【Trick】conda指令安装yml文件中的python依赖

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安装python依赖通常使用【pip】或【conda】指令，pip主要用于txt文件，conda主要用于yml文件。以下将给出其使用方法

pip

首先，创建一个YAML文件，列出python依赖项。

dependencies:
  - python=3.8
  - numpy
  - pandas
  - matplotlib

然后，打开终端，运行pip指令（假设文件名为requirements，实际应用时下列指令应该根据文件名进行修改）。

对于yml：

pip install -r requirements.yml

对于txt：

pip install -r requirements.txt

后续只需等待即可。 

conda

 首先，创建一个YAML文件，列出python依赖项。

channels:
  - defaults
dependencies:
  - python=3.8
  - numpy
  - pandas
  - matplotlib

然后，打开终端，运行conda指令（假设文件名为requirements，实际应用时下列指令应该根据文件名进行修改）。

对于yml：

conda env create -f requirements.yml

后续只需等待即可。 

实际案例

yml文件内容如下：

name: con_110
channels:
  - pytorch
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - backcall=0.2.0=pyhd3eb1b0_0
  - blas=1.0=mkl
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2022.07.19=h06a4308_0
  - certifi=2022.9.24=py37h06a4308_0
  - cudatoolkit=11.3.1=h2bc3f7f_2
  - debugpy=1.5.1=py37h295c915_0
  - decorator=5.1.1=pyhd3eb1b0_0
  - entrypoints=0.4=py37h06a4308_0
  - faiss-gpu=1.7.2=py3.7_h28a55e0_0_cuda11.3
  - ffmpeg=4.3=hf484d3e_0
  - freetype=2.11.0=h70c0345_0
  - giflib=5.2.1=h7b6447c_0
  - gmp=6.2.1=h2531618_2
  - gnutls=3.6.15=he1e5248_0
  - intel-openmp=2021.4.0=h06a4308_3561
  - ipykernel=6.15.2=py37h06a4308_0
  - ipython=7.31.1=py37h06a4308_1
  - jedi=0.18.1=py37h06a4308_1
  - joblib=1.1.0=pyhd3eb1b0_0
  - jpeg=9d=h7f8727e_0
  - jupyter_client=7.1.2=pyhd3eb1b0_0
  - jupyter_core=4.11.1=py37h06a4308_0
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.35.1=h7274673_9
  - libfaiss=1.7.2=hfc2d529_0_cuda11.3
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.3.0=h5101ec6_17
  - libgfortran-ng=7.5.0=ha8ba4b0_17
  - libgfortran4=7.5.0=ha8ba4b0_17
  - libgomp=9.3.0=h5101ec6_17
  - libiconv=1.15=h63c8f33_5
  - libidn2=2.3.2=h7f8727e_0
  - libpng=1.6.37=hbc83047_0
  - libsodium=1.0.18=h7b6447c_0
  - libstdcxx-ng=9.3.0=hd4cf53a_17
  - libtasn1=4.16.0=h27cfd23_0
  - libtiff=4.2.0=h85742a9_0
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.40.0=h7b6447c_0
  - libwebp=1.2.2=h55f646e_0
  - libwebp-base=1.2.2=h7f8727e_0
  - lz4-c=1.9.3=h295c915_1
  - matplotlib-inline=0.1.6=py37h06a4308_0
  - mkl=2021.4.0=h06a4308_640
  - mkl-service=2.4.0=py37h7f8727e_0
  - mkl_fft=1.3.1=py37hd3c417c_0
  - mkl_random=1.2.2=py37h51133e4_0
  - ncurses=6.3=h7f8727e_2
  - nest-asyncio=1.5.5=py37h06a4308_0
  - nettle=3.7.3=hbbd107a_1
  - numpy=1.21.2=py37h20f2e39_0
  - numpy-base=1.21.2=py37h79a1101_0
  - olefile=0.46=py37_0
  - openh264=2.1.1=h4ff587b_0
  - openssl=1.1.1q=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - parso=0.8.3=pyhd3eb1b0_0
  - pexpect=4.8.0=pyhd3eb1b0_3
  - pickleshare=0.7.5=pyhd3eb1b0_1003
  - pillow=8.4.0=py37h5aabda8_0
  - pip=21.2.2=py37h06a4308_0
  - prompt-toolkit=3.0.20=pyhd3eb1b0_0
  - ptyprocess=0.7.0=pyhd3eb1b0_2
  - py=1.11.0=pyhd3eb1b0_0
  - pygments=2.11.2=pyhd3eb1b0_0
  - pyparsing=3.0.9=py37h06a4308_0
  - python=3.7.11=h12debd9_0
  - python-dateutil=2.8.2=pyhd3eb1b0_0
  - pytorch=1.10.2=py3.7_cuda11.3_cudnn8.2.0_0
  - pytorch-mutex=1.0=cuda
  - pyzmq=22.3.0=py37h295c915_2
  - readline=8.1.2=h7f8727e_1
  - scikit-learn=1.0.2=py37h51133e4_1
  - scipy=1.7.3=py37hc147768_0
  - setuptools=58.0.4=py37h06a4308_0
  - six=1.16.0=pyhd3eb1b0_1
  - sqlite=3.37.2=hc218d9a_0
  - threadpoolctl=2.2.0=pyh0d69192_0
  - tk=8.6.11=h1ccaba5_0
  - torchvision=0.11.3=py37_cu113
  - tornado=6.1=py37h27cfd23_0
  - tqdm=4.62.3=pyhd3eb1b0_1
  - traitlets=5.1.1=pyhd3eb1b0_0
  - typing_extensions=3.10.0.2=pyh06a4308_0
  - wcwidth=0.2.5=pyhd3eb1b0_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.2.5=h7b6447c_0
  - zeromq=4.3.4=h2531618_0
  - zlib=1.2.11=h7f8727e_4
  - zstd=1.4.9=haebb681_0
  - pip:
    - charset-normalizer==2.0.12
    - click==8.0.4
    - docker-pycreds==0.4.0
    - gitdb==4.0.9
    - gitpython==3.1.27
    - idna==3.3
    - importlib-metadata==4.11.1
    - pathtools==0.1.2
    - promise==2.3
    - protobuf==3.19.4
    - psutil==5.9.0
    - pyyaml==6.0
    - requests==2.27.1
    - sentry-sdk==1.5.6
    - shortuuid==1.0.8
    - smmap==5.0.0
    - termcolor==1.1.0
    - urllib3==1.26.8
    - wandb==0.12.10
    - yaspin==2.1.0
    - zipp==3.7.0

conda指令如下：

conda env create -f environment.yml

运行过程如下：

(base) ubuntu@xwk2:~/GG$ conda env create -f environment.yml
Retrieving notices: ...working... done
Collecting package metadata (repodata.json): - WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.7.1.*, but conda is ignoring the .* and treating it as 1.7.1
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.9.0.*, but conda is ignoring the .* and treating it as 1.9.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.8.0.*, but conda is ignoring the .* and treating it as 1.8.0
WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.6.0.*, but conda is ignoring the .* and treating it as 1.6.0
done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 23.7.3
  latest version: 24.4.0

Please update conda by running

    $ conda update -n base -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main conda

Or to minimize the number of packages updated during conda update use

     conda install conda=24.4.0



Downloading and Extracting Packages
blas-1.0             | 6 KB      | ############################################################################## | 100% 
certifi-2022.9.24    | 154 KB    | ############################################################################## | 100% 
setuptools-58.0.4    | 775 KB    | ############################################################################## | 100% 
numpy-base-1.21.2    | 4.8 MB    | ############################################################################## | 100% 
ipykernel-6.15.2     | 189 KB    | ############################################################################## | 100% 
prompt-toolkit-3.0.2 | 259 KB    | ############################################################################## | 100% 
pygments-2.11.2      | 759 KB    | ############################################################################## | 100% 
python-3.7.11        | 45.3 MB   | ############################################################################## | 100% 
pyzmq-22.3.0         | 465 KB    | ############################################################################## | 100% 
libunistring-0.9.10  | 536 KB    | ############################################################################## | 100% 
entrypoints-0.4      | 16 KB     | ############################################################################## | 100% 
 ... (more hidden) ...


                                                                                                                         
Preparing transaction: done                                                                                              
Verifying transaction: done                                                                                              
Executing transaction: \ By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html                                
                                                                                                                         
\                                                                                                                        
                                                                                                                         
    Installed package of scikit-learn can be accelerated using scikit-learn-intelex.                                     
    More details are available here: https://intel.github.io/scikit-learn-intelex                                        
                                                                                                                         
    For example:      

        $ conda install scikit-learn-intelex
        $ python -m sklearnex my_application.py

    

done
Installing pip dependencies: \ Ran pip subprocess with arguments:
['/home/ubuntu/miniconda3/envs/con_110/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt', '--exists-action=b']
Pip subprocess output:
Collecting charset-normalizer==2.0.12
  Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
Collecting click==8.0.4
  Downloading click-8.0.4-py3-none-any.whl (97 kB)
Collecting docker-pycreds==0.4.0
  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)
Collecting gitdb==4.0.9
  Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)
Collecting gitpython==3.1.27
  Downloading GitPython-3.1.27-py3-none-any.whl (181 kB)
Collecting idna==3.3
  Downloading idna-3.3-py3-none-any.whl (61 kB)
Collecting importlib-metadata==4.11.1
  Downloading importlib_metadata-4.11.1-py3-none-any.whl (17 kB)
Collecting pathtools==0.1.2
  Downloading pathtools-0.1.2.tar.gz (11 kB)
Collecting promise==2.3
  Downloading promise-2.3.tar.gz (19 kB)
Collecting protobuf==3.19.4
  Downloading protobuf-3.19.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)
Collecting psutil==5.9.0
  Downloading psutil-5.9.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (280 kB)
Collecting pyyaml==6.0
  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)
Collecting requests==2.27.1
  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
Collecting sentry-sdk==1.5.6
  Downloading sentry_sdk-1.5.6-py2.py3-none-any.whl (144 kB)
Collecting shortuuid==1.0.8
  Downloading shortuuid-1.0.8-py3-none-any.whl (9.5 kB)
Collecting smmap==5.0.0
  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)
Collecting termcolor==1.1.0
  Downloading termcolor-1.1.0.tar.gz (3.9 kB)
Collecting urllib3==1.26.8
  Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)
Collecting wandb==0.12.10
  Downloading wandb-0.12.10-py2.py3-none-any.whl (1.7 MB)
Collecting yaspin==2.1.0
  Downloading yaspin-2.1.0-py3-none-any.whl (18 kB)
Collecting zipp==3.7.0
  Downloading zipp-3.7.0-py3-none-any.whl (5.3 kB)
Requirement already satisfied: six>=1.4.0 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from docker-pycreds==0.4.0->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 3)) (1.16.0)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from gitpython==3.1.27->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 5)) (3.10.0.2)
Requirement already satisfied: certifi>=2017.4.17 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from requests==2.27.1->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 13)) (2022.9.24)
Requirement already satisfied: python-dateutil>=2.6.1 in /home/ubuntu/miniconda3/envs/con_110/lib/python3.7/site-packages (from wandb==0.12.10->-r /home/ubuntu/GLC/condaenv.h1f9e76a.requirements.txt (line 19)) (2.8.2)
Building wheels for collected packages: pathtools, promise, termcolor
  Building wheel for pathtools (setup.py): started
  Building wheel for pathtools (setup.py): finished with status 'done'
  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=a66ada93957cf7fc7b3ef5bffd7844b018a4a97eaca9289f2e292761489ee2d8
  Stored in directory: /home/ubuntu/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2
  Building wheel for promise (setup.py): started
  Building wheel for promise (setup.py): finished with status 'done'
  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21503 sha256=884eb51f491d088c5fb1959e5be5f3ec41086ef58eac9c32cc737c5c83a45578
  Stored in directory: /home/ubuntu/.cache/pip/wheels/29/93/c6/762e359f8cb6a5b69c72235d798804cae523bbe41c2aa8333d
  Building wheel for termcolor (setup.py): started
  Building wheel for termcolor (setup.py): finished with status 'done'
  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4848 sha256=889e55b1d10f55c622c011af4442358d02ae5abd2629c38ed91bdaebd3dd542d
  Stored in directory: /home/ubuntu/.cache/pip/wheels/3f/e3/ec/8a8336ff196023622fbcb36de0c5a5c218cbb24111d1d4c7f2
Successfully built pathtools promise termcolor
Installing collected packages: zipp, smmap, urllib3, termcolor, importlib-metadata, idna, gitdb, charset-normalizer, yaspin, shortuuid, sentry-sdk, requests, pyyaml, psutil, protobuf, promise, pathtools, gitpython, docker-pycreds, click, wandb
  Attempting uninstall: psutil
    Found existing installation: psutil 5.8.0
    Uninstalling psutil-5.8.0:
      Successfully uninstalled psutil-5.8.0
Successfully installed charset-normalizer-2.0.12 click-8.0.4 docker-pycreds-0.4.0 gitdb-4.0.9 gitpython-3.1.27 idna-3.3 importlib-metadata-4.11.1 pathtools-0.1.2 promise-2.3 protobuf-3.19.4 psutil-5.9.0 pyyaml-6.0 requests-2.27.1 sentry-sdk-1.5.6 shortuuid-1.0.8 smmap-5.0.0 termcolor-1.1.0 urllib3-1.26.8 wandb-0.12.10 yaspin-2.1.0 zipp-3.7.0

done
#
# To activate this environment, use
#
#     $ conda activate con_110
#
# To deactivate an active environment, use
#
#     $ conda deactivate

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