Tensorflow GPU install instructions for ubuntu 16.04 - Deep learning
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README.md replace cudnn file with williamFalcon's new upload Oct 15, 2018

README.md

Tensorflow GPU install on ubuntu 16.04

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

After following these instructions you'll have:

  1. Ubuntu 16.04.
  2. Cuda 9.0 drivers installed.
  3. A conda environment with python 3.6.
  4. The latest tensorflow version with gpu support.

Step 0: Noveau drivers

Before you begin, you may need to disable the opensource ubuntu NVIDIA driver called nouveau.

Option 1: Modify modprobe file

  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
  1. reboot system
reboot
  1. On reboot, verify that noveau drivers are not loaded
lsmod | grep nouveau

If nouveau driver(s) are still loaded do not proceed with the installation guide and troubleshoot why it's still loaded.

Option 2: Modify Grub load command
From this stackoverflow solution

  1. When the GRUB boot menu appears : Highlight the Ubuntu menu entry and press the E key. Add the nouveau.modeset=0 parameter to the end of the linux line ... Then press F10 to boot.
  2. When login page appears press [ctrl + ALt + F1]
  3. Enter username + password
  4. Uninstall every NVIDIA related software:
sudo apt-get purge nvidia*  
sudo reboot   

Installation steps

  1. 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 curl   
  1. install nvidia drivers
# The 16.04 installer works with 16.10.
# download drivers
curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb

# download key to allow installation
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub

# install actual package
sudo dpkg -i ./cuda-repo-ubuntu1604_9.0.176-1_amd64.deb

#  install cuda (but it'll prompt to install other deps, so we try to install twice with a dep update in between
sudo apt-get update
sudo apt-get install cuda-9-0   

2a. reboot Ubuntu

sudo reboot

2b. check nvidia driver install

nvidia-smi   

# you should see a list of gpus printed    
# if not, the previous steps failed.   
  1. Install cudnn
wget https://s3.amazonaws.com/open-source-william-falcon/cudnn-9.0-linux-x64-v7.3.1.20.tgz
sudo tar -xzvf cudnn-9.0-linux-x64-v7.3.1.20.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*
  1. Add these lines to end of ~/.bashrc:
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
export PATH="$PATH:/usr/local/cuda/bin"

4a. Reload bashrc

source ~/.bashrc
  1. 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    

5a. Reload bashrc

source ~/.bashrc
  1. Create conda env to install tf
conda create -n tensorflow

# press y a few times 
  1. Activate env
source activate tensorflow   
  1. Install tensorflow with GPU support for python 3.6
pip install tf-nightly-gpu

# 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
  1. Test tf install
# start python shell   
python

# run test script   
import tensorflow as tf   

hello = tf.constant('Hello, TensorFlow!')

# when you run sess, you should see a bunch of lines with the word gpu in them (if install worked)
# otherwise, not running on gpu
sess = tf.Session()
print(sess.run(hello))