使用yml快速配置conda虚拟环境

根据自己的环境要求配置

channels:
  - conda-forge
dependencies:
  ##### Core scientific packages
  - python >=3.0
  - jupyter==1.0.0
  - pip
  - matplotlib==3.0.3
  - numpy==1.16.2
  - pandas==0.24.1
  - scipy==1.2.1

  ##### Machine Learning packages
  - scikit-learn==0.20.3
  #- xgboost==0.82

  ##### Deep Learning packages
  # Replace tensorflow with tensorflow-gpu if you want GPU support. If so,
  # you need a GPU card with CUDA Compute Capability 3.0 or higher support, and
  # you must install CUDA, cuDNN and more: see tensorflow.org for the detailed
  # installation instructions.
  - tensorflow==1.13.1
  - tensorflow-gpu==1.13.1

  # Optional: OpenAI gym is only needed for the Reinforcement Learning chapter.
  # There are a few dependencies you need to install first, check out:
  # https://github.com/openai/gym#installing-everything
  #- pip:
    #- gym[all]==0.10.9
    # If you only want to install the Atari dependency, uncomment this line instead:
    #- gym[atari]==0.10.9

  ##### Image manipulation
  - imageio==2.5.0
  - pillow==6.2.0
  - scikit-image==0.14.2

  ##### Extra packages (optional)
  # Nice utility to diff Jupyter Notebooks.
  #- nbdime==1.0.5

  # May be useful with Pandas for complex "where" clauses (e.g., Pandas
  # tutorial).
  - numexpr==2.6.9

  # Optional: these libraries can be useful in the classification chapter,
  # exercise 4.
  - nltk==3.4.5
  - pip:
    - urlextract==0.9

  # Optional: tqdm displays nice progress bars, ipywidgets for tqdm's notebook support
  - tqdm==4.31.1
  - ipywidgets==7.4.2

  # Optional: Some useful extensions to customize and configure jupyter notebooks
  - jupyter_contrib_nbextensions
  - jupyter_nbextensions_configurator

name: yourenvsname

conda env create -f yourymlname.yml

在终端中输入以上命令