1. sh NVIDIA-Linux-x86_64-410.73.run #或者系统设置里面直接选英伟达的驱动
reboot
nvidia-smi #检查驱动是否生效
2. sh Anaconda3-5.3.0-Linux-x86_64.sh #https://www.anaconda.com/download/#linux
source ~/.bashrc
3. sh cuda_9.0.176_384.81_linux.run #https://developer.nvidia.com/cuda-downloads
vim ~/.bashrc
export CUDA_HOME=/usr/local/cuda
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=/usr/local/cuda/lib64
souce ~/.bashrc
nvcc -V #执行成功表示安装成功
4. dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb #https://developer.nvidia.com/cudnn
dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
tar zxf cudnn-9.0-linux-x664-v7.tgz #cuDNN Library for Linux
cp cuda/include/cudnn.h /usr/local/cuda/include
cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
chmod a+r /usr/local/cuda/include/cudnn.h
chmod a+r /usr/local/cuda/lib64/libcudnn*
5. conda install python=3.6
pip install --upgrade pip
pip install tensorflow-gpu #速度慢可以换一个源: pip install --index-url https://pypi.douban.com/simple tensorflow-gpu
python
import tensorflow as tf
heeo = tf.constant('hello')
sess - tf.Session()
print(sess.run(heeo))
6. pip install opencv-contrib-python
7. https://github.com/xiaoxiaotao/tensorflow-flowers
