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。

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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:

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - lz4-c==1.9.4=h2bbff1b_0- git==2.34.1=haa95532_0- libtiff==4.4.0=h8a3f274_2- sip==4.19.8=py37h6538335_0- sqlite==3.35.4=h2bbff1b_0- libwebp==1.2.4=h2bbff1b_0- libwebp-base==1.2.4=h2bbff1b_0- wrapt==1.12.1=py37he774522_1- mkl_fft==1.3.0=py37h277e83a_2- zstd==1.5.0=h19a0ad4_1- matplotlib-base==3.4.3=py37h49ac443_0- icc_rt==2019.0.0=h0cc432a_1- pyreadline==2.1=py37_1- markdown==3.3.4=py37haa95532_0- certifi==2022.12.7=py37haa95532_0- libbrotlidec==1.0.9=h2bbff1b_7- qt==5.9.7=vc14h73c81de_0- tk==8.6.12=h2bbff1b_0- libbrotlienc==1.0.9=h2bbff1b_7- python==3.7.10=h7840368_100_cpython- pandas==1.2.4=py37hf11a4ad_0- lerc==3.0=hd77b12b_0- six==1.15.0=py37haa95532_0- cython==0.29.23=py37hd77b12b_0- ca-certificates==2022.10.11=haa95532_0- libpng==1.6.37=h2a8f88b_0- xz==5.2.8=h8cc25b3_0- brotli==1.0.9=h2bbff1b_7- libdeflate==1.8=h2bbff1b_5- mkl_random==1.2.1=py37hf11a4ad_2- tensorboard==1.14.0=py37he3c9ec2_0- openssl==1.1.1s=h2bbff1b_0- wincertstore==0.2=py37_0- libprotobuf==3.14.0=h23ce68f_0- tornado==6.2=py37h2bbff1b_0- brotli-bin==1.0.9=h2bbff1b_7- zlib==1.2.11=h62dcd97_4- absl-py==0.12.0=py37haa95532_0- libbrotlicommon==1.0.9=h2bbff1b_7- hdf5==1.10.4=h7ebc959_0- pip==21.0.1=py37haa95532_0- tensorflow-base==1.14.0=gpu_py37h55fc52a_0- astor==0.8.1=py37haa95532_0- coverage==5.5=py37h2bbff1b_2- pyqt==5.9.2=py37h6538335_2- tensorflow==1.14.0=gpu_py37h5512b17_0- freetype==2.10.4=hd328e21_0- vc==14.2=h21ff451_1- jpeg==9b=hb83a4c4_2- yaml==0.2.5=he774522_0- icu==58.2=ha925a31_3- scikit-learn==0.24.1=py37hf11a4ad_0- numpy-base==1.16.6=py37h5bb6eb2_3- 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。

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simpleWriting 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:

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - tornado==6.2=py37h2bbff1b_0- absl-py==0.12.0=py37haa95532_0- freetype==2.10.4=hd328e21_0- brotli-bin==1.0.9=h2bbff1b_7- pandas==1.2.4=py37hf11a4ad_0- sip==4.19.8=py37h6538335_0- zstd==1.5.0=h19a0ad4_1- libbrotlicommon==1.0.9=h2bbff1b_7- markdown==3.3.4=py37haa95532_0- matplotlib-base==3.4.3=py37h49ac443_0- tensorboard==1.14.0=py37he3c9ec2_0- jpeg==9b=hb83a4c4_2- libtiff==4.4.0=h8a3f274_2- six==1.15.0=py37haa95532_0- tk==8.6.12=h2bbff1b_0- libdeflate==1.8=h2bbff1b_5- git==2.34.1=haa95532_0- certifi==2022.12.7=py37haa95532_0- lerc==3.0=hd77b12b_0- openssl==1.1.1s=h2bbff1b_0- zlib==1.2.11=h62dcd97_4- astor==0.8.1=py37haa95532_0- libwebp==1.2.4=h2bbff1b_0- scikit-learn==0.24.1=py37hf11a4ad_0- brotli==1.0.9=h2bbff1b_7- tensorflow==1.14.0=gpu_py37h5512b17_0- pyqt==5.9.2=py37h6538335_2- tensorflow-base==1.14.0=gpu_py37h55fc52a_0- mkl_random==1.2.1=py37hf11a4ad_2- yaml==0.2.5=he774522_0- libbrotlidec==1.0.9=h2bbff1b_7- qt==5.9.7=vc14h73c81de_0- libpng==1.6.37=h2a8f88b_0- vs2015_runtime==14.27.29016=h5e58377_2- cython==0.29.23=py37hd77b12b_0- wincertstore==0.2=py37_0- icu==58.2=ha925a31_3- wrapt==1.12.1=py37he774522_1- xz==5.2.8=h8cc25b3_0- vc==14.2=h21ff451_1- sqlite==3.35.4=h2bbff1b_0- pip==21.0.1=py37haa95532_0- ca-certificates==2022.10.11=haa95532_0- python==3.7.10=h7840368_100_cpython- pyreadline==2.1=py37_1- libbrotlienc==1.0.9=h2bbff1b_7- mkl_fft==1.3.0=py37h277e83a_2- icc_rt==2019.0.0=h0cc432a_1- libwebp-base==1.2.4=h2bbff1b_0- coverage==5.5=py37h2bbff1b_2- hdf5==1.10.4=h7ebc959_0- numpy-base==1.16.6=py37h5bb6eb2_3- lz4-c==1.9.4=h2bbff1b_0- 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。

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ pip config unset global.index-urlWriting to /home/LIST_2080Ti/.config/pip/pip.conf(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - pyreadline- icc_rt- vc- 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。

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

5Found 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.

Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failed /Solving environment: | Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failed -UnsatisfiableError: The following specifications were found to be incompatible with each other: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：

- tensorflow-base==1.14.0=gpu_py37h55fc52a_0- zlib==1.2.11=h62dcd97_4- blas=1.0=mkl- setuptools==54.2.0- munkres=1.1.4=py_0- numpy==1.16.6Package fftw conflicts for:Package libgcc-ng conflicts for:- werkzeug=1.0.1=pyhd3eb1b0_0- scipy==1.6.3- keras-base=2.3.1=py37_0- six==1.15.0=py37haa95532_0- openssl==1.1.1s=h2bbff1b_0Package system conflicts for:- intel-openmp==2021.2.0- certifi==2022.12.7=py37haa95532_0- python==3.7.10=h7840368_100_cpython- _tflow_select=2.1.0=gpu- mkl_random==1.2.1=py37hf11a4ad_2- pip==21.0.1=py37haa95532_0Package tzdata conflicts for:- keras-applications=1.0.8=py_1- cudatoolkit=10.0.130=0Package libgcc conflicts for:- keras-preprocessing=1.1.2=pyhd3eb1b0_0- gast=0.4.0=py_0- hdf5==1.10.4=h7ebc959_0- libpng==1.6.37=h2a8f88b_0

完整冲突如下：

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.ymlWarning: 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.Collecting package metadata (repodata.json): doneSolving environment: | Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failed /Solving environment: | Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failed -UnsatisfiableError: The following specifications were found to be incompatible with each other:Output in format: Requested package -> Available versionsPackage tensorflow-base conflicts for:keras==2.3.1=0 -> tensorflow -> 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-> 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']Package zlib conflicts for:keras==2.3.1=0 -> tensorflow -> zlib[version='>=1.2.11, python[version='>=3.5'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.5'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, tensorflow==1.14.0 -> zlib[version='>=1.2.11, python -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.6'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.7'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python[version='>=3.7, zlib[version='>=1.2.11,=1.2.13,=1.2.12, python -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, matplotlib[version='>=2.2'] -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, python -> zlib[version='1.2.*|1.2.11|>=1.2.11,=1.2.13,=1.2.12, numpy[version='>=1.9.1'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15'] -> blas[version='*|*|1.0|1.1|1.0',build='openblas|openblas|mkl|openblas|mkl']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']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']Package setuptools conflicts for:joblib==1.0.1=pyhd3eb1b0_0 -> setuptoolspip -> setuptoolsseaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> setuptools[version=' munkresPackage numpy conflicts for:keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> numpy[version='>=1.9.1']seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15']seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> numpy[version='>=1.11.*|>=1.12.1,=1.14.6,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.19.5,=1.21.5,=1.18.5,=1.21.4,=1.17.5,=1.16.6,=1.19.4,=1.16.5,=1.19.2,=1.15.4,=1.18.4,=1.18.1,=1.9.3,=1.9.*|>=1.16,=1.21,=1.21.2,=1.20.2,=1.13.3,=1.11.3,=1.20.3,=1.21.6,=1.23.5,=1.21.6,=1.23.4,=1.20.3,=1.20.3,=1.21.6,=1.20.3,=1.21.6,=1.9|>=1.19,=1.21,=1.19,=1.21,=1.16,=1.21,=1.16.6,=1.21.2,=1.15.1, numpy[version='>=1.9.1']keras-base==2.3.1=py37_0 -> h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,=1.16.5,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.4,=1.19.4,=1.16.6,=1.19.2,=1.9.3,=1.8|>=1.8,=1.21.5,=1.21.2,=1.11.3,=1.20.3,=1.21.6,=1.23.5,=1.21.6,=1.23.4,=1.20.3,=1.20.3,=1.21.6,=1.20.3,=1.21.6,=1.18.1,=1.9|>=1.11|>=1.19,=1.21,=1.19,=1.21,=1.16,=1.21,=1.16.6,=1.21.2,=1.15.1, numpy[version='>=1.9.1']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,=1.14.6,=1.9.1|>=1.16.1|>=1.8.2|>=1.11']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,=1.14.6,=1.16.5,=1.16.6,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.20.3,=1.20.3,=1.21.6,=1.21.6,=1.23.5,=1.23.5,=1.21.6,=1.23.4,=1.23.4,=1.20.3,=1.21.6,=1.21.5,=1.20.3,=1.21.6,=1.21.4,=1.19.4,=1.19.2,=1.18.1,=1.9.3,=1.9|>=1.19,=1.21,=1.19,=1.21,=1.16,=1.21,=1.16.6,=1.21.2,=1.15.1, h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,=1.16.5,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.4,=1.19.4,=1.16.6,=1.19.2,=1.9.3,=1.8|>=1.8,=1.21.5,=1.21.2,=1.11.3, scipy[version='>=0.14'] -> fftw[version='>=3.3.9, scipy[version='>=1.0'] -> fftw[version='>=3.3.9, scipy[version='>=0.14'] -> fftw[version='>=3.3.9, 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']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']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']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']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']blas==1.0=mkl -> mkl -> libgcc-ng[version='>=11.2.0']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']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']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']keras==2.3.1=0 -> tensorflow -> libgcc-ng[version='>=5.4.0|>=7.5.0|>=9.4.0']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']tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> libgcc-ng[version='>=5.4.0']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']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']cudnn==7.6.5=cuda10.0_0 -> cudatoolkit[version='>=10.0, libgcc-ng[version='>=10.3.0|>=12|>=9.4.0|>=9.3.0']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']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']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']Package werkzeug conflicts for:werkzeug==1.0.1=pyhd3eb1b0_0keras==2.3.1=0 -> tensorflow -> werkzeug[version='>=0.11.10']Package scipy conflicts for:keras==2.3.1=0 -> keras-base=2.3.1 -> scipy[version='>=0.14']keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14']keras-base==2.3.1=py37_0 -> scipy[version='>=0.14']seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0']Package keras-base conflicts for:keras==2.3.1=0 -> keras-base=2.3.1keras-base==2.3.1=py37_0Package six conflicts for:keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> six[version='>=1.9.0']keras-base==2.3.1=py37_0 -> h5py -> sixkeras-base==2.3.1=py37_0 -> six[version='>=1.9.0']keras-applications==1.0.8=py_1 -> h5py -> sixkeras==2.3.1=0 -> keras-base=2.3.1 -> six[version='>=1.10.0|>=1.9.0']Package openssl conflicts for:joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.7, openssl[version='>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1e,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1l,=3.0.0,=1.1.1s,=1.1.1q,=1.1.1n,=1.1.1k,=1.1.1d,=1.1.1c,=1.1.1b, python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.5'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.7'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.5'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, tensorflow -> openssl[version='>=1.1.1l, python -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python[version='>=3.6'] -> openssl[version='1.0.*|>=1.0.2o,=1.0.2p,=1.1.1a,=1.1.1d,=1.1.1e,=1.1.1f,=1.1.1g,=1.1.1h,=1.1.1i,=1.1.1j,=1.1.1k,=1.1.1l,=1.1.1n,=1.1.1o,=1.1.1q,=1.1.1s,=3.0.7,=3.0.5,=3.0.3,=3.0.2,=3.0.0,=1.1.1m,=1.1.1c,=1.1.1b,=1.0.2n,=1.0.2m,=1.0.2l, python -> system==5.8wheel==0.36.2=pyhd3eb1b0_0 -> python -> system==5.8werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> system==5.8pip -> python[version='>=3'] -> system==5.8gast==0.4.0=py_0 -> python -> system==5.8keras-applications==1.0.8=py_1 -> python -> system==5.8Package intel-openmp conflicts for:blas==1.0=mkl -> mkl -> intel-openmp[version='2021.*|2022.*']keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> intel-openmp[version='>=2021.4.0, scipy[version='>=0.14'] -> intel-openmp[version='>=2021.4.0, scipy[version='>=1.0'] -> intel-openmp[version='>=2021.4.0, setuptools -> certifi[version='>=2016.09|>=2016.9.26']joblib==1.0.1=pyhd3eb1b0_0 -> setuptools -> certifi[version='>=2016.09|>=2016.9.26']Package python conflicts for:keras-base==2.3.1=py37_0 -> h5py -> python[version='2.6.*|2.7.*|3.5.*|3.6.*|>=2.7,=3.10,=3.11,=3.8,=3.9,=3.6,=3.5,=3.6']keras-base==2.3.1=py37_0 -> python[version='>=3.7, _tflow_select==2.1.0=gpukeras==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']tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> _tflow_select[version='==2.2.0|==2.3.0',build='eigen|mkl']Package mkl_random conflicts for:seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15'] -> mkl_random[version='>=1.0.2,=1.2.1,=1.0.4, numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,=1.2.1,=1.0.4, numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,=1.2.1,=1.0.4, numpy[version='>=1.9.1'] -> mkl_random[version='>=1.0.2,=1.2.1,=1.0.4, python -> pipthreadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> pipkeras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pippipkeras-base==2.3.1=py37_0 -> python[version='>=3.7, piptyping_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> pipjoblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pipmunkres==1.1.4=py_0 -> python -> pipwerkzeug==1.0.1=pyhd3eb1b0_0 -> python -> pipzipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pipseaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pipfonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pipkeras-applications==1.0.8=py_1 -> python -> pipwheel==0.36.2=pyhd3eb1b0_0 -> python -> pipPackage tzdata conflicts for:gast==0.4.0=py_0 -> python -> tzdatathreadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> tzdatawheel==0.36.2=pyhd3eb1b0_0 -> python -> tzdatakeras-applications==1.0.8=py_1 -> python -> tzdataseaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdatatyping_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> tzdatapip -> python[version='>=3.7'] -> tzdatajoblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdatamunkres==1.1.4=py_0 -> python -> tzdatawerkzeug==1.0.1=pyhd3eb1b0_0 -> python -> tzdatazipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdatakeras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdatafonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdataPackage keras-applications conflicts for:keras==2.3.1=0 -> keras-base=2.3.1 -> keras-applications[version='>=1.0.6']keras-applications==1.0.8=py_1keras-base==2.3.1=py37_0 -> keras-applications[version='>=1.0.6']Package cudatoolkit conflicts for:cudnn==7.6.5=cuda10.0_0 -> cudatoolkit[version='>=10.0, tensorflow -> cudatoolkit[version='10.2|10.2.*|11.0|11.0.*|11.1|11.1.*|>=11.2, scipy[version='>=0.14'] -> libgcc==5.2.0keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> libgcc==5.2.0Package keras-preprocessing conflicts for:keras==2.3.1=0 -> keras-base=2.3.1 -> keras-preprocessing[version='>=1.0.5']keras-preprocessing==1.1.2=pyhd3eb1b0_0keras-base==2.3.1=py37_0 -> keras-preprocessing[version='>=1.0.5']Package gast conflicts for:keras==2.3.1=0 -> tensorflow -> gast[version='>=0.2.0']gast==0.4.0=py_0Package hdf5 conflicts for: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,=1.10.4,=1.10.4,=1.10.5,=1.10.5,=1.10.5,=1.10.6,=1.10.6,=1.10.6,=1.12.1,=1.12.1,=1.12.1,=1.12.2,=1.10.2,=1.8.20,=1.8.18,=1.10.1, h5py -> hdf5[version='1.10.1|1.10.1.*|1.10.2.*|>=1.10.3,=1.10.4,=1.10.4,=1.10.4,=1.10.5,=1.10.5,=1.10.5,=1.10.6,=1.10.6,=1.10.6,=1.12.1,=1.12.1,=1.12.1,=1.12.2,=1.10.2,=1.8.20,=1.8.18,=1.10.1, tensorflow==1.14.0 -> libpng[version='>=1.6.37, tensorflow -> libpng[version='>=1.6.37, matplotlib[version='>=2.2'] -> libpng[version='>=1.6.34,=1.6.35,=1.6.36,=1.6.37, tensorflow -> __cuda- keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']Your installed version is: 2.31Note 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。

LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - vs2015_runtime- icc_rt- vc- 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：

name: catchannels:- conda-forge- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2dependencies:- _tflow_select=2.1.0=gpu- absl-py- astor- blas=1.0=mkl- brotli- brotli-bin- ca-certificates- certifi- coverage- cudatoolkit=10.0.130=0- cudnn=7.6.5=cuda10.0_0- cython- fonttools=4.25.0=pyhd3eb1b0_0- freetype- gast=0.4.0=py_0- git- hdf5- icu- joblib=1.0.1=pyhd3eb1b0_0- jpeg- keras=2.3.1=0- keras-applications=1.0.8=py_1- keras-base=2.3.1=py37_0- keras-preprocessing=1.1.2=pyhd3eb1b0_0- lerc- libbrotlicommon- libbrotlidec- libbrotlienc- libdeflate- libpng- libprotobuf- libtiff- libwebp- libwebp-base- lz4-c- markdown- matplotlib-base- mkl_fft- mkl_random- munkres=1.1.4=py_0- numpy-base- openssl- pandas- pip- pyqt- python- qt- scikit-learn- seaborn=0.11.2=pyhd3eb1b0_0- sip- six- sqlite- tensorboard- tensorflow- tensorflow-base- tensorflow-gpu=1.14.0=h0d30ee6_0- threadpoolctl=2.1.0=pyh5ca1d4c_0- tk- tornado- typing_extensions=3.7.4.3=pyha847dfd_0- werkzeug=1.0.1=pyhd3eb1b0_0- wheel=0.36.2=pyhd3eb1b0_0- wincertstore- wrapt- xz- yaml- zipp=3.4.1=pyhd3eb1b0_0- zlib- zstd- pip:- appdirs==1.4.4- astroid==2.5.6- cached-property==1.5.2- chardet==4.0.0- charset-normalizer==2.1.1- colorama==0.4.4- cycler==0.10.0- decorator==5.1.1- dill==0.3.4- emd-signal==1.2.2- flatbuffers==1.12- grpcio==1.32.0- h5py==2.8.0- idna==3.4- importlib-metadata==4.0.1- intel-openmp==2021.2.0- isort==5.8.0- jinja2==3.1.2- kiwisolver==1.3.1- lazy-object-proxy==1.6.0- markupsafe==2.1.1- matplotlib==3.4.2- mccabe==0.6.1- mkl==2021.2.0- mkl-service==2.3.0- mne==1.1.1- multiprocess==0.70.12.2- numpy==1.16.6- opt-einsum==3.3.0- packaging==21.3- pathos==0.2.8- pillow==8.2.0- pooch==1.6.0- pox==0.3.0- ppft==1.6.6.4- protobuf==3.16.0- pyasn1-modules==0.2.8- pydot==1.4.2- pydot-ng==2.0.0- pylint==2.8.2- pyparsing==2.4.7- python-dateutil==2.8.1- python-graphviz==0.16- pytz==2021.1- pyyaml==5.4.1- requests==2.28.1- scipy==1.6.3- setuptools==54.2.0- shadowsocks==3.0.0- shadowsocks-py==2.9.1- tbb==2021.2.0- tensorboard-data-server==0.6.1- tensorboard-plugin-wit==1.8.0- tensorflow-estimator==2.4.0- termcolor==1.1.0- toml==0.10.2- tqdm==4.64.1- typed-ast==1.4.3- vc- vs2015_runtime- pyreadline- icc_rt- i https://pypi.tuna.tsinghua.edu.cn/simple

报错如下：

The following specifications were found to be incompatible with your system:- feature:/linux-64::__cuda==11.7=0- feature:/linux-64::__glibc==2.31=0- feature:|@/linux-64::__cuda==11.7=0- feature:|@/linux-64::__glibc==2.31=0- brotli -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- brotli-bin -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- coverage -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- cython -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- freetype -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']- git -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- hdf5 -> libgfortran-ng -> __glibc[version='>=2.17']- icu -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- jpeg -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- keras==2.3.1=0 -> tensorflow -> __cuda- keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']- lerc -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- libbrotlicommon -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- libbrotlidec -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- libbrotlienc -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- libdeflate -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- libpng -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']- libprotobuf -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- libtiff -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- libwebp -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- libwebp-base -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- lz4-c -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']- matplotlib-base -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- mkl_fft -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- mkl_random -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- numpy-base -> libgcc-ng[version='>=11.2.0'] -> __glibc[version='>=2.17']- openssl -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- pandas -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- pyqt -> qt-main[version='>=5.15.6, __glibc[version='>=2.17|>=2.17, libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- qt -> qt-main=5.15.6 -> __glibc[version='>=2.17|>=2.17, libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- sip -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- sqlite -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- tensorboard -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']- tensorflow -> __cuda- tensorflow -> __glibc[version='>=2.17']- tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17, __cuda- tensorflow-base -> __glibc[version='>=2.17']- tensorflow-base -> cudatoolkit[version='>=11.2, __glibc[version='>=2.17, libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- tornado -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- wincertstore -> __win- wrapt -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- xz -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']- yaml -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']- zlib -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']- zstd -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']Your installed version is: 2.31Note 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：

name: catchannels:- conda-forge- defaultdependencies:- _tflow_select- absl-py- astor- blas- brotli- brotli-bin- ca-certificates- certifi- coverage- cudatoolkit- cudnn=7.6.5=cuda10.0_0- cython- fonttools=4.25.0=pyhd3eb1b0_0- freetype- gast- git- hdf5- icu- joblib=1.0.1=pyhd3eb1b0_0- jpeg- keras=2.3.1=0- keras-applications- keras-base- keras-preprocessing- lerc- libbrotlicommon- libbrotlidec- libbrotlienc- libdeflate- libpng- libprotobuf- libtiff- libwebp- libwebp-base- lz4-c- markdown- matplotlib-base- mkl_fft- mkl_random- munkres- numpy-base- openssl- pandas- pip- pyqt- python- qt- scikit-learn- seaborn=0.11.2=pyhd3eb1b0_0- sip- six- sqlite- tensorboard- tensorflow- tensorflow-base- tensorflow-gpu=1.14.0=h0d30ee6_0- threadpoolctl=2.1.0=pyh5ca1d4c_0- tk- tornado- typing_extensions=3.7.4.3=pyha847dfd_0- werkzeug- wheel=0.36.2=pyhd3eb1b0_0- wincertstore- wrapt- xz- yaml- zipp=3.4.1=pyhd3eb1b0_0- zlib- zstd- pip:- appdirs==1.4.4- astroid==2.5.6- cached-property==1.5.2- chardet==4.0.0- charset-normalizer==2.1.1- colorama==0.4.4- cycler==0.10.0- decorator==5.1.1- dill==0.3.4- emd-signal==1.2.2- flatbuffers==1.12- grpcio==1.32.0- h5py==2.8.0- idna==3.4- importlib-metadata==4.0.1- intel-openmp- isort==5.8.0- jinja2==3.1.2- kiwisolver==1.3.1- lazy-object-proxy==1.6.0- markupsafe==2.1.1- matplotlib==3.4.2- mccabe==0.6.1- mkl==2021.2.0- mkl-service==2.3.0- mne==1.1.1- multiprocess==0.70.12.2- numpy- opt-einsum==3.3.0- packaging==21.3- pathos==0.2.8- pillow==8.2.0- pooch==1.6.0- pox==0.3.0- ppft==1.6.6.4- protobuf==3.16.0- pyasn1-modules==0.2.8- pydot==1.4.2- pydot-ng==2.0.0- pylint==2.8.2- pyparsing==2.4.7- python-dateutil==2.8.1- python-graphviz==0.16- pytz==2021.1- pyyaml==5.4.1- requests==2.28.1- scipy- setuptools- shadowsocks==3.0.0- shadowsocks-py==2.9.1- tbb==2021.2.0- tensorboard-data-server==0.6.1- tensorboard-plugin-wit==1.8.0- tensorflow-estimator==2.4.0- termcolor==1.1.0- toml==0.10.2- tqdm==4.64.1- typed-ast==1.4.3- vc- vs2015_runtime- pyreadline- icc_rt- i https://pypi.tuna.tsinghua.edu.cn/simple

这个时候检查冲突报错：

The following specifications were found to be incompatible with your system: - feature:/linux-64::__cuda==11.7=0 - feature:/linux-64::__glibc==2.31=0 - feature:|@/linux-64::__cuda==11.7=0 - feature:|@/linux-64::__glibc==2.31=0 - keras==2.3.1=0 -> tensorflow -> __cuda - keras==2.3.1=0 -> tensorflow -> __glibc[version='>=2.17']Your installed version is: 2.31Note 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：

(venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda env create -f environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - kera- cudnn==7.6.5=cuda10.0_0- numpy-base- tensorflow-gpu==1.14.0=h0d30ee6_0- _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：

(venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda env create -f environment.ymlCollecting package metadata (repodata.json): doneSolving environment: failedResolvePackageNotFound: - _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.

libpng -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']blas -> _openmp_mutex[version='*|>=4.5',build=*_llvm]yaml -> libgcc-ng[version='>=9.4.0'] -> _openmp_mutex[version='>=4.5']zlib -> libgcc-ng[version='>=12'] -> _openmp_mutex[version='>=4.5']Package numpy conflicts for:matplotlib-base -> contourpy[version='>=1.0.1'] -> numpy[version='>=1.16']scikit-learn -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11.3,=1.14.6,=1.16.5,=1.16.6,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.5,=1.21.4,=1.19.4,=1.19.2,=1.9.3,=1.9|1.9.*|1.8.*|1.7.*|1.6.*|1.5.*']tensorflow-gpu -> numpy[version='1.11.*|1.12.*|>=1.11|>=1.11.0']keras-applications -> numpy[version='>=1.9.1']scikit-learn -> scipy -> numpy[version='>=1.11|>=1.18.1,=1.20.3,=1.20.3,=1.20.3,=1.20.3,=1.23.5,=1.21.6,=1.21.6,=1.23.4,=1.21.6,=1.21.6, numpy[version='>=1.15']pandas -> scipy -> numpy[version='1.5.*|>=1.11.3,=1.20.3,=1.20.3,=1.20.3,=1.20.3,=1.23.5,=1.21.6,=1.21.6,=1.23.4,=1.21.6,=1.21.6, pandas[version='>=0.23'] -> numpy[version='>=1.11.*|>=1.12.1,=1.14.6,=1.15.4,=1.16.5,=1.16.6,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.5,=1.21.4,=1.19.4,=1.19.2,=1.18.4,=1.18.1,=1.9.3,=1.9.*|>=1.20.3,=1.23.5,=1.21.6,=1.21.6,=1.23.4,=1.20.3,=1.20.3,=1.21.6,=1.20.3,=1.21.6,=1.11.3,=1.9']tensorflow-base -> h5py[version='>=2.9.0'] -> numpy[version='>=1.12.0|>=1.16.5,=1.17.5,=1.21.4,=1.23.4,=1.23.5,=1.19.4,=1.16.6,=1.19.2,=1.9.3,=1.9.1|>=1.16.1|>=1.13.3']keras-base -> numpy[version='>=1.9.1']tensorflow-base -> numpy[version='>=1.11|>=1.13.3,=1.14.6,=1.16.1,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.21.5,=1.19.2,=1.19']pandas -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11|>=1.11.*|>=1.12.1,=1.14.6,=1.15.4,=1.16.5,=1.16.6,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.5,=1.21.4,=1.19.4,=1.19.2,=1.18.4,=1.18.1,=1.9.3,=1.9.*|>=1.9|>=1.8|>=1.7|>=1.12|1.9.*|1.8.*|1.7.*|1.6.*']keras-preprocessing -> numpy[version='>=1.9.1']tensorboard -> numpy[version='>=1.12.0|>=1.16']keras-preprocessing -> scipy[version='>=0.14'] -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.11|>=1.11.3,=1.14.6,=1.16.5,=1.16.6,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.20.3,=1.20.3,=1.23.5,=1.23.5,=1.21.6,=1.21.6,=1.21.6,=1.23.4,=1.23.4,=1.20.3,=1.21.6,=1.21.5,=1.20.3,=1.21.6,=1.21.4,=1.19.4,=1.19.2,=1.18.1,=1.9.3,=1.9|1.9.*|1.8.*']tensorflow -> numpy[version='1.11.*|1.12.*|>=1.10.1|>=1.11.0|>=1.12.1|>=1.13.3|>=1.13.3,=1.14.6,=1.16.1|>=1.8.2|>=1.11']matplotlib-base -> numpy[version='>=1.11.3,=1.14.6,=1.15.4,=1.16.5,=1.16.6,=1.17.5,=1.17|>=1.19|>=1.20.3,=1.23.5,=1.21.6,=1.23.4,=1.19.5,=1.21.4,=1.18.5,=1.19.4,=1.19.2,=1.9.3, h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,=1.16.5,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.4,=1.19.4,=1.16.6,=1.19.2,=1.9.3,=1.8|>=1.8, numpy[version='>=1.11|>=1.14.6,=1.16.5,=1.17.5,=1.18.5,=1.19.5,=1.9.3, mkl-service[version='>=2, numpy[version='>=1.11.3, h5py -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|>=1.14.6,=1.16.5,=1.17.5,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.5,=1.23.4,=1.21.4,=1.19.4,=1.16.6,=1.19.2,=1.9.3,=1.8|>=1.8,=1.20.3,=1.23.5,=1.21.6,=1.21.6,=1.23.4,=1.20.3,=1.20.3,=1.21.6,=1.21.5,=1.20.3,=1.21.6,=1.18.1,=1.11.3,=1.9|>=1.11']tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> numpy[version='>=1.12.0|>=1.16.1,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.21.5,=1.19.2,=1.19|>=1.9.1']mkl_fft -> numpy[version='>=1.11|>=1.11.3,=1.14.6,=1.16.5,=1.16.6,=1.18.5,=1.19.5,=1.20.3,=1.21.6,=1.23.4,=1.21.5,=1.21.4,=1.19.2, keras[version='>=2.1.6'] -> tensorflow[version='>=2.2']keras-base -> tensorflow[version='>=2.2']tensorflowkeras-preprocessing -> keras[version='>=2.1.6'] -> tensorflow[version='>=2.2']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']Package astor conflicts for:tensorflow -> astor[version='>=0.6.0']astortensorflow-base -> astor[version='>=0.6.0']Package libgfortran5 conflicts for:blas -> libgfortran5[version='>=10.3.0|>=10.4.0|>=9.4.0|>=9.3.0']hdf5 -> libgfortran5[version='>=10.3.0|>=10.4.0|>=9.4.0|>=9.3.0']keras-base -> scipy[version='>=0.14'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']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']pandas -> scipy -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']keras-preprocessing -> scipy[version='>=0.14'] -> libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.4.0|>=9.3.0']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']scikit-learn -> libcblas[version='>=3.9.0, libgfortran5[version='>=10.3.0|>=10.4.0|>=11.3.0|>=9.3.0|>=9.4.0']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']Package jbig conflicts for:qt -> libtiff=4.0 -> jbig==2.1libwebp -> libtiff[version='>=4.3.0, jbig==2.1libtiff -> jbig==2.1Package yaml conflicts for:keras-base -> pyyaml -> yaml[version='0.1.4|0.1.6|>=0.1.7,=0.2.2,=0.2.5, fonttools[version='>=4.22.0']Package keras-preprocessing conflicts for:tensorflow -> keras-preprocessing[version='>=1.0.5']tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> keras-preprocessing[version='>=1.1.1|>=1.1.2,=1.1.2']tensorflow-base -> keras-preprocessing[version='>=1.0.5|>=1.1.1|>=1.1.2,=1.1.2']keras-preprocessingkeras-applications -> keras[version='>=2.1.6'] -> keras-preprocessing[version='1.0.2.*|>=1.0.5|>=1.1.0']Package theano conflicts for:keras-applications -> keras[version='>=2.1.6'] -> theanokeras-preprocessing -> keras[version='>=2.1.6'] -> theanoPackage icu conflicts for:pyqt -> qt-main[version='>=5.15.6, icu[version='54.*|>=58.2,=64.2,=67.1,=68.1,=68.2,=69.1,=70.1, icu[version='54.*|58.*|>=58.2,=64.2,=67.1,=68.1,=69.1, qt-main=5.15.6 -> icu[version='69.*|>=68.2,=70.1, icu[version='>=68.1,=68.2,=69.1,=70.1, tensorflow-base==2.11.0[build=cpu_py310*_0] -> icu[version='>=68.1,=68.2,=69.1,=70.1, icu[version='>=58.2,=64.2,=67.1, pyqt5-sip==12.11.0=py310heca2aa9_3 -> sippyqt -> sip[version='4.15.5|4.16.5|4.18|4.18.*|>=4.19.4,=6.5.1,=6.6.2,=6.7.2,=6.7.5,=4.18|>=4.16.4, tensorflow-tensorboardtensorflow-gpu -> tensorflow-tensorboardPackage dataclasses conflicts for:tensorflow-gpu -> werkzeug[version='>=0.11.10'] -> dataclassestensorboard -> werkzeug[version='>=1.0.1'] -> dataclassestensorflow -> werkzeug[version='>=0.11.10'] -> dataclasseswerkzeug -> dataclassesPackage ordereddict conflicts for:absl-py -> enum34 -> ordereddictpyqt -> enum34 -> ordereddictPackage protobuf conflicts for:tensorflow-gpu -> protobuf[version='>=3.1.0|>=3.2.0']tensorflow-base -> tensorboard[version='>=2.10, protobuf[version='>=3.4.0|>=3.6.0|>=3.9.2, protobuf[version='>=3.3.0|>=3.4.0|>=3.6.0|>=3.9.2|>=3.9.2,=3.8.0|>=3.6.1']tensorflow-base -> protobuf[version='>=3.3.0|>=3.6.1|>=3.9.2']tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> protobuf[version='>=3.9.2']tensorflow-gpu -> tensorflow-gpu-base==1.3.0 -> protobuf[version='>=3.3.0']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']Package packaging conflicts for:pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> packagingtensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> packagingtensorflow-base -> wheel[version='>=0.35, packaging[version='>=20.2']matplotlib-base -> packaging[version='>=20.0']tensorboard -> wheel[version='>=0.26'] -> packaging[version='>=20.2']pip -> wheel -> packaging[version='>=20.2']tensorflow-base -> packagingsip -> packagingPackage joblib conflicts for:scikit-learn -> joblib[version='>=0.11|>=1.0.0|>=1.1.1']joblib==1.0.1=pyhd3eb1b0_0Package libwebp-base conflicts for: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,=1.2.3, 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]matplotlib-base -> pillow[version='>=6.2.0'] -> libwebp-base[version='>=1.2.2,=1.2.4, libtiff[version='>=4.4.0, libwebp-base[version='>=1.1.0, qt-webengine=5.15 -> libwebp-base[version='>=1.1.0,=1.2.2,=1.2.4,=1.2.3, libwebp-base[version='>=1.1.0,=1.2.3,=1.2.4, libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']libbrotlidec -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']brotli-bin -> libbrotlidec==1.0.9=h166bdaf_8 -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']brotli -> libbrotlidec==1.0.9=h166bdaf_8 -> libbrotlicommon==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']libbrotlicommonPackage libssh2 conflicts for:hdf5 -> libcurl[version='>=7.87.0, libssh2[version='>=1.10.0,=1.9.0, libcurl[version='>=7.71.1, libssh2[version='>=1.10.0,=1.9.0,=1.8.0, curl -> libssh2[version='1.8.*|>=1.10.0,=1.9.0,=1.8.0, libcurl[version='>=7.86.0, libssh2[version='>=1.10.0,=1.9.0,=1.8.0, futures[version='>=3.1.1']tensorboard -> grpcio[version='>=1.6.3'] -> futures[version='>=2.2.0']tornado -> futurestensorflow -> grpcio[version='>=1.8.6'] -> futures[version='>=2.2.0|>=3.1.1']tensorflow-base -> grpcio[version='>=1.8.6'] -> futures[version='>=2.2.0|>=3.1.1']matplotlib-base -> tornado -> futuresPackage threadpoolctl conflicts for:scikit-learn -> threadpoolctl[version='>=2.0.0']threadpoolctl==2.1.0=pyh5ca1d4c_0Package html5lib conflicts for:tensorflow -> html5lib==0.9999999tensorflow -> bleach==1.5.0 -> html5lib[version='>=0.999,!=0.9999,!=0.99999, graalpy[version='>=22.3.0, curlgit -> curl[version='>=7.44.0,=7.59.0,=7.64.0,=7.64.1,=7.69.1,=7.71.1,=7.75.0,=7.77.0,=7.78.0,=7.79.1,=7.80.0,=7.81.0,=7.82.0,=7.83.1, werkzeug[version='>=0.11.10|>=0.11.15|>=1.0.1|>=0.14']werkzeugtensorflow-base -> tensorboard[version='>=2.11, werkzeug[version='>=0.11.10|>=0.11.15|>=1.0.1']tensorflow-gpu -> werkzeug[version='>=0.11.10']tensorflow -> werkzeug[version='>=0.11.10']Package brotli conflicts for:brotlimatplotlib-base -> fonttools[version='>=4.22.0'] -> brotli[version='>=1.0.1']fonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1']Package bleach conflicts for:tensorboard -> bleach[version='1.5.0|>=1.5.0, bleach==1.5.0tensorflow-gpu -> bleach==1.5.0Package lz4-c conflicts for:zstd -> lz4-c[version='>=1.8.1.2,=1.8.3,=1.9.2,=1.9.3,=1.9.3, zstd[version='>=1.5.2, lz4-c[version='>=1.8.3,=1.9.2,=1.9.3,=1.9.3, lz4 -> lz4-c=1.8.1lz4-cPackage libtiff conflicts for:libwebp -> libtiff[version='>=4.0.10,=4.1.0,=4.2.0,=4.3.0,=4.4.0,=4.5.0,=4.0.9, libtiff[version='4.0.*|>=4.0.10, qt[version='>=4.8.6, libtiff[version='4.0.*|>=4.0.10, pillow[version='>=6.2.0'] -> libtiff[version='>=4.0.10,=4.1.0,=4.2.0,=4.3.0,=4.3.0,=4.4.0,=4.5.0, gtk2 -> libtiff[version='>=4.0.3,=4.0.9,=4.1.0, mkl[version='>=2022.0.1, tbb=2021mkl_fft -> mkl[version='>=2022.1.0, tbb=2021blas -> mkl -> tbb=2021Package libgfortran4 conflicts for:blas -> libgfortran4[version='>=7.5.0']blas -> libgfortran-ng -> libgfortran4=7.5.0Package mpc conflicts for:tensorflow-gpu -> libgcc -> mpc[version='>=0.8.0']pyqt -> libgcc -> mpc[version='>=0.8.0']qt -> libgcc -> mpc[version='>=0.8.0']Package qt conflicts for:qtpyqt -> qt[version='4.8.*|5.6.*|5.9.*|>=5.12.5,=5.12.9,=5.9.7,=5.6.2,=4.8.6, google-auth[version='>=1.6.3, requests[version='>=2.20.0, tensorboard[version='>=2.11, requests[version='>=2.21.0|>=2.21.0, requests[version='>=2.21.0|>=2.21.0, pillow[version='>=6.2.0'] -> libwebpqt -> qt-webengine=5.15 -> libwebplibtiff -> libwebpPackage backports conflicts for:tensorflow-base -> backports.weakref[version='>=1.0rc1'] -> backportstornado -> ssl_match_hostname -> backportstensorflow -> backports.weakref[version='>=1.0rc1'] -> backportstensorflow-gpu -> backports.weakref==1.0rc1 -> backportsmatplotlib-base -> backports.functools_lru_cache -> backportsPackage pandas conflicts for:pandasseaborn==0.11.2=pyhd3eb1b0_0 -> pandas[version='>=0.23']Package enum34 conflicts for:tensorflow -> absl-py[version='>=0.1.6'] -> enum34[version='>=1.0.4']tensorflow -> enum34[version='>=1.1.6']tensorboard -> absl-py[version='>=0.4'] -> enum34[version='>=1.0.4']tensorflow-base -> absl-py[version='>=0.4.0'] -> enum34[version='>=1.0.4']pyqt -> enum34absl-py -> enum34tensorflow-base -> enum34[version='>=1.1.6']Package munkres conflicts for:matplotlib-base -> fonttools[version='>=4.22.0'] -> munkresfonttools==4.25.0=pyhd3eb1b0_0 -> munkresmunkresPackage zstd conflicts for:zstdlibtiff -> zstd[version='>=1.3.3,=1.4.0,=1.4.3,=1.4.4,=1.4.5,=1.4.9,=1.5.0,=1.5.2, qt-main[version='>=5.15.6, zstd[version='>=1.5.0,=1.5.2, libtiff[version='>=4.5.0, zstd[version='>=1.3.3,=1.4.0,=1.4.3,=1.4.4,=1.4.5,=1.4.9,=1.5.0,=1.5.2, qt-main=5.15.6 -> zstd[version='>=1.3.3,=1.4.0,=1.4.3,=1.4.4,=1.4.5,=1.4.8,=1.4.9,=1.5.0,=1.5.2,=1.5.1, libdeflate[version='>=1.10,=1.12,=1.13,=1.14,=1.16,=1.17,=1.8,=1.7, libtiff[version='>=4.0.10, libdeflate[version='>=1.10,=1.12,=1.13,=1.14,=1.8,=1.7, libtiff[version='>=4.5.0, libdeflate[version='>=1.10,=1.12,=1.13,=1.14,=1.16,=1.17,=1.8,=1.7, tensorflow-base==2.11.0[build=cuda112py39*_0] -> cudnn[version='>=7.6.5.32,=8.4.1.50,=8.2.1.32, cudnn[version='5.1|5.1.*|6.0.*']tensorflow-base -> cudnn[version='>=7.6.5.32,=8.4.1.50,=8.2.1.32, matplotlib[version='>=2.2'] -> pyqt[version='>=5.12.3,=5|>=5.6.0,=5.9.2, xorg-libx11 -> libxcb=1matplotlib-base -> pillow[version='>=6.2.0'] -> libxcb[version='>=1.13, qt-main[version='>=5.15.6, libxcb[version='>=1.13, libxcb[version='>=1.13, libcurl[version='>=7.87.0, libnghttp2[version='>=1.41.0,=1.43.0,=1.47.0, libcurl[version='>=7.86.0, libnghttp2[version='>=1.41.0,=1.43.0,=1.47.0, libcurl[version='>=7.71.1, libnghttp2[version='>=1.41.0,=1.43.0,=1.47.0, libtiff[version='>=4.5.0, lerc[version='>=2.2.1,=3.0,=4.0.0, lerc[version='>=2.2.1,=3.0,=4.0.0, libtiff[version='>=4.0.10, lerc[version='>=2.2.1,=3.0,=4.0.0, packaging -> pyparsing[version='=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,=2.0.2']matplotlib-base -> pyparsing[version='>=2.0.3,!=2.0.4,!=2.1.2,!=2.1.6|>=2.2.1|>=2.3.1']matplotlib-base -> packaging[version='>=20.0'] -> pyparsing[version='=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,=2.0.2']sip -> packaging -> pyparsing[version='=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,=2.0.2']Package pypy3.6 conflicts for:tornado -> pypy3.6[version='>=7.3.1|>=7.3.2|>=7.3.3']tornado -> python[version='>=3.6, pypy3.6[version='7.3.*|7.3.0.*|7.3.1.*|7.3.2.*|7.3.3.*']Package wrapt conflicts for:tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> wrapt[version='>=1.11.0|>=1.12.1,=1.11.1']tensorflow-base -> wrapt[version='>=1.11.0|>=1.12.1,=1.11.1']wraptPackage llvm-openmp conflicts for:mkl_fft -> mkl[version='>=2022.1.0, 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']blas -> openblas -> llvm-openmp[version='>=10.0.1|>=15.0.7|>=15.0.6|>=14.0.3']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']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']mkl_random -> mkl[version='>=2022.0.1, 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']Package libbrotlienc conflicts for:libbrotliencfonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1'] -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']brotli -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']brotli-bin -> libbrotlienc==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']Package zipp conflicts for:zipp==3.4.1=pyhd3eb1b0_0markdown -> importlib-metadata[version='>=4.4'] -> zipp[version='>=0.5']Package mkl-service conflicts for:mkl_fft -> mkl-service[version='>=2, mkl-service[version='>=2, tensorflow-base==2.11.0[build=cpu_py310*_0] -> snappy[version='>=1.1.8,=1.1.9, snappy[version='>=1.1.8,=1.1.9, 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']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']Package nose conflicts for:scikit-learn -> nosetensorboard -> numpy -> nosepandas -> numpy[version='>=1.7'] -> nosePackage cython conflicts for:keras-applications -> h5py -> cython==0.22cythonkeras-base -> h5py -> cython==0.22Package typing_extensions conflicts for:tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> typing_extensions[version='3.7.4.*|>=3.6.6|>=3.7.4,=3.7.4']typing_extensions==3.7.4.3=pyha847dfd_0tensorflow-base -> typing_extensions[version='3.7.4.*|>=3.6.6|>=3.7.4,=3.7.4']markdown -> importlib-metadata[version='>=4.4'] -> typing_extensions[version='>=3.6.4']Package toml conflicts for:pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> tomlcoverage -> tomli -> tomlsip -> tomlPackage brotli-bin conflicts for:brotli-binfonttools==4.25.0=pyhd3eb1b0_0 -> brotli[version='>=1.0.1'] -> brotli-bin==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']brotli -> brotli-bin==1.0.9[build='h7f98852_5|h166bdaf_8|h166bdaf_7|h7f98852_6']Package python-dateutil conflicts for:matplotlib-base -> python-dateutil[version='>=2.1|>=2.7']seaborn==0.11.2=pyhd3eb1b0_0 -> pandas[version='>=0.23'] -> python-dateutil[version='>=2.5.*|>=2.6.1|>=2.7.3|>=2.8.1']pandas -> python-dateutil[version='>=2.5.*|>=2.6.1|>=2.7.3|>=2.8.1']Package pypy3.8 conflicts for:tornado -> python[version='>=3.8, pypy3.8[version='7.3.*|7.3.11.*|7.3.9.*|7.3.8.*']tornado -> pypy3.8[version='>=7.3.8|>=7.3.9']Package distribute conflicts for:pip -> distributepython -> pip -> distributePackage fribidi conflicts for:qt -> pango -> fribidi[version='>=1.0.10,=1.0.9,=1.0.5, pillow[version='>=6.2.0'] -> fribidi[version='>=1.0.10, libiconv[version='1.15|1.15.*|>=1.15,=1.16,=1.17, qt-webengine=5.15 -> libiconv[version='1.14.*|1.15|>=1.15,=1.16,=1.17,=1.17, __glibc[version='>=2.17, __glibc[version='>=2.17|>=2.17, tensorflow[version='>=2.2'] -> __cuda- keras-base -> tensorflow[version='>=2.2'] -> __glibc[version='>=2.17']- pyqt -> qt-main[version='>=5.15.6, __glibc[version='>=2.17, qt-main=5.15.6 -> __glibc[version='>=2.17, __cuda- tensorflow -> __glibc[version='>=2.17']- tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17, __cuda- tensorflow-base -> __glibc[version='>=2.17']- tensorflow-base -> cudatoolkit[version='>=11.2, __glibc[version='>=2.17, tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda- tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']- wincertstore -> __winYour installed version is: 2.31Note 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。

(venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda install python=3.7Collecting package metadata (current_repodata.json): doneSolving environment: failed with initial frozen solve. Retrying with flexible solve.Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.Collecting package metadata (repodata.json): doneSolving environment: failed with initial frozen solve. Retrying with flexible solve.Solving environment: | Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failedUnsatisfiableError: 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.

IST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda remove -n venv1 --allRemove all packages in environment /home/LIST_2080Ti/anaconda3/envs/venv1:No packages found in /home/LIST_2080Ti/anaconda3/envs/venv1. Continuing environment removalLIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2 python=3.7.10Collecting package metadata (current_repodata.json): doneSolving environment: failed with repodata from current_repodata.json, will retry with next repodata source.Collecting package metadata (repodata.json): doneSolving environment: / Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failedUnsatisfiableError: Note that strict channel priority may have removed packages required for satisfiability.LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2 python=3.7Collecting package metadata (current_repodata.json): doneSolving environment: failed with repodata from current_repodata.json, will retry with next repodata source.Collecting package metadata (repodata.json): doneSolving environment: \ Found conflicts! Looking for incompatible packages.This can take several minutes.Press CTRL-C to abort.failedUnsatisfiableError: Note that strict channel priority may have removed packages required for satisfiability.LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2Collecting package metadata (current_repodata.json): doneSolving environment: done## Package Plan ##environment location: /home/LIST_2080Ti/anaconda3/envs/venv2Proceed ([y]/n)" />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：
 
The following specifications were found to be incompatible with your system:- feature:/linux-64::__cuda==11.7=0- feature:/linux-64::__glibc==2.31=0- feature:|@/linux-64::__cuda==11.7=0- feature:|@/linux-64::__glibc==2.31=0- cudatoolkit -> __glibc[version='>=2.17, __glibc[version='>=2.17|>=2.17, qt-main[version='>=5.15.6, __glibc[version='>=2.17, qt-main=5.15.6 -> __glibc[version='>=2.17, __cuda- tensorflow -> __glibc[version='>=2.17']- tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17, __cuda- tensorflow-base -> __glibc[version='>=2.17']- tensorflow-base -> cudatoolkit[version='>=11.2, __glibc[version='>=2.17, tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda- tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']- wincertstore -> __winYour installed version is: 2.31Note 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。
(venv2) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/njh$ ldd --versionldd (Ubuntu GLIBC 2.31-0ubuntu9.9) 2.31Copyright (C) 2020 Free Software Foundation, Inc.This is free software; see the source for copying conditions. There is NOwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.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：
conda update condaconda 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：
The following specifications were found to be incompatible with your system:- feature:/linux-64::__cuda==11.7=0- feature:/linux-64::__glibc==2.31=0- feature:|@/linux-64::__cuda==11.7=0- feature:|@/linux-64::__glibc==2.31=0- cudatoolkit -> __glibc[version='>=2.17, __glibc[version='>=2.17|>=2.17, qt-main[version='>=5.15.6, __glibc[version='>=2.17, qt-main=5.15.6 -> __glibc[version='>=2.17, __cuda- tensorflow -> __glibc[version='>=2.17']- tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17, __cuda- tensorflow-base -> __glibc[version='>=2.17']- tensorflow-base -> cudatoolkit[version='>=11.2, __glibc[version='>=2.17, tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda- tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']- wincertstore -> __winYour installed version is: 2.31Note 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
conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/conda-forge/conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/main/conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/pkgs/free/conda config --add channels https://mirrors.bfsu.edu.cn/anaconda/cloud/bioconda/conda config --add channels http://mirrors.aliyun.com/anaconda/cloud/bioconda/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/————————————————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。原文链接：https://blog.csdn.net/weixin_42001274/article/details/127209878
现在又开始检测了。报错：
The following specifications were found to be incompatible with your system:- feature:/linux-64::__cuda==11.7=0- feature:/linux-64::__glibc==2.31=0- feature:|@/linux-64::__cuda==11.7=0- feature:|@/linux-64::__glibc==2.31=0- cudatoolkit -> __glibc[version='>=2.17, __glibc[version='>=2.17|>=2.17, qt-main[version='>=5.15.6, __glibc[version='>=2.17, qt-main=5.15.6 -> __glibc[version='>=2.17, __cuda- tensorflow -> __glibc[version='>=2.17']- tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17, __cuda- tensorflow-base -> __glibc[version='>=2.17']- tensorflow-base -> cudatoolkit[version='>=11.2, __glibc[version='>=2.17, tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda- tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']- wincertstore -> __winYour installed version is: 2.31Note 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。
conda env create -f environment.ymlpip 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：
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/simpleLooking in indexes: https://pypi.tuna.tsinghua.edu.cn/simpleCollecting tensorflow-gpu==1.14.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/32/67/559ca8408431c37ad3a17e859c8c291ea82f092354074baef482b98ffb7b/tensorflow_gpu-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (377.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 377.1/377.1 MB 1.6 MB/s eta 0:00:00Collecting gast>=0.2.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5f/1c/b59500a88c5c3d9d601c5ca62b9df5e0964764472faed82a182958a922c5/gast-0.5.3-py3-none-any.whl (19 kB)Collecting grpcio>=1.8.6Downloading 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) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.8/4.8 MB 5.8 MB/s eta 0:00:00Collecting tensorboard=1.14.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.1/3.1 MB 3.9 MB/s eta 0:00:00Collecting tensorflow-estimator=1.14.0rc0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 488.5/488.5 kB 1.1 MB/s eta 0:00:00Collecting six>=1.10.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)Collecting keras-preprocessing>=1.0.5Downloading https://pypi.tuna.tsinghua.edu.cn/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 42.6/42.6 kB 3.0 MB/s eta 0:00:00Collecting astor>=0.6.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl (27 kB)Collecting google-pasta>=0.1.6Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 kB 2.8 MB/s eta 0:00:00Collecting numpy=1.14.5Downloading 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) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 15.7/15.7 MB 2.4 MB/s eta 0:00:00Collecting protobuf>=3.6.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e7/a2/3273c05fc5d959fa90de6453ebd6d45c6d4fab3ec212d631625ea5780921/protobuf-4.21.12-cp37-abi3-manylinux2014_x86_64.whl (409 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 409.8/409.8 kB 4.0 MB/s eta 0:00:00Collecting termcolor>=1.1.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/aa/f4/8ddd8a684b4c005345f45740a449d93d0af7ccecd91319d0f4426cf08b36/termcolor-2.2.0-py3-none-any.whl (6.6 kB)Collecting absl-py>=0.7.0Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dd/87/de5c32fa1b1c6c3305d576e299801d8655c175ca9557019906247b994331/absl_py-1.4.0-py3-none-any.whl (126 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 126.5/126.5 kB 4.7 MB/s eta 0:00:00Requirement 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)Collecting wrapt>=1.11.1Downloading 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) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 75.2/75.2 kB 5.2 MB/s eta 0:00:00Collecting keras-applications>=1.0.6Downloading https://pypi.tuna.tsinghua.edu.cn/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.7/50.7 kB 8.2 MB/s eta 0:00:00Collecting h5pyDownloading 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) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.3/4.3 MB 6.6 MB/s eta 0:00:00Requirement already satisfied: setuptools>=41.0.0 in /home/LIST_2080Ti/anaconda3/envs/venv2/lib/python3.7/site-packages (from tensorboard=1.14.0->tensorflow-gpu==1.14.0) (67.1.0)Collecting werkzeug>=0.11.15Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/27/be6ddbcf60115305205de79c29004a0c6bc53cec814f733467b1bb89386d/Werkzeug-2.2.2-py3-none-any.whl (232 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 232.7/232.7 kB 2.4 MB/s eta 0:00:00Collecting markdown>=2.6.8Downloading https://pypi.tuna.tsinghua.edu.cn/packages/86/be/ad281f7a3686b38dd8a307fa33210cdf2130404dfef668a37a4166d737ca/Markdown-3.4.1-py3-none-any.whl (93 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 93.3/93.3 kB 2.2 MB/s eta 0:00:00Collecting importlib-metadata>=4.4Downloading https://pypi.tuna.tsinghua.edu.cn/packages/26/a7/9da7d5b23fc98ab3d424ac2c65613d63c1f401efb84ad50f2fa27b2caab4/importlib_metadata-6.0.0-py3-none-any.whl (21 kB)Collecting MarkupSafe>=2.1.1Downloading 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)Collecting typing-extensions>=3.6.4Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0b/8e/f1a0a5a76cfef77e1eb6004cb49e5f8d72634da638420b9ea492ce8305e8/typing_extensions-4.4.0-py3-none-any.whl (26 kB)Collecting zipp>=0.5Downloading https://pypi.tuna.tsinghua.edu.cn/packages/37/7d/4a5221043904612db108bbe7d0ad7409015fb143bae137c72d9dfd7b75e1/zipp-3.12.1-py3-none-any.whl (6.7 kB)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-gpuSuccessfully 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 ':'
 ruamel_yaml.scanner.ScannerError: while scanning a simple keyin "", line 143, column 5:-i https://pypi.tuna.tsinghua.ed ... ^ (line: 143)could not find expected ':'in "", line 144, column 1:# prefix: D:\Program\Anaconda3\e ... ^ (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