ubuntu 16.04 笔记本双显卡安装tensorflow-

环境
联想笔记本 Y430P
ubuntu 16.04
GeForce GTX 850M
python3
tensorflow-gpu

前言

ubuntu默认使用集显，不支持CUDA，因此需要切换成独显GeForce GTX 850M。

CUDA必须是3.5以上的，GeForce GTX 850M是5.0
NVIDIA® GPU card with CUDA® Compute Capability 3.5 or higher

1. 安装显卡切换软件

sudo add-apt-repository ppa:nilarimogard/webupd8    #添加PPA更新源
sudo apt-get update                                 #刷新更新源列表
sudo apt-get install prime-indicator                #安装双显卡切换指示器

重启，右上角会有NVIDIA的图标

image.png

2. 禁用系统默认驱动

sudo chmod 666 /etc/modprobe.d/blacklist.conf       #修改blacklist.conf权限为可写可运行
sudo vim /etc/modprobe.d/blacklist.conf           #打开blacklist.conf

文件末尾添加这一行

blacklist nouveau

3. 查看GTX850M官方驱动的版本

最新的驱动是410

image.png

4. Ctrl+Alt+F1进入命令行模式

sudo service lightdm stop           #关闭图形系统
sudo apt-get install nvidia-410     #也就是刚才看的410
sudo reboot            #重启

5. 安装CUDA

sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
sudo apt install ./cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
sudo apt update

# Install CUDA and tools. Include optional NCCL 2.x
sudo apt install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \
    cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.2.1.38-1+cuda9.0 \
    libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0

6. 安装tensorflow-gpu

pip3 install tensorflow-gpu

7. 测试代码，第一次运行时间很长

python3 1_tf_reduce_mean.py

# 1_tf_reduce_mean.py

import tensorflow as tf
import numpy as np

#  Computes the mean of elements across dimensions of a tensor. (deprecated arguments)

#  tf.reduce_mean(
#      input_tensor,
#      axis=None,
#      keepdims=None,
#      name=None,
#      reduction_indices=None,
#      keep_dims=None
#      )

c = np.array([[3.,4], [5.,6], [6.,7]])

step = tf.reduce_mean(c, 1)
with tf.Session() as sess:
    print(sess.run(step))