1 cudnn安裝
按照官網(wǎng)教程進(jìn)行安裝：https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#download
1.1? 下載
從https://developer.nvidia.com/cudnn上下載cudnn相應(yīng)版本的壓縮包，若非tgz版本弦悉，修改后綴名為tgz
1.2 解壓縮
$ tar -xzvf cudnn-9.0-linux-x64-v7.tgz
1.3 復(fù)制和修改權(quán)限
$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include?
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64?
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
1.4 查看cudnn版本：
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A2
2 tensorflow安裝
2.1 安裝native pip, python2.7,GPU support ,tensorflow
$ sudo apt-get install python-pip python-dev ????????**安裝pip**
$ pip install tensorflow-gpu ???????????????**安裝tensorflow**
#若成功旭寿，則進(jìn)入驗(yàn)證階段崇败，否則執(zhí)行第三行指令
$ sudo pip install --upgrade?\
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.1-cp27-none-linux_x86_64.whl
2.2 驗(yàn)證
進(jìn)入python，執(zhí)行如下指令
# Python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
屏幕顯示 Hello, TensorFlow!后室，代表tensorflow安裝成功
