Oct/30/2018 Updated by


Python + GPUenvironment for Deep Learning

[Up] Japanese English

Make the python + GPU environment necessary to run the Deep Learning program.

[Prerequisite Conditions]

Creating the python + GPU environment

Use tensorflow-gpu instead of tensorflow. Other than that, create the python environment in exactly the same way as "Python Environment for Deep Learning".

  1. It is assumed that the environment of python 3.X has been installed on Anaconda. (X ≥ 7)
  2. Start "conda", and do the following steps in the "conda" window.
  3. Create a new python environment with conda. Here, the environment name is deep. At this moment, tensorflow runs only on python 3.6 or 3.5, so using 3.6.
  4.     (base) C:\Users\nitta>  conda create -n deep python=3.6 jupyter 
  5. Switch the new python environment.
  6.     (gpu) C:\Users\nitta>  conda activate deep  
  7. Install tensorflow-gpu .
  8.     (gpu) C:\Users\nitta>  pip install --ignore-installed --upgrade tensorflow-gpu  
  9. There may be a message saying to update the pip during installation of tensorflow. In that case, update pip according to the instructions.
  10. Install keras .
  11.     (gpu) C:\Users\nitta>  pip install keras  
  12. Install convenient modules .
  13.     (gpu) C:\Users\nitta>   pip install scipy scikit-learn pillow h5py matplotlib pandas  
  14. Install OpenCV .
  15.     (gpu) C:\Users\nitta>  conda install opencv  
  16. Install Natural Language ToolKit .
  17.     (gpu) C:\Users\nitta>  pip install nltk  
  18. Install gensim for word2vec .
  19.     (gpu) C:\Users\nitta>  pip install gensim