1'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']
  42. seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']
  43. keras-base==2.3.1=py37_0 -> numpy[version='>=1.9.1'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']
  44. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> numpy[version='>=1.9.1'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']
  46. Package setuptools conflicts for:
  47. joblib==1.0.1=pyhd3eb1b0_0 -> setuptools
  48. pip -> setuptools
  49. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> setuptools[version='<60.0.0']
  51. Package munkres conflicts for:
  52. munkres==1.1.4=py_0
  53. fonttools==4.25.0=pyhd3eb1b0_0 -> munkres
  55. Package numpy conflicts for:
  56. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> numpy[version='>=1.9.1']
  57. seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15']
  58. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> numpy[version='>=1.11.*|>=1.12.1,<2.0a0|>=1.14.6,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.19.5,<2.0a0|>=1.21.5,<2.0a0|>=1.18.5,<2.0a0|>=1.21.4,<2.0a0|>=1.17.5,<2.0a0|>=1.16.6,<2.0a0|>=1.19.4,<2.0a0|>=1.16.5,<2.0a0|>=1.19.2,<2.0a0|>=1.15.4,<2.0a0|>=1.18.4,<2.0a0|>=1.18.1,<2.0a0|>=1.9.3,<2.0a0|>=1.9.*|>=1.16,<2.0a0|>=1.21,<2.0a0|>=1.21.2,<2.0a0|>=1.20.2,<2.0a0|>=1.13.3,<2.0a0|>=1.11.3,<2.0a0|>=1.20.3,<1.27|>=1.21.6,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.20.3,<1.26|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.9|>=1.19,<1.26.0|>=1.21,<1.26.0|>=1.19,<1.25.0|>=1.21,<1.25.0|>=1.16,<1.23|>=1.21,<1.23|>=1.16.6,<1.23.0|>=1.21.2,<1.23.0|>=1.15.1,<2.0a0']
  59. keras-applications==1.0.8=py_1 -> numpy[version='>=1.9.1']
  60. keras-base==2.3.1=py37_0 -> h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.16.6,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.8|>=1.8,<1.14|>=1.21.5,<2.0a0|>=1.21.2,<2.0a0|>=1.11.3,<2.0a0|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*|>=1.20.3,<1.27|>=1.21.6,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.20.3,<1.26|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.18.1,<2.0a0|>=1.9|>=1.11|>=1.19,<1.26.0|>=1.21,<1.26.0|>=1.19,<1.25.0|>=1.21,<1.25.0|>=1.16,<1.23|>=1.21,<1.23|>=1.16.6,<1.23.0|>=1.21.2,<1.23.0|>=1.15.1,<2.0a0']
  61. keras-base==2.3.1=py37_0 -> numpy[version='>=1.9.1']
  62. keras==2.3.1=0 -> keras-base=2.3.1 -> numpy[version='1.11.*|1.12.*|>=1.10.1|>=1.11.0|>=1.12.1|>=1.13.3|>=1.13.3,<2.0a0|>=1.14.6,<2.0a0|>=1.9.1|>=1.16.1|>=1.8.2|>=1.11']
  63. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11|>=1.11.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<1.26|>=1.20.3,<1.27|>=1.20.3,<2.0a0|>=1.21.6,<1.27|>=1.21.6,<2.0a0|>=1.23.5,<1.27|>=1.23.5,<2.0a0|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.23.4,<2.0a0|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.21.5,<2.0a0|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.18.1,<2.0a0|>=1.9.3,<2.0a0|>=1.9|>=1.19,<1.26.0|>=1.21,<1.26.0|>=1.19,<1.25.0|>=1.21,<1.25.0|>=1.16,<1.23|>=1.21,<1.23|>=1.16.6,<1.23.0|>=1.21.2,<1.23.0|>=1.15.1,<2.0a0|1.9.*|1.8.*']
  64. keras-applications==1.0.8=py_1 -> h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.16.6,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.8|>=1.8,<1.14|>=1.21.5,<2.0a0|>=1.21.2,<2.0a0|>=1.11.3,<2.0a0|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*']
  66. Package fftw conflicts for:
  67. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> fftw[version='>=3.3.9,<4.0a0']
  68. seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0'] -> fftw[version='>=3.3.9,<4.0a0']
  69. keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> fftw[version='>=3.3.9,<4.0a0']
  71. Package libgcc-ng conflicts for:
  72. threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  73. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> libgcc-ng[version='>=10.3.0|>=12|>=7.2.0|>=7.3.0|>=9.4.0|>=9.3.0|>=7.5.0|>=4.9|>=11.2.0']
  74. gast==0.4.0=py_0 -> python -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  75. wheel==0.36.2=pyhd3eb1b0_0 -> python -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  76. pip -> python[version='>=3.7'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  77. blas==1.0=mkl -> mkl -> libgcc-ng[version='>=11.2.0']
  78. joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  79. keras-applications==1.0.8=py_1 -> h5py -> libgcc-ng[version='>=10.3.0|>=11.2.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=7.2.0']
  80. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> numpy[version='>=1.9.1'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  81. keras==2.3.1=0 -> tensorflow -> libgcc-ng[version='>=5.4.0|>=7.5.0|>=9.4.0']
  82. munkres==1.1.4=py_0 -> python -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  83. tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> libgcc-ng[version='>=5.4.0']
  84. zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  85. keras-base==2.3.1=py37_0 -> h5py -> libgcc-ng[version='>=10.3.0|>=11.2.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=7.2.0']
  86. cudnn==7.6.5=cuda10.0_0 -> cudatoolkit[version='>=10.0,<10.1'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0']
  87. typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  88. fonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1'] -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  89. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0|>=7.5.0|>=7.3.0|>=4.9|>=11.2.0|>=7.2.0']
  91. Package werkzeug conflicts for:
  92. werkzeug==1.0.1=pyhd3eb1b0_0
  93. keras==2.3.1=0 -> tensorflow -> werkzeug[version='>=0.11.10']
  95. Package scipy conflicts for:
  96. keras==2.3.1=0 -> keras-base=2.3.1 -> scipy[version='>=0.14']
  97. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14']
  98. keras-base==2.3.1=py37_0 -> scipy[version='>=0.14']
  99. seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0']
  101. Package keras-base conflicts for:
  102. keras==2.3.1=0 -> keras-base=2.3.1
  103. keras-base==2.3.1=py37_0
  105. Package six conflicts for:
  106. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> six[version='>=1.9.0']
  107. keras-base==2.3.1=py37_0 -> h5py -> six
  108. keras-base==2.3.1=py37_0 -> six[version='>=1.9.0']
  109. keras-applications==1.0.8=py_1 -> h5py -> six
  110. keras==2.3.1=0 -> keras-base=2.3.1 -> six[version='>=1.10.0|>=1.9.0']
  112. Package openssl conflicts for:
  113. joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*']
  114. keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> openssl[version='>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1l,<1.1.2a|>=3.0.0,<4.0a0|>=1.1.1s,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a']
  115. zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*']
  116. typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  117. pip -> python[version='>=3.7'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  118. keras-applications==1.0.8=py_1 -> python -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  119. seaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*']
  120. threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  121. gast==0.4.0=py_0 -> python -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  122. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  123. fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*']
  124. keras==2.3.1=0 -> tensorflow -> openssl[version='>=1.1.1l,<1.1.2a']
  125. munkres==1.1.4=py_0 -> python -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  126. wheel==0.36.2=pyhd3eb1b0_0 -> python -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*|1.0.1.*']
  127. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,<1.0.3a|>=1.0.2p,<1.0.3a|>=1.1.1a,<1.1.2a|>=1.1.1d,<1.1.2a|>=1.1.1e,<1.1.2a|>=1.1.1f,<1.1.2a|>=1.1.1g,<1.1.2a|>=1.1.1h,<1.1.2a|>=1.1.1i,<1.1.2a|>=1.1.1j,<1.1.2a|>=1.1.1k,<1.1.2a|>=1.1.1l,<1.1.2a|>=1.1.1n,<1.1.2a|>=1.1.1o,<1.1.2a|>=1.1.1q,<1.1.2a|>=1.1.1s,<1.1.2a|>=3.0.7,<4.0a0|>=3.0.5,<4.0a0|>=3.0.3,<4.0a0|>=3.0.2,<4.0a0|>=3.0.0,<4.0a0|>=1.1.1m,<1.1.2a|>=1.1.1c,<1.1.2a|>=1.1.1b,<1.1.2a|>=1.0.2n,<1.0.3a|>=1.0.2m,<1.0.3a|>=1.0.2l,<1.0.3a|1.0.2.*']
  129. Package system conflicts for:
  130. munkres==1.1.4=py_0 -> python -> system==5.8
  131. wheel==0.36.2=pyhd3eb1b0_0 -> python -> system==5.8
  132. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> system==5.8
  133. pip -> python[version='>=3'] -> system==5.8
  134. gast==0.4.0=py_0 -> python -> system==5.8
  135. keras-applications==1.0.8=py_1 -> python -> system==5.8
  137. Package intel-openmp conflicts for:
  138. blas==1.0=mkl -> mkl -> intel-openmp[version='2021.*|2022.*']
  139. keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> intel-openmp[version='>=2021.4.0,<2022.0a0']
  140. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> intel-openmp[version='>=2021.4.0,<2022.0a0']
  141. seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0'] -> intel-openmp[version='>=2021.4.0,<2022.0a0']
  143. Package certifi conflicts for:
  144. pip -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
  145. joblib==1.0.1=pyhd3eb1b0_0 -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']
  147. Package python conflicts for:
  148. keras-base==2.3.1=py37_0 -> h5py -> python[version='2.6.*|2.7.*|3.5.*|3.6.*|>=2.7,<2.8.0a0|>=3.10,<3.11.0a0|>=3.11,<3.12.0a0|>=3.8,<3.9.0a0|>=3.9,<3.10.0a0|>=3.6,<3.7.0a0|>=3.5,<3.6.0a0|3.4.*|3.3.*|>=3.6']
  149. keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0']
  151. Package _tflow_select conflicts for:
  152. _tflow_select==2.1.0=gpu
  153. tensorflow-gpu==1.14.0=h0d30ee6_0 -> _tflow_select==2.1.0=gpu
  154. keras==2.3.1=0 -> tensorflow -> _tflow_select[version='2.1.0|2.2.0|2.3.0|2.3.0|==2.1.0|==2.2.0|==2.3.0|==1.1.0|==1.3.0|==1.2.0',build='eigen|gpu|eigen|gpu|eigen|gpu|eigen|mkl|mkl|mkl']
  155. tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> _tflow_select[version='==2.2.0|==2.3.0',build='eigen|mkl']
  157. Package mkl_random conflicts for:
  158. seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15'] -> mkl_random[version='>=1.0.2,<2.0a0|>=1.2.1,<2.0a0|>=1.0.4,<2.0a0']
  159. keras-base==2.3.1=py37_0 -> numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,<2.0a0|>=1.2.1,<2.0a0|>=1.0.4,<2.0a0']
  160. keras-applications==1.0.8=py_1 -> numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,<2.0a0|>=1.2.1,<2.0a0|>=1.0.4,<2.0a0']
  161. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,<2.0a0|>=1.2.1,<2.0a0|>=1.0.4,<2.0a0']
  163. Package pip conflicts for:
  164. gast==0.4.0=py_0 -> python -> pip
  165. threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> pip
  166. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
  167. pip
  168. keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> pip
  169. typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> pip
  170. joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
  171. munkres==1.1.4=py_0 -> python -> pip
  172. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> pip
  173. zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
  174. seaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
  175. fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
  176. keras-applications==1.0.8=py_1 -> python -> pip
  177. wheel==0.36.2=pyhd3eb1b0_0 -> python -> pip
  179. Package tzdata conflicts for:
  180. gast==0.4.0=py_0 -> python -> tzdata
  181. threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> tzdata
  182. wheel==0.36.2=pyhd3eb1b0_0 -> python -> tzdata
  183. keras-applications==1.0.8=py_1 -> python -> tzdata
  184. seaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
  185. typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> tzdata
  186. pip -> python[version='>=3.7'] -> tzdata
  187. joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
  188. munkres==1.1.4=py_0 -> python -> tzdata
  189. werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> tzdata
  190. zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
  191. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
  192. fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
  194. Package keras-applications conflicts for:
  195. keras==2.3.1=0 -> keras-base=2.3.1 -> keras-applications[version='>=1.0.6']
  196. keras-applications==1.0.8=py_1
  197. keras-base==2.3.1=py37_0 -> keras-applications[version='>=1.0.6']
  199. Package cudatoolkit conflicts for:
  200. cudnn==7.6.5=cuda10.0_0 -> cudatoolkit[version='>=10.0,<10.1']
  201. keras==2.3.1=0 -> tensorflow -> cudatoolkit[version='10.2|10.2.*|11.0|11.0.*|11.1|11.1.*|>=11.2,<12']
  202. cudatoolkit==10.0.130=0
  204. Package libgcc conflicts for:
  205. keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> libgcc==5.2.0
  206. keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> libgcc==5.2.0
  208. Package keras-preprocessing conflicts for:
  209. keras==2.3.1=0 -> keras-base=2.3.1 -> keras-preprocessing[version='>=1.0.5']
  210. keras-preprocessing==1.1.2=pyhd3eb1b0_0
  211. keras-base==2.3.1=py37_0 -> keras-preprocessing[version='>=1.0.5']
  213. Package gast conflicts for:
  214. keras==2.3.1=0 -> tensorflow -> gast[version='>=0.2.0']
  215. gast==0.4.0=py_0
  217. Package hdf5 conflicts for:
  218. keras-base==2.3.1=py37_0 -> h5py -> hdf5[version='1.10.1|1.10.1.*|1.10.2.*|>=1.10.3,<1.10.4.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.6,<1.10.7.0a0|>=1.10.6,<1.10.7.0a0|>=1.10.6,<1.10.7.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.2,<1.12.3.0a0|>=1.10.2,<1.10.3.0a0|1.8.18|1.8.18.*|1.8.17|1.8.17.*|1.8.17.*|1.8.15.*|>=1.8.20,<1.9.0a0|>=1.8.18,<1.8.19.0a0|>=1.10.1,<1.10.2.0a0|1.8.17|1.8.16|1.8.15.1|1.8.14|1.8.13|1.8.9',build='mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_mpich_*|mpi_openmpi_*|mpi_openmpi_*']
  219. keras-applications==1.0.8=py_1 -> h5py -> hdf5[version='1.10.1|1.10.1.*|1.10.2.*|>=1.10.3,<1.10.4.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.4,<1.10.5.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.5,<1.10.6.0a0|>=1.10.6,<1.10.7.0a0|>=1.10.6,<1.10.7.0a0|>=1.10.6,<1.10.7.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.1,<1.12.2.0a0|>=1.12.2,<1.12.3.0a0|>=1.10.2,<1.10.3.0a0|1.8.18|1.8.18.*|1.8.17|1.8.17.*|1.8.17.*|1.8.15.*|>=1.8.20,<1.9.0a0|>=1.8.18,<1.8.19.0a0|>=1.10.1,<1.10.2.0a0|1.8.17|1.8.16|1.8.15.1|1.8.14|1.8.13|1.8.9',build='mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_openmpi_*|mpi_mpich_*|mpi_mpich_*|mpi_openmpi_*|mpi_openmpi_*']
  221. Package libpng conflicts for:
  222. tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> libpng[version='>=1.6.37,<1.7.0a0']
  223. keras==2.3.1=0 -> tensorflow -> libpng[version='>=1.6.37,<1.7.0a0']
  224. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> libpng[version='>=1.6.34,<1.7.0a0|>=1.6.35,<1.7.0a0|>=1.6.36,<1.7.0a0|>=1.6.37,<1.7.0a0']The following specifications were found to be incompatible with your system:
  226.   - feature:/linux-64::__cuda==11.7=0
  227.   - feature:/linux-64::__glibc==2.31=0
  228.   - feature:|@/linux-64::__cuda==11.7=0
  229.   - feature:|@/linux-64::__glibc==2.31=0
  230.   - keras==2.3.1=0 -> tensorflow -> __cuda
  231.   - keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']
  233. Your installed version is: 2.31
  235. Note that strict channel priority may have removed packages required for satisfiability.

6 将conda安装转为pip安装

因为按照前面的方法问题巨多，因此将采用直接删除报错的版本号。仍旧有4个包找不到。然后把这四个包移动到pip下。

  1. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
  2. Collecting package metadata (repodata.json): done
  3. Solving environment: failed
  4.  
  5. ResolvePackageNotFound: 
  6.   - vs2015_runtime
  7.   - icc_rt
  8.   - vc
  9.   - pyreadline

 这次修改后，检查冲突用了好久了。

 仍旧是超多冲突。即便删除了版本，仍旧有茫茫多的冲突报错。

yml文件内容如下：

  1. name: cat
  2. channels:
  3.   - conda-forge
  4.   - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  5.   - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
  6.   - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  7.   - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
  8.   - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
  9.  
  10. dependencies:
  11.   - _tflow_select=2.1.0=gpu
  12.   - absl-py
  13.   - astor
  14.   - blas=1.0=mkl
  15.   - brotli
  16.   - brotli-bin
  17.   - ca-certificates
  18.   - certifi
  19.   - coverage
  20.   - cudatoolkit=10.0.130=0
  21.   - cudnn=7.6.5=cuda10.0_0
  22.   - cython
  23.   - fonttools=4.25.0=pyhd3eb1b0_0
  24.   - freetype
  25.   - gast=0.4.0=py_0
  26.   - git
  27.   - hdf5
  28.   - icu
  29.   - joblib=1.0.1=pyhd3eb1b0_0
  30.   - jpeg
  31.   - keras=2.3.1=0
  32.   - keras-applications=1.0.8=py_1
  33.   - keras-base=2.3.1=py37_0
  34.   - keras-preprocessing=1.1.2=pyhd3eb1b0_0
  35.   - lerc
  36.   - libbrotlicommon
  37.   - libbrotlidec
  38.   - libbrotlienc
  39.   - libdeflate
  40.   - libpng
  41.   - libprotobuf
  42.   - libtiff
  43.   - libwebp
  44.   - libwebp-base
  45.   - lz4-c
  46.   - markdown
  47.   - matplotlib-base
  48.   - mkl_fft
  49.   - mkl_random
  50.   - munkres=1.1.4=py_0
  51.   - numpy-base
  52.   - openssl
  53.   - pandas
  54.   - pip
  55.   - pyqt
  56.   - python
  57.   - qt
  58.   - scikit-learn
  59.   - seaborn=0.11.2=pyhd3eb1b0_0
  60.   - sip
  61.   - six
  62.   - sqlite
  63.   - tensorboard
  64.   - tensorflow
  65.   - tensorflow-base
  66.   - tensorflow-gpu=1.14.0=h0d30ee6_0
  67.   - threadpoolctl=2.1.0=pyh5ca1d4c_0
  68.   - tk
  69.   - tornado
  70.   - typing_extensions=3.7.4.3=pyha847dfd_0
  71.   - werkzeug=1.0.1=pyhd3eb1b0_0
  72.   - wheel=0.36.2=pyhd3eb1b0_0
  73.   - wincertstore
  74.   - wrapt
  75.   - xz
  76.   - yaml
  77.   - zipp=3.4.1=pyhd3eb1b0_0
  78.   - zlib
  79.   - zstd
  80.   - pip:
  81.     - appdirs==1.4.4
  82.     - astroid==2.5.6
  83.     - cached-property==1.5.2
  84.     - chardet==4.0.0
  85.     - charset-normalizer==2.1.1
  86.     - colorama==0.4.4
  87.     - cycler==0.10.0
  88.     - decorator==5.1.1
  89.     - dill==0.3.4
  90.     - emd-signal==1.2.2
  91.     - flatbuffers==1.12
  92.     - grpcio==1.32.0
  93.     - h5py==2.8.0
  94.     - idna==3.4
  95.     - importlib-metadata==4.0.1
  96.     - intel-openmp==2021.2.0
  97.     - isort==5.8.0
  98.     - jinja2==3.1.2
  99.     - kiwisolver==1.3.1
  100.     - lazy-object-proxy==1.6.0
  101.     - markupsafe==2.1.1
  102.     - matplotlib==3.4.2
  103.     - mccabe==0.6.1
  104.     - mkl==2021.2.0
  105.     - mkl-service==2.3.0
  106.     - mne==1.1.1
  107.     - multiprocess==0.70.12.2
  108.     - numpy==1.16.6
  109.     - opt-einsum==3.3.0
  110.     - packaging==21.3
  111.     - pathos==0.2.8
  112.     - pillow==8.2.0
  113.     - pooch==1.6.0
  114.     - pox==0.3.0
  115.     - ppft==1.6.6.4
  116.     - protobuf==3.16.0
  117.     - pyasn1-modules==0.2.8
  118.     - pydot==1.4.2
  119.     - pydot-ng==2.0.0
  120.     - pylint==2.8.2
  121.     - pyparsing==2.4.7
  122.     - python-dateutil==2.8.1
  123.     - python-graphviz==0.16
  124.     - pytz==2021.1
  125.     - pyyaml==5.4.1
  126.     - requests==2.28.1
  127.     - scipy==1.6.3
  128.     - setuptools==54.2.0
  129.     - shadowsocks==3.0.0
  130.     - shadowsocks-py==2.9.1
  131.     - tbb==2021.2.0
  132.     - tensorboard-data-server==0.6.1
  133.     - tensorboard-plugin-wit==1.8.0
  134.     - tensorflow-estimator==2.4.0
  135.     - termcolor==1.1.0
  136.     - toml==0.10.2
  137.     - tqdm==4.64.1
  138.     - typed-ast==1.4.3
  139.     - vc
  140.     - vs2015_runtime
  141.     - pyreadline
  142.     - icc_rt
  143.     - i https://pypi.tuna.tsinghua.edu.cn/simple

报错如下： 

  1. The following specifications were found to be incompatible with your system:
  2.  
  3.   - feature:/linux-64::__cuda==11.7=0
  4.   - feature:/linux-64::__glibc==2.31=0
  5.   - feature:|@/linux-64::__cuda==11.7=0
  6.   - feature:|@/linux-64::__glibc==2.31=0
  7.   - brotli -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  8.   - brotli-bin -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  9.   - coverage -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  10.   - cython -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  11.   - freetype -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
  12.   - git -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  13.   - hdf5 -> libgfortran-ng -> __glibc[version='>=2.17']
  14.   - icu -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  15.   - jpeg -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  16.   - keras==2.3.1=0 -> tensorflow -> __cuda
  17.   - keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']
  18.   - lerc -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  19.   - libbrotlicommon -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  20.   - libbrotlidec -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  21.   - libbrotlienc -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  22.   - libdeflate -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  23.   - libpng -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']
  24.   - libprotobuf -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  25.   - libtiff -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  26.   - libwebp -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  27.   - libwebp-base -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  28.   - lz4-c -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']
  29.   - matplotlib-base -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  30.   - mkl_fft -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  31.   - mkl_random -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  32.   - numpy-base -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']
  33.   - openssl -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  34.   - pandas -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  35.   - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  36.   - python -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  37.   - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  38.   - scikit-learn -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  39.   - sip -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  40.   - sqlite -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  41.   - tensorboard -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']
  42.   - tensorflow -> __cuda
  43.   - tensorflow -> __glibc[version='>=2.17']
  44.   - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  45.   - tensorflow-base -> __cuda
  46.   - tensorflow-base -> __glibc[version='>=2.17']
  47.   - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  48.   - tk -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  49.   - tornado -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  50.   - wincertstore -> __win
  51.   - wrapt -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  52.   - xz -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']
  53.   - yaml -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']
  54.   - zlib -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  55.   - zstd -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  56.  
  57. Your installed version is: 2.31
  58.  
  59. Note that strict channel priority may have removed packages required for satisfiability.

这次报错比之前的更多，还不如按照之前的搞呢。

之前的即为：直接删除版本号，然后仍旧找不到的将其剪切到pip部分安装。

7 以第5步的为准，修改后的yml如下所示：

  1. name: cat
  2. channels:
  3.   - conda-forge
  4.   - default
  5.  
  6. dependencies:
  7.   - _tflow_select
  8.   - absl-py
  9.   - astor
  10.   - blas
  11.   - brotli
  12.   - brotli-bin
  13.   - ca-certificates
  14.   - certifi
  15.   - coverage
  16.   - cudatoolkit
  17.   - cudnn=7.6.5=cuda10.0_0
  18.   - cython
  19.   - fonttools=4.25.0=pyhd3eb1b0_0
  20.   - freetype
  21.   - gast
  22.   - git
  23.   - hdf5
  24.   - icu
  25.   - joblib=1.0.1=pyhd3eb1b0_0
  26.   - jpeg
  27.   - keras=2.3.1=0
  28.   - keras-applications
  29.   - keras-base
  30.   - keras-preprocessing
  31.   - lerc
  32.   - libbrotlicommon
  33.   - libbrotlidec
  34.   - libbrotlienc
  35.   - libdeflate
  36.   - libpng
  37.   - libprotobuf
  38.   - libtiff
  39.   - libwebp
  40.   - libwebp-base
  41.   - lz4-c
  42.   - markdown
  43.   - matplotlib-base
  44.   - mkl_fft
  45.   - mkl_random
  46.   - munkres
  47.   - numpy-base
  48.   - openssl
  49.   - pandas
  50.   - pip
  51.   - pyqt
  52.   - python
  53.   - qt
  54.   - scikit-learn
  55.   - seaborn=0.11.2=pyhd3eb1b0_0
  56.   - sip
  57.   - six
  58.   - sqlite
  59.   - tensorboard
  60.   - tensorflow
  61.   - tensorflow-base
  62.   - tensorflow-gpu=1.14.0=h0d30ee6_0
  63.   - threadpoolctl=2.1.0=pyh5ca1d4c_0
  64.   - tk
  65.   - tornado
  66.   - typing_extensions=3.7.4.3=pyha847dfd_0
  67.   - werkzeug
  68.   - wheel=0.36.2=pyhd3eb1b0_0
  69.   - wincertstore
  70.   - wrapt
  71.   - xz
  72.   - yaml
  73.   - zipp=3.4.1=pyhd3eb1b0_0
  74.   - zlib
  75.   - zstd
  76.   - pip:
  77.     - appdirs==1.4.4
  78.     - astroid==2.5.6
  79.     - cached-property==1.5.2
  80.     - chardet==4.0.0
  81.     - charset-normalizer==2.1.1
  82.     - colorama==0.4.4
  83.     - cycler==0.10.0
  84.     - decorator==5.1.1
  85.     - dill==0.3.4
  86.     - emd-signal==1.2.2
  87.     - flatbuffers==1.12
  88.     - grpcio==1.32.0
  89.     - h5py==2.8.0
  90.     - idna==3.4
  91.     - importlib-metadata==4.0.1
  92.     - intel-openmp
  93.     - isort==5.8.0
  94.     - jinja2==3.1.2
  95.     - kiwisolver==1.3.1
  96.     - lazy-object-proxy==1.6.0
  97.     - markupsafe==2.1.1
  98.     - matplotlib==3.4.2
  99.     - mccabe==0.6.1
  100.     - mkl==2021.2.0
  101.     - mkl-service==2.3.0
  102.     - mne==1.1.1
  103.     - multiprocess==0.70.12.2
  104.     - numpy
  105.     - opt-einsum==3.3.0
  106.     - packaging==21.3
  107.     - pathos==0.2.8
  108.     - pillow==8.2.0
  109.     - pooch==1.6.0
  110.     - pox==0.3.0
  111.     - ppft==1.6.6.4
  112.     - protobuf==3.16.0
  113.     - pyasn1-modules==0.2.8
  114.     - pydot==1.4.2
  115.     - pydot-ng==2.0.0
  116.     - pylint==2.8.2
  117.     - pyparsing==2.4.7
  118.     - python-dateutil==2.8.1
  119.     - python-graphviz==0.16
  120.     - pytz==2021.1
  121.     - pyyaml==5.4.1
  122.     - requests==2.28.1
  123.     - scipy
  124.     - setuptools
  125.     - shadowsocks==3.0.0
  126.     - shadowsocks-py==2.9.1
  127.     - tbb==2021.2.0
  128.     - tensorboard-data-server==0.6.1
  129.     - tensorboard-plugin-wit==1.8.0
  130.     - tensorflow-estimator==2.4.0
  131.     - termcolor==1.1.0
  132.     - toml==0.10.2
  133.     - tqdm==4.64.1
  134.     - typed-ast==1.4.3
  135.     - vc
  136.     - vs2015_runtime
  137.     - pyreadline
  138.     - icc_rt
  139.     - i https://pypi.tuna.tsinghua.edu.cn/simple

这个时候检查冲突报错：

  1. The following specifications were found to be incompatible with your system:
  2.  
  3.   - feature:/linux-64::__cuda==11.7=0
  4.   - feature:/linux-64::__glibc==2.31=0
  5.   - feature:|@/linux-64::__cuda==11.7=0
  6.   - feature:|@/linux-64::__glibc==2.31=0
  7.   - keras==2.3.1=0 -> tensorflow -> __cuda
  8.   - keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']
  9.  
  10. Your installed version is: 2.31
  11.  
  12. Note that strict channel priority may have removed packages required for satisfiability.

找到有一个说法是安装依赖包时候，频道里conda-forge和default混合导致的。一旦把它改成只使用conda-forge问题就能解决。

 Conda glibc依赖冲突 - 问答 - 腾讯云开发者社区-腾讯云

因此采用删除.yml中channel中的default。 

现在又在执行检测和安装了。

报错：

  1. (venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda env create -f environment.yml
  2. Collecting package metadata (repodata.json): done
  3. Solving environment: failed
  4.  
  5. ResolvePackageNotFound: 
  6.   - kera
  7.   - cudnn==7.6.5=cuda10.0_0
  8.   - numpy-base
  9.   - tensorflow-gpu==1.14.0=h0d30ee6_0
  10.   - _tflow_select

我把后面带版本号的删除版本号，不带版本号的直接移动到pip后进行安装。 

报错：

  1. (venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda env create -f environment.yml
  2. Collecting package metadata (repodata.json): done
  3. Solving environment: failed
  4.  
  5. ResolvePackageNotFound: 
  6.   - _tflow_select

再次把这个包_tflow_select移动到pip后安装。

再次报错：Found conflicts! Looking for incompatible packages.

  1. libpng -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
  2. blas -> _openmp_mutex[version='*|>=4.5',build=*_llvm]
  3. yaml -> libgcc-ng[version='>=9.4.0'] -> _openmp_mutex[version='>=4.5']
  4. zlib -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']
  5.  
  6. Package numpy conflicts for:
  7. matplotlib-base -> contourpy[version='>=1.0.1'] -> numpy[version='>=1.16']
  8. scikit-learn -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.5,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.9|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*']
  9. tensorflow-gpu -> numpy[version='1.11.*|1.12.*|>=1.11|>=1.11.0']
  10. keras-applications -> numpy[version='>=1.9.1']
  11. scikit-learn -> scipy -> numpy[version='>=1.11|>=1.18.1,<2.0a0|>=1.20.3,<1.23|>=1.20.3,<1.25|>=1.20.3,<1.26|>=1.20.3,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.21.6,<1.25|>=1.21.6,<1.23']
  12. seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15']
  13. pandas -> scipy -> numpy[version='1.5.*|>=1.11.3,<2.0a0|>=1.20.3,<1.23|>=1.20.3,<1.25|>=1.20.3,<1.26|>=1.20.3,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.21.6,<1.25|>=1.21.6,<1.23']
  14. seaborn==0.11.2=pyhd3eb1b0_0 -> pandas[version='>=0.23'] -> numpy[version='>=1.11.*|>=1.12.1,<2.0a0|>=1.14.6,<2.0a0|>=1.15.4,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.5,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.18.4,<2.0a0|>=1.18.1,<2.0a0|>=1.9.3,<2.0a0|>=1.9.*|>=1.20.3,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.20.3,<1.26|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.11.3,<2.0a0|>=1.9']
  15. tensorflow-base -> h5py[version='>=2.9.0'] -> numpy[version='>=1.12.0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.21.4,<2.0a0|>=1.23.4,<2.0a0|>=1.23.5,<2.0a0|>=1.19.4,<2.0a0|>=1.16.6,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.9.1|>=1.16.1|>=1.13.3']
  16. keras-base -> numpy[version='>=1.9.1']
  17. tensorflow-base -> numpy[version='>=1.11|>=1.13.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.1,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.21.5,<2.0a0|>=1.19.2,<1.20|>=1.19']
  18. pandas -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11|>=1.11.*|>=1.12.1,<2.0a0|>=1.14.6,<2.0a0|>=1.15.4,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.5,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.18.4,<2.0a0|>=1.18.1,<2.0a0|>=1.9.3,<2.0a0|>=1.9.*|>=1.9|>=1.8|>=1.7|>=1.12|1.9.*|1.8.*|1.7.*|1.6.*']
  19. keras-preprocessing -> numpy[version='>=1.9.1']
  20. tensorboard -> numpy[version='>=1.12.0|>=1.16']
  21. keras-preprocessing -> scipy[version='>=0.14'] -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11|>=1.11.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<1.26|>=1.20.3,<1.27|>=1.20.3,<2.0a0|>=1.23.5,<1.27|>=1.23.5,<2.0a0|>=1.21.6,<1.27|>=1.21.6,<2.0a0|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.23.4,<2.0a0|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.21.5,<2.0a0|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.18.1,<2.0a0|>=1.9.3,<2.0a0|>=1.9|1.9.*|1.8.*']
  22. tensorflow -> numpy[version='1.11.*|1.12.*|>=1.10.1|>=1.11.0|>=1.12.1|>=1.13.3|>=1.13.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.1|>=1.8.2|>=1.11']
  23. matplotlib-base -> numpy[version='>=1.11.3,<2.0a0|>=1.14.6,<2.0a0|>=1.15.4,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.17.5,<2.0a0|>=1.17|>=1.19|>=1.20.3,<2.0a0|>=1.23.5,<2.0a0|>=1.21.6,<2.0a0|>=1.23.4,<2.0a0|>=1.19.5,<2.0a0|>=1.21.4,<2.0a0|>=1.18.5,<2.0a0|>=1.19.4,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0']
  24. keras-applications -> h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.16.6,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.8|>=1.8,<1.14|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*']
  25. mkl_random -> numpy[version='>=1.11|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.9.3,<2.0a0']
  26. mkl_random -> mkl-service[version='>=2,<3.0a0'] -> numpy[version='>=1.11.3,<2.0a0']
  27. keras-base -> h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.17.5,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.5,<2.0a0|>=1.23.4,<2.0a0|>=1.21.4,<2.0a0|>=1.19.4,<2.0a0|>=1.16.6,<2.0a0|>=1.19.2,<2.0a0|>=1.9.3,<2.0a0|>=1.8|>=1.8,<1.14|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*|>=1.20.3,<1.27|>=1.23.5,<1.27|>=1.21.6,<1.27|>=1.21.6,<1.26|>=1.23.4,<1.26|>=1.20.3,<1.26|>=1.20.3,<1.25|>=1.21.6,<1.25|>=1.21.5,<2.0a0|>=1.20.3,<1.23|>=1.21.6,<1.23|>=1.18.1,<2.0a0|>=1.11.3,<2.0a0|>=1.9|>=1.11']
  28. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> numpy[version='>=1.12.0|>=1.16.1,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.21.5,<2.0a0|>=1.19.2,<1.20|>=1.19|>=1.9.1']
  29. mkl_fft -> numpy[version='>=1.11|>=1.11.3,<2.0a0|>=1.14.6,<2.0a0|>=1.16.5,<2.0a0|>=1.16.6,<2.0a0|>=1.18.5,<2.0a0|>=1.19.5,<2.0a0|>=1.20.3,<2.0a0|>=1.21.6,<2.0a0|>=1.23.4,<2.0a0|>=1.21.5,<2.0a0|>=1.21.4,<2.0a0|>=1.19.2,<2.0a0']
  30.  
  31. Package tensorflow conflicts for:
  32. keras-applications -> keras[version='>=2.1.6'] -> tensorflow[version='>=2.2']
  33. keras-base -> tensorflow[version='>=2.2']
  34. tensorflow
  35. keras-preprocessing -> keras[version='>=2.1.6'] -> tensorflow[version='>=2.2']
  36. tensorflow-gpu -> tensorflow[version='2.10.0|2.10.0|2.10.0|2.10.0|2.11.0|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.1|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.8.0|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.1|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.7.0|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.2|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0|2.6.0',build='cuda110py37hba838d9_2|cuda110py39h22e3326_2|cuda112py38hbe5352d_2|cuda111py38h48e9d96_2|cuda102py37h80be449_0|cuda102py38h4357c17_0|cuda110py39h016931e_0|cuda111py39h50553a9_0|cuda112py38ha230376_0|cuda111py39h50553a9_1|cuda112py38ha230376_1|cuda102py38h4357c17_1|cuda102py39h87695c4_1|cuda110py37h4801193_1|cuda110py39h016931e_1|cuda102py37h80be449_2|cuda111py39h594ad97_2|cuda112py37h474db6c_2|cuda112py38hab8ae04_2|cuda110py39ha53fd0e_2|cuda112py39h01bd6f0_0|cuda112py310he87a039_0|cuda111py39hd57d6a4_0|cuda111py37h7cf2244_0|cuda110py38h502d20a_0|cuda102py38h32e99bf_0|cuda112py39h01bd6f0_0|cuda111py37h7cf2244_0|cuda111py39hd57d6a4_0|cuda112py38hded6998_0|cuda102py38h32e99bf_0|cuda110py38h502d20a_0|cuda112py310he87a039_0|cuda110py37h68f1ac2_0|cuda111py37h7cf2244_0|cuda111py39hd57d6a4_0|cuda112py310he87a039_0|cuda110py38h502d20a_0|cuda102py38h32e99bf_0|cuda110py37h68f1ac2_0|cuda110py38h502d20a_0|cuda102py38h32e99bf_0|cuda112py310he87a039_0|cuda112py310he87a039_0|cuda112py38hded6998_0|cuda112py310he87a039_0|cuda112py39h01bd6f0_0|cuda112py38hded6998_0|cuda112py37h01c6645_0|cuda112py39h01bd6f0_0|cuda111py310hffb2d60_0|cuda111py39hd57d6a4_0|cuda112py38hded6998_0|cuda102py310hcf4adbc_0|cuda111py38h2d198b7_0|cuda102py37ha17b477_0|cuda112py37h01c6645_0|cuda110py39hcfb7b87_0|cuda111py37h7cf2244_0|cuda102py39h30a2e9f_0|cuda110py310h5096daf_0|cuda112py39h01bd6f0_0|cuda110py39hcfb7b87_0|cuda110py37h68f1ac2_0|cuda102py37ha17b477_0|cuda110py310h5096daf_0|cuda102py39h30a2e9f_0|cuda102py310hcf4adbc_0|cuda112py39h01bd6f0_0|cuda112py37h01c6645_0|cuda112py38hded6998_0|cuda111py310hffb2d60_0|cuda111py38h2d198b7_0|cuda102py37ha17b477_0|cuda111py310hffb2d60_0|cuda110py310h5096daf_0|cuda110py39hcfb7b87_0|cuda111py38h2d198b7_0|cuda102py310hcf4adbc_0|cuda102py39h30a2e9f_0|cuda112py37h01c6645_0|cuda102py310hcf4adbc_0|cuda102py39h30a2e9f_0|cuda102py37ha17b477_0|cuda110py310h5096daf_0|cuda110py39hcfb7b87_0|cuda110py37h68f1ac2_0|cuda111py38h2d198b7_0|cuda111py310hffb2d60_0|cuda112py37h01c6645_0|cuda112py38hded6998_0|cuda111py37hf54207c_2|cuda112py39h23446aa_2|cuda110py38h09c20b0_2|cuda110py37h41dd380_2|cuda102py39h87695c4_2|cuda102py38h4357c17_2|cuda111py38h6ed5851_2|cuda110py38h1096b06_1|cuda102py37h80be449_1|cuda112py39h9333c2f_1|cuda112py37hada678f_1|cuda111py38h862ebb2_1|cuda111py37h557cc93_1|cuda112py39h9333c2f_0|cuda112py37hada678f_0|cuda111py38h862ebb2_0|cuda111py37h557cc93_0|cuda110py38h1096b06_0|cuda110py37h4801193_0|cuda102py39h87695c4_0|cuda111py39h383fce0_2|cuda111py37hc404611_2|cuda112py37h3e4f0e2_2|cuda110py38hc4b1a70_2|cuda102py37h4cd87c6_2|cuda102py38hc567ca3_2|cuda102py39hff8942c_2|cuda112py39h9dc3950_2']
  37.  
  38. Package astor conflicts for:
  39. tensorflow -> astor[version='>=0.6.0']
  40. astor
  41. tensorflow-base -> astor[version='>=0.6.0']
  42.  
  43. Package libgfortran5 conflicts for:
  44. blas -> libgfortran5[version='>=10.3.0|>=10.4.0|>=9.4.0|>=9.3.0']
  45. hdf5 -> libgfortran5[version='>=10.3.0|>=10.4.0|>=9.4.0|>=9.3.0']
  46. keras-base -> scipy[version='>=0.14'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']
  47. blas -> libgfortran-ng -> libgfortran5[version='10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.4.0|10.4.0|10.4.0|10.4.0|11.1.0|11.1.0|11.1.0|11.1.0|11.1.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.3.0|11.3.0|11.3.0|11.3.0|12.1.0|12.1.0|12.2.0|9.5.0|9.5.0|9.5.0|9.5.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.3.0.*|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.4.0.*',build='h0ffbd86_9|h0ffbd86_12|h0ffbd86_14|h0ffbd86_15|h62347ff_4|h62347ff_6|h62347ff_7|h62347ff_8|h62347ff_10|h62347ff_11|h62347ff_12|h62347ff_13|h62347ff_16|h6e911d1_17|hab08dfb_18|hb56cab1_4|hb56cab1_8|hb56cab1_10|hb56cab1_11|hb56cab1_14|hb56cab1_15|hb56cab1_16|h6c583b3_4|h6c583b3_8|h5c6108e_8|h5c6108e_10|h5c6108e_14|h6a973e8_17|h39d6296_18|hdcd56e2_16|h337968e_19|h337968e_18|hdcd56e2_17|h39d6296_19|h6a973e8_16|h5c6108e_16|h5c6108e_15|h5c6108e_13|h5c6108e_12|h5c6108e_11|h5c6108e_9|h6c583b3_7|h6c583b3_6|h6c583b3_5|hfbd5096_19|hfbd5096_18|he3294f5_17|he3294f5_16|hb56cab1_13|hb56cab1_12|hb56cab1_9|hb56cab1_7|hb56cab1_6|hb56cab1_5|hab08dfb_19|h6e911d1_16|h62347ff_15|h62347ff_14|h62347ff_9|h62347ff_5|h42c683c_19|h42c683c_18|h0ffbd86_17|h0ffbd86_16|h0ffbd86_13|h0ffbd86_11|h0ffbd86_10|h0ffbd86_8']
  48. pandas -> scipy -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']
  49. keras-preprocessing -> scipy[version='>=0.14'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']
  50. seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']
  51. scikit-learn -> libcblas[version='>=3.9.0,<4.0a0'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.3.0|>=9.4.0']
  52. hdf5 -> libgfortran-ng -> libgfortran5[version='10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.3.0|10.4.0|10.4.0|10.4.0|10.4.0|11.1.0|11.1.0|11.1.0|11.1.0|11.1.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.2.0|11.3.0|11.3.0|11.3.0|11.3.0|12.1.0|12.1.0|12.2.0|9.5.0|9.5.0|9.5.0|9.5.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.4.0|9.3.0.*|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.5.0|8.4.0.*',build='h0ffbd86_9|h0ffbd86_12|h0ffbd86_14|h0ffbd86_15|h62347ff_4|h62347ff_6|h62347ff_7|h62347ff_8|h62347ff_10|h62347ff_11|h62347ff_12|h62347ff_13|h62347ff_16|h6e911d1_17|hab08dfb_18|hb56cab1_4|hb56cab1_8|hb56cab1_10|hb56cab1_11|hb56cab1_14|hb56cab1_15|hb56cab1_16|h6c583b3_4|h6c583b3_8|h5c6108e_8|h5c6108e_10|h5c6108e_14|h6a973e8_17|h39d6296_18|hdcd56e2_16|h337968e_19|h337968e_18|hdcd56e2_17|h39d6296_19|h6a973e8_16|h5c6108e_16|h5c6108e_15|h5c6108e_13|h5c6108e_12|h5c6108e_11|h5c6108e_9|h6c583b3_7|h6c583b3_6|h6c583b3_5|hfbd5096_19|hfbd5096_18|he3294f5_17|he3294f5_16|hb56cab1_13|hb56cab1_12|hb56cab1_9|hb56cab1_7|hb56cab1_6|hb56cab1_5|hab08dfb_19|h6e911d1_16|h62347ff_15|h62347ff_14|h62347ff_9|h62347ff_5|h42c683c_19|h42c683c_18|h0ffbd86_17|h0ffbd86_16|h0ffbd86_13|h0ffbd86_11|h0ffbd86_10|h0ffbd86_8']
  53.  
  54. Package jbig conflicts for:
  55. qt -> libtiff=4.0 -> jbig==2.1
  56. libwebp -> libtiff[version='>=4.3.0,<4.5.0a0'] -> jbig==2.1
  57. libtiff -> jbig==2.1
  58.  
  59. Package yaml conflicts for:
  60. keras-base -> pyyaml -> yaml[version='0.1.4|0.1.6|>=0.1.7,<0.2.0a0|>=0.2.2,<0.3.0a0|>=0.2.5,<0.3.0a0']
  61. yaml
  62.  
  63. Package fonttools conflicts for:
  64. fonttools==4.25.0=pyhd3eb1b0_0
  65. matplotlib-base -> fonttools[version='>=4.22.0']
  66.  
  67. Package keras-preprocessing conflicts for:
  68. tensorflow -> keras-preprocessing[version='>=1.0.5']
  69. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> keras-preprocessing[version='>=1.1.1|>=1.1.2,<1.2|>=1.1.2']
  70. tensorflow-base -> keras-preprocessing[version='>=1.0.5|>=1.1.1|>=1.1.2,<1.2|>=1.1.2']
  71. keras-preprocessing
  72. keras-applications -> keras[version='>=2.1.6'] -> keras-preprocessing[version='1.0.2.*|>=1.0.5|>=1.1.0']
  73.  
  74. Package theano conflicts for:
  75. keras-applications -> keras[version='>=2.1.6'] -> theano
  76. keras-preprocessing -> keras[version='>=2.1.6'] -> theano
  77.  
  78. Package icu conflicts for:
  79. pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> icu[version='54.*|>=58.2,<59.0a0|>=64.2,<65.0a0|>=67.1,<68.0a0|>=68.1,<69.0a0|>=68.2,<69.0a0|>=69.1,<70.0a0|>=70.1,<71.0a0|58.*']
  80. qt -> icu[version='54.*|58.*|>=58.2,<59.0a0|>=64.2,<65.0a0|>=67.1,<68.0a0|>=68.1,<69.0a0|>=69.1,<70.0a0']
  81. qt -> qt-main=5.15.6 -> icu[version='69.*|>=68.2,<69.0a0|>=70.1,<71.0a0']
  82. tensorflow-base -> icu[version='>=68.1,<69.0a0|>=68.2,<69.0a0|>=69.1,<70.0a0|>=70.1,<71.0a0']
  83. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> icu[version='>=68.1,<69.0a0|>=68.2,<69.0a0|>=69.1,<70.0a0|>=70.1,<71.0a0']
  84. matplotlib-base -> icu[version='>=58.2,<59.0a0|>=64.2,<65.0a0|>=67.1,<68.0a0']
  85. icu
  86.  
  87. Package sip conflicts for:
  88. sip
  89. pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> sip
  90. pyqt -> sip[version='4.15.5|4.16.5|4.18|4.18.*|>=4.19.4,<=4.19.8|>=6.5.1,<6.6.0a0|>=6.6.2,<6.7.0a0|>=6.7.2,<6.8.0a0|>=6.7.5,<6.8.0a0|>=4.18|>=4.16.4,<4.18']
  91.  
  92. Package tensorflow-tensorboard conflicts for:
  93. tensorflow -> tensorflow-tensorboard
  94. tensorflow-gpu -> tensorflow-tensorboard
  95.  
  96. Package dataclasses conflicts for:
  97. tensorflow-gpu -> werkzeug[version='>=0.11.10'] -> dataclasses
  98. tensorboard -> werkzeug[version='>=1.0.1'] -> dataclasses
  99. tensorflow -> werkzeug[version='>=0.11.10'] -> dataclasses
  100. werkzeug -> dataclasses
  101.  
  102. Package ordereddict conflicts for:
  103. absl-py -> enum34 -> ordereddict
  104. pyqt -> enum34 -> ordereddict
  105.  
  106. Package protobuf conflicts for:
  107. tensorflow-gpu -> protobuf[version='>=3.1.0|>=3.2.0']
  108. tensorflow-base -> tensorboard[version='>=2.10,<2.11'] -> protobuf[version='>=3.4.0|>=3.6.0|>=3.9.2,<3.20']
  109. tensorboard -> protobuf[version='>=3.3.0|>=3.4.0|>=3.6.0|>=3.9.2|>=3.9.2,<3.20|>=3.8.0|>=3.6.1']
  110. tensorflow-base -> protobuf[version='>=3.3.0|>=3.6.1|>=3.9.2']
  111. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> protobuf[version='>=3.9.2']
  112. tensorflow-gpu -> tensorflow-gpu-base==1.3.0 -> protobuf[version='>=3.3.0']
  113. tensorflow -> protobuf[version='3.0.0b2|3.0.0|3.1.0|>=3.1.0|>=3.2.0|>=3.3.0|>=3.4.0|>=3.6.0|>=3.6.1']
  114.  
  115. Package packaging conflicts for:
  116. pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> packaging
  117. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> packaging
  118. tensorflow-base -> wheel[version='>=0.35,<1'] -> packaging[version='>=20.2']
  119. matplotlib-base -> packaging[version='>=20.0']
  120. tensorboard -> wheel[version='>=0.26'] -> packaging[version='>=20.2']
  121. pip -> wheel -> packaging[version='>=20.2']
  122. tensorflow-base -> packaging
  123. sip -> packaging
  124.  
  125. Package joblib conflicts for:
  126. scikit-learn -> joblib[version='>=0.11|>=1.0.0|>=1.1.1']
  127. joblib==1.0.1=pyhd3eb1b0_0
  128.  
  129. Package libwebp-base conflicts for:
  130. libwebp -> libwebp-base[version='1.1.0|1.1.0.*|1.2.0.*|1.2.1.*|1.2.2.*|1.2.3.*|1.2.4.*|>=1.2.4,<2.0a0|>=1.2.3,<2.0a0',build=2]
  131. libtiff -> libwebp -> libwebp-base[version='1.1.0|1.1.0.*|1.2.0.*|1.2.1.*|1.2.2.*|1.2.3.*|1.2.4.*',build=2]
  132. matplotlib-base -> pillow[version='>=6.2.0'] -> libwebp-base[version='>=1.2.2,<2.0a0|>=1.2.4,<2.0a0']
  133. libwebp -> libtiff[version='>=4.4.0,<4.5.0a0'] -> libwebp-base[version='>=1.1.0,<1.2.0a0']
  134. qt -> qt-webengine=5.15 -> libwebp-base[version='>=1.1.0,<1.2.0a0|>=1.2.2,<2.0a0|>=1.2.4,<2.0a0|>=1.2.3,<2.0a0']
  135. libtiff -> libwebp-base[version='>=1.1.0,<1.2.0a0|>=1.2.3,<2.0a0|>=1.2.4,<2.0a0']
  136. libwebp-base
  137.  
  138. Package libbrotlicommon conflicts for:
  139. libbrotlienc -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  140. libbrotlidec -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  141. brotli-bin -> libbrotlidec==1.0.9=h166bdaf_8 -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  142. brotli -> libbrotlidec==1.0.9=h166bdaf_8 -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  143. libbrotlicommon
  144.  
  145. Package libssh2 conflicts for:
  146. hdf5 -> libcurl[version='>=7.87.0,<8.0a0'] -> libssh2[version='>=1.10.0,<2.0a0|>=1.9.0,<2.0a0']
  147. tensorflow -> libcurl[version='>=7.71.1,<8.0a0'] -> libssh2[version='>=1.10.0,<2.0a0|>=1.9.0,<2.0a0|>=1.8.0,<2.0.0a0']
  148. git -> curl -> libssh2[version='1.8.*|>=1.10.0,<2.0a0|>=1.9.0,<2.0a0|>=1.8.0,<2.0.0a0']
  149. tensorflow-base -> libcurl[version='>=7.86.0,<8.0a0'] -> libssh2[version='>=1.10.0,<2.0a0|>=1.9.0,<2.0a0|>=1.8.0,<2.0.0a0']
  150.  
  151. Package futures conflicts for:
  152. tensorboard -> futures[version='>=3.1.1']
  153. tensorboard -> grpcio[version='>=1.6.3'] -> futures[version='>=2.2.0']
  154. tornado -> futures
  155. tensorflow -> grpcio[version='>=1.8.6'] -> futures[version='>=2.2.0|>=3.1.1']
  156. tensorflow-base -> grpcio[version='>=1.8.6'] -> futures[version='>=2.2.0|>=3.1.1']
  157. matplotlib-base -> tornado -> futures
  158.  
  159. Package threadpoolctl conflicts for:
  160. scikit-learn -> threadpoolctl[version='>=2.0.0']
  161. threadpoolctl==2.1.0=pyh5ca1d4c_0
  162.  
  163. Package html5lib conflicts for:
  164. tensorflow -> html5lib==0.9999999
  165. tensorflow -> bleach==1.5.0 -> html5lib[version='>=0.999,!=0.9999,!=0.99999,<0.99999999']
  166.  
  167. Package curl conflicts for:
  168. python -> graalpy[version='>=22.3.0,<22.3.1.0a0'] -> curl
  169. git -> curl[version='>=7.44.0,<8|>=7.59.0,<8.0a0|>=7.64.0,<8.0a0|>=7.64.1,<8.0a0|>=7.69.1,<8.0a0|>=7.71.1,<8.0a0|>=7.75.0,<8.0a0|>=7.77.0,<8.0a0|>=7.78.0,<8.0a0|>=7.79.1,<8.0a0|>=7.80.0,<8.0a0|>=7.81.0,<8.0a0|>=7.82.0,<8.0a0|>=7.83.1,<8.0a0']
  170.  
  171. Package werkzeug conflicts for:
  172. tensorboard -> werkzeug[version='>=0.11.10|>=0.11.15|>=1.0.1|>=0.14']
  173. werkzeug
  174. tensorflow-base -> tensorboard[version='>=2.11,<2.12'] -> werkzeug[version='>=0.11.10|>=0.11.15|>=1.0.1']
  175. tensorflow-gpu -> werkzeug[version='>=0.11.10']
  176. tensorflow -> werkzeug[version='>=0.11.10']
  177.  
  178. Package brotli conflicts for:
  179. brotli
  180. matplotlib-base -> fonttools[version='>=4.22.0'] -> brotli[version='>=1.0.1']
  181. fonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1']
  182.  
  183. Package bleach conflicts for:
  184. tensorboard -> bleach[version='1.5.0|>=1.5.0,<1.5.1.0a0']
  185. tensorflow -> bleach==1.5.0
  186. tensorflow-gpu -> bleach==1.5.0
  187.  
  188. Package lz4-c conflicts for:
  189. zstd -> lz4-c[version='>=1.8.1.2,<1.8.2.0a0|>=1.8.3,<1.8.4.0a0|>=1.9.2,<1.9.3.0a0|>=1.9.3,<1.10.0a0|>=1.9.3,<1.9.4.0a0']
  190. libtiff -> zstd[version='>=1.5.2,<1.6.0a0'] -> lz4-c[version='>=1.8.3,<1.8.4.0a0|>=1.9.2,<1.9.3.0a0|>=1.9.3,<1.10.0a0|>=1.9.3,<1.9.4.0a0']
  191. zstd -> lz4 -> lz4-c=1.8.1
  192. lz4-c
  193.  
  194. Package libtiff conflicts for:
  195. libwebp -> libtiff[version='>=4.0.10,<4.5.0a0|>=4.1.0,<4.5.0a0|>=4.2.0,<4.5.0a0|>=4.3.0,<4.5.0a0|>=4.4.0,<4.5.0a0|>=4.5.0,<4.6.0a0|>=4.0.9,<4.5.0a0']
  196. libtiff
  197. qt -> libtiff[version='4.0.*|>=4.0.10,<4.5.0a0']
  198. pyqt -> qt[version='>=4.8.6,<5.0'] -> libtiff[version='4.0.*|>=4.0.10,<4.5.0a0']
  199. matplotlib-base -> pillow[version='>=6.2.0'] -> libtiff[version='>=4.0.10,<4.4.0a0|>=4.1.0,<4.4.0a0|>=4.2.0,<4.4.0a0|>=4.3.0,<4.4.0a0|>=4.3.0,<4.5.0a0|>=4.4.0,<4.5.0a0|>=4.5.0,<4.6.0a0']
  200. qt -> gtk2 -> libtiff[version='>=4.0.3,<4.0.8|>=4.0.9,<4.5.0a0|>=4.1.0,<4.5.0a0']
  201.  
  202. Package tbb conflicts for:
  203. mkl_random -> mkl[version='>=2022.0.1,<2023.0a0'] -> tbb=2021
  204. mkl_fft -> mkl[version='>=2022.1.0,<2023.0a0'] -> tbb=2021
  205. blas -> mkl -> tbb=2021
  206.  
  207. Package libgfortran4 conflicts for:
  208. blas -> libgfortran4[version='>=7.5.0']
  209. blas -> libgfortran-ng -> libgfortran4=7.5.0
  210.  
  211. Package mpc conflicts for:
  212. tensorflow-gpu -> libgcc -> mpc[version='>=0.8.0']
  213. pyqt -> libgcc -> mpc[version='>=0.8.0']
  214. qt -> libgcc -> mpc[version='>=0.8.0']
  215.  
  216. Package qt conflicts for:
  217. qt
  218. pyqt -> qt[version='4.8.*|5.6.*|5.9.*|>=5.12.5,<5.13.0a0|>=5.12.9,<5.13.0a0|>=5.9.7,<5.10.0a0|>=5.6.2,<5.7.0a0|>=4.8.6,<5.0|5.6.0|4.8.6|4.8.5']
  219.  
  220. Package requests conflicts for:
  221. tensorboard -> google-auth[version='>=1.6.3,<3'] -> requests[version='>=2.20.0,<3.0.0dev']
  222. tensorflow-base -> tensorboard[version='>=2.11,<2.12'] -> requests[version='>=2.21.0|>=2.21.0,<3']
  223. tensorboard -> requests[version='>=2.21.0|>=2.21.0,<3']
  224.  
  225. Package libwebp conflicts for:
  226. libwebp
  227. matplotlib-base -> pillow[version='>=6.2.0'] -> libwebp
  228. qt -> qt-webengine=5.15 -> libwebp
  229. libtiff -> libwebp
  230.  
  231. Package backports conflicts for:
  232. tensorflow-base -> backports.weakref[version='>=1.0rc1'] -> backports
  233. tornado -> ssl_match_hostname -> backports
  234. tensorflow -> backports.weakref[version='>=1.0rc1'] -> backports
  235. tensorflow-gpu -> backports.weakref==1.0rc1 -> backports
  236. matplotlib-base -> backports.functools_lru_cache -> backports
  237.  
  238. Package pandas conflicts for:
  239. pandas
  240. seaborn==0.11.2=pyhd3eb1b0_0 -> pandas[version='>=0.23']
  241.  
  242. Package enum34 conflicts for:
  243. tensorflow -> absl-py[version='>=0.1.6'] -> enum34[version='>=1.0.4']
  244. tensorflow -> enum34[version='>=1.1.6']
  245. tensorboard -> absl-py[version='>=0.4'] -> enum34[version='>=1.0.4']
  246. tensorflow-base -> absl-py[version='>=0.4.0'] -> enum34[version='>=1.0.4']
  247. pyqt -> enum34
  248. absl-py -> enum34
  249. tensorflow-base -> enum34[version='>=1.1.6']
  250.  
  251. Package munkres conflicts for:
  252. matplotlib-base -> fonttools[version='>=4.22.0'] -> munkres
  253. fonttools==4.25.0=pyhd3eb1b0_0 -> munkres
  254. munkres
  255.  
  256. Package zstd conflicts for:
  257. zstd
  258. libtiff -> zstd[version='>=1.3.3,<1.3.4.0a0|>=1.4.0,<1.5.0.0a0|>=1.4.3,<1.5.0.0a0|>=1.4.4,<1.5.0.0a0|>=1.4.5,<1.5.0a0|>=1.4.9,<1.5.0a0|>=1.5.0,<1.6.0a0|>=1.5.2,<1.6.0a0']
  259. pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> zstd[version='>=1.5.0,<1.6.0a0|>=1.5.2,<1.6.0a0']
  260. libwebp -> libtiff[version='>=4.5.0,<4.6.0a0'] -> zstd[version='>=1.3.3,<1.3.4.0a0|>=1.4.0,<1.5.0.0a0|>=1.4.3,<1.5.0.0a0|>=1.4.4,<1.5.0.0a0|>=1.4.5,<1.5.0a0|>=1.4.9,<1.5.0a0|>=1.5.0,<1.6.0a0|>=1.5.2,<1.6.0a0']
  261. qt -> qt-main=5.15.6 -> zstd[version='>=1.3.3,<1.3.4.0a0|>=1.4.0,<1.5.0.0a0|>=1.4.3,<1.5.0.0a0|>=1.4.4,<1.5.0.0a0|>=1.4.5,<1.5.0a0|>=1.4.8,<1.5.0a0|>=1.4.9,<1.5.0a0|>=1.5.0,<1.6.0a0|>=1.5.2,<1.6.0a0|>=1.5.1,<1.6.0a0']
  262.  
  263. Package libdeflate conflicts for:
  264. libdeflate
  265. libtiff -> libdeflate[version='>=1.10,<1.11.0a0|>=1.12,<1.13.0a0|>=1.13,<1.14.0a0|>=1.14,<1.15.0a0|>=1.16,<1.17.0a0|>=1.17,<1.18.0a0|>=1.8,<1.9.0a0|>=1.7,<1.8.0a0']
  266. qt -> libtiff[version='>=4.0.10,<4.5.0a0'] -> libdeflate[version='>=1.10,<1.11.0a0|>=1.12,<1.13.0a0|>=1.13,<1.14.0a0|>=1.14,<1.15.0a0|>=1.8,<1.9.0a0|>=1.7,<1.8.0a0']
  267. libwebp -> libtiff[version='>=4.5.0,<4.6.0a0'] -> libdeflate[version='>=1.10,<1.11.0a0|>=1.12,<1.13.0a0|>=1.13,<1.14.0a0|>=1.14,<1.15.0a0|>=1.16,<1.17.0a0|>=1.17,<1.18.0a0|>=1.8,<1.9.0a0|>=1.7,<1.8.0a0']
  268.  
  269. Package cudnn conflicts for:
  270. tensorflow -> tensorflow-base==2.11.0[build=cuda112py39*_0] -> cudnn[version='>=7.6.5.32,<8.0a0|>=8.4.1.50,<9.0a0|>=8.2.1.32,<9.0a0']
  271. tensorflow-gpu -> cudnn[version='5.1|5.1.*|6.0.*']
  272. tensorflow-base -> cudnn[version='>=7.6.5.32,<8.0a0|>=8.4.1.50,<9.0a0|>=8.2.1.32,<9.0a0']
  273. cudnn
  274.  
  275. Package pyqt conflicts for:
  276. pyqt
  277. seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> pyqt[version='>=5.12.3,<5.13.0a0|>=5|>=5.6.0,<5.7.0a0|>=5.9.2,<5.10.0a0']
  278.  
  279. Package libxcb conflicts for:
  280. qt -> xorg-libx11 -> libxcb=1
  281. matplotlib-base -> pillow[version='>=6.2.0'] -> libxcb[version='>=1.13,<1.14.0a0']
  282. pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> libxcb[version='>=1.13,<1.14.0a0']
  283. qt -> libxcb[version='>=1.13,<1.14.0a0']
  284.  
  285. Package libnghttp2 conflicts for:
  286. hdf5 -> libcurl[version='>=7.87.0,<8.0a0'] -> libnghttp2[version='>=1.41.0,<2.0a0|>=1.43.0,<2.0a0|>=1.47.0,<2.0a0']
  287. tensorflow-base -> libcurl[version='>=7.86.0,<8.0a0'] -> libnghttp2[version='>=1.41.0,<2.0a0|>=1.43.0,<2.0a0|>=1.47.0,<2.0a0']
  288. tensorflow -> libcurl[version='>=7.71.1,<8.0a0'] -> libnghttp2[version='>=1.41.0,<2.0a0|>=1.43.0,<2.0a0|>=1.47.0,<2.0a0']
  289.  
  290. Package lerc conflicts for:
  291. lerc
  292. libwebp -> libtiff[version='>=4.5.0,<4.6.0a0'] -> lerc[version='>=2.2.1,<3.0a0|>=3.0,<4.0a0|>=4.0.0,<5.0a0']
  293. libtiff -> lerc[version='>=2.2.1,<3.0a0|>=3.0,<4.0a0|>=4.0.0,<5.0a0']
  294. qt -> libtiff[version='>=4.0.10,<4.5.0a0'] -> lerc[version='>=2.2.1,<3.0a0|>=3.0,<4.0a0|>=4.0.0,<5.0a0']
  295.  
  296. Package pyparsing conflicts for:
  297. tensorflow-base -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
  298. matplotlib-base -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1|>=2.3.1']
  299. matplotlib-base -> packaging[version='>=20.0'] -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
  300. sip -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
  301.  
  302. Package pypy3.6 conflicts for:
  303. tornado -> pypy3.6[version='>=7.3.1|>=7.3.2|>=7.3.3']
  304. tornado -> python[version='>=3.6,<3.7.0a0'] -> pypy3.6[version='7.3.*|7.3.0.*|7.3.1.*|7.3.2.*|7.3.3.*']
  305.  
  306. Package wrapt conflicts for:
  307. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> wrapt[version='>=1.11.0|>=1.12.1,<1.13|>=1.11.1']
  308. tensorflow-base -> wrapt[version='>=1.11.0|>=1.12.1,<1.13|>=1.11.1']
  309. wrapt
  310.  
  311. Package llvm-openmp conflicts for:
  312. mkl_fft -> mkl[version='>=2022.1.0,<2023.0a0'] -> llvm-openmp[version='>=10.0.0|>=11.0.0|>=12.0.1|>=14.0.3|>=15.0.6|>=9.0.1|>=15.0.7|>=11.1.0']
  313. blas -> openblas -> llvm-openmp[version='>=10.0.1|>=15.0.7|>=15.0.6|>=14.0.3']
  314. blas -> llvm-openmp[version='>=10.0.0|>=11.0.0|>=11.0.1|>=11.1.0|>=12.0.1|>=13.0.1|>=14.0.4|>=9.0.1']
  315. scikit-learn -> blas=[build=openblas] -> llvm-openmp[version='>=10.0.0|>=11.0.0|>=11.0.1|>=11.1.0|>=12.0.1|>=13.0.1|>=14.0.4|>=9.0.1']
  316. mkl_random -> mkl[version='>=2022.0.1,<2023.0a0'] -> llvm-openmp[version='>=10.0.0|>=11.0.0|>=11.1.0|>=12.0.1|>=14.0.3|>=15.0.6|>=9.0.1|>=15.0.7']
  317.  
  318. Package libbrotlienc conflicts for:
  319. libbrotlienc
  320. fonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1'] -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  321. brotli -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  322. brotli-bin -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  323.  
  324. Package zipp conflicts for:
  325. zipp==3.4.1=pyhd3eb1b0_0
  326. markdown -> importlib-metadata[version='>=4.4'] -> zipp[version='>=0.5']
  327.  
  328. Package mkl-service conflicts for:
  329. mkl_fft -> mkl-service[version='>=2,<3.0a0']
  330. mkl_random -> mkl-service[version='>=2,<3.0a0']
  331.  
  332. Package snappy conflicts for:
  333. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> snappy[version='>=1.1.8,<2.0a0|>=1.1.9,<2.0a0']
  334. tensorflow-base -> snappy[version='>=1.1.8,<2.0a0|>=1.1.9,<2.0a0']
  335.  
  336. Package blas-devel conflicts for:
  337. blas -> blas-devel==3.9.0[build='7_blis|7_openblas|8_blis|8_mkl|10_blis|11_linux64_mkl|12_linux64_openblas|12_linux64_blis|13_linux64_openblas|13_linux64_blis|13_linux64_mkl|14_linux64_mkl|16_linux64_blis|16_linux64_mkl|16_linux64_openblas|15_linux64_mkl|15_linux64_blis|15_linux64_openblas|14_linux64_blis|14_linux64_openblas|12_linux64_mkl|11_linux64_openblas|11_linux64_blis|10_mkl|10_openblas|9_mkl|9_openblas|9_blis|8_openblas|7_mkl|5_netlib']
  338. scikit-learn -> blas=[build=openblas] -> blas-devel==3.9.0[build='7_mkl|8_mkl|10_mkl|11_linux64_mkl|13_linux64_mkl|15_linux64_mkl|8_openblas|10_openblas|12_linux64_openblas|13_linux64_openblas|14_linux64_openblas|15_linux64_openblas|16_linux64_openblas|11_linux64_openblas|9_openblas|7_openblas|16_linux64_mkl|14_linux64_mkl|12_linux64_mkl|9_mkl']
  339.  
  340. Package nose conflicts for:
  341. scikit-learn -> nose
  342. tensorboard -> numpy -> nose
  343. pandas -> numpy[version='>=1.7'] -> nose
  344.  
  345. Package cython conflicts for:
  346. keras-applications -> h5py -> cython==0.22
  347. cython
  348. keras-base -> h5py -> cython==0.22
  349.  
  350. Package typing_extensions conflicts for:
  351. tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> typing_extensions[version='3.7.4.*|>=3.6.6|>=3.7.4,<3.8|>=3.7.4']
  352. typing_extensions==3.7.4.3=pyha847dfd_0
  353. tensorflow-base -> typing_extensions[version='3.7.4.*|>=3.6.6|>=3.7.4,<3.8|>=3.7.4']
  354. markdown -> importlib-metadata[version='>=4.4'] -> typing_extensions[version='>=3.6.4']
  355.  
  356. Package toml conflicts for:
  357. pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> toml
  358. coverage -> tomli -> toml
  359. sip -> toml
  360.  
  361. Package brotli-bin conflicts for:
  362. brotli-bin
  363. fonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1'] -> brotli-bin==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  364. brotli -> brotli-bin==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']
  365.  
  366. Package python-dateutil conflicts for:
  367. matplotlib-base -> python-dateutil[version='>=2.1|>=2.7']
  368. seaborn==0.11.2=pyhd3eb1b0_0 -> pandas[version='>=0.23'] -> python-dateutil[version='>=2.5.*|>=2.6.1|>=2.7.3|>=2.8.1']
  369. pandas -> python-dateutil[version='>=2.5.*|>=2.6.1|>=2.7.3|>=2.8.1']
  370.  
  371. Package pypy3.8 conflicts for:
  372. tornado -> python[version='>=3.8,<3.9.0a0'] -> pypy3.8[version='7.3.*|7.3.11.*|7.3.9.*|7.3.8.*']
  373. tornado -> pypy3.8[version='>=7.3.8|>=7.3.9']
  374.  
  375. Package distribute conflicts for:
  376. pip -> distribute
  377. python -> pip -> distribute
  378.  
  379. Package fribidi conflicts for:
  380. qt -> pango -> fribidi[version='>=1.0.10,<2.0a0|>=1.0.9,<2.0a0|>=1.0.5,<2.0a0']
  381. matplotlib-base -> pillow[version='>=6.2.0'] -> fribidi[version='>=1.0.10,<2.0a0']
  382.  
  383. Package libiconv conflicts for:
  384. git -> libiconv[version='1.15|1.15.*|>=1.15,<2.0.0a0|>=1.16,<2.0.0a0|>=1.17,<2.0a0']
  385. qt -> qt-webengine=5.15 -> libiconv[version='1.14.*|1.15|>=1.15,<2.0.0a0|>=1.16,<2.0.0a0|>=1.17,<2.0a0|>=1.17,<2.0.0a0']The following specifications were found to be incompatible with your system:
  386.  
  387.   - feature:/linux-64::__cuda==11.7=0
  388.   - feature:/linux-64::__glibc==2.31=0
  389.   - feature:|@/linux-64::__cuda==11.7=0
  390.   - feature:|@/linux-64::__glibc==2.31=0
  391.   - cudatoolkit -> __glibc[version='>=2.17,<3.0.a0']
  392.   - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  393.   - keras-base -> tensorflow[version='>=2.2'] -> __cuda
  394.   - keras-base -> tensorflow[version='>=2.2'] -> __glibc[version='>=2.17']
  395.   - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  396.   - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  397.   - tensorflow -> __cuda
  398.   - tensorflow -> __glibc[version='>=2.17']
  399.   - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  400.   - tensorflow-base -> __cuda
  401.   - tensorflow-base -> __glibc[version='>=2.17']
  402.   - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  403.   - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  404.   - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  405.   - wincertstore -> __win
  406.  
  407. Your installed version is: 2.31
  408.  
  409. Note that strict channel priority may have removed packages required for satisfiability.

报了上面的一大堆conflicts后，我就找解决方案。

看到有人说是python版本不匹配，并不是上面的——_glibc的问题。思路来自这里。

安装新环境和python版本时，Conda glibc依赖冲突 - 问答 - 腾讯云开发者社区-腾讯云

8 执行 conda install python=3.7

有报错了，仍旧是冲突。 

  1. (venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda install python=3.7
  2. Collecting package metadata (current_repodata.json): done
  3. Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  4. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
  5. Collecting package metadata (repodata.json): done
  6. Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  7. Solving environment: | 
  8. Found conflicts! Looking for incompatible packages.
  9. This can take several minutes.  Press CTRL-C to abort.
  10. failed                                                                                                                                                                                            
  11.  
  12. UnsatisfiableError: 
  13. Note that strict channel priority may have removed packages required for satisfiability.

奇怪的事这次冲突还不详细。

会不会是这个虚拟环境装了乱七八糟的东西，因此删除了venv1，新建虚拟环境2，再来。此时安装python版本时候仍旧报错。

UnsatisfiableError: 
Note that strict channel priority may have removed packages required for satisfiability.

  1. IST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda remove -n venv1 --all
  2.  
  3. Remove all packages in environment /home/LIST_2080Ti/anaconda3/envs/venv1:
  4.  
  5. No packages found in /home/LIST_2080Ti/anaconda3/envs/venv1. Continuing environment removal
  6. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2 python=3.7.10
  7. Collecting package metadata (current_repodata.json): done
  8. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
  9. Collecting package metadata (repodata.json): done
  10. Solving environment: / 
  11. Found conflicts! Looking for incompatible packages.
  12. This can take several minutes.  Press CTRL-C to abort.
  13. failed                                                                                                                                                                                            
  14.  
  15. UnsatisfiableError: 
  16. Note that strict channel priority may have removed packages required for satisfiability.
  17.  
  18. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2 python=3.7
  19. Collecting package metadata (current_repodata.json): done
  20. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
  21. Collecting package metadata (repodata.json): done
  22. Solving environment: \ 
  23. Found conflicts! Looking for incompatible packages.
  24. This can take several minutes.  Press CTRL-C to abort.
  25. failed                                                                                                                                                                                            
  26.  
  27. UnsatisfiableError: 
  28. Note that strict channel priority may have removed packages required for satisfiability.
  29.  
  30. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2
  31. Collecting package metadata (current_repodata.json): done
  32. Solving environment: done
  33.  
  34. ## Package Plan ##
  35.  
  36.   environment location: /home/LIST_2080Ti/anaconda3/envs/venv2
  37.  
  38.  
  39.  
  40. Proceed ([y]/n)? y
  41.  
  42. Preparing transaction: done
  43. Verifying transaction: done
  44. Executing transaction: done
  45. #
  46. # To activate this environment, use
  47. #
  48. #     $ conda activate venv2
  49. #
  50. # To deactivate an active environment, use
  51. #
  52. #     $ conda deactivate
  53.  
  54. Retrieving notices: ...working... done
  55. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda activate venv2
  56. (venv2) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda install python=3.7.10
  57. Collecting package metadata (current_repodata.json): done
  58. Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  59. Collecting package metadata (repodata.json): done
  60. Solving environment: failed with initial frozen solve. Retrying with flexible solve.
  61. Solving environment: / 
  62. Found conflicts! Looking for incompatible packages.
  63. This can take several minutes.  Press CTRL-C to abort.
  64. failed                                                                                                                                                                                            
  65.  
  66. UnsatisfiableError: 
  67. Note that strict channel priority may have removed packages required for satisfiability.

9 遇到上面冲突怎么办

使用这个方式解决了。

UnsatisfiableError: Note that strict channel priority may have removed packagesconda【成功解决】_ACMSunny的博客-CSDN博客

再次安装环境，继续报错：

  1. The following specifications were found to be incompatible with your system:
  2.  
  3.   - feature:/linux-64::__cuda==11.7=0
  4.   - feature:/linux-64::__glibc==2.31=0
  5.   - feature:|@/linux-64::__cuda==11.7=0
  6.   - feature:|@/linux-64::__glibc==2.31=0
  7.   - cudatoolkit -> __glibc[version='>=2.17,<3.0.a0']
  8.   - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  9.   - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  10.   - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  11.   - tensorflow -> __cuda
  12.   - tensorflow -> __glibc[version='>=2.17']
  13.   - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  14.   - tensorflow-base -> __cuda
  15.   - tensorflow-base -> __glibc[version='>=2.17']
  16.   - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  17.   - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  18.   - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  19.   - wincertstore -> __win
  20.  
  21. Your installed version is: 2.31
  22.  
  23. Note that strict channel priority may have removed packages required for satisfiability.

使用：ldd --version查询一下。我安装的2.31就是这个东东。

  1. (venv2) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/njh$ ldd --version
  2. ldd (Ubuntu GLIBC 2.31-0ubuntu9.9) 2.31
  3. Copyright (C) 2020 Free Software Foundation, Inc.
  4. This is free software; see the source for copying conditions.  There is NO
  5. warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
  6. Written by Roland McGrath and Ulrich Drepper.

而上面报错的那些正是我需要安装的却与2.31不兼容。

conda - UnsatisfiableError glibc 和 cudatoolkit - IT工具网的方法是更新conda：

  1. conda update conda
  2.  
  3. conda update --all

同时把defaults，conda-forge,bioconda加入到channel里。

conda config --append channels defaults --append channels conda-forge --append channels bioconda

运行安装环境语句conda env create -f environment.yml，等结果。

仍旧一堆错。

结果如下：

  1. The following specifications were found to be incompatible with your system:
  2.  
  3.   - feature:/linux-64::__cuda==11.7=0
  4.   - feature:/linux-64::__glibc==2.31=0
  5.   - feature:|@/linux-64::__cuda==11.7=0
  6.   - feature:|@/linux-64::__glibc==2.31=0
  7.   - cudatoolkit -> __glibc[version='>=2.17,<3.0.a0']
  8.   - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  9.   - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  10.   - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  11.   - tensorflow -> __cuda
  12.   - tensorflow -> __glibc[version='>=2.17']
  13.   - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  14.   - tensorflow-base -> __cuda
  15.   - tensorflow-base -> __glibc[version='>=2.17']
  16.   - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  17.   - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  18.   - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  19.   - wincertstore -> __win
  20.  
  21. Your installed version is: 2.31
  22.  
  23. Note that strict channel priority may have removed packages required for satisfiability.

一阵操作猛如虎，回看错误个个有。

10 然后采取增加镜像源的方式

  1. conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/conda-forge/
  2. conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/main/
  3. conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/free/
  4. conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/bioconda/
  5. conda config --add channels http://mirrors.aliyun.com/anaconda/cloud/bioconda/
  6. conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/
  7. conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/
  8. conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  9. ————————————————
  10. 版权声明：本文为CSDN博主「weixin_42001274」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
  11. 原文链接：https://blog.csdn.net/weixin_42001274/article/details/127209878

现在又开始检测了。报错：

  1. The following specifications were found to be incompatible with your system:
  2.  
  3.   - feature:/linux-64::__cuda==11.7=0
  4.   - feature:/linux-64::__glibc==2.31=0
  5.   - feature:|@/linux-64::__cuda==11.7=0
  6.   - feature:|@/linux-64::__glibc==2.31=0
  7.   - cudatoolkit -> __glibc[version='>=2.17,<3.0.a0']
  8.   - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  9.   - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  10.   - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  11.   - tensorflow -> __cuda
  12.   - tensorflow -> __glibc[version='>=2.17']
  13.   - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  14.   - tensorflow-base -> __cuda
  15.   - tensorflow-base -> __glibc[version='>=2.17']
  16.   - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  17.   - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  18.   - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  19.   - wincertstore -> __win
  20.  
  21. Your installed version is: 2.31
  22.  
  23. Note that strict channel priority may have removed packages required for satisfiability.

11 删除channel中的default和conda update --strict-channel-priority --all

继续试验：

conda update --strict-channel-priority --all

来自：python - resolving package resolutions in conda - Stack Overflow

且删除channel中的default
Conda glibc依赖冲突 - 问答 - 腾讯云开发者社区-腾讯云

12 最终解决方案

以上就是我每天碰壁碰出来的结果，事实发现，这些办法都不能解决我的问题。

pip安装和conda安装配置环境我都试了，packagenotfound可以通过添加源来解决。而conflicts涉及到源码之类的，简直无能为力。因此决定暂时放弃这个方法。

开始使用，一次一安装的方式去干。

就是程序需要用到什么就安装什么。

尽可能的减少环境内包的数量和可能产生的冲突。

使用这个方法需要注意以下几点：

（1）你的源环境是否使用TensorFlow，如果使用一定要安装正确版本的TensorFlow，然后再安装其它包。

（2）可以先安装一些常用的包，比如numpy，pandas,matplotlib,scipy等等。也要根据你自己常用的情况去选择。

（3）可以看一下你程序内导入的包。

万万没想到，当我不使用这两种整体方式配置环境时候，之前的那些奇形怪状的死活有冲突安装不上的包一股脑都安装了。

  1. conda env create -f environment.yml
  2.  
  3. pip install -r requirements.txt

 安装命令为：

pip install tensorflow-gpu==1.14.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

之所以用1.14.0是我的源环境是这样的。你可以根据你自己的环境修改。想知道你的配置列表。可以直接cmd——激活你的环境——conda list，上面会显示你的TensorFlow的版本号。

如下：

  1. LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/njh/CHB-MIT-DATA/epilepsy_eeg_classification$ pip install tensorflow-gpu==1.14.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
  2. Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
  3. Collecting tensorflow-gpu==1.14.0
  4.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/32/67/559ca8408431c37ad3a17e859c8c291ea82f092354074baef482b98ffb7b/tensorflow_gpu-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (377.1 MB)
  5.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 377.1/377.1 MB 1.6 MB/s eta 0:00:00
  6. Collecting gast>=0.2.0
  7.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5f/1c/b59500a88c5c3d9d601c5ca62b9df5e0964764472faed82a182958a922c5/gast-0.5.3-py3-none-any.whl (19 kB)
  8. Collecting grpcio>=1.8.6
  9.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dc/e9/6e97a958c2a6603d9eb93e94b73381e2df8eb13865cdb166fc8f4dee8772/grpcio-1.51.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)
  10.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.8/4.8 MB 5.8 MB/s eta 0:00:00
  11. Collecting tensorboard<1.15.0,>=1.14.0
  12.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1 MB)
  13.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.1/3.1 MB 3.9 MB/s eta 0:00:00
  14. Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0
  15.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488 kB)
  16.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 488.5/488.5 kB 1.1 MB/s eta 0:00:00
  17. Collecting six>=1.10.0
  18.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
  19. Collecting keras-preprocessing>=1.0.5
  20.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
  21.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 42.6/42.6 kB 3.0 MB/s eta 0:00:00
  22. Collecting astor>=0.6.0
  23.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl (27 kB)
  24. Collecting google-pasta>=0.1.6
  25.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57 kB)
  26.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 kB 2.8 MB/s eta 0:00:00
  27. Collecting numpy<2.0,>=1.14.5
  28.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6d/ad/ff3b21ebfe79a4d25b4a4f8e5cf9fd44a204adb6b33c09010f566f51027a/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)
  29.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 15.7/15.7 MB 2.4 MB/s eta 0:00:00
  30. Collecting protobuf>=3.6.1
  31.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e7/a2/3273c05fc5d959fa90de6453ebd6d45c6d4fab3ec212d631625ea5780921/protobuf-4.21.12-cp37-abi3-manylinux2014_x86_64.whl (409 kB)
  32.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 409.8/409.8 kB 4.0 MB/s eta 0:00:00
  33. Collecting termcolor>=1.1.0
  34.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/aa/f4/8ddd8a684b4c005345f45740a449d93d0af7ccecd91319d0f4426cf08b36/termcolor-2.2.0-py3-none-any.whl (6.6 kB)
  35. Collecting absl-py>=0.7.0
  36.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dd/87/de5c32fa1b1c6c3305d576e299801d8655c175ca9557019906247b994331/absl_py-1.4.0-py3-none-any.whl (126 kB)
  37.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 126.5/126.5 kB 4.7 MB/s eta 0:00:00
  38. Requirement already satisfied: wheel>=0.26 in /home/LIST_2080Ti/anaconda3/envs/venv2/lib/python3.7/site-packages (from tensorflow-gpu==1.14.0) (0.38.4)
  39. Collecting wrapt>=1.11.1
  40.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/49/a8/528295a24655f901148177355edb6a22b84abb2abfadacc1675643c1434a/wrapt-1.14.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (75 kB)
  41.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 75.2/75.2 kB 5.2 MB/s eta 0:00:00
  42. Collecting keras-applications>=1.0.6
  43.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50 kB)
  44.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.7/50.7 kB 8.2 MB/s eta 0:00:00
  45. Collecting h5py
  46.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/be/de1e591bec008ed92d3829b985757b8bc2d34179feef5e181530876a4f9d/h5py-3.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)
  47.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.3/4.3 MB 6.6 MB/s eta 0:00:00
  48. Requirement already satisfied: setuptools>=41.0.0 in /home/LIST_2080Ti/anaconda3/envs/venv2/lib/python3.7/site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (67.1.0)
  49. Collecting werkzeug>=0.11.15
  50.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/27/be6ddbcf60115305205de79c29004a0c6bc53cec814f733467b1bb89386d/Werkzeug-2.2.2-py3-none-any.whl (232 kB)
  51.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 232.7/232.7 kB 2.4 MB/s eta 0:00:00
  52. Collecting markdown>=2.6.8
  53.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/86/be/ad281f7a3686b38dd8a307fa33210cdf2130404dfef668a37a4166d737ca/Markdown-3.4.1-py3-none-any.whl (93 kB)
  54.      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 93.3/93.3 kB 2.2 MB/s eta 0:00:00
  55. Collecting importlib-metadata>=4.4
  56.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/26/a7/9da7d5b23fc98ab3d424ac2c65613d63c1f401efb84ad50f2fa27b2caab4/importlib_metadata-6.0.0-py3-none-any.whl (21 kB)
  57. Collecting MarkupSafe>=2.1.1
  58.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/88/8c8cce021ac1b1eedde349c6a41f6c256da60babf95e572071361ff3f66b/MarkupSafe-2.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB)
  59. Collecting typing-extensions>=3.6.4
  60.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0b/8e/f1a0a5a76cfef77e1eb6004cb49e5f8d72634da638420b9ea492ce8305e8/typing_extensions-4.4.0-py3-none-any.whl (26 kB)
  61. Collecting zipp>=0.5
  62.   Downloading https://pypi.tuna.tsinghua.edu.cn/packages/37/7d/4a5221043904612db108bbe7d0ad7409015fb143bae137c72d9dfd7b75e1/zipp-3.12.1-py3-none-any.whl (6.7 kB)
  63. Installing collected packages: tensorflow-estimator, zipp, wrapt, typing-extensions, termcolor, six, protobuf, numpy, MarkupSafe, grpcio, gast, astor, absl-py, werkzeug, keras-preprocessing, importlib-metadata, h5py, google-pasta, markdown, keras-applications, tensorboard, tensorflow-gpu
  64. Successfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astor-0.8.1 gast-0.5.3 google-pasta-0.2.0 grpcio-1.51.1 h5py-3.8.0 importlib-metadata-6.0.0 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.4.1 numpy-1.21.6 protobuf-4.21.12 six-1.16.0 tensorboard-1.14.0 tensorflow-estimator-1.14.0 tensorflow-gpu-1.14.0 termcolor-2.2.0 typing-extensions-4.4.0 werkzeug-2.2.2 wrapt-1.14.1 zipp-3.12.1

安装TensorFlow-gpu版本时候自动安装一波包。

Successfully installed MarkupSafe-2.1.2 absl-py-1.4.0 astor-0.8.1 gast-0.5.3 google-pasta-0.2.0 grpcio-1.51.1 h5py-3.8.0 importlib-metadata-6.0.0 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.4.1 numpy-1.21.6 protobuf-4.21.12 six-1.16.0 tensorboard-1.14.0 tensorflow-estimator-1.14.0 tensorflow-gpu-1.14.0 termcolor-2.2.0 typing-extensions-4.4.0 werkzeug-2.2.2 wrapt-1.14.1 zipp-3.12.1

报错缺少mne时候，安装mne又装了一堆包。

Successfully installed appdirs-1.4.4 certifi-2022.12.7 charset-normalizer-3.0.1 cycler-0.11.0 decorator-5.1.1 fonttools-4.38.0 idna-3.4 jinja2-3.1.2 kiwisolver-1.4.4 matplotlib-3.5.3 mne-1.3.0 packaging-23.0 pillow-9.4.0 pooch-1.6.0 pyparsing-3.0.9 python-dateutil-2.8.2 requests-2.28.2 scipy-1.7.3 tqdm-4.64.1 urllib3-1.26.14

报错缺少pandas时候，安装pandas只安装了pandas和pytz.

Successfully installed pandas-1.3.5 pytz-2022.7.1

然后再调整了一下里面使用文件的路径。使用相对路径报错的是找不到文件。所以，在服务器我用的是绝对路径。

然后事情就完成了。

万万没想到，我几天没有搞定的事情，一个个安装的时候竟然如此顺利。

13 问题分析

很多人使用上面的方式都解决了问题，只有我用了前面的所有方法，直到自己不使用整体配置环境的方式才解决问题。

报错的原因有很多。

比如packagenotfound，可能需要加入镜像源就能解决。

比如found conflicts，可能需要修改版本，或者删除版本号能解决。而我实验了各种方式，这个conflicts始终无法解决。直到自己手动配置环境才可以。

第12步手动配置，总共也没花多少时间就解决了问题。

希望前面的12个坑能够给你以借鉴。

另外，一般情况下，个人项目不会太大，手动不使用整体配置可能会更好更快的完成。

conda env create -f environment.yml

pip install -r requirements.txt

或许对于大项目有用，但是对于小项目来说，它带来的问题远远比它带来的便利要大。

————————————————————

14 could not find expected ':'

  1.  ruamel_yaml.scanner.ScannerError: while scanning a simple key
  2.       in "<unicode string>", line 143, column 5:
  3.             -i https://pypi.tuna.tsinghua.ed ... 
  4.             ^ (line: 143)
  5.     could not find expected ':'
  6.       in "<unicode string>", line 144, column 1:
  7.         # prefix: D:\Program\Anaconda3\e ... 
  8.         ^ (line: 144)

 yml配置文件遇到“：”或者“-”后面必须留一个空格！

15 参考文章

（1）pip配置环境

linux环境根据requirements.txt搭建python虚拟环境_小小鱼er的博客-CSDN博客_根据requirement创建虚拟环境

Python项目部署到服务器上_李俊的博客的博客-CSDN博客_python项目部署到服务器

（2）conda配置服务器环境

Anaconda 复制或移植已有环境（复制到别的服务器上）_anaconda复制环境_℡ヾNothing-_哥的博客-CSDN博客

使用ananconda直接在服务器之间快速迁移环境 - 哔哩哔哩

将你的Python代码部署到云服务器上_Pythonwill的博客-CSDN博客_如何用python部署云端服务器

在服务器上搭建自己的python环境(针对小白)_西瓜6的博客-CSDN博客_服务器环境里python

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