Anaconda创建跟别人环境配置一样的虚拟环境（coda env creat -f environment.yml）

当我们跑别人在github上的代码时，往往需要配置跟作者一样的环境。当作者导出自己的环境配置时，一般都是.yml文件，这时候需要输入命令行来实现配置一模一样的环境。

导出的yml文件一般配置如下：

name: vin_old_tf
channels:
  - anaconda
  - intel
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _tflow_select=2.1.0=gpu
  - absl-py=0.8.1=py37_0
  - astor=0.8.0=py37_0
  - attrs=19.3.0=py_0
  - backcall=0.1.0=py37_0
  - binutils_impl_linux-64=2.31.1=h6176602_1
  - binutils_linux-64=2.31.1=h6176602_9
  - blas=2.14=openblas
  - bleach=3.1.0=py37_0
  - bzip2=1.0.8=h7b6447c_0
  - c-ares=1.15.0=h7b6447c_1001
  - ca-certificates=2020.7.22=0
  - cairo=1.16.0=hfb77d84_1002
  - certifi=2020.6.20=py37_0
  - cloudpickle=1.2.2=py_1
  - coloredlogs=14.0=py37hc8dfbb8_1
  - cudatoolkit=10.0.130=0
  - cudnn=7.6.5=cuda10.0_0
  - cupti=10.0.130=0
  - cycler=0.10.0=py_2
  - cytoolz=0.10.1=py37h516909a_0
  - dask-core=2.9.2=py_0
  - dbus=1.13.12=h746ee38_0
  - decorator=4.4.1=py_0
  - defusedxml=0.6.0=py_0
  - entrypoints=0.3=py37_0
  - expat=2.2.9=he1b5a44_2
  - ffmpeg=4.1.3=h167e202_0
  - fontconfig=2.13.1=h86ecdb6_1001
  - freetype=2.9.1=h8a8886c_1
  - gast=0.3.2=py_0
  - gcc_impl_linux-64=7.3.0=habb00fd_1
  - gcc_linux-64=7.3.0=h553295d_9
  - giflib=5.2.1=h516909a_1
  - glib=2.63.1=h5a9c865_0
  - gmp=6.1.2=h6c8ec71_1
  - gnutls=3.6.5=hd3a4fd2_1002
  - google-pasta=0.1.8=py_0
  - graphite2=1.3.13=h23475e2_0
  - grpcio=1.16.1=py37hf8bcb03_1
  - gst-plugins-base=1.14.5=h0935bb2_0
  - gstreamer=1.14.5=h36ae1b5_0
  - gxx_impl_linux-64=7.3.0=hdf63c60_1
  - gxx_linux-64=7.3.0=h553295d_9
  - h5py=2.10.0=nompi_py37h513d04c_101
  - harfbuzz=2.4.0=h9f30f68_3
  - hdf5=1.10.5=nompi_h3c11f04_1104
  - humanfriendly=8.2=py37hc8dfbb8_0
  - icu=64.2=he1b5a44_1
  - imageio=2.6.1=py37_0
  - imbalanced-learn=0.6.2=py_0
  - importlib_metadata=1.3.0=py37_0
  - intelpython=2020.0=1
  - ipykernel=5.1.3=py37h39e3cac_1
  - ipython=7.11.1=py37h39e3cac_0
  - ipython_genutils=0.2.0=py37_0
  - ipywidgets=7.5.1=py_0
  - jasper=1.900.1=hd497a04_4
  - jedi=0.15.2=py37_0
  - jinja2=2.10.3=py_0
  - joblib=0.13.2=py37_1
  - jpeg=9c=h14c3975_1001
  - jsonschema=3.2.0=py37_0
  - jupyter=1.0.0=py37_7
  - jupyter_client=5.3.4=py37_0
  - jupyter_console=6.1.0=py_0
  - jupyter_core=4.6.1=py37_0
  - keras=2.3.1=py37_0
  - keras-applications=1.0.8=py_0
  - keras-preprocessing=1.1.0=py_1
  - kiwisolver=1.1.0=py37hc9558a2_0
  - lame=3.100=h7b6447c_0
  - ld_impl_linux-64=2.33.1=h53a641e_7
  - libblas=3.8.0=14_openblas
  - libcblas=3.8.0=14_openblas
  - libclang=9.0.1=default_hde54327_0
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=9.1.0=hdf63c60_0
  - libgfortran-ng=7.3.0=hdf63c60_0
  - libgpuarray=0.7.6=h14c3975_1003
  - libiconv=1.15=h63c8f33_5
  - liblapack=3.8.0=14_openblas
  - liblapacke=3.8.0=14_openblas
  - libllvm9=9.0.1=hc9558a2_0
  - libopenblas=0.3.7=h5ec1e0e_6
  - libopencv=4.2.0=py37_2
  - libpng=1.6.37=hbc83047_0
  - libprotobuf=3.11.2=hd408876_0
  - libsodium=1.0.16=h1bed415_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - libtiff=4.1.0=h2733197_0
  - libuuid=2.32.1=h14c3975_1000
  - libwebp=1.0.2=h56121f0_5
  - libxcb=1.13=h1bed415_1
  - libxkbcommon=0.9.1=hebb1f50_0
  - libxml2=2.9.9=hea5a465_1
  - mako=1.1.0=py_0
  - markdown=3.1.1=py37_0
  - markupsafe=1.1.1=py37h7b6447c_0
  - matplotlib=3.1.2=py37_1
  - matplotlib-base=3.1.2=py37h250f245_1
  - mistune=0.8.4=py37h7b6447c_0
  - more-itertools=8.0.2=py_0
  - nbconvert=5.6.1=py37_0
  - nbformat=4.4.0=py37_0
  - ncurses=6.1=he6710b0_1
  - nettle=3.4.1=h1bed415_1002
  - networkx=2.4=py_0
  - notebook=6.0.2=py37_0
  - nspr=4.24=he1b5a44_0
  - nss=3.47=he751ad9_0
  - numpy=1.16.4=py37h99e49ec_0
  - numpy-base=1.16.4=py37h2f8d375_0
  - olefile=0.46=py_0
  - opencv=4.2.0=py37_2
  - openh264=1.8.0=hd408876_0
  - openssl=1.1.1g=h7b6447c_0
  - pandas=1.0.3=py37h0573a6f_0
  - pandoc=2.2.3.2=0
  - pandocfilters=1.4.2=py37_1
  - parso=0.5.2=py_0
  - pcre=8.43=he6710b0_0
  - pexpect=4.7.0=py37_0
  - pickleshare=0.7.5=py37_0
  - pillow=6.0.0=py37h34e0f95_0
  - pip=19.3.1=py37_0
  - pixman=0.38.0=h7b6447c_0
  - prometheus_client=0.7.1=py_0
  - prompt_toolkit=3.0.2=py_0
  - protobuf=3.11.2=py37he6710b0_0
  - ptyprocess=0.6.0=py37_0
  - py-opencv=4.2.0=py37h5ca1d4c_2
  - pygments=2.5.2=py_0
  - pygpu=0.7.6=py37hc1659b7_1000
  - pyparsing=2.4.6=py_0
  - pyqt=5.12.3=py37h8685d9f_3
  - pyrsistent=0.15.6=py37h7b6447c_0
  - python=3.7.6=h0371630_2
  - python-dateutil=2.8.1=py_0
  - python_abi=3.7=1_cp37m
  - pytz=2020.1=py_0
  - pywavelets=1.1.1=py37hc1659b7_0
  - pyyaml=5.3.1=py37h8f50634_0
  - pyzmq=18.1.0=py37he6710b0_0
  - qt=5.12.5=hd8c4c69_1
  - qtconsole=4.6.0=py_1
  - readline=7.0=h7b6447c_5
  - scikit-image=0.16.2=py37hb3f55d8_0
  - scikit-learn=0.22.1=py37h22eb022_0
  - scipy=1.3.2=py37he2b7bc3_0
  - send2trash=1.5.0=py37_0
  - setuptools=44.0.0=py37_0
  - six=1.13.0=py37_0
  - sqlite=3.30.1=h7b6447c_0
  - tensorboard=1.14.0=py37hf484d3e_0
  - tensorflow=1.14.0=gpu_py37h4491b45_0
  - tensorflow-base=1.14.0=gpu_py37h8d69cac_0
  - tensorflow-estimator=1.14.0=py_0
  - tensorflow-gpu=1.14.0=h0d30ee6_0
  - termcolor=1.1.0=py37_1
  - terminado=0.8.3=py37_0
  - testpath=0.4.4=py_0
  - theano=1.0.4=py37he1b5a44_1001
  - tk=8.6.10=hed695b0_0
  - toolz=0.10.0=py_0
  - tornado=6.0.3=py37h7b6447c_0
  - tqdm=4.41.1=py_0
  - traitlets=4.3.3=py37_0
  - ujson=2.0.3=py37he6710b0_0
  - wcwidth=0.1.7=py37_0
  - webencodings=0.5.1=py37_1
  - werkzeug=0.16.0=py_0
  - wheel=0.33.6=py37_0
  - widgetsnbextension=3.5.1=py37_0
  - wrapt=1.11.2=py37h7b6447c_0
  - x264=1!152.20180806=h7b6447c_0
  - xorg-kbproto=1.0.7=h14c3975_1002
  - xorg-libice=1.0.10=h516909a_0
  - xorg-libsm=1.2.3=h84519dc_1000
  - xorg-libx11=1.6.9=h516909a_0
  - xorg-libxext=1.3.4=h516909a_0
  - xorg-libxrender=0.9.10=h516909a_1002
  - xorg-renderproto=0.11.1=h14c3975_1002
  - xorg-xextproto=7.3.0=h14c3975_1002
  - xorg-xproto=7.0.31=h14c3975_1007
  - xz=5.2.4=h14c3975_4
  - yaml=0.2.5=h516909a_0
  - zeromq=4.3.1=he6710b0_3
  - zipp=0.6.0=py_0
  - zlib=1.2.11=h7b6447c_3
  - zstd=1.3.7=h0b5b093_0
  - pip:
    - dlib==19.19.0
    - pyqt5-sip==4.19.18
    - pyqtchart==5.12
    - pyqtwebengine==5.12.1
prefix: /home/rubin/anaconda3/envs/vin_old_tf

如上述代码所示：name是创建虚拟环境之后在anaconda/envs文件夹下虚拟环境的名称，比如这个为：vin_old_tf；channels、dependents、pip都是需要下载的包名；prefix则是自己anaconda文件下虚拟环境的路径，

需要把这个路径改为自己的文件夹。

接下来配置和创建虚拟环境，输入如下命令行：

conda env create -f environment.yml

然后按enter，系统就开始自动下载啦，等一段时间之后就安装和配置成功了。

PS：看安装成功与否可以看/home/rubin/anaconda3/envs/文件夹下有没有创建的虚拟环境的文件名，有的话就是安装成功了。