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conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】 14 could not find expected ':'

2年前分類：Toy博客閱讀(61)違法舉報(bào)

這篇具有很好參考價(jià)值的文章主要介紹了conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】 14 could not find expected ':'。希望對(duì)大家有所幫助。如果存在錯(cuò)誤或未考慮完全的地方，請(qǐng)大家不吝賜教，您也可以點(diǎn)擊"舉報(bào)違法"按鈕提交疑問(wèn)。

閱讀須知：長(zhǎng)文，將近10萬(wàn)字。主要原因是報(bào)了太多錯(cuò)，記錄了太多bug。

前面的11步驟是我的試錯(cuò)過(guò)程，直到第12/13步才解決。沒(méi)耐心的可以直接從目錄跳到第12步最后。

整篇文章簡(jiǎn)而言之：笨方法在一些時(shí)候或許是最好的方法，且是最省時(shí)間最省力氣的做法。

下面看一看我的一把辛酸淚吧。

————————————————————

事情的起源是想把本機(jī)程序配置到服務(wù)器運(yùn)行以減少運(yùn)行時(shí)間。我之前試了pip和pipreqs安裝依賴，報(bào)錯(cuò)卻隨著我的修改而越來(lái)越多。

于是我決定試一試conda環(huán)境配置解決這個(gè)問(wèn)題。

按照CSDN博主：℡ヾNothing-_哥所說(shuō)，只需要四步，一如大象裝冰箱一樣簡(jiǎn)單。就可以搞定移植環(huán)境后的程序配置。

Anaconda 復(fù)制或移植已有環(huán)境（復(fù)制到別的服務(wù)器上）_anaconda復(fù)制環(huán)境_℡ヾNothing-_哥的博客-CSDN博客

于是我就按照他的方法搞了起來(lái)。

前面的：克隆環(huán)境——激活環(huán)境——導(dǎo)出配置都順利完成，唯有最后一步配置環(huán)境時(shí)候出了問(wèn)題。

conda env create -f environment.yml

大問(wèn)題。

下面就是我的報(bào)錯(cuò)和解決歷程了。

1 報(bào)錯(cuò)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.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound: 
  - 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

我看到有人說(shuō)清華源下包可能更齊全，然后就添加了清華源。

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

于是迎來(lái)了第二波報(bào)錯(cuò)，與原來(lái)的報(bào)錯(cuò)缺包情況相差無(wú)幾。

2 報(bào)錯(cuò)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.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound: 
  - 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 看來(lái)源不怎么影響包是否缺失。

于是決定刪除第二步的配置。將ResolvePackageNotFound: 找不到的版本號(hào)刪掉，然后報(bào)錯(cuò)由原來(lái)的54個(gè)變成了4個(gè)。

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ pip config unset global.index-url
Writing 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.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound: 
  - pyreadline
  - icc_rt
  - vc
  - vs2015_runtime

4 pip與conda

但是我毫無(wú)疑問(wèn)更改了依賴包的版本，因此并不是太合理，于是決定按照上面參考文章那樣，直接將conda無(wú)法安裝的包改由pip安裝。

直接將報(bào)錯(cuò)的內(nèi)容復(fù)制到environment.yml的pip后面，并將前面conda內(nèi)的相關(guān)報(bào)錯(cuò)刪除即可。

后來(lái)查詢知道，pip包遠(yuǎn)比conda包多，所以，conda會(huì)遇到更多的缺包現(xiàn)象。

conda	pip
包內(nèi)容	二進(jìn)制	.whl和源碼
是否需要編譯	不需要	需要
安裝包類型	Python、C、R等任何類型	僅限于Python
是否支持環(huán)境管理	是，可以創(chuàng)建多個(gè)環(huán)境	否，需要借助virtualenv or venv等其它工具
依賴包檢查	檢查十分嚴(yán)格	檢查不嚴(yán)格
包來(lái)源	Anaconda repo and cloud	PyPI
包數(shù)量	約1500個(gè)	約150000個(gè)

圖來(lái)自：【基礎(chǔ)知識(shí)】pip和conda,你會(huì)選擇誰(shuí)？ - 騰訊云開發(fā)者社區(qū)-騰訊云

pip的包大約是conda包的100倍。

因此把conda安裝改為pip安裝就有了依據(jù)。

這里還有兩篇對(duì)比conda和pip的文章，寫得很好，有空的可以看看。

Anaconda和pip使用總結(jié) conda與pip的區(qū)別_taoqick的博客-CSDN博客_anaconda pip

python使用pip與conda 的區(qū)別_pip安裝和conda安裝的區(qū)別_weixin_42641188的博客-CSDN博客

pip 和conda_知更鳥k的博客-CSDN博客_pip和conda

5?Found conflicts! Looking for incompatible packages.

當(dāng)我把conda無(wú)法安裝的包轉(zhuǎn)到pip安裝后，上面的ResolvePackageNotFound消失，但是現(xiàn)在出現(xiàn)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

這次就要把版本號(hào)刪除掉以解決沖突問(wèn)題。

刪除版本號(hào)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==1.16.6

Package 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_0

Package 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_0

Package tzdata conflicts for:

- keras-applications=1.0.8=py_1

- cudatoolkit=10.0.130=0

Package 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

完整沖突如下：

(base) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
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.
Collecting package metadata (repodata.json): done
Solving 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 versions

Package tensorflow-base conflicts for:
keras==2.3.1=0 -> tensorflow -> 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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']
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']

Package zlib conflicts for:
keras==2.3.1=0 -> tensorflow -> zlib[version='>=1.2.11,<1.3.0a0']
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']
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']
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']
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']
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']
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']
tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> zlib[version='>=1.2.11,<1.3.0a0']
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']
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']
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']
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.*']
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']
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']
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']
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']

Package blas conflicts for:
blas==1.0=mkl
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']
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 -> setuptools
pip -> setuptools
seaborn==0.11.2=pyhd3eb1b0_0 -> matplotlib[version='>=2.2'] -> setuptools[version='<60.0.0']

Package munkres conflicts for:
munkres==1.1.4=py_0
fonttools==4.25.0=pyhd3eb1b0_0 -> munkres

Package 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,<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']
keras-applications==1.0.8=py_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,<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']
keras-base==2.3.1=py37_0 -> 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,<2.0a0|>=1.14.6,<2.0a0|>=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,<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.*']
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.*']

Package fftw conflicts for:
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> fftw[version='>=3.3.9,<4.0a0']
seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0'] -> fftw[version='>=3.3.9,<4.0a0']
keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> fftw[version='>=3.3.9,<4.0a0']

Package libgcc-ng conflicts for:
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']
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,<10.1'] -> 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_0
keras==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=2.3.1
keras-base==2.3.1=py37_0

Package six conflicts for:
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> six[version='>=1.9.0']
keras-base==2.3.1=py37_0 -> h5py -> six
keras-base==2.3.1=py37_0 -> six[version='>=1.9.0']
keras-applications==1.0.8=py_1 -> h5py -> six
keras==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,<1.0.3a|>=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.*']
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']
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.*']
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.*']
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.*']
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.*']
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.*']
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.*']
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.*']
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.*']
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.*']
keras==2.3.1=0 -> tensorflow -> openssl[version='>=1.1.1l,<1.1.2a']
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.*']
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.*']
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.*']

Package system conflicts for:
munkres==1.1.4=py_0 -> python -> system==5.8
wheel==0.36.2=pyhd3eb1b0_0 -> python -> system==5.8
werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> system==5.8
pip -> python[version='>=3'] -> system==5.8
gast==0.4.0=py_0 -> python -> system==5.8
keras-applications==1.0.8=py_1 -> python -> system==5.8

Package 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,<2022.0a0']
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> intel-openmp[version='>=2021.4.0,<2022.0a0']
seaborn==0.11.2=pyhd3eb1b0_0 -> scipy[version='>=1.0'] -> intel-openmp[version='>=2021.4.0,<2022.0a0']

Package certifi conflicts for:
pip -> 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,<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']
keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0']

Package _tflow_select conflicts for:
_tflow_select==2.1.0=gpu
tensorflow-gpu==1.14.0=h0d30ee6_0 -> _tflow_select==2.1.0=gpu
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']
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,<2.0a0|>=1.2.1,<2.0a0|>=1.0.4,<2.0a0']
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']
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']
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']

Package pip conflicts for:
gast==0.4.0=py_0 -> python -> pip
threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> pip
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
pip
keras-base==2.3.1=py37_0 -> python[version='>=3.7,<3.8.0a0'] -> pip
typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> pip
joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
munkres==1.1.4=py_0 -> python -> pip
werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> pip
zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
seaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> pip
keras-applications==1.0.8=py_1 -> python -> pip
wheel==0.36.2=pyhd3eb1b0_0 -> python -> pip

Package tzdata conflicts for:
gast==0.4.0=py_0 -> python -> tzdata
threadpoolctl==2.1.0=pyh5ca1d4c_0 -> python[version='>=3.5'] -> tzdata
wheel==0.36.2=pyhd3eb1b0_0 -> python -> tzdata
keras-applications==1.0.8=py_1 -> python -> tzdata
seaborn==0.11.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
typing_extensions==3.7.4.3=pyha847dfd_0 -> python[version='>=3.5'] -> tzdata
pip -> python[version='>=3.7'] -> tzdata
joblib==1.0.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
munkres==1.1.4=py_0 -> python -> tzdata
werkzeug==1.0.1=pyhd3eb1b0_0 -> python -> tzdata
zipp==3.4.1=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata
fonttools==4.25.0=pyhd3eb1b0_0 -> python[version='>=3.6'] -> tzdata

Package 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_1
keras-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,<10.1']
keras==2.3.1=0 -> tensorflow -> cudatoolkit[version='10.2|10.2.*|11.0|11.0.*|11.1|11.1.*|>=11.2,<12']
cudatoolkit==10.0.130=0

Package libgcc conflicts for:
keras-base==2.3.1=py37_0 -> scipy[version='>=0.14'] -> libgcc==5.2.0
keras-preprocessing==1.1.2=pyhd3eb1b0_0 -> scipy[version='>=0.14'] -> libgcc==5.2.0

Package 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_0
keras-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_0

Package 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,<1.10.4.0a0|>=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_*']
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_*']

Package libpng conflicts for:
tensorflow-gpu==1.14.0=h0d30ee6_0 -> tensorflow==1.14.0 -> libpng[version='>=1.6.37,<1.7.0a0']
keras==2.3.1=0 -> tensorflow -> libpng[version='>=1.6.37,<1.7.0a0']
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:

  - 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: 2.31

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

6 將conda安裝轉(zhuǎn)為pip安裝

因?yàn)榘凑涨懊娴姆椒▎?wèn)題巨多，因此將采用直接刪除報(bào)錯(cuò)的版本號(hào)。仍舊有4個(gè)包找不到。然后把這四個(gè)包移動(dòng)到pip下。

LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~$ conda env create -f /home/LIST_2080Ti/2080/CHB-MIT-DATA/epilepsy_eeg_classification/environment.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound: 
  - vs2015_runtime
  - icc_rt
  - vc
  - pyreadline

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

?這次修改后，檢查沖突用了好久了。

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

?仍舊是超多沖突。即便刪除了版本，仍舊有茫茫多的沖突報(bào)錯(cuò)。

yml文件內(nèi)容如下：

name: cat
channels:
  - 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/msys2

dependencies:
  - _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

報(bào)錯(cuò)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,<5.16.0a0'] -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - python -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']
  - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - scikit-learn -> 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,<3.0.a0']
  - tensorflow-base -> __cuda
  - tensorflow-base -> __glibc[version='>=2.17']
  - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  - tk -> 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: 2.31

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

這次報(bào)錯(cuò)比之前的更多，還不如按照之前的搞呢。

之前的即為：直接刪除版本號(hào)，然后仍舊找不到的將其剪切到pip部分安裝。

7 以第5步的為準(zhǔn)，修改后的yml如下所示：

name: cat
channels:
  - conda-forge
  - default

dependencies:
  - _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

這個(gè)時(shí)候檢查沖突報(bào)錯(cuò)：

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: 2.31

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

找到有一個(gè)說(shuō)法是安裝依賴包時(shí)候，頻道里conda-forge和default混合導(dǎo)致的。一旦把它改成只使用conda-forge問(wèn)題就能解決。

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

?Conda glibc依賴沖突 - 問(wèn)答 - 騰訊云開發(fā)者社區(qū)-騰訊云

因此采用刪除.yml中channel中的default。?

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

現(xiàn)在又在執(zhí)行檢測(cè)和安裝了。

報(bào)錯(cuò)：

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

ResolvePackageNotFound: 
  - kera
  - cudnn==7.6.5=cuda10.0_0
  - numpy-base
  - tensorflow-gpu==1.14.0=h0d30ee6_0
  - _tflow_select

我把后面帶版本號(hào)的刪除版本號(hào)，不帶版本號(hào)的直接移動(dòng)到pip后進(jìn)行安裝。?

報(bào)錯(cuò)：

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

ResolvePackageNotFound:?
? - _tflow_select

再次把這個(gè)包_tflow_select移動(dòng)到pip后安裝。

再次報(bào)錯(cuò)：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,<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.*']
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,<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']
seaborn==0.11.2=pyhd3eb1b0_0 -> numpy[version='>=1.15']
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']
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']
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']
keras-base -> numpy[version='>=1.9.1']
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']
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.*']
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,<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.*']
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']
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']
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.*']
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']
mkl_random -> mkl-service[version='>=2,<3.0a0'] -> numpy[version='>=1.11.3,<2.0a0']
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']
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']
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']

Package tensorflow conflicts for:
keras-applications -> keras[version='>=2.1.6'] -> tensorflow[version='>=2.2']
keras-base -> tensorflow[version='>=2.2']
tensorflow
keras-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']
astor
tensorflow-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,<4.0a0'] -> 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==2.1
libwebp -> libtiff[version='>=4.3.0,<4.5.0a0'] -> jbig==2.1
libtiff -> jbig==2.1

Package yaml conflicts for:
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']
yaml

Package fonttools conflicts for:
fonttools==4.25.0=pyhd3eb1b0_0
matplotlib-base -> 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,<1.2|>=1.1.2']
tensorflow-base -> keras-preprocessing[version='>=1.0.5|>=1.1.1|>=1.1.2,<1.2|>=1.1.2']
keras-preprocessing
keras-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'] -> theano
keras-preprocessing -> keras[version='>=2.1.6'] -> theano

Package icu conflicts for:
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.*']
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']
qt -> qt-main=5.15.6 -> icu[version='69.*|>=68.2,<69.0a0|>=70.1,<71.0a0']
tensorflow-base -> icu[version='>=68.1,<69.0a0|>=68.2,<69.0a0|>=69.1,<70.0a0|>=70.1,<71.0a0']
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']
matplotlib-base -> icu[version='>=58.2,<59.0a0|>=64.2,<65.0a0|>=67.1,<68.0a0']
icu

Package sip conflicts for:
sip
pyqt -> pyqt5-sip==12.11.0=py310heca2aa9_3 -> sip
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']

Package tensorflow-tensorboard conflicts for:
tensorflow -> tensorflow-tensorboard
tensorflow-gpu -> tensorflow-tensorboard

Package dataclasses conflicts for:
tensorflow-gpu -> werkzeug[version='>=0.11.10'] -> dataclasses
tensorboard -> werkzeug[version='>=1.0.1'] -> dataclasses
tensorflow -> werkzeug[version='>=0.11.10'] -> dataclasses
werkzeug -> dataclasses

Package ordereddict conflicts for:
absl-py -> enum34 -> ordereddict
pyqt -> enum34 -> ordereddict

Package protobuf conflicts for:
tensorflow-gpu -> protobuf[version='>=3.1.0|>=3.2.0']
tensorflow-base -> tensorboard[version='>=2.10,<2.11'] -> protobuf[version='>=3.4.0|>=3.6.0|>=3.9.2,<3.20']
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']
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 -> packaging
tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> packaging
tensorflow-base -> wheel[version='>=0.35,<1'] -> 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 -> packaging
sip -> packaging

Package joblib conflicts for:
scikit-learn -> joblib[version='>=0.11|>=1.0.0|>=1.1.1']
joblib==1.0.1=pyhd3eb1b0_0

Package 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,<2.0a0|>=1.2.3,<2.0a0',build=2]
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]
matplotlib-base -> pillow[version='>=6.2.0'] -> libwebp-base[version='>=1.2.2,<2.0a0|>=1.2.4,<2.0a0']
libwebp -> libtiff[version='>=4.4.0,<4.5.0a0'] -> libwebp-base[version='>=1.1.0,<1.2.0a0']
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']
libtiff -> libwebp-base[version='>=1.1.0,<1.2.0a0|>=1.2.3,<2.0a0|>=1.2.4,<2.0a0']
libwebp-base

Package libbrotlicommon conflicts for:
libbrotlienc -> 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']
libbrotlicommon

Package libssh2 conflicts for:
hdf5 -> libcurl[version='>=7.87.0,<8.0a0'] -> libssh2[version='>=1.10.0,<2.0a0|>=1.9.0,<2.0a0']
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']
git -> curl -> libssh2[version='1.8.*|>=1.10.0,<2.0a0|>=1.9.0,<2.0a0|>=1.8.0,<2.0.0a0']
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']

Package futures conflicts for:
tensorboard -> futures[version='>=3.1.1']
tensorboard -> grpcio[version='>=1.6.3'] -> futures[version='>=2.2.0']
tornado -> futures
tensorflow -> 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 -> futures

Package threadpoolctl conflicts for:
scikit-learn -> threadpoolctl[version='>=2.0.0']
threadpoolctl==2.1.0=pyh5ca1d4c_0

Package html5lib conflicts for:
tensorflow -> html5lib==0.9999999
tensorflow -> bleach==1.5.0 -> html5lib[version='>=0.999,!=0.9999,!=0.99999,<0.99999999']

Package curl conflicts for:
python -> graalpy[version='>=22.3.0,<22.3.1.0a0'] -> curl
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']

Package werkzeug conflicts for:
tensorboard -> werkzeug[version='>=0.11.10|>=0.11.15|>=1.0.1|>=0.14']
werkzeug
tensorflow-base -> tensorboard[version='>=2.11,<2.12'] -> 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:
brotli
matplotlib-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,<1.5.1.0a0']
tensorflow -> bleach==1.5.0
tensorflow-gpu -> bleach==1.5.0

Package lz4-c conflicts for:
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']
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']
zstd -> lz4 -> lz4-c=1.8.1
lz4-c

Package libtiff conflicts for:
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']
libtiff
qt -> libtiff[version='4.0.*|>=4.0.10,<4.5.0a0']
pyqt -> qt[version='>=4.8.6,<5.0'] -> libtiff[version='4.0.*|>=4.0.10,<4.5.0a0']
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']
qt -> gtk2 -> libtiff[version='>=4.0.3,<4.0.8|>=4.0.9,<4.5.0a0|>=4.1.0,<4.5.0a0']

Package tbb conflicts for:
mkl_random -> mkl[version='>=2022.0.1,<2023.0a0'] -> tbb=2021
mkl_fft -> mkl[version='>=2022.1.0,<2023.0a0'] -> tbb=2021
blas -> mkl -> tbb=2021

Package libgfortran4 conflicts for:
blas -> libgfortran4[version='>=7.5.0']
blas -> libgfortran-ng -> libgfortran4=7.5.0

Package 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:
qt
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']

Package requests conflicts for:
tensorboard -> google-auth[version='>=1.6.3,<3'] -> requests[version='>=2.20.0,<3.0.0dev']
tensorflow-base -> tensorboard[version='>=2.11,<2.12'] -> requests[version='>=2.21.0|>=2.21.0,<3']
tensorboard -> requests[version='>=2.21.0|>=2.21.0,<3']

Package libwebp conflicts for:
libwebp
matplotlib-base -> pillow[version='>=6.2.0'] -> libwebp
qt -> qt-webengine=5.15 -> libwebp
libtiff -> libwebp

Package backports conflicts for:
tensorflow-base -> backports.weakref[version='>=1.0rc1'] -> backports
tornado -> ssl_match_hostname -> backports
tensorflow -> backports.weakref[version='>=1.0rc1'] -> backports
tensorflow-gpu -> backports.weakref==1.0rc1 -> backports
matplotlib-base -> backports.functools_lru_cache -> backports

Package pandas conflicts for:
pandas
seaborn==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 -> enum34
absl-py -> enum34
tensorflow-base -> enum34[version='>=1.1.6']

Package munkres conflicts for:
matplotlib-base -> fonttools[version='>=4.22.0'] -> munkres
fonttools==4.25.0=pyhd3eb1b0_0 -> munkres
munkres

Package zstd conflicts for:
zstd
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']
pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> zstd[version='>=1.5.0,<1.6.0a0|>=1.5.2,<1.6.0a0']
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']
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']

Package libdeflate conflicts for:
libdeflate
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']
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']
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']

Package cudnn conflicts for:
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']
tensorflow-gpu -> cudnn[version='5.1|5.1.*|6.0.*']
tensorflow-base -> cudnn[version='>=7.6.5.32,<8.0a0|>=8.4.1.50,<9.0a0|>=8.2.1.32,<9.0a0']
cudnn

Package pyqt conflicts for:
pyqt
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']

Package libxcb conflicts for:
qt -> xorg-libx11 -> libxcb=1
matplotlib-base -> pillow[version='>=6.2.0'] -> libxcb[version='>=1.13,<1.14.0a0']
pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> libxcb[version='>=1.13,<1.14.0a0']
qt -> libxcb[version='>=1.13,<1.14.0a0']

Package libnghttp2 conflicts for:
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']
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']
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']

Package lerc conflicts for:
lerc
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']
libtiff -> lerc[version='>=2.2.1,<3.0a0|>=3.0,<4.0a0|>=4.0.0,<5.0a0']
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']

Package pyparsing conflicts for:
tensorflow-base -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=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='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=2.0.2']
sip -> packaging -> pyparsing[version='<3,>=2.0.2|>=2.0.2,!=3.0.5|>=2.0.2,<3|>=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,<3.7.0a0'] -> 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,<1.13|>=1.11.1']
tensorflow-base -> wrapt[version='>=1.11.0|>=1.12.1,<1.13|>=1.11.1']
wrapt

Package llvm-openmp conflicts for:
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']
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,<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']

Package libbrotlienc conflicts for:
libbrotlienc
fonttools==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_0
markdown -> importlib-metadata[version='>=4.4'] -> zipp[version='>=0.5']

Package mkl-service conflicts for:
mkl_fft -> mkl-service[version='>=2,<3.0a0']
mkl_random -> mkl-service[version='>=2,<3.0a0']

Package snappy conflicts for:
tensorflow -> tensorflow-base==2.11.0[build=cpu_py310*_0] -> snappy[version='>=1.1.8,<2.0a0|>=1.1.9,<2.0a0']
tensorflow-base -> snappy[version='>=1.1.8,<2.0a0|>=1.1.9,<2.0a0']

Package blas-devel conflicts for:
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']
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 -> nose
tensorboard -> numpy -> nose
pandas -> numpy[version='>=1.7'] -> nose

Package cython conflicts for:
keras-applications -> h5py -> cython==0.22
cython
keras-base -> h5py -> cython==0.22

Package 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,<3.8|>=3.7.4']
typing_extensions==3.7.4.3=pyha847dfd_0
tensorflow-base -> typing_extensions[version='3.7.4.*|>=3.6.6|>=3.7.4,<3.8|>=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 -> toml
coverage -> tomli -> toml
sip -> toml

Package brotli-bin conflicts for:
brotli-bin
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']
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,<3.9.0a0'] -> 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 -> distribute
python -> pip -> distribute

Package fribidi conflicts for:
qt -> pango -> fribidi[version='>=1.0.10,<2.0a0|>=1.0.9,<2.0a0|>=1.0.5,<2.0a0']
matplotlib-base -> pillow[version='>=6.2.0'] -> fribidi[version='>=1.0.10,<2.0a0']

Package libiconv conflicts for:
git -> libiconv[version='1.15|1.15.*|>=1.15,<2.0.0a0|>=1.16,<2.0.0a0|>=1.17,<2.0a0']
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:

  - 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,<3.0.a0']
  - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - keras-base -> tensorflow[version='>=2.2'] -> __cuda
  - keras-base -> tensorflow[version='>=2.2'] -> __glibc[version='>=2.17']
  - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow -> __cuda
  - tensorflow -> __glibc[version='>=2.17']
  - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-base -> __cuda
  - tensorflow-base -> __glibc[version='>=2.17']
  - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  - wincertstore -> __win

Your installed version is: 2.31

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

報(bào)了上面的一大堆conflicts后，我就找解決方案。

看到有人說(shuō)是python版本不匹配，并不是上面的——_glibc的問(wèn)題。思路來(lái)自這里。

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

安裝新環(huán)境和python版本時(shí)，Conda glibc依賴沖突 - 問(wèn)答 - 騰訊云開發(fā)者社區(qū)-騰訊云

8 執(zhí)行 conda install python=3.7

有報(bào)錯(cuò)了，仍舊是沖突。?

(venv1) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda install python=3.7
Collecting package metadata (current_repodata.json): done
Solving 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): done
Solving 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.
failed                                                                                                                                                                                            

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

奇怪的事這次沖突還不詳細(xì)。

會(huì)不會(huì)是這個(gè)虛擬環(huán)境裝了亂七八糟的東西，因此刪除了venv1，新建虛擬環(huán)境2，再來(lái)。此時(shí)安裝python版本時(shí)候仍舊報(bào)錯(cuò)。

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 --all

Remove all packages in environment /home/LIST_2080Ti/anaconda3/envs/venv1:

No packages found in /home/LIST_2080Ti/anaconda3/envs/venv1. Continuing environment removal
LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda create -n venv2 python=3.7.10
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: / 
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.
failed                                                                                                                                                                                            

UnsatisfiableError: 
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=3.7
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: \ 
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.
failed                                                                                                                                                                                            

UnsatisfiableError: 
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
Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/LIST_2080Ti/anaconda3/envs/venv2



Proceed ([y]/n)? y

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate venv2
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Retrieving notices: ...working... done
LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda activate venv2
(venv2) LIST_2080Ti@ubuntu-SYS-7049GP-TRT:~/2080/CHB-MIT-DATA/epilepsy_eeg_classification$ conda install python=3.7.10
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving 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.
failed                                                                                                                                                                                            

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

9 遇到上面沖突怎么辦

使用這個(gè)方式解決了。

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

再次安裝環(huán)境，繼續(xù)報(bào)錯(cuò)：

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,<3.0.a0']
  - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow -> __cuda
  - tensorflow -> __glibc[version='>=2.17']
  - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-base -> __cuda
  - tensorflow-base -> __glibc[version='>=2.17']
  - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  - wincertstore -> __win

Your installed version is: 2.31

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

使用：ldd --version查詢一下。我安裝的2.31就是這個(gè)東東。

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

而上面報(bào)錯(cuò)的那些正是我需要安裝的卻與2.31不兼容。

conda - UnsatisfiableError glibc 和 cudatoolkit - IT工具網(wǎng)的方法是更新conda：

conda update conda

conda update --all

同時(shí)把defaults，conda-forge,bioconda加入到channel里。

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

運(yùn)行安裝環(huán)境語(yǔ)句conda env create -f environment.yml，等結(jié)果。

仍舊一堆錯(cuò)。

結(jié)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,<3.0.a0']
  - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow -> __cuda
  - tensorflow -> __glibc[version='>=2.17']
  - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-base -> __cuda
  - tensorflow-base -> __glibc[version='>=2.17']
  - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  - wincertstore -> __win

Your installed version is: 2.31

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

一陣操作猛如虎，回看錯(cuò)誤個(gè)個(gè)有。

10 然后采取增加鏡像源的方式

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/
————————————————
版權(quán)聲明：本文為CSDN博主「weixin_42001274」的原創(chuàng)文章，遵循CC 4.0 BY-SA版權(quán)協(xié)議，轉(zhuǎn)載請(qǐng)附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/weixin_42001274/article/details/127209878

現(xiàn)在又開始檢測(cè)了。報(bào)錯(cuò)：

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,<3.0.a0']
  - cudnn -> __glibc[version='>=2.17|>=2.17,<3.0.a0']
  - pyqt -> qt-main[version='>=5.15.6,<5.16.0a0'] -> __glibc[version='>=2.17,<3.0.a0']
  - qt -> qt-main=5.15.6 -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow -> __cuda
  - tensorflow -> __glibc[version='>=2.17']
  - tensorflow -> cudatoolkit[version='11.1|11.1.*'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-base -> __cuda
  - tensorflow-base -> __glibc[version='>=2.17']
  - tensorflow-base -> cudatoolkit[version='>=11.2,<12'] -> __glibc[version='>=2.17,<3.0.a0']
  - tensorflow-gpu -> tensorflow==2.11.0=cuda112py39h01bd6f0_0 -> __cuda
  - tensorflow-gpu -> tensorflow==2.6.2=cuda111py37hf54207c_2 -> __glibc[version='>=2.17']
  - wincertstore -> __win

Your installed version is: 2.31

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

11?刪除channel中的default和conda update --strict-channel-priority --all

繼續(xù)試驗(yàn)：

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

來(lái)自：python - resolving package resolutions in conda - Stack Overflow

且刪除channel中的default conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'
Conda glibc依賴沖突 - 問(wèn)答 - 騰訊云開發(fā)者社區(qū)-騰訊云

12 最終解決方案

以上就是我每天碰壁碰出來(lái)的結(jié)果，事實(shí)發(fā)現(xiàn)，這些辦法都不能解決我的問(wèn)題。

pip安裝和conda安裝配置環(huán)境我都試了，packagenotfound可以通過(guò)添加源來(lái)解決。而conflicts涉及到源碼之類的，簡(jiǎn)直無(wú)能為力。因此決定暫時(shí)放棄這個(gè)方法。

開始使用，一次一安裝的方式去干。

就是程序需要用到什么就安裝什么。

盡可能的減少環(huán)境內(nèi)包的數(shù)量和可能產(chǎn)生的沖突。

使用這個(gè)方法需要注意以下幾點(diǎn)：

（1）你的源環(huán)境是否使用TensorFlow，如果使用一定要安裝正確版本的TensorFlow，然后再安裝其它包。

（2）可以先安裝一些常用的包，比如numpy，pandas,matplotlib,scipy等等。也要根據(jù)你自己常用的情況去選擇。

（3）可以看一下你程序內(nèi)導(dǎo)入的包。

萬(wàn)萬(wàn)沒(méi)想到，當(dāng)我不使用這兩種整體方式配置環(huán)境時(shí)候，之前的那些奇形怪狀的死活有沖突安裝不上的包一股腦都安裝了。

conda env create -f environment.yml

pip install -r requirements.txt

?安裝命令為：

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

之所以用1.14.0是我的源環(huán)境是這樣的。你可以根據(jù)你自己的環(huán)境修改。想知道你的配置列表?？梢灾苯觕md——激活你的環(huán)境——conda list，上面會(huì)顯示你的TensorFlow的版本號(hào)。

如下：

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

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
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting tensorflow-gpu==1.14.0
  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)
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Collecting gast>=0.2.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/5f/1c/b59500a88c5c3d9d601c5ca62b9df5e0964764472faed82a182958a922c5/gast-0.5.3-py3-none-any.whl (19 kB)
Collecting grpcio>=1.8.6
  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)
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Collecting tensorboard<1.15.0,>=1.14.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1 MB)
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Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488 kB)
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Collecting six>=1.10.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting keras-preprocessing>=1.0.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
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Collecting astor>=0.6.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl (27 kB)
Collecting google-pasta>=0.1.6
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57 kB)
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Collecting numpy<2.0,>=1.14.5
  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)
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Collecting protobuf>=3.6.1
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e7/a2/3273c05fc5d959fa90de6453ebd6d45c6d4fab3ec212d631625ea5780921/protobuf-4.21.12-cp37-abi3-manylinux2014_x86_64.whl (409 kB)
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Collecting termcolor>=1.1.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/aa/f4/8ddd8a684b4c005345f45740a449d93d0af7ccecd91319d0f4426cf08b36/termcolor-2.2.0-py3-none-any.whl (6.6 kB)
Collecting absl-py>=0.7.0
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dd/87/de5c32fa1b1c6c3305d576e299801d8655c175ca9557019906247b994331/absl_py-1.4.0-py3-none-any.whl (126 kB)
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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)
Collecting wrapt>=1.11.1
  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)
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Collecting keras-applications>=1.0.6
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50 kB)
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Collecting h5py
  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)
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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)
Collecting werkzeug>=0.11.15
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/27/be6ddbcf60115305205de79c29004a0c6bc53cec814f733467b1bb89386d/Werkzeug-2.2.2-py3-none-any.whl (232 kB)
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Collecting markdown>=2.6.8
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/86/be/ad281f7a3686b38dd8a307fa33210cdf2130404dfef668a37a4166d737ca/Markdown-3.4.1-py3-none-any.whl (93 kB)
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Collecting importlib-metadata>=4.4
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/26/a7/9da7d5b23fc98ab3d424ac2c65613d63c1f401efb84ad50f2fa27b2caab4/importlib_metadata-6.0.0-py3-none-any.whl (21 kB)
Collecting MarkupSafe>=2.1.1
  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)
Collecting typing-extensions>=3.6.4
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/0b/8e/f1a0a5a76cfef77e1eb6004cb49e5f8d72634da638420b9ea492ce8305e8/typing_extensions-4.4.0-py3-none-any.whl (26 kB)
Collecting zipp>=0.5
  Downloading 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-gpu
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

安裝TensorFlow-gpu版本時(shí)候自動(dòng)安裝一波包。

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

報(bào)錯(cuò)缺少mne時(shí)候，安裝mne又裝了一堆包。

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

報(bào)錯(cuò)缺少pandas時(shí)候，安裝pandas只安裝了pandas和pytz.

Successfully installed pandas-1.3.5 pytz-2022.7.1

然后再調(diào)整了一下里面使用文件的路徑。使用相對(duì)路徑報(bào)錯(cuò)的是找不到文件。所以，在服務(wù)器我用的是絕對(duì)路徑。

然后事情就完成了。

萬(wàn)萬(wàn)沒(méi)想到，我?guī)滋鞗](méi)有搞定的事情，一個(gè)個(gè)安裝的時(shí)候竟然如此順利。

13 問(wèn)題分析

很多人使用上面的方式都解決了問(wèn)題，只有我用了前面的所有方法，直到自己不使用整體配置環(huán)境的方式才解決問(wèn)題。

報(bào)錯(cuò)的原因有很多。

比如packagenotfound，可能需要加入鏡像源就能解決。

比如found conflicts，可能需要修改版本，或者刪除版本號(hào)能解決。而我實(shí)驗(yàn)了各種方式，這個(gè)conflicts始終無(wú)法解決。直到自己手動(dòng)配置環(huán)境才可以。

第12步手動(dòng)配置，總共也沒(méi)花多少時(shí)間就解決了問(wèn)題。

希望前面的12個(gè)坑能夠給你以借鑒。

另外，一般情況下，個(gè)人項(xiàng)目不會(huì)太大，手動(dòng)不使用整體配置可能會(huì)更好更快的完成。

conda env create -f environment.yml

pip install -r requirements.txt

或許對(duì)于大項(xiàng)目有用，但是對(duì)于小項(xiàng)目來(lái)說(shuō)，它帶來(lái)的問(wèn)題遠(yuǎn)遠(yuǎn)比它帶來(lái)的便利要大。

————————————————————

14 could not find expected ':'

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

conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】

14 could not find expected ':'

?yml配置文件遇到“：”或者“-”后面必須留一個(gè)空格！

15 參考文章

（1）pip配置環(huán)境

linux環(huán)境根據(jù)requirements.txt搭建python虛擬環(huán)境_小小魚er的博客-CSDN博客_根據(jù)requirement創(chuàng)建虛擬環(huán)境

Python項(xiàng)目部署到服務(wù)器上_李俊的博客的博客-CSDN博客_python項(xiàng)目部署到服務(wù)器

（2）conda配置服務(wù)器環(huán)境

Anaconda 復(fù)制或移植已有環(huán)境（復(fù)制到別的服務(wù)器上）_anaconda復(fù)制環(huán)境_℡ヾNothing-_哥的博客-CSDN博客

使用ananconda直接在服務(wù)器之間快速遷移環(huán)境 - 嗶哩嗶哩

將你的Python代碼部署到云服務(wù)器上_Pythonwill的博客-CSDN博客_如何用python部署云端服務(wù)器

在服務(wù)器上搭建自己的python環(huán)境(針對(duì)小白)_西瓜6的博客-CSDN博客_服務(wù)器環(huán)境里python文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-400722.html

到了這里，關(guān)于conda env create -f environment.yml報(bào)錯(cuò)ResolvePackageNotFound和Found conflicts的解決方案【已解決】 14 could not find expected ':'的文章就介紹完了。如果您還想了解更多內(nèi)容，請(qǐng)?jiān)谟疑辖撬阉鱐OY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章，希望大家以后多多支持TOY模板網(wǎng)！

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【conda】conda create 環(huán)境報(bào)錯(cuò)CondaHTTPError: HTTP 000 CONNECTION FAILED for url ＜https://conda.anaconda.o
目錄 ????背景 ????報(bào)錯(cuò) ????網(wǎng)上解決方案 ????分析 ???? 我的解決方案（Linux版） ??原因分析 ??解決步驟 ????教訓(xùn) 整理不易，歡迎一鍵三連?。?！送你們一條美麗的--分割線-- ????????操作系統(tǒng)：Linux ????????conda創(chuàng)建虛擬環(huán)境： conda create -n xxx python=3.
2024年02月04日
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參考 | conda 一直在 solving environment: / 解決辦法
瞅瞅 C:Users{用戶名} 下面有沒(méi)有一個(gè) .condar 的文件: 如果沒(méi)有, 在命令行輸入這個(gè)命令 conda config --add channels r 然后 C:Users{用戶名} 應(yīng)該就會(huì)有 .condar 這個(gè)文件了打開清華源 anaconda 鏡像站, 復(fù)制框里內(nèi)容用記事本打開 C:Users{用戶名} 下的 .condar 文件, 將上面內(nèi)容復(fù)制到里
2024年01月17日
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解決pycharm中無(wú)法添加conda environment 問(wèn)題
解決pycharm中無(wú)法添加conda environment 問(wèn)題針對(duì)有些pycharm使用愛好者，添加python interpreter時(shí)，想通過(guò)conda environment 來(lái)添加，結(jié)果發(fā)現(xiàn)conda environment 中顯示conda executable is not found 的問(wèn)題，下面步驟可以解決該問(wèn)題： step1：點(diǎn)擊conda environment step2: 點(diǎn)擊conda executable 中的“文件包”表
2024年02月13日
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Conda安裝第三方庫(kù)卡在solving environment
安裝第三方庫(kù)（如pytorch)卡在solving environment步驟刪除鏡像路徑設(shè)置ssl 親測(cè)有效
2024年02月13日
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解決conda創(chuàng)建環(huán)境，環(huán)境路徑并非是conda安裝目錄下的envs或我們?cè)O(shè)置的目錄
有些同學(xué)可能遇到使用conda創(chuàng)建環(huán)境，環(huán)境的路徑總是在C盤，但是明明conda安裝在D盤，而且配置文件的默認(rèn)路徑也是D盤。其實(shí)原因很簡(jiǎn)單，因?yàn)樵O(shè)置的默認(rèn)路徑?jīng)]有足夠的權(quán)限。 1.怎么查看默認(rèn)的目錄呢第一種方法：找到 .condarc 文件，一般在 C:Usersadministrator.condarc ，以文
2024年02月11日
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Conda python管理環(huán)境environments 三從入門到精通
Conda系列：翻譯: Anaconda 與 miniconda的區(qū)別 Miniconda介紹以及安裝 Conda python運(yùn)行的包和環(huán)境管理入門 Conda python管理環(huán)境environments 一從入門到精通 Conda python管理環(huán)境environments 二從入門到精通激活環(huán)境對(duì)于在環(huán)境中制作軟件至關(guān)重要好使。激活需要兩個(gè)主要功能：向 PATH 添加
2024年01月23日
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