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env_driver_cuda_tf_installation.md

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GPU server environment setup and other configuration related knowledge.

Server

  • linux ubuntu 16.04
  • 4 GeForce GTX 1080 Ti

Environment setup

  • Install nvidia 1080Ti graphics driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get install nvidia-396

Afterwards, you can check the Installation with the nvidia-smi command, which will report all your CUDA-capable devices in the system.

  • Download and install Anaconda Anaconda3-5.1.0-Linux-x86_64.sh from here.

  • Download CUDA 9.0 cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb from here.

  • Install CUDA as cmd below

sudo dpkg -i cuda-repo-ubuntu1604-9-0-local_9.0.176-1_amd64.deb
sudo apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda

  • Download cuDNN for CUDA 9.0 'cuDNN v7.0.5 Library for Linux' cudnn-9.0-linux-x64-v7.solitairetheme8 from here

  • Install cuDnn

tar zxvf cudnn-9.0-linux-x64-v7.solitairetheme8
sudo cp -P cuda/include/cudnn.h /usr/local/cuda-9.0/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda-9.0/lib64
sudo chmod a+r /usr/local/cuda-9.0/lib64/libcudnn*

  • Check if CUDA installation correctly as here

  • Add environment path by vim ~/.bash_profile, and source ~/.bash_profile

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-9.0/lib64:/usr/local/cuda-9.0/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda-9.0
export PATH=$PATH:/usr/local/cuda-9.0/bin:/home/ubuntu/anaconda3/bin

Then you can create and config your virtual environment.

Virtual Environment

  • my virtual environment
conda create --name env_gpu_py35 python=3.5
source activate env_gpu_py35
pip install tensorflow-gpu keras
conda install scikit-learn
conda install jupyter