高速ymlを使ってconda仮想環境を設定します.


mymlを使って急速にconda環境を設定します.
  • 自分の環境要求に応じて構成される.
  • `conda env create-f yourymlname.yml`
  • 自分の環境に応じて配置する.
    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端末に以上のコマンドを入力します.