Tensorflow GPU install on ubuntu 16.04

These instructions are intended to set up a deep learning environment for GPU-powered tensorflow.

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Before you begin, you may need to disable the nouveau (see https://nouveau.freedesktop.org/wiki/) video driver in Ubuntu. The nouveau driver is an opensource driver for nVidia cards. Unfortunately, this dynamic library can cause problems with the GeForce 1080ti and can result in linux not being able to start xwindows after you enter your username and password. You can disable the nouveau drivers like so:

1. After you boot the linux system and are sitting at a login prompt, press ctrl+alt+F1 to get to a terminal screen. Login via this terminal screen.
2. Create a file: /etc/modprobe.d/nouveau
3. Put the following in the above file...

blacklist nouveau
options nouveau modeset=0

Note: Once the above changes have been made, you should be able to reboot the linux box and login using the default Ubuntu 16.04 method.

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After following these instructions you'll have:

1. Ubuntu 16.04.
2. Cuda 8 drivers installed.
3. A conda environment with python 3.6.
4. The latest tensorflow version with gpu support.

NOTE: Pay SPECIAL attention to step 3 and say NO to installing the graphics driver.

0. update apt-get

sudo apt-get update

1. Install apt-get deps

sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev   

2. install nvidia drivers

# The 16.04 installer works with 16.10.
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
apt-get update
#  Added this to make sure we don't drag down the newest version of cuda!
apt-get install cuda=8.0.61-1 -y

2a. check nvidia driver install

nvidia-smi   

# you should see a list of gpus printed    
# if not, the previous steps failed.   

3. install cuda toolkit (MAKE SURE TO SELECT N TO INSTALL NVIDIA DRIVERS)

wget https://s3.amazonaws.com/personal-waf/cuda_8.0.61_375.26_linux.run   
sudo sh cuda_8.0.61_375.26_linux.run   # press and hold s to skip agreement   

# Do you accept the previously read EULA?
# accept

# Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
# ************************* VERY KEY ****************************
# ******************** DON"T SAY Y ******************************
# n

# Install the CUDA 8.0 Toolkit?
# y

# Enter Toolkit Location
# press enter


# Do you want to install a symbolic link at /usr/local/cuda?
# y

# Install the CUDA 8.0 Samples?
# y

# Enter CUDA Samples Location
# press enter    

# now this prints: 
# Installing the CUDA Toolkit in /usr/local/cuda-8.0 …
# Installing the CUDA Samples in /home/liping …
# Copying samples to /home/liping/NVIDIA_CUDA-8.0_Samples now…
# Finished copying samples.

4. Install cudnn

wget https://s3.amazonaws.com/open-source-william-falcon/cudnn-8.0-linux-x64-v6.0.tgz  
sudo tar -xzvf cudnn-8.0-linux-x64-v6.0.tgz   
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*

5. Add these lines to end of ~/.bashrc:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda

6. Reload bashrc

source ~/.bashrc

7. Install miniconda

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh   

# press s to skip terms   

# Do you approve the license terms? [yes|no]
# yes

# Miniconda3 will now be installed into this location:
# accept the location

# Do you wish the installer to prepend the Miniconda3 install location
# to PATH in your /home/ghost/.bashrc ? [yes|no]
# yes    

8. Reload bashrc

source ~/.bashrc

9. Create conda env to install tf

conda create -n tensorflow

# press y a few times 

10. Activate env

source activate tensorflow   

11. Install tensorflow with GPU support for python 3.6
    Note: You need to specify the 1.4.1 version as the 1.5 requires CUDA 9.0

pip install tensorflow-gpu==1.4.1

# If the above fails, try the part below
# pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp36-cp36m-linux_x86_64.whl

12. Test tf install

# start python shell   
python

# run test script   
import tensorflow as tf   

hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))