Install Nvidia driver with dkms may help you

DKMS is Dynamic Kernel Module Support, which can help you when you upgrade your kernel with the out come modules.

To install Nvidia Graphic Card driver with DKMS support, install DKMS first.

$ sudo apt-get install dkms

Then install the driver, take NVIDIA-Linux-x86_64-367.35.run for example

$ chmod +x  NVIDIA-Linux-x86_64-367.35.run
$ sudo sh NVIDIA-Linux-x86_64-367.35.run --dkms

When the installation finish

$ dkms status

You can find  "nvidia, 367.35, 3.19.0-64-generic, x86_64: installed".

And you can build all modules for current kernel by

$ dkms autoinstall

And for a specifi kernel by

$ dkms autoinstall -k 4.4.15 

Or install a module for all kernels

$ dkms install -m nvidia -v 367.35 --all

Reference  https://wiki.archlinux.org/index.php/Dynamic_Kernel_Module_Support

Tensorflow's docker image with gpu supported doesn't work

Nvidia driver要先裝好

https://github.com/NVIDIA/nvidia-docker/wiki/Installation

先裝docker-engine

https://docs.docker.com/engine/installation/

 再裝nvidia-docker

https://github.com/NVIDIA/nvidia-docker

使用Tensorflow Docker image(注1)

$ nvidia-docker pull gcr.io/tensorflow/tensorflow:latest-gpu

$ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu sh -c 'python -m tensorflow.models.image.mnist.convolutional'

結果 not find libcudnn.so

離開 container (Ctrl+D)

進入 container，使用bash

 $ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash

 連結

$ ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.4 /usr/lib/x86_64-linux-gnu/libcudnn.so

Run again

$ python -m tensorflow.models.image.mnist.convolutional

結果

https://www.tensorflow.org/versions/r0.9/get_started/os_setup.html#docker-installation 

注1： If cannot connect to the Docker daemon. Is the docker daemon running on this host?

http://stackoverflow.com/questions/33562109/docker-command-cant-connect-to-docker-daemon