How to set up tensorflow with CUDA 8 cuDNN 5.1 in virtualenv with Python 3.5 on Ubuntu 16.04 http://ksopyla.com/2017/02/tensorflow-gpu-virtualenv-python3/

ubuntu16_tensorflow_cuda8.sh

# This is shorthened version of blog post 
#  http://ksopyla.com/2017/02/tensorflow-gpu-virtualenv-python3/

# update packages
sudo apt-get update
sudo apt-get upgrade

#Add the ppa repo for NVIDIA graphics driver
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt-get update

#Install the recommended driver (currently nvidia-378)
sudo ubuntu-drivers autoinstall
sudo reboot

#check if drivers were installed
nvidia-smi



#############################################
# Instal CUDA Toolkit 8.0 for x64 Ubuntu 16.04 
wget -O cuda_8_linux.run https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
sudo chmod +x cuda_8_linux.run
./cuda_8.0.61_375.26_linux.run

#Do you accept the previously read EULA?
#accept
#Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 367.48?
#n (we installed drivers previously)
#Install the CUDA 8.0 Toolkit?
#y
#Enter Toolkit Location:
#/usr/local/cuda-8.0 (enter)
#Do you wish to run the installation with ‚sudo’?
#y
#Do you want to install a symbolic link at /usr/local/cuda?
#y 
#Install the CUDA 8.0 Samples?
#y 
#Enter CUDA Samples Location:
#enter 

# Install cuDNN
# go to website and download cudnn-8 https://developer.nvidia.com/cudnn
tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz 

# copy libs to /usr/local/cuda folder
sudo cp -P cuda/include/cudnn.h /usr/local/cuda/include
sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

# isntall python 3 and virtual env
sudo apt install python3-pip
sudo apt install python3-venv

# create virtual environment for tensorflow
python3 -m venv tfenv
source tfenv/bin/activate

# Instal tensorflow package with gpu support
(tfenv)$ pip install tensorflow-gpu
#or CPU version
(tfenv)$ pip install tensorflow


# check installation, run simple python scipt from console
$ python

import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
tf_session = tf.Session()
x = tf.constant(1)
y = tf.constant(1)
print(tf_session.run(x + y))

it would be nice to add this wget method to get the cudnn file:
https://gist.github.com/mjdietzx/0ff77af5ae60622ce6ed8c4d9b419f45

Can you tell us what virtual env you got this working in. Trying to do something similar with ESXI.
Keep hitting an issue logging back into the Ubuntu UI when I install the NVIDIA graphics driver with specific version. i.e. sudo apt install nvidia-381 . The UI Accepts the user/password, but then just kicks me back to login. To fix it I run sudo apt-get purge nvidia*

It seems to accept sudo ubuntu-drivers autoinstall and allows me to log in. However the nvidia-smi command isn't found. Any ideas, pointers welcomed.

Thanks for these instructions. Unfortunately I was unable to get this to work, seems like the versions of the software are a moving target. Here are steps that worked for me yesterday (1/30/18): http://www.sixthdoor.com/deep-learning-setup-tensorflow-gpu-1-4-on-ubuntu-16-04/